Palabras clave
Precipitation
Weather types
Cluster analysis
Seasonal climate variability
América Central
Precipitación
Tipos de tiempo atmosférico
Análisis de conglomerados
Variabilidad climática estacional
Cómo citar
Resumen
Técnicas de aprendizaje no supervisado se emplean para estudiar la relación entre la circulación atmosférica y la precipitación sobre América Central y sus áreas circundantes. Específicamente, el algoritmo de agrupamiento k-means++ se aplica a tres conjuntos de datos de baja resolución del reanálisis ERA interim, estos son candidatos a representar el vector de estado atmosférico y cada uno contiene su variabilidad temporal completa. Los conjuntos de datos probados son: a) campos de viento a 925, 800 y 200 hPa, b) lo mismo que “a)” más la energía potencial convectiva disponible y c) lo mismo que “a)” más el vapor de agua en la columna total. Se calculan métricas de agrupamiento, a saber, el criterio de relación de varianza, el criterio de silueta y el error cuadrático medio, para cuantificar la calidad del agrupamiento. Los grupos se interpretan como weather types, configuraciones recurrentes del vector de estado atmosférico asociadas con estados observables del tiempo atmosférico. El número correcto de grupos para cada conjunto de datos se determina con una prueba de normalidad de Monte Carlo para asegurar la existencia de grupos reales. El objetivo principal es obtener un conjunto de weather types que contengan elementos que caractericen la transición de y hacia la temporada de lluvias en la vertiente del Pacífico de América Central, así como otros elementos del ciclo estacional de precipitación regional, como las canículas. Además de las métricas estadísticas, para seleccionar entre conjuntos de datos y un número plausible de grupos, se presta atención a las características temporales de los grupos. La literatura existente no proporciona un conjunto de weather types adecuado para analizar transiciones estacionales y las diferencias en los mecanismos asociados con los máximos estacionales de lluvia.
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Derechos de autor 2024 Fernán Sáenz S., Eric J. Alfaro, Hugo G. Hidalgo