Version 1
: Received: 22 March 2024 / Approved: 26 March 2024 / Online: 26 March 2024 (11:23:33 CET)
How to cite:
Krasnopolsky, V. Applying Machine Learning in Numerical Weather and Climate Modeling Systems. Preprints2024, 2024031566. https://doi.org/10.20944/preprints202403.1566.v1
Krasnopolsky, V. Applying Machine Learning in Numerical Weather and Climate Modeling Systems. Preprints 2024, 2024031566. https://doi.org/10.20944/preprints202403.1566.v1
Krasnopolsky, V. Applying Machine Learning in Numerical Weather and Climate Modeling Systems. Preprints2024, 2024031566. https://doi.org/10.20944/preprints202403.1566.v1
APA Style
Krasnopolsky, V. (2024). Applying Machine Learning in Numerical Weather and Climate Modeling Systems. Preprints. https://doi.org/10.20944/preprints202403.1566.v1
Chicago/Turabian Style
Krasnopolsky, V. 2024 "Applying Machine Learning in Numerical Weather and Climate Modeling Systems" Preprints. https://doi.org/10.20944/preprints202403.1566.v1
Abstract
In this paper major machine learning (ML) tools and the most important applications developed elsewhere for numerical weather and climate modeling systems (NWCMS) are reviewed. NWCMSs are briefly introduced. The most important papers published in this field in recent years are reviewed. The advantages and limitations of the ML approach in applications to NWCMS are briefly discussed. Currently, this field is experiencing explosive growth. Several important papers are published every week. Thus, this paper should be considered a simple introduction to the problem.
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.