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Revista de Matemática Teoría y Aplicaciones

Print version ISSN 1409-2433

Rev. Mat vol.28 n.2 San José Jul./Dec. 2021

http://dx.doi.org/10.15517/rmta.v28i2.42322 

Artículo

Análisis de los conglomerados de precipitación y sus cambios estacionales sobre América Central para el periodo 1976-2015

Analysis of precipitation clusters and their seasonal changes over Central America for the 1976-2015 period

Tito Maldonado1 

Eric J. Alfaro2 

Hugo G. Hidalgo3 

1Universidad de Costa Rica, Centro de Investigaciones Geofísicas (CIGEFI), San José, Costa Rica; tito.maldonado@ucr.ac.cr

2Universidad de Costa Rica, Centro de Investigaciones Geofísicas (CIGEFI), Escuela de Física y Centro de Investigación en Ciencias del Mar y Limnología (CIMAR), San José, Costa Rica; erick.alfaro@ucr.ac.cr

3Universidad de Costa Rica, Centro de Investigaciones Geofísicas (CIGEFI) y Escuela de Física, San José, Costa Rica; hugo.hidalgo@ucr.ac.cr

Resumen

La ubicación geográfica de América Central juega un papel importante en la descripción de la variabilidad climática de la región. Está rodeada por dos grandes masas de agua, el Pacífico Tropical del Este en el lado occidental y el Mar Caribe en el lado oriental. La región es sensible al efecto de los sistemas dinámicos tanto a gran escala como a escala regional que actúan en sus proximidades, siendo la topografía el principal modulador local de la variabilidad en la región. Para tener en cuenta esta variabilidad climática espacial se utilizaron 57 estaciones meteorológicas con datos diarios de precipitación en América Central para el periodo 1976-2015. Se definieron índices mensuales para describir cuánto y cómo llueve: acumulado total mensual (ACU), cantidad de días con lluvia (DCP), porcentaje de días que no sobrepasan el percentil 20 (extremos secos), y el porcentaje de días que exceden el percentil 80 (extremos húmedos). Por medio de técnicas de aprendizaje automatizado (análisis de conglomerados) se estimaron un número óptimo de grupos para cada variable. La optimización se realizó utilizando el estadístico de brecha. Se encontró un patrón de grupos localizados principalmente en la vertiente Pacífico y Caribe, mientras que en todas las variables se identificó un grupo localizado en la región del Caribe de Costa Rica. Al analizar los cambios en los 40 años de análisis, no se encontraron cambios ni tendencias significativos en las escalas de tiempo mensual estacional y anual ni a nivel de estación, grupal ni regional.

Palabras clave: América Central; precipitación; análisis de conglomerados; aprendizaje automatizado; estadístico de brecha; análisis de tendencias; variabilidad climática.

Abstract

The geographical location of Central America plays a significant role in describing the climate variability of the region. It is surrounded by two large water masses, the Eastern Tropical Pacific ocean on the western side and the Caribbean Sea on the eastern side. The region is sensitive to the effect of both large-scale and regional-scale dynamical systems acting in its vicinity, being the topography the main local modulator of the variability in the region. To account this spatial climate variability we used 57 meteorological stations with daily precipitation data in Central America for the period 1976-2015. Monthly indices were defined to describe how much and how it rains: monthly total accumulated (ACU), number of days with rain (DCP), percentage of days that do not exceed the 20th percentile (dry extremes), and the percentage of days that exceeds the percentile 80th (wet extreme). Using automated learning techniques (cluster analysis), an optimal number of groups was estimated for each variable. Optimization was performed using the gap statistic. A pattern of groups located primarily on the Pacific and Caribbean slopes was found, while in all variables a group located in the Caribbean region of Costa Rica was identified. When analyzing the changes in the 40 years of analysis, no significant changes or trends were found in the monthly seasonal or annual time scales or at the station, group or regional level.

Keywords: Central America; precipitation; cluster analysis; trend analysis; unsupervised machine learning; gap statistic; climate variability.

Mathematics Subject Classification: 62H30, 91C20, 86A10, 86-08.

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Financiamiento y agradecimientos

Los autores agradecen a los siguientes proyectos inscritos en la Universidad de Costa Rica: 805-C0-610 (Fondo de Estímulo), C0-074, B8-766 (Redes), B9-454 (Grupos), B0-810 y EC-497 (VarClim).

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Recibido: 03 de Julio de 2020; Revisado: 25 de Noviembre de 2020; Aprobado: 25 de Noviembre de 2020

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