Data-driven Hydrological Modeller
Forecast, Evaluation, Hydrology Monitoring and Forecast
Summary:
Kenza is a research scientist in the Hydrology Team at ECMWF. She works on developing data-driven models to better forecast and understand floods across the globe.
Professional interests:
- Machine learning
- Uncertainty quantification
- Climate change adaptation
Career background:
Education
- PhD 'Predicting precipitation over High Mountain Asia with Gaussian Processes', University of Cambridge (2025)
- MRes Environmental Data Science, University of Cambridge (2020)
- MSci Physics, Imperial College London (2019)
Additional Research Experience
- Frontier Development Lab (2022)
- Cambridge University Science and Policy Exchange (2020-2021)
- Geophysical Fluid Dynamics Group, University of Oxford (2018)
- Planetary Science Group, University of Oxford (2017)
- British Antarctic Survey (2016)
- 2024
- Kenza Tazi, Andrew Orr, Javier Hernandez-González, Scott Hosking, Richard E. Turner (November 2024) Downscaling precipitation over High-mountain Asia using multi-fidelity Gaussian processes: improved estimates from ERA5, Hydrology and Earth System Sciences. DOI: 10.5194/hess-28-4903-2024
- 2023
- Emiliano Díaz, Gherardo Varando, Fernando Iglesias-Suarez, Gustau Camps-Valls, Kenza Tazi, Kara Lamb, Duncan Watson-Parris (May 2023) Learning causal drivers of PyroCb. DOI: 10.5194/egusphere-egu23-16846
- 2022
- Kenza Tazi, Emiliano Díaz Salas-Porras, Ashwin Braude, Daniel Okoh, Kara D. Lamb, Duncan Watson-Parris, Paula Harder, Nis Meinert (June 2022) Pyrocast: a Machine Learning Pipeline to Forecast Pyrocumulonimbus (PyroCb) Clouds, Tackling Climate Change with Machine Learning (NeurIPS 2022). DOI: 10.48550/arxiv.2211.13052
- Emiliano Díaz Salas-Porras, Kenza Tazi, Ashwin Braude, Daniel Okoh, Kara D. Lamb, Duncan Watson-Parris, Paula Harder, Nis Meinert (June 2022) Identifying the Causes of Pyrocumulonimbus (PyroCb), https://doi.org/10.48550/arXiv.2211.08883. DOI: 10.48550/arxiv.2211.08883
- Vidhi Lalchand, Kenza Tazi, Talay M. Cheema, Richard E. Turner, Scott Hosking (June 2022) Kernel Learning for Explainable Climate Science, 16th Bayesian Modelling Applications Workshop at UAI, 2022 . DOI: 10.48550/arxiv.2209.04947
- 2020
- (October 2020) Evaluation and comparison of a machine learning cloud identification algorithm for the SLSTR in polar regions, Remote Sensing of Environment. DOI: 10.1016/j.rse.2020.111999