Machine Learning in Weather Prediction and Climate Analyses-Applications and Perspectives

In this paper, we performed an analysis of the 500 most relevant scientific articles published since 2018, concerning machine learning methods in the field of climate and numerical weather prediction using the Google Scholar search engine. The most common topics of interest in the abstracts were identified, and some of them examined in detail: in numerical weather prediction research-photovoltaic and wind energy, atmospheric physics and processes; in climate research-parametrizations, extreme events, and climate change. With the created database, it was also possible to extract the most commonly examined meteorological fields (wind, precipitation, temperature, pressure, and radiation), methods (Deep Learning, Random Forest, Artificial Neural Networks, Support Vector Machine, and XGBoost), and countries (China, USA, Australia, India, and Germany) in these topics. Performing critical reviews of the literature, authors are trying to predict the future research direction of these fields, with the main conclusion being that machine learning methods will be a key feature in future weather forecasting.

Automated summary

In this paper, an analysis of the most relevant scientific articles on machine learning methods in the field of climate and numerical weather prediction was conducted. The common topics of interest and the most frequently examined meteorological fields, methods, and countries were identified through the analysis of abstracts. The authors predicted that machine learning methods will play a key role in future weather forecasting based on critical reviews of the literature.