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  • 1
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    Probabilistic Precipitation-Type Forecasting Based on GEFS Ensemble Forecasts of Vertical Temperature Profiles (2017)
    American Meteorological Society
    Details
    Publication Date: 2017-03-21
    Description: A Bayesian classification method for probabilistic forecasts of precipitation type is presented. The method considers the vertical wet-bulb temperature profiles associated with each precipitation type, transforms them into their principal components, and models each of these principal components by a skew normal distribution. A variance inflation technique is used to de-emphasize the impact of principal components corresponding to smaller eigenvalues, and Bayes’s theorem finally yields probability forecasts for each precipitation type based on predicted wet-bulb temperature profiles. This approach is demonstrated with reforecast data from the Global Ensemble Forecast System (GEFS) and observations at 551 METAR sites, using either the full ensemble or the control run only. In both cases, reliable probability forecasts for precipitation type being either rain, snow, ice pellets, freezing rain, or freezing drizzle are obtained. Compared to the model output statistics (MOS) approach presently used by the National Weather Service, the skill of the proposed method is comparable for rain and snow and significantly better for the freezing precipitation types.
    Print ISSN: 0027-0644
    Electronic ISSN: 1520-0493
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 2
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    A Perfect Prognosis Scheme for Forecasting Warm-Season Lightning over Florida (2008)
    American Meteorological Society
    Details
    Publication Date: 2008-06-01
    Description: This study develops and evaluates a statistical scheme for forecasting warm-season lightning over Florida. Four warm seasons of analysis data from the Rapid Update Cycle (RUC) and lightning data from the National Lightning Detection Network are used in a perfect prognosis technique to develop a high-resolution, gridded forecast guidance product for warm-season cloud-to-ground (CG) lightning over Florida. The most important RUC-derived parameters are used to develop equations producing 3-hourly spatial probability forecasts for one or more CG flashes, as well as the probability of exceeding various flash count percentile thresholds. Binary logistic regression is used to develop the equations for one or more flashes, while a negative binomial model is used to predict the amount of lightning, conditional on one or more flashes occurring. The scheme is applied to output from three mesoscale models during an independent test period (the 2006 warm season). The evaluation is performed using output from the National Centers for Environmental Prediction (NCEP) 13-km RUC (RUC13), the NCEP 12-km North American Mesoscale Model, and local high-resolution runs of the Weather Research and Forecasting (WRF) Model for a domain over south Florida. Forecasts from all three mesoscale models generally show positive skill through the 2100–2359 UTC period with respect to a model containing only climatology and persistence (L-CLIPER) and persistence alone. A forecast example using the high-resolution WRF Model is shown for 16–17 August 2006. Although the exact timing and placement of forecast lightning are not perfect, there generally is good agreement between the forecasts and their verification, with most of the observed lightning occurring within the higher forecast probability contours.
    Print ISSN: 0027-0644
    Electronic ISSN: 1520-0493
    Topics: Geography , Geosciences , Physics
    Published by American Meteorological Society
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  • 3
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    A Statistical Procedure to Forecast Warm Season Lightning over Portions of the Florida Peninsula (2006)
    American Meteorological Society
    Details
    Publication Date: 2006-10-01
    Description: Sixteen years of cloud-to-ground lightning data from the National Lightning Detection Network and morning radiosonde-derived parameters are used to develop a statistical scheme to provide improved forecast guidance for warm season afternoon and evening lightning for 11 areas of the Florida peninsula serviced by Florida Power and Light Corporation (FPL). Logistic regression techniques are used to develop equations predicting whether at least one flash will occur during the noon–midnight period in each area, as well as the amount of lightning that can be expected during this same period, conditional on at least one flash occurring. For the amount of lightning, the best results are achieved by creating four quartile categories of flash count based on climatology, and then using three logistic equations and a decision tree approach to determine the most likely quartile. A combination of forward stepwise screening and cross validation are used to select the best combination of predictors that are most likely to generalize to independent data. Results show the guidance equations to be superior to persistence on both the dependent dataset and during cross validation. The greatest skill scores are achieved for predicting whether at least one flash will occur, as well as predicting the number of flashes to within one quartile of that observed. These results demonstrate that the equations possess forecast skill and will provide useful guidance for the probability and amount of lightning in each of the 11 FPL service areas.
    Print ISSN: 0882-8156
    Electronic ISSN: 1520-0434
    Topics: Geography , Physics
    Published by American Meteorological Society
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  • 4
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    LAMP Upgraded Convection and Total Lightning Probability and “Potential” Guidance for the Conterminous United States (2019)
    American Meteorological Society
    Details
    Publication Date: 2019-10-01
    Description: 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.
    Print ISSN: 0882-8156
    Electronic ISSN: 1520-0434
    Topics: Geography , Physics
    Published by American Meteorological Society
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