Space-Time Assessment of Extreme Precipitation in Cuba between 1980 and 2019 from Multi-Source Weighted-Ensemble Precipitation Dataset

Abstract

Precipitation extremes such as heavy rainfall and floods are of great interest for climate scientists, particularly for small islands vulnerable to weather phenomena such as hurricanes. In this study, we investigated the spatio-temporal evolution of extreme rainfall over Cuba from 1980 to 2019, separating the dry and rainy periods. In addition, a ranking of extreme precipitation events was performed, which provides the number of events, the area affected, and a ranking of their magnitude by considering the magnitude of anomalies. The analysis was conducted using daily data from the multi-source weighted-ensemble precipitation (MSWEPv2). In determining the extreme precipitation ranking, the daily extreme precipitation anomaly was calculated with respect to the 95th percentile climatological distribution, giving a measure of the rarity of the event for each day and each grid point. For a more detailed analysis regarding the ranking, a separation was made by regions applying the K-mean methodology. The months belonging to the rainy period of the year presented the highest amount of precipitation above the 95th percentile compared to results obtained for the dry period. Of the six months belonging to the cyclonic season, in five of them Cuba was affected, directly or indirectly, by a tropical cyclone. The years 1982–83 and 1998 presented the highest-ranking value for the dry and rainy periods, respectively. Moreover, a trend analysis revealed an increase in the trend of occurrence of extreme events and a decrease in the percentage of the area affected. The analysis by regions showed a similar behavior to that carried out for all of Cuba. It was found that the warm phase of the ENSO events influenced approximately ~22% of the occurrence of extreme events for both periods.

Analysis of the Gálvez–Davison Index for the Forecasting Formation and Evolution of Convective Clouds in the Tropics: Western Cuba

“…In this region, the average temperatures are around 25 • C, and the relative humidity is generally high, with values ranging between 75% and 95%. Also, this region is one of the three different zones for precipitation regimes in Cuba, according to previous studies [26]. The rainy season occurs from May to October and is especially pronounced in September and October; sea breezes prevail on both the north and south coasts.…”

“…In addition, it could show regions with values lower than 30 corresponding to the subsiding movements of the systems. Hurricane Irma (case not shown) was reported as one of the most extreme precipitation events affecting Cuba since 1980 [26], with maximum accumulated precipitation of 225 mm in the northern part of the study region. The maximum sustained winds were higher than 295 km/h, and it was the worst coastal flooding to date, with wave heights between 8 and 10 m [39].…”

“…In this case, similar results to Michael were obtained, with a limitation: the translation movement of the hurricane was not well forecasted, and although the precipitation and cloudiness matched with reality, the bands of cloudiness associated with it reached a geographical location one hour later than expected. Hurricane Irma (case not shown) was reported as one of the most extreme precipitation events affecting Cuba since 1980 [26], with maximum accumulated precipitation of 225 mm in the northern part of the study region. The maximum sustained winds were higher than 295 km/h, and it was the worst coastal flooding to date, with wave heights between 8 and 10 m [39].…”

Machine and deep learning approaches to understand and predict habitat suitability for seabird breeding

“…In addition, differences in marine current patterns between regions (e.g. Arriaza et al, 2008 ) and the influence of extreme climate events such as rainfall and hurricanes (Alvarez‐Socorro et al, 2021 ) should cause differences in local oceanography (e.g. SST, Chl a ) between coastal areas of Cuba.…”

Event‐based extreme precipitation variability analysis over a part of the Hindu Kush Himalayan region

“…This dataset's advantage is that it uses the complementary strengths of gauge‐, satellite‐ and reanalysis‐based data to provide reliable precipitation estimates and outperformed the other satellite estimates ERA‐Interim, PERSIANN‐CDR and TRMM in the Himalayan catchment (Abro et al, 2020). This dataset has been widely used in various studies (Alvarez‐Socorro et al, 2021; Awange et al, 2019; Chen et al, 2020) over different parts of the globe and found that it provides a reliable estimation of precipitation in the Indus River basin (Dahri et al, 2021).…”