Probabilistic Quantitative Precipitation Estimates with NOAA/NSSL Ground Radar-Based National Mosaic and QPE System

The uncertainty structure of radar quantitative precipitation estimation (QPE) is a major issue for many applications and has been the subject of many studies. Beyond the characterization, the question of how to efficiently communicate this information to end-users is still outstanding. By using the WSR-88D radar network and rain gauge datasets across the conterminous US, a preliminary investigation of this subject has been carried out within the framework of the NOAA/NSSL Ground Radar-Based National Mosaic QPE. We derive probability distributions of rainfall rates instead of single values using a model quantifying the relation between radar reflectivity and the corresponding “true” rainfall. The probabilistic model ingests a variety of sources of error in radar QPE as well as the impact of correction algorithms on the radar signal. Ensembles of reflectivity-to-rain rate relationships accounting explicitly for rain typology were derived at a 5min/1km scale. This approach preserves the fine space/time sampling properties of the radar and conditions probabilistic QPE on the rain rate and rainfall type when computing probabilistic quantitative precipitation estimates (PQPE). The model components were estimated on the basis of a 1-year-long data sample. This PQPE model enables us to estimate rainfall probability maps and to generate radar rainfall ensembles. Maps of the rainfall exceedance probability for specific thresholds (e.g. precipitation return periods) are produced, given a radar rainfall map.

Communicating uncertainty information is not an easy task. Forecasters at NCEP's Weather Prediction Center were involved in the interpretation of the usefulness of the probabilistic QPE products during the Flash Flood and Intense Rainfall (FFaIR) experiment during the summer of 2013. User feedback has been instrumental in refining the use of QPE uncertainty relative to precipitation return periods in the context of flash flood forecasting.