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Found 3 papers, 2 papers with code
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.
Compressing multidimensional weather and climate data into neural networks
We propose a new method of compressing this multidimensional weather and climate data: a coordinate-based neural network is trained to overfit the data, and the resulting parameters are taken as a compact representation of the original grid-based data.
ENS-10: A Dataset For Post-Processing Ensemble Weather Forecasts
We propose the ENS-10 prediction correction task for improving the forecast quality at a 48-hour lead time through ensemble post-processing.
