20.500.12556/RUL-164588 Ensemble weather forecast post-processing with a flexible probabilistic neural network approach Ensemble forecast post-processing is a necessary step in producing accurate probabilistic forecasts. Many post-processing methods operate by estimating the parameters of a predetermined probability distribution; others operate on a per-lead-time or per-station basis. All of the aforementioned factors either limit the expressive power of the methods in question or require additional models, one for each lead time and station. We propose a novel, neural network-based method that produces forecasts for all lead times jointly and requires a single model for all stations. We incorporate normalizing spline flows as flexible parametric distribution estimators, which enables us to model complex forecast distributions. Furthermore, we demonstrate the effectiveness of our method in the context of the EUPPBench benchmark, where we conduct 2-m temperature forecast post-processing for stations in a subregion of Europe. We show that our novel method exhibits state-of-the-art performance on the benchmark, improving upon other well-performing entries. Additionally, by providing a detailed comparison of three variants of our novel post-processing method, we elucidate the reasons why our method outperforms per-lead-time-based approaches and approaches with distributional assumptions. ensemble forecast machine learning post-processing napovedi ansambla strojno učenje poprocesiranje true false true Angleški jezik Slovenski jezik Članek v reviji 2024-11-04 12:08:21 2024-11-04 12:08:23 2024-11-27 03:50:46 0000-00-00 00:00:00 2024 0 0 Str. 4156–4177 iss. 764, pt. A Vol. 150 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.85: 551.515 0035-9009 10.1002/qj.4809 213518083 Quart_J_Royal_Meteoro_Soc_-_2024_-_Mlakar_-_Ensemble_weather_forecast_post‐processing_with_a_flexible_probabilistic_neural.pdf Quart_J_Royal_Meteoro_Soc_-_2024_-_Mlakar_-_Ensemble_weather_forecast_post‐processing_with_a_flexible_probabilistic_neural.pdf 1 1A6E659BBAE9ABF5697D4928E3FEAEF1 42de27d01036d1a68af72d4a89adfc6b3c69f90c1429776552d41a87d2344510 5ea93fce-abe6-11ef-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=196190 https://rmets.onlinelibrary.wiley.com/doi/10.1002/qj.4809 0 c6eb044f-9a9c-11ef-b232-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=195119 Fakulteta za računalništvo in informatiko 0 0 0