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Abstract I will discuss the Neural GCM, which we built by building a dynamical core in JAX and then training the parameterization on ERA5 on 5-day forecasts. The quality of the forecasts up to 1 year portends a potential revolution in improving model …

Abstract Mathematical and numerical models of the atmosphere and ocean rely on various assumptions, approximations, and simplifications. Over the past decade, significant progress has been made in elucidating their structure and interconnections, …

Abstract While AI has been disrupting conventional weather forecasting, we are only beginning to witness the impact of AI on long-term climate simulations. The fidelity and reliability of climate models has been limited by computing capabilities. These …

Abstract This presentation will cover some of the recent advances in weather forecasting, learning directly from data using machine learning techniques. It will discuss some of the limitations and pitfalls of training ML models for scientific …

Abstract Since the start of the Covid-19 pandemic, major health inequalities have been highlighted in many countries. People on low incomes and belonging to ethno-racial minorities are the hardest hit by the pandemic. Based on an analysis of data from a …

Abstract The stunning recent advances in AI content generation rely on cutting-edge, generative deep learning algorithms and architectures trained on massive amounts of text, image, and video data. With different training data, these algorithms and …

Abstract Data assimilation is the set of key mathematical methods used to optimally combine observations and numerical model outputs. Data assimilation (DA) is critical to adjust the initial condition of meteorological forecasts, to estimate model …

Abstract By studying the effects of the Covid-19 pandemic on the scale of a municipality in Seine-Saint-Denis, the poorest department in mainland France, we will examine the reproduction of social inequalities in health and the transformation of …