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 recent emergence of the National Digital Forecast Database as the flagship product of the National Weather Service has resulted in an increased demand for forecast guidance products on fine-mesh grids. Unfortunately, fine-grid forecasts with geographically regionalized statistical models are usually plagued by nonmeteorological discontinuities at regional boundaries. This study treats the problem in a regionalized Global Forecast System (GFS)-based model output statistics (MOS) application that produces 6-h probabilistic quantitative precipitation forecasts (PQPFs) on a 4-km grid up to 192 h in advance. The technique involves incorporating areal overlap in the geographical regionalization and weighting multiple PQPFs in region-overlap zones. The degree of overlap ranges from about 20 km along meteorologically significant regional boundaries to about 150 km at quasi-arbitrary boundaries. The forecast-weighting constants for a grid point in an overlap zone vary in direct proportion to the distances to the closest associated regional boundaries.The application of the region-overlap and forecast-weighting techniques resulted in retention of sharp PQPF gradients along meteorologically significant regional boundaries and prevention of artificial discontinuities at quasi-arbitrary boundaries. The eradication of the discontinuities in the forecast patterns was achieved without sacrificing forecast skill. While the regionalization was customized for producing highspatial-resolution 6-h PQPFs over the contiguous United States with a specialized gridded MOS application, the region-overlap and forecast-weighting techniques may have general applicability. Also, the quality of the 6-h PQPFs was not strongly dependent on customization of the regionalization. 1 Note that statistical prediction equations could be developed from one set of points and then applied to another set. Thus, the equations could be developed from station data and then applied (forecasts issued) on a grid.

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.