most of TRMM’s limitations and is central to ensuring the availability of remotely sensed precipitation measurements for climate research (NRC 2007a).

Through its technological innovations, TRMM has enabled the following scientific accomplishments for hydrology and climate: establishing rainfall climatology, quantifying the diurnal cycle of precipitation and convective intensity, and profiling latent heating (NRC 2006). TRMM data have also contributed to operational use: near-real-time TRMM-based multisatellite estimations of rainfall are being used to detect floods in the United States and especially overseas where conventional information is lacking. The National Oceanic and Atmospheric Administration’s (NOAA) National Environmental Satellite Data and Information Service uses TRMM data as part of its Tropical Rainfall Potential Program to estimate flood potential in hurricanes (Box 6.1, Figure 6.1). The National Aeronautics and Space Administration’s (NASA) TRMM-based Multisatellite Precipitation Analysis is used globally to detect floods and monitor rain for agricultural uses. The Naval Research Laboratory Monterey and the National Centers for Environmental Prediction use TRMM data as a key part of their multisatellite rain estimates. TRMM data are central to the success of these efforts because of their accuracy and the significant sampling coverage by TRMM in the tropics.

The scientific accomplishments and operational advantages of TRMM have spurred the development of the GPM follow-on mission, scheduled for launch in 2013 (NRC 2007a). GPM will consist of a core spacecraft with a dual-frequency precipitation radar and a multifrequency microwave radiometric imager with high-frequency capabilities to serve as an orbiting “precipitation physics laboratory.” In addition to the core spacecraft, GPM will include a constellation of current and planned satellites with passive microwave radiometers. Together, the system will provide calibrated global precipitation at 2- to 4-hr intervals.


Of the seasonal changes that occur on Earth’s land surface, perhaps the most profound is the accumulation and melt of seasonal snow cover. Snow influences climate, weather, and the water balance. Snow cover has significant effects on energy and mass exchange between Earth’s surface and atmosphere and is an important reservoir of fresh water. Its high albedo changes the surface radiation balance; its low thermal diffusivity insulates the ground; and it is a wet, cold surface in the context of heat and moisture fluxes. Therefore, snow cover exerts a huge influence on the hydrologic cycle during the winter and spring for much of Earth’s land area. Near many mountain ranges, the seasonal snow cover is the dominating source of runoff, filling rivers and recharging aquifers that more than a billion people depend on for their water resources (Barnett et al. 2005a). Snow affects large-scale atmospheric circulation. Early-season snow cover variability in the northern hemisphere, for example, leads to altered circulation patterns, suggesting implications for climate predictability (Cohen and Entekhabi 1999).

For four decades, satellite remote sensing instruments have measured snow properties. These weekly measurements represent one of the longest satellite-derived climate data records, which now enables scientists to study long-term trends in seasonal snow cover (Frei and Robinson 1999). At optical wavelengths, sensors such as the NOAA Advanced Very High Resolution Radiometer (AVHRR) and the Landsat Thematic Mapper (TM) have been used to produce maps of snow cover at both continental and drainage-basin scales. In the EOS era, snow-cover products are available from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Multiangle Imaging Spectroradimeter (MISR), and the Advanced Spaceborne Thermal Emission Reflection and Radiometer (ASTER). Snow-water equivalent (the depth of liquid water that the snowpack would produce if it melted) is regularly estimated at coarse spatial resolution from passive microwave data, including SSM/R, SSM/I, and the EOS instrument AMSR-E in a time series that goes back to 1978. However, at finer spatial resolution, necessary for the mountains, measuring snow-water equivalent is a difficult problem; and a proposed sensor for Snow and Cold Land Processes (SCLP) is recommended as one of 17 high-priority missions for launch before 2020 (NRC 2007a).

König et al. (2001) and Dozier and Painter (2004) have reviewed developments in remote sensing of snow and ice. Among them is the use of snow-covered area from MODIS in hydrologic analysis and modeling (Box 6.2, Figure 6.2). Through updates of a runoff model with measurements of snow cover, seasonal streamflow forecasts have been improved (McGuire et al. 2006). Unlike surface measurements, satellite observations are able to show the distribution of snow over the topography, revealing that considerable snow at higher elevations remains after all snow has disappeared from the surface measurement stations.

An additional property measured from MODIS is snow albedo. In the current generation of climate and snowmelt models, snow albedo is typically either prescribed or represented by empirical aging functions, when truly it is a dynamic variable affected by grain growth and light-absorbing impurities. Newer analyses of snow cover are incorporating the seasonal evolution of both the snow cover and its albedo. In the visible part of the spectrum, clean, deep snow is bright and white, irrespective of the size of the grains. Beyond the visible wavelengths in the near infrared and shortwave infrared, however, snow is one of the most “colorful” substances in nature. Newly fallen snow usually has a fine grain size, but metamorphism and sintering throughout the winter and spring increase the grain size, bond grains together, and reduce reflectance in wavelengths beyond about 0.8 μm (Warren 1982). This behavior of snow is important to the snowpack’s energy balance because the decrease in albedo often occurs during the spring when the

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