Vassili Kitsios

Jan 5, 2025 | aihub.org | Vassili Kitsios |Lucy Smith

NASA/GSFC, MODIS Rapid Response Team, Jacques DescloitresBy Vassili Kitsios, CSIROA new machine-learning weather prediction model called GenCast can outperform the best traditional forecasting systems in at least some situations, according to a paper by Google DeepMind researchers published last month in Nature. Using a diffusion model approach similar to artificial intelligence (AI) image generators, the system generates multiple forecasts to capture the complex behaviour of the atmosphere.

Dec 9, 2024 | csiro.au | Vassili Kitsios

By  Vassili Kitsios 9 December 2024 4 min read A new machine-learning weather prediction model called GenCast can outperform the best traditional forecasting systems in at least some situations, according to a paper by Google DeepMind researchers published today in Nature. Using a diffusion model approach similar to artificial intelligence (AI) image generators, the system generates multiple forecasts to capture the complex behaviour of the atmosphere.

Dec 5, 2024 | tolerance.ca | Vassili Kitsios

By Vassili Kitsios, Senior Research Scientist, Climate Forecasting, CSIRO A new machine-learning weather prediction model called GenCast can outperform the best traditional forecasting systems in at least some situations, according to a paper by Google DeepMind researchers published today in Nature.

Dec 4, 2024 | theconversation.com | Vassili Kitsios

A new machine-learning weather prediction model called GenCast can outperform the best traditional forecasting systems in at least some situations, according to a paper by Google DeepMind researchers published today in Nature. Using a diffusion model approach similar to artificial intelligence (AI) image generators, the system generates multiple forecasts to capture the complex behaviour of the atmosphere.

Oct 6, 2023 | nature.com | Vassili Kitsios

AbstractNavigating a path toward net-zero, requires the assessment of physical climate risks for a broad range of future economic scenarios, and their associated carbon concentration pathways. Climate models typically simulate a limited number of possible pathways, providing a small fraction of the data needed to quantify the physical risk. Here machine learning techniques are employed to rapidly and cheaply generate output mimicking these climate simulations.