Q-triplet characterization of atmospheric time series at Antofagasta: A missing values problem

TL;DR

This paper analyzes long-term atmospheric data from Antofagasta, Chile, using q-triplet methodology to understand climate dynamics despite challenges posed by missing and irregular data points.

Contribution

It introduces a comprehensive statistical approach employing q-triplet analysis to characterize climate time series with missing and irregular data.

Findings

q-triplet values follow the pattern q_sens<1<q_stat<q_rel

Identified distinctive behavior in atmospheric variables

Demonstrated effectiveness of q-triplet in non-equilibrium systems

Abstract

Located in northern Chile (23.7{\deg}S, 70.4{\deg}W), Antofagasta has an exceptionally arid and stable climate characterized by minimal precipitation and consistent weather patterns. Nevertheless, despite these climate conditions being meaningful for several research and practical applications, our understanding of weather dynamics remains limited. The available meteorological data from 1969 to 2016 is analogical, which presents a significant challenge to analyze because these records are riddled with missing values, some measurements were taken at irregular measuring intervals, making it an interesting puzzle to grasp the Antofagasta's climate scenario. To overcome this issue, we present a comprehensive statistical analysis of atmospheric temperature, pressure, and humidity time series. Our analytical approach involves the q-triplet calculation method, serving as a powerful tool to identify distinctive behavior within systems under non-equilibrium states. Our results suggest that, in general, the q-triplet values satisfy the condition $q_\text{sens}<1<q_\text{stat}<q_\text{rel}$, a pattern that has been observed in previous studies.