Descriptions of Earth Exploration-Satellite Service Parameters Related to Table 2.1
AIR TEMPERATURE PROFILES
Global air temperature profiles are critical to numerical weather predictions (NPWs) because temperature is inversely related to air density and therefore to the differential gravitational forces on air that help drive local and global winds. Temperature also serves as a tracer of atmospheric motion. Although satelliteborne infrared imaging spectrometers can measure these profiles, clouds often introduce significant errors, particularly in the lower troposphere, in certain polar seasons, and in “baroclinic” regions, that commonly exert a disproportionate influence on future weather events. Current operational weather satellites combine both microwave and infrared spectrometer data to take advantage of the relative strengths of each; this sensor combination probably makes the single most important contribution of weather satellite data to the dramatic improvements achieved in providing useful global weather predictions up to a week in advance. Although the 50-60 GHz oxygen absorption bands provide most such data, they are generally supplemented by other bands that help correct the results for precipitation, humidity, and surface effects, discussed below in this appendix. In addition, it has been found that the original operational temperature sounding microwave instruments (microwave sounding units, or MSUs) can be calibrated across satellites to yield a very sensitive indicator of global warming in the middle troposphere with accuracies on the order of 0.1 K per decade. These observations are being continued using successor instruments such as advanced MSUs (AMSUs). Systematic radio frequency interfer-
ence (RFI) at levels too low to be otherwise detected could, in principle, introduce errors in such measurements.
Observations of global precipitation are important to both weather forecasts and climate studies. They are particularly useful in monitoring severe storms such as hurricanes and damaging fronts. Precipitation is important not only to safety, agriculture, and commerce, but also to hydrology and predictions of floods, soil moisture, and sea surface salinity (SSS). Since the locations of convective precipitating cells cannot be predicted well, and because they sometimes reside under higher cloud shields such as those obscuring hurricanes and other severe storms, only microwave sensors can reveal their intensities and locations. Precipitation is generally observed using the same sensors as those used for water vapor, which include (1) window-channel sensors at frequencies such as 18.7, 22, 23.8, 31.4, 37, and 89 GHz that observe raindrop emission against colder backgrounds such as ocean and low-emissivity soil (e.g., Special Sensor Microwave/Imager [SSM/I], Special Sensor Microwave Imager/Sounder [SSMI/S], Advanced Microwave Scanning Radiometer-Earth [AMSR-E]), and (2) the opaque water vapor resonance 176-191 GHz in combination with lower frequencies such as 89, 150, and 164-168 GHz; glaciated cell tops are particularly visible and sensitive to convective strength. In addition, the opaque oxygen bands 50-56 GHz are useful because they are sensitive to ice particle size distributions and therefore to the heavier precipitation rates (e.g., AMSU, SSMI/S).
SEA SURFACE SALINITY
Sea surface salinity is a critical missing parameter that scientists need in order to meet climate research goals. Measuring global SSS over time will contribute to scientists’ understanding of change in the global Earth system and how the system responds to natural and human-induced change. Global measurements of SSS can be achieved to ~0.2 practical salinity units using space-based passive microwave radiometry at 1.4 GHz and radar scatterometry at 1.26 GHz.1 These measurements can provide significant new information about how global precipitation, evaporation, and the water cycle are changing. Global SSS variability provides key insight regarding freshwater flow into and out of the ocean associated with precipitation, evaporation, ice melting, and river runoff. Global SSS measurements will also provide important background about how climate variation induces changes in global ocean circulation. The combination of global SSS and sea surface temperature
See http://aquarius.nasa.gov/science.php; accessed on January 15, 2010.
(SST) measurements can be used to determine seawater density, which regulates ocean circulation and the formation of water masses.
SEA SURFACE TEMPERATURE
Global all-weather sea surface temperature data are critical for NWP and climate research. SST measurements are important for understanding heat exchange and coupling between the ocean and atmosphere, and SST data are required by operational ocean analyses in order to properly constrain upper-ocean circulation and thermal structure.2 SST measurements in clear air can be obtained using electrooptical (traditional) instruments; however, clouds prevent these measurements, so passive microwave measurements within the 5 to 6 GHz region are critical for obtaining coverage in areas that are seasonally cloud-covered. For example, areas in the U.S. Exclusive Economic Zone off the coast of Washington and Oregon are not imaged with traditional satellite SST sensors for weeks at a time owing to persistent stratus cloud cover, necessitating an all-weather solution. The standard SST measurement uncertainty for space-based SST measurements is 0.5 K at 50 km (passive microwave, all-weather capability). To achieve this standard for microwave measurements, interfering signal power within a (typical) receiving bandwidth of 350 MHz (e.g., AMSR-E) must be below approximately –126 dBm3 using a factor of 10 power margin. For reference, this power level is effectively 3 dB higher than recommended levels from International Telecommunication Union-Radio (ITU-R) RS.1029, but still far below the level of interference that would be considered acceptable for nearly all other communication and signal systems. Space-based SST measurements near 6 GHz near land are impacted primarily by land-based emitters operating in the fixed service within full compliance of their regulations. Less pervasive RFI impacts are encountered from shipboard radar. For SST measurements using 10.7 GHz such as TRMM’s TMI and AMSR-E, substantive RFI is incurred from geostationary transmitters operating immediately adjacent to the upper edge of the 10.7 GHz EESS band segment as depicted in Figure 2.16 in Chapter 2 in this report.
Global, high-quality soil moisture measurements are expected to advance weather forecasting and Earth hydrology studies significantly. A proposed NASA
mission, Soil Moisture Active Passive (SMAP), would provide measurements of soil moisture using 1.4 GHz passive microwave radiometry and 1.26 GHz radar scatterometer to measure soil moisture to approximately 4 percent uncertainty with about 1.5 kg/m2 surface vegetation water content. Soil moisture measurements at higher frequencies, such as those planned for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) near 6 and 10 GHz, will also provide additional data refresh reducing data latency and measurements capable of producing soil moisture estimates to approximately 8 percent uncertainty at 50 km horizontal spatial resolution.
A National Centers for Environmental Prediction (NCEP) Scientific Assessment has determined that the NCEP Eta model requires soil moisture to properly calculate the energy fluxes at the surface. To support the model, the U.S. Department of Commerce requires measurements at the surface with a horizontal resolution of 50 km, mapping uncertainty of 3 km, and measurement accuracy of approximately 10 cm of water per 1 meter column of soil.4
SEA SURFACE WIND VECTOR
Space-based remote sensing of sea surface winds (SSWs) depends on precision measurements of polarimetric upwelling microwave emissions from the ocean surface at 10.7-37.0 GHz. High-quality SST measurements based on 6 GHz region brightness temperatures are also required to produce the best SSW direction product. Global SSW data are critical for high-quality NWP forecasts, developing tropical cyclone warnings, aircraft and ship operations, ship routing, and other civil and military operations. SSW data constitute one of the most important parameters (Environmental Data Records, or EDRs) in operational meteorological remote sensing. The accuracy of the wind vector products obtained from WindSat retrievals to date has reached or exceeded that available from active scatterometer systems such as QuikScat at moderate to high wind speeds, and the ability of microwave radiometers to simultaneously measure atmospheric and sea temperature properties motivates attempts to improve the accuracy of the radiometer products further.
One of the first applications of space-based passive microwave imagery was for monitoring sea ice characteristics. The Electrical Scanning Microwave Radiometer (ESMR) data set provides the earliest all-weather, all-season imagery of polar
sea ice. Some satellite data of sea ice in the visible and infrared wavelengths were available in the late 1960s and early 1970s (before the introduction of space-based passive microwave observations), but since the polar regions are either dark or cloud-covered for much of the year, the generation of consistent, long-term data records from visible and infrared sensing was not practical.
Passive microwave data introduced a major advance in the usefulness of satellite sea ice imaging. The value of passive microwave data for sea ice studies derives from the large contrast in microwave emissivities between sea ice and open water. At 19.35 GHz, open water has an emissivity of approximately 0.44, whereas various sea ice types have emissivities ranging from approximately 0.8 to 0.97. The resulting contrast in microwave brightness temperatures allows accurate estimates of sea ice concentrations (percentages of ocean area covered by sea ice) and hence the identification of sea ice distributions throughout the region of observation, as well as temporal variations of these distributions throughout the time period of observation.5
WATER VAPOR PROFILES
Global water vapor profiles are essential to the NWP of rainfall and drought, and they help constrain such predictions in general. As in the case of temperature profile measurements, combined microwave and infrared spectral data can yield near-all-weather global performance despite most clouds. Two different types of microwave observations are used, those in transparent bands within which the water vapor absorption stands out against the colder ocean background (ocean partially reflects the extremely cold cosmic background radiation), or against that of cold low-emissivity land. No profile information is usually retrieved, only an estimate of the column-integrated abundance. The frequencies most often used for this purpose include 18.7, 22, 23.8, 31.4, 37, and 89 GHz. To improve retrieval accuracies, these channels are often dual-polarized (horizontal and vertical) and scanned at a constant angle of incidence (e.g., SSM/I, SSM/IS, and AMSR-E). In addition, the opaque water vapor resonance near 183 GHz is often used in combination with some of the lower frequencies; these frequencies generally include 89, 150, 164-168, and 176-191 GHz, but must be used in combination with temperature profile information to yield the most accurate results (e.g., AMSU, SSM/IS). Instruments retrieving water vapor profiles are generally used to retrieve other parameters simultaneously, such as cloud water content, precipitation rate, ice and snow information, and so on.
The ability of microwave radiometers to measure water vapor and cloud water directly is a significant capability, provided by no other remote sensing system. Radars measure cloud reflectivity, which has a strong dependence on water droplet size. Uncertainty in the cloud droplet size distribution makes radar measurements of cloud water inaccurate. Because liquid water is a strong absorber (and hence emitter) of microwave energy, the volume of cloud water can be more accurately measured with microwave radiometers. The microwave technique is also far more accurate than infrared or optical methods owing to the high reflectance, or albedo, of clouds at these wavelengths.
NUMERICAL WEATHER PREDICTION
In general NWP models such as the European Centre for Medium-Range Weather Forecasts (ECMWF), Navy NoGAPS use a full range of passive microwave data: 19.35, 22.235, 23.6-24.0, 31.3-31.8, 37, 50.3-57.3, 85.5, 89, 150, 176-190 GHz operationally. Although space-based global microwave observations have their largest impact where other data sources are sparse, significant positive impact is also identified in data-rich areas. It is estimated that in the Southern Hemisphere, microwave observations provide 60 to 70 percent of the impact of all satellite data in the ECMWF model. The total proportional impact, that is, the relative reduction if a particular band is lost, is over 50 percent for the band 54-57 GHz alone. Similarly, a loss of 24 GHz data would represent 30 percent of the total impact from microwave measurements. Note that these estimates assume that all other data remain intact, so the loss of more than one channel is more serious than a linear combination of losses would suggest. Other bands at a similar level of importance are 31.3-31.8, 57-59, 89, and 183.31 GHz.6
Although the protective stratospheric ozone layer is indeed recovering, the need for passive millimeter- and submillimeter-wave monitoring continues today. The ability to monitor the individual abundance, spatial distribution, and temporal trend of each of the trace species that contributes to the depletion process allows the efficacy of the Montreal Protocol to be directly verified. More importantly, recent, large-scale changes in the stratospheric makeup suggest that the rate of recovery of the ozone layer may be slowing.
Millimeter- and submillimeter-wave frequencies distributed from approximately 183 to 916 GHz are ideally suited for observing ice clouds.7 These high frequencies are necessary in order for scattering to be the dominant interaction mechanism. The wide range of frequencies accommodates the large dynamic range of ice water path that occurs in nature and is incorporated in the Submillimeter Infrared Radiometer Ice Cloud Experiment (SIRICE) mission that is currently in pre-Phase A development at NASA. See Table 2.4 in Chapter 2 of this report.