edu.ucar.opensky::articles:26352 UCAR/NCAR - Library OpenSky Support PO Box 3000 Boulder CO 80307-3000 opensky@ucar.edu http://opensky.ucar.edu/ homepage pointOfContact 2025-12-24T19:30:36.116295 ISO 19115-2 Geographic Information - Metadata - Part 2: Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) https://n2t.org/ark:/85065/d7cr5zcj https://doi.org/10.48550/arXiv.1603.07285 https Related Preprint #1 A guide to convolution arithmetic for deep learning Deep learning forecast uncertainty for precipitation over the western United States 2023-06-01T00:00:00Z publication https://n2t.org/ark:/85065/d7cr5zcj https://doi.org/10.1175/MWR-D-22-0268.1 Hu, W. author Ghazvinian, M. author Chapman, William author Sengupta, A. author Ralph, F. M. author Delle Monache, L. author UCAR/NCAR - Library publisher Reliably quantifying uncertainty in precipitation forecasts remains a critical challenge. This work examines the application of a deep learning (DL) architecture, Unet, for postprocessing deterministic numerical weather predictions of precipitation to improve their skills and for deriving forecast uncertainty. Daily accumulated 0-4-day precipitation fore-casts are generated from a 34-yr reforecast based on the West Weather Research and Forecasting (West-WRF) mesoscale model, developed by the Center for Western Weather and Water Extremes. The Unet learns the distributional parameters associated with a censored, shifted gamma distribution. In addition, the DL framework is tested against state-of-the-art benchmark methods, including an analog ensemble, nonhomogeneous regression, and mixed-type meta-Gaussian distribu-tion. These methods are evaluated over four years of data and the western United States. The Unet outperforms the benchmark methods at all lead times as measured by continuous ranked probability and Brier skill scores. The Unet also produces a reliable estimation of forecast uncertainty, as measured by binned spread-skill relationship diagrams. Addition-ally, the Unet has the best performance for extreme events (i.e., the 95th and 99th percentiles of the distribution) and for these cases, its performance improves as more training data are available. SIGNIFICANCE STATEMENT: Accurate precipitation forecasts are critical for social and economic sectors. They also play an important role in our daily activity planning. The objective of this research is to investigate how to use a deep learning architecture to postprocess high-resolution (4 km) precipitation forecasts and generate accurate and reliable forecasts with quantified uncertainty. The proposed approach performs well with extreme cases and its per-formance improves as more data are available in training. 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