Our People
Deep S. Banerjee
Modelling Scientist
EO Data-Model Evaluation
Research interests
I am interested in Numerical Ocean Models (Biogeo chemical and physical), Data Assimilation, Ocean Observation networks and model validation and Machine learning. I am presently developing an Artificial Neural Network-based model in the Atlantic-European North West Shelf domain to improve biogeochemical model forecasts and analyses, with the ultimate objective of providing better predictions related to harmful events such as eutrophication.
Recent publications
Assimilation of machine‐learning‐predicted nitrate to improve the quality of phytoplankton forecasting in the shelf‐sea environment. 2026-04-22
DOI: https://doi.org/10.1002/qj.70156 ISSN: https://portal.issn.org/resource/ISSN/0035-9009 ISSN: https://portal.issn.org/resource/ISSN/1477-870X
Machine-learning-derived surface nitrate dataset for the Northwest European Shelf. 2026-04-22
DOI: https://doi.org/10.5281/ZENODO.19695959
NECCTON Report on ML and DA developments for data-model integration in T4.2.1-T4.4.3 (D4.4). 2026-02-23
DOI: https://doi.org/10.5281/ZENODO.18745261
Hybrid Physics–AI Ecosystem Simulations Improve Biogeochemical Predictions in Temperate Shelf Seas. 2026-02-05
DOI: https://doi.org/10.31223/X5C74R
Marine data assimilation in the UK: the past, the present, and the vision for the future. 2025-08-05
DOI: https://doi.org/10.5194/os-21-1709-2025 ISSN: https://portal.issn.org/resource/ISSN/1812-0792
A Digital Twin Ocean: Can we improve Coastal Ocean Forecasts using targeted Marine Autonomy?. 2025
DOI: https://doi.org/10.5194/EGUSPHERE-2025-3346
Combining machine learning with data assimilation to improve the quality of phytoplankton forecasting in a shelf sea environment. 2025
DOI: https://doi.org/10.48550/ARXIV.2508.02400
Improved understanding of nitrate trends, eutrophication indicators, and risk areas using machine learning. 2025
DOI: https://doi.org/10.5194/BG-22-3769-2025 WOSUID: https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Publons&SrcAuth=Publons_CEL&KeyUT=WOS:001709847300001&DestLinkType=FullRecord&DestApp=WOS_CPL
Modelling primary production: multitude of theories, or multitude of languages?. 2025
DOI: https://doi.org/10.5194/EGUSPHERE-2025-6256
Bivariate sea-ice assimilation for global-ocean analysis–reanalysis. 2023-09-15
DOI: https://doi.org/10.5194/os-19-1375-2023
Copernicus Ocean State Report, issue 6. 2022-08-31
DOI: https://doi.org/10.1080/1755876x.2022.2095169 ISSN: https://portal.issn.org/resource/ISSN/1755-876X ISSN: https://portal.issn.org/resource/ISSN/1755-8778
The Atlantic Meridional Overturning Circulation forcing the mean sea level in the Mediterranean Sea through the Gibraltar transport . 2022-03-28
DOI: https://doi.org/10.5194/egusphere-egu22-10212
The CMCC Global Ocean Reanalysis System (C-GLORS): a series of consolidated eddy-permitting ocean reanalyses. 2021-09-13
https://symp-bonn2021.sciencesconf.org/data/357215.pdf
Are ocean-moored buoys redundant for prediction of Indian monsoon?. 2021-08
DOI: https://doi.org/10.1007/s00703-021-00792-3 ISSN: https://portal.issn.org/resource/ISSN/0177-7971 ISSN: https://portal.issn.org/resource/ISSN/1436-5065
High-Resolution Operational Ocean Forecast and Reanalysis System for the Indian Ocean. 2020-08-01
DOI: https://doi.org/10.1175/bams-d-19-0083.1 ISSN: https://portal.issn.org/resource/ISSN/0003-0007 ISSN: https://portal.issn.org/resource/ISSN/1520-0477
Ensemble based regional ocean data assimilation system for the Indian Ocean: Implementation and evaluation. 2019
http://www.scopus.com/inward/record.url?eid=2-s2.0-85072578789&partnerID=MN8TOARS
Impact of dynamical representational errors on an Indian Ocean ensemble data assimilation system. 2019
http://www.scopus.com/inward/record.url?eid=2-s2.0-85072556315&partnerID=MN8TOARS
LETKF-ROMS: An improved predictability system for the Indian Ocean. 2018-11-01
https://incois.gov.in/documents/rain_reports/ESSO-INCOIS-MDG-TR-03_(2018).pdf
