Faculty member Dr. Pierre Kirstetter leads the development of probabilistic precipitation retrievals
Faculty member Dr. Pierre Kirstetter leads the development of probabilistic precipitation retrievals for hazard applications in the National Weather Service
Progress in precipitation science and applications is critical to advancing weather and water budget studies and to predicting natural hazards caused by extreme events, from local to global scales. It requires more than just one deterministic precipitation “best estimate” to adequately cope with the intermittent, highly skewed distribution that characterizes precipitation. Probabilistic Quantitative Precipitation Estimation (PQPE) is an approach that integrates remote sensing, meteorology, hydrology, and artificial intelligence to advance precipitation estimation, processes understanding, and applications. It increases the information content through the consideration of uncertainty in the remote sensing of precipitation and advances the quantification of risk associated with precipitation-related hazards such as flash flooding. The approach described in Kirstetter et al. (WRR, 2015) was tested for radar networks by NOAA/NSSL scientists and NWS forecasters in the HMT-Hydro Experiment in 2019 to evaluate new tools and techniques through real-time testbed operations for the improvement of flash flood detection and warning operations (https://inside.nssl.noaa.gov/flash/hwt-hydro/). Through collaboration with the NOAA Multi-Radar/Multi-Sensor team, it is now running in real-time in the MRMS System Experimental Product Viewer at: https://mrms.nssl.noaa.gov/qvs/mrms_v12/. NASA and NOAA support the current development of PQPE for the latest generation of global satellite precipitation estimates. More details can be found at https://www.researchgate.net/project/Probabilistic-Precipitation-Estimates.