Machine Learning Models Applied to Weather Series Analysis

Abstract

In recent years the explosion in high-performance computing systems and high-capacity storage has led to an exponential increase in the amount of information, generating the phenomenon of big data and the development of automatic processing models like machine learning analysis. In this paper a machine learning time series analysis was experimentally developed in relation to the paroxysmal meteorological event “cloudburst” characterized by a very intense storm, concentrated in a few hours and highly localized. These extreme phenomena such as hail, overflows and sudden floods are found in both urban and rural areas. The predictability over time of these phenomena is very short and depends on the event considered, therefore it is useful to add data driven methods to the deterministic modeling tools to get the anticipated predictability of the event, also known as nowcasting. The detailed knowledge of these phenomena, together with the development of simulation models for the propagation of cloudbursts, can be a useful tool for monitoring and mitigating risk in civil protection contingency plans.