University of Hyderabad, Hyderabad
Sreenivas Pentakota obtained his Ph.D. from Andhra University. He worked as a Scientist at the Indian Institute of Tropical Meteorology (IITM) from 2009 to 2022 and joined as a Professor at the Center for Earth Ocean and Atmospheric Sciences (CEOAS), at the Univeristy of Hyderabad. During his tenure at IITM, he was actively involved in the “Monsoon Mission” program of MoES. Implemented a new Ensemble Kalman Filter (LETKF) based Ocean-Atmospheric coupled data assimilation system for the operational monsoon prediction model. He has published around 28 research papers in high-impact factor journals. He has proven scientific expertise in varied research areas related to oceanography, climate, and modeling as evidenced by the variety of publication records. He is a Life member of the Indian Meteorological Society and Ocean Society of India and was selected Young Associate of the Indian Academy of Sciences in 2019.
Lectures by Fellows/Associates
Swapan K Ghosh, University of Mumbai, Mumbai
An advanced Ensemble Kalman Filter based Ocean-Atmospheric coupled Data Assimilation system and its impact in enhancing the Indian monsoon predictions
The Ocean and Atmosphere observations have increased tremendously in recent decades due to satellites and improved observational networks. Since monsoon prediction partly depends on the state of the Ocean and Atmosphere (i.e., model starting point or “analysis”), these observations can be used to improve the analysis, thereby, the predictions. The recent improvements in high-performance computing and data assimilation research under Monsoon Mission have aided us in implementing the local ensemble transform Kalman filter-based Ocean-Atmospheric coupled data assimilation system to the operational monsoon prediction model. The new system incorporates theoretically advanced features of flow-dependency and ensemble-based analysis, and the predictions using the new system simulate the large-scale monsoon features and convection canters well and improve Indian summer-monsoon-rainfall prediction skill with a gain of one month lead time. The present study reports the enhancements and explores probable mechanisms responsible for the improvement. The study is vital to operational agencies adopting advanced data assimilation methods, particularly to boost monsoon predictions.