[A-TT29] Machine Learning Techniques in Weather, Climate, Ocean, Hydrology and Disease Predictions

[ATT29-05] Update of global maps of Alisov's climate classification using an unsupervised machine-learning algorithm

*Ryu Shimabukuro¹, Tomohiko Tomita², Ken-ichi Fukui³ (1.Graduate School of Science and Technology, Kumamoto University, 2.Faculty of Advanced Science and Technology, Kumamoto University, 3.The Institute of Scientific and Industrial Research, Osaka University)

Keywords:Alisov's climate classification, Air mass, Front, unsupervised machine-learning

Alisov's climate classification was proposed in 1954, and it focuses on the January–July changes in large-scale air mass zones and their fronts. In this study, data clustering by machine learning was applied to global reanalysis data to quantitatively and objectively determine air mass zones, which were then used to classify the global climate. The differences in air mass zones between two half-year seasons were used to determine climatic zones, which were then subdivided into continental or maritime climatic regions or according to east–west climatic differences. This study began by questioning whether the global climate can be divided into four air mass zones as Alisov did in the 1950s. The results showed that Alisov's four air mass zones from the 1950s were supported from a modern data-driven perspective using high-quality global reanalysis data. In addition, the clustering technique accurately captured frontal precipitation between air mass zones in the mid-and high latitudes. This study, thus, renews Alisov's climate classification for the first time in almost 70 years and applies data-driven machine learning to establish a standard for air-mass-based climate classification. This paper has been peer-reviewed once in Progress in Earth and Planetary Science (PEPS) and a revised manuscript is in preparation.