Review
Version 1
Preserved in Portico This version is not peer-reviewed
Machine Learning Methods in Climate Prediction: A Survey
Version 1
: Received: 21 September 2023 / Approved: 25 September 2023 / Online: 26 September 2023 (13:45:25 CEST)
Version 2 : Received: 28 October 2023 / Approved: 30 October 2023 / Online: 30 October 2023 (17:09:12 CET)
Version 2 : Received: 28 October 2023 / Approved: 30 October 2023 / Online: 30 October 2023 (17:09:12 CET)
A peer-reviewed article of this Preprint also exists.
Chen, L.; Han, B.; Wang, X.; Zhao, J.; Yang, W.; Yang, Z. Machine Learning Methods in Weather and Climate Applications: A Survey. Appl. Sci. 2023, 13, 12019. Chen, L.; Han, B.; Wang, X.; Zhao, J.; Yang, W.; Yang, Z. Machine Learning Methods in Weather and Climate Applications: A Survey. Appl. Sci. 2023, 13, 12019.
Abstract
Weather and climate prediction have been crucial in human history for enabling effective agricultural planning, safeguarding against natural disasters, and facilitating strategic decision-making in various sectors. In this context, the need for accurate and timely forecasting is highly significant. Machine learning has the potential to improve the accuracy and speed of weather and climate prediction. This comprehensive survey assesses the efficacy of Machine Learning (ML) and Machine Learning-Enhanced (ML-Enhanced) methodologies in short-term weather forecasting and medium-to-long-term climate prediction. Acknowledging the historical importance of weather and climate prediction as crucial tools affecting human life and societal functions, the paper scrutinizes over 20 methods, highlighting six that appear to be at the forefront of ML applications in meteorological forecasting. While ML shows promising results in short-term weather prediction, its role remains supplementary in medium-to-long-term climate forecasting due to the complexity of climate conditions and limited data samples. The study finds ML to be fast, scalable, and relatively accurate; however, traditional methods remain indispensable, particularly for medium-to-long-term forecasts, where understanding the mechanisms of meteorological changes is pivotal.
Keywords
Machine-learning; Weather prediction; Climate prediction; Survey
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
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.
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