Statistical Analysis of weather variables of Antofagasta

TL;DR

This paper analyzes the statistical properties of weather variables in Antofagasta, revealing their distributional characteristics, autocorrelation patterns, and seasonal behaviors using various statistical techniques.

Contribution

It applies a comprehensive statistical analysis including deseasonalization, distribution assessment, and autocorrelation measurement to weather data from Antofagasta, providing new insights into their behavior.

Findings

Distributions are symmetrical with heavy tails.

Variables exhibit high autocorrelation up to one year.

Weather variables have positive kurtosis and are heavily autocorrelated.

Abstract

The statistical behavior of weather variables of Antofagasta is described, especially the daily data of air as temperature, pressure and relative humidity measured at 08:00, 14:00 and 20:00. In this article, we use a time series deseasonalization technique, Q-Q plot, skewness, kurtosis and the Pearson correlation coefficient. We found that the distributions of the records are symmetrical and have positive kurtosis, so they have heavy tails. In addition, the variables are highly autocorrelated, extending up to one year in the case of pressure and temperature.