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November 2024
Published:
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
Published:
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
Event date:
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.
Estimation of water quality parameters using remote sensing data and machine learning models
Event date:
Webinar with Alireza Taheri Dehkordi, Lund University. The global decline in water quality, exacerbated by climate change and population growth, underscores the need for continuous and accurate monitoring of water quality parameters (WQPs). Remote sensing (RS) data, especially from multispectral satellites like Sentinel-2 and Landsat-8, offers large-scale, periodic observations for tracking WQPs. However, deriving accurate estimates solely from RS data is complex due to the intricate relationships between spectral bands and water quality indicators. This talk presents two novel machine learning approaches that leverage advanced RS data processing to enhance water quality monitoring.