Precipitation Estimation Using Combined Radar and Microwave Radiometer Observations from GPM- Initial Studies

Monday, 15 December 2014: 8:45 AM
William S Olson1, Mircea Grecu2, Stephen J Munchak3, Steven F McLaughlin4, Ziad S Haddad5, Kwo-Sen Kuo3, Lin Tian2, Benjamin T Johnson1 and Hirohiko Masunaga6, (1)Joint Center for Earth Systems Technology, Baltimore, MD, United States, (2)Goddard Earth Sciences Technology and Research, Greenbelt, MD, United States, (3)Earth System Science Interdisciplinary Center, COLLEGE PARK, MD, United States, (4)Science Systems and Applications, Inc., Lanham, MD, United States, (5)Jet Propulsion Laboratory, Pasadena, CA, United States, (6)Nagoya University, Nagoya, Japan
In the Global Precipitation Measurement (GPM) mission, the Dual-Frequency Precipitation Radar - GPM Microwave Imager (DPR-GMI) combined radar-radiometer precipitation algorithm will provide, in principle, the most accurate and highest resolution estimates of surface rainfall rate and precipitation vertical structure from a spaceborne observing platform. In addition to direct applications of these precipitation estimates, they will serve as a crucial reference for cross-calibrating passive microwave precipitation profile estimates from the GPM radiometer constellation. And through the microwave radiometer estimates, the combined algorithm calibration will ultimately be propagated to GPM infrared-microwave multisatellite estimates of surface rainfall.

The GPM combined DPR-GMI precipitation algorithm is based upon an ensemble filtering technique. At each DPR footprint location, an initial estimate is made of the distribution of possible precipitation profiles consistent with DPR Ku reflectivity observations and a priori information regarding the intercepts of the assumed size distributions of precipitation particles and parameters describing environmental conditions. This Ku-consistent profile distribution is filtered using coincident DPR Ka reflectivities, the vertical path-integrated attenuation at Ku and Ka bands, and GMI brightness temperature observations. The resulting filtered distribution of precipitation profiles is consistent with all of the available data and a priori information; the mean of the profiles gives the best estimate of precipitation, and the standard deviation is a measure of the uncertainty of that estimate.

The DPR-GMI algorithm will be evaluated by comparing estimated reflectivity and precipitation profiles against ground-based polarimetric radar data, and also by checking that the “best fit” precipitation distributions lead to forward radiative model simulations that are generally unbiased with respect to the observations. The impacts of multiple scattering, footprint subgrid-scale variability, representation of precipitation particle size distributions, scattering properties of ice-phase precipitation, and surface characteristics on forward radiative modeling and estimated precipitation will be discussed at the conference.