Digital Twins for Global Precipitation and Fire Weather Using Deep Learning
Centre for Climate Change Research, Indian Institute of Tropical Meteorology, Pune and Jackson School of Geosciences, University of Texas, Austin
Tuesday, Apr 11, 2023, 2:00 pm
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Digital twins, virtual replicas of physical systems, have recently gained significant attention for their potential to simulate and predict various environmental phenomena. In this presentation, Manmeet will present a novel approach to developing digital twins for global precipitation and fire weather prediction using cutting-edge deep learning techniques. The talk will cover the development of a sophisticated deep learning model that data from multiple sources. The model aims to enhance the accuracy and efficiency of environmental modeling, paving the way for more informed decision-making processes in the context of climate change and natural disaster management. Manmeet will discuss the model's performance in predicting precipitation patterns and fire weather conditions, as well as its potential applications. Moreover, the presentation will explore future directions for research, including the integration of other environmental factors and the expansion of the digital twin concept to additional weather-related phenomena. Through the innovative combination of deep learning and digital twin technology, Manmeet's research aspires to contribute to the ongoing efforts of the scientific community in understanding and mitigating the impacts of climate change and extreme weather events on our planet.
SPEAKER BIO: Manmeet Singh is Scientist at the Centre for Climate Change Research, Indian Institute of Tropical Meteorology, Pune and an Associate at Jackson School of Geosciences, The University of Texas at Austin, Austin, USA. He was 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.
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