An overview of the National Weather Service's centralized statistical quantitative precipitation forecasts

Abstract

The current operational statistical quantitative precipitation forecast (QPF) system of the US National Weather Service (NWS) is described. This system produces categorical QPF for stations within the contiguous US and Alaska, and utilizes the Model Output Statistics (MOS) technique applied to output from the NWS Nested Grid Model (NGM). Operational forecasts from this system improve significantly over the gridpoint precipitation forecasts available directly from the NGM. Although most NGM QPF guidance is disseminated to forecasters in categorical form, the categorical forecasts are based on probabilistic information produced by the MOS regression equations. This probabilistic information is shown to have significant skill relative to forecasts produced by using climatic variables, and expected-value estimates from the set of NGM MOS probabilities show promise in helping to fine-tune the categorical forecasts. Since the development of the NGM MOS QPF system, advances in computer processing power have enabled the more frequent introduction and enhancement of operational numerical weather prediction models. Precipitation estimates have evolved as well, with traditional manual observations being replaced by a network of automated observing sites and radar-based estimates. These changes mean that future MOS systems will need to be based on short and potentially unstable samples of model output and predictand data. Strategies for adapting the MOS technique to this environment are discussed. Skillful MOS guidance still can be produced under these conditions, as an experimental QPF system based on a limited sample of data from the NWS eta-coordinate model has shown. Results from these experiments are described, as well as the ongoing development of MOS QPF guidance based on the NWS Global Spectral Model.