Probabilistic Quantitative Precipitation Estimates with Ground-Based Radar Networks

Explicitly integrating uncertainty as an integral part radar quantitative precipitation estimation (QPE) is needed at fine spatiotemporal scales (e.g. 1-km/5-min) for hydrological modeling, storm prediction, and flash flood monitoring. These applications require more than just one deterministic “best estimate” to adequately cope with the intermittent, highly skewed distribution that characterizes precipitation. Probabilistic QPE (PQPE) was introduced across the conterminous US with the NOAA/NSSL Multi-Radar/Multi-Sensor to increase the values of QPE for risk decision processes. Probability distributions of precipitation rates are computed instead of deterministic values to describe the range and values of possible rate given the radar observations. It uses models quantifying the relation between radar reflectivity and the corresponding “true” precipitation.

By preserving the fine space/time sampling properties of the sensor and by increasing the information content in QPE, precipitation probability maps compare favorably to the deterministic QPE. For the first time, the Hydrometeorology Testbed (HMT) Multi-Radar / Multi-Sensor (MRMS) Hydro Experiment (HMT-Hydro) 2019 allowed NWS forecasters and NSSL scientists to investigate the use of probabilistic grids for flash flood warning decision making. The presentation will discuss the evaluation of precipitation probability maps in this context.