HR: 1340h
AN: A23A-0924    [Abstracts]
TI: A Merged Atmospheric Water Data Set from the A-Train
AU: * Fetzer, E J
EM: Eric.J.Fetzer@jpl.nasa.gov
AF: Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr, Pasadena, CA 91109 United States
AU: Irion, F W
EM: Fredrick.W.Irion@jpl.nasa.gov
AF: Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr, Pasadena, CA 91109 United States
AU: Lambrigtsen, B H
EM: Bjorn.H.Lambrigtsen@jpl.nasa.gov
AF: Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr, Pasadena, CA 91109 United States
AU: Read, W G
EM: William.G.Read@jpl.nasa.gov
AF: Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr, Pasadena, CA 91109 United States
AU: Waliser, D E
EM: Duane.E.Waliser@jpl.nasa.gov
AF: Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr, Pasadena, CA 91109 United States
AB: 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. We are combining these observations into a long-term data record as part of the NASA Energy and Water Cycle Study (NEWS) program. Observations include temperature, water vapor, cloud fraction, cloud top pressure, cloud top temperature, cloud liquid water content, and cloud ice content. Because the A-Train satellites fly in formation, 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.
DE: 0320 Cloud physics and chemistry
DE: 0322 Constituent sources and sinks
DE: 0350 Pressure, density, and temperature
DE: 0368 Troposphere: constituent transport and chemistry
SC: Atmospheric Sciences [A]
MN: Fall Meeting 2005