Improved seasonal prediction skill of rainfall for the Primera season in Central America

Abstract

This study explores the predictive skill of seasonal rainfall characteristics for the first rainy (and planting) season, May–June, in Central America. Statistical predictive models were built using a Model Output Statistics (MOS) technique based on canonical correlation analysis, in which variables that forecast with the Climate Forecast System version 2 (CFSv2) were used as candidate predictors for the observed total precipitation, frequency of rainy days and mean number of extremely dry and wet events in the season. CFSv2 initializations from February to April were explored. The CFSv2 variables used in the study consist of rainfall, as in a typical MOS technique, and a combination of low‐level winds and convective available potential energy (CAPE), a blend that has been previously shown to be a good predictor for convective activity. The highest predictive skill was found for the seasonal frequency of rainy days, followed by the mean frequency of dry events. In terms of candidate predictors, the zonal transport of CAPE (uCAPE) at 925 hPa offers higher skill across Central America than rainfall, which is attributed in part to the high model uncertainties associated with precipitation in the region. As expected, dynamical model predictors initialized in February provide lower skill than those initialized later. Nonetheless, the skill is comparable for March and April initializations. These results suggest that the National Meteorological and Hydrological Services in Central America, and the Central American Regional Climate Outlook Forum, can produce earlier more skilful forecasts for May–June rainfall characteristics than previously stated.

Sub-monthly evolution of the Caribbean Low-Level Jet and its relationship with regional precipitation and atmospheric circulation

“…Therefore lagged links with regional circulation and hydroclimate (e.g., Serra et al 2010;Martin and Schumacher 2011) do not account for the underlying evolution of the CLLJ in modulating its climate signature. This is a potentially important shortcoming in investigating causal relationships and their underpinning mechanisms involving rapidly-evolving atmospheric dynamical features, such as the CLLJ, and ultimately hinders our understanding of the generation of sub-seasonal hydroclimate events over Central America (e.g., Alfaro et al 2018).…”

Comparison of parameters of the generalized extreme value distribution associated with extreme rainfall events in Central America

“…Generalmente, el concepto de evento extremo está relacionado principalmente con la magnitud del impacto y no con la frecuencia con la que ocurren (Retana, 2012). Sin embargo, Alfaro et al (2018) y Maldonado et al (2016Maldonado et al ( , 2013 han usado la frecuencia de eventos extremos para estudiar la predictibilidad en América Central, a lo largo del ciclo anual de las precipitaciones, y han encontrado buena habilidad predictiva de los modelos ajustados. En este contexto, 114 se propone la DGVE como un modelo probabilístico para eventos de lluvia extrema.…”

Asociación entre algunos índices de sequía e impactos socio-productivos en el Pacífico Norte de Costa Rica

“…Los índices de sequía utilizados fueron los siguientes: Índice Normalizado de Sequía, conocido en inglés como Standardized Precipitation Index (SPI; Mckee, 1993) para 6 meses y 12 meses específicamente, para los meses octubre y diciembre, lo cual describiría eventos anuales y durante la época lluviosa de la vertiente del Pacífico de Costa Rica (mayo-octubre, Alfaro et al, 2018a), el Índice Modificado de Anomalía de Lluvia conocido en inglés como Modified Rainfall Anomaly Index (mRAI) (Hänsel et al, 2016), el Índice de Severidad de Sequía y el Índice Hidrológico de Palmer, conocidos en inglés como Palmer Drought Severity Index (PDSI) (Palmer, 1965) y Palmer Hydrological Drought Index (PHDI) respectivamente y usados para periodos de sequía extensos (Dai, 2011), el Índice Z de Sequía de Palmer, conocido como Palmer Drought Z-Index, utilizado para sequías agrícolas (Zargar et al, 2011) y el Índice de Reconocimiento de Sequías, conocido como Reconnaissance Drought Index (RDI) (Tsakiris & Vangelis, 2005). A excepción del SPI, todos los índices fueron calculados mensualmente para cada una de las 20 estaciones y después se calcula la media anual para cada uno de ellos.…”