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Morphologic Investigation of Thunderstorm Initiates and GIS Attributes with Testing for Improved Operational Nowcasting of Thunderstorms & their Severity in New Jersey Dr. Paul Croft1, Alan Cope2, Danielle Fadeski3, Alexis Ottati3, Jackie Parr3 Faculty Research Advisor 1 National Weather Service2 Undergraduate Student 3 To Improve… Skill Confidence Precision in Convective Forecasting Why Improve the convective initiation forecast? What We Forecast… What Really Happens… “…a 40% chance of showers and thunderstorms…” Convective Objectives Determine Convective Initiation patterns PHI CWA and nearby region Movement, Intensity and Coverage Use online database to assist in enhanced operational forecasting of thunderstorm initiation, coverage, and severity in real-time Establish operational archive and forecast database http://hurri.kean.edu/~keancast/thunder/thunder.html Data Collection & Methods Study Period: 2000 – 2010 10 Summer Seasons: June, July & August (one test season) Mapped daily radar every 3 hours between 12 UTC and 00 UTC Recorded cell/area radar intensities of 30 & 50 dBz Classification of each day and identify initiation locations/patterns 500mb flow, surface synoptic pattern, and combinations of the two Southwest Warm Front Event Contaminate Classification of Convective Activity for 1200 – 0000 UTC Null Event Contaminate Null After 15 UTC Before 15 UTC No Activity Research to Operations: Thunder Dome Preferred locations of initiation from archive Empirical probabilities of occurrence developed Critical threshold values and field patterns associated with activity Discern an “E” from “C” or “Null” day with greater confidence 500 flow Sfc Synoptic Probabilities Locations CDC http://hurri.kean.edu/~keancast/thunder/thunder.html Diagnostics Pattern of parameters Causative Factors Building an Operational Conceptual Model Determine 500 mb flow type (e.g., West flow cases) 500 mb flow- WEST Day Type Sample Size % Chance Event Contaminate Null 79 63 68 38% 30% 32% 68% chance of initiation to occur with West flow Forecasting with Operational Conceptual Model Determine Surface Feature (e.g., Cold Front) Surface Feature- COLD FRONT Day Type Sample Size % Chance Event Contaminate Null 85 78 25 45% 41% 13% 86% chance of initiation to occur with surface cold front Applying the Operational Conceptual Model Using a combination (500mb+Surface Feature) e.g., West flow and Cold Front West-Cold font Day Type Sample Size % Chance Event Contaminate Null 27 21 10 47% 36% 17% preferred region for initiation for event cold front and 500 mb flow 83% chance of initiation with W-CF combination Contaminate cold front cases show no preference for initiation location Use of Diagnostic Patterns/Thresholds… 32% Chance Event PWAT Event 64% Chance Contaminate PWAT Contaminate 4% Chance Null PWAT Null Operational Testing & Verification Outline areas of initiation; cells, areas, or lines & where for severe Indicate whether forecast day of interest will be: E, C, N & if Severe Date/Type: June 1, 2009/Event How successful? Time of Forecast Obs/Predict Event Previous Afternoon 2010 Success Rate Contaminate88% Null Event 7 Early Morning 2010 3 79% 0 Contaminate 2 Early Morning 2009 7 85% 0 Null 4 Early Morning 2009/2010 0 81% 4 1200 UTC 500 mb flow: NW Number sequence of cell initiation Sfc Pattern: High P Severe: Yes Student: Match location to highest MOS POP axis & compare with gridded/zone Lightning Data STP for Coverage Severe versus Non-Severe Develop a Lightning Climatology Event Days, SW Flow Event Days, NW Flow Can break down hourly to show diurnal evolution… Can assist in verification and determining coverage/impacts… What’s the pattern in time? Event Days, SW Flow Event Days, NW Flow What’s the Coverage of Convective Activity? (short term forecasting: 0-6h & 6-12h) Storm Total Precipitation (STP) Consider the first (12-18z) and second (18-00z) halves of the day See progression/development of cells after initiation locations Mapped values from website products 0.1 inch signifies “likely” precipitation related to day’s convection 1.0+ inches suggest thunderstorm with heavy rainfall and intensity/severity Composites of Coverage/Intensity Suggests greater risk regions Amounts and possible severe storms What about probability/location of Severity? 48.5 % Severe Half E-COLD create Severe Weather 25.6% Severe One-fourth C-COLD create severe weather Diagnosing Events: Non-Severe vs. Severe Non-Severe Severe Omega at 700mb for Cold Front EVENT days: 00-09 Non-Severe Omega at 700mb for Cold Front CONTAMINATE days: 00-09 Severe GIS tie-in to Models & NDFD: Explaining Convection Use high resolution GIS-based grid with 1-km grid of study region with details of the forecast region and locations Relate specific physiographic features in the area to the preferred locations of convective initiation and its severity GIS grid calculations focus on land use and land cover, elevation, distance to coast, and slope and can be related to model output Risk assessment and management; warning specificity & public information statements; visualizations in time and space Automation and animation for response planning/preparation GIS Assisted Convective Forecasting If we know the characteristics of the Grid Box (Elevation, Land Cover, Population, etc.) If we know the synoptic regime & 500mb Flow (SW CF, etc.) Combine this information with CDC composite variable or parameter values (PWAT, Omega, etc.) GIS Assisted Prediction of Convective Initiation characteristics, impacts, & risks Summary & Conclusions Comprehensive Prediction of Convective Initiation haveWhere, the ability to improve… We know: Who, We What, How, When, & Why of initiation We can: Distinguish CoverageSkill and Intensity/Severity Confidence Now Provide: Operational Products Precision with Online Archive Now Identify: Operational Conceptual Model & Cause/Effect in Convective Forecasting Next: Refine, Enhance, Automate (GIS-based radar data) Future steps: GIS-grid assisted forecasting Future purposes: Risk assessment and management Acknowledgements Thanks to the Kean University Department of Geology & Meteorology Faculty & Staff, Student Majors, and Adam Gonsiewski, undergraduate student of Millersville University for their assistance with this project. This presentation was prepared by Kean University and the National Weather Service under a sub-award with the University Corporation for Atmospheric Research (UCAR) under Cooperative Agreement with the National Oceanic and Atmospheric Administration (NOAA), U.S. Department of Commerce (DOC).