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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.
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.
The U.S. National Blend of Models provides statistically postprocessed, high-resolution multimodel ensemble guidance, providing National Weather Service forecasters with a calibrated, downscaled starting point for producing digital forecasts. Forecasts of 12-hourly probability of precipitation (POP12) over the contiguous United States are produced as follows: 1) Populate the forecast and analyze cumulative distribution functions (CDFs) to be used later in quantile mapping. Were every grid point processed without benefit of data from other points, 60 days of training data would likely be insufficient for estimating CDFs and adjusting the errors in the forecast. Accordingly, “supplemental” locations were identified for each grid point, and data from the supplemental locations were used to populate the forecast and analyzed CDFs used in the quantile mapping. 2) Load the real-time U.S. and Environment Canada (now known as Environment and Climate Change Canada) global deterministic and ensemble forecasts, interpolated to ⅛°. 3) Using CDFs from the past 60 days of data, apply a deterministic quantile mapping to the ensemble forecasts. 4) Dress the resulting ensemble with random noise. 5) Generate probabilities from the ensemble relative frequency. 6) Spatially smooth the forecast using a Savitzky–Golay smoother, applying more smoothing in flatter areas. Forecasts of 6-hourly quantitative precipitation (QPF06) are more simply produced as follows: 1) Form a grand ensemble mean, again interpolated to ⅛°. 2) Quantile map the mean forecast using CDFs of the ensemble mean and analyzed distributions. 3) Spatially smooth the field, similar to POP12. Results for spring 2016 are provided, demonstrating that the postprocessing improves POP12 reliability and skill, as well as the deterministic forecast bias, while maintaining sharpness and spatial detail.
Localized Aviation MOS Program (LAMP) convection and lightning probability and “potential” guidance forecasts for the conterminous United States, developed by the Meteorological Development Laboratory (MDL), have been produced operationally and made available to aviation and other users through the National Digital Guidance Database (NDGD) since April 2014. In response to user requests for improved skill and resolution of these forecasts, MDL has recently made extensive upgrades, and a switch to the new LAMP guidance was made in January 2018. Upgrades include improved spatial and temporal resolution of the predictands, which were enabled by first time LAMP use of finescale radar reflectivity products from the Multi-Radar Multi-Sensor (MRMS) system, total lightning observations from a ground-based lightning sensing system, and finescale model output from the High Resolution Rapid Refresh (HRRR) model. This article describes how these new data inputs are applied in the LAMP model to obtain improved skill and sharpness of the convection and total lightning probability forecasts. Strengths and limitations in LAMP performance are shown through verification statistics and example verification maps for a selected intense convective storm case.
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