| WeatherBench: a benchmark data set for data‐driven weather forecasting S Rasp, PD Dueben, S Scher, JA Weyn, S Mouatadid, N Thuerey Journal of Advances in Modeling Earth Systems 12 (11), e2020MS002203, 2020 | 476 | 2020 |
| Using extreme learning machines for short-term urban water demand forecasting S Mouatadid, J Adamowski Urban water journal 14 (6), 630-638, 2017 | 110 | 2017 |
| Input selection and data-driven model performance optimization to predict the Standardized Precipitation and Evaporation Index in a drought-prone region S Mouatadid, N Raj, RC Deo, JF Adamowski Atmospheric research 212, 130-149, 2018 | 99 | 2018 |
| Coupling the maximum overlap discrete wavelet transform and long short-term memory networks for irrigation flow forecasting S Mouatadid, JF Adamowski, MK Tiwari, JM Quilty Agricultural Water Management 219, 72-85, 2019 | 93 | 2019 |
| Online learning with optimism and delay GE Flaspohler, F Orabona, J Cohen, S Mouatadid, M Oprescu, ... International Conference on Machine Learning, 3363-3373, 2021 | 38 | 2021 |
| Adaptive bias correction for improved subseasonal forecasting S Mouatadid, P Orenstein, G Flaspohler, J Cohen, M Oprescu, E Fraenkel, ... Nature Communications 14 (1), 3482, 2023 | 27 | 2023 |
| A machine learning approach to non-uniform spatial downscaling of climate variables S Mouatadid, S Easterbrook, AR Erler 2017 IEEE international conference on data mining workshops (ICDMW), 332-341, 2017 | 23 | 2017 |
| Prediction of SPEI using MLR and ANN: A case study for Wilsons Promontory Station in Victoria S Mouatadid, RC Deo, JF Adamowski 2015 IEEE 14th International Conference on Machine Learning and Applications …, 2015 | 11 | 2015 |
| SubseasonalClimateUSA: a dataset for subseasonal forecasting and benchmarking S Mouatadid, P Orenstein, G Flaspohler, M Oprescu, J Cohen, F Wang, ... Advances in Neural Information Processing Systems 36, 2024 | 7 | 2024 |
| Learned benchmarks for subseasonal forecasting S Mouatadid, P Orenstein, G Flaspohler, M Oprescu, J Cohen, F Wang, ... arXiv preprint arXiv:2109.10399, 2021 | 7 | 2021 |
| Development of a Daily Multilayer Cropland Soil Moisture Dataset for China Using Machine Learning and Application to Cropping Patterns Y Liu, D Chen, S Mouatadid, X Lu, M Chen, Y Cheng, Z Xie, B Jia, H Wu, ... Journal of Hydrometeorology 22 (2), 445-461, 2021 | 4 | 2021 |
| Non-uniform spatial downscaling of climate variables S Mouatadid, S Easterbrook, A Erler 7th International workshop on clmate informatics, 2017 | 3 | 2017 |
| RFID-based Location System for Forest Search and Rescue Missions S Mouatadid, Z Fatara, Y Salih-Alj 2013 4th International Conference on Intelligent Systems, Modelling and …, 2013 | 2 | 2013 |
| Recovering the parameters underlying the Lorenz-96 chaotic dynamics S Mouatadid, P Gentine, W Yu, S Easterbrook arXiv preprint arXiv:1906.06786, 2019 | 1 | 2019 |
| Wavelet-Long Short-Term Memory Networks: An Approach to Irrigation Flow Forecasting S Mouatadid, JF Adamowski, MK Tiwari, JM Quilty | 1 | 2019 |
| Correcting Historical Errors: An investigation of systematic errors in Arrhenius's 1896 climate model SM Easterbrook, S Fassnacht, A Hurka, S Mouatadid AGU Fall Meeting Abstracts 2018, A13O-2679, 2018 | 1 | 2018 |
| Advancing Ensemble Subseasonal Forecasting with Machine Learning S Totz, J Cohen, G Flaspohler, S Mouatadid, P Orenstein, L Mackey, ... 103rd AMS Annual Meeting, 2023 | | 2023 |
| Machine Learning for Improved Weather and Climate Predictions S Mouatadid University of Toronto (Canada), 2023 | | 2023 |
| Applying Machine Learning to Improve Subseasonal-to-Seasonal (S2S) Forecasts S Mouatadid, J Cohen, L Mackey 100th American Meteorological Society Annual Meeting, 2020 | | 2020 |
| The Arrhenius Model of Climate Change as a Classroom Experiment SM Easterbrook, A Hurka, S Fassnacht, S Mouatadid AGU Fall Meeting 2018, 2018 | | 2018 |
Stephan RaspSenior Research Scientist at Google ResearchVerified email at tum.de
