"My research is driven by curiosity and passion to uncover nature's mysteries, especially within the ocean. With abundant resources and collaborative colleagues, I appreciate the opportunities PML offers. My current research enhances marine biogeochemical understanding using Machine Learning and Data Assimilation, benefiting ecosystem forecasts and strengthening PML's potential in scientific community."
Following the completion of MSc in Physics, Deep began his career as Guest Lecturer at SS College (Government-sponsored) followed by Assistant Professor in Department of Physics at SKFGI Engineering College in India. Subsequently, Deep joined the Indian National Centre for Ocean Information Services (INCOIS), an autonomous research institution under the Government of India, as Project Scientist in Data Assimilation and Ocean Modelling division.
At INCOIS, Deep's primary focus was on developing and implementing a state-of-the-art Data Assimilation system for the Indian Ocean Region, using ROMS (Regional Ocean Modelling System) - LETKF (Local Ensemble Transform Kalman Filter) framework. Concurrently, he continued to enhance the INCOIS-Global Ocean Data Assimilation System based on Modular Ocean Model (MOM)-3DVar. This effort significantly improved real-time and near real-time ocean reanalysis production, leading to enhanced predictions of tropical cyclones in the Indian Ocean domain.
Later, Deep joined the Euro-Mediterranean Center on Climate Change (CMCC), Italy as Research Associate in the Ocean Modelling and Data Assimilation division. He contributed to the enhancement of the global ocean data assimilation system, CGLORS, funded by Copernicus Marine Service (CMEMS), with a focus on the NEMO-OceanVar framework. Subsequently, he engaged into working with Community Earth System Model (CESM) to introduce a Sea Ice Data Assimilation scheme in NEMO-CICE coupled framework to improve the ocean and sea ice states for a better seasonal climate prediction.
Presently, Deep serves as a Modelling Scientist at Plymouth Marine Laboratory (PML) in Marine System Modelling division. With his previous eight years’ experience in working with various numerical ocean/sea ice models and data assimilation schemes, Deep is presently developing an Artificial Neural Network-based model in the Atlantic-European North West Shelf domain. This research work aims to improve biogeochemical model forecasts and analyses, with the ultimate objective of providing better predictions related to harmful events such as eutrophication.
At INCOIS, Deep's primary focus was on developing and implementing a state-of-the-art Data Assimilation system for the Indian Ocean Region, using ROMS (Regional Ocean Modelling System) - LETKF (Local Ensemble Transform Kalman Filter) framework. Concurrently, he continued to enhance the INCOIS-Global Ocean Data Assimilation System based on Modular Ocean Model (MOM)-3DVar. This effort significantly improved real-time and near real-time ocean reanalysis production, leading to enhanced predictions of tropical cyclones in the Indian Ocean domain.
Later, Deep joined the Euro-Mediterranean Center on Climate Change (CMCC), Italy as Research Associate in the Ocean Modelling and Data Assimilation division. He contributed to the enhancement of the global ocean data assimilation system, CGLORS, funded by Copernicus Marine Service (CMEMS), with a focus on the NEMO-OceanVar framework. Subsequently, he engaged into working with Community Earth System Model (CESM) to introduce a Sea Ice Data Assimilation scheme in NEMO-CICE coupled framework to improve the ocean and sea ice states for a better seasonal climate prediction.
Presently, Deep serves as a Modelling Scientist at Plymouth Marine Laboratory (PML) in Marine System Modelling division. With his previous eight years’ experience in working with various numerical ocean/sea ice models and data assimilation schemes, Deep is presently developing an Artificial Neural Network-based model in the Atlantic-European North West Shelf domain. This research work aims to improve biogeochemical model forecasts and analyses, with the ultimate objective of providing better predictions related to harmful events such as eutrophication.
- 114-GLORAN CMEMS LOT 2 Research programme funded by Mercator Ocean.
- CMCC_C3S2_370 project, funded by Copernicus Marine Services.
- High-resolution Operational Ocean Forecast and reanalysis System (HOOFS) under the Ministry of Earth Sciences, Govt. of India.
- Bivariate sea-ice assimilation for global ocean Analysis/Reanalysis EGUsphere [preprint], February 2023, accepted for publication in Ocean Science Journal https://doi.org/10.5194/egusphere-2023-254
- High-Resolution Operational Ocean Forecast and Reanalysis System for the Indian Ocean, Bulletin of the American Meteorological Society, 101(8), E1340-E1356. August 2022, http://dx.doi.org/10.1175/BAMS-D-19-0083.1
- Impact of Dynamical Representational Errors on an Indian Ocean Ensemble Data Assimilation System, Quarterly Journal of the Royal Meteorological Society (QJRMS), August 2019, https://doi.org/10.1002/qj.3649
- Ensemble based Regional ocean data assimilation system for the Indian Ocean: Implementation and Evaluation, Journal of Ocean Modelling, Volume 143, November 2019, 101470, https://doi.org/10.1016/j.ocemod.2019.101470
- Are Ocean Moored Buoys Redundant for Prediction of Indian Monsoon?', Meteorol Atmos Phys 133, 1075–1088, April 2021 https://doi.org/10.1007/s00703-021-00792-3