Monday, March 26, 2018. 1:30PM. Webinar: Improved Statistical Postpro
cessing of Precipitation for the NWS Forecasts and Hydrologic Applications.
Thomas M. Hamill, NOAA Earth System Research Lab. Sponsored by **Cen
ter for Satellite Research and Applications.** More information here.

Accurate numerical forecasts of precipitation have many purposes, including providing forcings to hydrologic models used to predict river flo w and Great Lakes water levels. Commonly, the raw model guidance may exhibi t both random and systematic errors, such as an overabundance of light prec ipitation and not enough heavy precipitation. If the raw guidance is used t o provide precipitation forcings to hydrologic predictions, the accuracy of those predictions will suffer. A potential solution is the statistical pos tprocessing of the numerical guidance, using past forecasts and observation s (or analyses) to statistically adjust the current model guidance. Statist ical postprocessing of precipitation amount is quite challenging; to have a n adequate training sample of unusual events such as heavy precipitation, e ither a long-time series of past forecasts and analyses are needed (i.e., r eforecasts) or advanced statistical methods to leverage shorter training da ta sets.

In this seminar [she] will describe an advanced precip itation postprocessing algorithm in development that is intended for operat ional use in the NWS roughly a year hence. Each of the ensemble predictions are statistically postprocessed to adjust for biases in the mean amount an d for excessive confidence. Short training data sets (the last 60 days of f orecasts and analyses) are used. Biases in the mean amount are addressed th rough "quantile mapping" using cumulative distributions functions (CDFs) of analyzed and forecast precipitation. To address the small training sample size, the training sample used to populate CDFs at a particular location ar e supplemented by data from other locations with similar precipitation clim atologies and terrain characteristics. The final probabilities are determin ed from a weighted linear combination of "dressed" ensemble members.

The resultant probabilistic forecasts are greatly improved, as will be demonstrated with verification statistics and some case studies. A fter presentation of results, [she] welcome discussion about the potential relevance of this algorithm for forcing hydrologic predictions of the Great Lakes.

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