Developing deep-learning models for weather-climate forecasts

AI and deep learning are rapidly becoming essential tools in weather and climate science. This presentation will cover our recent work using these techniques to enhance predictions across various timescales and phenomena. We have successfully applied architectures like convolutional neural networks, transformers, and generative models to forecast events like ENSO and the Indian Ocean Dipole, as well as to correct biases in traditional climate models and to downscale coarse-resolution outputs using diffusion framework. Looking ahead, I will also discuss our efforts in building AI large models for ensemble subseasonal-to-decadal forecasting and the exciting prospect of creating AI agents dedicated to climate research.