The existence of several gridded precipitation products (GPP) has facilitated studies related to climate change, climate modeling, as well as a better understanding of the physical processes underpinning this key variable. Due to complexities in estimating rainfall, gridded datasets exhibit different levels of accuracy across regions, even when they are developed at relatively high resolution or using sophisticated procedures. The performance of 16 GPP are evaluated over the Caribbean region, which includes the Caribbean Islands, and portions of Central South America. Monthly data for sixty weather stations are used as a reference for the period 1983–2010. The 16 GPP include six products based on station data only, two that combine ground station and satellite information, two merging station and reanalysis information, four based on reanalysis, and two using multisource information. The temporal resolution of the GPP ranged between daily and monthly and spatial resolution from 0.033° to 0.5°. The methodological approach employed combined a comparison of regional and sub-regional precipitation annual cycles, the Kling–Gupta efficiency (KGE) index, as well as several metrics derived from the standardized precipitation index (SPI). Overall, the best performances were obtained from GPCC025 and MSWEP2, likely reflecting the positive impact of the large number of station data utilized in their development. It is also demonstrated that a higher spatial resolution does not always mean better accuracy. There is a need for this kind of assessment when undertaking climate studies in regions like the Caribbean where resolution is a significant consideration. ERA5 performed best among the reanalyses analyzed and has the potential to be used to develop regionally based GPP by applying bias correction or downscaling techniques. The methodological approach employed provides a comprehensive and robust evaluation of the relative strengths and weaknesses of GPP in the Caribbean region.
During 2020, the Dominican Republic received the impact of several tropical organisms. Among those that generated the greatest losses in the country, tropical storm Isaias stands out because of the significant precipitation (327.6 mm at Sabana del Mar during 29–31 July 2020) and flooding it caused. The study analyzes the behavior of the products of the Flash Flood Guidance System (FFGS) and the Nowcasting and Very Short Range Prediction System (Spanish acronym SisPI) for the quantitative precipitation forecast (QPF) of the precipitation generated by Isaias on 30 July 2020 over the Dominican Republic. Traditional categorical verification and featured-based spatial verification methods are used in the study, taking as observation the quantitative precipitation estimation of GPM. The results show that both numerical weather prediction systems are powerful tools for QPF and also to contribute to the prevention and mitigation of disasters caused by the extreme hydro-meteorological event analyzed. For the forecast of rain occurrence, the HIRESW-NMMB product of FFGS presented the highest ability with a CSI greater than 0.4. The HIRESW-ARW and SisPI products not only presented high rates of false alarms but also performed better in forecasting heavy rain values. The results of the verification based on objects with the MODE are consistent with those obtained in the verification by categories. The HIRESW-NMMB product underestimated the intense rainfall values by approximately 60 mm, while HIRESW-ARW and SisPI tools presented minor differences, the latter being the one with the greatest skill.
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