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Found 7 papers, 1 papers with code
AI Foundation Models for Weather and Climate: Applications, Design, and Implementation
In particular, we are witnessing the rise of AI foundation models that can perform competitively on multiple domain-specific downstream tasks.
Hard-Constrained Deep Learning for Climate Downscaling
In order to conserve physical quantities, here we introduce methods that guarantee statistical constraints are satisfied by a deep learning downscaling model, while also improving their performance according to traditional metrics.
Wildfire risk forecast: An optimizable fire danger index
This approach has two benefits: (1) the NFDRS IC parameters can be improved for each region using actual observed fire events, and (2) the internal variables remain intact for interpretations by specialists instead of meaningless hidden layers as in traditional neural networks.
Controlling Weather Field Synthesis Using Variational Autoencoders
One of the consequences of climate change is anobserved increase in the frequency of extreme cli-mate events.
A comparative study of stochastic and deep generative models for multisite precipitation synthesis
Future climate change scenarios are usually hypothesized using simulations from weather generators.
Extreme Precipitation Seasonal Forecast Using a Transformer Neural Network
An impact of climate change is the increase in frequency and intensity of extreme precipitation events.
Decadal Forecasts with ResDMD: a Residual DMD Neural Network
Operational forecasting centers are investing in decadal (1-10 year) forecast systems to support long-term decision making for a more climate-resilient society.
