Our People
Eviatar Bach
Lecturer in Mathematics of Environmental Data Science
Research interests
I am interested in data assimilation and environmental inverse problems, as well as predictability of the Earth system. I work on development of new methods, particularly incorporating machine learning, and their mathematical foundations.
Recent publications
Nesterov acceleration for ensemble Kalman inversion and variants. 2025-08
DOI: https://doi.org/10.1016/j.jcp.2025.114063
Learning Enhanced Ensemble Filters. 2025-04
DOI: https://doi.org/10.48550/arxiv.2504.17836
Forecast error growth: A dynamic-stochastic model. 2025-03
DOI: https://doi.org/10.48550/arxiv.2411.06623
Learning Optimal Filters Using Variational Inference. 2025-03
DOI: https://doi.org/10.48550/ARXIV.2406.18066
Inverse Problems and Data Assimilation: A Machine Learning Approach. 2024-10
DOI: https://doi.org/10.48550/ARXIV.2410.10523
High‐Dimensional Covariance Estimation From a Small Number of Samples. 2024-09
DOI: https://doi.org/10.1029/2024MS004417
The South Atlantic Dipole via multichannel singular spectrum analysis. 2024-07-05
URI: https://centaur.reading.ac.uk/117071/ DOI: https://doi.org/10.1038/s41598-024-62089-w ISSN: https://portal.issn.org/resource/ISSN/2045-2322
Improved subseasonal prediction of South Asian monsoon rainfall using data-driven forecasts of oscillatory modes. 2024-04-09
DOI: https://doi.org/10.1073/pnas.2312573121
Filtering dynamical systems using observations of statistics. 2024-03-01
DOI: https://doi.org/10.1063/5.0171827
A global assessment of the effects of solar farms on albedo, vegetation, and land surface temperature using remote sensing. 2024-01
DOI: https://doi.org/10.1016/j.solener.2023.112198
A Multi‐Model Ensemble Kalman Filter for Data Assimilation and Forecasting. 2023-01
DOI: https://doi.org/10.1029/2022MS003123
EnsembleKalmanProcesses.jl: Derivative-free ensemble-based model calibration. 2022-12-15
DOI: https://doi.org/10.21105/joss.04869
Towards physics-inspired data-driven weather forecasting: integrating data assimilation with a deep spatial-transformer-based U-NET in a case study with ERA5. 2022-03-16
DOI: https://doi.org/10.5194/gmd-15-2221-2022
Impacts of 319 wind farms on surface temperature and vegetation in the United States. 2022-02-01
DOI: https://doi.org/10.1088/1748-9326/ac49ba
parasweep: a template-based utility for generating, dispatching, and post-processing of parameter sweeps. 2021-01-13
URI: https://centaur.reading.ac.uk/116998/
Advances in Coupled Data Assimilation, Ensemble Forecasting, and Assimilation of Altimeter Observations. 2020-11
DOI: https://doi.org/10.36071/clivar.79.2020 ISSN: https://portal.issn.org/resource/ISSN/1026-0471
Local Atmosphere–Ocean Predictability: Dynamical Origins, Lead Times, and Seasonality. 2019-11-01
DOI: https://doi.org/10.1175/JCLI-D-18-0817.1
Strongly Coupled Data Assimilation in Multiscale Media: Experiments Using a Quasi‐Geostrophic Coupled Model. 2019-06
DOI: https://doi.org/10.1029/2019MS001652
Climate model shows large-scale wind and solar farms in the Sahara increase rain and vegetation. 2018-09-07
