| Exploring deep learning capabilities for surge predictions in coastal areas T Tiggeloven, A Couasnon, C van Straaten, S Muis, PJ Ward Scientific reports 11 (1), 17224, 2021 | 59 | 2021 |
| Using explainable machine learning forecasts to discover subseasonal drivers of high summer temperatures in western and central Europe C Van Straaten, K Whan, D Coumou, B Van den Hurk, M Schmeits Monthly Weather Review 150 (5), 1115-1134, 2022 | 42 | 2022 |
| The influence of aggregation and statistical post‐processing on the subseasonal predictability of European temperatures C van Straaten, K Whan, D Coumou, B van den Hurk, M Schmeits Quarterly Journal of the Royal Meteorological Society 146 (731), 2654-2670, 2020 | 25 | 2020 |
| Statistical postprocessing and multivariate structuring of high-resolution ensemble precipitation forecasts C van Straaten, K Whan, M Schmeits Journal of Hydrometeorology 19 (11), 1815-1833, 2018 | 25 | 2018 |
| Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives S Materia, LP García, C van Straaten, S O, A Mamalakis, L Cavicchia, ... Wiley Interdisciplinary Reviews: Climate Change 15 (6), e914, 2024 | 12 | 2024 |
| Correcting Subseasonal Forecast Errors with an Explainable ANN to Understand Misrepresented Sources of Predictability of European Summer Temperatures C van Straaten, K Whan, D Coumou, B van den Hurk, M Schmeits Artificial Intelligence for the Earth Systems 2 (3), e220047, 2023 | 11 | 2023 |
| Artificial Intelligence for Prediction of Climate Extremes: State of the art, challenges and future perspectives S Materia, LP García, C van Straaten, A Mamalakis, L Cavicchia, ... arXiv preprint arXiv:2310.01944, 2023 | 7 | 2023 |
| Exploring deep learning capabilities for surge predictions in coastal areas, Sci. Rep., 11, 17224 T Tiggeloven, A Couasnon, C van Straaten, S Muis, PJ Ward | 7 | 2021 |
| Regional Modelling of Water Stress; Irrigation water requirement meets water availability in the Oum Er Rbia basin JW van Straaten | 4 | 2017 |
| Predicting food‐security crises in the Horn of Africa using machine learning T Busker, B van den Hurk, H de Moel, M van den Homberg, ... Earth's Future 12 (8), e2023EF004211, 2024 | 3 | 2024 |
| Improving sub-seasonal forecasts by correcting missing teleconnections using ANN-based post-processing C van Straaten, K Whan, D Coumou, B van den Hurk, M Schmeits EGU General Assembly Conference Abstracts, EGU22-1686, 2022 | 1 | 2022 |
| Disentangling wave-like atmospheric trends in the northern hemisphere midlatitudes C van Straaten, T Happé, F D'Andrea, D Coumou EGU24, 2024 | | 2024 |
| Machine Learning to improve and understand sub-seasonal forecasts of European temperature JW van Straaten | | 2023 |
| Strengthening gradients in the tropical west Pacific connect to European summer temperatures on sub-seasonal timescales C van Straaten, D Coumou, K Whan, B van den Hurk, M Schmeits Weather and Climate Dynamics Discussions 2023, 1-20, 2023 | | 2023 |
| Using explainable machine learning forecasts to discover sub-seasonal C van Straaten, K Whan, D Coumou, B van den Hurk, M Schmeits | | 2022 |
| Towards rapid surge predictions: Exploring deep learning approaches to predict temporal evolution of surge levels in coastal areas T Tiggeloven, A Couasnon, C van Straaten, S Muis, P Ward AGU Fall Meeting Abstracts 2021, A45J-1988, 2021 | | 2021 |
| Exploring deep learning approaches to predict hourly evolution of surge levels T Tiggeloven, A Couasnon, C van Straaten, S Muis, P Ward EGU General Assembly Conference Abstracts, EGU21-8638, 2021 | | 2021 |
