| Neural networks for postprocessing ensemble weather forecasts S Rasp, S Lerch Monthly Weather Review 146 (11), 3885-3900, 2018 | 375 | 2018 |
| Evaluating probabilistic forecasts with scoringRules A Jordan, F Krüger, S Lerch arXiv preprint arXiv:1709.04743, 2017 | 240 | 2017 |
| Statistical postprocessing for weather forecasts–review, challenges and avenues in a big data world S Vannitsem, JB Bremnes, J Demaeyer, GR Evans, J Flowerdew, S Hemri, ... Bulletin of the American Meteorological Society, 1-44, 2020 | 190 | 2020 |
| Forecaster's dilemma: extreme events and forecast evaluation S Lerch, TL Thorarinsdottir, F Ravazzolo, T Gneiting Statistical Science, 106-127, 2017 | 169 | 2017 |
| Log‐normal distribution based Ensemble Model Output Statistics models for probabilistic wind‐speed forecasting S Baran, S Lerch Quarterly Journal of the Royal Meteorological Society 141 (691), 2289-2299, 2015 | 125 | 2015 |
| Comparison of non-homogeneous regression models for probabilistic wind speed forecasting S Lerch, TL Thorarinsdottir Tellus A: Dynamic Meteorology and Oceanography 65 (1), 21206, 2013 | 114 | 2013 |
| Predictive inference based on Markov chain Monte Carlo output F Krüger, S Lerch, T Thorarinsdottir, T Gneiting International Statistical Review 89 (2), 274-301, 2021 | 102 | 2021 |
| Mixture EMOS model for calibrating ensemble forecasts of wind speed S Baran, S Lerch Environmetrics 27 (2), 116-130, 2016 | 79 | 2016 |
| Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison B Schulz, S Lerch Monthly Weather Review 150 (1), 235-257, 2022 | 68 | 2022 |
| Combining predictive distributions for the statistical post-processing of ensemble forecasts S Baran, S Lerch International Journal of Forecasting 34 (3), 477-496, 2018 | 67 | 2018 |
| Towards implementing artificial intelligence post-processing in weather and climate: Proposed actions from the Oxford 2019 workshop SE Haupt, W Chapman, SV Adams, C Kirkwood, JS Hosking, ... Philosophical Transactions of the Royal Society A 379 (2194), 20200091, 2021 | 57 | 2021 |
| Similarity-based semilocal estimation of post-processing models S Lerch, S Baran Journal of the Royal Statistical Society Series C: Applied Statistics 66 (1 …, 2017 | 52 | 2017 |
| Precipitation sensitivity to the uncertainty of terrestrial water flow in WRF-Hydro: An ensemble analysis for central Europe J Arnault, T Rummler, F Baur, S Lerch, S Wagner, B Fersch, Z Zhang, ... Journal of Hydrometeorology 19 (6), 1007-1025, 2018 | 45 | 2018 |
| Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting B Schulz, M El Ayari, S Lerch, S Baran Solar Energy 220, 1016-1031, 2021 | 44 | 2021 |
| Probabilistic predictions from deterministic atmospheric river forecasts with deep learning WE Chapman, L Delle Monache, S Alessandrini, AC Subramanian, ... Monthly Weather Review 150 (1), 215-234, 2022 | 37 | 2022 |
| Remember the past: a comparison of time-adaptive training schemes for non-homogeneous regression MN Lang, S Lerch, GJ Mayr, T Simon, R Stauffer, A Zeileis Nonlinear Processes in Geophysics 27 (1), 23-34, 2020 | 36 | 2020 |
| Simulation-based comparison of multivariate ensemble post-processing methods S Lerch, S Baran, A Möller, J Groß, R Schefzik, S Hemri, M Graeter Nonlinear Processes in Geophysics 27 (2), 349-371, 2020 | 34 | 2020 |
| Machine learning for total cloud cover prediction A Baran, S Lerch, M El Ayari, S Baran Neural Computing and Applications 33 (7), 2605-2620, 2021 | 31 | 2021 |
| Forecasting wind gusts in winter storms using a calibrated convection‐permitting ensemble F Pantillon, S Lerch, P Knippertz, U Corsmeier Quarterly Journal of the Royal Meteorological Society 144 (715), 1864-1881, 2018 | 30 | 2018 |
| Probabilistic solar forecasting: Benchmarks, post-processing, verification T Gneiting, S Lerch, B Schulz Solar Energy 252, 72-80, 2023 | 25 | 2023 |
Sándor BaranProfessor, University of DebrecenVerified email at inf.unideb.hu
