13 citations as recorded by crossref.

1. A two‐step machine‐learning approach to statistical post‐processing of weather forecasts for power generation Á. Baran & S. Baran 10.1002/qj.4635
2. Comparison of multivariate post‐processing methods using global ECMWF ensemble forecasts M. Lakatos et al. 10.1002/qj.4436
3. Improving categorical and continuous accuracy of precipitation forecasts by integrating Empirical Quantile Mapping and Bernoulli-Gamma-Gaussian distribution L. Li et al. 10.1016/j.atmosres.2023.107133
4. Truncated generalized extreme value distribution‐based ensemble model output statistics model for calibration of wind speed ensemble forecasts S. Baran et al. 10.1002/env.2678
5. Probabilistic solar forecasting: Benchmarks, post-processing, verification T. Gneiting et al. 10.1016/j.solener.2022.12.054
6. The EUPPBench postprocessing benchmark dataset v1.0 J. Demaeyer et al. 10.5194/essd-15-2635-2023
7. Ensemble forecasting for intraday electricity prices: Simulating trajectories M. Narajewski & F. Ziel 10.1016/j.apenergy.2020.115801
8. Statistical post‐processing of visibility ensemble forecasts S. Baran & M. Lakatos 10.1002/met.2157
9. Preface: Advances in post-processing and blending of deterministic and ensemble forecasts S. Hemri et al. 10.5194/npg-27-519-2020
10. Forecasting: theory and practice F. Petropoulos et al. 10.1016/j.ijforecast.2021.11.001
11. Generative machine learning methods for multivariate ensemble postprocessing J. Chen et al. 10.1214/23-AOAS1784
12. Spatially Coherent Postprocessing of Cloud Cover Ensemble Forecasts 10.1175/MWR-D-21-0046.1
13. Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting B. Schulz et al. 10.1016/j.solener.2021.03.023