Expertise
Probability forecasts and quantification of uncertainty.Weather and climate dynamics.
Extreme events and impact on society.
Interpretability and verification of AI models.
Affiliations
2024 - now Citadel, London, UK2018 - 2024 Vrije Universiteit Amsterdam, NL
2018 - 2024 Royal Netherlands Meteorological Institute, de Bilt, NL
Interviews
2023 Algemeen Dagblad (newspaper)2023 Nu.nl (newspaper)
2020 Nieuw Nederland Podcast (spotify)
Publications
van Straaten, C., Whan, K., Coumou, D., van den Hurk, B. & Schmeits, M. (2023) Correcting sub-seasonal forecast errors with an explainable ANN to understand misrepresented sources of predictability of European summer temperatures Artificial Intelligence for the Earth Systems, 10.1175/AIES-D-22-0047.1
van Straaten, C., Whan, K., Coumou, D., van den Hurk, B. & Schmeits, M. (2022) Using explainable machine learning forecasts to discover sub-seasonal drivers of high summer temperatures in western and central Europe Monthly Weather Review, 10.1175/MWR-D-21-0201.1
Tiggeloven, T., Couasnon, A., van Straaten, C., Muis, S. & Ward, P.J. (2021) Exploring deep learning capabilities for surge predictions in coastal areas. Nature Scientific Reports, 10.1038/s41598-021-96674-0
van Straaten, C., Whan, K., Coumou, D., van den Hurk, B. & Schmeits, M. (2020) The influence of aggregation and statistical post-processing on the sub-seasonal predictability of European temperatures. QJRMS, 10.1002/qj.3810
van Straaten, C., Whan, K. & Schmeits, M. (2018) Statistical postprocessing and multivariate structuring of high-resolution ensemble precipitation forecasts. Journal of Hydrometeorology, 10.1175/JHM-D-18-0105.1