Cross-validation of Precipitation Identification from NOAA/NSSL Ground-Radar-Based National Mosaic QPE (NMQ) System and Crowd-Sourcing-Based mPING Weather Reports

The U.S. Weather Surveillance Radar-1988 Doppler (WSR-88D) network (known as NEXRAD) has played a critical role in weather-related services provided by the National Weather Service (NWS). Based on NEXRAD measurements, NOAA's National Severe Storms Laboratory (NSSL) and the University of Oklahoma (OU) have developed a Next Generation National Mosaic and Multisensor QPE System (NMQ-Q2). Since June 2006, the system has been generating high-resolution national 3-D reflectivity mosaic grids (31 levels) and a suite of severe weather and QPE products at a 1-km horizontal resolution and 5-min update cycle .The data are disseminated across government agencies and universities in real time and have been utilized in applications including data assimilation, Numerical Weather Prediction (NWP) model verification and aviation product development. One of the most concerned weather issues by the public is the type of precipitation on the ground. NMQ-Q2 identifies the near-surface precipitation type through integrating the reflectivity measured by WSR-88D and the temperature and humidity profiles provided by the weather model analysis (RUC/RAP). A better identification of precipitation type will be achieved by integrating the polarimetric radar-based hydrometeor classification algorithm (HCA), which will be installed in the upgraded NMQ system (Q3) in 2014. In 2006, NOAA/NSSL initialized a Precipitation Identification near the Ground (PING) Project, aiming to collect ground-truth reports of precipitation type throughout the continental United States (CONUS) to validate the remote-sensing-based precipitation identification. Recently, a simple “app” called mPING (meteorological Phenomena Identification Near the Ground) was developed by NSSL researchers to run on “smart” phones or, more generically, web-enabled devices with GPS location capabilities. Using mPING, anyone with a smart phone or a web access can pass surface observations of precipitation to the meteorology community. The current study shows the first ever cross-validation of precipitation identification from the crowd-sourcing-based mPING weather reports and the ground-radar-based NMQ-Q2 system. The mPING and NMQ-Q2 datasets analyzed in this study include all the spatiotemporally matched data pairs since the mPING was deployed in mid-December 2012. According to the analysis results, mPING has proven to be not only very popular, but also capable of providing consistent, accurate observational data to research community.