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Bedartha Goswami (University of Tübingen, Germany)
Date & Time
Tue, 29 August 2023, 11:00 to 12:30
Madhava Lecture Hall and Online

Recent years have seen a boom in the application of statistical machine learning and artificial intelligence methods to tackle problems in meteorology and climate science. In this talk, I will present three projects ongoing in my group that use machine learning concepts to understand climate variability. I will show how we can use similarity-based networks of climate time series data to reveal new propagation pathways of extreme rainfall over South Asia that might potentially be useful for early warning systems of extreme monsoonal rain. Next, I will present how we can use principal component analysis in combination with Gaussian Mixture Models to categorize extreme phases of the El Niño Southern Oscillation (ENSO) and show how the Eastern Pacific El Niño is better modeled as two separate categories: a weaker canonical' form and an extreme El Niño, both of which show distinct idiosyncratic onset and development. Last, I will present how we can leverage deep learning to develop purely data-driven models for subseasonal-to-seasonal forecasting of the ENSO that are at par with state-of-the-art physics based weather prediction models. Finally, we will briefly talk about what these recent developments mean for understanding and modelling climate variability over the Indian subcontinent.

Zoom link:
Meeting ID: 812 1143 2021
Passcode: 292930