LAMP Upgraded Convection and Total Lightning Probability and “Potential” Guidance for the Conterminous United States

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.

[1]  Kenneth L. Cummins,et al.  A Combined TOA/MDF Technology Upgrade of the U.S. National Lightning Detection Network , 1998 .

[2]  Jerome P. Charba,et al.  Quality control of gridded national radar reflectivity data , 2005 .

[3]  Operational 2-h thunderstorm guidance forecasts to 24 hours on a 20-km grid , 2009 .

[4]  Eric C. Bruning,et al.  Initial Geostationary Lightning Mapper Observations , 2019, Geophysical Research Letters.

[5]  Scott D. Rudlosky,et al.  Evaluating ENTLN performance relative to TRMM/LIS , 2015 .

[6]  R. M. Reap.  Climatological Characteristics and Objective Prediction of Thunderstorms over Alaska , 1991 .

[7]  Bob Glahn,et al.  A LAMP–HRRR MELD for Improved Aviation Guidance , 2017 .

[8]  Conrad L. Ziegler,et al.  The Implementation of an Explicit Charging and Discharge Lightning Scheme within the WRF-ARW Model: Benchmark Simulations of a Continental Squall Line, a Tropical Cyclone, and a Winter Storm , 2013 .

[9]  GFS-BASED MOS THUNDERSTORM GUIDANCE FOR ALASKA , 2007 .

[10]  J. Schaefer.  The critical success index as an indicator of Warning skill , 1990 .

[11]  Jerome P. Charba,et al.  High-Resolution GFS-Based MOS Quantitative Precipitation Forecasts on a 4-km Grid , 2011 .

[12]  Jean-Pierre Pinty,et al.  Simulation of a supercellular storm using a three‐dimensional mesoscale model with an explicit lightning flash scheme , 2007 .

[13]  Raúl E. López,et al.  Lightning Casualties and Damages in the United States from 1959 to 1994 , 2000 .

[14]  D. Dowell,et al.  Numerical Simulations of Lightning and Storm Charge of the 29–30 May 2004 Geary, Oklahoma, Supercell Thunderstorm Using EnKF Mobile Radar Data Assimilation , 2014 .

[15]  Kristin M. Calhoun,et al.  Multi-Radar Multi-Sensor (MRMS) Severe Weather and Aviation Products: Initial Operating Capabilities , 2016 .

[16]  Timothy J. Lang,et al.  Relationships between Convective Storm Kinematics, Precipitation, and Lightning , 2002 .

[17]  Vladimir A. Rakov,et al.  Performance characteristics of the ENTLN evaluated using rocket-triggered lightning data , 2015 .

[18]  Judy E. Ghirardelli,et al.  The Meteorological Development Laboratory’s Aviation Weather Prediction System , 2010 .

[19]  J. Charba,et al.  Regionalization in Fine-Grid GFS MOS 6-h Quantitative Precipitation Forecasts , 2011 .

[20]  William J. Koshak,et al.  The GOES-R GeoStationary Lightning Mapper (GLM) , 2012 .

[21]  John Krause,et al.  A Simple Algorithm to Discriminate between Meteorological and Nonmeteorological Radar Echoes , 2016 .

[22]  Jian Zhang,et al.  Weather Radar Coverage over the Contiguous United States , 2002 .

[23]  Paul R. Krehbiel,et al.  The Timing of Cloud-to-Ground Lightning Relative to Total Lightning Activity , 2011 .

[24]  H. Glahn,et al.  A Local AFOS MOS Program (LAMP) and its Application to Wind Prediction , 1986 .

[25]  J. Charba.  Supplemental Automated Quality Control of MRMS Reflectivity Products for LAMP Convection and Lightning Forecast Guidance Applications , 2017 .

[26]  V. Rakov,et al.  An Update on the Performance Characteristics of the NLDN , 2014 .

[27]  Donald S. Foster,et al.  Automated 12–36 Hour Probability Forecasts of Thunderstorms and Severe Local Storms , 1979 .

[28]  S. Goodman,et al.  Forecasting Lightning Threat Using Cloud-Resolving Model Simulations , 2009 .

[29]  K. Cummins,et al.  The Intracloud Lightning Fraction in the Contiguous United States , 2017 .

[30]  Patrick King,et al.  Investigating the Potential of Using Radar Echo Reflectivity to Nowcast Cloud-to-Ground Lightning Initiation over Southern Ontario , 2010 .

[31]  Richard E. Orville,et al.  Development of the National Lightning Detection Network , 2008 .

[32]  Boris Katz,et al.  Recent Changes Implemented into the Global Forecast System at NMC , 1991 .

[33]  A. Clark,et al.  Impact of Storm-Scale Lightning Data Assimilation on WRF-ARW Precipitation Forecasts during the 2013 Warm Season over the Contiguous United States , 2015 .

[34]  Tsutomu Takahashi,et al.  Riming Electrification as a Charge Generation Mechanism in Thunderstorms , 1978 .

[35]  William R. Burrows,et al.  Warm Season Lightning Probability Prediction for Canada and the Northern United States , 2005 .

[36]  V. Chandrasekar,et al.  A Semisupervised Robust Hydrometeor Classification Method for Dual-Polarization Radar Applications , 2015 .

[37]  Jerry M. Straka,et al.  Charge structure and lightning sensitivity in a simulated multicell thunderstorm , 2005 .

[38]  G. Grell,et al.  A North American Hourly Assimilation and Model Forecast Cycle: The Rapid Refresh , 2016 .

[39]  Lawrence D. Carey,et al.  Electrical and multiparameter radar observations of a severe hailstorm , 1998 .

[40]  B. Lynn,et al.  An Evaluation of the Efficacy of Using Observed Lightning to Improve Convective Lightning Forecasts , 2015 .

[41]  W. Briggs.  Statistical Methods in the Atmospheric Sciences , 2007 .

[42]  Lawrence D. Carey,et al.  The Relationship between Precipitation and Lightning in Tropical Island Convection: A C-Band Polarimetric Radar Study , 2000 .

[43]  Wolfgang Schulz,et al.  Lightning locating systems: Insights on characteristics and validation techniques , 2015 .

[44]  P. Krehbiel,et al.  Accuracy of the Lightning Mapping Array , 2003 .

[45]  M. Murphy.  8.2 CLOUD LIGHTNING PERFORMANCE AND CLIMATOLOGY OF THE U.S. BASED ON THE UPGRADED U.S. NATIONAL LIGHTNING DETECTION NETWORK , 2015 .

[46]  H. Fuelberg,et al.  A Perfect Prognosis Scheme for Forecasting Warm-Season Lightning over Florida , 2008 .

[47]  W. Gallus,et al.  Neighborhood-Based Verification of Precipitation Forecasts from Convection-Allowing NCAR WRF Model Simulations and the Operational NAM , 2010 .

[48]  E. Mansell,et al.  Numerically Simulated Electrification and Lightning of the 29 June 2000 STEPS Supercell Storm , 2006 .

[49]  M. Marchand.  Assimilation of Lightning Data Using a Nudging Method Involving Low-Level Warming , 2014 .

[50]  Probabilistic Lightning Forecast Guidance for Aviation , 2004 .

[51]  Jian Zhang,et al.  A Real-Time Algorithm for Merging Radar QPEs with Rain Gauge Observations and Orographic Precipitation Climatology , 2014 .

[52]  H. Glahn,et al.  Use of Model Output Statistics for Predicting Ceiling Height , 1972 .

[53]  M. D. Tran,et al.  Evaluation of ENTLN Performance Characteristics Based on the Ground Truth Natural and Rocket‐Triggered Lightning Data Acquired in Florida , 2017 .

[54]  G. Brier.  VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY , 1950 .

[55]  E. Krider,et al.  Lightning Direction-Finding Systems for Forest Fire Detection , 1980 .

[56]  A. Fierro,et al.  Application of a Lightning Data Assimilation Technique in the WRF-ARW Model at Cloud-Resolving Scales for the Tornado Outbreak of 24 May 2011 , 2012 .

[57]  Lin Tang,et al.  A Physically Based Precipitation–Nonprecipitation Radar Echo Classifier Using Polarimetric and Environmental Data in a Real-Time National System , 2014 .

[58]  Louis J. Wicker,et al.  Electrification and Lightning in an Idealized Boundary-Crossing Supercell Simulation of 2 June 1995* , 2004 .

[59]  Jeff W. Brogden,et al.  Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities , 2016 .

[60]  Lawrence D. Carey,et al.  A multiparameter radar case study of the microphysical and kinematic evolution of a lightning producing storm , 1996 .

[61]  E. Mansell,et al.  Simulated three‐dimensional branched lightning in a numerical thunderstorm model , 2002 .

[62]  Lawrence D. Carey,et al.  Radar Nowcasting of Cloud-to-Ground Lightning over Houston, Texas , 2011 .

[63]  Steven J. Goodman,et al.  Three Years of TRMM Precipitation Features. Part I: Radar, Radiometric, and Lightning Characteristics , 2005 .

[64]  Craig S. Schwartz,et al.  Generating Probabilistic Forecasts from Convection-Allowing Ensembles Using Neighborhood Approaches: A Review and Recommendations , 2017 .

[65]  H. Glahn,et al.  The Use of Model Output Statistics (MOS) in Objective Weather Forecasting , 1972 .

[66]  Donald W. Burgess,et al.  Recording, Archiving, and Using WSR-88D Data , 1993 .

[67]  L. Carey,et al.  2 USING WSR-88 D REFLECTIVITY FOR THE PREDICTION OF CLOUD-TO-GROUND LIGHTNING : A CENTRAL NORTH CAROLINA STUDY , 2004 .

[68]  S. J. Weiss,et al.  Assessing Advances in the Assimilation of Radar Data and Other Mesoscale Observations within a Collaborative Forecasting-Research Environment , 2010 .

[69]  K. Cummins,et al.  Combined Satellite- and Surface-Based Estimation of the Intracloud Cloud-to-Ground Lightning Ratio over the Continental United States , 2001 .

[70]  Timothy D. Crum,et al.  The WSR-88D and the WSR-88D Operational Support Facility , 1993 .

[71]  Eric Rogers.  The NCEP North American Mesoscale Modeling System: Final Eta model/analysis changes and preliminary experiments using the WRF-NMM , 2005 .

[72]  Robert A. Clark,et al.  Vertically Integrated Liquid Water—A New Analysis Tool , 1972 .