Journal of Atmospheric and Oceanic Technology · 1993 · 1K citations · 0 references
Forecasting MethodologyEngineeringWeather ForecastingGeophysicsNumerical Weather PredictionData ScienceMeteorological MeasurementRadar Signal ProcessingReal-time Automated IdentificationHydrometeorologyMeteorologySynthetic Aperture RadarPredictive AnalyticsRadar ApplicationForecastingRadar ImagingRadarContiguous RegionThunderstorm IdentificationRadar Image ProcessingShort-term Forecasting
The methodology is grounded in concepts underlying thunderstorm detection and tracking. The study presents a real‑time automated system for identifying, tracking, and short‑term forecasting thunderstorms using volume‑scan weather radar data. Storms are defined as contiguous regions exceeding reflectivity and size thresholds, identified at discrete time steps, matched across times via an optimization scheme that handles mergers and splits, and their future position and size are forecast with a weighted linear fit to the track history. Performance of the detection and forecast was evaluated for the summer 1991 season, with results presented.
A methodology is presented for the real-time automated identification, tracking, and short-term forecasting of thunderstorms based on volume-scan weather radar data. The emphasis is on the concepts upon which the methodology is based. A “storm” is defined as a contiguous region exceeding thresholds for reflectivity and size. Storms defined in this way are identified at discrete time intervals. An optimization scheme is employed to match the storms at one time with those at the following time, with some geometric logic to deal with mergers and splits. The short-term forecast of both position and size is based on a weighted linear fit to the storm track history data. The performance of the detection and forecast were evaluated for the summer 1991 season, and the results are presented.