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Found 5 papers, 0 papers with code
Online model error correction with neural networks: application to the Integrated Forecasting System
In this article, we propose to develop a model error correction for the operational Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts using a neural network.
On some limitations of data-driven weather forecasting models
As in many other areas of engineering and applied science, Machine Learning (ML) is having a profound impact in the domain of Weather and Climate Prediction.
Online model error correction with neural networks in the incremental 4D-Var framework
Data assimilation is used to estimate the system state from the observations, while machine learning computes a surrogate model of the dynamical system based on those estimated states.
A comparison of combined data assimilation and machine learning methods for offline and online model error correction
We compare online and offline learning using the same framework with the two-scale Lorenz system, and show that with online learning, it is possible to extract all the information from sparse and noisy observations.
Using machine learning to correct model error in data assimilation and forecast applications
This yields a class of iterative methods in which, at each iteration a DA step assimilates the observations, and alternates with a ML step to learn the underlying dynamics of the DA analysis.
