edu.ucar.opensky::articles:26211 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-24T19:38:29.628401 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d7k93chx https://doi.org/10.7289/V5W9574V https Related Dataset #1 NOAA Next Generation Radar (NEXRAD) Level II Base Data The tornado probability algorithm: A probabilistic machine learning tornadic circulation detection algorithm 2023-03-01T00:00:00Z publication https://n2t.org/ark:/85065/d7k93chx https://doi.org/10.1175/WAF-D-22-0123.1 Sandmæl, T. N. author Smith, B. R. author Reinhart, A. E. author Schick, I. M. author Ake, M. C. author Madden, J. G. author Steeves, R. B. author Williams, S. S. author Elmore, K. L. author Meyer, Tiffany author UCAR/NCAR - Library publisher A new probabilistic tornado detection algorithm was developed to potentially replace the operational tornado detection algorithm (TDA) for the WSR-88D radar network. The tornado probability algorithm (TORP) uses a random forest machine learning technique to estimate a probability of tornado occurrence based on single-radar data, and is trained on 166 145 data points derived from 0.58-tilt radar data and storm reports from 2011 to 2016, of which 10.4% are tornadic. A variety of performance evaluation metrics show a generally good model performance for discriminating between tornadic and nontornadic points. When using a 50% probability threshold to decide whether the model is predicting a tornado or not, the probability of detection and false alarm ratio are 57% and 50%, respectively, showing high skill by several metrics and vastly outperforming the TDA. The model weaknesses include false alarms associated with poor-quality radial velocity data and greatly reduced performance when used in the western United States. Overall, TORP can provide real-time guidance for tornado warning decisions, which can increase forecaster confidence and encourage swift decision-making. It has the ability to condense a multitude of radar data into a concise object-based information readout that can be displayed in visualization software used by the National Weather Service, core partners, and researchers. UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact PDF Text theme Resource Type 2016-01-01T00:00:00Z publication Copyright 2023 American Meteorological Society (AMS). None eng geoscientificInformation