AI for Climate Risk: Difference between revisions
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AI for Climate | ==== AI Foundation Model for Weather and Climate ==== | ||
AI for | * [https://huggingface.co/papers/2409.13598 '''Prithvi WxC'''](open-source) In collaboration with NASA, IBM built a '''general-purpose AI foundation model''' that could be customized for a range of practical weather and climate applications, at varying spatial scales. Potential applications include creating targeted forecasts from local weather data, predicting extreme weather events, improving the spatial resolution of global climate simulations, and improving the representation of physical processes in conventional weather and climate models. | ||
AI for | * [https://arxiv.org/abs/2405.13063 '''Aurora'''] A foundation model developed by Microsoft that can do high-resolution (11-km) weather forecast and even air pollution forecast. | ||
AI for exposure | ==== AI for Weather forecast ==== | ||
* [https://deepmind.google/discover/blog/graphcast-ai-model-for-faster-and-more-accurate-global-weather-forecasting/ '''GraphCast'''] Global weather forecast model developed by Google DeepMind. | |||
* [https://github.com/198808xc/Pangu-Weather '''Panggu'''](open-source) Global weather forecast model developed by Huawei, China | |||
* '''[https://github.com/tpys/FuXi Fuxi]'''(open-source) Global weather forecast model developed by Fudan University, China | |||
* [https://arxiv.org/abs/2202.11214#:~:text=FourCastNet%2C%20short%20for%20Fourier%20Forecasting,0.25%5E%7B%5Ccirc%7D%20resolution. '''FourCastNet'''] | |||
* [https://arxiv.org/abs/2404.00411 '''Aardvark weather: end-to-end data-driven weather forecasting'''] An end-to-end weather forecasting system proposed to replace the entire operational numerical weather forecast pipeline. Aardvark directly ingests raw observations and is capable of outputting global gridded forecasts, as well as local station forecasts. | |||
* [https://arxiv.org/abs/2312.15796 '''GenCast''': Diffusion-based ensemble forecasting for medium-range weather] | |||
* [https://arxiv.org/abs/2312.15796 '''Neural general circulation models''' for weather and climate] | |||
==== Climate Downscaling ==== | |||
* [https://github.com/Earth-Intelligence-Lab/LocalizedWeatherGNN/ '''Localized weather GNN'''] by Earth Intelligence Lab It downscales gridded weather forecasts, such as ERA5, to provide accurate off-grid predictions. | |||
* [https://arxiv.org/abs/2309.15214 '''Residual Corrective Diffusion Modeling''' for Km-scale Atmospheric Downscaling] | |||
==== Climate Risk Forecasting ==== | |||
* [https://www.sciencedirect.com/science/article/pii/S2589915524000191 A nonstationary stochastic simulator for clustered regional hydroclimatic extremes to characterize compound flood risk] | |||
* [https://github.com/jtbuch/smlfire1.0 A stochastic ML model of wildfire activity in western US] | |||
* [https://onlinelibrary.wiley.com/doi/10.1111/ele.14018 A statistical model] to create gridded (4-km spatial resolution), monthly predictions of burn area as a function of climatic variables. | |||
==== AI for exposure mapping ==== | |||
Latest revision as of 17:50, 21 October 2024
AI Foundation Model for Weather and Climate
- Prithvi WxC(open-source) In collaboration with NASA, IBM built a general-purpose AI foundation model that could be customized for a range of practical weather and climate applications, at varying spatial scales. Potential applications include creating targeted forecasts from local weather data, predicting extreme weather events, improving the spatial resolution of global climate simulations, and improving the representation of physical processes in conventional weather and climate models.
- Aurora A foundation model developed by Microsoft that can do high-resolution (11-km) weather forecast and even air pollution forecast.
AI for Weather forecast
- GraphCast Global weather forecast model developed by Google DeepMind.
- Panggu(open-source) Global weather forecast model developed by Huawei, China
- Fuxi(open-source) Global weather forecast model developed by Fudan University, China
- FourCastNet
- Aardvark weather: end-to-end data-driven weather forecasting An end-to-end weather forecasting system proposed to replace the entire operational numerical weather forecast pipeline. Aardvark directly ingests raw observations and is capable of outputting global gridded forecasts, as well as local station forecasts.
- GenCast: Diffusion-based ensemble forecasting for medium-range weather
- Neural general circulation models for weather and climate
Climate Downscaling
- Localized weather GNN by Earth Intelligence Lab It downscales gridded weather forecasts, such as ERA5, to provide accurate off-grid predictions.
- Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling
Climate Risk Forecasting
- A nonstationary stochastic simulator for clustered regional hydroclimatic extremes to characterize compound flood risk
- A stochastic ML model of wildfire activity in western US
- A statistical model to create gridded (4-km spatial resolution), monthly predictions of burn area as a function of climatic variables.