Verification and Refinement of GPM Ground Validation Data at Wallops Flight Facility
Monday, 15 December 2014
The Precipitation Research Facility (PRF) at NASA’s Goddard Space Flight Center - Wallops Flight Facility (WFF) employs numerous remote sensing and in-situ instruments for the Global Precipitation Measurement (GPM) ground validation program. Among these assets are the NPOL S-band radar, D3R Ku/Ka-band radar, 2-D video disdrometers, Parsivel2 disdrometers, tipping bucket, and weighing bucket rain gauges. The WFF PRF has deployed these instruments in the Mid-Atlantic Delmarva region, and is actively collecting data for GPM ground validation. The NPOL radar is an observational tool to bridge the spatial difference between ground and satellite observations. Connecting the points near the ground between NPOL and other instruments leads to the extension of the full column through the atmosphere for direct comparisons with GPM observations. This in turn provides critical feedback to physical algorithm developers. Therefore, the verification and refinement of observations from radar, rain gauges, and disdrometers are critical to the GPM ground validation program by providing confidence in the underlying data used to validate satellite measurements. This research entails comparisons of data and derived products between validation instruments to verify and quantify differences in reflectivity, computed rain rates, and derived drop-size-distribution (DSD) parameters. Results comparing reflectivity from NPOL and 2-D video disdrometers indicate agreement within 1 dB from several significant rain events with light wind. Mixed-phase and windy events show greater variability. Rain rates and DSD parameters will show significant variability depending on computational methods chosen for each instrument. Comparisons between two radar-based dual-polarimetric approaches for estimating rain rates will be discussed, in addition to the retrieval of DSD parameters. Careful consideration of specific events will be needed for the best possible datasets for GPM statistical and physical validation.