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Featured Paper: ONEKANA: Modelling Thermal Inequalities in African Cities
Published:
The new Climate Change AI Nordics Featured Paper is "ONEKANA: Modelling Thermal Inequalities in African Cities" by Sabine Vanhuysse and colleagues. This research addresses the pressing issue of thermal disparities in rapidly urbanizing African cities, where vulnerable populations are disproportionately affected by extreme heat due to environmental and socioeconomic factors.
November 2024
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Welcome to the Climate Change AI Nordics Newsletter, November 2024! Read about recent and coming seminars, workshops, and publications from the network's researchers.
Featured Preprint: Continuous Ensemble Weather Forecasting with Diffusion Models
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In Featured Preprints, preprints from affiliated researchers are summarized and featured at Climate Change AI Nordics. This one features "Continuous Ensemble Weather Forecasting with Diffusion Models", from Martin Andrae, Tomas Landelius, Joel Oskarsson, and Fredrik Lindsten.
Frontiers in machine learning for weather forecasting
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Webinar with Joel Oskarsson, Linköping University. Recent years have seen rapid progress in using machine learning models for weather forecasting. These models show impressive performance, matching or even outperforming existing physics-based models, while running in a fraction of the time. This is fundamentally and rapidly changing the landscape of weather forecasting today. In this talk I will discuss the factors that enabled this paradigm shift, the core machine learning methods used and the research questions at the bleeding edge of machine learning for weather. In particular I will focus on how current methods can be extended to regional and probabilistic forecasting. For regional forecasting I will showcase graph-based methods for building limited area weather forecasting models. I will also discuss how generative machine learning methods can enable probabilistic forecasting, giving much-needed estimates of uncertainty and allowing for predicting extreme weather events.