Weather and air quality simulations have greatly improved over the past decade. These advances are in large part due to improved data assimilation, atmospheric physics and chemistry models, faster computers, and observations that improve, validate, evaluate, and initialize deterministic and ensemble forecast models. National Oceanic and Atmospheric Administration (NOAA)/National Weather Service (NWS) forecasters issue lifesaving warnings with as much lead time as scientifically possible, and work with researchers to improve forecast and warning lead times with better geographic precision and reduced false alarms for all hazardous weather (Uccellini, 2017). The Environmental Protection Agency (EPA), industrial stakeholders, as well as state and local governments rely on accurate air quality simulations to identify cost-effective strategies to improve health.
Over the next decade data assimilation and modeling efforts will emphasize simulating a number of coupled components of the Earth system rather than just the individual atmosphere, ocean, sea-ice, land (including ice and snow), and atmospheric composition components. These efforts are aimed at extending skillful forecasts into the subseasonal range (up to 60 days), and on delivering lifesaving and economic societal benefits. Satellite observations, combined with data assimilation and numerical prediction models, will be essential components of this fully coupled Earth system framework. There are numerous scientific, computational, and observational challenges associated with achieving such an integrated weather and air quality prediction system, but the societal benefits associated with more skillful weather forecasts across all lead times would truly be transformative.
The Panel on Weather and Air Quality: Minutes to Subseasonal identified 10 primary science questions and quantified objectives to address these challenges, all of which will require observations from both research and operational satellite systems. In particular, observations from research satellites will be
NOTE: This chapter was written by members of the Panel on Weather and Air Quality: Minutes to Subseasonal and is provided for reference only. Any study finding or consensus recommendation will appear in Chapters 1-5, the report from the survey steering committee.
essential for advancing capabilities, while observations from operational systems will be critical to sustaining forecast systems.
Table 7.1 summarizes the panel’s scientific/application priorities, as addressed in its questions/goals and the corresponding measurement objectives.
For an Earth system framework, representing the lowest part of the atmosphere (the planetary boundary layer, or PBL) is crucial for weather and air quality forecasting because this is where people live and work, and because it is integral to the exchange of energy, momentum, and mass between the atmosphere and the surface (land, ocean, and ice). Due to the strong diurnal cycles of boundary layer processes, and their implications for air quality, convective initiation, and severe weather outbreaks, a combination of geostationary and polar-orbiting satellites, airborne platforms, and ground-based networks will be needed for the measurements. These include satellite infrared and microwave sounders, and radio occultation from the Global Navigation Satellite System (GNSS). In particular, three-dimensional (3D) horizontal wind vector measurements from spaceborne wind profilers (Baker et al., 2014) would be transformative to weather and air quality forecasting, both in the PBL and in the free troposphere. High-temporal-resolution vertical profiling of the PBL and throughout the free troposphere at a national scale would improve severe weather (a national priority)1 and air quality forecasting.
Local variations in Earth’s surface characteristics also affect the exchange of energy, moisture, and pollutants between the surface and the free atmosphere through changes in the PBL turbulence, local convergence, and vertical motion (Kilpatrick et al., 2016). Over the oceans, the average magnitude of vertical transport due to these inhomogeneities is roughly 10 to 100 times the magnitude of the long-term mean flux needed to warm the oceans (Steffen, 2014; IPCC AR5 Report, 2015). Locally, and on short time scales, there are much larger variations in energy, moisture, and air pollutant transport from the PBL into the free atmosphere and vice versa, and this impacts local weather and air quality (Steffen, 2014). These surface spatial variations also modify precipitation and contribute to local temperature extremes, which are important considerations for the hydrological cycle, human health, and agriculture; these in turn can modify the surface air temperature (Kilpatrick and Xie, 2015).
It is vital to know where, when, and how clouds form, whether they precipitate or not, and how they affect the radiative balance at the surface. Improvements in our ability to assess the availability of freshwater from precipitation and to examine the occurrence and characteristics of extreme precipitation events will require considerable improvement in the accuracy of cloud precipitation (and cloud-aerosol-radiative balance) processes in Earth system models at scales from microphysical to regional to global. Cloud Resolving Models (CRMs) cannot be improved without observational constraints to assess the fidelity of existing microphysical schemes and processes (Stephens and Ellis, 2008; Hagos et al., 2014). High-quality measurements of cloud precipitation particles, microphysical properties, and vertical motions (velocities and areal extent of vertical updrafts) will be needed to constrain these models as they become capable of explicitly resolving cloud microphysical processes at the convective updraft scales.
This collective of new observations will lead to improved weather forecasts—namely, by improving the initialization (through data assimilation) and representation of key atmospheric processes (e.g., deep convective energy exchanges between the PBL and the upper troposphere, aerosol-cloud-precipitation interactions, and convective initiation) and Earth system couplings (e.g., land-, ocean-, and ice-surface conditions and boundary layer processes). New observations such as these would, for example, allow time-resolved monitoring of convective processes to improve their representation in numerical weather models—from small-scale cloud-resolving models to the global-scale weather and climate. Such improvements are crucial for extending our predictions into the subseasonal range; for improving around-the-clock
1 H.R. 353—Weather Research and Forecasting Innovation Act of 2017, 115th Congress.
TABLE 7.1 Summary of Science and Applications Questions and Their Priorities
|Science and Applications Questions||Highest Priority Measurement Objectives (MI=Most Important, VI=Very Important)|
|W-1||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?||(MI) W-1a. Determine the effects of key boundary layer processes on weather, hydrological, and air quality forecasts at minutes to subseasonal time scales.|
|W-2||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?||(MI) 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.|
|W-3||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?||(VI) 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.|
|W-4||Why do convective storms, heavy precipitation, and clouds occur exactly when and where they do?||(MI) 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.|
|W-5||What processes determine the spatiotemporal structure of important air pollutants and their concomitant adverse impact on human health, agriculture, and ecosystems?||(MI) 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).|
|W-6||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?||The objective associated with this question was ranked Important. See subsequent sections for details.|
|W-7||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?||The objective associated with this question was ranked Important. See subsequent sections for details.|
|W-8||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?||The objective associated with this question was ranked Important. See subsequent sections for details.|
|W-9||What processes determine cloud microphysical properties and their connections to aerosols and precipitation?|
|W-10||How do clouds affect the radiative forcing at the surface and contribute to predictability on time scales from minutes to subseasonal?|
lifesaving prediction of the timing, location, and threat level of severe weather (minutes to days); and for providing for more skillful and comprehensive “environmental” forecasts.
The current health and economic costs (e.g., human morbidity and mortality, ecosystem degradation, and crop yield reductions) of air pollution are large and growing, particularly in developing countries. While satellite measurements have significantly advanced atmospheric composition and health assessment research over the last decade, the current observational networks for tropospheric methane, particulates, and ozone have deficiencies that do not allow for characterization of their 3D distributions, especially at “nose-level.” Nor are the measurements of these important pollutants and key climate change species sufficiently accurate to monitor their variations and trends. These deficiencies include poor spatiotemporal coverage, limited information on particulate speciation, and insufficient measurements of ozone and secondary particulate precursor species. These deficiencies further impact our ability to develop strategies to mitigate the adverse health, economic, and environmental damage done by air pollution. A particular concern is that there are no replacements planned for several aging U.S. satellite instruments2 that, when gone, will leave atmospheric and health scientists with a greatly diminished capability to quantify 3D particulate concentrations and chemical speciation. Continuation of the current data records, the power of which increases as the records lengthen, is critical for assessing the long-term effects of particulate pollution, ozone, and methane on air quality, human health, ecosystems, and climate.
Space-based remote sensing provides key information on weather and air quality of the Earth system on global and local scales. These measurements are a vital component in the global monitoring and prediction of the state of our atmosphere. New measurements and observations will be needed to address the science objectives identified by this panel. In Table 7.2, the highest priority science and application objectives are mapped to the Targeted Observables that will strongly contribute to addressing those objectives.3 Note that most measurements will contribute data needed for more than one objective, and thus coordinated planning, such as formation flying, could substantially enhance each individual measurement. In addition to the new observations, planned operational weather satellites are also key in achieving these objectives, as these satellite systems can help with specifying the environmental conditions of the event being monitored by the new technologies. Advancing the science and applications of weather and air quality will also require combining the satellite measurements with the appropriate data assimilation and numerical model simulations. These fundamental science questions can be addressed only with advanced capabilities in observations, modeling and data assimilation systems, instrument platforms, and computing facilities.
This chapter identifies a set of challenges that must be addressed by collaboration of research and operational agencies to advance weather and air quality research. The National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA), U.S. Geological Survey (USGS), other government agencies, academia, and private industry all engage in weather research. NASA, perhaps best exemplified by the competitively driven investments of the Earth Science Technology Office (ESTO), has the unique role and capability in instrument technology development and new mission conceptualization. NASA has repeatedly demonstrated the capacity, when leveraged, to pioneer the next generations of instrument platforms, formation flying, and quality measurements that address relevant technologies, science questions, and applications. NOAA and USGS need to play two roles: agile on-ramping and adaption of these new technologies into operational systems, and deployment of the operational systems in support of new research endeavors.
2 For example, collocated Moderate-Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations provide a three-dimensional (3D) distribution of aerosols on a global scale; both instruments are far beyond their designed life span.
3 Not mapped here are cases where the Targeted Observables may provide a narrow or an indirect benefit to the objective, although such connections may be cited elsewhere in this report.
TABLE 7.2 Priority Targeted Observables Mapped to the Science and Applications Objectives That Were Ranked as Most Important (MI) or Very Important (VI)
|Priority Targeted Observables||Science and Applications Objectives|
|Aerosol Vertical Profiles||W-1a, W-2a, W-5a|
|Aerosol Properties||W-1a, W-2a, W-3a, W-4a, W-5a|
|Temperature, Water Vapor, PBL Height||W-1a, W-2a, W-3a, W-4a|
|Atmospheric Winds||W-1a, W-2a, W-4a|
|Precipitation and Clouds||W-1a, W-2a, W-3a, W-4a|
|Ice Elevation||W-2a, W-3a|
|Snow Depth and Snow Water Equivalent||W-2a, W-3a|
|Soil Moisture||W-2a, W-3a|
|Ocean Surface Winds and Currents||W-1a, W-2a, W-3a|
|Vegetation, Snow, and Surface Energy Balance||W-3a|
|Cloud Microphysics and Vertical Motion||W-4a|
INTRODUCTION AND VISION
Perhaps someday in the dim future it will be possible to advance the computations faster than the weather advances and at a cost less than the saving to mankind due to the information gained. But that is a dream.4
The air above us influences our lives through both its movement (i.e., weather) and its quality. The National Centers for Environmental Information (NCEI) tracks and evaluates weather events that have great economic and societal impact. NCEI estimates that between 1980 and 2016, the United States sustained 200 weather-related disasters where overall costs reached or exceeded $1 billion for each event (including a consumer price index adjustment to 2016; NOAA NCEI, 2017). In 2015 there were 522 fatalities and 2,143 injuries from weather-related events in the United States, with losses totaling over $4.8 billion (see Figure 7.1). Floods and heat waves are two weather phenomena that have contributed to significant economic loss over the last several years and caused more than half of the fatalities and injuries. The 10-year (2006-2015) average flood casualty in the United States is 82 deaths per year. High temperatures and drought can cost billions of dollars to agriculture industries. Heat waves strain the healthcare system and can cause traffic hazards as highway and railroad buckle under extreme heat.
During August and September 2017, the United States experienced three major disasters associated with the landfall of Hurricanes Harvey, Irma, and Maria. Harvey contributed to epic flooding in coastal Texas and Louisiana, with at least 30 fatalities in Houston alone. Irma resulted in more than 134 deaths, with mass evacuations and destruction in Florida and across the Caribbean. A direct hit on Puerto Rico by Hurricane Maria devastated the island’s electrical grid and caused significant damage to the infrastructure and economy, with numerous fatalities reported along the hurricane’s path. Additional disasters were asso-
4 In 1922, well before the advent of digital computing, Lewis Richardson published the visionary “Weather Prediction by Numerical Process,” and with it the first true numerical weather forecast.
ciated with multiple significant wildfires in drought-parched northwest U.S. states (with many areas rated as in an exceptional drought), as well as northern California, where wildfire blazes driven by high winds killed dozens of people, wiped out communities and livelihoods, and destroyed thousands of homes and other structures.
Poor outdoor air quality, due to both gaseous and particulate matter (PM) constituents, contributes to millions of premature deaths annually worldwide and also negatively impacts agriculture yields and can degrade infrastructure. Poor air quality is a leading cause of premature deaths due to environmental and occupational risks (Figure 7.2). In the United States roughly $65 billion is spent annually on mitigating air pollution, resulting in $2 trillion in benefits, including over 160,000 cases of reduced infant and adult premature mortality (US EPA, 2011). By 2060, 6 to 9 million premature deaths worldwide are expected in association with poor air quality, with the associated annual global welfare costs projected to rise from U.S.
$3 trillion in 2015 to U.S. $18 to $25 trillion in 2060 (OECD, 2016). Even in the United States, where air quality is relatively good, in comparison with other parts of the world, air pollution is still a leading health risk factor (Lim et al., 2012), and there remain areas across the United States that are still not attaining air quality standards needed to protect health and the environment (US EPA, 2016).
Furthermore, we cannot characterize weather-related economic issues exclusively in terms of cost or loss avoidance. Improved forecast capability will reduce losses to property and life, but the economic benefits will also include new business development (e.g., the benefits from improved energy production and utilization). Enhanced remote sensing ability and capacity will undoubtedly create new commercial opportunities and jobs. Just as information content from past generations of satellites were partially responsible for the creation of U.S. weather industries, future space-based developments will spawn new industries and corporations as well. When looking across sectors ranging from insurance to manufacturing, the U.S. economy is reported to receive at least $30 billion per year from such earth observation products as satellite-derived weather information (Wigbels, 2008). This will only increase as we pursue advanced new remote sensing capabilities.
Advances in our understanding of weather and air quality processes rely on careful and precise measurements of key variables. Observations are essential for developing numerical representations of the relevant processes that model-based predictions are based on and for providing the initial conditions for the model simulations. Space-based remote sensing provides essential measurements on the state of our atmosphere, notably by providing routine and consistent national observations for short-term forecasting (i.e., minutes to hours to several days), as well as global measurements that are essential for medium-
to long-range global forecasts (i.e., 7 to 14 days). Since their introduction nearly 60 years ago, satellite measurements have substantially improved our forecasts and are a cornerstone of modern numerical weather prediction (NWP; WMO, 2012). Accurate forecast challenges still remain: forecasting convective initiation earlier, pinpointing the exact location of severe weather outbreaks, anticipating heat waves and dry periods, and better predicting hurricanes and other extreme weather events particularly in a changing climate (Powell and Aberson, 2001; NASEM, 2016b; NHC, 2016). Quantitative and heavy precipitation forecasts remain challenging, leading to significant societal implications with regard to reservoir and dam management, impacts on agriculture and on some modes of transportation. Extending weather prediction accuracy out to two weeks and beyond will have immense positive societal impacts by providing confident information to planning by decision makers and emergency managers.
Our ability to simulate the dynamics and chemistry that shape atmospheric composition, including air quality forecasts, has improved with advances in weather modeling and availability of satellite data of tropospheric trace gases and pollutants. Satellite data allow pollutant emissions and concentrations to be constrained, pollution plumes to be tracked throughout the troposphere, and photochemical processes to be inferred. As a result of the improved spatial coverage and reduced uncertainty in air quality products from satellite measurements, the health community has explored the use of these satellite data for applications that draw strong statistical inferences between pollutants and health outcomes. Satellite data of air pollutants and robust air quality modeling and forecasting allow for the development of effective pollution mitigation strategies to protect human and ecosystem health, including crops. For example, recent estimates of U.S. NOx emissions from automobiles inferred from satellite observations are about a factor of two lower than in current emission inventories (Travis et al., 2016), which, if proven to be true, will have ramifications in terms of future pollutant controls costs.
Addressing the objectives and goals described in this chapter will contribute to societal benefits that range from information for short-term needs, such as accurate severe weather forecasts and air quality simulations that protect life and property, to improved monthly to subseasonal precipitation and temperature outlooks that anticipate extremes in heat, droughts, and flooding, to a longer-term scientific understanding necessary for future applications that will benefit society in ways still to be realized.
Our ability to simulate the past, current, and future state of the atmosphere has improved over the past decade (Bauer et al., 2015) These advances are in large part due to improved atmospheric physics and chemistry models and data assimilation methods, faster computers, and observations that improve, validate, and initialize deterministic and ensemble forecast models. Maintaining and improving our weather forecasting and air quality monitoring and simulation capabilities require observations from both research and operational satellite systems. NASA research missions explore cutting-edge flight missions with newly matured technologies with managed risk. NOAA and USGS necessarily follow risk-averse paths that do not allow for interruption of measurements critical to their core atmospheric, hydrologic, terrestrial, ecosystems, and oceanic stewardship mandates. History has shown that these diverse strategic approaches can be complementary. While a challenge, due to limited resources, operational requirements, and data distribution, there have been successful transitions from research instruments to operational applications.
Observations from research satellites are essential for advancing and demonstrating capabilities, while observations from operational systems are critical to sustaining forecast systems; their synergistic use also is critically important. The operational utilization of new research instruments has led to improved weather and air quality forecasts. For example, observations from the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Atmospheric Infrared Sounder (AIRS) are routinely incorporated in operational numerical
weather prediction models (Bormann and Thépaut, 2004). Observations from geostationary satellites provide the environmental perspective needed for interpreting observations from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat (Mitrescu et al., 2008). The ability to leverage research and operational satellite observations for mutual benefit is crucial. The increasing demands for timely and accurate forecasts require new remote sensing technologies and sampling strategies from both research and operational satellite systems. Geostationary sensors provide high temporal resolution over a given hemisphere, while low-Earth orbiters provide global coverage with higher spatial resolution than if placed in geostationary orbits. Small satellites and CubeSats can provide opportunities not only to test new technologies and focus sampling strategies but also to fly low-cost missions and constellations. The Afternoon Constellation (A-Train) and Cyclone Global Navigation Satellite System (CYGNSS) constellations demonstrate the value of formation flying and should pave the way for future mission collaborations (Ardanuy, 2011).
The upcoming decade will likely experience an exponential growth in MicroSat, NanoSat, and CubeSat instrumentation, flights, and observations (NASEM, 2016a), with the potential for a commensurate explosive surge in collective contribution to Earth system science, application, and operations—from space weather to hydrology, spanning the atmosphere, ocean, and land surface. The emergence of “U-class” miniaturized satellites could significantly transform how we plan and conduct future Earth and space science research and operations. These new observations are rapidly increasing in observational capability and will need to be effectively incorporated into prediction models.
Improved satellite observations must keep pace with improved computing technologies and software development. Increased computer power and improved software engineering will enable data assimilation systems and forecasting models to move steadily toward exascale computing with commensurate higher fidelity, wider range of represented physical processes representation, increased temporal and spatial resolution, increased number of high-resolution ensembles, and longer lead times. Applications’ demands will also require global high-resolution modeling and forecasting. To take full advantage of these computational advances, it is essential that future satellite observations obtain data at the spatial resolution and temporal refresh that best initialize these models.
Observations are also needed to understand processes that contribute to creating an integrated understanding of our atmosphere and its interactions with the surface. While both weather and air quality models are improving through direct inclusion of more physical processes, some key physics are still not fully resolved in our models. Observations are needed to support process studies that provide the understanding necessary for appropriate model parameterizations. For example, precipitation formation results from complex relationships between air motion, aerosol type and concentration, and microphysical processes within the cloud system. The dynamics of the cloud system respond to and drive the microphysical responses, yet the microphysical processes within the system feed back on the dynamics through downdrafts, entrainment, precipitation, and radiation (Stevens and Seifert, 2008; Stevens and Feingold, 2009). Today’s models are unable to accurately represent how the potential energy is built up by deep convection, or how it gets intermittently transferred to the extratropical wave train in localized bursts, influencing weather far downstream (Haddad et al., 2016). An appropriate suite of measurements, perhaps flown in formation, can provide the necessary observational constraints to understand these relationships.
Benefits of Prior Efforts
In the last decade, we have witnessed enormous benefits from a number of weather and air quality-focused satellite missions. A few limited examples are listed here (more are available in the 2015 NASA Senior Review):
- A demonstration that high-spectral resolution infrared instruments such as the EOS-era Atmospheric Infrared Sounder (AIRS; launched in 2002) provide significant improvement and impact on numerical weather forecasting by providing critical initial condition information on temperature and humidity profiles. Similar capabilities from the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Infrared Atmospheric Sounding Interferometer (IASI; launched in 2006, 2012) and the NOAA/NASA Cross-track Infrared Sounder (CrIS; launched in 2011) have augmented this crucial observational resource. A time sequence of atmospheric profiles enables a consistent analysis of the preconvective and convective environment across the entire region of the storm system, providing the necessary data to improve the timeliness of severe storm predictions (Weisz et al., 2015) As noted in the 2015 NASA Earth Science Senior Review, AIRS data are also of significant importance to the FAA and aviation community. AIRS, IASI, and the Ozone Monitoring Instrument (OMI; launched in 2004) provided sulfur dioxide and ash data for airline traffic safety and management during the Eyjafjallajökull eruption in 2010 that disrupted air travel for several weeks.
- The Earth System Science Pathfinder (ESSP) CloudSat mission (launched in 2006) flies in the A-Train and was the first radar in space designed to elucidate the internal structure of clouds, including estimates of their vertically resolved liquid and ice contents, and providing unique information on light precipitation and snowfall. CloudSat observations have been instrumental for clarifying fundamental processes such as cloud-radiation feedbacks, including aerosol-cloud-rainfall interactions (Lebo et al., 2017), and the linkages between the water cycle and radiative forcing (J.-L. Li et al., 2013; Andrews et al., 2010; Waliser, et al., 2013). CloudSat data have been used for the evaluation of existing parameterizations of clouds, convection, precipitation, and related microphysical processes in numerical weather prediction and climate projection models (Stephens et al., 2010; Matus, 2015).
- Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO; launched in 2006) provides unique cloud and aerosol products, including vertical profiles of the backscatter and polarization. The use of CALIPSO data in combination with CloudSat, Moderate-Resolution Imaging Spectroradiometer (MODIS), and Cloud-Earth Radiant Energy System (CERES) observations led to more than 500 peer-reviewed publications between the 2013 and the 2015 Senior Reviews. CALIPSO aerosol vertical profiles are used in data assimilation tests at the U.S. Naval Research Laboratory, the European Centre for Medium Range Weather Forecasts (ECMWF), and the Japanese Meteorological Agency (JMA). Detection of volcanic ash plumes by CALIPSO is used in support of commercial aviation operations. The U.S. Environmental Protection Agency (EPA) and several state agencies are using aerosol CALIPSO data to assess air quality and develop strategies to mitigate pollution-induced reduction to visibility.
- The Quick Scatterometer satellite (QuikSCAT; launched in 1999) provided wide areal coverage of ocean-surface wind speed and direction that proved extremely valuable for atmospheric and ocean forecasts as well as nowcasting of extreme weather for many of the national agencies routinely identified in NASA’s Senior Reviews. Although developed as a NASA research mission, QuikSCAT supplied data that were routinely assimilated into operational numerical weather forecast models. QuikSCAT also provide information on the water content in vegetation’s upper canopy and on the age and motion of sea ice, and improved the tracking of icebergs. Recent studies have shown that these observations are useful for identifying areas of ocean upwelling and higher productivity.
- A unique and valuable addition to the observational characterization of the atmosphere and the advancement of weather forecasting came from the Global Navigation Satellite System (GNSS) radio occultation sounding (often referred to as Global Positioning System Radio Occultation or GPSRO).
- The technique was first applied in 1995, and came to fuller fruition with the 2006 launch of the relatively low cost Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission composed of six microsatellites. The data include electron counts in the ionosphere and, key for weather, atmospheric profiles of temperature, moisture, and pressure in the troposphere and stratosphere. These soundings have had a significant positive impact on weather forecasting, especially above the oceans, within the polar regions, and in other areas hard to sample with in situ means (such as upper troposphere and lower stratosphere; Collard and Healy, 2003).
- Over the last decade, the use of satellite data of air pollutants, such as OMI nitrogen dioxide and OMI sulfur dioxide, for scientific research and health and air quality applications has grown dramatically. Ongoing retrieval algorithm development is not only improving existing data sets of pollutants but also allowing for new data sets to be developed, such as for AIRS ammonia (Warner et al., 2017), an air toxin. These improved data sets are now at a quality to demonstrate the efficacy of environmental legislation, to inform the development of mitigation strategies, and to constrain emission fluxes and concentrations of some important air pollutants (Krotkov et al., 2016).
- OMI and the Ozone Mapping Profiler Suite (OMPS; launched in 2012) continue to monitor the health of the stratospheric ozone layer, a record begun in 1970 with Nimbus-4/Backscatter Ultraviolet (BUV). The satellite data indicate that the size and depth of the ozone hole seem to have stabilized over the last decade. Observations from the Microwave Limb Sounder (MLS; launched in 2004) make vital contributions to our understanding of the chemical and dynamical processes that affect the stratospheric ozone layer.
- The goal of the Tropical Rainfall Measuring Mission (TRMM; launched in 1997) was to provide monthly averages of rainfall over the Earth’s surface in large grid boxes. This was achieved with a complementary suite of active and passive sensors flown on the one satellite. The extended lifetime mission of 17 years yielded rainfall estimates at a higher spatial resolution, every 3 hours and in near real time. The longer record enabled observations to study synoptic variability of precipitation more fully. The observations provided a first description of the diurnal cycle of precipitation over the oceans and tropical land areas (e.g., Nesbitt and Zipser, 2003). The assimilation experiments of Hou et al. (2004) demonstrated the value of using the TRMM observations to yield more realistic storm features and better 5-day storm track prediction and precipitation forecasts for Hurricanes Bonnie and Floyd. Lien et al. (2016) also demonstrated that the assimilation of the TRMM project derived data improved 5-day global model forecasts. The Global Precipitation Measurement (GPM; mission launched in February 2014) succeeds and improves upon the TRMM project. Both missions are joint projects of NASA and the Japan Aerospace Exploration Agency (JAXA).
A major challenge to the continued use of the current Earth Science Division (ESD) satellite missions is spacecraft health and safety (NASA, 2015), along with additional risks associated with old software and aging operating systems. A rapid decline in capability of these missions will have adverse consequences on our observing capability. This will weaken our understanding of our atmosphere and slow down the steady gains in weather and air quality forecasts achieved over the last two decades (Box 7.1). This will reduce our ability to respond to hazardous weather and air quality conditions (Box 7.2). New missions that address the objectives and questions in the next section will help the nation address the reducing capability of these aging missions.
Science and Applications Challenges
Satellite measurements of meteorological variables are essential to sustaining and improving skill in forecasts of weather and air quality. Additionally, satellite observations of trace gases and aerosols, along with accurate representation of meteorological processes, are critical for our understanding of how natural processes and anthropogenic emissions influence air quality and climate. Improving models often requires looking to the past, and satellite observations are routinely used in hindcasts to improve modeling capabilities through sensitivity studies or to trace emissions to their sources. This section outlines several important scientific and application challenges that, once addressed, will enable further improvements in weather and air quality modeling.
There is a need for observations that improve our understanding of complex atmospheric physical and chemical processes and their interactions with the land and oceans. Observing, understanding, and modeling atmospheric boundary layer physical and chemical processes have long been wanting, including measuring basic state variables such as temperature, humidity, and wind. Quite often our current retrieval capabilities are limited to a single coarse-grained observation of the lowest 1,000 m, which is insufficient to describe and resolve the physical and chemical processes in this key region. It is an observational and technological challenge to make these observations on a global scale.
Another challenge is to bridge the gap between weather forecasts and seasonal outlooks to enable meaningful forecast skill that extends well into and through the subseasonal range of 2 weeks to 2 months. Improved understanding of the sources of subseasonal predictability (e.g., El Niño Southern Oscillation [ENSO], Madden-Julian Oscillation [MJO]), require better observations of the coupled Earth system, and better modeling and data assimilation of the coupled system (NASEM, 2016c). Dynamical subseasonal forecasts are beginning to demonstrate skill and provide a path to useful 1-week to 2-month forecasts. Advances in this area offer great potential to impact decision support for early preparation against high-impact weather events and to benefit society in areas of food security, agriculture, water and energy management, hazard preparation and response, transportation planning and safety, and so on.
Convection and precipitation play a primary role in many high-impact weather events over a broad range of temporal and spatial scales (e.g., severe storms, tropical cyclones, floods, etc.). These high-impact events have proven particularly difficult to accurately simulate and forecast in part because convective clouds often exist at scales that are only partially resolved by observations and regional/global models. Furthermore, it is difficult to observe and represent the multiscale interactions that are critical to convective organization and evolution.
A major challenge for air quality simulation is to better understand these convective systems, and in particular to resolve mesoscale organization and processes that are often associated with air quality episodes. These processes include lake-, bay-, and ocean-breeze circulations and complex terrain-induced circulations. Our ability to run cloud-resolving models that account for this finer scale convection organization over the globe requires new computing technologies. The White House-led agreement between the Department of Energy (DOE), National Science Foundation (NSF), and Department of Commerce (DOC) establishes a path to exascale computing that could enable 1 km mesh grids capable of resolving deep convection in about one decade (Holdren and Donovan, 2016).
There is a need to incorporate new observations and gained knowledge into research and operational weather and air quality models. Improving weather and air quality predictive capabilities requires a strong connection between observations from research satellites and their use in operational data assimilation and forecast systems. The challenge is that there is a significant difference in culture and risk tolerance between NASA and NOAA/USGS. While NASA’s strategy embraces the advancement of “understanding of Earth and develop technologies to improve the quality of life on our home planet,” NOAA’s “science serving society” focus is more applied: “To understand and predict changes in climate, weather, oceans and coasts. . . . To conserve and manage coastal and marine ecosystems and resources.” Similarly, the USGS mission is to serve “the Nation by providing reliable scientific information to describe and understand the Earth; minimize loss of life and property from natural disasters; manage water, biological, energy, and mineral resources; and enhance and protect our quality of life.” History has shown that these diverse strategic approaches can be highly complementary. NASA has historically succeeded when charged with proving new technologies—advancing their technological maturity to a level where operational agencies can harvest them to realize cost savings and quality improvements. The operational agencies must put high priority on rapidly advancing the utilization of new measurements when they are first available.
There is a need to create and maintain comprehensive observations of meteorology and atmospheric composition that are required to initialize and constrain forecasts and accurately capture how emissions and weather impact air quality. The atmosphere is coupled to surface processes through fluxes of heat, momentum, radiation, and constituents (e.g., water, methane). However, observational estimates of these fluxes from space are deficient. To improve the estimates of these fluxes and to better understand the interaction of atmospheric boundary layer with clouds and convection, better measurements from space of temperature, water vapor, and vector wind in the free troposphere and boundary layer are necessary.
A big challenge in improving our understanding of clouds and precipitation is gaining insights into the processes that govern where precipitation forms and its intensity. Specifically, we need to document the environmental factors that determine the relative contributions of various ice and liquid phase processes to precipitation development and how these processes relate to convective motions in the atmosphere. These are the processes that are currently very crudely parameterized in most weather models, yet ultimately predict precipitation and consequently societal impacts. Observations are needed that can more directly constrain these rates on various weather scales and quantify their dependence on the environment.
A major challenge for using satellite data for the quantitative determination of the health effects of pollutants over time is accurately inferring the correspondence between the observed quantity (e.g., aerosol optical depth in the atmospheric column) and aerosol type, surface concentrations, and surface fluxes. These linkages have important implications for many applications, including those of the health, air quality, atmospheric composition, ecosystems (including crop yields), weather, and climate communities. Typically, models are used to estimate the vertical distribution of trace gases and aerosols (which is strongly affected by boundary layer processes) and to convert aerosol optical depth to particulate matter, and this adds additional uncertainty to observational uncertainty.
There is a wide recognition on the need to reduce the uncertainties associated with methane sources (e.g., wetlands, energy extraction, and permafrost thaw), not only because of its importance as a climate gas but also for its contribution to degrading air quality by elevating tropospheric ozone. Consequently, reducing global methane concentrations has been proposed as way to mitigate ozone pollution. Methane budget uncertainties have confounded emission estimates and attribution of observed variations and trends in regional and global methane concentrations, and have led to a wide range of methane source and regional emissions. Because of these large budget uncertainties, there is a need for the development of a comprehensive, long-term methane observing strategy, including of the processes that affect methane emissions.
There is a need for adequate computing power to assimilate the data into numerical weather predication and air quality models and run deterministic and ensemble forecasts at sufficiently high fidelity, resolution, and lead times. Data assimilation, including for coupled systems, is needed to blend the model fields with the observations. Satellite observations provide information, usually in the form of radiances or reflectivity, about the atmosphere and surface. Models provide physically and chemically consistent information, but are prone to systematic errors. Accurate estimates of the model background and satellite observation error statistics are essential for optimally combining the information from both observations and models via data assimilation and thus provide accurate initial conditions and retrospective analyses. Meteorological reanalyses are essential to understand weather processes and to infer atmospheric composition and greenhouse gas fluxes from atmospheric observations. Chemical reanalysis is increasing as the necessary tools, needed observations, and computing power are becoming available to conduct these analyses.
With the huge data volume from observing system and data assimilation outputs, building accessible data archives, with analytic services, is also needed. The transition of research (new findings and discoveries, new measurements, new modeling components) to operations and applications needs to be accelerated. This requires improved support for collaboration between model/assimilation and observation specialists.
Opportunities exist to address the preceding challenges and realize significant benefits in key scientific and applications areas. These opportunities are provided by (1) new instruments, technology, and platforms; (2) the combination of research and operational measurements from different platforms (e.g., spaceborne, airborne, and ground based); and (3) enhanced modeling capabilities with improved data assimilation capabilities.
An opportunity to improve our weather and air quality monitoring and forecasting capabilities requires observations from both research and operational satellite systems. Observations from research satellites are critical for advancing capabilities, while observations from operational systems are essential to sustaining forecast systems; their synergistic use also is critically important. The ability to leverage research and operational satellite observations for mutual benefit is imperative in conducting weather and air quality remote sensing research. The increasing demands for timely and accurate forecasts require new remote sensing technologies and sampling strategies from both research and operational satellite systems. Geostationary sensors provide high temporal resolution over a given hemisphere, while low-Earth orbiters provide global coverage with higher spatial resolution than if placed in geostationary orbits. Small satellites and CubeSats can provide opportunities to not only test new technologies and focus sampling strategies but also to fly low-cost missions and constellations.
In its evolution of environmental remote sensing capabilities, NOAA (formerly the Environmental Science Services Administration—ESSA) relied on new technology demonstrations by NASA that were subsequently transferred into NOAA missions. The earliest examples include the Nimbus satellite series, which demonstrated new capabilities for ESSA implementation (one example is the High-resolution Infra-Red Sounder [HIRS] on Nimbus 5, which became the operational sounder on the Television Infrared Observation Satellite Program [TIROS]). This Operational Satellite Improvement Program (OSIP) started in the 1970s and continued in the 1980s, when NASA added a sounding capability to the NOAA geostationary imager; the Visible Infrared Spin-Scan Radiometer (VISSR) became the VISSR Atmospheric Sounder (VAS) in 1980. OSIP was canceled in 1982.
As noted in the National Academies report From Research to Operations in Weather Satellites and Numerical Weather Prediction: Crossing the Valley of Death (2000), a replacement for OSIP was never developed. Instead, NOAA continued with a procurement practice of specifying the instrument performance
and having the contractor deliver the instrument for flight on the operational satellites. This procedure limits the iterative development process that was so successful in the OSIP program.
There is now an opportunity to resurrect an OSIP-like agreement between NASA and NOAA, and NASA and USGS. One such approach would be to exploit the capabilities offered by the NASA Earth Science Technology Office (ESTO) through a multiagency funding and coordination mechanism. The intent would be to resurrect an interagency technology maturation process to provide atmospheric observing technology “on-ramps” that would account for the strengths of the two agencies: NOAA’s low-risk and sustainable measurement set evolving as NASA matures new observing technologies to a high technology readiness level.
PRIORITIZED SCIENCE OBJECTIVES AND ENABLING MEASUREMENTS
New instruments and technologies may provide observations critical for understanding fundamental atmospheric processes. For example, current passive sensors (e.g., MODIS) enable wind estimation by feature tracking (e.g., clouds) in time sequences of images; these wind vectors have been widely used in weather forecasting. Active sensors (e.g., Doppler wind lidars from spaceborne platforms) will be able to track motions indicated by molecular, aerosol, and dust backscatter and measure the vertical profile of the horizontal wind vector. (Note that the Atmospheric Dynamics Mission [ADM]-Aeolus mission will measure line-of-sight winds, not the horizontal wind vector.) Combining the strengths of both active and passive techniques, particularly when co-located (either with sensors on the same satellite platform or flying in formation), can provide insight on the development and evolution of cloud and precipitation particles, and the influences from aerosols. Similarly, active and passive sensing of methane could advance the substantial efforts to close the methane budget. Most likely, uncertainties in other important budgets could also be reduced.
Based on these challenges and opportunities, the following scientific questions and goals in weather and air quality were deemed important, and were categorized as Most Important (MI), Very Important (VI), and Important (I).
- W-1. 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? (MI)
- W-2. 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? (MI)
- W-3. How do spatial variations in surface characteristics (influencing ocean and atmospheric dynamics, thermal inertia, and water) modify transfer between domains (air, ocean, land, and cryosphere) and thereby influence weather and air quality? (VI)
- W-4. Why do convective storms, heavy precipitation, and clouds occur exactly when and where they do? (MI)
- W-5. What processes determine the spatiotemporal structure of important air pollutants and their concomitant adverse impact on human health, agriculture, and ecosystems? (MI)
- W-6. 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? (I)
- W-7. 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? (I)
- W-8. 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? (I)
- W-9. What processes determine cloud microphysical properties and their connections to aerosols and precipitation? (I)
- W-10. How do clouds affect the radiative forcing at the surface and contribute to predictability on time scales from minutes to subseasonal? (I)
W-1: Planetary Boundary Layer Dynamics
Question W-1. 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?
People live and work in the PBL, or the lowest layer of the atmosphere that is directly influenced by its contact with the surface. The vast majority of the economy, agriculture, aquaculture, water management, transportation, and tourism, are sensitive to weather. Cloud and precipitation forecasts are driven by the exchange of water vapor through the PBL. This near-surface layer of the atmosphere is relatively poorly modeled, as is the exchange of energy, moisture, and pollutants between this layer, the surface, and the free atmosphere. These exchanges are critical to weather and climate because the bulk of the interactions with solar heating and evaporation that drive the atmosphere and ocean take place within the PBL rather than the free atmosphere. For forecasts longer than a few days, errors in these exchanges lead to substantial and growing errors in weather forecast models. As an added benefit, more accurate representation of PBL processes in weather prediction models and improved estimates of particulate matter (PM) size distributions and composition can improve modeling of cloud formation and atmospheric radiative transfer.
In recognition of the importance of the PBL for weather and air quality forecasting, this goal and objective was categorized as Most Important.
Objective W-1a. Determine the effects of key boundary layer processes on weather, hydrological, and air quality forecasts at minutes to subseasonal time scales.
In order to adequately represent the key boundary layer processes responsible for the exchange of energy, moisture, and pollutants between the surface and the free troposphere, high-resolution, diurnally resolved, 3D measurements of horizontal wind, temperature, humidity, and aerosol and trace gases are required.
PBL processes show a strong diurnal cycle. For instance, the PBL height can increase by an order of magnitude from near sunrise to midafternoon over land (Stull, 1998). Additionally, satellite measurements of global PBL processes are challenging because the remotely sensed signals from the PBL also contain information about the surface and the cloud-free troposphere. Geostationary satellites can fully resolve the diurnal cycle over a specific region but, without hyperspectral temperature and humidity sounding capability, have difficulty in resolving the vertical structure of the PBL. It is challenging even for polar-orbiting satellites to resolve the vertical structure of the PBL. PBL processes also vary from day to day, but the variations are generally not as strong as the diurnal variations.
Because of the strong diurnal cycles of PBL processes, a combination of geostationary and polar orbiting satellites, airborne platforms, and ground-based networks (e.g., wind profilers and radiometers) are needed for PBL measurements. Also needed are satellite infrared and microwave sounders and radio occultation measurements from the Global Navigation Satellite System (GNSS). This heterogeneous observing network would provide the needed global observations of PBL at the appropriate vertical resolution.
The measurement basis of the key PBL variables includes three-dimensional (3D) temperature, water vapor, and horizontal wind vector, and aerosol and trace gas (e.g., ozone) concentrations. They also include two-dimensional (2D; in the horizontal direction) PBL height, cloud liquid water path, cloud base, and precipitation.
Three-dimensional variables (except wind) can be measured by a variety of instruments on board polar-orbiting and geostationary satellites, airborne platforms (e.g., Aircraft Meteorological Data Relay [AMDAR]), and ground-based networks (e.g., wind profilers, Atmospheric Emitted Radiance Interferometer [AERI], Raman lidars), and through data assimilation using a meteorological model (including chemical processes). For the 3D horizontal wind vector, scatterometer measurements of near-surface wind over ocean are available, and multiangle VIS/IR measurements may occasionally reach the PBL. At present, ground-based networks are crucial for the measurements of air quality variables and horizontal vector (e.g., wind profilers over land and offshore structures, and on ocean buoys). The 3D horizontal wind vector measurements from spaceborne wind profilers will be transformative to weather and air quality forecasting.
Two-dimensional variables can be measured by a variety of instruments on board polar-orbiting and geostationary satellites and ground-based networks (e.g., radiosonde, microwave radiometer, ceilometer, disdrometer, and radar). For instance, PBL height can be measured by lidars (e.g., from CALIPSO), while cloud base can be observed by combining CloudSat radar and CALIPSO lidar measurements.
For trade space analysis, a vertical resolution of 0.2 km for all 3D variables is requested to resolve the vertical structure, as the PBL can be as shallow as a few tens of meters. This requirement can be relaxed during the daytime when the PBL is deep (e.g., over semiarid regions in summer).
Considering the large spatial variation of aerosol and trace gas concentrations (partly due to surface heterogeneities, such as urban-rural or land-water contrast), a horizontal resolution of 5 km is requested for these variables. Consistent with the requirement from other objectives, a horizontal resolution of 10 km is requested for precipitation. For all other variables, 20 km resolution is requested. A reduced horizontal resolution of 20 km for all variables would have reduced effectiveness but would still be beneficial.
A 3-hourly temporal resolution is requested for all variables except air quality variables (for which 2-hourly resolution is requested to better resolve the diurnal cycle). Less frequent sampling than 3-hourly would have difficulty in realistically resolving the diurnal cycle.
To make the measurements most useful, the uncertainty requirement differs for different variables. For instance, it is 1 m/s for horizontal wind vector, 0.3 g/kg for humidity, 0.3 K for temperature, and 20 percent for precipitation.
Comparing Program of Record (POR) versus new measurements, continuing operation of current (U.S. and international) missions (e.g., VIIRS, CrIS, AMSR, CALIPSO, GOES-16) will contribute to all measurements discussed here. The panel anticipates that measurements as listed in the Science and Applications Traceability Matrix (SATM; e.g., precipitation) will be continued. Also helpful are planned missions from international partners. For instance, the ESA ADM-Aeolus mission (Reitebuch, 2012; to be launched in 2018) will provide the Doppler lidar wind profile measurements.
To fully address our measurement needs, however, new measurement capabilities are needed. These capabilities build upon existing or planned missions and hence have a high technology readiness level
with fewer risks than brand-new technologies. In particular, with the experience to be gained from the ADM-Aeolus wind profiling mission, a new wind profiling mission is needed to provide sufficient vertical resolution in the PBL and in the troposphere. This is addressed as an identified need/gap and associated Candidate Measurement Approach in Targeted Observable 4 (TO-4) in Appendix C. More useful than GPM alone would be a combined GPM + CloudSat mission for cloud and precipitation measurements. This is similarly addressed in TO-5 in Appendix C.
Recognizing the cross-panel importance of the PBL and the challenges of obtaining 3D measurements of horizontal wind, temperature, humidity, and aerosol and trace gases in the PBL, optimal approaches could be as follows:
- To combine Doppler lidar wind profiling with hyperspectral infrared sounding to provide the 3D structure of horizontal wind. This can be further combined with near-surface microwave scatterometer wind over ocean and ground-based wind profiling, high vertical resolution profiles from radiosondes, and commercial aircraft measurements during takeoff and landing.
- To combine microwave and hyperspectral infrared sounding with GNSS radio occultation to provide the 3D temperature and humidity. This can be further combined with ground-based systems (e.g., Raman lidar) and high vertical resolution profiles from radiosondes and commercial aircraft measurements during takeoff and landing (including water vapor measurements from commercial aircraft).
- To combine with the PBL measurement approaches for trace gases and aerosols as described later in the subsections on air pollution and methane.
- To bring Doppler lidar and hyperspectral infrared observations together in a dynamically consistent way through data assimilation and numerical modeling.
Connections and Linkages to Other Panels
Because the PBL interacts with surface processes, the priorities here are important to the objectives of the Global Hydrological Cycles and Water Resources Panel (through near-surface atmospheric quantities such as wind speed and surface quantities such as precipitation) and the Marine and Terrestrial Ecosystems and Natural Resource Management Panel (through near-surface atmospheric quantities such as wind speed and aerosol and trace gases).
Furthermore, the priorities here are important to all five integrating themes: carbon cycle and water cycle (as mentioned earlier); extreme events (some of which are closely linked to PBL processes, such as air pollution); technological innovation (e.g., wind profile measurements); and applications (as human activities occur primarily in the lower part of the PBL).
W-2: Longer Range Environmental Predictions
Question W-2. 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?
There are increasingly more demands to extend operational environmental forecasts beyond the typical 7- to 10-day weather forecasts into useful multiweek subseasonal forecasts. Significant progress toward useful subseasonal forecasts has been made in the past few years, as sources of predictability have become better understood and modeled (Barnston et al., 2009; Brunet et al., 2010; Gottschalck et al., 2010; Lin et al., 2010; Vitart et al., 2010; Marshall et al., 2011; Waliser, 2011; Zhang, 2013; Marshall et al., 2014;
Scaife et al., 2014; Mo and Lyon, 2015). Examples of benefits and applications include information on changes in the risks of extreme events, and opportunities for optimizing resource decisions with regard to water security (availability, quality, and management), food security (agriculture, livestock, and fisheries), energy security (demand and production), hazard preparation and response, human health (air quality episodes, urban heat stress, and disease vectors), ecosystems stewardship, and so on.
Although many scientific and technological challenges remain, a great return on investment can be expected if scientific understanding and modeling capabilities associated with subseasonal prediction can be advanced. These advances hinge on maintaining relevant, and in some cases enabling new, satellite-based observing resources. The challenges include the following:
- Improving the observations and data assimilation techniques to significantly reduce errors in the atmospheric initial state that amplify and propagate during the course of the forecast;
- Developing accurate models and parameterizations of complex subgrid scale processes that have significant importance to subseasonal phenomena and predictability, including deep convection and mesoscale organization of storm systems, convective vertical mass transport and propagation of tropical potential energy into the midlatitudes, atmospheric boundary layer processes, aerosol-cloud-precipitation interactions, ocean mixed-layer and sea-ice processes, land-surface and land-atmosphere interactions involving root zone and surface soil moisture, snow processes and vegetation dynamics, stratosphere-troposphere interactions;
- Identifying and characterizing sources of subseasonal predictability, including natural modes of variability (e.g., ENSO, MJO, IOD, QBO), slowly evolving Earth system components, and some elements of “external forcing” (e.g., annual phenological cycle, natural and anthropogenic emissions of aerosols);
- Understanding and quantifying how the preceding sources individually and collectively influence the development of disruptive and extreme events (e.g., MJO and ENSO influences on tropical cyclone locations and frequency, midlatitude blocking events, sudden stratospheric warming events, multiscale variations of the monsoons);
- Identifying the essential elements for coupling Earth system components that, when modeled, will fully exploit the available predictability for applications and societal benefits; and
- Improving data assimilation methods using all observations to improve prediction accuracy from short-range to subseasonal time scale.
This science question/objective was categorized as Most Important by the Weather and Air Quality Panel.
Objective 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 skill by 50 percent for forecast times of 1 week to 2 months.
While a number of operational forecast centers have developed subseasonal forecast systems, most are experimental. National and international community efforts, such as the North America Multi-Model Ensemble (NMME; Kirtman et al., 2013); the NOAA Modeling, Analysis, Predictions and Projections (MAPP) program Subseasonal-to-Seasonal (S2S) Task Force; and the World Climate Research Programme (WCRP)-World Weather Research Program (WWRP) joint S2S Prediction Project (Vitart et al., 2012), are under way to develop and improve these systems, and define useful metrics (e.g., Wheeler and Hendon, 2004; Neena
- Developing/improving the initialization of atmospheric variables (particularly global tropospheric horizontal vector winds, and including better utilization of data in cloudy/precipitating regions) and surface variables critical to subseasonal prediction (e.g., sea ice, snow, soil moisture, ocean mixed layer) that are significantly underobserved today.
- Developing optimal strategies for initializing deterministic and ensemble subseasonal forecasting systems, considering the roles and predictability associated with the atmosphere, ocean, land, and cryosphere.
- Constructing initial conditions that better utilize satellite data in cloudy and precipitating regions where significant challenges remain in data assimilation methodology. Similarly, contending with anthropogenic sources of microwaves (e.g., radio frequency interference) that limit use of passive microwave observations (e.g., for soil moisture, freeze-thaw).
- Reducing systematic model errors in the underlying physical processes and subseasonal relevant phenomena that affect subseasonal forecast skill.
- Defining appropriate metrics to quantify increases in subseasonal prediction skill.
- Developing coupled atmosphere-land-ocean data assimilation methodologies.
Addressing this objective requires building on research and development advances in observing systems, including interagency and other programmatic considerations.
Observations are critical to sustaining and more fully developing subseasonal predictions for societal benefits. This requires (1) sustaining current observations relevant to subseasonal prediction; (2) establishing and sustaining new observations that enable more-accurate, longer-lead subseasonal forecasts; and (3) demonstrating new research-oriented observations that will improve our understanding and predicting of subseasonal processes and phenomena. Advances across these areas require improved relevant global initial atmospheric, boundary layer, and surface conditions; and process understanding and assimilation/modeling capabilities of atmospheric convection, mesoscale storm organization, and atmosphere and ocean boundary layers, including surface characteristics. Space-based observations need to be complemented by in situ networks and research campaigns for synergistic use in process research, satellite measurement validation, and development and validation of subseasonal forecast models.
This section focuses on observations of the vertical profiles of atmospheric temperature, humidity, and horizontal winds, as the other observational requirements are discussed elsewhere in this chapter (see also the details for item W-2 in the Consolidated SATM in Appendix B).
Temperature, humidity, and horizontal vector wind profiles for subseasonal forecasting improvements have requirements for 1 km vertical resolution, with horizontal resolution goals of 5 km (near-term goal) and 3 km (longer-term goal). In addition, there are near-term goals for 3-hour refresh and longer-term goals for 90-minute refresh (NOAA, 1999; WMO, 2017). The POR polar-orbiting and geostationary imagers and sounders partially satisfy these requirements, but complementary observing system enhancements are needed to meet them.
Trade Space for Horizontal Vector Wind Fields
Atmospheric motion vectors (AMVs) are produced by tracking cloud features and water vapor gradients using time sequences of geostationary images or overlapping images from polar orbiting satellites. The polar orbiting satellites generally provide winds over the polar regions, although global AMVs have been generated using the Advanced Very High Resolution Radiometer (AVHRR) on the dual Meteorological Operational Satellite Program (MetOp)-A/B satellites separated in orbit by ~45 minutes. These AMVs lack vertical coverage and resolution; the associated uncertainty in the vector height attribution is the primary contributor to AMV observation error (Velden and Bedka, 2009). Further, they rely on the assumption that tracked cloud or moisture features are indicative of atmospheric motions. In the tropics, where upper-air wind measurements from radiosonde observations are scarce, observation of the 3D horizontal vector winds is more critical than in the mid- and high-latitudes, as the geostrophic balance relationship does not apply and the mass and wind fields become uncorrelated. Such data, were they to become available, would have a significant impact (ECMWF, 2016; Lillo and Parsons, 2017) on extended-range weather prediction.
To meet the long-standing need for 3D wind fields, the options are geostationary hyperspectral infrared sounders, polar-orbiting Doppler wind lidars, and small hyperspectral IR imaging sounders on a low Earth orbit (LEO) satellite constellation (several satellites each in multiple orbit planes). Five to six evenly spaced geostationary satellites would provide near global coverage (between approximately +/–55 degrees latitude). The World Meteorological Organization (WMO) “Vision for the GOS in 2025” recommended at least six IR hyperspectral geostationary sounders separated by no more than 70 degrees of longitude (WMO, 2017). Tracking features in hyperspectral radiance retrievals in clear skies (from soundings with vertical resolution of roughly 1 km) and cloudy skies (with cloud tops defined within 0.5 km) minimizes altitude assignment errors, thus addressing a major problem of motion vectors. Tracking atmospheric motions with wind lidars—as indicated by molecular, aerosol, and dust backscatter—offers good coverage in clear regions. While the early demonstration of this active remote sensing by Aeolus expected later this decade will provide only the radial component of the wind vector, full vector resolution is anticipated, given recent advances in laser technology.
Trade Space for Temperature and Moisture Profiles
The POR advanced infrared polar-orbiting sounders (e.g., AIRS, IASI, or CrIS) provide global temperature and moisture soundings with ~15 km horizontal and ~1 km vertical resolution under clear sky conditions. These hyperspectral IR sounders provide forecast impacts per instrument that are greater than any other single satellite instrument, highlighting the value of increased vertical resolution. Under cloudy conditions microwave sounders provide temperature and humidity profiles, but with less vertical and horizontal resolution. The POR has only two orbits (midmorning and early afternoon), which is less than the recommended ~4-hour revisit obtained with three optimally spaced orbits.
Rapid advancements in CubeSat technologies have led to hyperspectral IR sounder mission concepts (e.g., CubeSat Infrared Atmospheric Sounder—CIRAS) for relatively inexpensive missions that could be flown in the immediate future—either as demonstrations or as a new operational series with a suitable sustainment strategy.
Global Navigation Satellite System-Radio Occultation (GNSS-RO) provides soundings with excellent vertical resolution but inadequate horizontal resolution due to the occultation geometry. Constellations of hundreds of GNSS-RO satellites to increase the temporal sampling have been suggested.
For improved temperature and moisture profile temporal resolution, geostationary satellites must be considered. High spectral (and hence vertical) resolution geostationary IR sounders with rapid scanning would enable monitoring of important low-level information (including PBL evolution and convective initiation), and offer better estimation of surface temperature and emissivity. Numerical experiments (Sieglaff
et al., 2009; J.-L. Li et al., 2011; Li et al., 2012; Revercomb, 2012; Weisz et al., 2015) have shown that such observations, were they to be available over the continental United States, would lead to a profound improvement in NOAA’s ability to forecast convective initiation and confidently deliver lifesaving severe storm warnings on forecast. Europe is launching a geostationary hyperspectral IR sounder in 2021 with a spatial resolution of 4 km; the higher spatial resolution enables better clear sky sounding coverage but biases the soundings away from cloudy skies. A geostationary microwave capability would offer coverage in cloudy regions, but with poorer vertical resolution. The anticipated 50 km resolution also poses a challenge for achieving definition of small-scale features.
Connections and Linkages to Other Panels
The subseasonal objectives are closely linked to those in the PBL (see the preceding subsection), slowly varying surface properties (see the following subsection), and improved understanding of physical processes associated with convection, clouds, and radiation (discussed later, in the subsections on Questions W-4, W-9, and W-10). In addition they are linked to the Hydrology Panel objectives related to hazardous event preparedness and mitigation via long-lead forecast information (e.g., floods, droughts, wildfire potential). The objectives to improve subseasonal forecasts and the modeling of coupled processes (e.g., soil moisture, snowpack, sea-ice, near surface ocean conditions) are linked to climate objectives to improve seasonal and longer-term forecasts. The climate objectives related to connections between weather/climate extremes and large-scale circulation patterns are also critical to subseasonal forecasting and sources of predictability.
W-3: Surface Spatial Variations Impacts on Mass and Energy Transfers
Question W-3. How do spatial variations in surface characteristics (influencing ocean and atmospheric dynamics, thermal inertia, and water) modify transfer between domains (air, ocean, land, and cryosphere) and thereby influence weather and air quality?
Local variations in surface characteristics affect the exchange of energy, moisture, and pollutants between the surface and the free-atmosphere through changes in the PBL turbulence, local convergence, and vertical motion (Kilpatrick, 2016). These exchanges are critical to weather and climate prediction because the bulk of the interactions with solar heating and evaporation that drive the atmosphere and ocean take place in the boundary layer rather than the free atmosphere. Air quality is also strongly influenced by small-scale weather events related to small spatial scale differences in elevation and surface characteristics, such as at the interfaces between land, ocean, bays, and large lakes (Lyons and Cole, 1976; Wilczak and Glendening, 1988; Banta et al., 2005, 2011; Loughner et al., 2014). One of the greatest weaknesses in air quality forecasts is the inability to properly capture these local variabilities, leading to poorly forecasted air quality events.
The Weather and Air Quality Panel categorized this science question/objective as Very Important.
Objective 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 percent over oceans or 25 percent over land and ice surfaces averaged over a 100 × 100 km region and 2- to 3-day time period.
Over the ocean, the average magnitude of vertical transport due to these variations in surface properties is roughly 10 to 100 times the magnitude of the long-term mean flux needed to warm the oceans (Steffen,
2014; IPCC AR5 Report, 2015). Local daily increases in surface fluxes of energy, moisture and air pollutant increase the exchange between the boundary layer and the free atmosphere, and impact weather and air quality locally and downwind (Song et al., 2009; Kilpatrick and Xie, 2015). These surface spatial variations also modify precipitation and local temperature extremes, which are important considerations for agriculture and for the hydrological cycle, which in turn modify the surface air temperature (Kilpatrick and Xie, 2015).
The relevant surface characteristics are roughness, temperature, soil moisture, snow water equivalent (SWE), sea-ice thickness, surface type (soil, vegetation, snow, water, ice, etc.), ocean mixed-layer depth and current vectors (Figure 7.3).
The required accuracy and several new variables are unique for each domain (land, ocean, and ice). Note that the science objective could be achieved with regional- or domain-specific measurements, and that finer spatial temporal scale observations than listed earlier are expected to be needed to meet the accuracy requirements. This objective must leverage the POR (e.g., altimetry, IR sea-surface temperature [SST], land brightness temperatures) and in situ observations (e.g., land-type data sets, Argo).
This goal focuses on observations over large regions of the world, tying small-scale surface variability to the synoptic and global scale for weather, water availability, and the energy cycle. There are a vast number of measurements that could be made to increase the understanding of key processes and their consequences in the boundary layer. Some of the required parameters are already known in a broad sense or are routinely measured as part of the in situ observing network or the satellite POR. Most of the in situ observations will continue to be acquired over land. Open-ocean in situ surface observations from ships and buoys are limited; fortunately, the ocean surface is more amenable to satellite observations than land and sea ice. Subsurface ocean observations—specifically, Argo (Freeland et al., 2010)—have been remarkably successful and are expected to continue. While many variables are needed for this goal, and are well served by the POR and are of low risk, there are a few innovative satellite observations that are not part of the POR.
The new satellite-based observations (listed in the order in which they appear in the following text) are the height of the boundary layer, and high-resolution (~5 km) ocean surface winds and ocean surface currents. The following technological developments would be useful: (1) microwave sounders designed to determine near-surface air temperature and humidity as well as continue the passive microwave observations of SST and sea-ice coverage; (2) Ku- or Ka-band radars to measure upper canopy characteristics and snow depth; (3) Doppler radar measurements of ice motion; and (4) the depth of the ocean’s mixed layer.
Trade Space for Atmospheric Observations
Columnar water vapor (all sky) can be measured with polar-orbiting and geostationary IR and microwave sounders combined with GNSS-RO. Cloud fraction is measured with polar-orbiting and geostationary radiometers. High resolution of roughly 300 m is highly desirable, but the rapid refresh from geostationary satellites is more important. Lidar (e.g., CALIPSO) can also measure boundary-layer height, but the spatial coverage is very limited for a nadir-pointing instrument. The high-resolution requirement is essential to accurately observe gradients. Measurement of the boundary-layer height is innovative, but it is a highly useful rather than a crucial measurement for this objective. The accuracy requirements have not been investigated in detail to determine the accuracy needs and observational uncertainty.
Trade Space for Land Observations
The following observations are expected from the in situ and ground-based systems: near-surface air temperature and humidity, surface pressure, and surface vector winds. These observations are not available with sufficient spatial density in most locations.
Land-surface temperature can be determined from satellite IR radiometers complemented by modeling or microwave radiances. However, land temperature changes are highly dependent on cloud cover and
type, which are not well captured in the models, and clouds degrade the IR observations. Soil moisture (corresponding to a few cm depth) can be measured from multichannel radiometry (Jackson et al., 2010—with L-band—and from scatterometry (Brocca et al., 2011)—where C-band has the greater utility (Wagner et al., 2013). These are demonstrated technologies; however, the observations do not penetrate the surface as far as desired. These observations can be obtained from the scatterometry POR; however, sufficient innovation in spatial and temporal resolution would lead to improved accuracy and utility. Land-surface emissivity can be measured with a multiangle multichannel radiometer (e.g., Multi-angle Imaging Spectroradiometer [MISR]) and modeling (Z.L. Li et al., 2013). These are used for converting brightness temperatures to temperatures, and beam filling is the largest source of error for these observations. More study is required to assess the capability and risk associated with the proposed application.
Upper canopy moisture content can be measured through multichannel radiometers (Zhang and Zhou, 2015) or scatterometers at high inclination angle (Steele-Dunne et al., 2012; Saatchi et al., 2013)—e.g., a QuikSCAT-like design for Ka- or Ku-band—or both types of instruments (Arakelyan et al., 2009). The scatterometer has the advantage of seeing through cloud cover, but the radiometer can provide additional detail about the canopy. Both techniques observe the upper canopy unless the viewing angle is too close to nadir, in which case lower level canopies and the soil influence the observations. The radiometer observations can be obtained through the POR. Much finer resolution scatterometer and radiometer observations would be innovative and very helpful in achieving the science objectives.
Two-dimensional precipitation can be measured by dual-frequency radiometers, satellite radar, surface weather radar, and rain gauges. A sampling of half an hour is desired, which can be done only with a constellation of many satellites or from rain gauges and weather radar. In the continental United States, this is better achieved with weather radar, but for most of the world the satellite constellation is required.
Trade Space for Ocean Observations
Scatterometer observations are well suited for measuring ocean surface vector winds (Wentz et al., 2016), and have a strong heritage. There are many approaches to determine wind speed, but most science applications require wind vector. For example, a polarimetric radiometer can accurately retrieve wind direction if the wind speed is >3 m/s (Wentz et al., 2013), and have greater wind direction uncertainty than scatterometers at higher wind speeds (Rucciardulli et al., 2012). SAR can provide wind vectors (Zhang et al., 2012), but with inadequate directional accuracy.
High spatial resolution is needed to observe closer to the coast and to determine spatial derivatives. A spatial resolution of 5 km could be achieved with relative cost-effectiveness by using Ka-band, with similar accuracy to the Ku-band used on prior U.S., Japanese, and Indian instruments. This higher resolution allows innovation while continuing the climate record. A Ka-band instrument has moved through the NASA ESTO development program, and foreign countries have expressed interest in partnering on a launch of a vector wind sensor. Future scatterometer orbits are planned to be near the 6 AM/6 PM equatorial crossing time, and these will supplement the coverage from the advanced scatterometer (ASCAT). However, temporal variability of the winds over the oceans is so great that additional coverage is needed to meet WMO and science needs (Bourassa et al., 2010).
Ocean surface currents are one of the least well-observed ocean essential climate variables (Simmons et al., 2015). Novel approaches to measuring ocean-surface vector currents have been developed using a Doppler scatterometer (see preceding paragraph) or Synthetic Aperture Radar (SAR; Rouault et al., 2010); however, SAR provides only one vector component (away from the sensor), which is not adequate because currents vary on the inertial scale and full vectors are needed for science and applications. The addition of Doppler capability to a Ka-band scatterometer is the only addition needed to allow such a scatterometer to measure vector currents. This application is sensitive to the wavelength. A 10 km resolution with 5 km
in-swath spacing is desired to resolve currents and derivatives of currents (Bourassa and McBeth-Ford, 2010; Rodriguez et al., 2017). The risks associated with an instrument have been reduced through successful completion of the ESTO program (Kumar and Bauer, 2016). Subsurface currents are provided by in situ measurements.
Sea-ice motion has been inferred from passive microwave observations (Kwok et al., 1998) and from active sensors such as SAR (Curlander et al., 1985) and scatterometers (Zhao et al., 2002). This is accomplished through feature tracking; therefore, routine SAR resolution or high-resolution scatterometers are desirable. A Doppler scatterometer should be quite capable because the signal from ice is much stronger than the signal from open water; however, the risk has not been assessed. The passive microwave approach has the advantage of continuing a long time series; however, there is not an established POR for these measurements. The Doppler scatterometer approach is innovative and it is the same instrument as noted for ocean-surface currents. The risks are much greater for Doppler approach than for other methods. A high resolution, Ka- or Ku-band scatterometer would be very effective for feature tracking, largely mitigating this risk.
Sea-surface temperature can be measured from a radiometer at IR or microwave frequencies (Wentz, 2000). IR has the advantage of higher resolution, but microwaves have the large advantage of seeing through clouds that often block IR observations. Ideally, IR and microwave observations would be obtained together to better determine errors in the historical record. There is no POR for microwave observations. Subsurface ocean temperatures are provided by in situ measurements.
Significant wave height (SWH; Young et al., 2011) and sea-surface height (SSH; Chelton et al., 2001) can be measured through satellite radar altimetry (Hemer et al., 2010; Masters et al., 2012). However, coverage is so sparse that SWH is typically estimated from vector wind observations and wave models (Simmons et al., 2015). Wide-swath vector wind observations can be combined with models to determine wave parameters with far better sampling than altimetry (Cardone et al., 2004). These are well-tested approaches. The sampling density of wind observations is less than WMO requirements, but much better than SWH observations.
The vertically integrated energy (ocean heat content) requires altimetry for the column thickness and in situ observations of temperature, salinity, and ocean bottom pressure profiles. They are linked to SSH through the density of the water column and measurement of mass by gravity observations.
Sea-surface salinity can be measured by L-band radiometry, provided there are coincident observations of sea-surface temperature and surface roughness (historically from a scatterometer, without vector capability). The in situ system currently measures salinity substantially more accurately (Belward et al., 2016); therefore, these observations are not optimal to be taken from space unless doing so is justified through other variables (e.g., soil moisture). Salinity observations are not part of the POR or crucial for the proposed science. Subsurface salinity profiles are provided by in situ measurements.
Near-surface air temperature and humidity can be usefully determined from microwave radiometry, provided there is a spectral channel sensitivity to sea-surface temperature (Bourassa et al., 2010; Jackson and Wick, 2010; Roberts et al., 2010; Smith et al., 2012). Satellite observations are slightly noisier than buoy observations, but have much better spatial sampling. These observations can be taken in a manner that is synergetic with other microwave observations.
Trade Space for Cryosphere Observations
Sea-ice surface temperature is determined from IR and microwave radiometers complemented by modeling (Comiso, 2002), while ice-surface emissivity can be measured by a multichannel radiometer. Cloud cover substantially limits the usefulness of these IR observations, but in the absence of clouds will be more accurate than historical microwave observations. Two additional problems are contaminants on the surface and distinguishing ice from water. Accuracy needs are coarse for these observations.
Snow coverage can be measured with visible and microwave radiometry (Armstrong and Brodzik, 2001). The depth and snow water equivalent can be estimated under different conditions from passive microwave, high-frequency radar, and lidar. High resolution is desired because of the lack of uniformity of the depth and snow type. Snow albedo (Stroeve et al., 2005) and emissivity are estimated from a combination of radiometric observations of IR, microwaves, and visible light. Snow coverage, albedo, and emissivity observations are well established. Snow water equivalent and depth measurements are innovative and crucial for a variety of applications (e.g., hydrology and subseasonal forecasting), but they are not essential for the science goal.
Floating sea ice thickness can be estimated from freeboard (height of the ice surface above the sea surface) observations from satellite radar and laser altimetry (Laxon et al., 2003), although snow cover on the ice is an issue. However, the current temporal and spatial sampling is inadequate for the shorter weather, subseasonal, and climate extremes time scales.
Connections and Linkages to Other Panels
This objective is related to the Hydrology and Climate chapter objectives (Chapters 6 and 9) because the small-scale surface features, including man-made changes, cause changes in weather patterns and consequently the energy and water cycles (NASEM, 2016). Extreme weather and air quality events are important parts of Hydrology and Climate, and require similarly high temporal and spatial observing strategies. Surface characteristics influence the local and regional ecosystems that are important for ecosystems and Solid Earth objectives. These surface characteristics serve as the bottom boundary conditions for the PBL and subseasonal forecasts, and they change in response to variability in the PBL, subseasonal variations, and cloud distributions. Spatial gradients in surface features also occur in areas that are prone to rainfall, and in some cases contribute to the differences in local rainfall. Rain-related extremes and disasters including landslides are important for Hydrology, Solid Earth, and Climate. The small differences in surface features can also cause areas of greater likelihood of extreme surface pollution (see the subsections on W-5 and W-6). The local variability in wind stress influences local sea-level height, and the energy and mass budgets at the ice-ocean and ice-air interfaces are influenced by small-scale differences of ice-water change, ocean temperature and salinity change, and most importantly, by the change in albedo associated with melt ponds. The smaller scale spatial changes influence the spatial and seasonal bias in air-sea turbulent energy fluxes, which are sufficiently large to impact the ocean’s mode water and atmospheric water vapor, and hence impact the energy budget.
W-4: Convective Storm Formation Processes
Question W-4. Why do convective storms, heavy precipitation, and clouds occur exactly when and where they do?
Predicting the occurrence and location of convective storms, and how they evolve into severe weather, is critical for accurate forecasting of hazardous weather. Better predictions of atmospheric water at all spatial and temporal scales is needed to predict hazardous weather with lead times of minutes, to days, to weeks. Reliable longer lead time (>30 minutes) warnings for severe weather events will remain elusive without adequate measurement and modeling of the convective transport parameters throughout their life cycle.
In addition to its role in local severe weather, convection also impacts the large-scale atmospheric circulations, which then influence weather across the globe. For example, the Madden-Julian Oscillation (MJO), a fluctuation of organized convection near the equator, can force midlatitude circulation patterns
that lead to weather extremes such as droughts, floods, heat waves, and extended cold snaps. Thus, the timing and location of organized convection in one location may have immediate impacts on convective and nonconvective weather elsewhere. These predictions are also integral to air quality forecasting, as convection can effectively ventilate pollutants in the planetary boundary layer to the free troposphere.
Over the next decade, the resolution of weather and climate models will improve to explicitly represent cloud and convective processes. High-resolution weather modeling is necessary to reliably project the rainfall extremes that are important for predicting future flood risk, and hence, for informing decisions regarding urban planning, flood protection, and the design of resilient infrastructure. Climate modeling experiments are now being run at very high (<5 km grid spacing) resolution and provide potential added value to future projections of convective precipitation (Kendon et al., 2014).
The Weather and Air Quality Panel categorized this science question/objective as Most Important.
Objective 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.
Convective processes redistribute water, heat, and momentum through the depth of the troposphere. To improve the prediction of convective processes, observations of the various physical mechanisms within the clouds and local environment that act to produce precipitation are needed. This includes the cloud microphysical properties and the vertical motions within convective storms that are associated with heavy precipitation. These process-oriented measurements should enable new insights to inform the next generation of cloud and precipitation models for weather forecasting. It is imperative that these measurements constrain and define these processes that are critical for more accurate weather forecasts and predictions of the water cycle.
In addition, the interactions between aerosols, trace constituents, and convection are not well understood, particularly with respect to the specific type, scales, and strengths of the convection and the transformation of types of aerosols (dust, black carbon, sulfate, and nitrate). This will require satellite observations to obtain the global context, along with enhanced ground-based networks and suborbital measurements to sample the full vertical profile of aerosol through both the boundary layer and free atmosphere.
Current cloud-resolving models typically overpredict convective updrafts and the associated production of snow and graupel (Varble et al., 2014; Fan et al., 2015). This misrepresentation of precipitation rates biases model depictions of the tails of the precipitation distribution and adversely affects forecasts of extreme precipitation events, including flooding and long-term droughts.
Current global weather forecast models use convective parameterization schemes based on statistics to resolve convective motions rather than simulate the dynamical processes themselves. These deep convection parameterizations are not generally designed to operate in kilometer-scale models (despite recent scale-aware parameterizations; Grell and Freitas, 2014), and many of the assumptions of these schemes (e.g., that the cloud coverage is small compared to the grid square) are violated at these resolutions.
To improve the prediction of convective processes in both cloud-resolving models and larger scale models using convective parameterizations, observations of the various physical mechanisms within clouds that act to produce precipitation are required. Precipitation processes fundamentally couple vertical velocities to hydrometeor production. Thus, measurements must establish relationships between microphysical processes and cloud-scale dynamics. Accurate measurements of cloud water content and surface precip-
itation at cloud-process-resolving scales is needed along with estimates of particle size distributions and phase. Determining updraft strength for storms provides a foundation for assessing the transport of water vapor, chemical species, and aerosols from the lower troposphere into the middle and upper troposphere.
Liquid and ice particles have significantly different backscatter and attenuation characteristics at different frequencies. Multifrequency active and passive observations can distinguish between hydrometeor types. Wideband passive observations at frequencies ≦ 37 GHz are sensitive to heavy rain, 37-166 GHz observations are sensitive to moderate and light rain, and frequencies greater than 89 GHz are sensitive to scattering by ice particles. An example baseline radar system would include a three-frequency system centered upon scanning Ku-, Ka-, and W-band (e.g., 13, 35, and 94 GHz) radars, with Doppler capability at all frequencies.
Addressing this objective requires measurement of particle vertical velocities ideally with ~20 cm/s accuracy or better in cloud and stratiform precipitation, and at least 50 cm/s accuracy inside deep convection. Radar reflectivity of ice particles should be measured with a sensitivity of approximately –30 dBZ in cloud and –10 dBZ in precipitation (Jackson et al., 2016). The mean particle diameter estimated from multifrequency reflectivity observations can be used to estimate the terminal fall speed and density and habit. Measurements should be acquired at a vertical resolution of at least 250 m to resolve the vertical structure in the storm, and a horizontal resolution of 1 km in cloud and light precipitation. A horizontal resolution of 2 km is preferred to resolve convection. All measurements are to be acquired over a swath of a few tens of kilometers to sufficiently cover the convective-scale storm system.
The TRMM, GPM, and CloudSat missions together have demonstrated the utility of satellite-based Ku-, Ka-, and W-band (14, 35, and 94 GHz) radar observations. The only planned international mission with Doppler capability is the ESA/JAXA EarthCARE mission. The planned Time-Resolved Observations of Precipitation Structure and Storm Intensity with a Constellation of Smallsats (TROPICS) mission proposes a constellation of CubeSats to provide passive multispectral microwave observations for precipitation estimates. Formation flying, as in the A-Train, can also provide needed observations from a combined platform.
Characterizing the prestorm environment is also important, particularly the vertical distribution of the horizontal wind, temperature, and moisture. This can be accomplished over oceanic areas largely with operational weather satellites. The observations need to be made at a temporal and spatial resolution that enables interpretation of the environment and the life cycle of the storm and in conjunction with the active sensors. Given that most heavy convection occurs in the nonpolar regions, hyperspectral observations on geostationary satellites can provide the needed profiles of temperature, moisture, and horizontal winds at the required space and time scales. Observations of cloud boundaries (e.g., cloud top and areal coverage) will also be needed and can be attained from visible and infrared images on operational geostationary and polar orbiting satellites.
Connections and Linkages to Other Panels
This weather objective links to the Climate, Hydrology, Ecosystem, and Solid Earth panels. Current forecast models are unable to reproduce spatial patterns and frequency of precipitation events, which is critical for understanding the change in extreme weather events (Sun et al., 2007). Additionally, most models underestimate the intensity of precipitation, which in turn affects the characterization of extreme flooding (Stephens et al., 2012). At the same time surface processes (e.g., topography, soil moisture, and vegetation heterogeneities) are crucial for the initiation of deep convection.
W-5: Air Pollution Processes and Distributions
Question W-5. What processes determine the spatiotemporal structure of important air pollutants and their concomitant adverse impact on human health, agriculture, and ecosystems?
One out of every nine premature deaths is related to conditions linked to air pollution. Of those deaths around 4 million/year are related to outdoor air pollution (WHO, 2016; Cohen et al., 2017) costing the global economy about $225 billion in lost labor income annually and more than $5 trillion in welfare losses (World Bank, 2016). Just one fire season in Indonesia in 2015 led to an estimated 100,000 excess deaths from severe haze (Koplitz et al., 2016). Wildfire pollution also affects large U.S. populations (Figure 7.4). The degradation of air quality in many countries over the last decade has been well documented by satellite air pollutant data (Duncan et.al, 2016; Geddes et al., 2016; Ma et al., 2016). By 2060, 6 to 9 million annual premature deaths are expected, with annual global welfare costs projected to rise to U.S. $18 to $25 trillion (OECD, 2016). Ecosystem health is also degraded by air pollution (Figure 7.5), such as acid rain, eutrophication of water bodies, and oxidation of plant tissue by ozone (O3). Global crop yield reductions are estimated at about 10 percent annually, costing tens of billions (USD) in economic losses
(Van Dingenen et al., 2009; Avnery et al., 2011). Despite the social and economic costs, there is little or no current reliable information on air pollution levels and the associated health risks for most of the world’s population (de Sherbinin et al., 2014).
The Weather and Air Quality Panel categorized this science question/objective as Most Important.
Objective 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 percent of vertically resolved tropospheric fields (including surface concentrations) of speciated particulate matter (PM), ozone (O3), and nitrogen dioxide (NO2).
Knowledge of the processes driving the spatial distribution of pollutants, especially surface concentrations of particulate matter (PM; e.g., smoke, dust, and oxidized chemicals), ozone (O3), and nitrogen dioxide (NO2), is a key motivation for using satellite data for health and air quality research with direct applications to effective mitigation strategy development.
PM impacts human health more than any other pollutant, and plays major roles in climate, weather, and environmental damage. It is also the most complex pollutant, as the particles come in sizes that span orders of magnitude, can contain thousands of species, can be of different phases, and interact continuously with their surrounding gaseous environment. Particles can also be both primary (directly emitted) and secondary (formed) in the atmosphere. Composition, phase, and size influence their health and environmental effects. Exposure to high levels of O3 reduces lung function, causes breathing problems, aggravates asthma, and damages plants. NO2 affects lung function, contributes to both O3 and PM formation, and adds to acid rain and eutrophication.
Assessment of the effects of air pollution on ecosystem and human health and the development of effective mitigation strategies require the establishment and maintenance of a robust observing strategy for both the spatial distribution of pollutants as well as the ancillary data that are necessary to estimate
emissions and understand chemical/dynamical processes that determine pollutant distributions. First, a comprehensive observing strategy for the spatial distribution of PM (including speciation), O3, and NO2 within the boundary layer and lower free troposphere can be best met by a combination of space-based observations, and expansion of aircraft and ground-based observations in conjunction with chemical transport modeling to capture surface levels. Current in situ networks have limited spatial and temporal coverage and do not capture the chemistry and transport that often occur above the boundary layer, but impact “nose-level” concentrations. However, they measure surface PM properties, such as speciation, and surface O3, which are not currently inferable from space-based instruments. Given the variety of uses, the accuracy of the estimated pollutant concentrations from the system of satellites, ground-based networks, and models should be within 10 percent. Second, another critical component of this observing strategy is the measurements that are necessary to estimate pollutant emissions, which may be inferred from satellite observations of their spatial distributions in conjunction with chemical transport model. Space, suborbital and high-quality ancillary observations of PM and O3 precursors (e.g., sulfur dioxide [SO2], NO2, carbon monoxide [CO]) are important for better characterizing emission sources that impact PM and O3. Some pollutant losses (e.g., deposition) may also be estimated from satellite observations of their concentrations. Third, robust meteorological observations are necessary to constrain a chemistry transport models’ dynamical processes that influence pollutant transport. Last, other ancillary data that are required include both in situ and satellite observations of ecosystems that may be used to assess their health. The required meteorological and ecosystem observations are presented in the Consolidated Science and Applications Traceability Matrix in Appendix B and discussed elsewhere in this report.
Identifying the major pollution sources of aerosols, both anthropogenic and natural, requires hourly observations at a spatial scale of at least 1 km2. Passive imagers making measurements at visible and ultraviolet wavelengths and lidars are two approaches to take from satellite platforms. Horizontal resolution for monitoring trace gas species will be at a resolution that depends upon the pollutant, but should be smaller than approximately 10 km. Satellite observations, along with suborbital and ground-based observations, and atmospheric models, are required to provide spatiotemporal 3D pollutant fields. Improved satellite observations will increase the resolution and accuracy beyond that currently achieved with the available systems. New satellite observations would enhance horizontal information at resolutions necessary to ultimately resolve human and ecosystem exposures and important atmospheric processes at relevant scales, recognizing that the models can provide more detailed horizontal resolutions along with vertical structure than achieved by satellite observations alone.
Observations from the current, but aging, Moderate-Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging Spectroradiometer (MISR) give information directly related to PM, such as aerosol optical depth (AOD) and other light scattering properties, and continue to be invaluable. Several upcoming missions promise to continue the records of MODIS and MISR. Upcoming geosynchronous satellite instruments (e.g., on GOES-S and GOES-T, and Tropospheric Emissions: Monitoring of Pollution [TEMPO]), including the current GOES-16 Advanced Baseline Imager (ABI) and Himawari-8/9 Advanced Himawari Imagers (AHI), are viewed as filling the potential void left by the limited remaining lifetimes of MISR and MODIS, but they do not or will not provide more comprehensive information on PM properties. The upcoming Flexible Combined Imager (FCI) onboard the European Meteosat Third Generation (MTG) satellite will present a global constellation of satellites with high spatial and temporal resolution aerosol observations. The polar-orbiting Multi-Angle Imager for Aerosols (MAIA) mission is still in the formulation stage and will provide enhanced information on PM properties for health applications. However, it will have a lifetime
of about 2 years. The planned deployment of the polar-orbiting MetOp-SG Multi-Viewing Multi-Channel Multi-Polarization Imaging (3MI) instrument, a 2D wide field of view radiometer, will provide AOD and may provide more global information on aerosol characteristics, but will lack the temporal coverage (i.e., not geostationary Earth orbit [GEO]) desired for health and atmospheric chemistry studies. Planning would need to commence immediately to launch instruments with capabilities similar to, but beyond, MODIS and MISR to ensure long-term and stable continuation of their records. However, neither MODIS nor MISR provide the specific PM properties of most interest—for example, concentration (ground-level or elevated) and composition—but this critical need could be met with an expansion of in situ networks.
Spaceborne instruments that use ultraviolet through visible wavelengths of light, such as the Ozone Monitoring Instrument (OMI) and the recently launched, polar-orbiting instrument Tropospheric Monitoring Instrument (TROPOMI), provide atmospheric O3 and NO2 columns, which have very little information on the vertical distribution of O3 within the troposphere. The upcoming geosynchronous TEMPO instrument is designed to observe the temporal evolution of pollutants throughout the day and may have somewhat more sensitivity to lower tropospheric O3 (Zoogman et al., 2017) but will not give surface-level concentrations required for most applications. Therefore, surface and aircraft observations of O3 remain indispensable, and an expansion of these observational networks would benefit health and ecosystem applications.
Global, or near-global coverage of pollutants (e.g., PM, O3, and NO2) with diurnal variation is important to link air quality to health and ecosystem damages and to identify source impacts on air quality. Several upcoming missions will provide partial coverage of the northern hemisphere midlatitudes. The TEMPO mission will observe much of North America, and its sister missions will observe East Asia (GEMS) and Europe (Sentinel). However, this planned network covers regions where air quality is largely improving. It does not cover much of South Asia, Africa, and the tropics, where population growth is rapid and over two billion additional people are expected by 2050. Monitoring the spatial and temporal evolution of air pollution over these developing regions will be a critical priority in the next few decades for issues of global human and ecosystems health and climate. For more detail on possible observing strategies for these important regions, see the Consolidated Science and Applications Traceability Matrix in Appendix B.
Connections and Linkages to Other Panels
Air pollution distributions strongly depend on meteorological processes, so this science question/objective connects to other science questions/objectives, such as Question W-1, within the Weather and Air Quality Panel. In addition, it has connections to the Ecosystems Panel, as air pollution degrades forest health and reduces crop yields, and to the Climate subpanel, as PM and O3 are climate forcers.
W-6: Air Pollution Processes and Trends
Question W-6. 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?
Long-term, consistent, multi-instrument/multiplatform data records for a wide variety of pollutants are critical for robust quantitative determination in a range of health, air quality, and atmospheric composition applications. They have proven very powerful in assessing sources of emissions and for epidemiologic analyses, and their power will increase as the record lengthens. Increasing the range of species being characterized will enhance the use of the observations (e.g., to assess the health impacts of PM species).
This science question/objective was categorized as Important by the Weather and Air Quality Panel.
Objective 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 percent per year), which are necessary for the determination of controlling processes and estimation of health effects and impacts on agriculture and ecosystems.
The goal is to create a comprehensive and long-term air quality observing network of satellite and complementary in situ observations. The fidelity of these long-term satellite data records depends on careful interconsistent calibration of the various individual data sets. Long-term and comprehensive data from in situ networks are a necessity for the determination of the credibility of these multidecadal satellite data records.
Air pollutant observations should capture the changes in both the pollutants of most concern (e.g., PM and O3) and also important chemical intermediates (e.g., NO2 and volatile/semivolatile organic compounds), with sufficient spatial and temporal resolution to both test atmospheric models as well as to be directly used in specific applications, such as emission trend, ecosystem impact, and health analyses. Long-term trend analysis, when used along with similar long-term ground-based observations and modeling, suggests an accuracy of 20 percent for pollutant concentrations.
The underlying technologies used in the current POR satellite are viable in this application, although increasing spatial resolution and chemical characterization are important to extending how those observations are being used and will be used in the future.
Connections and Linkages to Other Panels
As air pollution variations and trends strongly depend on variations and trends in meteorology, so this science question/objective connects to other science questions/objectives, such as Question W-1, within the Weather and Air Quality Panel. In addition, it has connections to the Ecosystems subpanel, as air pollution degrades ecosystems viability and reduces crop yields, and to the Climate subpanel, as PM and O3 are climate forcers.
W-7: Tropospheric Ozone Processes and Trends
Question W-7. 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?
Observing the 3D structure of tropospheric ozone (O3) and how it evolves over time is central to understanding many aspects of the chemistry and dynamics of the troposphere and lower stratosphere. O3 is important as a key oxidant and as a more readily observable precursor to the tropospheric hydroxyl radical (OH), the atmosphere’s primary oxidant responsible for cleansing the atmosphere of, as well as creating, other pollutants, including some greenhouse gases (GHGs). Therefore, O3 is central to tropospheric trace gas chemistry. In addition, tropospheric O3 is also an important GHG, and its radiative forcing is highly
dependent on altitude and latitude (Myhre et al., 2013). At Earth’s surface, it is an important air pollutant, harming both humans and plants.
The Weather and Air Quality Panel categorized this science question/objective as Important.
Objective W-7a. Characterize tropospheric O3 variations, including stratospheric-tropospheric exchange of O3 and impacts on surface air quality and background levels.
Despite decades of study there are still shortcomings in our ability to accurately predict O3 and OH levels through the United States and around the globe, and this adversely affects our ability to simulate tropospheric chemistry. Underlying questions to be answered include: What are the main factors leading to the errors in today’s modeling capabilities? Are there missing related emissions sources? Are other processes more important? Determining the causes of observed tropospheric O3 variations, from hourly to seasonal to interannual time scales, and km to global spatial scales, is necessary for enhancing predictive capability of today’s atmospheric chemistry models that are used for health, air quality, ecosystem, climate, and agricultural management.
The objective is to determine the anthropogenic and natural sources of tropospheric O3 and its spatiotemporal variations. This requires fine horizontal resolutions (e.g., 5 × 5 km2 or better) and fine vertical resolution (e.g., <500 m) from the surface through the lower stratosphere. Daily observations are optimal, such as for tracking tropospheric pollutant plumes and stratospheric intrusions.
Observing the vertical distributions of tropospheric O3 requires a combination of continuous observations from both in situ (e.g., ozonesondes) and satellite instruments. Ozonesondes have historically provided the best vertical resolution, although their global spatial and temporal coverage is sparse. Data collected on commercial aircraft (e.g., In-service Aircraft for a Global Observing System [IAGOS]) also give information on the vertical structure of O3. Expansion of the ozonesonde and commercial airliner networks would provide better temporal and spatial coverage of in situ observations, including for the purpose of validation of satellite O3 data.
Spaceborne instruments that use UV/VIS wavelengths of light, such as the Ozone Monitoring Instrument (OMI) and the recently launched Tropospheric Monitoring Instrument (TROPOMI), give atmospheric O3 columns, which have very little information on the vertical distribution of O3 within the troposphere. However, column data are useful because of global coverage and because various techniques allow the data to constrain the tropospheric column. The upcoming Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument may have more sensitivity to lower tropospheric O3 (Zoogman et al., 2017).
The High Resolution Dynamics Limb Sounder (HIRDLS), a filter radiometer that measures IR wavelengths, was designed to provide unprecedented vertical gradient information (~1 km) for O3, temperature, and several nitrogen-containing species in the upper troposphere and lower stratosphere (UT/LS), a dynamically active and important region for the exchange of O3-rich stratospheric air to the troposphere. After launch activation of the HIRDLS instrument revealed that the optical path had become blocked, and only 20 percent of the aperture could view the Earth’s atmosphere, requiring major algorithm development to account for the blockage and its other impacts. Nevertheless, the limited HIRDLS data demonstrated the utility of the instrument for UT/LS research relative to the Microwave Limb Sounder (MLS) and Michelson
Interferometer for Passive Atmospheric Sounding (MIPAS) instruments, which have coarser vertical resolutions (~3 km) in the UT/LS.
Ancillary observations of O3 precursors will also help constrain the budget and distribution of tropospheric O3. For instance, satellite data of lightning distributions and temporal variations will be useful to infer the middle and upper tropospheric NOx lightning source, which is not currently reliably discerned in tropospheric column NO2 data. This is important, as lightning NOx is a major driver of tropospheric OH and also contributes to O3 formation in the free troposphere, where O3 is radiatively important. Other ancillary satellite observations that are useful include tropospheric columns of NO2 and CO. Tropospheric OH and O3 share many of the same drivers. Therefore, constraint of the concentrations and variations of O3 and these drivers provides indirect constraints on tropospheric OH, which is not observable from space and is difficult to measure in situ, especially because of its large spatiotemporal variability.
Connections and Linkages to Other Panels
This science question has connections to the Climate subpanel, although there is no specific Climate science question/objective for tropospheric O3.
W-8: Methane Source Trends and Processes
Question W-8. 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?
Current observations of atmospheric methane (CH4) do not adequately constrain its emission source strengths and their variations. CH4 is a key contributor to rising background ozone (O3) levels that contribute to urban and regional smog, as well as being responsible for a significant portion of global warming directly and through the increased O3 levels (Ciais et al., 2013). It also impacts the atmospheric oxidizing capacity, the ability of the atmosphere to remove trace gases. Since preindustrial times, the atmospheric CH4 burden has more than doubled because of anthropogenic emissions. Natural sources are expected to increase as well in response to a warming climate.
This science question/objective was categorized as Important by the Weather and Air Quality Panel.
Objective W-8a. Reduce uncertainty in tropospheric CH4 concentrations and in CH4 emissions, including uncertainties in the factors that affect natural fluxes.
In order to close the CH4 budget, it is critical to constrain the strengths and distributions of the many types of natural and anthropogenic CH4 sources and the primary sink, reaction with the hydroxyl radical (OH). However, available observational data of CH4 and OH have proven inadequate to (1) constrain methane’s global and regional source types and strengths, and (2) explain observed atmospheric trends and variation over the last few decades (Houweling et al., 2017; Rigby et al., 2017; Turner et al., 2017).
Spatially resolved (e.g., 4 × 4 km2) observations of CH4 are necessary to separate CH4 source types, which are often co-located or in close proximity, and to infer fluxes. Ancillary observations of carbon monoxide (CO), CH4 isotopes, and ethane (C2H6) will help separate the various anthropogenic and nat-
ural CH4 sources. Hourly CH4 observations are desired, as, for example, fluxes from wetlands often vary substantially throughout the day. Desired precision is <1 percent with an accuracy of <5 ppbv.
Closure of the CH4 budget requires a combination of continuous observations, which will draw upon the strengths of surface networks and both passive and active spaceborne instruments. Such a comprehensive network will likely have sufficient coverage, sampling, and precision to constrain global, regional, and sectoral thermogenic and biogenic CH4 emission sources. Our current understanding of CH4 distributions and processes is founded mostly on sparse, but precise and accurate ground-based in situ measurements from global monitoring networks.
Passive satellite observations, such as from the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY, now defunct), the Greenhouse Gases Observing Satellite (GOSAT), the recently launched Tropospheric Monitoring Instrument (TROPOMI), and the upcoming Geostationary Carbon Cycle Observatory (GeoCARB) missions, lack spatial coverage over some large source regions, such as cloudy or low-light environments (e.g., Arctic, wetlands, monsoons). They also lack the required sensitivity to quantitatively derive regional CH4 sources with critically low uncertainties.
Active (laser) remote sensing technology will likely be a key step in obtaining global measurements of atmospheric CH4 that will complement data from in situ and passive sensors (Figure 7.6). Active sensors detect CH4 in the absence of sunlight (i.e., at night and at high latitudes in all seasons), in the presence of scattered or optically thin clouds and aerosols, and over land and water surfaces. Though unproven from space, CH4 lidar technology has been demonstrated on multiple aircraft platforms, and the key instrument components have a long heritage from previous space missions (e.g., Ice, Cloud, and Land Elevation Satellite [ICESat; 2003-2009], Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation [CALIPSO], and Cloud-Aerosol Transport System [CATS]). The joint Franco-German Methane Remote Sensing Lidar Mission (MERLIN) is scheduled for launch in 2020 and is the only active trace gas mission currently in
development (Kiemle et al., 2011; Stephan et al., 2011). Its target sensitivity is 8 to 36 ppb, with 50 km horizontal resolution. While it will serve as a proof-of-concept, its projected lifetime is only 3 years, and there will likely be significant biases associated with its two-wavelength technique. Innovative multiwavelength techniques in future lidar instruments may reduce these biases (Chen et al., 2012, 2014; Sun and Abshire, 2012; Abshire et al., 2013, 2014; Ramanathan et al., 2013, 2015).
Connections and Linkages to Other Panels
This science question has connections to the Climate subpanel, as CH4 is a potent greenhouse gas, and the Ecosystems subpanel, as natural CH4 emission sources include wetlands and thawing permafrost.
W-9: Role of Cloud Microphysical Processes
Question W-9. What processes determine cloud microphysical properties and their connections to aerosols and precipitation?
Precipitation forecasts cover different space and time scales, from what is happening outside someone’s home to seasonal forecasts over a particular watershed for agriculture applications. While precipitation forecasts have improved over the last decade, more accurate forecasts of the location and intensity will help people as they plan their daily lives as well as assist businesses in their planning. An important step in better precipitation forecasts requires observations of the coupling between cloud water content, vertical mass fluxes, and precipitation yield; these are needed to develop new formulations for models operating at these fine spatial scales. As the computing power continues to increase, cloud resolving models now used in regional models will operate on global domains (Tao et al., 2017).
The scientific community has identified a coming challenge for an improved understanding of in-cloud microphysical processes (e.g., deposition, aggregation, riming, accretion, melting, entrainment, and evaporation) and the circulation patterns that lead to precipitation (Bony et al., 2015; WCRP, 2015). While our understanding largely comes from theoretical studies and explorations using appropriate cloud models and simulation, observations provide the constraints that guide these explorations and document the environmental factors that determine the roles of various in-cloud processes. Currently, these processes are crudely parameterized in most weather models even though they are critical to the storm precipitation. Models that explicitly represent these processes show extreme sensitivity to precipitation based on the choice of process rates. The set of observational constraints that have been possible with past and present observations have not proven sufficient to constrain the models, and new observations spanning all weather scales are needed to quantify these processes and their dependence on the storm environment.
This science question/objective was categorized as Important by the Weather and Air Quality Panel.
Objective W-9a. Characterize the microphysical processes and interactions of hydrometeors by measuring the hydrometeor distribution and precipitation rate to within 5 percent.
Achieving this objective requires observations of cloud microphysical property profiles (e.g., water content, particle size, and number concentration), precipitation, cloud-scale vertical and horizontal motions, profiles of aerosol properties that provide information on cloud condensation nuclei concentrations, and the environmental conditions around the storm. Current observations provide only limited insight into the highly uncertain relationships between air motions, aerosols, microphysical processes, and precipitation formation that models must accurately represent. Priority observations must characterize the microphysical
processes and interactions of hydrometeors by measuring the hydrometeor distribution and precipitation rate to within 5 percent.
Providing observational constraints on convective precipitation efficiency requires, at a minimum, accurate measurements of ice water path (IWP) and precipitation on cold-cloud-process-resolving scales. To further relate these to the vertical mass fluxes responsible for transporting ice mass to the upper troposphere requires corresponding estimates of hydrometeor fall speed and vertical air motions. Aerosols impact a cloud by influencing the initial droplet spectrum activated in an updraft near the cloud base. Observations of cloud vertical motions, precipitation, and aerosol geophysical properties can be used to constrain microphysical process rates. Characterizing the storm environment is also important, particularly wind, temperature, and moisture profiles and the boundary layer structure. The observations need to be made at a temporal and spatial resolution that enables interpretation of the environment and the life cycle of the storm. The weather satellite observations of record can provide the environmental conditions. Hyperspectral infrared observations on geostationary satellites (Li et al., 2011) can provide the needed profiles of temperature, moisture, and to a certain degree, winds at the required space and time scales in nonpolar regions.
Observation of integrated ice mass, particle fall velocities, vertical air motions, and surface precipitation rates would satisfy a need to constrain precipitation efficiency, convective mass fluxes, and sedimentation rates in weather and global models. Accurately modeling the interactions of hydrometeors and precipitation rate at a minimum requires observations of the ratio of ice mass aloft to precipitation flux at the surface over the range of conditions encountered in nature. This will require a combination of passive and active measurement systems.
Radar reflectivity measurements at multiple frequencies provide a means to derive the mean particle diameter, along with estimates of terminal fall speed, density, and habit. Liquid and ice particles have significantly different backscatter and attenuation characteristics at different frequencies. Multifrequency active and passive observations can distinguish between these hydrometeor types. Wideband passive observations are sensitive to heavy rain (37 GHz), moderate and light rain (37-166 GHz) and ice scattering (89 GHz). Radar reflectivities supply additional constraints on vertical distribution of particles. The radar (e.g., TRMM, GPM, and CloudSat) missions provide heritage for the needed instruments and have demonstrated the utility of satellite-based Ku-, Ka-, and W-band (e.g., 14, 35, and 94 GHz) radar observations for profiling the full spectrum of condensed water throughout the atmosphere. The only planned international mission with Doppler capability is the ESA/JAXA EarthCARE mission (Sy et al., 2014). Doppler velocities at Ku-, Ka-, and W-band can now be measured to the desired accuracy at a horizontal resolution of at least 4 km for the full spectrum of precipitation.
Instantaneous IWP uncertainty has to be better than 50 percent for IWP in the range 3-1000 gm-2. The uncertainty of mean particle size (e.g., mass-mean diameter, over the range 40-600 µm) should approach 25 percent for column-mean and 50 percent for vertically resolved measurements. A spatial resolution of 4 km is desirable in stratiform precipitation; 2 km resolution is required in the ice portions of convective cores. Submillimeter radiances provide sensitivity to integrated ice mass. Example approaches to submillimeter wavelength radiometry include the airborne Conical Scanning Submillimeter-wave Imaging Radiometer (CoSSIR; Evans et al., 2005). Radars have now flown in space with sufficient frequency diversity to
sample the full spectrum of atmospheric ice with the added ability to retrieve the rainfall below (Stephens et al., 2008; Hou et al., 2015). The first spaceborne submillimeter radiometer, the Ice Cloud Imager (ICI), is planned to launch on the MetOp-SG satellites. It has been demonstrated that IWP can be inferred to accuracies better than 50 percent over a range from 2 to 1000 gm-2 from submillimeter radiances (Evans et al., 2002). This range of IWP could be expanded to cover the full spectrum of suspended and precipitating ice by coordinating the observations with an infrared radiometer and a lower frequency passive microwave in a constellation modeled after the highly successful A-Train (L’Ecuyer and Jiang, 2010).
Instantaneous surface precipitation rate (mm/hr) uncertainties are estimated at 50 percent. Multifrequency radar (Ku-/Ka-band in moderate precipitation; Ka-/W-band in light precipitation) need observations at 2 km spatial resolution, with 4 km resolution being more realistic in heavier precipitation, where Ku-band will be required.
Vertical velocity (m/s) with an accuracy of 0.2 m/s (stratiform); 0.5 m/s (convection) can be determined with Doppler velocity approach at one or more frequencies (depending on precipitation intensity and location in cloud). Recent literature suggests that substantial progress could be made with simultaneous measurements of vertical velocity and IWP to accuracies of 20 cm/s and 50 percent, respectively, on individual storm scales (Saleeby and van den Heever, 2013; Varble et al., 2014; Tao et al., 2017). Combined Doppler velocity with terminal fall speed estimates from mean particle size can be used to infer vertical air motions.
Aerosol loading of environment is determined from lidar observations of aerosol backscatter, extinction, and depolarization at 532 and 1064 nm, with a horizontal resolution of 100 m and a vertical resolution of approximately 50 m. Active lidars are likely not to provide a sampling of the entire storm environment. Coupling the lidar with the imaging systems can provide a more complete analysis of the aerosol loading of the environment. Total aerosol optical depth of the environment around the storms can also be retrieved with operational imagers on geostationary and polar orbiting satellites. Observations of cloud boundaries (e.g., cloud top and areal coverage) will also be needed and can largely be attained from POR visible and infrared imagers planned for operational geostationary and polar orbiting satellites.
Connections and Linkages to Other Panels
Observations are needed to constrain cloud microphysical properties, precipitation, and storm dynamics processes on various weather scales and quantify their dependence on the environment. Global climate models (GCMs) need appropriate observations that define the relationships between ice-phase precipitation processes, detrainment rates, and surface rainfall. The lack of global measurements of atmospheric ice water path, for example, has led to an order of magnitude uncertainty in GCMs, and recent evidence suggests that model improvement over time has been slow (Jiang et al., 2012; Su et al., 2013). Better precipitation forecasts also support the Hydrology Panel by better defining the amount of water that falls on the ground.
W-10: Clouds and Radiative Forcing
Question W-10. How do clouds affect the radiative forcing at the surface and contribute to predictability on time scales from minutes to subseasonal?
Clouds in Earth’s atmosphere occur with widely varying spatial and temporal scales. Clouds have a first-order effect on the radiative balance at the surface, and the radiative heating/cooling within the planetary boundary layer and atmosphere. The radiative effect of clouds is fundamentally linked to cloud-aerosol processes.
Inadequate representation of clouds in numerical weather forecast models can lead to significant temperature biases and errors in subsequent forecasts of severe weather, transportation hazards, renewable energy, agriculture, and flooding hazards. Multiyear satellite- and aircraft-based studies (Rossow and Schiffer, 1999; Wood and Field, 2011) have shown that 15 percent of the global cloudiness is at or below a 10 km horizontal scale. Even high-resolution, convective-allowing models run at 3 km horizontal scale are unable to capture this significant fraction of cloudiness. Consequently, high-resolution weather forecast errors result from this inability to adequately represent these small-scale clouds and the subsequent effects on boundary-layer evolution. The proposed very high resolution global measurements of clouds are critical for weather forecasts from which widespread economic and safety-related decisions are made hourly. They are essential both for model improvement (especially, subgrid-scale cloud representation) and for effective all-sky radiance data assimilation to improve global and regional short-range weather forecasts. A similar challenge with representation of subgrid clouds (Bauer et al., 2015; Furtado, 2016) contributes to errors in coupled atmospheric-ocean models producing subseasonal (roughly 2 to 8 weeks) guidance.
This science question/objective was categorized as Importantby the Weather and Air Quality Panel.
Objective 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.
The goal of this objective is to observe and understand the effects of clouds on the radiative balance at the surface. This emphasis on radiative forcing from clouds (and related aerosols) complements the focus on cloud microphysics processes (see section on Question W-9) and on deep convective clouds (see section on Question W-4). The science objectives related to surface properties (see section on Question W-3) also require these improved surface shortwave and longwave radiation fluxes and related cloud effects. Related measurements of aerosols and aerosol-cloud interaction are also necessary to improve accuracy of overall cloud and radiation effects on weather models.
The relevant measurements are the three-dimensional distribution of water vapor and temperature, horizontal and vertical winds, hydrometeors and aerosols, the horizontal distribution of precipitation, and cloud information (e.g., cloud fraction, depth, and droplet size). These parameters affect the radiative fluxes at the surface and within the atmosphere. Global measurements, including the polar regions, are required, along with the ability to accurately discriminate between clouds and land-surface areas with similar thermal characteristics. The ability to determine the radiative properties of clouds and aerosols on all scales, including small-scale cumulus clouds, is required, along with the need to quantify the effects of cloud radiative fluxes of all scales. The global measurements of cloud information represent the new requirements relevant to this science question; the other geophysical variables overlap with many of the other Weather and Air Quality Panel priorities.
Current space-based measurements include those from polar and geostationary VIS, IR, and MW sensors (e.g., MODIS and VIIRS, CERES, GOES-16, AVHRR, MeteoSat, and Himawari-8). Complementary ground-based systems include ceilometers (e.g., at airports in the United States) and very limited surface radiation networks (e.g., U.S. Surface Radiation Budget Network [SURFRAD]).
Additional required space-based observations include high-resolution VIS/IR imagers. Resolutions down to 200 m horizontal would be desirable, although 1 km horizontal would be acceptable. A higher-resolution Visible/Infrared Imaging Radiometer Suite (VIIRS)-like instrument, with the day-night band and an update frequency of 3 hours would be ideal, although a 6-hour frequency would be acceptable. Complementary ground-based observations, such as the globally distributed, higher density surface-based radiation stations and the U.S. DOE/ARM sites are also needed.
Connections and Linkages to Other Panels
The measurements required for this science question/objective are closely linked to those of the Climate Panel. Both emphasize the combination of spaceborne, airborne, and ground-based measurements to measure fluxes and the impact of clouds. Both emphasize cloud physical properties and radiative fluxes, and both recognize the importance of aerosols to cloud formation and properties and those impacts on the radiative fluxes.
RESULTING SOCIETAL BENEFIT
The state of our atmosphere exerts a strong influence on human activities. A goal of this chapter is to determine the appropriate questions that will eventually lead to forecasting capabilities that minimize the adverse impacts on people and maximize the positive socioeconomic consequences. Attaining these goals will require new observations combined with operational satellite observations and improved capabilities in data assimilation and numerical simulations. The maximum benefit can be achieved only by also identifying the most adversely affected populations and the most impacted economic sectors, and through the knowledge and flexibility of decision makers who will make use of the improved predictions. This section is restricted to the valuation of improved weather and air quality forecasts and its benefits. To be of value, these predictions must be timely, accurate, and relevant. The science questions and objectives laid out in the preceding section need to be answered to achieve more accurate weather forecasts and air quality simulations in the coming decade.
Improvements in current forecast models along with the incorporation of observations within the data assimilation systems have been useful in providing appropriate weather guidance to decision makers. However, forecasting heavy precipitation remains a challenge, and without understanding the microphysical processes involved in convective growth, the simulation of the physics of a convective storm at a scale of 1-3 km is not possible. The limitations of the convective resolving models are impacting the ability to identify and predict strong winds and heavy rainfall with sufficient lead time for people and organizations to react. New instruments and technologies to measure the interactions between storm updraft, cloud water, and ice with the ensuing formation of precipitation will provide constraints on physical representations in storm models. There will be significant societal benefits in preparing for heavy rains if it can be determined exactly when and where heavy precipitations occurs. One example is the September 2016 flood in Ellicott City, Maryland, that resulted from a combination of unprecedented rainfall, unfavorable topography, and past land use and infrastructure decisions based on historic climate norms for the area. While a heavy rain event was forecasted 5 days in advance, it was more extreme than predicted and was a very rare event that resulted in flash flooding (NOAA NWS, 2016). Advances in precipitation forecasts along with improved hydrology models are believed to be a key factor for improving the outcome of such an event in the future.
Not currently observing the time evolution of the PBL’s vertical structure on a national scale limits our ability to most accurately forecast the PBL’s collapse, the initiation of convection, the outbreak of severe weather, and the location and intensity of tornados, hail, and damaging straight-line winds. As of mid-
May, the tornado death toll for 2017 has risen to 34.5 By providing NOAA/NWS forecasters with better information, it would be possible to provide more reliable information to the public earlier, warning on the forecasts rather than the actual observations of the severe weather itself. High temporal resolution (“minute-scale”) temperature and moisture soundings over the continental United States could be an effective tool in addressing this requirement.
Accurate operational subseasonal forecast systems directly support decision making, and the potential impact of useful subseasonal forecasts is extensive. Examples of expected use include (1) agriculture and fisheries at a local/regional level as well as for food security concerns at national and international levels; (2) water availability and management at local/regional levels as well as for security concerns at national and international levels; (3) hazard preparation and response, including for floods, tropical cyclones, and other severe storms; (4) health considerations, including those related to air and water quality, vector-borne diseases, and severe heat and cold conditions; (5) energy production and generation (e.g., wind, hydroelectric) for demand related to anomalous temperature conditions; (6) transportation, including ship routing and guidance for potential Arctic passages; and (7) military planning and security concerns related to many of the preceding items. While the potential of subseasonal prediction to yield actionable information in these areas is evident, bringing this promise to fruition still requires considerable advances in research and operations, both of which depend critically on specific types of observations. Hurricanes Harvey, Irma, and Maria in the late summer and fall of 2017 resulted in an unprecedented damage for the United States (states and territories) from both extraordinary precipitation and wind damage. Improved 2-week prediction on the potential for extreme weather events such as tropical cyclones or severe convective storms (e.g., Brunet et al., 2010; Vitart et al., 2017; Vitart and Robertson, 2017) enabled by improved global observations, data assimilation, and Earth system prediction models will be critical for these longer range decision-making areas for the United States (NASEM, 2017).
Satellites collect data on pollutant concentrations that are not captured by traditional networks—for example, over the ocean and above the surface. The information from satellites is particularly powerful when linked with the long-term ground-based observational air quality measurement networks, research and commercial aircraft measurements, and increasingly refined models. Satellite observations provide information that can be assimilated to better predict where pollutant levels will be high and to determine where to target emission reductions to most effectively reduce adverse impacts. The benefit to society is that satellite data of fire locations and pollutant concentrations are being used by health and air quality managers to support decision-making activities. Wildfires throughout North America, such as during the summer of 2017, can expose residents in rural and urban areas to unhealthy and often hazardous levels of particulate matter for weeks at a time. Satellite data and air quality models are used to inform fire management efforts to minimize the exposure of the population to severe haze and also to issue air quality alerts so that people may take action to limit their exposure.
Since Hurricanes Andrew (1992) and Katrina (2005), there has been significant progress in providing useful information to the public that must prepare for the hurricane landfall. With the improvement in the meteorological knowledge of the atmospheric physics, the technology of the observations from satellites and the ground, and the operational implementation of both, the associated forecasts (e.g., timing, location, and intensity of hurricane landfall, predicted winds, flooding, storm surge, etc.) have achieved increased public confidence and enabled civil and emergency managers to understand and prepare for likely outcomes days to over a week in advance. The National Weather Service now can work closely and effectively with the emergency management community at the federal, state, and local levels—a successful cooperation that likely saved many lives in Texas, Louisiana, Florida, Puerto Rico, and the U.S. Virgin Islands
5 C. Dolce and L. Lam, 2017, “2017 U.S. Tornado Deaths Near Three Dozen, and More Than Half Have Been in Mobile Homes,” Weather.com, May 17, https://weather.com/storms/tornado/news/tornado-death-toll-may-2017.
this past hurricane season. Further improvement in the forecast skill, from more advanced observations of weather and air quality along with state-of-the-art assimilation into numerical models, will enable earlier and more accurate predictions (of potential fire as well as severe weather). When effectively communicated to the public, these forecasts will lead to increased probabilities of reduced risk to the public and better outcomes from disastrous circumstances for the millions that are directly affected. This means leveraging the maturing science of observing and predicting severe weather, water, and climate-related events into comprehensive, reliable, and optimal management of societal preparation, response, and recovery.
Society will always be sensitive to weather and air quality, and thus the importance of better predictions for the protection of lives and property and continued economic growth will remain an ongoing challenge. The global suite of international operational geostationary satellites provide valuable imagery at spectral resolutions that can complement low Earth orbit NASA research missions by providing the spatial and temporal context of the scene being observed. The next decade provides a unique opportunity for NASA, NOAA, and USGS to work collaboratively to gather new measurements that make improved prediction a reality.
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