Extreme Precipitation

The U. S. Weather Research Program supports research and development projects on extreme precipitation by providing funding for the Hydrometeorology Testbed (HMT). HMT conducts research on precipitation and weather conditions that can lead to flooding and fosters transition of scientific advances and new tools into NWS forecasting operations. HMT projects emphasize the development of prototype tools for flood and extreme precipitation forecasting and winter weather forecasting improvements that are tested with NWS forecasters.

Quantitative Precipitation Estimation (QPE) and Forecasting (QPF)  

Work supported by OWAQ will balance and complement HMT’s established capabilities with a much greater effort in quantitative precipitation estimation (QPE) and forecasting (QPF).  In particular, USWRP funds from OWAQ will enable the development and testing of advanced ensemble forecasting systems, QPF/PQPF decision support tools to improve operational hydrometeorology forecasts, and new verification techniques. New data fusion methodologies for improving QPE are also part of the HMT portfolio supported by OWAQ. The testbed offers a unique and technically sophisticated venue for testing the latest, promising developments in QPE and QPF/PQPF, with the direct collaboration of research scientists and operational forecasters.  With support from the USWRP, HMT represents a unique opportunity for progress in QPE and QPF/PQPF research with concurrent rapid implementation in operations.

Verification

Utilization of reforecast datasets has been shown to dramatically improve QPF forecast skill by statistically correcting weather forecasts using a long set of retrospective weather forecasts generated using a fixed numerical weather prediction model. A quantitative analysis of the reforecast output is being performed on Atmospheric River data and an analysis to include other forecast variables is currently underway. From this analysis, the best forecast variables for predicting extreme precipitation events on the West Coast will be determined for the reforecast dataset. Plans are underway to conduct a similar analysis in the southeast U.S. to better understand the type of precipitation events that present the greatest QPF challenges. OWAQ supported verification activities also include evaluation of microphysical parameterizations and QPF performance in experimental, high resolution prediction models such as the Experimental Regional Ensemble Forecast system (ExREF).

Research to Operations    

Part of HMT was established to accelerate the transfer of scientific and technological innovations into operations at the Weather Prediction Center (WPC). HMT-WPC has hosted an annual Winter Weather Experiment, an Atmospheric River Retrospective Forecasting Experiment, and the Flash Flood Intensive Rainfall Experiment (FFaIR). HMT will also partner with the National Weather Service (NWS) Western Region to bring ensemble QPF grids into the Sacramento, CA forecast office. The grids will be made available to the AWIPS display so that forecasters can quantitatively evaluate the performance of the HMT ensemble against other operational models. Other forecast offices will test and evaluate the ensemble QPF model and if successful, the HMT ensemble model grids will be made available to all Western Region forecast offices.

Improved Understanding

Improved understanding of precipitation processes is the foundation for all of the HMT activities described above. This includes processes ranging from cool season orographic rainfall on the west coast to warm season convection in the southeast U.S. The objective is to improve forecasting extreme precipitation events for both numerical weather prediction (NWP) models and human forecasters. While human forecasters rely on NWP model guidance for many aspects of a weather forecast, it is the human recognition of local conditions, model error and bias, and past experience that is often most critical to successful forecasts of high-impact events. Therefore, improving both NWP guidance and forecaster awareness is key to improving the precipitation forecast.

For further information, please visit the HMT website.