5.4
A combined atmospheric water data set for hydrology studies

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Tuesday, 31 January 2006: 12:00 PM
A combined atmospheric water data set for hydrology studies
A305 (Georgia World Congress Center)
Eric J. Fetzer, JPL, Pasadena, CA; and F. W. Irion, B. H. Lambrigtsen, W. G. Read, and D. E. Waliser

Presentation PDF (229.0 kB)

Current limitations in observations of atmospheric water in its gas, liquid and solid phases are central to many unresolved questions in hydrology and climate science. Several of the instruments in NASA A-Train satellite constellation of the Earth Observing System (EOS) measure atmospheric water quantities useful in resolving these questions. These instruments included the Atmospheric Infrared Sounder (AIRS), the Advance Microwave Scanning Radiometer for EOS (AMSR-E) and the Moderate-resolution Imaging Spectroradiometer (MODIS) all on Aqua, the Microwave Limb Sounder (MLS) on Aura, the Advanced Microwave Sounding Unit-B (AMSU-B) on the NOAA-16 satellite, and the Cloudsat radar. We are combining these observations into a long-term data record as part of NASA Energy and Water Cycle Study (NEWS) program. AIRS, AMSR-E, MODIS and MLS all measure water vapor. MODIS and AIRS measure cloud fraction, top pressure and top temperature, while MODIS and AMSR-E observe cloud liquid water. MLS and the AMSU-B determine the presence of ice clouds. When launched, Cloudsat will obtain profiles of cloud liquid and ice water. Because these satellites fly in formation as part of the A-Train, these measurements are made with overlapping spatial coverage and time coincidence of a few minutes or less. These sampling characteristics preserve the instantaneous relationship between water vapor, cloud liquid and cloud ice. The merged data set will provide observational constraints on atmospheric numerical models of the hydrologic cycle. Some of challenges inherent in this work include reconciling similar quantities observed by different instruments, placing observations from different sampling grids into useful formats, merging data sets with different height coverage, and distilling relevant quantities from very large data sets of several years' duration. We will give examples of applications of this data set to atmospheric processes, focusing on the Madden-Julian Oscillation and other aspects of tropical deep convection.