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

Bogdan Bochenek,Zbigniew Ustrnul

doi:10.3390/atmos13020180

Bogdan Bochenek, Zbigniew Ustrnul

Open Access

https://doi.org/10.3390/atmos13020180

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Abstract

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.

Highlights

The beginning of the 21st century, with the advent of big data, efficient supercomputers with Graphics Processing Units (GPU), and scientific interest in emerging new methods, turned out to be crucial in the history of machine learning [1]
The main goal of this study is to present a review of the machine learning methods and applications within the main topics of meteorology, as well as in climate analyses
(published since 2018), obtained from the Google Scholar search engine google.com/, accessed on 10 November 2021), which were related to the phrases “numerical, which were related to the weather prediction” and “machine learning”—250 papers, and “climate” and “machine phrases “numerical weather prediction” and “machine learning”—250 papers, and “clilearning”—250 papers

Summary

Introduction

The beginning of the 21st century, with the advent of big data, efficient supercomputers with Graphics Processing Units (GPU), and scientific interest in emerging new methods, turned out to be crucial in the history of machine learning [1]. Detailed reviews of machine learning algorithms, as the most important subgroup of artificial intelligence methods (Figure 1) in atmospheric science, can be found in many thematic articles [2,3,4]. In these publications, one can find details about many methods and their classifications. The most interesting group of techniques was found to be supervised learning (Figure 1), the most dominant group in the recent publications in the field. In that group we find methods, such as Decision Trees, e.g., Random

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