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)
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.
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
With the rapid development of artificial intelligence, machine learning is gradually becoming popular in predictions in all walks of life. In meteorology, It is gradually competing with traditional climate predictions dominated by physical models. This survey aims to consolidate the current understanding of Machine Learning (ML) applications in weather and climate prediction—a field of growing importance across multiple sectors including agriculture and disaster management. Building upon an exhaustive review of more than 20 methods highlighted in existing literature, this survey pinpointed eight techniques that show particular promise for improving the accuracy of both short-term weather and medium-to-long-term climate forecasts. According to the survey, while ML demonstrates significant capabilities in short-term weather prediction, its application in medium-to-long-term climate forecasting remains limited, constrained by factors such as intricate climate variables and data limitations. Current literature tends to focus narrowly on either short-term weather or medium-to-long-term climate forecasting, often neglecting the relationship between the two, as well as general neglect of modelling structure and recent advances. By providing an integrated analysis of models spanning different time scales, this survey aims to bridge these gaps, thereby serving as a meaningful guide for future interdisciplinary research in this rapidly evolving field.
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.
Received:
30 October 2023
Commenter:
LIUYI CHEN
Commenter's Conflict of Interests:
Author
Comment:
The main changes in the article are: 1. the addition of an ML model for short-term weather prediction, GraphCast; 2. the consideration of an additional ML-enhanced method that uses ML as a parameterized simulation of a physical model; 3. the enhancement of the comparison of the results of the various methods.
Commenter: LIUYI CHEN
Commenter's Conflict of Interests: Author