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Proceedings Paper

A potential DSD retrieval process for dual-frequency precipitation radar (DPR) on board GPM
Author(s): Minda Le; V. Chandresekar
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Paper Abstract

Global Precipitation Measurement (GPM) is poised to be the next generation precipitation monitoring system from space after the Tropical rainfall measurement (TRMM) mission. The GPM mission is centered on the deployment of a core observatory satellite with an active dual-frequency radar DPR, operating at Ku- and Ka- band. Two independent observations from DPR provide the possibility to retrieve two independent parameters from gamma drop size distribution (DSD), namely median volume diameter (D0) and scaled intercept (NW), at each resolution volume. Dual-frequency method proposed for the DPR radar can be formulated in terms of integral equations and the two DSD parameters D0 and NW can be estimated at each bin based on the assumed microphysical models of hydrometeors. One known error in the dual frequency retrievals is the dual-valued problem when retrieving D0 from DFR for rain. Rose and Chandrasekar (2006)[1], remedied the bi-valued problem by assuming a linear model with height for D0 as well as NW (in log scale) in rain. The algorithm with the linear assumption was evaluated by Le et al. (2009) [2] based on the whole vertical profile including rain, melting ice, and ice region through a hybrid method. The hybrid method combines forward retrieval by Meneghini et al (1997) [3] in frozen and melting region and the linear assumption in rain region. The retrieval process uses recursive procedure to optimize DSD parameters at the bottom of rain by constructing the cost function along the vertical profile. This retrieval algorithm is applied to tropical storm Earl, a category 4 hurricane captured by APR-2 precipitation radar during the Genesis and Rapid Intensification Processes (GRIP) campaign in 2010.

Paper Details

Date Published: 8 November 2012
PDF: 11 pages
Proc. SPIE 8523, Remote Sensing of the Atmosphere, Clouds, and Precipitation IV, 852307 (8 November 2012); doi: 10.1117/12.977597
Show Author Affiliations
Minda Le, Colorado State Univ. (United States)
V. Chandresekar, Colorado State Univ. (United States)


Published in SPIE Proceedings Vol. 8523:
Remote Sensing of the Atmosphere, Clouds, and Precipitation IV
Tadahiro Hayasaka; Kenji Nakamura; Eastwood Im, Editor(s)

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