HR: 1340h
AN: A53D-1440    [Abstracts]
TI: Evaluating CloudSat Ice Water Retrievals Using a Cloud Resolving Model: Sensitivities to Frozen Particle Properties and Implications for Model-Data Comparisons
AU: * Woods, C P
EM: Woods@jpl.nasa.gov
AF: Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
AU: Waliser, D
AF: Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
AU: Li, F
AF: Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
AU: Austin, R
AF: Colorado State University, Department of Atmospheric Sciences, Fort Collins, CO 80523, United States
AU: Stephens, G
AF: Colorado State University, Department of Atmospheric Sciences, Fort Collins, CO 80523, United States
AU: Vane, D
AF: Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, United States
AU: Tao, W
AF: NASA Goddard Space Flight Center, Laboratory for Atmospheres, Greenbelt, MD 20771, United States
AU: Tompkins, A
AF: European Center for Medium-Range Weather Forecasts, Berkshire RG2, Reading, 9AX, United Kingdom
AB: The sensitivities of CloudSat ice water content retrievals to frozen particle characteristics are tested by generating CloudSat-like retrievals from profiles of known ice water content. First, `truth' values of total ice water content are generated by a cloud-resolving model (MM5). The MM5 model profiles are generated using the Reisner- Thompson microphysical parameterization scheme, which allows for the existence of multiple types of frozen particles (cloud ice, snow and graupel). Next, a 94-GHz reflectivity simulator, called QuickBeam, is used to generate a CloudSat-like view of the model generated profiles. Since reflectivity is highly dependent on the characteristics of the scattering particles (e.g., density, size distribution), a set of tests are performed to determine the sensitivity of the reflectivity to the assumed properties of cloud ice and snow particles. Finally, the CloudSat ice water content retrieval algorithm is applied to the profiles of 94-GHz reflectivity, producing 'simulated retrieved' values of ice water content, which can be compared to the `truth' values. The comparisons suggest that CloudSat ice water content retrievals are sensitive to the frozen particle properties often parameterized in models (e.g., particle density, particle size distributions). The sensitivity tests provide a better understanding of how the different components of the frozen water mass impact the ice water content retrieved by CloudSat. Such information is important when comparing the measurements to modeled frozen water mass quantities, including those from various levels of sophistication in global climate models. Additionally, we demonstrate how information gained in this study may be used for improving the retrieval system. A simple height-based retrieval correction that effectively corrects for the vertically varying characteristics of frozen particles is examined.
DE: 0320 Cloud physics and chemistry
DE: 3311 Clouds and aerosols
DE: 3337 Global climate models (1626, 4928)
DE: 3354 Precipitation (1854)
DE: 3360 Remote sensing
SC: Atmospheric Sciences [A]
MN: 2007 Fall Meeting