edu.ucar.opensky::articles:43773 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-24T17:50:41.185454 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.net/ark:/85065/d78d01qk Evaluating Machine Learning–based probabilistic lightning forecasts using the HRRR: A comparison to three forecast baselines 2025-05-01T00:00:00Z publication https://n2t.net/ark:/85065/d78d01qk https://doi.org/10.1175/WAF-D-24-0140.1 Sobash, Ryan A. author Ahijevych, David author UCAR/NCAR - Library publisher Probabilistic forecasts of lightning for the CONUS were generated by postprocessing the HRRR with neural networks (NNs). These NN probability forecasts (NNPFs) were produced for HRRR forecasts in 2021&#8211;22 using NNs trained with 0000 UTC HRRR forecasts from 2019 to 2020 paired with &#8805;1 observed cloud-to-ground (CG1) flash as the target variable. CG1-NNPF skill was evaluated against three baselines: smoothed HRRR-based surrogate lightning forecasts, SPC probabilistic thunderstorm outlooks, and calibrated ensemble thunderstorm guidance from the High-Resolution Ensemble Forecast (HREF) system. The hourly maximum updraft speed (UP) diagnostic was the most skillful HRRR surrogate for predicting CG1, outperforming diagnostics such as hourly maximum lightning threat and updraft helicity. The UP forecasts were compared to the NNPFs by using the most skillful combination of UP threshold and smoothing length scale at each forecast hour. The 4- and 1-h CG1-NNPFs exceeded the skill of the UP forecasts in 2021 for nearly all forecast hours, with reduced skill differences overnight compared to the daytime. The NNPFs exhibited excellent reliability, with slight overforecasting of probabilities overnight. The NNPFs were more skillful than NOAA Storm Prediction Center (SPC) Thunderstorm Outlooks in both 2021 and 2022, especially overnight, while during the daytime, the SPC forecasts had similar or slightly greater skill. Finally, the NNPFs outperformed calibrated thunder guidance from the HREF system evaluated across forecasts in 2022. These findings imply that using NNs for thunderstorm prediction can improve upon existing non&#8211;machine learning baselines from deterministic and ensemble systems and may improve operational SPC thunderstorm forecasts. 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 <span style="font-family:Arial;font-size:10pt;font-style:normal;font-weight:normal;" data-sheets-root="1">Copyright 2025 American Meteorological Society (AMS).</span> None eng geoscientificInformation