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Improving probabilistic ensemble forecasts of convection through the application of QPF-POP relationships

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Tuesday, 25 January 2011
Improving probabilistic ensemble forecasts of convection through the application of QPF-POP relationships
Christopher J. Schaffer, NOAA/NWS, Peachtree City, GA; and W. A. Gallus Jr. and M. Segal
Manuscript (445.1 kB)

New approaches of post-processing quantitative precipitation forecasts (QPFs) from an ensemble were used to generate probability of precipitation (POP) tables in order to develop a forecasting method that could outperform a traditional method that relies upon calibration of POP forecasts derived using equal-weighting of ensemble members. Early warm season 10-member ensemble output from the NOAA Hazardous Weather Testbed Spring Experiments was used, with 29 cases serving as a training set to create the POP tables and 20 cases used as a test set. The new approaches use QPF-POP relationships based on two properties termed characteristic precipitation amount and agreement. In the first approach, POPs were based on a binned precipitation amount and the number of ensemble members with 6-hour precipitation accumulations greater than given thresholds. In a second approach, a neighborhood method was used to find the number of points in a given neighborhood area around each of the domain grid points with precipitation greater than a threshold, while also considering the binned amount representative of the neighborhood. This approach, although considering only a single ensemble member, yielded forecasts of only marginally lower skill compared to those obtained by 10-member ensembles in the first approach. After application of a correction for forecast overestimation, a third approach using a combination of methods produced forecasts that were improved statistically significantly compared to the calibrated traditional method's forecasts. The second approach on its own showed skill comparable to that obtained by a traditional calibrated 10-member ensemble, so adopting this approach alone could potentially save computer resources which could then be used for model refinements, only sacrificing a small amount of increased skill that could have been obtained by including the other approaches used in the third approach.