The National Weather Service (NWS)’s flash flood and river forecast operations rely on rapidly updated, high-resolution quantitative precipitation estimates (QPE). At present, this information is provided primarily by weather radar (WSR-88Ds) and precipitation gauge networks; however, in regions where these networks are sparse, such as orographic regions, real-time GOES satellite QPE products can provide critical information for filling in these gaps.
In the OHD-STAR-HMT led activity, a current-GOES version of the official GOES-R rainfall rate algorithm will be augmented to add dual-polarimetric radar-based rain rates to calibrate the satellite data. This augmentation is expected to improve the accuracy of the QPE products in regions where radar coverage is too sparse and fragmented to accurately estimate precipitation (e.g., Alaska and Puerto Rico).
The radar-calibrated GOES-R rainfall rates will then be fused with available gauge and radar QPE to create a high resolution and spatially seamless data set using existing and emerging statistical multisensor fusion techniques. To facilitate the transition of the augmented GOES-R rainfall algorithm to NWS river and flash flood operations, the resulting products will be evaluated using data from selected regions, including HMT-SEPS, in collaboration with scientists at HMT, the National Water Center, and NWS forecasters using NWS modeling tools.
Contact: Rob Cifelli