AI for Climate Risk: Difference between revisions
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==== AI for Flooding Risk ==== | ==== AI for Flooding Risk ==== | ||
* [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 ==== | ==== AI for exposure ==== | ||
Revision as of 00:32, 16 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.
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
Climate Downscaling
Climate Risk Forecasting
AI for Wildfire Risk
AI for Flooding Risk
- 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.