Evaluation of Precipitation Vertical Profiles Estimated by GPM-Era Satellite-Based Passive Microwave Retrievals

Author:

Utsumi Nobuyuki12,Turk F. Joseph1,Haddad Ziad S.1,Kirstetter Pierre-Emmanuel3456,Kim Hyungjun178

Affiliation:

1. a Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

2. b Nagamori Institute of Actuators, Kyoto University of Advanced Science, Kyoto, Japan

3. c School of Meteorology, University of Oklahoma, Norman, Oklahoma

4. d School of Civil Engineering and Environmental Science, University of Oklahoma, Norman, Oklahoma

5. e Advanced Radar Research Center, National Weather Center, Norman, Oklahoma

6. f NOAA/National Severe Storms Laboratory, Norman, Oklahoma

7. g Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, Los Angeles, California

8. h Institute of Industrial Science, The University of Tokyo, Tokyo, Japan

Abstract

AbstractPrecipitation estimation based on passive microwave (MW) observations from low-Earth-orbiting satellites is one of the essential variables for understanding the global climate. However, almost all validation studies for such precipitation estimation have focused only on the surface precipitation rate. This study investigates the vertical precipitation profiles estimated by two passive MW-based retrieval algorithms, i.e., the emissivity principal components (EPC) algorithm and the Goddard profiling algorithm (GPROF). The passive MW-based condensed water content profiles estimated from the Global Precipitation Measurement Microwave Imager (GMI) are validated using the GMI + Dual-Frequency Precipitation Radar combined algorithm as the reference product. It is shown that the EPC generally underestimates the magnitude of the condensed water content profiles, described by the mean condensed water content, by about 20%–50% in the middle-to-high latitudes, while GPROF overestimates it by about 20%–50% in the middle-to-high latitudes and more than 50% in the tropics. Part of the EPC magnitude biases is associated with the representation of the precipitation type (i.e., convective and stratiform) in the retrieval algorithm. This suggests that a separate technique for precipitation type identification would aid in mitigating these biases. In contrast to the magnitude of the profile, the profile shapes are relatively well represented by these two passive MW-based retrievals. The joint analysis between the estimation performances of the vertical profiles and surface precipitation rate shows that the physically reasonable connections between the surface precipitation rate and the associated vertical profiles are achieved to some extent by the passive MW-based algorithms.

Publisher

American Meteorological Society

Subject

Atmospheric Science

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