Evaluation of Sixteen Gridded Precipitation Datasets over the Caribbean Region Using Gauge Observations
2. Data and Methods
2.1. Study Area
3. Results and Discussion
3.1. The Mean Annual Cycle of Precipitation
3.2. Evaluation of GPP Based on the Modified Efficiency Index of Kling-Gupta
- Based on the KGE index, GPCC025 and GPCC05 (gauge only), MSWEP and MSWEP2 (gauge, satellite, and reanalysis), and CHELSA (gauge and reanalysis) display an overall good performance, including for each season and the whole year. Of the two datasets that merge rain-gauge and satellite information, we find that, at least in our study domain, PERSIANNCDR performs worse than CHIRPS. We suggest that the use of a coarse precipitation dataset (2.5°) such as GPCP , possibly reduces the skill of PERSIANNCDR to adequately represent precipitation features in the Caribbean.
- CRU and PRECL datasets are among the station-based products with the lowest performance in representing precipitation variability across the Caribbean. This is likely due to the lower number of surface station taken into account in their development, as compared with other station based GPP such as GPCC025, GPCC05, and HERREAULT.
- Although we expected similar performance for HERREAULT as GPCC, considering the former was built upon a GPCC product , this was not observed. In this regard, we found significant differences between the GPCC version 7 (used in HERREAULT) and version 8 (evaluated here), as shown by their low correlations over our study domain (Figure 6). This could in part explain the differences in the performance between HERREAULT and the GPCC products used in this paper. Nevertheless, further investigation should be carried out due to the potential to generate a more accurate regional high-resolution product (such as HERREAULT) based on the GPCC full data version 8.
- The lowest KGE scores, suggesting a relatively poor performance, correspond to most reanalyses (except for ERA5) and consistently CPCGLOBAL. The reanalysis products are among the GPP with the lowest performance, which reflects the limitations of the current atmospheric models to represent the fine-scale precipitation processes. This might be due to limitations in representing sub-grid convection and land-surface parameterization schemes. This result also reveals a constraint related to the spatial resolution needed to adequately represent the small islands of the Lesser Antilles. A possible solution to this limitation could be the incorporation of high-resolution, non-hydrostatic, regional models .
- Although ERA5 significantly underestimates precipitation across the Caribbean, it is the reanalysis product with the best performance based on the KGE scores. This is seen in the better correlation and variability scores with ground stations. This suggests its potential for use in the development of new GPP products for the Caribbean especially with bias correction techniques employed—which may prove easier than trying to improve the linear correlations . For example, it is hypothesized that the CHELSA product, which was developed based on ERA-Interim , may perform even better over the region if using ERA5 instead, which is currently the primary reanalysis model from the ECMWF.
3.3. GPP Evaluation Based on SPI
- With respect to the applied methodological approach, we conclude that:
- The combined analysis of the annual precipitation cycle and KGE and SPI metrics, provide a robust and comprehensive understanding of the performance of the considered precipitation products for the Caribbean. While the comparison of the annual precipitation cycles between GPP and observations provided useful preliminary information about the best and worst GPP, the KGE index added a global and objective picture of the GPP abilities by integrating correlation, bias, and variability. At the same time, SPIs gave insights about the GPP skill with respect to other important features of precipitation. For instance, ERA5 was the worst reanalysis product in representing the annual precipitation cycle for the Caribbean, underestimating the total monthly precipitation. However, it performed better than other GPP in the SPI analysis, mainly because of its higher correlation with observations. Similarly, GPP, such as CHELSA or TERRACLIMATE, were among the best for some individual performance indicators (e.g., DF and DE statistics) but did not show similar ability in other ones (e.g., SPIAA moderate and severe (Figure 8b,c)).
- With respect to the annual cycle analysis, we conclude that:
- The analysis of the annual precipitation cycle revealed a high correspondence between GPP and observations across the Caribbean as whole, as well as for the two sub-regions analysed. Features such as the MSD and the maximum of rainfall during November were well represented for the Western and Eastern sub-regions respectively. There was a better performance for the majority of the GPP over the Western Caribbean when compared with the eastern side. This might either be due to the low station density used in the development of these GPP, especially over the Eastern Caribbean, or the inability of the reanalysis products to properly represent the small territories and the precipitation processes that happen on these small land areas. Considering this analysis, we suggest that the best performing products for the whole Caribbean and the sub-regions were the gauge-based GPCC025 and GPCC05 and the gauge and satellite-based CHIRPS.
- With respect to the evaluation based on the KGE index we highlight the following:
- Although correlation has a strong influence in the KGE scoring, it was the worst component captured by the GPP. We suggest that for further improvement or development of GPP for use in the Caribbean, improving correlation should be a primary goal.
- Generally speaking, the reanalysis products were among the lowest-performing GPP group, although some of them performed better than PERSIANCDR (gauge and satellite) and CPCGLOBAL (gauge, satellite, and reanalysis). The lower representativeness of reanalysis suggests deficiencies of atmospheric models to reproduce the small-scale precipitation processes. In this group, ERA5 had the best performance, despite its significant precipitation underestimation. This result calls the attention of the potential users of ERA5 to develop new regional precipitation products by using bias correction or downscaling techniques.
- On the basis of the KGE analysis, GPCC025, GPCC05, MSWEP, and MSWEP2 yielded better results for both annual and seasonal time scales. We suggest that the use of higher surface station density in these GPP highly influenced this outcome, notwithstanding that their development methodologies are different.
- With respect to the evaluation based on SPI, we observe the following:
- Overall, the GPP fail in representing accurately the SPI-0.5 statistics, having different ratings for different statistics. The GPP with best performance were MSWEP, MSWEP2, ERA5, and CHELSA. For SPI-1.5, the GPP better resemble the observations with a discernible distinction between the worst and best performers, and a tendency to overestimate the maximum consecutive months with drought and the number of drought events. In general, products such as MSWEP2, ERA5, CHELSA, and MSWEP were the most accurate.
- Considering the areas affected by different SPI categories, all GPP perform better for pluvial conditions than for drought for all SPI lengths. For wet periods, the correlations were higher, and the amplitudes were greater but closer to the observations. The GPCC025, MSWEP, MSWEP2, CHIRPS, and HERREAULT products had the best performance.
- The strategy to explore GPP performance across varying SPI statistics as well as using the temporal variations of the areas affected by pluvial and drought conditions are complementary. This provides a more robust evaluation of the GPP and avoids potential bias in the interpretation of results.
Conflicts of Interest
|No.||Station Name||Country||Latitude||Longitude||No.||Station Name||Country||Latitude||Longitude|
|1||Central Farm (CAYO)||Belize||17.1||−89.0||31||Nuevitas||Cuba||21.6||−77.2|
|2||Melinda (STANN Creek)||Belize||17.0||−88.3||32||Manzanillo||Cuba||20.2||−77.2|
|3||Phillip Goldson International Airport||Belize||17.5||−88.3||33||Las Tunas||Cuba||20.9||−76.9|
|4||Cabo San Antonio||Cuba||21.9||−85.0||34||Jucarito||Cuba||20.7||−76.9|
|5||Paso Real de San Diego||Cuba||22.6||−83.3||35||Orange||Jamaica|
|7||La Fe||Cuba||21.7||−82.8||37||La Jíquima||Cuba||20.9||−76.5|
|8||Punta del Este||Cuba||21.6||−82.5||38||Universidad||Cuba||20.0||−75.8|
|10||Owen Robert A. Georgetown||Cayman||19.3||−81.4||40||Maisí||Cuba||20.2||−74.1|
|12||Aguada de Pasajeros||Cuba||22.4||−80.8||42||Barahona||Dominican Republic||18.2||−71.1|
|13||Cienfuegos||Cuba||22.2||−80.4||43||Padre Las Casas||Dominican Republic||18.7||−70.9|
|14||Santo Domingo||Cuba||22.6||−80.2||44||Santo Domingo||Dominican Republic||−69.5|
|15||Sagua la Grande||Cuba||22.8||−80.1||45||Las Americas||Dominican Republic||18.3||−69.4|
|16||Topes de Collante||Cuba||21.9||−80.0||46||San Juan||Puerto Rico||18.5||−66.1|
|17||Yabú||Cuba||22.5||−80.0||47||St. Thomas||Virgin Islands||18.3||−65.0|
|18||Trinidad||Cuba||21.8||−80.0||48||St. Croix||Virgin Islands||17.7||−64.8|
|19||Sancti Spíritus||Cuba||22.0||−79.4||49||National Agricultural Station||St. Kitts||17.3||−62.7|
|20||Jibaro||Cuba||21.7||−79.2||50||V.C. Bird International Airport||Antigua||17.1||−61.5|
|21||Júcaro||Cuba||21.6||−78.9||51||St. Augustine||Trinidad & Tobago||10.6||−61.4|
|23||Camilo Cienfuegos||Cuba||22.2||−78.8||53||Melville Hall Airport||Dominica||15.3||−61.3|
|24||Florida||Cuba||21.5||−78.3||54||Piarco||Trinidad & Tobago||10.6||−61.3|
|25||Esmeralda||Cuba||21.8||−78.1||55||Arnosvale/E.T. Joshua Airport||St. Vincent||13.1||−61.2|
|26||Santa Cruz del Sur||Cuba||20.7||−78.0||56||Lamentin||Martinique||14.6||−61.0|
|28||Bsavannah||Jamaica||58||Crown Point||Trinidad & Tobago||11.2||−60.8|
|29||Cabo Cruz||Cuba||19.8||−77.7||59||Caribbean Institute for Meteorology and Hidrology||Barbados||13.2||−59.6|
|30||Palo Seco||Cuba||21.1||−77.3||60||Grantley Adams Airport||Barbados||13.1||−59.5|
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|No||Abbreviation||Sources||Full Name||Spatial Resolution||Temporal Coverage||References|
|1||CRU||G||Climatic Research Unit (CRU) Time-Series (CRUTS4.01)||0.5°|
|2||CPCGlobal||G||Climate Prediction Center (CPC) Unified||0.5°|
|3||PRECL||G||PRECipitation REConstruction over Land (PREC/L)||0.5°|
|4||GPCC05||G||Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis||0.5°|
|5||GPCC025||G||Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis||0.25°|
|6||HERREAULT||G||High resolution Regional statistical downscaled GPCC v7 for the Caribbean||0.033°|
|7||CHIRPS||G,S||Climate Hazards group Infrared Precipitation with Stations (CHIRPS)||0.05°|
|8||PERSIANNCDR||G,S||Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Climate Data Record (CDR) PERSIANN-CDR||0.25°||1983–present|||
|9||TERRACLIMATE||G,R||High-resolution global dataset of monthly climate and climatic water balance from 1958–2015||0.0416°|
|10||CHELSA||G,R||Climatologies at high resolution for the earth’s land surface areas||0.0083°|
|11||CFSR||R||National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR)||0.3125°||1979–2010|||
|12||ERAi||R||European Center for Medium-range Forecast Reanalysis Interim (ERA-Interim)||0.25°||1979–2017|||
|13||ERA5||R||European Center for Medium-range Forecast Reanalysis||0.25°||1979–present|||
|14||JRA55||R||Japanese 55-year Reanalysis (JRA-55)||0.56°||1959–present|||
|15||MSWEP||G,S,R||Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1||0.25°|
|16||MSWEP2||G,S,R||Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 2||0.1°|
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Centella-Artola, A.; Bezanilla-Morlot, A.; Taylor, M.A.; Herrera, D.A.; Martinez-Castro, D.; Gouirand, I.; Sierra-Lorenzo, M.; Vichot-Llano, A.; Stephenson, T.; Fonseca, C.; Campbell, J.; Alpizar, M. Evaluation of Sixteen Gridded Precipitation Datasets over the Caribbean Region Using Gauge Observations. Atmosphere 2020, 11, 1334. https://doi.org/10.3390/atmos11121334
Centella-Artola A, Bezanilla-Morlot A, Taylor MA, Herrera DA, Martinez-Castro D, Gouirand I, Sierra-Lorenzo M, Vichot-Llano A, Stephenson T, Fonseca C, Campbell J, Alpizar M. Evaluation of Sixteen Gridded Precipitation Datasets over the Caribbean Region Using Gauge Observations. Atmosphere. 2020; 11(12):1334. https://doi.org/10.3390/atmos11121334Chicago/Turabian Style
Centella-Artola, Abel, Arnoldo Bezanilla-Morlot, Michael A. Taylor, Dimitris A. Herrera, Daniel Martinez-Castro, Isabelle Gouirand, Maibys Sierra-Lorenzo, Alejandro Vichot-Llano, Tannecia Stephenson, Cecilia Fonseca, Jayaka Campbell, and Milena Alpizar. 2020. "Evaluation of Sixteen Gridded Precipitation Datasets over the Caribbean Region Using Gauge Observations" Atmosphere 11, no. 12: 1334. https://doi.org/10.3390/atmos11121334