Simulated space-borne radar data using cloud-resolving model for GPM/DPR algorithm development

The goal of this study is to develop a space-borne radar simulator using cloud-resolving model data as input. The primary users of the data are expected to be algorithm developers for the Dual-frequency (Ku/Ka-band) Precipitation Radar (DPR) that will be flown on the Global Precipitation Measurement (GPM) satellite. To obtain a good understanding of the algorithm performance, the simulator mimics the scanning geometry of the dual-frequency radar with a inner swath of about 120 km, which contains matched-beam data from both frequencies, and an outer swath from 120 to 245 km over which only Ku-band data will be acquired. Another important aspect of the simulator is that it accounts for all of the model inputs including cloud and rain water, cloud ice, snow and water vapor and their effects on the scattering and extinction of the radar signals. Nominally, the particle size distributions of the snow and rain are taken to be exponential distributions with fixed intercepts, N₀, and with a slope parameter determined by the equivalent water content. In the future, the exponential distribution assumption will be generalized to a gamma model with a varying shape parameter to assess the effects of this parameter on the retrieval algorithms. Although the model data do not presently contain mixed phase hydrometeors, the Yokoyama-Tanaka melting model is used along with the Bruggeman effective dielectric constant to replace rain and snow particles, where both are present, with mixed phase particles while preserving the snow/water fraction. To generate simulated data for testing the Surface Reference Technique (SRT), the simulation algorithm includes a module used to compute return power from the surface given as input the normalized radar cross section of the surface,σ⁰, at each frequency and incidence angle. The simulated σ⁰ data are modeled from realizations of jointly Gaussian random variables with means, variances and correlations obtained from measurements of σ⁰ from the JPL APR2 (2nd generation Airborne Precipitation Radar) data, which operates at approximately the same frequencies as the DPR. In this study, simulated data are generated using the three dimensional Goddard Cumulus Ensemble (GCE) model for TRMM-LBA experiment (Jan. 26, 1999) that has 256 X 512 grid points with 1km horizontal resolution and 70 vertical layers extending to a maximum height of about 23km. The model adopts a one-moment bulk parameterization of cloud microphysics with 2-liquid (rain and cloud liquid) and 3-ice (snow, cloud ice and graupel) classes of hydrometeors. We will discuss the general capabilities of the radar simulator, present some sample results and show how they can be used to assess the performance of the radar retrieval algorithms proposed for the Dual-Frequency GPM radar.