Articles | Volume 15, issue 8
Geosci. Model Dev., 15, 3433–3445, 2022
https://doi.org/10.5194/gmd-15-3433-2022
Geosci. Model Dev., 15, 3433–3445, 2022
https://doi.org/10.5194/gmd-15-3433-2022
Development and technical paper
02 May 2022
Development and technical paper | 02 May 2022

Efficient high-dimensional variational data assimilation with machine-learned reduced-order models

Romit Maulik et al.

Model code and software

AIEADA/LSTM_Var_Prototype: GMD-2021- 65 415: AIEADA 1.0: Efficient high-dimensional variational data assimilation with machine-learned reducedorder models (GMD_v1) Romit Maulik https://doi.org/10.5281/zenodo.6382921

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Short summary
In numerical weather prediction, data assimilation is frequently utilized to enhance the accuracy of forecasts from equation-based models. In this work we use a machine learning framework that approximates a complex dynamical system given by the geopotential height. Instead of using an equation-based model, we utilize this machine-learned alternative to dramatically accelerate both the forecast and the assimilation of data, thereby reducing need for large computational resources.