B
Science and Applications Traceability Matrix
The Science and Applications Traceability Matrix (SATM; Table B.1) provided the basis for much of the committee’s deliberations and forms the foundation of its recommendations. The process for developing this matrix, including the central role of the panels, is summarized in Chapter 3.
The SATM is organized by panel and color coded, with blue-shaded columns identifying science and applications questions and objectives and green-shaded columns identifying associated observation and measurement needs to address those questions and objectives. Note that Table 3.3 is identical to the blue portion of the SATM. The content of the green columns represents panel guidance to the steering committee. The steering committee did more extensive implementation analysis (including cost analysis through the Cost Assessment and Technical Evaluation (CATE) process, where appropriate) to determine its observing system priorities (Table 3.3). As such, the green columns should not be taken as recommendations or even definitive guidance, but rather as noncomprehensive suggestions.
Columns in the SATM consist of the following:
- Societal or Science Question. The top-level science or applications question driving the research need.
- Earth Science/Application Objective. A specific objective needed to address the related science or societal question.
- Science/Applications Importance. The relative priority of pursuing a given objective, ranked as Most Important, Very Important, or Important (described further in Chapter 3).
- Geophysical Observable. The geophysical parameter to be observed in order to pursue the related objective.
- Measurement Parameters. The measurement specifications associated with the observable.
- Example Measurement Approaches. Examples of measurement methods that can be used to measure the observable to achieve the requirements of the measurement parameters. Entries in these columns reflect the judgment of the panels, but are not definitive. A thorough trade analysis was not performed to identify the best measurement approach for each observation, no instrument or
- mission design was performed, and no costing was established. Note that examples in either the Program of Record (POR) or Targeted Observable (TO) subcolumns are not complete and may even be absent for a number of reasons, due to the complexity of the SATM process. Blank cells should not be considered as evidence that no relevant POR is available or new measurement (TO) is needed.
- Method. Candidate measurement methods.
- POR. Examples of POR instruments or missions that have made or will make similar measurements. The POR numbers refer to the Committee on Earth Observing Satellites (CEOS) Catalog categories, as summarized in Table B.2. Listed names are instruments or missions that have been specifically identified.
- TO. Entries in the ESAS 2017 Targeted Observables table (Appendix C) that may, depending on implementation approach, contribute to the needed measurement(s).
Table B.1 begins on next set of facing pages.
TABLE B.2 Committee on Earth Observing Satellites Catalog Categories
Program of Record Number | Instrument Technology |
---|---|
1 | Absorption-band microwave (MW) radiometer/spectrometer |
2 | Atmospheric lidar |
3 | Broadband radiometer |
4 | Cloud and precipitation radar |
5 | Doppler lidar |
6 | Global Navigation Satellite Systems (GNSS) radio-occultation receiver |
7 | GNSS receiver |
8 | Gradiometer/accelerometer |
9 | High-resolution optical imager |
10 | High-resolution nadir-scanning infrared (IR) spectrometer |
11 | High-resolution nadir-scanning shortwave (SW) spectrometer |
12 | Imaging radar (Synthetic Aperture Radar) |
13 | Laser Retroreflector |
14 | Laser altimeter |
15 | Lightning imager |
16 | Limb-scanning IR spectrometer |
17 | Limb-scanning MW spectrometer |
18 | Limb-scanning SW spectrometer |
19 | Magnetometer |
20 | Medium-resolution IR spectrometer |
21 | Medium-resolution IR spectro-radiometer |
22 | Multichannel/direction/polarization radiometer |
23 | Multipurpose imaging MW radiometer |
24 | Multipurpose imaging visible (VIS)/IR radiometer |
25 | Narrow-band channel IR radiometer |
26 | Nonscanning MW radiometer |
27 | Radar altimeter |
28 | Radar scatterometer |
29 | Radio-positioning system |
30 | Satellite-to-satellite ranging system |
31 | Solar irradiance monitor |
TABLE B.1 ESAS 2017 Consolidated Science and Applications Traceability Matrix
KEY: | Square brackets: Nonspace observations or related commitments Curly brackets: Space observations with non-NASAS/NOAA/USGS assets, such as non-U.S. or databuys |
GLOBAL HYDROLOGICAL CYCLES AND WATER RESOURCES PANEL | |||||||
---|---|---|---|---|---|---|---|
SCIENCE | MEASUREMENT | ||||||
Societal or Science Question/Goal | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
QUESTION H-1. Coupling the Water and Energy Cycle. How is the water cycle changing? Are changes in evapotranspiration and precipitation accelerating, with greater rates of evapotranspiration and thereby precipitation, and how are these changes expressed in the space-time distribution of rainfall, snowfall, evapotranspiration, and the frequency and magnitude of extremes such as droughts and floods? | H-1a. Develop and evaluate an integrated Earth system analysis with sufficient observational input to accurately quantify the components of the water and energy cycles and their interactions, and to close the water balance from headwater catchments to continental-scale river basins. | Most Important | Energy and water fluxes in the boundary or surface layer: solar (direct and reflected) and longwave radiation (downwelling and emitted), sensible and latent heat exchange, and soil heat flux. | Surface solar and longwave radiation balances, which are needed to estimate the other energy balance parameters, to within 10 W/m2 accuracy at 1 km resolution globally, four times daily. | Downscale CERES-like observations to finer spatial resolutions (1 km) and eliminate systematic errors. See H-1b and H-1c. | POR-1, 3, 10, 20, 21, 23, 24, 25 | TO-17, 18 |
Model and data integration with capabilities to estimate moist processes in atmosphere, land and terrestrial biosphere. | POR-1, 6, 9 | TO-13 | |||||
H-1b. Quantify rates of precipitation and its phase (rain and snow/ice) worldwide at convective and orographic scales suitable to capture flash floods and beyond. | Most Important | Precipitation rate and phase (rain or snow). | Diurnal cycle of precipitation at 1 (desirable) or 4 km (needed) scales (rain and snow) with accuracy of 0.2 mm/hr for rainfall and 1 mm/hr for snow, at finer scales in selected areas such as mountainous regions. | Multi-frequency radar and radiometer system similar to GPM/CloudSat as well as aerosol capabilities for continued improvement in precipitation process understanding, precipitation rate observations, and long term monitoring for change detection. | POR-4, 23, 25 | TO-5, 13 | |
H-1c. Quantify rates of snow accumulation, snowmelt, ice melt, and sublimation from snow and ice worldwide at scales driven by topographic variability. | Most Important | Snow water equivalent (SWE). | Global SWE at 1 (desirable) or 4 km (needed) resolution every 3-5 days, to 10% accuracy for SWE values to 1 m. | Existing passive microwave for global scale okay for SWE values to ~200 mm. Problematic for deep snow in heterogeneous terrain. | POR-23 | TO-16, 19 | |
In mountains, SWE at ~100 m resolution suitable for SWE values to 2.5 m. | In mountains, measure depth (Ka-band radar or laser altimeter) and density (SAR). | POR-17 (KaRIn, SWOT) | TO-16, 19 | ||||
Snow and glacier albedo and temperature. | Spectral albedo of subpixel snow and glaciers at weekly intervals to an accuracy to estimate absorption of solar radiation to 10%. Ice/snow surface temperature to ±1 K. At spatial resolution of 30 to 100 m. | Imaging spectrometer to understand seasonal variability. Develop methods and calibration for multispectral sensors for weekly worldwide coverage. Panchromatic multiangle radiometer. Thermal emission radiometer for temperature. | POR-22 | TO-18 | |||
QUESTION H-2. Prediction of Changes. How do anthropogenic changes in climate, land use, water use, and water storage, interact and modify the water and energy cycles locally, regionally, and globally and what are the short- and long-term consequences? | H-2a. Quantify how changes in land use, water use, and water storage affect evapotranspiration rates, and how these in turn affect local and regional precipitation systems, groundwater recharge, temperature extremes, and carbon cycling. | Very Important | Latent heat flux at 3 (desirable) to 6 hour (useful) resolution during daytime intervals and at 1 km spatial scale with better than 10 W/m2 accuracy. | Temperature of soil and vegetation separately, 40-100 m spatial resolution, accuracy of ±1 K, at a temporal frequency to resolve the diurnal cycle. | Emitted infrared radiation in 4 µm and 11 µm wavelength regions, possibly free flyers to get desired frequency of four times daily. | POR-3, 9, 10, 20, 24, 25 | TO-13, 18 |
Boundary layer vapor pressure deficit profile and near-surface humitidy, 1-10 km resolution, at least four times during daytime with better than 1 hPa accuracy. Boundary-layer wind speed over land, including heterogeneous terrain, is needed to estimate surface fluxes. | AIRS for atmospheric moisture. Add capability for near surface profile (profile within the first 100 m). | POR-1, 6, 20, 23 | TO-4, 13 | ||||
Soil moisture profile to 4% volumetric accuracy in top 1 m of the soil column. | Passive microwave dual-channel L- and P-band radiometer, with polarization for four Stokes parameters, at spatial resolution 20-60 km. Active microwave dual-channel L- and P-band radar, VV, HH, and HV polarization at spatial resolution of 100 m to 1 km. | POR-23 | TO-17 |
GLOBAL HYDROLOGICAL CYCLES AND WATER RESOURCES PANEL | |||||||
---|---|---|---|---|---|---|---|
SCIENCE | MEASUREMENT | ||||||
Societal or Science Question/Goal | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
Albedo of vegetation and soil separately, to an accuracy to estimate absorption of solar radiation to 10 W/m2, at weekly intervals at field scale 30-60 m spatial resolution. | Imaging spectrometer to develop methods and to calibrate multispectral sensors for worldwide coverage. | POR-22, 24 | TO-18 | ||||
H-2b. Quantify the magnitude of anthropogenic processes that cause changes in radiative forcing, temperature, snowmelt, and ice melt, as they alter downstream water quantity and quality. | Important | Snow and ice albedo, contaminant type (dust, soot) and concentration, land cover. Surface temperature. Glacier, river, and lake mapping and characterization. | Spectral snow and ice albedo, optical properties and concentrations of contaminants (dust and soot), surface temperature to ±1 K. | Imaging spectrometry at resolution to capture topographic variability, typically ~30 m. Lidar to measure vegetation properties. Thermal emission radiometer for temperature, prefer to 30 m spatial resolution. 5-10 m spatial multiband imaging for worldwide coverage of land, rivers, lakes, and glaciers. | POR-3, 9, 12, 14, 22 | TO-18, 20 | |
H-2c. Quantify how changes in land use, land cover, and water use related to agricultural activities, food production, and forest management affect water quality and especially groundwater recharge, threatening sustainability of future water supplies. | Most Important | Recharge rates (i.e., space-time rates of change in groundwater storage and availability) at 1 km (desired) up to 10 km (useful) scale globally at 10-day intervals with accuracy of better than ±1 mm/day | Soil moisture profile to 4% volumetric accuracy in top 1 m of the soil column. | See H-2a for Measurement Approach to soil moisture. | POR-23 | TO-17 | |
Changes in vadose zone moisture and in groundwater storage. Changes in groundwater levels. Changes in snow water equivalent. | Gravimetric methods. | POR-30 | TO-9, 17 | ||||
Land-surface deflection to 1 cm accuracy, 100 m spatial resolution. | L-band InSAR. Perhaps combined with airborne lidar. | POR-12 | TO-19 | ||||
Differences between precipitation and evapotranspiration to an accuracy whereby estimates of their difference have smaller errors than the magnitude of groundwater recharge. | See H-2a for Measurement Parameters for evapotranspiration. | See H-2a for Measurement Approaches for evapotranspiration. | - | TO-17 | |||
Rainfall at fine space (1 km) and time (15 min) resolution in selected areas to properly capture accumulation at field scales and partition between canopy intercept, infiltration and runoff. | High-resolution geostationary radar. Observationally constrained mesoscale models; typical constraints are active microwave backscattering coefficients, passive microwave brightness temperatures, and geostationary VIS/IR measurements. | - | TO-5, 9, 17 | ||||
QUESTION H-3. Availability of Freshwater and Coupling with Biological Cycles. How do changes in the water cycle impact local and regional freshwater availability, alter the biotic life of streams, and affect ecosystems and the services these provide? | H-3a. Develop methods and systems for monitoring water quality for human health and ecosystem services. | Important | Turbidity, total suspended sols and suspended sols particle size distribution in estuaries and coastal regions, salinity to 10 psu, temperature to 1 K, and chlorophyll. | At spatial scales small enough to resolve streams, ~10 m. Appropriate scale and resolution for onsite management for water quality. | Imaging spectrometer for worldwide land coverage and to develop methods and calibrate multispectral sensors for more frequent coverage. | - | TO-3, 18 |
H-3b. Monitor and understand the coupled natural and anthropogenic processes that change water quality, fluxes, and storages in and between all reservoirs (atmosphere, rivers, lakes, groundwater, and glaciers), and the response to extreme events. | Important | Land cover and vegetation condition, soil moisture, land use, burned area after fire, and terrain slope. | At scales small enough to resolve local areas contributing to water quality and landslides: at spatial resolution of 100 m (desirable) to 1 km (useful). | Models that link precipitation, land use, land cover, and topography to water quality. | POR-9, 10, 12, 21 | TO-3, 18, 20 | |
H-3c. Determine structure, productivity, and health of plants to constrain estimates of evapotranspiration. | Important | Vegetation biophysical condition (color and water content), vapor pressure deficit between vegetation and atmosphere, soil moisture profile, leaf area index and vegetation fraction, broadband and spectral albedo of vegetation. | Water use efficiency of plants as they respond to moisture stress. | Models that link vegetation’s radiative signal with processes of evapotranspiration. | POR-10 | Modeling | |
Structure of vegetation canopy. | Amount of woody biomass and leaf area index. 1 to 4 points sample points per square meter with 2 to 4 returns per point for moderately to heavily forested regions | Lidar, P-band radar. | POR-12, 14 | TO-20 |
GLOBAL HYDROLOGICAL CYCLES AND WATER RESOURCES PANEL | |||||||
---|---|---|---|---|---|---|---|
SCIENCE | MEASUREMENT | ||||||
Societal or Science Question/Goal | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
and areas with intensive agriculture. Vegetation fraction at 30-100 m resolution and vertical profile of vegetation (canopy-understory-bare soil) | |||||||
Photosynthetic rate. | Solar induced fluorescence. | Imaging spectrometry within the Fraunhofer bands, but at 100 m scale. | POR-32 | - | |||
QUESTION H-4. How does the water cycle interact with other Earth system processes to change the predictability and impacts of hazardous events and hazard-chains (e.g., floods, wildfires, landslides, coastal loss, subsidence, droughts, human health, and ecosystem health), and how do we improve preparedness and mitigation of water-related extreme events? | H-4a. Monitor and understand hazard response in rugged terrain and land margins to heavy rainfall, temperature and evaporation extremes, and strong winds at multiple temporal and spatial scales. This socioeconomic priority depends on success of addressing H-1b and H-1c, H-2a, and H-2c. | Very Important | Magnitude and frequency of severe storms. Depth and extent of floods. | Precipitation, snowmelt, water depth, and water flow in soil at time and space scales consistent with events. | For precipitation and snow: Similar to SWOT but at finer spatial resolution. | See H-1b and H-1c | See H-1b and H-1c |
For River Discharge: Similar to SWOT but at finer spatial resolution. | POR-26 (SWOT) | ||||||
H-4b. Quantify key meteorological, glaciological, and solid Earth dynamical and state variables and processes controlling flash floods, and rapid hazard chains to improve detection, prediction, and preparedness. (This is a critical socioeconomic priority that depends on success of addressing H-1b, H-1c, and H-4a). | Important | Rainfall intensity and volume for storms in the 95th percentile of values specific to areas, especially estimates in mountainous terrain where other measurement sources are not available, soil moisture, SWE, and glacier changes. | Precipitation, snowmelt, and flow in soil and glaciers at time and space scales consistent with events. | See measurement approaches associated with Objective H-2c. | POR-23 | TO-17 | |
H-4c. Improve drought monitoring to forecast short-term impacts more accurately and to assess potential mitigations. This is a critical socioeconomic priority that depends on success of addressing H-1b, H-1c, and H-2c. | Important | Soil moisture, vegetation moisture, cumulative evapotranspiration, and SWE. | See specifications associated with Objective H-1a. | See measurement approaches associated with Objective H-1a. | POR-12, 23 | TO-17 | |
H-4d. Understand linkages between anthropogenic modification of the land, including fire suppression, land use, and urbanization on frequency of, and response to, hazards. This is tightly linked to H-2a, H-2b, H-4a, H-4b, and H-4c. | Important | Susceptibility of forest and brushlands to fire, land use change, urban characteristics. | Dry fuel load. | Imaging spectrometer for worldwide land coverage and to develop methods and calibrate multispectral sensors for more frequent coverage. | TO-18 | ||
Land use and land cover, global scale monthly at 30-100 m resolution, selected areas annually at 5-10 m resolution, surface soil moisture, surface temperature, evapotranspiration at scale of topographic variability, typically ~30 m. | For LULCC: Multispectral sensors at varying resolution to merge spatial and time scales. | POR-9, 11, 12 | |||||
For Soil Moisture: Multispectral sensors at varying resolution to merge spatial and time scales. | TO-20 | ||||||
Urban form and textures at scales to resolve distinctive features, typically 5-10 m, annually. | Imaging spectrometer for worldwide land coverage and to develop methods and calibrate multispectral sensors for more frequent coverage. | POR-9, 11, 12 | TO-18, 20 |
WEATHER AND AIR QUALITY PANEL | |||||||
---|---|---|---|---|---|---|---|
SCIENCE | MEASUREMENT | ||||||
Societal or Science Question/Goal | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
QUESTION W-1. Planetary Boundary Layer Dynamics. What planetary boundary layer (PBL) processes are integral to the air-surface (land, ocean and sea ice) exchanges of energy, momentum, and mass, and how do these impact weather forecasts and air quality simulations? | W-1a. Determine the effects of key boundary layer processes on weather, hydrological, and air quality forecasts at minutes to subseasonal time scales. | Most Important | 3D temperature in PBL | Horizontal resolution 20 km, vertical resolution 0.2 km, temporal resolution 3 hr, 0.3 K/0.3 K | Polar/geo IR and microwave sounders, complemented by airborne and surface observations | POR-1, 6, 20, 24, 25 | TO-13 |
3D humidity in PBL | Horizontal resolution 20 km, vertical resolution 0.2 km, temporal resolution 3 hr, 0.3 g/kg | Polar/geo IR and microwave sounders, complemented by airborne and surface observations | POR-1, 6, 20, 24, 25 | TO-13 | |||
3D horizontal wind vector in PBL | Horizontal resolution 20 km, vertical resolution 0.2 km, temporal resolution 3 hr, 1 m/s | Doppler wind lidar, AMVs from multiangle VIS/IR (occasionally reaching PBL), scatterometer measurements of near-surface winds over ocean | POR-25 | TO-4, 11 | |||
3D PM component and trace gas (ozone, NO2) concentrations | Horizontal resolution 5 km, vertical resolution 0.2 km, temporal resolution 2 hr | See approaches listed under W-6 below. | POR-2, 10, 11, 22 | TO-1, 12 | |||
2D PBL height | Horizontal resolution 20 km, temporal resolution 3 hr, 0.1 km | Lidar (e.g., CALIPSO) | POR-2, 6 | TO-1, 13 | |||
2D PBL cloud LWP | Horizontal resolution 20 km, 20% | Microwave radiometer | POR-1, 23 | TO-5 | |||
2D cloud base | Horizontal resolution 20 km, 0.1 km | Lidar | POR-2, 4 | TO-5 | |||
2D precipitation | Horizontal resolution 10 km, 20% | Passive microwave (e.g., GPM), radar; complemented by rain gauges and radar over land | POR-1, 4, 23 | TO-5 | |||
QUESTION W-2. Larger Range Environmental Predictions. How can environmental predictions of weather and air quality be extended to seamlessly forecast Earth system conditions at lead times of 1 week to 2 months? | W-2a. Improve the observed and modeled representation of natural, low-frequency modes of weather/climate variability (e.g., MJO, ENSO), including upscale interactions between the large-scale circulation and organization of convection and slowly varying boundary processes to extend the lead time of useful prediction skills by 50% for forecast times of 1 week to 2 months. Advances require improved: (1) Process understanding and assimilation / modeling capabilities of atmospheric convection, mesoscale organization, and atmosphere and ocean boundary layers, (2) Global initial conditions relevant to these quantities/processes. Observations needed for boundary layer, surface conditions, and convection are described in W-1, W-3, and W-4, respectively. | Most Important | Vertical temperature profile | Boundary layer through middle atmosphere; threshold Horizontal resolution 5 km, objective Horizontal resolution 3 km, both at 1 km Vertical resolution; threshold refresh 3 hr, objective refresh global 90 min and CONUS 60 min; measured with 1 K rms | Polar/geo IR and microwave sounders PLUS GNSS-RO | POR-1, 6, 20, 25 | TO-13 |
Vertical water vapor profile | Boundary layer through middle atmosphere; threshold Horizontal resolution 5 km, objective Horizontal resolution 3 km, both at 1 km Vertical resolution; threshold refresh 3 hr, objective refresh global 90 min and CONUS 60 min; measured with 10% LTH rms and 20% UTH rms | Polar/geo IR and microwave sounders PLUS GNSS-RO | POR-1, 6, 20, 25 | TO-13 | |||
Vertical profiles of horizontal vector winds | Boundary layer through middle atmosphere; threshold Horizontal resolution 5 km, objective Horizontal resolution 3 km, both at 1 km Vertical resolution; threshold refresh 3 hr, objective refresh global 90 min and CONUS 60 min; measured at 3 m/s rms | Doppler wind lidar | POR-5 | TO-4 | |||
AMVs from IR, WV, and visible imagers and hyperspectral sounders | POR-1, POR-20 | TO-13 | |||||
Vertical profile of atmospheric O3, aerosols, and dust for subseasonal | From surface through middle atmosphere mid-troposphere for aerosols and dust; through stratosphere for ozone. | Lidar, stereo visible, UV backscatter, MW limb sounding | POR-11, 17, 18 | TO-12 | |||
Vertical distributions of clouds and precipitation particles | From surface through lower stratosphere; Vertical resolution 1 km/10 km; ice water path to within 25%, LWP to within 25% | MW for LWP, submillimeter with radar for IWP; GNSS-RO (L-band) dual-pol - LHCP is new | POR-2, 4 | TO-5 |
WEATHER AND AIR QUALITY PANEL | |||||||
---|---|---|---|---|---|---|---|
SCIENCE | MEASUREMENT | ||||||
Societal or Science Question/Goal | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
Precipitation: total amount and rate | Horizontal resolution 10 km, 20% | Passive microwave (e.g., GPM), radar; complemented by rain gauges and radar over land | POR-4, 23 | TO-5 | |||
Surface pressure | To within 1 mb | TO-5 | |||||
Vertical profiles of latent heating | GPROF from TRMM, also from CloudSat, GPM | POR-23 | TO-5 | ||||
Sea-ice coverage | 5 km resolution; 80% coverage daily; uncertainty 10%; 10 km horizontal | Doppler scatterometer or scatterometer, SAR, high-resolution imager, [ice stations] | POR-11, 12, 21, 23, 28 | TO-11 | |||
Sea-surface temperature | 0.2 K random uncertainty in 25 × 25 km area; 80% daily coverage; 3 to 5 km resolution. | IR radiometer, microwave radiometer, [complemented by in situ buoys and gliders] | POR-11, 21, 23, 24 | ||||
Land-surface temperature | 0.6 K random uncertainty in 25 × 25 km area, 80% daily coverage, 3-5 km resolution, with 1 km resolution desired. | IR radiometer (e.g., MODIS, VIIRS, AIRS, CrIS), complemented by modeling | POR-11, 21, 23 | TO-18 | |||
Snow coverage (for exposed land and ice) | An average of 1‐2 samples (overpasses) per day per 100 to 200 km region; 1 to 10 km resolution; random errors of two times the resolution. | Visible imager (coverage), passive microwave, radar, and lidar (for snow depth/water equivalent) | POR-11, 21, 23 | TO-16 | |||
Soil moisture (surface to root zone) | Random errors of 10% in fraction of saturation, while 1 km resolution is desired, 25 km is useful. | Multichannel radiometer, scatterometer (e.g., SMOS, SMAP). NOTES: C-band scatterometry has worked well in Europe, whereas in the US radiometry is more common. Both seem to work. | POR-12, 23, 27 | TO-17 | |||
Ocean mixed layer depth (heat content), sea-surface height, and bottom pressure | Global refresh 10 days; Horizontal 25 km; 0.5 W/m2/yr per decade. | Altimeter (e.g., Jason, SARAL), gravimeter (e.g., GRACE), [in situ profiles] | POR-27 | TO-10 | |||
Sea-ice thickness | 50 cm; 10 km; 24 hr. | Altimeter (e.g., Jason, ICESat-2) | POR-14 | TO-7 | |||
Snow water equivalent | Horizon resolution of 20 km, once per day, 10%, Desire 4 km resolution, on a 3 to 5 day scale. | Passive microwave, radar, and SAR | POR-17, 23 | TO-16, 19 | |||
QUESTION W-3. Surface Spatial Variations Impacts on Mass and Energy Transfers. How do spatial variations in surface characteristics (influencing ocean and atmospheric dynamics, thermal inertia, and water) modify transfer between domains (air, ocean, land, cryosphere) and thereby influence weather and air quality? | W-3a. Determine how spatial variability in surface characteristics modifies regional cycles of energy, water and momentum (stress) to an accuracy of 10 W/m2 in the enthalpy flux, and 0.1 N/m2 in stress, and observe total precipitation to an average accuracy of 15% over oceans and/or 25% over land and ice surfaces averaged over a 100 × 100 km region and 2-to 3-day time period. | Very Important | Ocean surface vector wind or surface wind stress | An average of 1‐2 samples (overpasses) per day per 100 to 200 km region; 5 to 10 km resolution; 0.02 N/m2 for 100 km scales and 1 to 2 day averages (this is analogous to vector component wind random errors <1 m/s for the proposed sampling). | Scatterometer OR polarimetric radiometer. NOTES: SAR could provide wind vectors but directional accuracy not sufficient to calculate curl. | POR-28 | TO-11 |
Ocean surface vector current | An average of 1‐2 samples (overpasses) per day per 100 to 200 km region for a high inclination orbit; 5 to 10 km resolution; Random errors ≤0.02 m/s for 100 km scales and 1 to 2 day averages (this is analogous to current random errors <0.5 m/s for the proposed sampling); Coincidence with wind observations. | Doppler scatterometer, HF radar (near coastal only, roughly 100 km from shore). NOTES: Wide swath altimetry will be complementary but is not an alternative. SAR could provide one vector component, but the accuracy and sampling are questionable. Accurate surface currents (true surface, not subsurface) are new and unique. | TO-11 | ||||
Subsurface current | Already exceeded by the global drifting buoy network: 1,250 drifting buoys with global ocean coverage and hourly locations. | [Surface drifting buoys drogued to 15 m depth, gliders] | TERRESTRIAL | TERRESTRIAL |
WEATHER AND AIR QUALITY PANEL | |||||||
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question/Goal | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
Sea-ice motion | 3 km per day; 25 km horizontal, 24 hr. | Doppler scatterometer or scatterometer, SAR, high-resolution imager, (ice stations). NOTES: Synergetic with observation of sea-ice age and extent, soil moisture, vegetation, snow, ocean mixed layer and surface currents. | POR-11, 12, 21 | TO-11 | |||
Sea-ice coverage | 5 km resolution; 80% coverage daily; uncertainty 10%; 10 km horizontal | Doppler scatterometer or scatterometer, SAR, high-resolution imager, [ice stations] | POR-11, 12, 21, 23, 28 | TO-11 | |||
Sea-surface temperature | 0.2 K random uncertainty in 25 × 25 km area; 80% daily coverage; 3 to 5 km resolution. | IR radiometer, microwave radiometer, [complemented by in situ buoys and gliders] | POR-11, 21, 23, 24 | ||||
Sea-ice surface temperature | 0.6 K random uncertainty in 25 × 25 km area; 80% daily coverage; 3-5 km resolution. | IR radiometer (e.g., MODIS, VIIRS, AIRS, CrIS), complemented by modeling | POR-11, 21, 23 | TO-18 | |||
Land-surface temperature | 0.6 K random uncertainty in 25 × 25 km area, 80% daily coverage, 3-5 km resolution, with 1 km resolution desired. | IR radiometer (e.g., MODIS, VIIRS, AIRS, CrIS), complemented by modeling | POR-11, 21, 23 | TO-18 | |||
Snow coverage (for exposed land and ice) | An average of 1‐2 samples (overpasses) per day per 100 to 200 km region; 1 to 10 km resolution; random errors of boundaries of two times the resolution. | Visible imager (coverage), passive microwave, radar, and lidar (for snow depth/water equivalent) | POR-11, 21, 23 | TO-16 | |||
Soil moisture (surface to root zone) | Random errors of 10% in fraction of saturation while 1 km is desired, 25 km is useful. | Multichannel radiometer, scatterometer (e.g., SMOS, SMAP). NOTES: C-band scatterometry has worked well in Europe, whereas in the US radiometry is more common. Both seem to work. | POR-12, 23, 28 | TO-15, 17 | |||
Upper canopy moisture content | Random errors of 10% in fraction of saturation. | Multichannel radiometer, high frequency and high inclination angle scatterometer | TO-15, 17 | ||||
Significant wave height | 5cm random error for a 25 km × 25 km area in one overpass. | Altimeter (for swell, wind wave compoent is well-estimated from surface winds), complemented by wave buoys | POR-26, 27 | TO-21 | |||
Columnar water vapor (all sky) | An average of 1‐2 samples (overpasses) per day per 50 km region; 5 to 10 km resolution; clear sky RMS errors within 3 mm; NWP needs higher revisit (1-6 hr). | Polar/geo IR and microwave sounders PLUS GNSS-RO | POR-21, 23, 24 | ||||
Cloud fraction | An average of 1‐2 samples (overpasses) per day per 50 km region, 5 to 10 km resolution, random errors <1 K in brightness temperature | Polar/geo IR | POR-11, 21, 24 | ||||
Ocean mixed layer depth (heat content), sea-surface height, and bottom pressure | Global refresh 10 days; Horizontal 7 km; 0.5 W/m2/yr per decade. | Altimeter (e.g., JASON, SARAL), gravimeter (e.g., GRACE), [in situ profiles] | POR-27 | TO-10 | |||
Boundary-layer height (via air temperature profile) | An average of 1‐2 samples (overpasses) per day per 50 km region; 5 to 10 km resolution; random errors 10 m in boundary‐layer height. | Lidar (e.g., CALIPSO) | POR-2, 6 | TO-1 | |||
Land surface emissivity | Horizon resolution of 20 km; once per day; 20%. Desire 0.1 km, resolve diurnal cycle 10% | Multiangle multichannel radiometer | POR-11, 21, 24 | TO-13 |
WEATHER AND AIR QUALITY PANEL | |||||||
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question/Goal | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
Ice surface emissivity | Horizon resolution of 20 km; once per day; 0.02. | Multichannel radiometer | POR-11, 21, 23 | TO-13 | |||
Sea-surface height | 10 cm random variability; six hourly; 10 km resolution. | Wide-swath altimeter, supported by microwave water vapor radiometer | POR-26, 27 | TO-21 | |||
Sea-surface salinity | An average of 1‐2 samples (overpasses) per 10 days per 100 to 200 km region; 50 km resolution; random erros of 0.2 psu in monthly average on a 100 × 100 km scale. | L-band radiometer, with co-aligned L-band scatterometer for roughness correction (e.g., SMAP, Aquarius, SMOS) | POR-12, 23 | TO-15 | |||
Sea-ice thickness | 50 cm; 10 km; 24 hr. | Altimeter (e.g., JSON, ICESat-2) | POR-14 | TO-7 | |||
Snow water equivalent | Horizon resolution of 20 km; once per day; 20%. Desire 4 km resolution, on a 3-5 day scale. | Passive microwave, radar, and SAR | POR-17, 23 | TO-16, 19 | |||
Snow albedo and emissivity | Horizon resolution of 20 km; once per day; 0.01; 5 km resolution is desired. | Multichannel radiometer, microwave and IR/Vis | POR-11, 21, 22, 23, 24 | TO-13 | |||
2D surface precipitation | Ideally half hourly, but any additional sampling would be very valuable | Dual-frequencey radiometry, radar (e.g., GPM), [rain gauges and radar over land] | POR-4, 23 | TO-5 | |||
2D ocean surface color | An average of 1‐2 samples (overpasses) per day per 100 to 200 km region; 5 to 10 km resolution; random errors of 10 per meter. | Radiometry (e.g., PACE), optical imager (e.g., MODIS), OLCI, SLGI) | POR-1, 21, 23, 24 | TO-3 | |||
Vegetation characteristics | Land cover type, leaf-area index, vegatation fraction, canopy height | IR and visible radiometry, MODIS, VIIRS, imaging lidar (GEDI and ICESat2) | POR-9, 10, 12 | TO-20 | |||
Near surface air temperature and humidity | Horizon resolution of 20 km; temporal resolution of 3 hr; 0.3 K. | Microwave sounder (ocean), possibly hyperspectral IR for clear skies | POR-1, 6 | TO-13 | |||
QUESTION W-4. Convective Storm Formulation Process. Why do convective storms, heavy precipitation, and clouds occur exactly when and where they do? | W-4a. Measure the vertical motion within deep convection to within 1 m/s and heavy precipitation rates to within 1 mm/hour to improve model representation of extreme precipitation and to determine convective transport and redistribution of mass, moisture, momentum, and chemical species. | Most Important | Vertical velocity | Global coverage; sample area 200 × 200 km; 5 year mission; Horizontal resolution 2 km; vertical resolution 200 m; temporal resolution 1 min over a 20-30 min period; accuracy 1 m/s. | Doppler radar | TO-5 | |
Precipitation rate | Global coverage; sample area 200 × 200 km; 5 year mission; Horizontal resolution 1 km; temporal resolution 1-5 min; accuracy 1 mm/hr. | Microwave, radar (e.g., GPM), [ground-based gauges and radar] | TO-5 | ||||
3D condensate | Accuracy 0.1 g/kg | Submillimeter multiple frequencies 180-900 GHz | TO-5 | ||||
Vertical profiles of horizontal | Accuracy 1 m/s | Doppler wind lidar | POR-5 | TO-4 | |||
winds | AMVs from IR/hyperspectral for wind estimation | POR-1, 20 | TO-13 | ||||
3D water vapor | Vertical resolution 1 km; spatial resolution 500 m; temporal resolution 15 min; accuracy 0.5 g/kg. | IR, hyperspectral, [in situ: rawinsonde, aircraft] | POR-20 | TO-13 | |||
QUESTION W-5. Air Pollution Processes and Distribution. What processes determine the spatio-temporal structure of important air pollutants and their concomitant adverse impact on human health, agriculture, and ecosystems? | W-5a. Improve the understanding of the processes that determine air pollution distributions and aid estimation of global air pollution impacts on human health and ecosystems by reducing uncertainty to <10% of vertically‐resolved tropospheric fields (including surface concentrations) of speciated particulate matter (PM), ozone (O3), and nitrogen dioxide (NO2). | Most Important | PM concentration and properties, including speciation | PM: Aerosol Optical Depth to infer PM from 0‐2 km layer; Six observations during daylight hours to get diurnal distribution. 5 × 5 km2 horizontal resolution. Spectral properties to infer PM speciation. | Combine advanced space-based observations, aircraft and ground-based observations with chemical transport modeling to infer surface levels. Geosynchronous orbit (GEO) to get temporal evolution and high horizontal resolution, in addition to LEO to get global coverage and allow for tracking long-range transport of pollution. A satellite at Lagrange point-1 may provide daylight-side coverage potentially hourly. PM: radiometric and polarimetric | POR-21 (MODIS), POR-22 (MISR, MAIA, 3MI), POR-24 (VIIRS), POR-11 (TEMPO) | TO-1, 2 |
WEATHER AND AIR QUALITY PANEL | |||||||
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question/Goal | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
instrument (e.g., NASA EV MAIA, MISR) | |||||||
Ozone (O3) concentration | O3: Chappuis and other UV bands to infer O3 from 0-2 km layer, and supported by modeling to infer surface level. Six observations during daylight hours to get diurnal distribution. Vertical resolution 500 m within BL. Horizontal resolution 5 × 5 km2. | UV/visible spectrometer at geo (e.g., TEMPO); commercial aircraft vertical observations during takeoff/landing | POR-11 (OMI, TEMPO, TROPOMI), POR-21 (GEMS) | ||||
NO2 (nitrogen dioxide) concentration | NO2: Lower tropospheric vertical distribution to infer NO2 from 0-2 km layer. Six observations during daylight hours to get diurnal distribution. Vertical resolution 500 m within BL. Horizontal resolution 5 × 5 km2. | UV/visible (e.g., Aura OMI, ESA TROPOMI, TEMPO); commercial aircraft vertical observations during takeoff/landing | POR-11 (OMI, TEMPO, TROPOMI), POR-21 (GEMS) | ||||
QUESTION W-6. Air Pollution Processes and Trends. What processes determine the long-term variations and trends in air pollution and their subsequent long-term recurring and cumulative impacts on human health, agriculture, and ecosystems? | W-6a. Characterize long-term trends and variations in global, vertically resolved speciated particulate matter (PM), ozone (O3), and nitrogen dioxide (NO2) trends (within 20%/yr), which are necessary for the determination of controlling processes and estimation of health effects and impacts on agriculture and ecosystems. | Important | PM concentration and properties, including speciation | PM: Aerosol Optical Depth to infer PM from 0‐2 km layer; Six observations during daylight hours to get diurnal distribution. 5 × 5 km2 horizontal resolution. Spectral properties to infer PM speciation. | Combine advanced space-based observations, aircraft and ground-based observations with chemical transport modeling to infer surface levels. Geosynchronous orbit (GEO) to get temporal evolution and high horizontal resolution, in addition to LEO to get global coverage and allow for tracking long-range transport of pollution. A satellite at Lagrange point 1 may provide daylight-side coverage potentially hourly. PM: radiometric and polarimetric instrument (e.g., NASA EV MAIA, MISR) | POR-21 (MODIS), POR-22 (MISR, MAIA, 3MI), POR-24 (VIIRS), POR-11 (TEMPO) | TO-1, 2 |
O3 (ozone) concentration | O3: Chappuis and other UV bands to infer O3 from 0-2 km layer, and supported by modeling to infer surface level. Six observations during daylight hours to get diurnal distribution. Vertical resolution 500 m within BL. Horizontal resolution 5 × 5 km2. | UV/visible spectrometer at geo (e.g., TEMPO); commercial aircraft vertical observations during takeoff/landing | POR-11 (OMI, TEMPO, TROPOMI), POR-21 (GEMS) | ||||
NO2 (nitrogen dioxide) concentration | NO2: Lower tropospheric vertical distribution to infer NO2 from 0-2 km layer. Six observations during daylight hours to get diurnal distribution. Vertical resolution 500 m within BL. Horizontal resolution 5 × 5 km2. | UV/visible (e.g., Aura OMI, ESA TROPOMI, TEMPO); commercial aircraft vertical observations during takeoff/landing | POR-11 (OMI, TEMPO, TROPOMI), POR-21 (GEMS) | ||||
QUESTION W-7. Tropospheric Ozone Processes and Trends. What processes determine observed tropospheric ozone (O3) variations and trends and what are the concomitant impacts of these changes on atmospheric composition/chemistry and climate? | W-7a. Characterize tropospheric O3 variations, including stratospheric-tropospheric exchange of O3 and impacts on surface air quality and background levels. | Important | O3 (ozone) concentration | O3: Vertical distribution within the troposphere and lower stratosphere through a combination of ozonesondes (0.5 km vertical resolution, weekly sampling, to 70 hPa) and satellites (e.g., 0.5 km in vertical resolution in upper troposphere, lower stratosphere; 5.5 km2 column observation with near surface (0-2 km) sensitivity). | Filter radiometer (e.g., Aura HIRDLS) for upper troposphere/lower stratosphere O3 in conjunction with an ozonesonde network and commercial aircraft observations during takeoff/landing. | POR-11 (HIRDLS, TEMPO), POR-15 (LIS, GLM), POR-21 (GEMS) |
WEATHER AND AIR QUALITY PANEL | |||||||
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question/Goal | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
QUESTION W-8. Methane Source Trends and Processes. What processes determine observed atmospheric methane (CH4) variations and trends and what are the subsequent impacts of these changes on atmospheric composition/chemistry and climate? | W-8a. Reduce uncertainty in tropospheric CH4 concentrations and in CH4 emissions, including uncertainties in the factors that affect natural fluxes. | Important | CH4 (methane) concentration | CH4 column (LEO): 7 × 7 km2 horizontal resolution; daily overpass; precision = 0.6% (upcoming TROPOMI specifications – full physics method). CH4 column (GEO): 4 × 4 km2 horizontal resolution; hourly observations; precision = 1.0% (GEO-CAPE specifications) Both TROPOMI and GEO-CAPE may be able to resolve large point sources on daily scales. |
Passive instruments give global coverage of columns (e.g., SCIAMACHY), but stymied by clouds and low light conditions. Emissions estimated from a model using satellite-observed methane and proxies (e.g., inundation depth) for emissions. | POR-10 (GOSAT, GOSAT2), POR-11 (TROPOMI) | TO-6 |
Active: pencil pixel size; along track coverage, precision = 1.0% (specifications for upcoming MERLIN) | Active lidar instruments give data in regions that passive instruments cannot, such as night, low light and/or cloudy environments (e.g., monsoons, Arctic). | POR-2 (MERLIN) | |||||
QUESTION W-9. Role of Cloud Microphysical Processes. What processes determine cloud microphysical properties and their connections to aerosols and precipitation? | W-9a. Characterize the microphysical processes and interactions of hydrometeors by measuring the hydrometeor distribution and precip rate to within 5%. | Important | 3D hydrometeor concentration and drop size distribution | 0.5 g/kg | Microwave, IR | TO-5 | |
Vertical temperature profile | Horizontal resolution: 3 km; 1 km vertical; refresh; global 90 minutes, CONUS 60 minutes. | Microwave and IR sounders, GNSS-RO | POR-1, 6, 23 | TO-13 | |||
Vertical water vapor profile | Horizontal resolution: 3 km; 1 km vertical; refresh; global 90 min CONUS 60 minutes. | Microwave and IR sounders, GNSS-RO | POR-23 | TO-13 | |||
Vertical profiles of horizontal wind vector | Horizontal resolution: 3 km; 1 km vertical; refresh; global 90 minutes and CONUS 60 minutes. | Doppler wind lidar, AMVs from IR, WV and visible imagers and sounders | TO-13 | ||||
Precipitation rate | 1 mm/hr accuracy; 2 km horizontal resolution; 1 min temporal refresh over a 20-30 min period. | TO-5 | |||||
Aerosol concentration | Aerosol optical depth (300 m resolution) | Nadir and multiangle radiometers (MODIS, MISR), lidars (CALYPSO, HRSL), Sun photometers (ground based) for calibration/validation. | POR-1, 2, 4, 10, 12 | TO-2, TO-1 | |||
QUESTION W-10. Clouds and Radiative Forcing. How do clouds affect the radiative forcing at the surface and contribute to predictability on time scales from minutes to subseasonal? | W-10a. Quantify the effects of clouds of all scales on radiative fluxes, including on the boundary layer evolution. Determine the structure, evolution and physical/dynamical properties of clouds on all scales, including small-scale cumulus clouds. | Important | High-resolution 2D cloud fraction, helpful to also have estimates of cloud depth, and cloud droplet distribution; Ground-based radiation, water vapor, horizontal and vertical winds, temperature; Hydrometeors, temperature, moisture, winds from the boundary layer through the troposphere and into the UTLS; 3D aerosols, hydrometeors, vertical and horizontal winds, water vapor, temperature, precipitation. | Within 2% for cloud fraction over a 5 × 5 km area; spatial resolution 200 m desirable. | High-resolution visible/IR | POR-1, 11, 21, 23, 24 |
MARINE AND TERRESTRIAL ECOSYSTEMS AND NATURAL RESOURCES MANAGEMENT PANEL | |||||||
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question/Goal | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
QUESTION E-1. Ecosystem Structure, Function, and Biodiversity. What are the structure, function, and biodiversity of Earth’s ecosystems, and how and why are they changing in time and space? | E-1a. Quantify the distribution of the functional traits, functional types, and composition of vegetation and marine biomass, spatially and over time. | Very Important | Chemical properties of vegetation, aquatic biomass, and soils | Land, inland aquatic, costal zone, and shallow coral reef: Spectral radiance (10 nm; 380-2500 nm); GSD = 30-45 m; Revisit = ~15 days; SNR = 400:1 VNIR/250:1 SWIR at 25% reflectance; IT of ~5 ms. Ocean: Spectral radiance (5 nm; 380-1050 nm); GSD = 0.25-1.0 km; Revisit = <2 days; SNR = 1000:1 at TOA clear sky ocean radiance (PACE) |
High-fidelity imaging spectrometer (150-200 km swath from Sun-synchronous LEO). For ocean, imaging spectrometer with wider swath and larger pixels. | Landsat-8 and -9 plus ESA Sentinal-2a, -2b, -2c, and -2d missions 30 m multispectra 3-4 day equatorial revisit frequency POR complement TO-18. PACE for ocean. | TO-18 |
Western hemisphere coastal ocean, inland waters: Geostationary; 100-300 m; 5-10 nm; 2-3 hr repeat; GSD = ~250 m | GEO spectrometers (e.g., GEO-CAPE) | TO-3 | |||||
E-1b. Quantify the three-dimensional (3D) structure of terrestrial vegetation and 3D distribution of marine biomass within the euphotic zone, spatially and over time. | Most Important | 3D physical structure of vegetation and aquatic biomass | Land: Imaging waveform acquired in swaths; desired sampling = 1 ha cells with 10-25 m footprint size; global sampling every 5 years. Ocean: ~2 m vertical resolution subsurface to ~3 optical depths; High spectral resolution lidar (or similar) technique to retrieve vertical particle backscatter and vertical extinction profiles; ≤1 km footprint at sea surface; global |
Imaging lidar (Land: 1064 nm; Ocean: 532 nm and 355 nm). NOTES: GEDI to deploy in 2019 for two years of canopy structure and biomass sampling; Synergy with ICESat-2 (not ideal for land vegetation) to launch circa 2020; Synergy with NISAR radar mission launches in 2022; Synergy with BIOMASS P-band mission to launch in 2020. | TO-22 and TO-20 for land lidar; TO-1 if it meets specs for ocean and TO-10 lidar | ||
E-1c. Quantify the physiological dynamics of terrestrial and aquatic primary producers. | Most Important | Primary Observable: Chemical properties of vegetation and aquatic biomass, and soils | Land, inland aquatic, coastal zone, and shallow coral reef: Spectral radiance (10 nm; 380-2500 nm); GSD = 30-45 m; Revisit = ~15 days; SNR = 400:1 VNIR/250:1 SWIR at 25% reflectance; IT of ~5 ms. Ocean: Spectral radiance (5 nm; 380-1050 nm); GSD = 0.25-1.0 km; Revisit = <2 days; SNR = 1000:1 at TOA Clear sky ocean radiance (PACE) |
High-fidelity imaging spectrometer (150-200 km swath from sun-sync LEO). For ocean, imaging spectrometer with wider swath and larger pixels. | Landsat-8 and -9 plus ESA Sentinal-2a, -2b, -2c, and -2d missions 30 m multispectra 3-4 day equatorial revisit frequency. For ocean, PACE plus BioArgo (in situ). | TO-18 | |
Supporting observable: Solar-induced fluorescence | 400-790 nm; 0.3 nm bandwidth (FWHM) | SIF spectrometer. NOTES: SIF sensors are in orbit and are planned, but require other measurements for interpretation. Science community indicates the need for concurrent imaging spectrometer (hyperspectral) and lidar measurements | GOME-2 POR and assume GEOCARB and FLEX will also be POR GEDI as POR lidar |
TO-18 | |||
Supporting observable: Thermal IR imaging | 8-12 microns (cloudband at 1.6 microns); multispectral; GSD = 50-100 m; revisit = <15 days; day and night measurements. 150-200 km swath from Sun-synchronous LEO. | TIR imager (e.g., Landsat TIR). NOTES: Needs to be coupled with the spectrometer to determine physiology (including ET). | Landsat-8 and 9, MODIS, VIIRS plus ECOSTRESS are POR | TO-17 | |||
Water particles for biomass accounting | Western Hemisphere Coastal Waters; Geostationary; 100-300 m; 5-10 nm; 2-3 hr repeat; GSD = ~250 m | GEO spectrometers (e.g., GEO-CAPE) | TO-3 | ||||
Net radiation and temperature for ET | Geostationary | Net radiation (e.g., GOES) | NOAA GOES-16 and GOES-S is POR |
MARINE AND TERRESTRIAL ECOSYSTEMS AND NATURAL RESOURCES MANAGEMENT PANEL | |||||||
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question/Goal | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
E-1d. Quantify moisture status of soils. | Important | Soil moisture | Combined radar (L-, P-band) and radiometer | Soil moisture (e.g., SMAP, SMOS) | SMAP, SMOS | TO-17 | |
E-1e. Support targeted species detection and analysis (e.g., foundation species, invasive species, indicator species, etc.). | Important | Plant species and/or aquatic biomass classification | Land, inland aquatic, costal zone, and shallow coral reef: Spectral radiance (10 nm; 380-2500 nm); GSD = 30-45 m; Revisit = ~15 days; SNR = 400:1 VNIR/250:1 SWIR at 25% reflectance; IT of ~5 ms. Land: Lidar; 1064 nm; Imaging waveform acquired in swaths; desired sampling = 1 ha cells with 10-25 m footprint size; global sampling every 5 years. |
High-fidelity imaging spectrometer (150-200 km swath from LEO, e.g., VSWIR or HyspIRI) | Landsat-8 and -9 plus ESA Sentinal-2a, -2b, -2c, and -2d missions 30 m multispectra 3-4 day equatorial revisit frequency POR and complement TO-18 for plant functional types | TO-18 | |
Open Ocean, Land Vegetation mapping: Spectral radiance (5 nm; 380-1050 nm); GSD = 0.25-1.0 km; Revisit = <2 days; SNR = 1000:1 at TOA clear sky ocean radiance (PACE). | High-fidelity imaging spectrometer (1500-2000 km swath from LEO, e.g., PACE) | SeaWiFS, MODIS, VIIRS and Sentinel-3 (all at reduced spectral resolution) and PACE | TO-10 | ||||
Western hemisphere coastal waters:Geostationary; 100-300 m; 5-10 nm; 2-3 hr repeat; GSD = ~250 m | GEO spectrometers (e.g., GEO-CAPE) | TO-3 | |||||
QUESTION E-2. Fluxes Between Ecosystems, Atmosphere, Oceans, and Solid Earth. What are the fluxes (of carbon, water, nutrients, and energy) between ecosystems and the atmosphere, the ocean, and solid Earth, and how and why are they changing? | E-2a. Quantify the fluxes of CO2 and CH4 globally at spatial scales of 100 to 500 km and monthly temporal resolution with uncertainty <25% between land ecosystems and atmosphere and between ocean ecosystems and atmosphere. | Most Important | Active and passive observations of atmospheric CO2, CH4, and CO concentrations | High accuracy column measurements with near-surface sensitivity, Global coverage in all seasons with 1-3 day revisit, Footprint resolution of ≤4 km CO2: random error <1 ppm, systematic error <0.2 ppm CH4: random error <10 ppb, systematic error <0.5 ppb CO: random error <10 ppb |
Active and/or passive NIR observations for total column CO2, CH4; TIR/SWIR observations of CO | GOSAT, GOSAT-2, OCO-2, OCO-3, GeoCARB, TEMPO, TROPOMI aboard Sentinel-5p | TO-6 |
GPP, respiration, and decomposition, and biomass burning | Global, daily, 30 m / 300 m | Possible: VIIRS, Landsat, lidar topography, commercial sat data | Landsat-8 and -9 plus ESA Sentinal-2a, -2b, -2c, and -2d missions 30 m multispectra 3-4 day equatorial revisit frequency POR | TO-6 and TO-18 | |||
3D winds | Global, daily, 5 km | Lidar winds | TO-4 | ||||
Air-sea delta pCO2 and air-sea gas transfer coef | global/daily, ≤100 km (less if apprp) | Possible: VIIRS, Aquarius FO, QuikSCAT FO | VIIRS, QuikSCAT | ||||
E-2b. Quantify the fluxes from land ecosystems between aquatic ecosystems. | Important | Riverine transport of nutrients, organic matter and other constituents to oceans and inland waters | River discharge, water quality (POC, DOC, nutrients, CDOM, turbidity); high revisit (2-3 days) | Possible: MODIS, VIIRS, Landsat, Sentinel-2 water quality | MODIS, VIIRS, Landsat, Sentinel-2 water quality | TO-3 | |
E-2c. Assess ecosystem subsidies from solid Earth. | Important | Dust inputs, soil erosion, landslides, black carbon | High spatial resolution (1 m), bare-Earth topography at 0.1 m vertical accuracy Spectral radiance (10 nm; 380-2500 nm); GSD = 30-45 m | Aircraft lidar; VSWIR | MISR | ||
QUESTION E-3. Fluxes Within Ecosystems. What are the fluxes (of carbon, water, nutrients, and energy) within ecosystems, and how and why are they changing? | E-3a. Quantify the flows of energy, carbon, water, nutrients, and so on, sustaining the life cycle of terrestrial and marine ecosystems and partitioning into functional types. | Most Important | GPP, respiration, litterfall and decomposition, nonPS vegetation, functional types | Global, daily, 30 m / 300 m Daily SIF measurements | MODIS, VIIRS, Landsat, lidar topography, commercial sat data SIF from GOSAT, GOME-2, and FLEX | Landsat-8 and -9 plus ESA Sentinal-2a, -2b, -2c, and -2d missions 30 m multispectra 3-4 day equatorial revisit frequency POR GOSAT and GOME-2 |
TO-18 and TO-20 |
CO2, CO, CH4, etc. fluxes from biomass burning | Global. Daily, 300 m. | e.g., {Sentinel-3} MODIS and VIIRS | MODIS and VIIRS POR | TO-6 |
MARINE AND TERRESTRIAL ECOSYSTEMS AND NATURAL RESOURCES MANAGEMENT PANEL | |||||||
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question/Goal | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
ET and root zone moisture | Globally, weekly, ~50 km. | Soil moisture (e.g., SMAP, SMOS, HDO from TES) | SMAP, TES | TO-17 | |||
Aquatic NPP, PhytoC and Chl, NCP, Export from the euphotic zone, N2 fixation and calcification, partitioned into functional types | Hyperspectral with spatial resolution 1 km and 1-2 day revisit for open ocean (PACE). Multi-spectral regional imaging with <200 m spatial resolution and <1 day revisit for coastal and inland waters (GEO-CAPE). |
Imaging spectrometer (e.g., PACE, VIIRS); In situ ocean measurements and modeling; high-fidelity imaging spectrometer (150-200 km swath from Sun-synchronous LEO) for aquatic and coastal waters. | MODIS, VIIRS, PACE, Sentinel-3, Landsat-8 and -9 plus ESA Sentinal-2a, -2b, -2c, and -2d missions 30 m multispectra 3-4 day equatorial revisit frequency POR | TO-3 and TO-18 | |||
E-3b. Understand how ecosystems support higher trophic levels of food webs. | Important | Rates of herbivory on terrestrial vegetation | See E-2a and E-3a | See E-2a and E-3a | |||
Zooplankton population dynamics and secondary production | Modeling plus new sensor concept, possibly lidar. | ||||||
QUESTION E-4. How is carbon accounted for through carbon storage, turnover, and accumulated biomass? Have all of the major carbon sinks been quantified and how are they changing in time? | E-4a. Improve assessments of the global inventory of terrestrial C pools and their rate of turnover. | Important | Aboveground carbon density (biomass) | Global, daily 30 m / 300 m lidar; 1064 nm; Imaging waveform acquired in swaths; desired sampling = 1ha cells with 10-25 m footprint size; global sampling every 5 years. | GEDI, ICESat-2, Landsat-8, Sentinel-2, MODIS, VIIRS | TO-20 and TO-22 | |
Terrestrial GPP, respiration, decomposition and biomass burning | See E-2a and E-3a | See E-2a and E-3a | TO-6 and TO-18 | ||||
E-4b. Constrain ocean C storage and turnover. | Important | Air-sea CO2 fluxes | Hyperspectral with spatial resolution 1 km and 1-2 day revisit for open ocean. | NPP for biological contribution | PACE | ||
Vertical profile of export flux and water mass ventilation ages | Hyperspectral with spatial resolution 1 km and 1-2 day revisit for open ocean. | NPP (see above) plus in situ measurements (e.g., ARGO) and particle flux modeling. | PACE (for NPP) | TO-3 and TO-10 | |||
QUESTION E-5. Carbon Sinks. Are carbon sinks stable, are they changing, and why? | E-5a. Discover ecosystem thresholds in altering C storage. | Important | Roles of temperature and moisture, changes in community structure (incl. invasives), sea-level rise, ocean upwelling, etc. on C storage | See E-1 through E-4 | Imaging spectrometer (e.g., PACE, MODIS, VIIRS); In situ ocean measurements and modeling | MODIS, VIIRS, PACE, Sentinel-3, Landsat-8 and -9 plus ESA Sentinal-2a, -2b, -2c, and -2d missions 30 m multispectra 3-4 day equatorial revisit frequency POR | TO-22 and TO-20 for land Lidar; TO-1 if it meets specs for ocean and TO-10 lidar |
E-5b. Discover cascading perturbations in ecosystems related to carbon storage. | Important | Cascading ecological perturbations (e.g., pine beetles, plankton or algal blooms, permafrost thawing, wildfire) | Hyperspectral with spatial resolution 1 km and 1-2 day revisit for open ocean. | Imaging spectrometer (e.g., PACE, MODIS, VIIRS); In situ ocean measurements and modeling | MODIS, VIIRS, PACE, Sentinel-3, Landsat-8 and -9 plus ESA Sentinal-2a, -2b, -2c, and -2d missions 30 m multispectra 3-4 day equatorial revisit frequency POR | TO-3, TO-18, TO-20, and TO-22 | |
E-5c. Understand ecosystem response to fire events. | Important | Wild and prescribed fires including active fire and burn area detection, GPP; 3D physical structure of vegetation; Chemical properties of vegetation | Global. Daily, 300 m. | Active fire detection using MODIS; VIIRS and the proposed TIR mission; (same as in E-1c but with eight bands); Burn area assessment using Landsat and Sentinel-2, MODIS, VIIRS and the proposed VSWIR imaging spectrometer (400 to 2500 nm) (same as in E-1e); Vegetation structure/fuel load assessment using lidar (same as in E-1b), SAR (Sentinel 1), and black carbon/smoke using CALIPSO, GEDI, GLAS, ICESat-1/2 | Landsat-8 and -9 plus ESA Sentinal-2a, -2b, -2c, and -2d missions 30 m multispectra 3-4 day equatorial revisit frequency POR | TO-2, TO-6, TO-12, TO-18, and TO-22 |
CLIMATE VARIABILITY AND CHANGE PANEL | |||||||
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
QUESTION C-1. Sea-level Rise: Ocean Heat Storage and Ice Melt. How much will sea-level rise, globally and regionally, over the next decade and beyond, and what will be the role of ice sheets and ocean heat storage? | C-1a. Determine the global mean sea-level rise to within 0.5 mm/yr over the course of a decade. | Most Important | Sea-surface height | Space/Time Sampling: 7 km along track/10 day; Space/Time Coverage: global/10 days; Accuracy/Stability: 3 cm at 7 km and 1 mm/global/yr | Radar altimeter, with microwave water vapor radiometer and precision orbit | POR-26, 27 | TO-21 |
Terrestrial reference frame | Space/Time Sampling: monthly; Space/Time Coverage: global/year-decade; Accuracy/Stability: 1 mm/0.1 mm/yr/decade | GNSS RO | POR-7, 13 (GRACE-FO, Jason-3, LAGEOS, GRASP) | ||||
Ocean mass distribution | Space/Time Sampling: 300 km2/monthly; Space/Time Coverage: global/monthly; Accuracy/Stability: 15 mm at 300 km2, 0.1 mm/yr/decade | Gravity (e.g., GRACE FO) | POR-30 | TO-9 | |||
C-1b. Determine the change in the global oceanic heat uptake to within 0.1 W/m2 over the course of a decade. | Most Important | Sea-surface height | Space/Time Sampling: 7 km along track/10 day, equivalent to 150 km2/10 day; Space/Time Coverage: global/10 days; Accuracy/Stability: 3 mm at 7 km, 1 mm/global/yr | Radar altimeter, with microwave water vapor radiometer and precision orbit | POR-26, 27 | TO-21 | |
Ocean mass distribution | Space/Time Sampling: 300 km2/monthly; Space/Time Coverage: global/monthly; Accuracy/Stability: 15 mm at 300 km2, 0.1 mm/yr/decade | Gravity (e.g., GRACE FO) | POR-30 | ||||
Ocean temperature and salinity profile | Space/Time Sampling: 3 degrees × 3 degrees/10 day, equivalent to 150 km2/10 day; Space/Time Coverage: global/10 days; Accuracy/Stability: 0.01 deg/0.01 psu | [in situ, such as Argo] | [Argo, Deep Argo] | TERRESTRIAL | |||
C-1c. Determine the changes in total ice-sheet mass balance to within 15 Gton/yr over the course of a decade and the changes in surface mass balance and glacier ice discharge with the same accuracy over the entire ice sheets, continuously, for decades to come. | Most Important | Ice-sheet mass | Horizontal resolution/range: 100 km / Global; Temporal sampling: Monthly; Precision: 1 cm water equivalent on scale of 200 km | Gravity (e.g., GRACE FO), NISAR/Landsat, [reanalysis], Operation IceBridge. | POR-30 | ||
Ice-sheet velocity | Horizontal resolution/range: 100 m / pole to pole; Temporal sampling: weekly to daily; Precision: 1 m/yr in fast flow areas, 1 cm/yr near ice divides | SAR (e.g., NISAR), Landsat | POR-12 | TO-19 | |||
Ice-sheet elevation | Vertical resolution/range: 10-20 cm; Horizontal resolution/range: 100 m/pole to pole; Temporal sampling: weekly to daily; Precision: 10-20 cm | Operation IceBridge, ICESat-2, {WorldView satellites}, GLISTIN | POR-14 | TO-20 | |||
Ice-sheet thickness, ice-shelf thickness | Vertical resolution/range: 10 m pole to pole; Horizontal resolution/range: 100 m/pole to pole; Temporal sampling: yearly; Precision: 10 m | Operation IceBridge, ICESat-2 (ice shelf), {WorldView satellites} | POR-14 | TO-20 | |||
Ice-sheet bed elevation, ice-shelf cavity shape | Vertical resolution/range: 30 m; Horizontal resolution/range: 100 m/pole to pole; Temporal sampling: one time; Precision: 30 m | Operation IceBridge, EVS-2 OMG, new EVS Antarctica | TO-20 | ||||
Ice-sheet surface mass balance | Vertical resolution/range: 1 mm/yr; Horizontal resolution/range: 5 km/pole to pole; Temporal sampling: monthly; Precision: 1 mm/yr | Gravity (e.g., GRACE FO), ICESat-2, Operation IceBridge, [re-analysis data] | POR-14, 30 | TO-20 | |||
C-1d. Determine regional sea-level change to within 1.5-2.5 mm/yr over the course of a decade (1.5 corresponds to a | Very Important | Sea-surface height | Space/Time Sampling: 250 m/weekly at midlatitudes; Space/Time Coverage: 20 days/global; Accuracy: 10 cm | e.g., SWOT to be launched 2021 | TO-21 |
CLIMATE VARIABILITY AND CHANGE PANEL | |||||||
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
~6000 km2 region, 2.5 corresponds to a ~4000 km2 region). | Land vertical motions | Space/Time Sampling: 100 m along coast; Space/Time Coverage: global coastline/monthly; Accuracy/stability: 1 mm/1 mm/yr | [ground GPS] | TERRESTRIAL | |||
Ocean mass distribution | Same as C-1a | POR-30 | |||||
Wind vector | Space/Time Sampling: 25 × 25 km/weekly; Space/Time Coverage: global/weekly; Accuracy/stability: 2 m/s speed, 15 degree direction | METOP A/B, Oceansat-3, HY-2B | POR-28 | TO-11 | |||
QUESTION C-2. Climate Feedback and Sensitivity. How can we reduce the uncertainty in the amount of future warming of Earth as a function of fossil fuel emissions, improve our ability to predict local and regional climate response to natural and anthropogenic forcings, and reduce the uncertainty in global climate sensitivity that drives uncertainty in future economic impacts and mitigation/adaptation strategies? | C-2a. Reduce uncertainty in low and high cloud feedback by a factor of 2. | Most Important | Top of Atmosphere Shortwave (SW), Longwave (LW) and Net Radiative Fluxes for All-sky and Clear-sky conditions | Space/Time Sampling: 100 km, monthly; Space/Time Coverage: global, decadal trends; Accuracy/Stability: global SW TOA flux at 0.3% (95% confidence), global LW TOA flux at 0.6% (95% confidence); Ultimate requirement is on use for Cloud Radiative Effect for SW and LW | Earth radiation monitor (e.g., CERES), RBI for space-time-angle sampling, CLARREO-like intercalibration (to achieve decadal trend accuracy/stability) | POR-3, 24 | TO-14 |
Cloud fraction | Space/Time Sampling: 100 km, monthly; Space/Time Coverage: global, decadal trends; Accuracy/Stability: 0.1% relative to global mean (1σ) | Cloud imagers (e.g., MODIS/VIIRS), or lidar (e.g., CALIPSO) or HSRL | POR-24 | TO-2 | |||
Cloud optical depth | Space/Time Sampling: 100 km, monthly; Space/Time Coverage: global, decadal trends; Accuracy/Stability: 0.3% relative to global mean log optical depth (95% confidence) | Cloud imagers (e.g., MODIS/VIIRS) plus CLARREO-like intercalibration (to achieve optical depth trend accuracy/stability) | POR-24 | TO-14 | |||
Cloud infrared emissivity | Space/Time Sampling: 100 km, monthly; Space/Time Coverage: global, decadal trends | Cloud imagers (e.g., MODIS/VIIRS), or lidar (e.g., CALIPSO) or HSRL | POR-24 | TO-2 or TO-14 | |||
Cloud top height | Space/Time Sampling: 100 km, monthly; Space/Time Coverage: global, decadal trends; Accuracy/Stability: 0.04 K (1σ) | Cloud imagers (e.g., MODIS/VIIRS) plus CLARREO-like intercalibration or lidar (e.g., CALIPSO) or HSRL | POR-24 | TO-2 or TO-14 | |||
Cloud effective radiating temperature | Space/Time Sampling: 100 km, monthly; Space/Time Coverage: global, decadal trends; Accuracy/Stability: 0.04 K (1σ) | Cloud imagers (e.g., MODIS/VIIRS) plus CLARREO-like intercalibration | POR-24 | TO-2 or TO-14 | |||
Cloud phase (water, ice) | Space/Time Sampling: 100 km, monthly; Space/Time Coverage: global, decadal trends | Cloud imagers (e.g., MODIS/VIIRS), or lidar (e.g., CALIPSO) or HSRL | POR-24 | TO-14 | |||
Cloud particle size | Space/Time Sampling: 100 km, monthly; Space/Time Coverage: global, decadal trends | Cloud imagers (e.g., MODIS/VIIRS) plus CLARREO-like intercalibration | POR-24 | TO-14 | |||
C-2b. Reduce uncertainty in water vapor feedback by a factor of 2. | Very Important | Atmospheric water vapor and temperature profiles | Vertical Resolution/Coverage: 2 km from 0 to 15 km altitude; Space/Time Sampling: 100 km horizontal resolution/monthly average Time/Space Coverage: decadal trends/global; Accuracy/Stability: 0.03 K (1σ) | IR sounders (e.g., CrIS, IASI) plus CLARREO-like intercalibration, GNSS-RO for zonal temperature trend in 5-20 km altitude | POR-6, 20 | TO-14 | |
C-2c. Reduce uncertainty in temperature lapse rate feedback by a factor of 2. | Very Important | Atmospheric temperature profile | Vertical Resolution/Coverage: 2 km from 0 to 15 km altitude; Space/Time Sampling: 2 km vertical resolution, 100 km horizontal resolution/monthly Time/Space Coverage: decadal | IR sounders (e.g., CrIS, IASI) plus CLARREO-like intercalibration, GNSS-RO for zonal temperature trend in 5-20 km altitude | POR-6, 20; Suborbital: Global Climate Observing System Reference Upper-Air Network (GRUAN), NWS Upper-Air Observations Program | TO-14 |
CLIMATE VARIABILITY AND CHANGE PANEL | |||||||
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
trends/global; Accuracy/Stability: 0.03 K (1σ) | |||||||
C-2d. Reduce uncertainty in carbon cycle feedback by a factor of 2. | Most Important | See Objectives C-3a, C-3c, C-3d, and C-3e | See Objective C-3a, C-3c, and C-3e | ||||
C-2e. Reduce uncertainty in snow/ice albedo feedback by a factor of 2. | Important | Surface Snow and Ice Coverage | Space/Time Sampling: 10 km horizontal resolution/monthly average; Time/Space Coverage: decadal trends regional and global; Accuracy/Stability: 1% (1σ) | MODIS/VIIRS for snow/ice cover and surface albedo. CERES for TOA albedo | POR-3, 24 | ||
Surface albedo and Top of Atmosphere albedo (spectral and broadband) | Space/Time Sampling: 100 km horizontal resolution/monthly average; Time/Space Coverage: decadal trends regional and global; Accuracy/Stability: 1% (1σ) | MODIS/VIIRS for snow/ice cover and surface albedo. CERES for TOA albedo | POR-3, 24 | ||||
C-2f. Determine the decadal average in global heat storage to 0.1 W/m2 (67% confidence) and interannual variability to 0.2 W/m2 (67% confidence). | Very Important | Same as for Objective C-1b | See Objective C-1b | POR-3, 24 | |||
Global net radiation | Space/Time Sampling: 100 km, monthly; Space/Time Coverage: Monthly to decadal, Global; Interannual stability/drift of calibration less than 0.1 W/m2 | Earth radiation monitor (e.g., CERES), RBI for space-time-angle sampling, CLARREO-like intercalibration (to achieve decadal trend accuracy/stability) | POR-3, 24 | TO-14 | |||
Total solar irradiance | Space/Time Sampling: full solar disk, daily; Space/Time Coverage: monthly to decadal; Accuracy/Stability: 0.01%/0.01% per decade | Total solar irradiance (e.g., TSIS) | TSIS on ISS, JPSS | ||||
C-2g. Quantify the contribution of the upper troposphere and stratosphere (UTS) to climate feedbacks and change by determining how changes in UTS composition and temperature affect radiative forcing with a 1-sigma uncertainty of 0.05 W/m2 over the course of the decade. | Very Important | Vertical profiles of temperature in UTS (upper troposphere and stratosphere), for quantifying radiative forcing | Vertical resolution and range: <1 km in UTLS, <3 km in mid-upper stratosphere; Horizontal resolution/range: 5° latitude × 10° longitude / Global; Temporal sampling: weekly; Precision: 1-2 K. | UV, visible, and infrared solar, lunar, and stellar occultation (e.g., ACE-FTS, GOMOS, HALOE, POAM, SAGE) High precision, high vertical resolution (<1 km), climate (trend) quality, but poor spatial coverage unless using a constellation. Infrared and microwave limb emission (e.g., HIRDLS, MIPAS, MLS, Odin SMR) Many species, 3-4 km vertical resolution, night and day, global coverage Visible limb scattering (e.g., SME, OMPS-LP, OSIRIS) Lidar (e.g., CALIPSO-CALIOP) High (meters) vertical resolution Radar (e.g., CloudSat CPR) Nadir-viewing solar reflection/scattering (e.g., AIM-CIPS, MODIS, OCO-2) Good (km) horizontal resolution |
POR-6, 16, 17, 18 ACE-FTS (not global), Auro MLS (not 1 km verical resolution; not self-calibrating), GPS-RO (extreme path length), MSU/SSU/AMSU (10 km vertical resolution; not self-calibrating) |
TO-13 | |
Vertical profiles of radiatively active gas concentrations in the UTS, for quantifying radiative forcing | Concentration of O3: Vertical resolution and range: <1 km in UTLS, <3 km in mid-upper stratosphere; Horizontal resolution/range: 5° latitude × 10° longitude / Global; Temporal sampling: weekly; Precision: 10%. Concentration of H2O: Vertical resolution and range: <1 km in UTLS, <3 km in mid-upper stratosphere; Horizontal resolution/range: 5° latitude × 10° longitude / Global; Temporal sampling: weekly; Precision: 10%. |
POR-6, 16, 17, 18 ACE-FTS (not global; not 1 km vertical resolution), Aura MLS (not 1 km vertical resolution; not self-calibrating), OMPS-L (not 1 km vertical resolution; not self-calibrating (?)); Suborbital measurements: Network for the Detection of Atmospheric Composition Change, Global Climate Observing System Reference Upper-Air Network (GRUAN), Southern Hemisphere Additional Ozonesondes (SHADOZ) SAGE III (ISS) (not global; 18 Feb 2017 launch) |
TO-12 |
CLIMATE VARIABILITY AND CHANGE PANEL | |||||||
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
Vertical profiles of UTS aerosol radiative properties, for quantifying radiative forcing | Vertical resolution and range: <1 km in UTLS; Horizontal resolution/range: 5° latitude × 10° longitude / Global; Temporal sampling: weekly; Precision: 30%. | POR-2, 16, 18 ACE-FTS [not global; not 1 km vertical resolution], Odin OSIRIS [not 1 km vertical resolution; not polar night; no particle size info], CALIPSO CALIOP [near end-of-life] SAGE III (ISS) [not global] |
TO-1, TO-2 | ||||
Volcanic and biomass burning emissions, for process studies (sources and transport) | Volcanic Aerosol: Vertical resolution and range: <1 km in UTLS; Horizontal resolution/range: 5° latitude × 10° longitude / Global; Temporal sampling: daily during events; Precision: 30%. Concentrations of gases from volcanic emissions: Vertical resolution and range: <1 km in UTLS; Horizontal resolution/range: 5° latitude × 10° longitude / Global; Temporal sampling: daily during events; Precision: ~20%, but species dependent. |
POR-2, 16, 17, 18 Volcanic Aerosol: CALIPSO CALIOP [near end-of-life] Concentrations of gases from volcanic emissions: ACE-FTS [not global; not 1 km vertical resolution], MLS [not 1 km vertical resolution; not NO2] SAGE III (ISS) [not enough sampling to track the sources] |
TO-1, TO-2, TO-12 | ||||
Deep convective clouds, for process studies (sources) | Vertical resolution and range: <1 km in UTLS; Horizontal range: Global; Horizontal and temporal sampling: event-specific, episodic; Precision: 30%. | POR-1, 4, 10, 17, 21, 23 Aura MLS (not 1 km vertical resolution), Aqua AIRS, Aqua AMSR-E, Aqua MODIS, CloudSat |
TO-5 | ||||
Small-scale transport and the Brewer-Dobson circulation, for process studies (transport) | Vertical resolution and range: <1 km in UTLS, <3 km in mid-upper stratosphere; Horizontal resolution/range: 5° latitude × 10° longitude / Global; Temporal sampling: event-driven for small scale, weekly for BD; Precision: 30%. | POR-16, 17 ACE-FTS [not global; not 1 km vertical resolution], Aura MLS [not 1 km vertical resolution] |
TO-12, TO-13 | ||||
Dynamical features such as the polar vortex, for process studies (transport) | Vertical resolution and range: <1 km in UTLS, <3 km in mid-upper stratoshpere; Horizontal resolution/range: 5° latitude × 10° longitude / Global; Temporal sampling: daily to weekly; Precision: 1-2 K (T), 30% (UV). | POR-6, 16, 17, 18 ACE-FTS [not global; not 1 km vertical resolution], Aura MLS [not 1 km vertical resolution], GPS-RO [extreme path length] |
TO-12, TO-13 | ||||
Planetary and gravity waves, for process studies (transport) | Vertical resolution and range: <1 km from cloud top to stratopause; Horizontal resolution/range: 5° latitude × 10° longitude / Global; Temporal sampling: daily to weekly; Precision: 1-2 K (T). | POR-6, 16, 17, 18 ACE-FTS [not global; not 1 km vertical resolution], Aura MLS [not 1 km vertical resolution], GPS-RO [extreme path length] |
TO-12, TO-13 | ||||
Stratospheric ozone and related constituents, for process studies (chemistry) | Vertical resolution and range: <1 km from cloud top to stratopause; Horizontal resolution/range: 5° latitude × 10° longitude / Global; Temporal sampling: daily to weekly; Precision: 5% (O3), 10% (others). | POR-16, 17, 18 ACE-FTS [not global; not 1 km vertical resolution], Aura MLS [not 1 km vertical resolution; only some of the constituents]); Suborbital measurements: Network for the Detection of Atmospheric Composition |
TO-12 |
CLIMATE VARIABILITY AND CHANGE PANEL | |||||||
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
Change, Global Climate Observing System Reference Upper-Air Network (GRUAN), Southern Hemisphere Additional Ozonesondes (SHADOZ) SAGE III [not global; only some of the constituents] | |||||||
C-2h. Reduce the IPCC AR5 total aerosol radiative forcing uncertainty by a factor of 2. | Most Important | AEROSOLS. Aerosol Size (0.05 μm and larger), vertical profiles of mass concentrations (10/cm3 and greater), effective radius. Also needed: (1) column properties (aerosol optical depth, burden as aerosol mass concentration), (2) plume properties (e.g., characterizing aerosol type, plume height, and plume thickness), and (3) characteristics at cloud base. Physical properties: (1) ability to distinguish size of those aerosols most important to become cloud condensation nuclei (CCN, 0.1 μm radius and smaller), from those most radiatively active (0.5 μm and larger), (2) aerosol source/chemical composition, (3) hygroscopicity, Need to separate absorption from total extinction, and obtain vertical profiles of both extinction and absorption, as well as relative humidity. | Horizontal resolution requirements: at high (1 km) to very high (<100 m); Vertical resolution requirement: Information is needed at low (column integrals) and high (<500 m) resolution. Temporal sampling: weekly; Precision: 30%. Accuracy requirements: vertically and horizontally resolved aerosol number and mass concentration (100%), effective variance (50%), and effective radius (10%) over the 0.1 to 1 μm radius range. Vertical distribution of temperature (0.2°C), humidity (uncertainty 0.3 g/kg). | Nadir viewing radiometers (e.g., MODIS); Multiangle viewing radiometers (MISR); Current technology lidar (CALIPSO); More modern technology lidar (HSRL); HSRL can be used for vertically resolved profiles that better enable (1) distinguishing aerosols and clouds, (2) increased sensitivity (detecting lower concentrations of aerosol), (3) better resolution of aerosol amounts nearer the surface (lower altitude), and (4) more accurate aerosol optical depths. Multiangle, multispectral polarimeters can provide improved column integrated information on aerosol composition such as refractive index (including absorption), particle size, and variance. The combination of HSRL and a multiangle, multispectral polarimeter has improved accuracy beyond each individual instrument. | POR-1, 2, 5, and Terrestrial. Also, POR-7, 13 (GRACE-FO, Jason-3, LAGEOS, GRASP) | TO-1, TO-2, TO-12, TO-18, TO-20 | |
CLOUDS. Nearby (to aerosol fields) and simultaneous measurements of cloud properties (cloud water path, thickness, altitude, condensate phase, cloud particle size, anvil extent and thickness), and precipitation characteristics. Derive estimates (with uncertainty) of cloud base vertical motions and maximum updraft velocities in cloud. Lidar (e.g., CALIOP) to separate cloud and aerosol. Determine properties of the aerosol ingested into clouds and cloud systems, and obtain a sufficiently long and diverse (in cloud types and locations) record to enable statistical interpretation of aerosol-cloud interactions. Determine drizzle frequency and amount (e.g., CloudSat). |
Determination of cloud height, cloud cover, microphysical properties. Horizontal resolution: 5 degrees latitude × 10 degrees longitude/Global. Temporal sampling: weekly. Precision: 30%. Improving the confidence levels through increased accuracy in various cloud fields:
|
Nadir and multi angular radiometers (MODIS, MISR); Lidars (CALIPSO, HSRL); W-band radar reflectivity profiles (e.g., CloudSat) that accounts for light rain/snow; microwave brightness temperatures; shortwave reflectances in the near IR and visible bands; stereo photogrammetric methods that build on and advance measurements pioneered by the MISR instrument on the Terra satellite. | POR-1, 2, 4, 5, 10, 20, 23, 24 | TO-1, TO-2, TO-5, TO-13 | |||
ENVIRONMENT. Meteorological properties in vicinity of aerosols | Atmospheric water vapor and temperature profiles: vertical | See C-12. IR sounders (e.g., CrIS, IASI) plus CLARREO-like | POR-5, 6, 7, 10, 20, 24 | TO-1, TO-13, TO-14 |
CLIMATE VARIABILITY AND CHANGE PANEL | |||||||
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
and clouds (temperature, winds, humidity) to characterize the environment in which the cloud is forming. | resolution/coverage: 2 km from 0 to 15 km altitude. Space/Time Sampling: 25 km horizontal resolution/monthly avg. Time/Space Coverage: decadal trends/global. Accuracy/Stability: 0.03 K (1σ) 3D Winds (see E-2, from Weather Panel, and C-3f) Surface winds (see C-4a): (U10, windspeed and direction) 20 km spatial resolution; 3 hr revisit; 0.5 m/s instantaneous uncertainty, 0.1 m/s monthly uncertainty, decadal stability to 0.05 m/s/decade; direction to 15 degrees instantaneous, monthly to 10 deg. |
intercalibration, GNSS-RO for zonal temperature trend in 5-20 km altitude | |||||
QUESTION C-3. Carbon Cycle, Including Carbon Dioxide and Methane. How large are the variations in the global carbon cycle and what are the associated climate and ecosystem impacts in the context of past and projected anthropogenic carbon emissions? | C-3a. Quantify CO2 fluxes at spatial scales of 100-500 km and monthly temporal resolution with uncertainty <25% to enable regional-scale process attribution explaining year-to-year variability by net uptake of carbon by terrestrial ecosystems (i.e., determine how much carbon uptake results from processes such as CO2 and nitrogen fertilization, forest regrowth, and changing ecosystem demography.) | Very Important | Biomass and biomass change | See E-3a | See E-3a | ||
GPP, respiration, and decomposition, and biomass burning | See E-3a | See E-3a | |||||
Atmospheric CO2 | Random error: XCO2: goal = 1 ppm, threshold = 3 ppm; Systematic error: XCO2: goal = 0.2 ppm, threshold = 0.5 ppm; Mission duration: 3‐5 years provides a snapshot of current conditions; Trends and interannual variability will require systematic measurements >10 yr | NearIR for total column plus thermal for separation of boundary layer and upper, ground-based and aircraft in situ measurements for linking to WMO calibration scales ground-truth | Orbital: POR-11 (OCO2, OCO-3-on-ISS), POR-24 (GOSAT, GOSAT-2), GeoCARB; Suborbital: NOAA Global Greenhouse Gas Reference Network, Total Carbon Column Observing Network | TO-6 | |||
Solar-induced flourescence | See E-1c | Ecosystem Panel, European FLEX Mission 2020, GOME-2, OCO-2 | POR-11, POR-32 (FLEX) | ||||
Leaf Area Index, Enhanced Vegetation Index, Normalized Difference Vegetation Index | Similar to or better than MODIS | ||||||
Atmospheric CO | Similar to or better than MOPITT | POR-11, Geo-CARB | TO-6 | ||||
C-3b. Reliably detect and quantify emissions from large sources of CO2 and CH4, including from urban areas, from known point sources such as power plants, and from previously unknown or transient sources such as CH4 leaks from oil and gas operations. | Important | Atmospheric CO2 and/or CH4 | Geostationary or other mapping capability. Measurement comparability <0.2 ppm for CO2 0.5 ppb for CH4 over time scales of decades will be needed to track changes in emissions. Spatial scale <1 km. | Geo or other mapping | Geo-CARB | TO-6 | |
C-3c. Provide early warning of carbon loss from large and vulnerable reservoirs such as tropical forests and permafrost. | Important | Atmospheric CO2 and/or CH4 | Random error: XCO2: goal = 1 ppm, threshold = 3 ppm; Systematic error: XCO2: goal = 0.2 ppm, threshold = 0.5 ppm; Random Error: XCH4 goal = 6 ppb, threshold = 12 ppb; Systematic error: goal = 2.5 ppb, threshold = 5 ppb | Geo or other mapping | POR-2 (ASCENDS, MERLIN), Geo-CARB | TO-6 | |
Biomass change | See E-3a | See E-3a | |||||
C-3d. Provide regional-scale process attribution for carbon uptake by ocean to within 25% | Important | Surface roughness—air sea gas transfer coefficient | See W-3a (i.e., ocean surface vector wind/surface wind stress) | See E-9 | |||
Winds | See W-3a, E-2 | (from Weather Panel) |
CLIMATE VARIABILITY AND CHANGE PANEL | |||||||
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
(especially in coastal regions and the Southern Ocean). | Atmospheric CO2 | Note ocean uptake signals of order 0.1 ppm and cannot be measured with current satellite sensor technology | No existing or proposed spaceborne XCO2 sensor is capable of detecting ocean flux signals to enable useful flux quantification | ||||
Ocean color | (from Ecosystem panel) | (from Ecosystem panel) | POR-21 (PACE), POR-24 (VIIRS) | TO-18 | |||
Salinity | (from Ecosystems or Weather Panel) | (from Ecosystems or Weather Panel) | POR-23 | ||||
C-3e. Quantify CH4 fluxes from wetlands at spatial scales of 300 km × 300 km and monthly temporal resolution with uncertainty better than 3 mg CH4 m−2 day−1 in order to establish predictive process, based understanding of dependence on environmental drivers such as temperature, carbon availability, and inundation. | Important | CH4 fluxes | Random Error: goal = 6 ppb threshold = 12 ppb; Systematic error: goal = 2.5 ppb, threshold = 5 ppb; Mission duration: 3‐5 years provides a snapshot of current conditions; Spatial coverage: Tropics, Boreal | POR-2 (MERLIN), POR-11 (TropOMI), GeoCARB | TO-6 | ||
C-3f. Improve atmospheric transport for data assimilation/inverse modeling. | Important | 3D Winds | See E-2 | See W-1 and W-3 | |||
PBL depth | See W-1 | See W-1 | |||||
Convective transport | See W-4 | See W-4 | |||||
Surface pressure | See W-2, to within 1 mb | See W-2 | TO-5 | ||||
C-3g. Quantify the tropospheric oxidizing capacity of OH, critical for air quality and dominant sink for CH4 and other GHGs. | Important | Abundance of gases lost by reaction with OH, such as HCFC-22, HFC-32 and HFC-152a | Vertical resolution and range: 1 km / mid-upper troposphere; Horizontal resolution/range: 50 km / global; Temporal sampling: Weekly; Precision: HCFC-22: ~10% (20 ppt), HFC-32: ~30% (3 ppt), HFC-152a: ~50% (4 ppt). Profiles of CO in the mid to upper global troposphere would provide useful information. This objective must have a strong suborbital component to be achieved. | There are the appropriate sample approaches: a) UV, visible, and infrared solar, lunar, and stellar occultation (e.g., ACE-FTS, GOMOS, HALOE, POAM, SAGE); b) Infrared and microwave limb emission (e.g., HIRDLS, MIPAS, MLS, Odin SMR). | Orbital: ACE-FTS, MLS, MOPITT, Odin SMR, SAGE III, TANSO-FTS. Suborbital: NASA ATom and other airborne surveys of tropospheric OH that include the tropical troposphere; continued ground-based observations of methyl chloroform and other well mixed gases with well defined emission source strength that are primarily lost by reaction with tropospheric OH. | ||
QUESTION C-4. Atmosphere-Ocean Flux Quantifications. How will the Earth system respond to changes in air-sea interactions? | C-4a. Improve the estimates of global air-sea fluxes of heat, momentum, water vapor (i.e., moisture) and other gases (e.g., CO2 and CH4) to the following global accuracy in the mean on local or regional scales: (1) radiative fluxes to 5 W/m2, (2) sensible and latent heat fluxes to 5 W/m2, (3) winds to 0.1 m/s, and (4) CO2 and CH4 to within 25%, with appropriate decadal stabilities. | Very Important | Surface vector winds | (U10, windspeed and direction) 20 km spatial resolution; 3 hr revisit; 0.5 m/s instantaneous uncertainty, 0.1 m/s monthly uncertainty, decadal stability to 0.05 m/s/decade; direction to 15 degrees instantaneous, monthly to 10 degrees | Scatterometer, Doppler scatterometer, passive microwave, SAR (possible new versions based on GPM microwave imager, or Compact Ocean Wind Vector Radiometer; Vector winds highly desirable for momentum fluxes) | POR-28; SSM/I, SSM/IS, AMSR-2, GMI, MWI. Move POR-7 CYGNSS (Mission ID 740, Instrument ID 1669, Unique ID 740-1669) to POR-33 |
TO-11 (if scatterometer and tuned to stress) |
10 m air humidity and temperature and SST | (BL atmos) 20 km horizontal resolution; 500 m vertical resolution with 10 m at surface; 3 hr revisit; monthly 0.2°C uncertainty (temperature), 0.3 g/kg (humidity) | For temperature and humidity: IR and microwave sounders (e.g., AIRS, AMSU, ATMS), GNSS-RO | POR-1, (6) | TO-13 | |||
Sea-surface temperature (skin) | 0.2 K random uncertainty in 25 × 25 km area; 80% daily coverage; 3 to 5 km resolution. | IR or microwave radiometery | POR-23 AMSR-2 (Mission ID 459, Instrument ID 883, Unique ID 459-883) with caveats (see notes) | ||||
Radiative fluxes | Global and regional bias of 5 W/m2, spatial resolution of 25 km, temporal | Like CERES on Terra/Aqua | POR-3 |
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
sampling 3 hr. Accuracy of 5 W/m2, decadal stability of 0.3 W/m2/decade. | |||||||
Surface currents | An average of 1-2 samples (overpasses) per day per 100 to 200 km region for a high inclination orbit; 5 to 10 km resolution; Random errors ≤0.02 m/s for 100 km scales and 1 to 2 day averages (this is analogous to current random errors <0.5 m/s for the proposed sampling); Coincidence with vector wind observations. | Altimeter (e.g., Jason-3) or Doppler scatterometer for surface current | |||||
Wave heights | (Hs, significant wave height) 25 km spatial resolution; 3 hr revisit; 0.5 m uncertainty | Altimeter (e.g., Jason-3) for significant wave height | POR-26, 27 | TO-21 | |||
XCO2 and XCH4 (dry air mole fraction of these species) | 25% uncertainty monthly average | ||||||
C-4b. Better quantify the role of surface waves in determining wind stress; demonstrate the validity of Monin-Obukhov similarity theory and other flux-profile relationships at high wind speeds over the ocean. | Important | Wave heights | (Hs, significant wave height) 25 km spatial resolution; 3 hr revisit; 0.5 m uncertainty | (Hs, significant wave height) Jason altimeter | POR-26, 27 | TO-21 | |
Surface layer profiles of temperature and humidity | (BL atmos) 20 km horizontal resolution; 10 m vertical resolution with 10 m at surface and near-surface; 3 hr revisit; monthly 0.2°C uncertainty (temperature), 0.3 g/kg (humidity) | (BL Atmos) For temperature and humidity: IR and microwave sounders (e.g., AIRS, AMSU, ATMS), GNSS-RO | POR-1 | TO-13 | |||
BL wind profiles | (U10, windspeed and direction) 20 km spatial resolution; 3 hr revisit; 0.5 m/s instantaneous uncertainty | (U10, windspeed or stress) Scatterometer, Doppler scatteroemter, passive microwave, SAR; possible new versions based on GPM microwave imager, or Compact Ocean Wind Vector Radiometer; Vector winds highly desirable for momentum fluxes | POR-23, 28, 33 | TO-11 (if scatterometer and tuned to stress) | |||
C-4c. Improve bulk flux parameterizations, particularly in extreme conditions and high-latitude regions reducing uncertainty in the bulk transfer coefficients by a factor of 2. | Important | Turbulent heat fluxes: direct covariance flux estimates of latent and sensible heat flux and simultaneous independent measurements of surface layer air temperature and humidity, sea-surface temperature, surface-relative surface layer winds. Momentum flux: direct covariance flux estimates of stress and simultaneous independent measurements of surface layer air temperature, sea-surface temperature, surface-relative surface layer winds, directional wave spectra. Gas fluxes: direct covariance flux estimates of gas fluxes and measurements of surface layer air temperature, sea-surface temperature, surface-relative surface layer winds, and gas partial- |
Accuracy: Direct covariance flux estimates 20% (stress), 30% uncertainty (heat, moisture, gases) on a point-by-point basis; Wind speed at 0.5 m/s instantaneous uncertainty; 0.2 K instantaneous uncertainty in surface layer air and sea-surface temperature; 0.3 g/kg instantaneous uncertainty in surface layer humidity; gas partial pressure difference to 4 μatm. Wave steepness data needs resolution of waves at 50 to 200 m wavelengths. Increased data needed at a variety of regimes: momentum/turbulent heat flux: wind speeds globally greater than 15 m/s; momentum/turbulent heat flux at stable conditions (air-sea temperature difference >2°C); momentum flux: measurements in strongly coupled swell-dominated regions; gas exchange at all wind speeds, all stability conditions; all wind speeds for gas exchanges. All momentum/heat/moisture/gas fluxes: |
Satellite observations: improved drag coefficients: simultaneous but independent surface stess and wind speed measurements (e.g., scatterometer for stress; wind speeds from passive microwave, scatterometers; wave steepness from SAR. Other turbulent direct covariance fluxes needed for bulk flux parameterizations unobtainable from satellite. In situ: direct covariance flux measurements and simultaneous independent bulk measurements, wave information, for all fluxes from buoys, expendable platforms at sufficient elevation | Satellite: SAR, POR-(1), CFOSAT In situ: OceanSITES buoys, OOI buoys, ships of opportunity, towers, individu research experiments and associated assets | TO-11 (if scatterometer and tuned to stress) al |
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
pressure gradients between atmospheric and ocean surface layers | measurements needed in marginal ice zone regions. | ||||||
C-4d. Evaluate the effect of surface CO2 gas exchange, oceanic storage, and impact on ecosystems, and improve the confidence in the estimates and reduce uncertainties by a factor of 2. | Important | Surface CO2 gas exchange: Wind speeds; partial pressure CO2 in equilibrium with surface water air above | Surface CO2 gas exchange: 25% uncertainty monthly average | Wind speeds: scatterometer, Doppler scatterometer, passive microwave, SAR CO2 partial pressures: in situ measurements | POR-28; SSM/I, SSM/IS, AMSR-2, GMI, MWI. Move POR-7 CYGNSS (Mission ID 740, Instrument ID 1669, Unique ID 740-1669) to POR-33 | TO-11 | |
QUESTION C-5. Aerosols and Aerosol Cloud Interactions. A. How do changes in aerosols (including their interactions with clouds, which constitute the largest uncertainty in total climate forcing) affect Earth’s radiation budget and offset the warming due to greenhouse gases? B. How can we better quantify the magnitude and variability of the emissions of natural aerosols, and the anthropogenic aerosol signal that modifies the natural one, so that we can better understand the response of climate to its various forcings? | C-5a. Improve estimates of the emissions of natural and anthropogenic aerosols and their precursors via observational constraints. | Very Important | Vertical profiles of aerosol mass concentrations, and including particle size (effective radius). Boundary layer concentrations are most relevant for sources; vertical profiles are most relevant for removal processes (settling and precipitation removal). Also needed: (1) dust, smoke, and other aerosol plume properties (e.g., characterizing height and thickness), and (2) characteristics in the column to assess removal rates. Precipitation phases and rates. Surface properties: (1) soil moisture; (2) topography; (3) soil type and vegetation coverage; (4) ocean surface characteristics (bubbles, waves); (5) sea-surface temperature. Surface vector winds. Fire radiative power, vegetation type and plume lofting height for fire mass consumption rate, smoke properties and plume height. Aerosol precursor gases that include NOx, SO2, DMS, VOCs, NH4, and co-emitted gases that include CO and CO2. |
Aerosol mass: Horizontal resolution requirements: at high (1 km) to very high (<100 m); Vertical resolution requirement: Information is needed at low (column integrals) and high (<500 m) resolution. Temporal sampling: weekly; Precision: 30%. Accuracy requirements: vertically and horizontally resolved aerosol mass concentration (100%) and effective variance (50%). (U10, windspeed and direction) 20 km spatial resolution; 3 hr revisit; 0.5 m/s instantaneous uncertainty, 0.1 m/s monthly uncertainty, decadal stability to 0.05 m/s/decade; direction to 15 degrees instantaneous, monthly to 10 degrees. (Hs, significant wave height) 25 km spatial resolution; 3 hr revisit; 0.5 m uncertainty. SST: 0.2 K random uncertainty in 25 × 25 km area; 80% daily coverage; 3 to 5 km resolution. Gas measurements: NOx, SO2, DMS, VOCs, NH4, CO, and CO2. |
See C-2h for Aerosol Measurement Approaches, C-4a for air-sea exchange that includes these below, Ecosystem for vegetation, biomass burning, dust, and possibly gases C-40. Scatterometer, Doppler scatterometer, passive microwave, SAR (possible new versions based on GPM microwave imager, or Compact Ocean Wind Vector Radiometer; Vector winds highly desirable for momentum fluxes) SST: C-42. IR and/or microwave radiometry C-44B. Altimeter (e.g., Jason-3) for significant wave height |
POR-2, 5, 7, 9, 10, 11, 12, 14, 20, 21, 22, 24, 25, 28, 32 | TO-1, TO-2, TO-3, TO-4, TO-6, TO-10, TO-11, TO-15, TO-17, TO-18, TO-22 |
C-5b. Characterize the properties and distribution in the atmosphere of natural and anthropogenic aerosols, including properties that affect their ability to interact with and modify clouds and radiation. | Important | AEROSOLS: (1) extinction, absorption, AOD, AAOD (spectrally resolved), polarization, single scattering albedo, (2) size and shape, (3) vertical and horizontal distribution, (4) hydroscopicity, composition (probably not from space implices need for ground and airborne help), (5) ancillary useful variables (CO, isoprene, relative humidity, etc.). | This is a refinement of Objective C-5a. As noted, level of detail probably requires complementary data to space-based measurements, the latter of which are more suited to characterizing emissions and transport rather than detailed characteristics of the aerosols | ||||
C-5c. Quantify the effect that aerosol has on cloud formation, cloud height, and cloud properties (reflectivity, lifetime, | Very Important | CLOUDS: (1) cloud cover, optical depth, reflectivity, (2) vertical and horizontal distribution (plus cloud overlap plus morphology including | See Objective C-2h | Nadir and multiangular radiometers (MODIS, MISR); lidars (CALIPSO, HSRL); microwave brightness temperatures; shortwave reflectances in | POR-1, 2, 4, 10, 12, 14, 20, 23, 24, 25 | TO-1, TO-5, TO-14 |
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
cloud phase), including semi-direct effects. | organization and convective/stratiform character), (3) colocation with precipitation, aerosls, aerosol sources, vertical velocity, (4) condensate state, number, phase, (5) within cloud variability. | the near IR and visible bands; stereo photogrammetric methods that builds on and advances measurements pioneered by the MISR instrument on the Terra satellite. | |||||
C-5d. Quantify the effect of aerosol-induced cloud changes on radiative fluxes (reduction in uncertainty by a factor of 2) and impact on climate (circulation, precipitation). | Important | Forcing and response: (1) radiative fluxes, (2) precipitation, (3) climate variables (winds, temperature, etc.). | See Objective C-2h | Nadir and multi angular radiometers (MODIS, MISR); Lidars (CALIPSO, HSRL); microwave brightness temperatures; shortwave reflectances in the near IR and visible bands; stereo photogrammetric methods that builds on and advances measurements pioneered by the MISR instrument on the Terra satellite. | POR-1, 23, 5, 6, 7, 10, 20, 23, 24, 25, 28, 31 | TO-1, TO-5, TO-14 | |
QUESTION C-6. Seasonal to Decadal Predictions, Including Changes and Extremes (C-6 and C-7). Can we significantly improve seasonal to decadal forecasts of societally relevant climate variables? [see footnote 1] | C-6a. Decrease uncertainty, by a factor of 2, in quantification of surface and subsurface ocean states for initialization of seasonal-to-decadal forecasts. | Very Important | Sea-surface height | Spatial: 1-3 km; Temporal: approximately weekly | 3 nadir-looking altimeters working together (e.g., SWOT) | POR-27 | |
Sea-surface salinity | Spatial: 5-10 km; Temporal: 2-3 days; Uncertainty 0.1-0.2 psu | Aquarius-like (SMOS, SMAP) | POR-23 | ||||
Sea-ice thickness | Freeboard height (from which thickness is derived); <3 cm uncertainty; Spatial: few km; Temporal: 10 days | Altimetry (e.g., ICESat-2, CryoSat-2) | POR-13, 14, 27, 29 | ||||
Sea-ice fraction | Spatial: 1 km; Temporal: daily | Microwave imagers (e.g., AMSR) | POR-1, 23 | TO-11 | |||
Sea-surface temperature | Spatial: 1-3 km; Temporal: resolved diurnal cycle | Microwave, IR imagers | POR-1, 9, 10, 24 | ||||
Surface vector winds | Spatial: few km; Temporal: daily | Scatterometer (e.g., QuikSCAT) | POR-23, 28 | TO-11 (if scatterometer and tuned to stress) | |||
Subsurface temperatures | Spatial: 1 km horizontial, 1 m vertical; Temporal: daily; Accuracy: 0.07°C | Lidar | |||||
Surface currents | Spatial: 5-10 km, wide swath; Temporal: 1-2/day; Random errors ≤1 m/s | Doppler scatterometer | TO-11 | ||||
Ocean mass | Spatial:100 km; Accuracy: 2 cm | Gravity (e.g., GRACE-FO) | POR-30 | ||||
C-6b. Decrease uncertainty, by a factor of 2, in quantification of land surface states for initialization of seasonal forecasts. | Important | Soil moisture | Daily at 10 km, to within 0.04 volumetric percent | L-band radar and radiometer (e.g., SMAP) | POR-12, 23 | ||
Freeze-thaw state | Weekly at 10 km | Passive microwave radiometers, scatterometers, SAR | POR-12, 23 | ||||
Total water storage | Weekly at 100 km, to within 0.04 volumetric percent on average | Gravity (e.g., GRACE-FO) | POR-30 | ||||
Vegetation phenology (FPAR) | Weekly at 10 km, to within 0.05 | MODIS, AVHRR-like vegetation measurements | POR-9, 21, 24 | ||||
Snow water equivalent | Daily at 10 km, to within 1 cm SWE | Combination of sensors, e.g., later altimetry, polarimetric imaging radar, microwave imaging radar, radiometers | POR-1, 9, 12, 14, 23, 24, 25, 28 | ||||
C-6c. Decrease uncertainty, by a factor of 2, in quantification of stratospheric states for initialization of seasonal-to-decadal forecasts. | Important | Polar vortex winds | Spatial: 5 degrees latitude/longitude; Temporal: daily | Radiometers and limb-sensing instrument for vertical resolution (infer observable using geostrophic approximation) | POR-16, 17, 18 | ||
QUESTION C-7. How are decadal-scale global atmospheric and ocean circulation patterns changing, and what are the effects of | C-7a. Quantify the changes in the atmospheric and oceanic circulation patterns, reducing the uncertainty by a factor of 2, with desired confidence levels | Very Important | COMMON TO ALL C-7: Observables to characterize the modes of circulation and trends in the global dynamical, thermodynamical, and water | COMMON TO ALL C-7: Sampling that allows the resolution of processes on spatial scales better than 0.5 degrees latitude‐longitude. This would also | COMMON TO ALL C-7: Nadir-viewing and limb soundings in the visible, infrared, microwave. Lidar-in-space. Wind profiler. Aerosols, water | POR-1, 3, 4, 5, 9, 11, 12, 16, 21, 23, 24, 25, 26, 27, 28, 31 | TO-1, 2, 4, 5, 9, 11, 12, 13, 15, 16, 17, 18, 20, 21 |
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
these changes on seasonal climate processes, extreme events, and longer term environmental change? | of 67% (likely in IPCC parlance). | systems: land and ocean surface temperature, vertical profile of atmospheric temperature and moisture, clouds, 3D wind profiles, surface to 5 km depth ocean temperature, salinity and currents. For ENSO and interannual time scales: SST, subsurface ocean properties, tropical winds. For AMOC, and decadal time scales in particular: SST and salinity to infer water density and ocean currents to determin stream functions. For changes in the position and intensity of the jets: (1) vertically resolved temperature in the UT/LS, to specify tropopause height, (2) precipitation at surface, as confirmation of Hadley cell location, (3) O3 and H2O in the UT/LS with higher vertical resolution, to enable definition of chemical tropopause, (4) vertical distribution of stratospheric and tropospheric aerosols. For regional climate quanitification: observations of tracer transport (e.g., aerosols), moisture and cloud properties. SST, SSS, SSH, subsurface ocean temperatures and salinity; sea-ice extent, thickness, thermodynamic state, and albedo. |
enable comparisons of the simulation fidelity between increasing high-resolution (better than 0.5 degrees latitude‐longitude) models over the next decade and the observations. Measurement Requirements: Vertical Resolution and Range: ~1 km. (surface to lower stratosphere). Measurement Requirements. Horizontal resolution/range: 1/2° latitude × 1/2° longitude / Global; Temporal Sampling: minimum daily; Precision: 20%; Sufficient accuracy to address the regional variability and detection-attribution of forced climate change at the 67% confidence level or better, and to be used in conjunction with model simulations and reanalyses. |
vapor, cloud, and sea-ice remote sensing. | |||
C-7b. Quantify the linkage between natural (e.g., volcanic) and anthropogenic (greenhouse gases, aerosols, land-use) forcings and oscillations in the climate system (e.g., MJO, NAO, ENSO, QBO). Reduce the uncertainty by a factor of 2. Confidence levels desired: 67%. | Important | POR-1, 2, 3, 4, 5, 11, 15, 16, 17, 22, 24, 31 | TO-1, 2, 3, 4, 5, 6, 9, 11, 12, 13, 14, 15, 16, 17, 18, 20 | ||||
C-7c. Quantify the linkage between global climate sensitivity and circulation change on regional scales including the occurrence of extremes and abrupt changes. Quantify the expansion of the Hadley cell to within 0.5 degrees latitude per decade (67% confidence desired); changes in the strength of AMOC to within 5% per decade (67% confidence desired); changes in ENSO spatial patterns, amplitude, and phase (67% confidence desired). | Very Important | POR-1, 3, 4, 12, 23, 24, 25, 26, 28, 31 | TO-1, 4, 5, 13, 14 | ||||
C-7d. Quantify the linkage between the dynamical and thermodynamic state of the ocean upon atmospheric weather patterns on decadal time scales. Reduce the uncertainty by a factor of 2 (relative to decadal prediction uncertainty in IPCC, 2013). Confidence level: 67% (likely). | Important | POR-1, 3, 4, 12, 21, 25, 26, 27, 28, 29 | TO-1, 4, 5, 11, 13, 15, 20 | ||||
C-7e. Provide observational verification of models used for climate projections. Are the models simulating the observed evolution of the large-scale patterns in the atmosphere and ocean circulation, such as the frequency and magnitude of ENSO events, strength of AMOC, and the poleward expansion of the subtropical jet (to a 67% level correspondence with the observational data). | Important | POR-1, 3, 4, 5, 11, 12, 16, 17, 21, 23, 24, 25, 26, 28, 31 | TO-1, 4, 5, 6, 9, 11, 12, 13, 15, 16, 17, 18, 20 |
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
QUESTION C-8. Causes and Effects of Polar Amplification. What will be the consequences of amplified climate change already observed in the Arctic and projected for Antarctica on global trends of sea-level rise, atmospheric circulation, extreme weather events, global ocean circulation, and carbon fluxes? | C-8a. Improve our understanding of the drivers behind polar amplification by quantifying the relative impact of snow/ice-albedo feedback versus changes in atmospheric and oceanic circulation, water vapor, and lapse rate feedback. | Very Important | Sea-ice concentration/extent/type | Daily at 10 km resolution | Continuity of multichannel passive microwave (e.g., SSMI, SSMIS), sea-ice classification with dual pol SAR (e.g., ENVISAT, Radarsat) or scatterometers. | TO-11, 15, 17 | |
Sea-ice thickness | Daily at 1 km resolution | Laser and radar altimetry (e.g., ICESat2, CryoSat) | |||||
Atmospheric boundary layer (surface temperature profiles, surface-air fluxes, water vapor, clouds). | Daily at 25 km spatial resolution, 200 m vertical resolution in the planetary boundary layer | Sounders and imagers at high horizontal resolution | |||||
Atmospheric soundings (tropopause and lower stratosphere) | |||||||
Snow cover extent | Daily at 10 km resolution | Continuity of multichannel passive microwave (e.g., SSMI, SSMIS) | |||||
C-8b. Improve understanding of high-latitude variability and midlatitude weather linkages (impact on midlatitude extreme weather and changes in storm tracks from increased polar temperatures, loss of ice and snow cover extent, and changes in sea level from increased melting of ice sheets and glaciers). | Very Important | Sea-ice concentration/extent/type | Daily at 10 km resolution, within 5% | See Objective C-8a | |||
Sea-ice thickness | Daily at 1 km resolution, within 20 cm | See Objective C-8a | |||||
Atmospheric boundary layer (surface temperature profiles, surface-air fluxes, water vapor, clouds). | Daily at 25 km spatial resolution, 200 m vertical resolution in the planetary boundary layer | See Objective C-8a | |||||
Atmospheric soundings (tropopause and lower stratosphere) | See Objective C-8a | ||||||
Snow cover extent | Daily at 10 km resolution | See Objective C-8a | |||||
Sea-surface temperatures | Daily at a few kilometers, within 0.1 K | Microwave, IR instr (e.g., VIIRS, MODIS) | |||||
Snow depth on land and sea ice | Daily at 1 km, within 10 km, within 20cm | Collocated Ku/Ka-band radar altimeter | |||||
Snow water equivalent | Daily at 10 km, to within 1 cm SWE | Combination of sensors (e.g., laser altimetry, polarimetric imaging radar, microwave imaging radar, radiometers) | |||||
C-8c. Improve regional-scale seasonal to decadal predictability of Arctic and Antarctic sea-ice cover, including sea-ice fraction (within 5%), ice thickness (within 20 cm), location of the ice edge (within 1 km), timing of ice retreat and ice advance (within 5 days). | Very Important | Sea-ice concentration/extent/type | Daily at 10 km resolution within 5% | See Objective C-8a | |||
Sea-ice thickness | Daily at 1 km resolution, within 20 cm | See Objective C-8a | |||||
Snow on sea ice | Daily at 100 m resolution, within 5 cm | Collocated Ku/Ka-band radar altimeter, IceBridge | |||||
Sea-ice motion and deformation | 3‐day and weekly, 10 km resolution within 10 km | Continuity of multichannel passive microwave (e.g., SSMI, SSMIS) | |||||
Melt pond fraction | Daily, 1 km resolution | Visible imagery (e.g., MODIS, VIIRS) | |||||
Sea-surface temperatures | Daily at a few kilometers, within 0.1 K | Microwave, IR instr (e.g., VIIRS, MODIS) | |||||
Snow cover extent | Daily at 10 km resolution, within 10 km | See Objective C-8a | |||||
Atmospheric boundary layer (surface temperature profiles, surface-air fluxes, water vapor, clouds). | Daily at 25 km spatial resolution, 200 m vertical resolution in the planetary boundary layer | See Objective C-8a | |||||
C-8d. Determine the changes in Southern Ocean carbon uptake due to climate change and associated atmosphere/ocean circulations. | Very Important | Atmospheric pCO2 (i.e., within the atmospheric boundary layer) | Monthly at 100 km by 100 km spatial scales. Air-sea pCO2 difference accurate to ±3 to 15 μatm, implying no more than ±2 to 10 μatm uncertainty in atmospheric pCO2. Aim for 25% total flux uncertainty. | Schimel el al. RFI#2 submission: high-resolution spectroscopic observations of reflected sunlight in near infrared CO2 and CH4, such as provided by OCO-2. Possibility of retrieving full flux from atmospheric inversion will require multiple satellites. |
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
Further modeling and OSSE work will help to confirm requirements. | Kawa et al. RFI#2: lidar retrieval (e.g., ASCENDS) Singh et al. RFI#2: 2-μm lidar (e.g., IPDA lidar) Regression methods: use altimetery for sea-surface height, ocean color for biological productivity/upper ocean upwelling |
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Upper ocean pCO2 (i.e., within the mixed layer) | Monthly at 100 km by 100 km spatial scales | Mannino et al RFI#2: ocean color (e.g., GEO-CAPE: high-temporal measurements of top of the atmosphere radiances from 350-900 nm (minimum of 25 bands; SNR > 1000) plus 1240 nm (SNR > 250) and 1640 nm (SNR > 180) with ~1000 biogeochemical Argo floats profiling every 10 days to provide upper in situ measurements | |||||
Surface wind speed | Surface wind: monthly means at 100 km resolution; would prefer daily at oceanic eddy-resolving resolution | Wind: scatterometer preferred, passive microwave wind speed possible | TO-11 (if scatterometer and tuned to stress) | ||||
SST or mixed-layer temperature | SST: monthly means at 100 km resolution; would prefer daily at eddy-resolving resolution to minimize or study eddy impacts | SST: microwave SST (microwave needed because of persistent cloud cover in region) | |||||
C-8e. Determine how changes in atmospheric circulation, turbulent heat fluxes, sea-ice cover, fresh water input, and ocean general circulation affect bottom water formation. | Important | Atmospheric boundary layer temperature | Temperature at 2 m elevation to 0.2°C, and surface-specific humidity, both at spatial scale of oceanic eddies | Humidity inferred from brightness temperature (e.g., SSM/I). Temperature inferred fromreanalysis or infrared or microwave atmospheric profiler (e.g., AIRS or AMSU) with near surface measurement capability. | |||
Surface wind speed | Surface wind: monthly means at 100 km resolution; would prefer daily at oceanic eddy-resolving resolution | Wind: scatterometer preferred, passive microwave wind speed possible | |||||
Surface temperature, salinity, density SST, or mixed layer temperature surface salinity (not easy from space at cold temperatures) or melt water input? | SST and SSS: 2-5 day sampling intervals, at eddy scales. Goal to obtain density to 0.03 kg/m3 or temperature to 0.2°C, consistent with threshold criteria used for mixed-layer depth definition. Freshwater input/ice melt could also be helpful on monthly time scales. | SST: microwave SST (microwave needed because of persistent cloud cover at high latitudes) SSS: surface salinity desirable. Very difficult observation at cold temperatures. Use Argo profiling floats and climatology if satellite retrievals not feasible. Freshwater input, ice melt: GRACE, sea-ice extent, ice thickness |
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C-8f. Determine how permafrost-thaw-driven land cover changes affect turbulent heat fluxes, above and belowground carbon pools, and resulting greenhouse gas fluxes (carbon dioxide, methane) in | Important | Freeze-thaw state | Weekly, at 100 m horizontal and 5-10 cm vertical resolution | Passive microwave radiometers, scatterometers, SAR; InSAR, C- and L-band SAR; Tomographic SAR; Airborne EM; GPR; SMAP | POR-12(PALSAR) POR-12(Sentinel-1) POR-23 (SMAP) |
TO-17 | |
Active layer thickness | Biweekly (except in winter, when no measurement is needed) at 100 m horizontal and 5 cm vertical resolution | AirMOSS (P-band SAR); UAVSAR (L-band InSAR); Airborne-EM; OIB low-frequency radars; ground based (or airborne) GPR | POR-12 (L-band PALSAR POR-12 (NISAR) |
CLIMATE VARIABILITY AND CHANGE PANEL | |||||||
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
the Arctic, as well as their impact on Arctic amplification. | Lake and wetland fraction | Bimonthly at 100-250 m | Optical instruments (e.g., MODIS, Landsat, SPOT, Sentinel-2), active SAR instruments (C and L bands) | POR-12(PALSAR) POR-12(Sentinel-1) POR-9 (Landsat) POR-9 (Sentinel-2) POR-27 (SWOT) POR-21 (MODIS) |
TO-18 | ||
Snow water equivalent | Daily at 1 km, within 1 cm SWE | Combination of sensors: e.g., laser altimetry, polarimetric imaging radar, microwave imaging radar, radiometers | POR-23 (AMSR-2) POR-23 (SMOS) |
TO-16 | |||
Snow cover extent | Daily at 1 km resolution, within 2 days uncertainty | Need for continuity of multichannel passive microwave; optical and infrared instruments (e.g., MODIS) | POR-24 (VIIRS) POR-9 (Landsat) POR-9 (Sentinel-2) POR-21 (MODIS) |
TO-18 | |||
Surface elevation | Annually at 50 m resolution, within 2 cm uncertainty | Active SAR instruments (Interferometry), Lidar; US L-band SAR; DLR Tandem-X and Tandem-L; ICESat-ATLAS | POR-12 (L-band PALSAR) POR-12 (S-band NISAR) POR-12(C-band Sentinel-1) POR-14 (ICESat-ATLAS) |
TO-19, 20 | |||
Permafrost thickness and 3D geometry | Once every 10 years, with 100 m horizontal resolution | Tomographic SAR; Satellite-based EM; GPR; P-band SAR; OIB VHF and UHF radars | POR-12 (P-band SAR) | ||||
Land surface temperature (LST) | Daily, with 1 km resolution and 1 K precision | MODIS, AVHRR, Sentinel | POR-24 (VIIRS)? POR-25 (Landsat) POR-25 (NISAR TIS) POR-22 (Sentinel-3) POR-21 (MODIS) |
TO-18 | |||
Land cover state and change | Monthly, with 30 m resolution | Landsat, SPOT, Sentinel-2, HyspIRI | POR-9 (Landsat) POR-9 (Sentinel-2) |
TO-18 | |||
Permafrost methane feedback (seep flux from thaw lakes) | Weekly during lake freeze-up season (Oct-Dec), ideally at 5-10 m, acceptable 30 m resolution | Quadpole L-band SAR (e.g., PALSAR) | POR-12(L-band PALSAR) | ||||
C-8g. Determine the amount of pollutants (e.g., black carbon, soot from fires, and other aerosols and dust) transported into polar regions and their impacts on snow and ice melt. | Important | Aerosol optical depth | See requirements for AEROSOLS in C-2h, C-5a, and requirements for particulate matter (PM) in W-1a, W-5a, W-6a | Nadir viewing Radiometers (e.g., MODIS); Multiangle viewing radiometers (MISR); Current technology lidar (CALIPSO); More modern technology lidar (HSRL); HSRL can be used for vertically resolved profiles that better enable (1) distinguishing aerosols and clouds, (2) increased sensitivity (detecting lower concentrations of aerosol), (3) better resolution of aerosol amounts nearer the surface (lower altitude), and (4) more accurate aerosol optical depths. Multiangle, multispectral polarimeters can provide improved column integrated information on aerosol composition such as refractive index (including absorption), particle size, and variance. The combination of HSRL and a multiangle, multispectral polarimeter has improved accuracy beyond each individual instrument. | |||
Aerosol absorption optical depth | See requirements for AEROSOLS in C-2h, C-5a |
CLIMATE VARIABILITY AND CHANGE PANEL | |||||||
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
Snow depth, cover, albedo on land, glaciers and sea ice | See requirements for H-1c | ||||||
From C-2h: Meteorological properties in vicinity of aerosols and clouds (temperature, winds, humidity) to characterize the environment in which the cloud is forming. | See requirements for C-2h | See C-12. IR sounders (e.g., CrIS, IASI) plus CLARREO-like intercalibration, GNSS-RO for zonal temperature trend in 5-20 km altitude | |||||
C-8h. Quantify high-latitude low cloud representation, feedbacks, and linkages to global radiation. | Important | Cloud properties (cloud fraction, cloud vertical distribution, cloud liquid water content, cloud ice water content, droplet effective radius, ice particle effective diameter, number concentration, in-cloud circulations, cloud top turbulence/entrainment, vertical velocity and cloud phase) | Cloud Fraction (%) <1%; Cloud Liquid Water Content +20%; Cloud Ice Water content +20%; Cloud top and base height <75 m | Continuity of lidar/radar instruments with need for more complete sampling and vertical resolution, synergistic passive-active instruments | |||
Radiation (surface upwelling and downwelling flux for longwave and shortwave; TOA fluxes), Turbulent radiative fluxes | Daily, within 5 W/m2 for radiative fluxes, within 5‐10 W/m2 for turbulent fluxes | Continuity of TOA radiative fluxes from CERES and MODIS/VIIRS | |||||
Atmospheric temperature and humidity | Need for collocated and finer spatial and vertical resolution boundary layer temperature and humidity | ||||||
Aerosol concentration and composition | Continued polar lidar needed (e.g., CALIOP), advanced lidar techniques like HSRL | ||||||
Sea-ice fraction | Daily, 10 km resolution, accuracy of 5% | ||||||
Sea-ice thickness | Daily at 1 km resolution, accuracy of 20 cm | ||||||
C-8i. Quantify how increased fetch, sea-level rise and permafrost thaw increase vulnerability of coastal communities to increased coastal inundation and erosion as winds and storms intensify. | Important | Wave heights | (Hs, significant wave height) 25 km spatial resolution; 3 hr revisit; 0.5 m uncertainty | Radar altimeter (e.g., Jason-3) for significant wave height | |||
Fetch/ice edge | Daily, at 10 km resolution, within 5 km | Passive and active microwave | |||||
Winds | (U10, windspeed) 10 km spatial resolution; 3 hr revisit; 2 m/s uncertainty | Scatterometer, passive microwave, SAR | TO-11 (if scatterometer and tuned to stress) | ||||
Ocean currents | Spatial: 5‐10 km, wide swath, random errors ≤1 m/s | Radar altimeter (e.g., Jason-3) for surface currents, Doppler scatterometer | |||||
QUESTION C-9. Ozone and Other Trace Gases in the Stratosphere and Troposphere. How are the abundances of ozone and other trace gases in the stratosphere and troposphere changing, and what are the implications for Earth’s climate? | C-9a. Quantify the amount of UV-B reaching the surface, and relate to changes in stratospheric ozone and atmospheric aerosols. | Important | Surface UVB | Global 5º latitude/10º longitude, surface only, weekly sampling, precision 5% | GOME-2; Suborbital: Baseline Surface Radiation Network (BSRN) | TROPOMI | |
Total column ozone | Global 1º latitude/2º longitude, surface only, weekly sampling for column, surface through stratosphere, precision 3 Dobson Units | POR-9, 11 SBUV-2, OMI, OMPS, GOME-2; Suborbital: Network for the Detection of Atmospheric Composition Change, Global Climate Observing System Reference Upper-Air Network (GRUAN), Southern Hemisphere Additional Ozonesondes |
TROPOMI |
CLIMATE VARIABILITY AND CHANGE PANEL | |||||||
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
Vertical profiles of temperature in UTS (upper troposphere and stratosphere), for quantifying radiative forcing | Vertical resolution and range: <1 km in UTLS, <3 km in mid-upper stratosphere; Horizontal resolution/range: 5° latitude × 10° longitude / Global; Temporal sampling: weekly; Precision: 1-2 K. | UV, visible, and infrared solar, lunar, and stellar occultation (e.g., ACE-FTS, GOMOS, HALOE, POAM, SAGE) High precision, high vertical resolution (<1 km), climate (trend) quality, but poor spatial coverage unless a constellation is used. Infrared and microwave limb emission (e.g., HIRDLS, MIPAS, MLS, Odin SMR)Many species, 3-4 km vertical resolution, night and day, global coverage Visible limb scattering (e.g., SME, OMPS-LP, OSIRIS). Lidar (e.g., CALIPSO-CALIOP). High (meters) vertical resolution. Radar (e.g., CloudSat CPR). Nadir-viewing solar reflection/scattering (e.g., AIM-CIPS, MODIS, OCO-2). Good (km) horizontal resolution Suborbital: Ground-based and balloon/aircraft measurements of ozone and ozone depleting substances to support emissions estimation and trend determination |
POR-6, 16, 17, 18ACE-FTS (not global), Auro MLS (not 1 km verical resolution; not self-calibrating), GPS-RO (extreme path length), MSU/SSU/AMSU (10 km vertical resolution; not self-calibrating); Suborbital: Global Climate Observing System Reference Upper-Air Network (GRUAN), NWS Upper-air Observations Program | TO-13 | |||
Vertical profiles of radiatively active gas concentrations in the UTS, for quantifying radiative forcing | Concentration of O3: Vertical resolution and range: <1 km in UTLS, <3 km in mid-upper stratosphere; Horizontal resolution/range: 5° latitude × 10° longitude / Global; Temporal sampling: weekly; Precision: 10%. Concentration of H2O: Vertical resolution and range: <1 km in UTLS, <3 km in mid-upper stratosphere; Horizontal resolution/range: 5° latitude × 10° longitude / Global; Temporal sampling: weekly; Precision: 10%. |
POR-6, 16, 17, 18 ACE-FTS (not global; not 1 km vertical resolution), Aura MLS (not 1 km vertical resolution; not self-calibrating), OMPS-L (not 1 km vertical resolution; not self-calibrating (?)); Suborbital measurements: Network for the Detection of Atmospheric Composition Change, Global Climate Observing System Reference Upper-Air Network (GRUAN), Southern Hemisphere Additional Ozonesondes (SHADOZ) SAGE III (ISS) (not global; 18 Feb 2017 launch) |
TO-12 | ||||
Vertical profiles of UTS aerosol radiative properties, for quantifying radiative forcing | Vertical resolution and range: <1 km in UTLS; Horizontal resolution/range: 5° latitude × 10° longitude / Global; Temporal sampling: weekly; Precision: 30%. | POR-2, 16, 18 ACE-FTS [not global; not 1 km vertical resolution], Odin OSIRIS [not 1 km vertical resolution; not polar night; no particle size info], CALIPSO CALIOP [near end-of-life] SAGE III (ISS) [not global] |
TO-1, 2 | ||||
Volcanic and biomass burning emissions, for process studies (sources and transport) | Volcanic Aerosol: Vertical resolution and range: <1 km in UTLS; Horizontal resolution/range: 5° latitude × 10° longitude / Global; Temporal sampling: daily during events; Precision: 30%. Concentrations of gases from volcanic emissions: Vertical resolution and range: <1 km in UTLS; Horizontal resolution/range: 5° latitude × 10° longitude / Global; Temporal sampling: daily during events; Precision: ~20%, but species dependent. |
POR-2, 16, 17, 18 Volcanic Aerosol: CALIPSO CALIOP [near end-of-life] Concentrations of gases from volcanic emissions: ACE-FTS [not global; not 1 km vertical resolution], MLS [not 1 km vertical resolution; not NO2] SAGE III (ISS) [not enough sampling to track the sources] |
TO-1, 2, 12 |
CLIMATE VARIABILITY AND CHANGE PANEL | |||||||
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
Deep convective clouds, for process studies (sources) | Vertical resolution and range: <1 km in UTLS; Horizontal range: Global; Horizontal and temporal sampling: event-specific, episodic; Precision: 30%. | POR-1, 4, 10, 17, 21, 23 Aura MLS (not 1 km vertical resolution), Aqua AIRS, Aqua AMSR-E, Aqua MODIS, CloudSat |
TO-5 | ||||
Small-scale transport and the Brewer-Dobson circulation, for process studies (transport) | Vertical resolution and range: <1 km in UTLS, <3 km in mid-upper stratosphere; Horizontal resolution/range: 5° latitude × 10° longitude / Global; Temporal sampling: event-driven for small scale, weekly for BD; Precision: 30%. | POR-16, 17 ACE-FTS [not global; not 1 km vertical resolution], Aura MLS [not 1 km vertical resolution] |
TO-12, 13 | ||||
Dynamical features such as the polar vortex, for process studies (transport) | Vertical resolution and range: <1 km in UTLS, <3 km in mid-upper stratosphere; Horizontal resolution/range: 5° latitude × 10° longitude / Global; Temporal sampling: daily to weekly; Precision: 1-2 K (T), 30% (UV). | POR-6, 16, 17, 18 ACE-FTS [not global; not 1 km vertical resolution], Aura MLS [not 1 km vertical resolution], GPS-RO [extreme path length] |
TO-12, 13 | ||||
Planetary and gravity waves, for process studies (transport) | Vertical resolution and range: <1 km from cloud top to stratopause; Horizontal resolution/range: 5° latitude × 10° longitude / Global; Temporal sampling: daily to weekly; Precision: 1-2 K (T). | POR-6, 16, 17, 18 ACE-FTS [not global; not 1 km vertical resolution], Aura MLS [not 1 km vertical resolution], GPS-RO [extreme path length] |
TO-12, 13 | ||||
Stratospheric ozone and related constituents, for process studies (chemistry) | Vertical resolution and range: <1 km from cloud top to stratopause; Horizontal resolution/range: 5° latitude × 10° longitude / Global; Temporal sampling: daily to weekly; Precision: 5% (O3), 10% (others). | POR-16, 17, 18 ACE-FTS [not global; not 1 km vertical resolution], Aura MLS [not 1 km vertical resolution; only some of the constituents]; Suborbital measurements: Network for the Detection of Atmospheric Composition Change, Global Climate Observing System Reference Upper-Air Network (GRUAN), Southern Hemisphere ADditional OZonesondes (SHADOZ), Advanced Global Atmospheric Gases Experiment and NOAA Halocarbons and Other Trace Species Network SAGE III [not global; only some of the constituents] |
TO-12 |
1 As noted in the text, all of the indicated measurements for Questions C-6 and C-7 would be useful, but the absence or excessive coarseness of any of the measurements would not be a “deal-breaker.” This question is best considered not as a motivation for a mission but rather as a beneficiary of measurements taken to address other questions. Indicating here which measurements are already being taken is, in a way, extraneous.
EARTH SURFACE AND INTERIOR PANEL | |||||||
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question/Goal | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
QUESTION S-1. How can large-scale geological hazards be accurately forecast in a socially relevant time frame? | S-1a. Measure the pre-, syn-, and posteruption surface deformation and products of Earth’s entire active land volcano inventory at a time scale of days to weeks. | Most Important | Land-surface deformation | At least two components of land-surface deformation and strain localization (e.g., surface fracturing) over length scales ranging from 10 m to 1,000 km and a precision of 1 mm at a sampling frequency related to the volcanic activity. Regionally sampled global coverage. | L- or S-band InSAR with ionospheric correction, [GPS/GNSS] | POR-12 (NISAR) | TO-19 |
Topography | High spatial resolution (5 m) bare-Earth topography at 1 m vertical accuracy over all volcanoes | Spacecraft swath-lidar or radar | POR-14 (ICESat-2) | TO-20 | |||
Ground-surface composition and changes over time | Hyperspectral VNIR/SWIR (at the ~ 30 m spatial scale) and TIR data (at the ~ 60 m spatial scale) with 1-2 week revisit time, acquiring continuously for periods of weeks to months prior to an eruption to detect trends and change | High spatial-resolution imaging/spectometry—e.g., ASTER, Hyperion, HyspIRI (last decadal survey), Landsat (high spatial). OMI, AIRS (high temporal) | POR-9 (ASTER, OLI, ETM+), POR-21 (MODIS), POR-24 (VIIRS, SEVIRI) | TO-18 | |||
Gas emissions, plume composition, particle size and temporal changes | Hyperspectral UV, NIR, SWIR, and TIR data (at ~1-10 km spatial scale) with daily revisit time. Multi- to hyperspectral VNIR/SWIR (at ~30 m) and TIR data (at ~60 m) with ~1 week revisit time. Acquiring continuously prior to and during eruptions to detect trends and measure eruptive emissions. Active (lidar and radar) and passive (MISR) data to characterize plume altitude | Global hyperspectral UV (e.g., OMI, OMPS) and TIR (e.g., AIRS, IASI) for SO2, H2S and ash; high-resolution NIR for CO2 (e.g., OCO-2). High spatial-resolution (e.g., ASTER, HyspIRI) for small plumes. Space-borne lidar and radar (e.g., CALIPSO, CloudSat), multiangle visible-NIR imagers (e.g., MISR) | POR-9 (ASTER), POR-11 (OMPS, OMI), POR-20 (AIRS), POR-21 (MODIS) | TO-1, 2, 18 | |||
Thermal output | Multispectral TIR data (including a 3-5 micron channel) at 100 m spatial resolution acquired at a temporal frequency of 1-24 hours to detect high-frequency changes in thermal output before and after an event | Moderate-resolution imaging/spectometry—e.g., ASTER, Landsat (high spatial) but at the high temporal scale of GOES, MODIS, AVHRR | POR-9 (ASTER), POR-21 (MODIS), POR-25 (ASTER, TIRS) | TO-18 | |||
S-1b. Measure and forecast interseismic, preseismic, coseismic, and postseismic activity over tectonically active areas on time scales ranging from hours to decades. | Most Important | Land-surface deformation | At least two components of land-surface deformation 10 m to 1,000 km resolution and precision of 1-10 mm at a sampling frequency related to seismic/tectonic activity. Ideally, resolution of 1 mm/week. Need more than 10 years of observations to measure interseismic deformation | L- or S-band InSAR with ionospheric correction, [GPS/GNSS]. | POR-12 (NISAR) | TO-19 | |
Large spatial scale gravity change | Gravity change for large events (GRACE and follow-on missions) | Gravity (e.g., GRACE-2) | POR-30 (GRACE-FO) | TO-9 | |||
Reference frame | Stable terrestrial reference frame at 1 mm/yr accuracy | [VLBI, SLR, GPS/GNSS] | POR-7, 13 (Jason-3, LAGEOS) | ||||
Topography | High spatial resolution (1 m), bare-Earth topography at 0.1 m vertical accuracy over selected tectonic areas | [aircraft/UAV lidar] | POR-14 (ICESat-2) | TO-20 | |||
Land cover change | High spatial resolution (1 m) stereo optical imagery | {Commercial optical} | POR-9 (Pleiades) | COMMERCIAL | |||
S-1c. Forecast and monitor landslides, especially those near population centers. | Very Important | Land-surface deformation | At least two components of land-surface deformation at <50 m spatial resolution and 1 mm/yr at a temporal frequency <seasonal (InSAR and GPS/GNSS) | L- or S-band InSAR, [GPS/GNSS] {Complements ground-based seismic data} | POR-12 (NISAR) | TO-19 | |
High-resolution topography | Spatial resolution 1-5 m, vertical 0.5 m | [aircraft/UAV lidar] | POR-14 (ICESat-2) | TO-20 | |||
Precipitation | Every 3 hours | Precipitation monitor (e.g., GPM) | POR-4 (GPM, CloudSat) | TO-5 | |||
Permafrost melt | Radar, optical imaging, and InSAR | {Radarsat-2, commercial 1 m optical} | POR-12, 23 (RADARSAT-2, SMAP, SMOS) | TO-17 |
EARTH SURFACE AND INTERIOR PANEL | |||||||
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question/Goal | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
High spatial resolution time series of distribution of vegetation and rock/soil composition | Hyperspectral VNIR/SWIR and TIR data at 30-45 m spatial resolution and ~ weekly temporal resolution | Moderate-resolution imaging/spectometry—e.g., ASTER, Landsat, Hyperion but at slightly improved spatial resolution and much improved temporal resolution | POR-9 (ASTER, Landsat, Hyperion, Sentinel-2) | TO-18 | |||
S-1d. Forecast, model, and measure tsunami generation, propagation, and run-up for major seafloor events. | Important | Topography and shallow bathymetry | High spatial resolution (1 m), bare-Earth topography at 0.1 m vertical accuracy over selected tectonic areas | [aircraft/UAV lidar] | POR-14 (ICESat-2) | TO-20 | |
Sea-surface tsunami waves | Wave height (0.1 m), period (seconds, minutes?) | Swath altimetry—e.g., SWOT {GPS/GNSS buoys, ocean altimetry, complements seafloor pressure changes} | POR-7, 27 (SWOT) | TO-21 | |||
Ionospheric waves | Ionospheric imaging at 10 km spatial resolution and 10 minute sampling from GPS/GNSS arrays | Radio occultation—e.g., GPS/GNSS, COSMIC | POR-6, 7 (COSMIC) | ||||
Global bathymetry and seamless nearshore bathymetry | Global marine gravity from swath radar altimetry (SWOT) | Swath altimetry | POR-27 (SWOT) | TO-21 | |||
Optical, radar, and InSAR change detection on demand with low-latency processing and distribution | Enable high spatial resolution spaceborne or aircraft asset that can provide timely information to relief efforts | {Commercial 1 m optical, GPS/GNSS} | POR-9 (Pleiades, RADARSAT2) | COMMERCIAL | |||
Rapid characterization of the magnitude of earthquakes | 1 Hz deformation time series | {Terrestrial seismic and GPS/GNSS networks} | POR-7, 13 (GNSS, GRACE-FO, Jason-3, LAGEOS, GRASP) | TERRESTRIAL | |||
All high-resolution visible to thermal IR imagery | Provide rapid acquisitions at the hours to 1-day time frame using either a constellation of small-sats and/or interconnectivity to other orbital assets in a sensor-web approach | Create new small-sat constellations to complement ground-based seismic, gas, thermal, scanning lidar monitoring systems. Expand on current orbital sensor webs such as ACE and the ASTER Urgent Request Protocol. NOTES: This is likely a NASA-specific focus, but could be partially satisfied with commercial participation. | POR-9 (Pleiades, Sentinel-2) | COMMERCIAL | |||
QUESTION S-2. How do geological disasters directly impact the Earth system and society following an event? | S-2a. Rapidly capture the transient processes following disasters for improved predictive modeling as well as response and mitigation through optimal retasking and analysis of space data. | Most Important | All high-resolution visible to thermal IR imagery | Provide rapid acquisitions at the hours to 1-day time frame using either a constellation of small-sats and/or interconnectivity to other orbital assets in a sensor-web approach | Create new small-sat constellations to complement ground-based seismic, gas, thermal, scanning lidar monitoring systems. Expand on current orbital sensor webs such as ACE and the ASTER Urgent Request Protocol. NOTES: This is likely a NASA-specific focus, but could be partially satisfied with commercial participation. | POR-9 (Pleiades, Sentinel-2) | COMMERCIAL |
Provide rapid deformation map acquisitions and interconnectivity to other sensors | At least two components of land-surface deformation over 10 m to 1000 km length scales at 10 mm precision and ASAP after the event. Adequate resolution of 1 cm/week for afterslip applications | InSAR | POR-12 (NISAR) | TO-19 | |||
S-2b. Assess surface deformation (<10 mm), extent of surface change (<100 m spatial resolution) and atmospheric contamination, and | Very Important | Land-surface deformation | At least two components of land-surface deformation and surface fracturing over length scales ranging from 10 m to 1,000 km and temporal resolution of 1 mm/yr at a sampling frequency related to the volcanic | L- or S-band InSAR with ionospheric correction, [GPS/GNSS] | POR-12 (NISAR) | TO-19 |
EARTH SURFACE AND INTERIOR PANEL | |||||||
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question/Goal | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
the composition and temperature of volcanic products following a volcanic eruption (hourly to daily temporal sampling). | activity (InSAR and GPS/GNSS) everywhere. | ||||||
Volume, composition, and temperature of all eruptive products and their changes over time | Hyperspectral VNIR/SWIR and TIR data at 30-45 m spatial resolution and ~ weekly temporal resolution, SAR backscatter data | Moderate-resolution imaging/spectometry—ASTER, Landsat, high-repeat time airborne/UAV data | POR-9 (ASTER), POR-21 (MODIS), POR-25 (ASTER, TIRS) | TO-18 | |||
Mass and energy fluxes across solid Earth/atmospheric boundary | Hyperspectral VNIR/SWIR and TIR data at 30-45 m spatial resolution and ~ weekly temporal resolution, High-rate SNR GPS/GNSS data | Moderate-resolution imaging/spectometry—ASTER, Landsat, high-repeat time airborne/UAV data | POR-9 (ASTER), POR-21 (MODIS), POR-25 (ASTER, TIRS) | TO-18 | |||
[Geospatial and numerical model development of future/continued hazard potential] | Hyperspectral VNIR/SWIR and TIR data at 30-45 m spatial resolution and ~ weekly temporal resolution. SAR backscatter data at >30 m (or better spatial resolution). Bare-Earth topography. High-rate SNR GPS/GNSS data. Synergy to past/future Landsat-style systems. Expanded GIS and integration with current databases. | Moderate-resolution imaging/spectometry—e.g., ASTER, Landsat, high-repeat time airborne/UAV data, [current plume dispersion, lahar and lava flow modeling] | POR-9 (ASTER), POR-21 (MODIS), POR-25 (ASTER, TIRS) | TO-18 | |||
S-2c. Assess co- and postseismic ground deformation (spatial resolution of 100 m and an accuracy of 10 mm) and damage to infrastructure following an earthquake. | Very Important | Land-surface deformation | At least two components of land-surface deformation at 100 m spatial resolution and 1 mm/yr at a temporal frequency related to the tectonic activity (InSAR and GPS/GNSS). Need more than 10 years of interseismic observations and 5 years of post seismic observations | L- or S-band InSAR with ionospheric correction, [GPS/GNSS] | POR-12 (NISAR) | TO-19 | |
Large spatial scale gravity change | Gravity change for large events | Gravity (e.g., GRACE-2) | POR-30 (GRACE-FO) | TO-9 | |||
Reference frame | Stable terrestrial reference frame at 1 mm/yr accuracy | [VLBI, SLR, GNSS] | POR-7, 13 (GRACE-FO, Jason-3, LAGEOS) | TO-9 | |||
Topography | High spatial resolution (1 m), bare-Earth topography at 0.1 m vertical accuracy over selected tectonic areas | [aircraft/UAV lidar] | POR-14 (ICESat-2) | TO-20 | |||
Optical imaging | Map surface rupture, liquefaction features and damage at spatial scales better than 5 m. | {Worldview}, [aircraft/drone imaging] | POR-9 (Pleiades) | COMMERCIAL, AIRBORNE | |||
QUESTION S-3. How will local sea-level change along coastlines around the world in the next decade to century? | S-3a. Quantify the rates of sea-level change and its driving processes at global, regional, and local scales, with uncertainty <0.1 mm/yr for global mean sea-level equivalent and <0.5 mm/yr sea-level equivalent at resolution of 10 km. | Most Important | Surface melt | Weekly during melt season, 1 m horizontal resolution | Imagery (e.g., Landsat, Aster, WorldView) | POR-9 (Pleiades) | TO-18 |
Ice topography | Monthly or less, uncertainty <(10 cm for mean, 25 cm/yr for change) over areas of 100 km2 | Satellite and suborbital lidar | POR-14 (ICESat-2) | TO-7 | |||
Snow density | 50 km resolution with accuracy of 2 cm RMS in terms of snow water equivalent (SWE), averaged monthly | POR-14 (ICESat-2), POR-26 (SWOT), POR-27 (CryoSat-2) | TO-16 | ||||
Gravity | Monthly, uncertainty 1 cm water-equivalent thickness at resolution of 200 km at equator | Gravity (e.g., GRACE-2) | POR-30 (GRACE-FO) | TO-9 | |||
3D surface deformation vectors on ice sheets | Monthly, cm/yr accuracy, 100 m resolution and better than seasonal sampling | InSAR | POR-12 (NISAR) | TO-19 | |||
Sea-surface height | Monthly, 2 cm height accuracy at 100 km resolution | Radar altimetry (e.g., Jason-3, Jason-CS, SWOT), [global tidal gauge network] | POR-26, 27 (Lason-3, SWOT) | TO-21 | |||
Terrestrial reference frame | Stability at <0.1 mm/yr | Possible (e.g., GRASP), [maintain high quality of co-located sites (minimum of 8-10)] | POR-7, 13 (GRACE-FO, Jason-3, LAGEOS, GRASP) |
EARTH SURFACE AND INTERIOR PANEL | |||||||
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question/Goal | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
In situ temperature/salinity | Comparable to Argo at 300 km resolution or better | N/A | |||||
Ice velocity | Monthly or less, uncertainty <10 cm/yr over areas of 100 km2 | InSAR | POR-12 (NISAR) | TO-19 | |||
High-resolution topography | Vertical accuracy of 10 cm, resolution 1 m | POR-14 (ICESat-2) | TO-20 | ||||
S-3b. Determine vertical motion of land along coastlines at uncertainty <1 mm/yr. | Most Important | Bare-earth topography | Global measurements made once to produce high-resolution (1-m horizontal, 10-cm vertical) bare-earth topographic model. Focused regional surveys at comparable resolution used to image landscape change from the globally established baseline. | [aircraft/UAV lidar, UAV SAR?]. NOTES: In cases where landscape changes are substantial and unobscured by vegetation, topographic models created from high-resolution stereo satellite imagery may supplement airborne lidar bare-earth elevation models. | POR-14 (ICESat-2) | TO-20 | |
Land-surface deformation | 5-10 mm vertical precision, <50 m horizontal, weekly | InSAR | POR-12 (NISAR) | TO-19 | |||
QUESTION S-4. What processes and interactions determine the rates of landscape change? | S-4a. Quantify global, decadal landscape change produced by abrupt events and by continuous reshaping of Earth’s surface from surface processes, tectonics, and societal activity. | Most Important | Bare-earth topography | Global measurements made once to produce high-resolution (1 m horizontal, 10 cm vertical) bare-earth topographic model. Focused regional surveys at comparable resolution used to image landscape change from the globally established baseline. | [aircraft/UAV lidar, UAV SAR?]. NOTES: In cases where landscape changes are substantial and unobscured by vegetation, topographic models created from high-resolution stereo satellite imagery may supplement airborne lidar bare-earth elevation models. | POR-14 (ICESat-2) | TO-20 |
Land-surface deformation | 5-10 mm vertical precision, <50 m horizontal, weekly | L- or S-band InSAR, [UAVSAR], [GNSS]. | POR-12 (NISAR) | TO-19 | |||
High spatial resolution time series of changes in optical surface characteristics | Optical ground characteristics at <1 m resolution with weekly repeat time | {Worldview-2 / 3 satellites} | POR-9 (Pleiades) | COMMERCIAL | |||
Measurement of rock-, soil-, water-, and ice-mass change | Satellite gravimetry | Gravity (e.g., GRACE-2) | POR-30 (GRACE-FO) | TO-9 | |||
Measurement of rainfall and snowfall rates | Multiple times per day via satellite constellation | Like GPM | POR-4 (GPM, CloudSat) | TO-5 | |||
Reflectance for freeze/thaw spatial and temporal distribution | <50 m horizontal, weekly | Radar reflectivity | POR-9 (Pleiades, RADARSAT2), POR-14 (NISAR | TO-19 | |||
S4b. Quantify weather events, surface hydrology, and changes in ice/water content of near-surface materials that produce landscape change. | Important | Measurement of rainfall and snowfall rates | Multiple times per day via satellite constellation | Precipitation monitor (e.g., GPM) | POR-4 (GPM, CloudSat) | TO-5 | |
Reflectance for freeze/thaw spatial and temporal distribution | <50 m horizontal, weekly | Radar reflectivity | POR-12 (NISAR) | TO-19 | |||
Optical characterization of spatial and temporal distribution of freeze/thaw | <1 m horizontal, weekly | {Worldview-2 / 3 satellites} | POR-9 (Pleiades) | COMMERCIAL | |||
Reflectance for snow depth/snow water equivalent | SWE at ~100 m resolution suitable for SWE values to 2.5 m. | Ka-band radar or laser altimeter (depth) and SAR (density) | POR-17 (KaRIn, SWOT) | TO-16, 19 | |||
InSAR | POR-12 (NISAR) | TO-19 | |||||
Soil/root zone moisture content | Soil state/moisture (e.g., SMAP) | POR-12 (NISAR), POR-23 (SMAP) | TO-17 | ||||
Vadose zone soil moisture at <5 m horizontal resolution with daily repeat times | [AIRMOSS] | AIRCRAFT | |||||
S4c. Quantify ecosystem response to and causes of landscape change. | Important | High spatial resolution time series of distribution of vegetation in VIS/NIR | NIR at <5 m with weekly to monthly repeat time | {Worldview-2 / 3 satellites} | POR-9 (ASTER), POR-21 (MODIS), POR-25 (ASTER, TIRS) | COMMERCIAL | |
Observations of canopy structure and carbon inventory | 1 m resolution canopy structure collected seasonally | [Aircraft/UAV waveform lidar] | POR-14 (ICESat-2) | TO-20 |
EARTH SURFACE AND INTERIOR PANEL | |||||||
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question/Goal | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
25 m resolution vertical structure observations collected seasonally and globally | GEDI | POR-14 (ICESat-2) | TO-20, 22 | ||||
Bare-earth topography | Global measurements made once to produce high-resolution (1 m horizontal, 10 cm vertical) bare-earth topographic model. Focused regional surveys at comparable resolution used to image landscape change from the globally established baseline. | [aircraft/UAV lidar, UAV SAR?]. NOTES: In cases where landscape changes are substantial and unobscured by vegetation, topographic models created from high-resolution stereo satellite imagery may supplement airborne lidar bare-earth elevation models | POR-14 (ICESat-2) | TO-20 | |||
Observations of ecosystem status and near-surface material composition | Hyperspectral VNIR/SWIR and TIR data at 30-45 m spatial resolution and ~ weekly temporal resolution | Moderate-resolution imaging/spectometry—e.g., Landsat, ASTER, Hyperion, but with improved spectral and temporal resolution | POR-9 (ASTER, OLI, ETM+ | ) TO-18 | |||
QUESTION S-5. How does energy flow from the core to Earth’s surface? | S-5a. Determine the effects of convection within Earth’s interior, specifically the dynamics of Earth’s core and its changing magnetic field and the interaction between mantle convection and plate motions. | Very Important | Monitor secular variation in Earth’s magnetic field | LEO Multi-point simultaneous magnetic field vector measurements with global coverage, 0.1 nT/component precision, 1 nT/component absolute. Multi-year continuous observations. | Magnetometers (e.g., SWARM). NOTES: There are currently no suitable LEO vector magnetic satellite missions planned beyond Swarm. | POR-19 (SWARM) | TO-8 |
Determine exchange of angular momentum between core and mantle from changes in earth rotation parameters | Observe changes in the Earth Orientation Parameters to 5 μs for the Length of Day and 50 μas for the corresponding xp, yp pole coordinates. Observe the nutation and precession of the Earth rotation axis to 0.0001″ for each component. | [VLBI] | POR-7, 13 (GRACE-FO, Jaso 3, LAGEOS, GRASP) | n- TO-9 | |||
Map surface topography—moderate resolution | Measure topography to 5 m horizontal and 10 cm vertical resolution | {TerraSAR Tandem-X} | COMMERCIAL | ||||
Map gravity field | Measure sea-surface height to 1 cm over 10 km distance | Radar altimetry (e.g., SWOT) | POR-26, 27 (SWOT) | TO-21 | |||
Determine plate motions and deformation and track the evolution of plate boundaries | Continuous GPS/GNSS 1 mm/yr horizontal, 2 mm/yr vertical, <500 km sampling interval | [GNSS] | POR-7 (GNSS) | ||||
SAR interferometry, 10 mm vertical, 100 mm horizontal | L-band InSAR with ionospheric correction | POR-12 (NISAR) | TO-19 | ||||
Marine or aeromagnetic high-resolution spatial magnetic anomalies, 10 nT, 1 km horizontal resolution | [aircraft magnetometer] | AIRCRAFT | |||||
Sea floor geodesy, 5 mm/yr horizontal, 10 mm/yr vertical | [GPS acoustics] | GPS | |||||
Improved reference frames through geodetic observations, 1 mm accuracy, 0.1 mm/yr stability horizontal and vertical | [VLBI, SLR, GNSS] | POR-7, 13 (GRACE-FO, Jaso 3, LAGEOS, GRASP) | n- | ||||
S-5b. Determine the water content in the upper mantle by resolving electrical conductivity to within a factor of 2 over horizontal scales of 1,000 km. | Important | Mantle conductivity determined from time series of global magnetic measurements | Multi-point simultaneous magnetic field vector measurements with global coverage, 0.1 nT/component precision, 1 nT/component absolute; multi-year, continuous observations. | Magnetometers (e.g., SWARM), but with larger number of satellites. NOTES: This objective requires multi-point measurements from a constellation of satellites taking oriented vector magnetic measurements. | POR-8, 19 (SWARM) | TO-8 | |
S-5c. Quantify the heat flow through the mantle and lithosphere within 10 mW/m2. | Important | Determine the heat flow through the land surface and volcanic activity | Night-time hyperspectral TIR at 30-45 m spatial resolution to determine surface heat flow to within 5 mW/m2 | Imaging (e.g., MODIS) | POR-25 (ASTER, TIRS), POR-21 (MODIS) | TO-18 |
EARTH SURFACE AND INTERIOR PANEL | |||||||
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SCIENCE | MEASUREMENT | ||||||
Societal or Science Question/Goal | Earth Science/Application Objective | Science/Application Importance | Geophysical Observable | Measurement Parameters | Example Measurement Approaches | ||
Method | POR | TO | |||||
Map depth of the Curie temperature isotherm | UAV spatial magnetic anomalies, 10 nT, 10 km horizontal resolution | [aircraft total field magnetics] | |||||
QUESTION S-6. How much water is traveling deep underground, and how does it affect geological processes and water supplies? | S-6a. Determine the fluid pressures, storage, and flow in confined aquifers at spatial resolution of 100 m and pressure of 1 kPa (0.1 m head). | Very Important | Topography | Topography at 10 m resolution | {TerraSAR Tandem-X}. NOTES: The required radar data have been collected by the TerraSAR Tandem-X mission although the data are not publically available. A negotiated data purchase may be less costly than a new NASA mission. | COMMERCIAL | |
Land-surface deformation | For seasonal variations: 1 cm/yr measured weekly at 10 m spatial sampling (which allows stacking for sub-cm secular trends) | L- or S-band InSAR, [GPS/GNSS] | POR-12 (NISAR) | TO-19 | |||
Surface water distribution | 100 m spatial, e.g., SWOT, stream gauge network, seasonally | Radar altimetry (e.g., SWOT) | POR-26, 27 (SWOT) | TO-21 | |||
S-6b. Measure all significant fluxes in and out of the groundwater system across the recharge area. | Important | Soil moisture, snow/SWE, rainfall | 1-5 km spatial, from SMAP, other radar, thermal inertia using TIR and VNIR data, and GPS reflections, weekly | Soil moisture (e.g., SMAP), rainfall (e.g., GPM) | TO-17 | ||
Gravity | Monthly, uncertainty 1 cm water-equivalent thickness at resolution of 100 km | Gravity (e.g., GRACE-2) | POR-30 (GRACE-FO) | TO-9 | |||
Topography | Vertical accuracy of 10 cm, resolution 1 m | SAR, lidar (suborbital) | POR-14 (ICESat-2) | TO-20 | |||
Deformation from fluid fluxes (uses several above measurements) | Spatiotemporal distribution of subsidence/uplift at 3 mm vertical per year, 5 m horizontal, weekly. Coverage over active reservoirs. | L- or S-band InSAR, [GPS/GNSS] | POR-12 (NISAR) | TO-19 | |||
Land-surface deformation | Spatiotemporal distribution of subsidence/uplift at 1 cm vertical, 5 m horizontal, weekly. Coverage over managed watersheds, other watersheds of interest | L- or S-band InSAR, [GPS/GNSS] | POR-12 (NISAR) | TO-19 | |||
S-6c. Determine the transport and storage properties in situ within a factor of 3 for shallow aquifers and an order of magnitude for deeper systems. | Important | Deformation from fluid fluxes (uses several above measurements) | Spatiotemporal distribution of subsidence/uplift at 3 mm/yr vertical, 5 m horizontal, weekly. Coverage over active reservoirs. | L- or S-band InSAR, [GPS/GNSS] | POR-12 (NISAR) | TO-19 | |
S-6d. Determine the impact of water-related human activities and natural water flow on earthquakes. | Important | Vertical surface deformation | Spatiotemporal distribution of subsidence/uplift at 3 mm/yr vertical, 5 m horizontal, weekly | L- or S-band InSAR, [GPS/GNSS] {seismic data and production/injection data from regulatory agencies} | POR-12 (NISAR) | TO-19 | |
QUESTION S-7. How do we improve discovery and management of energy, mineral, and soil resources? | S-7a. Map topography, surface mineralogic composition, distribution, thermal properties, soil properties/water content, and solar irradiance for improved development and management of energy, mineral, agricultural, and natural resources. | Important | Hyperspectral VSWIR reflectivity and TIR emissivity and surface temperature | Hyperspectral VNIR/SWIR and TIR data at 30-45 m spatial resolution and ~ weekly temporal resolution | Moderate-resolution imaging/spectometry (e.g., Landsat, Aster but with improved spectral and temporal resolution) | POR-9 (ASTER, OLI, ETM+) | TO-18 |
Thermal intertia | Hyperspectral TIR data at 30-45 m spatial resolution and ~ weekly temporal resolution. Day/night measurements needed at the 12-24 hour time scale. | Imaging (e.g., MODIS, ASTER) | POR-9 (ASTER, OLI, ETM+), POR-25 (ASTER, TIRS) | TO-18 | |||
Land-surface deformation | Spatiotemporal distribution of subsidence/uplift at 1 cm vertical, 5 m horizontal, weekly | L- or S-band InSAR with ionospheric correction, [GPS/GNSS] | POR-12 (NISAR) | TO-19 | |||
Topography | Topographic data at 30 m postings, 25 cm vertical | {TerraSAR Tandem-X} | COMMERCIAL | ||||
Solar irradiance | Real-time measurement of solar irradiance at better than 1 km resolution and 15-min cadence for managing solar power assisted energy grids. | Solar irradiance (e.g., GOES) |