# Machine Learning Methods in Weather and Climate Applications: A Survey

Article · Liuyi Chen, Bocheng Han, Xuesong Wang, Jiazhen Zhao, Wenke Yang, Zhengyi Yang

- Archive id: doi:10.3390/app132112019
- Published: 2023-11-03
- Venue: Applied Sciences
- DOI: https://doi.org/10.3390/app132112019
- Citations: 246
- Source URL: https://doi.org/10.3390/app132112019
- PDF: https://www.mdpi.com/2076-3417/13/21/12019/pdf?version=1699023313
- Provenance: openalex

## 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.

_Abstract reproduced under CC-BY (publisher license, via OpenAlex)._

Topics: [AI Weather & Climate Modeling](https://space-kitty.com/topics/ai-weather-climate)

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