Towards a Consistent Treatment of Surface Characteristics in the GPM DPR-GMI Combined Algorithm

Monday, 15 December 2014: 9:00 AM
Stephen J Munchak, Colorado State University, Fort Collins, CO, United States, William S Olson, Joint Center for Earth Systems Technology, Baltimore, MD, United States, Mircea Grecu, NASA/Goddard Space Flight Ctr, Greenbelt, MD, United States and Robert Meneghini, NASA Goddard Space Flight Center, Greenbelt, MD, United States
The Global Precipitation Measurement (GPM) core satellite GPM Microwave Imager (GMI) and Dual-frequency Precipitation Radar (DPR) comprise the most sophisticated platform for measuring precipitation from space. To make full use of both instruments, the GPM DPR-GMI combined algorithm retrieves precipitation profiles that are consistent with active and passive measurements. To accomplish this, forward models must be capable of simulating the measurements from both instruments using self-consistent physics.

Both passive and active measurements are sensitive to surface properties. In all but the heaviest rain, brightness temperatures are sensitive to surface emissivity, especially at low frequencies. Radar observations of the surface backscatter cross-section (σ0) are used to derive an estimate of the path-integrated attenuation, but require a rain-free reference value for comparison. Because emissivity and σ0 are related to the same physical parameters (e.g., dielectric constant, surface roughness), it is desirable for the forward models to be consistent when modeling them.

For the combined algorithm, we derive wind- σ0 relationships for each angle bin of DPR using matchups to GMI-derived wind estimates in clear sky conditions, where the rms error relative to ship and buoy observations is 2.1 m/s. Over land, angle-dependent emissivity- σ0 covariance matrices are estimated from GMI and DPR data in 14 different surface classes representing different levels of vegetation, snow cover, and boundaries. In the ensemble framework of the combined algorithm, it is possible to use these relationships to generate different realizations of surface properties that are consistent with the observed characteristics. The impact on rainfall estimates relative to earlier versions of the combined algorithm and the ability of the algorithm to estimate wind speed over water surfaces in raining conditions and surface emissivity over land will be demonstrated.