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Ellen M. Sukovich, F. Martin Ralph, Faye E. Barthold, David W. Reynolds, and David R. Novak


Extreme quantitative precipitation forecast (QPF) performance is baselined and analyzed by NOAA’s Hydrometeorology Testbed (HMT) using 11 yr of 32-km gridded QPFs from NCEP’s Weather Prediction Center (WPC). The analysis uses regional extreme precipitation thresholds, quantitatively defined as the 99th and 99.9th percentile precipitation values of all wet-site days from 2001 to 2011 for each River Forecast Center (RFC) region, to evaluate QPF performance at multiple lead times. Five verification metrics are used: probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), frequency bias, and conditional mean absolute error (MAEcond). Results indicate that extreme QPFs have incrementally improved in forecast accuracy over the 11-yr period. Seasonal extreme QPFs show the highest skill during winter and the lowest skill during summer, although an increase in QPF skill is observed during September, most likely due to landfalling tropical systems. Seasonal extreme QPF skill decreases with increased lead time. Extreme QPF skill is higher over the western and northeastern RFCs and is lower over the central and southeastern RFC regions, likely due to the preponderance of convective events in the central and southeastern regions. This study extends the NOAA HMT study of regional extreme QPF performance in the western United States to include the contiguous United States and applies the regional assessment recommended therein. The method and framework applied here are readily applied to any gridded QPF dataset to define and verify extreme precipitation events.

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Jerome P. Charba, David W. Reynolds, Brett E. McDonald, and Gary M. Carter


Comparative verification of operational 6-h quantitative precipitation forecast (QPF) products used for streamflow models run at National Weather Service (NWS) River Forecast Centers (RFCs) is presented. The QPF products include 1) national guidance produced by operational numerical weather prediction (NWP) models run at the National Centers for Environmental Prediction (NCEP), 2) guidance produced by forecasters at the Hydrometeorological Prediction Center (HPC) of NCEP for the conterminous United States, 3) local forecasts produced by forecasters at NWS Weather Forecast Offices (WFOs), and 4) the final QPF product for multi-WFO areas prepared by forecasters at RFCs. A major component of the study was development of a simple scoring methodology to indicate the relative accuracy of the various QPF products for NWS managers and possibly hydrologic users. The method is based on mean absolute error (MAE) and bias scores for continuous precipitation amounts grouped into mutually exclusive intervals. The grouping (stratification) was conducted on the basis of observed precipitation, which is customary, and also forecast precipitation. For ranking overall accuracy of each QPF product, the MAE for the two stratifications was objectively combined. The combined MAE could be particularly useful when the accuracy rankings for the individual stratifications are not consistent. MAE and bias scores from the comparative verification of 6-h QPF products during the 1998/99 cool season in the eastern United States for day 1 (0–24-h period) indicated that the HPC guidance performed slightly better than corresponding products issued by WFOs and RFCs. Nevertheless, the HPC product was only marginally better than the best-performing NCEP NWP model for QPF in the eastern United States, the Aviation (AVN) Model. In the western United States during the 1999/2000 cool season, the WFOs improved on the HPC guidance for day 1 but not for day 2 or day 3 (24–48- and 48–72-h periods, respectively). Also, both of these human QPF products improved on the AVN Model on day 1, but by day 3 neither did. These findings contributed to changes in the NWS QPF process for hydrologic model input.

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