32nd Conference on Broadcast Meteorology/31st Conference on Radar Meteorology/Fifth Conference on Coastal Atmospheric and Oceanic Prediction and Processes

Monday, 11 August 2003: 8:45 AM
Observing the Global Water Cycle from Space
Peter H. Hildebrand, NASA/GSFC, Greenbelt, MD; and P. Houser and C. A. Schlosser
Poster PDF (127.2 kB)
This paper presents an approach to measuring major components of the water cycle from space. Key elements of the global water cycle are discussed in terms of the storage of water—in the ocean, air, cloud and precipitation, in soil, ground water, snow and ice, and in lakes and rivers—and in terms of the global fluxes of water between these reservoirs. Approaches to measuring or otherwise evaluating the global water cycle are presented, and the limitations on known accuracy for many components of the water cycle are discussed, as are the characteristic spatial and temporal scales of the different water cycle components.

Using these observational requirements for a global water cycle observing system, the approach to measuring the global water cycle from space is developed. The capabilities of active and passive microwave instruments are discussed, as are supporting measurements from other sources. Examples of space observational systems, including TRMM/GPM precipitation measurement, cloud radars, soil moisture, sea surface salinity, temperature and humidity profiling, other measurement approaches are discussed, as are the assimilation of the microwave and other data into interpretative computer models.

The selection of orbits, and antenna size/beamwidth considerations then determine the sampling characteristics for satellite measurement systems. These considerations dictate a particular set of measurement possibilities, which are then matched to the observational sampling requirements based on the science. The result of this process is a network of satellite instrumentation systems, many in low Earth orbit, a few in geostationary orbit, and all tied together through a sampling network that feeds the observations into a data-assimilative computer model.

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