All known long-term records of forecasting performance for different types of precipitation forecasts in the National Weather Service were examined for relative skill and secular trends in skill. The largest upward trends were achieved by local probability of precipitation (PoP) forecasts for the periods 24-36 h and 36-48 h after 0000 and 1200 GMT. Over the last 13 years, the skill of these forecasts has improved at an average rate of 7.2% per 10-year interval. Over the same period, improvement has been smaller in local PoP skill in the 12-24 h range (2.0% per 10 years) and in the accuracy of "Yes/No" forecasts of measurable precipitation. The overall trend in accuracy of centralized quantitative precipitation forecasts of >0.5 in and >1.0 in has been slightly upward at the 0-24 h range and strongly upward at the 24-48 h range. Most of the improvement in these forecasts has been achieved from the early 1970s to the present. Strong upward accuracy trends in all types of precipitation forecasts within the past eight years are attributed primarily to improvements in numerical and statistical centralized guidance forecasts. The skill and accuracy of both measurable and quantitative precipitation forecasts is 35-55% greater during the cool season than during the warm season. Also, the secular rate of improvement of the cool season precipitation forecasts is 50-110% greater than that of the warm season. This seasonal difference in performance reflects the relative difficulty of forecasting predominantly stratiform precipitation of the cool season and convective precipitation of the warm season.
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.
The Meteorological Development Laboratory (MDL) of the National Weather Service (NWS) has developed high-resolution Global Forecast System (GFS)-based model output statistics (MOS) 6-and 12-h quantitative precipitation forecast (QPF) guidance on a 4-km grid for the contiguous United States. Geographically regionalized multiple linear regression equations are used to produce probabilistic QPFs (PQPFs) for multiple precipitation exceedance thresholds. Also, several supplementary QPF elements are derived from the PQPFs. The QPF elements are produced (presently experimentally) twice per day for forecast projections up to 156 h (6.5 days); probability of (measurable) precipitation (POP) forecasts extend to 192 h (8 days). Because the spatial and intensity resolutions of the QPF elements are higher than that for the currently operational gridded MOS QPF elements, this new application is referred to as high-resolution MOS (HRMOS) QPF.High spatial resolution and enhanced skill are built into the HRMOS PQPFs by incorporating finescale topography and climatology into the predictor database. This is accomplished through the use of specially formulated ''topoclimatic'' interactive predictors, which are formed as a simple product of a climatology-or terrain-related quantity and a GFS forecast variable. Such a predictor contains interactive effects, whereby finescale detail in the topographic or climatic variable is built into the GFS forecast variable, and dynamics in the large-scale GFS forecast variable are incorporated into the static topoclimatic variable. In essence, such interactive predictors account for the finescale bias error in the GFS forecasts, and thus they enhance the skill of the PQPFs.Underlying the enhanced performance of the HRMOS QPF elements is extensive use of archived fine-grid radar-based quantitative precipitation estimates (QPEs). The fine spatial scale of the QPE data supported development of a detailed precipitation climatology, which is used as a climatic predictive input. Also, the very large number of QPE sample points supported specification of rare-event (i.e., $1.50 and $2.00 in.) 6-h precipitation exceedance thresholds as predictands. Geographical regionalization of the PQPF regression equations and the derived QPF elements also contributes to enhanced forecast performance.Limited comparative verification of several 6-h model QPFs in categorical form showed the HRMOS QPF with significantly better threat scores and biases than corresponding GFS and operational gridded MOS QPFs.Limited testing of logistic regression versus linear regression to produce the 6-h PQPFs showed the feasibility of applying the logistic method with the very large HRMOS samples. However, objective screening of many candidate predictors with linear regression resulted in slightly better PQPF skill.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.