Manmeet Singh is presently at the Jackson School of Geosciences, The University of Texas at Austin, Austin, USA. He served as a Staff Scientist at the nodal national lab on weather and climate in India, Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Govt of India for 11 years from 2013-2024. He was also a Fulbright-Kalam fellow at the Jackson School of Geosciences, The University of Texas at Austin in 2021. His research interests include climate solutions to the problems on land, ocean and atmosphere using mathematical models, particularly numerical weather prediction systems. He is especially interested in AI/ML techniques, causal approaches, recurrence plots, complex networks and non-linear time series analysis for solving grand challenges in Earth System Science. He is an experienced climate modeller having contributed to the IITM Earth System Model simulations towards the IPCC AR6 report. Together with his PhD co-advisor, he developed and coupled the aerosol module of the IITM Earth System Model. He is active in teaching and has given invited talks at venues such as the NASA/UAH Seminar series, Microsoft India podcast among others. His PhD focussed on the impacts of the proposals suggesting volcanic eruptions as an analogue of solar geoengineering to halt climate change. Recently, his work has shown substantial improvements in high-impact short-range numerical weather predictions using deep learning and he has also developed novel physics inspired deep learning algorithms for high-resolution downscaling.

Abstract: In recent years, the confluence of data science, machine learning, and decision analytics has opened transformative pathways in understanding and addressing complex problems in weather and climate. This talk will explore how these interdisciplinary approaches are reshaping forecasting, risk assessment, and climate resilience planning. Drawing on recent case studies and ongoing research, we will examine the role of data-driven models in improving prediction accuracy, the integration of learning algorithms in climate simulations, and the use of decision frameworks to support adaptive responses to extreme weather events. The session aims to highlight both the opportunities and challenges in operationalizing these methods for real-world impact in a rapidly changing climate landscape.