PreprintCC BY-NC-ND 4.0arXiv
The interface of data assimilation and machine learning
Abstract
Data assimilation (DA) is the process of combining forecasts from a model with observations in order to optimally estimate the state of a system. This is critical for chaotic systems, such as the atmosphere, since if observations are not continually assimilated the model will quickly lose skill. DA is routinely performed (usually every 6 hours) at operational forecasting centres around the world. In this article we discuss the interface of machine learning (ML) and DA. This is still an emerging and quickly developing field, and this article tries to give an overview of some of the main topics and methods.
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BibTeX
@misc{bach2026interface,
title = {{The interface of data assimilation and machine learning}},
author = {Eviatar Bach},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.07496},
eprint = {2610.07496},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.07496},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - The interface of data assimilation and machine learning
AU - Eviatar Bach
PY - 2026
DA - 2026/10/05
PB - arXiv
DO - 10.48550/arxiv.2610.07496
UR - https://arxiv.org/abs/2610.07496
AB - Data assimilation (DA) is the process of combining forecasts from a model with observations in order to optimally estimate the state of a system. This is critical for chaotic systems, such as the atmosphere, since if observations are not continually assimilated the model will quickly lose skill. DA is routinely performed (usually every 6 hours) at operational forecasting centres around the world. In this article we discuss the interface of machine learning (ML) and DA. This is still an emerging and quickly developing field, and this article tries to give an overview of some of the main topics and methods.
ER -