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Found 5 papers, 1 papers with code
WeatherBench: A benchmark dataset for data-driven weather forecasting
Data-driven approaches, most prominently deep learning, have become powerful tools for prediction in many domains.
Machine Learning Emulation of 3D Cloud Radiative Effects
The treatment of cloud structure in numerical weather and climate models is often greatly simplified to make them computationally affordable.
Bridging observation, theory and numerical simulation of the ocean using Machine Learning
Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study.
A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts
Despite continuous improvements, precipitation forecasts are still not as accurate and reliable as those of other meteorological variables.
DiffDA: a Diffusion model for weather-scale Data Assimilation
The experiments also show that the initial conditions assimilated from sparse observations (less than 0. 77% of gridded data) and 48-hour forecast can be used for forecast models with a loss of lead time of at most 24 hours compared to initial conditions from state-of-the-art data assimilation in ERA5.
