Working papers
Mayer, M.J., Baran, A., Lerch, S., Horat, N., Yang, D., and Baran, S. (2025). Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods. arXiv:2508.15508.
Lerch, S., Schulz, B., Hess, R., Möller, A., Primo, C., Trepte, S., and Theis, S. (2025). Operational convection-permitting COSMO/ICON ensemble predictions at observation sites (CIENS). arXiv:2508.03845. Code on Github. Data on KITOpen.
Gneiting, T., Biegert, T., Kraus, K., Walz, E.-M., Jordan, A. I., and Lerch, S. (2025). Probabilistic measures afford fair comparisons of AIWP and NWP model output. arXiv:2506.03744. Code on Github.
Chen, J., Lerch, S., Schienle, M., Serafin, T., and Weron, R. (2025). Probabilistic intraday electricity price forecasting using generative machine learning. arXiv:2506.00044. Code on Github.
Mockert, F., Grams, C.M., Lerch, S., and Quinting, J. (2025). Windows of opportunity in subseasonal weather regime forecasting: A statistical-dynamical approach. arXiv:2505.02680. Code on Github.
Chen, J., Höhlein, K., and Lerch, S. (2025). Learning low-dimensional representations of ensemble forecast fields using autoencoder-based methods. arXiv:2502.04409. Code on Github.
Primo, C., Schulz, B., Lerch, S., and Hess, R. (2024). Comparison of Model Output Statistics and Neural Networks to Postprocess Wind Gusts. arXiv:2401.11896.
Schulz, B. and Lerch, S. (2022). Aggregating distribution forecasts from deep ensembles. arXiv:2204.02291. Code on Github.
Publications
Uttarwar, S.B., Lerch, S., Avesani, D., and Majone, B. (2025). Performance assessment of neural network models for seasonal weather forecast postprocessing in the Alpine region. Advances in Water Resources, in press. Code on Github.
Wilhelm, J., Quinting, J., Burda, M., Holborn, S., Ehret, U., Pena Sanchez, I., Lerch, S., Meyer, J., Verfürth, B., and Knippertz, P. (2025). TEEMLEAP -- A new testbed for exploring machine learning in atmospheric prediction for research and education. Journal of Advances in Modeling Earth Systems, 17, e2024MS004881. Code on Gitlab.
Bülte, C., Horat, N., Quinting, J., and Lerch, S. (2025). Uncertainty quantification for data-driven weather models. Artificial Intelligence for the Earth Systems, accepted for publication. Code on Github.
Horat, N., Klerings, S., and Lerch, S. (2025). Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning. Advances in Atmospheric Sciences, 42, 297−312. Code on Github.
Mockert, F., Grams, C.M., Lerch, S., Osman, M., and Quinting, J. (2024). Multivariate post-processing of probabilistic sub-seasonal weather regime forecasts. Quarterly Journal of the Royal Meteorological Society, 150, 4771-4787. Code on Github.
Feik, M., Lerch, S. and Stühmer, J. (2024). Graph Neural Networks and Spatial Information Learning for Post-Processing Ensemble Weather Forecasts. International Conference on Machine Learning (ICML) 2024 - Machine Learning for Earth System Modeling Workshop. Code on Github.
Kiefer, S., Lerch, S., Ludwig, P. and Pinto, J. (2024). Random Forests’ Postprocessing Capability of Enhancing Predictive Skill on Subseasonal Timescales - a Flow-Dependent View on Central European Winter Weather. Artificial Intelligence for the Earth Systems, 3, e240014.
Song, M., Yang, D., Lerch, S., Xia, X., Yagli, G.M., Bright, J.M., Shen, Y., Liu, B., Liu, X., and Mayer, M.J. (2024). Non-crossing quantile regression neural network as a calibration tool for ensemble weather forecasts. Advances in Atmospheric Sciences, 41, 417–1437.
Chen, J., Janke, T., Steinke, F., and Lerch, S. (2024). Generative machine learning methods for multivariate ensemble post-processing. Annals of Applied Statistics, 18, 159-183. Code on Github.
Höhlein, K., Schulz, B., Westermann, R. and Lerch, S. (2024). Postprocessing of Ensemble Weather Forecasts Using Permutation-invariant Neural Networks. Artificial Intelligence for the Earth Systems, 3, e230070. Code on Github.
Bracher, J., Koster, N., Krüger, F., and Lerch, S. (2024). Learning to forecast: The probabilistic time series forecasting challenge. The American Statistician, 78, 115-127. Code on Gitlab.
Horat, N. and Lerch, S. (2024). Deep learning for post-processing global probabilistic forecasts on sub-seasonal time scales. Monthly Weather Review, 152, 667-687. Code on Github.
Bracher, J., Rüter, L., Krüger, F., Lerch, S. and Schienle, M. (2023). Direction Augmentation in the Evaluation of Armed Conflict Predictions. International Interactions, 49, 989-1004. Preprint available on arXiv. Code on Github.
Kiefer, S., Lerch, S., Ludwig, P., and Pinto, J.G. (2023). Can Machine Learning Models be a Suitable Tool for Predicting Central European Cold Winter Weather on Subseasonal to Seasonal Timescales? Artificial Intelligence for the Earth Systems, 2, e230020. Code on Github.
Demaeyer, J., Bhend, J., Lerch, S., Primo, C., Van Schaeybroeck, B., Atencia, A. Ben Bouallègue, Z., Chen, J., Dabernig, M., Evans, G., Faganeli Pucer, J., Hooper, B., Horat, N., et al. (7 more) (2023). The EUPPBench postprocessing benchmark dataset v1.0. Earth System Science Data, 15, 2635–2653. Code on Github.
Lakatos, M., Lerch, S., Hemri, S., and Baran, S. (2023). Comparison of multivariate post-processing methods using global ECMWF ensemble forecasts. Quarterly Journal of the Royal Meteorological Society, 149, 856-877.
Gneiting, T., Lerch, S. and Schulz, B. (2023). Probabilistic solar forecasting: Benchmarks, post-processing, verification. Solar Energy, 252, 72-80. Code on Github.
Gneiting, T., Wolffram, D., Resin, J., Kraus, K., Bracher, J., Dimitriadis, T., Hagenmeyer, V., Jordan, A.I., Lerch, S., Phipps, K. and Schienle, M. (2023). Model diagnostics and forecast evaluation for quantiles. Annual Review of Statistics and Its Application, 10, 597-621. Code on Github.
Silini, R., Lerch, S., Mastrantonas, N., Kantz, H., Barreiro, M., and Masoller, C. (2022). Improving the prediction of the Madden-Julian Oscillation of the ECMWF model by post-processing. Earth System Dynamics, 13, 1157-1165.
Phipps, K., Lerch, S., Andersson, M., Mikut, R., Hagenmeyer, V. and Ludwig, N. (2022). Evaluating ensemble post-processing for wind power forecasts. Wind Energy, 25(8):1379-1405. Code on Github.
Lerch, S. and Polsterer, K.L. (2022). Convolutional autoencoders for spatially-informed ensemble post-processing. International Conference on Learning Representations (ICLR) 2022 - AI for Earth and Space Science Workshop. Code on Github.
Schulz, B. and Lerch, S. (2022). Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison. Monthly Weather Review, 150(1): 235-257. Data on KITOpen. Code on Github.
Chapman, W.E., Delle Monache, L., Alessandrini, S., Subramanian, A., Ralph, F.M., Xie, S.-P., Lerch, S. and Hayatbini, N. (2022). Probabilistic predictions from deterministic atmospheric river forecasts with deep learning. Monthly Weather Review, 150(1): 215-234. Code on Github.
Krüger, F., Lerch, S., Thorarinsdottir, T.L., and Gneiting, T. (2021). Predictive Inference Based on Markov Chain Monte Carlo Output. International Statistical Review, 89(2): 274-301. Code on GitHub.
Craig, G.C., Fink, A.H., Hoose, C., Janjic, T., Knippertz, P., Laurian, A., Lerch, S., Mayer, B., Miltenberger, A., Redl, R., Riemer, M., Tempest, K.I., and Wirth. V. (2021). Waves to Weather: Exploring the limits of predictability of weather. Bulletin of the American Meteorological Society, 102(11): E2151-E2164.
Schulz, B., El Ayari, M., Lerch, S. and Baran, S. (2021). Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting. Solar Energy, 220: 1016-1031.
Haupt, S.E., Chapman, W., Adams, S.V., Kirkwood, C., Hosking, J.S., Robinson, N.H., Lerch, S., and Subramanian, A.C. (2021). Towards implementing artificial intelligence post-processing in weather and climate: proposed actions from the Oxford 2019 workshop. Philosophical Transactions of the Royal Society A, 379, 20200091. Code on Github.
Vannitsem, S., Bremnes, J.B., Demaeyer, J., Evans, G.R., Flowerdew, J., Hemri, S., Lerch, S., Roberts, N., et al. (16 more) (2021). Statistical Postprocessing for Weather Forecasts - Review, Challenges and Avenues in a Big Data World. Bulletin of the American Meteorological Society, 102(3): E681–E699.
Baran, A., Lerch, S., El Ayari, M. and Baran, S. (2021). Machine learning for total cloud cover prediction. Neural Computing and Applications, 33: 2605–2620.
Lerch, S., Baran, S., Möller, A., Groß, J., Schefzik, R., Hemri, S., and Graeter, M. (2020). Simulation-based comparison of multivariate ensemble post-processing methods. Nonlinear Processes in Geophysics, 27: 349–371. Code on GitHub.
Lang, M. N., Lerch, S., Mayr, G. J., Simon, T., Stauffer, R., and Zeileis, A. (2020). Remember the past: A comparison of time-adaptive training schemes for non-homogeneous regression. Nonlinear Processes in Geophysics, 27: 23–34.
Jordan, A., Krüger, F., and Lerch, S. (2019). Evaluating probabilistic forecasts with scoringRules. Journal of Statistical Software, 90(12): 1–37. Code on GitHub.
Rasp, S. and Lerch, S. (2018). Neural networks for post-processing ensemble weather forecasts. Monthly Weather Review, 146(11): 3885–3900. Code on GitHub. Press coverage: KIT, CampusReport, InnovationOrigins, ingenieur.de.
Pantillon, F., Lerch, S., Knippertz, P., and Corsmeier, U. (2018). Forecasting wind gusts in winter storms using a calibrated convection-permitting ensemble. Quarterly Journal of the Royal Meteorological Society, 144(715): 1864–1881.
Arnault, J., Rummler, T., Baur, F., Lerch, S., Wagner, S., Fersch, B., Zhang, Z., Kerandi. N., Keil, C., and Kunstmann, H. (2018). Precipitation sensitivity to the uncertainty of terrestrial water flow in WRF-Hydro - An ensemble analysis for Central Europe. Journal of Hydrometeorology, 19(6): 1007–1025.
Baran, S. and Lerch, S. (2018). Combining predictive distributions for the statistical post-processing of ensemble forecasts. International Journal of Forecasting, 34(3): 477–496. arXiv preprint
Lerch, S., Thorarinsdottir, T.L., Ravazzolo, F., and Gneiting, T. (2017). Forecaster's Dilemma: Extreme Events and Forecast Evaluation. Statistical Science, 32(1): 106–127. Press coverage: Die Welt, Badische Neueste Nachrichten, Radio Regenbogen.
Lerch, S. and Baran, S. (2017). Similarity-based semilocal estimation of post-processing models. Journal of the Royal Statistical Society, Series C (Applied Statistics), 66(1): 29–51. arXiv preprint, preprint versions of figures (in color)
Baran, S. and Lerch, S. (2016). Mixture EMOS model for calibrating ensemble forecasts of wind speed. Environmetrics, 27(2): 116–130.
Baran, S. and Lerch, S. (2015). Log-normal distribution based EMOS models for probabilistic wind speed forecasting. Quarterly Journal of the Royal Meteorological Society, 141(691): 2289–2299.
Lerch, S. and Thorarinsdottir, T.L. (2013). Comparison of non-homogeneous regression models for probabilistic wind speed forecasting. Tellus A, 65: 21206.
Comments, outreach articles and other non-peer-reviewed publications
Horat, N. and Lerch, S. (2023). The potential of deep learning for post-processing sub-seasonal weather forecasts. S2S Prediction Project Newsletter, 24, 5-8.
Polsterer, K.L., D'Isanto, A., and Lerch, S. (2021). From photometric redshifts to improved weather forecasts: an interdisciplinary view on machine learning in astronomy. Conference Proceedings: Astronomical Data Analysis Software and Systems (ADASS) 2020.
Hemri, S., Lerch, S., Taillardat, M., Vannitsem, S. and Wilks, D.S. (2020). Preface to the special issue "Advances in post-processing and blending of deterministic and ensemble forecasts". Nonlinear Processes in Geophysics, 27, 519-521.
Dorninger, M., Ghelli, A. and Lerch, S. (2020). Editorial: Special collection on recent developments and application examples on forecast verification. Meteorological Applications, 27(4): e1934.
Lerch, S. (2019). Bessere Wettervorhersagen dank künstlicher Intelligenz (in German). ANG-Fokus, Vereinsheft der Aargauischen Naturforschenden Gesellschaft, 2019-2, 28-31.
Gneiting, T. and Lerch, S. (2017). Comments on: 'Random-projection ensemble classification' by T.I. Cannings and R.J. Samworth. Journal of the Royal Statistical Society Series B, 79(4): 1013.
R packages
Jordan, A., Krüger, F. and Lerch, S. (2016). R package scoringRules: Scoring rules for parametric and simulated distribution forecasts. CRAN.
Yuen, R.A., Baran, S., Fraley, C., Gneiting, T., Lerch, S., Scheuerer, M. and Thorarinsdottir, T.L. (2013). R package ensembleMOS: Ensemble Model Output Statistics. CRAN, contributions as author since version 0.8, July 2017.
Theses
Lerch, S. (2016). Probabilistic forecasting and comparative model assessment, with focus on extreme events. Ph.D. thesis. Karlsruhe Institute of Technology.
Last updated: August 25, 2025