Precipitation comparison for the CFSR, MERRA, TRMM3B42 and Combined Scheme datasets in Bolivia

Luis A. Blacutt, Dirceu L. Herdies, Luis Gustavo G. de Gonçalves, Daniel A. Vila, Marcos Andrade

Research output: Contribution to journalArticlepeer-review

93 Scopus citations

Abstract

An overwhelming number of applications depend on reliable precipitation estimations. However, over complex terrain in regions such as the Andes or the southwestern Amazon, the spatial coverage of rain gauges is scarce. Two reanalysis datasets, a satellite algorithm and a scheme that combines surface observations with satellite estimations were selected for studying rainfall in the following areas of Bolivia: the central Andes, Altiplano, southwestern Amazonia, and Chaco. These Bolivian regions can be divided into three main basins: the Altiplano, La Plata, and Amazon. The selected reanalyses were the Modern-Era Retrospective Analysis for Research and Applications, which has a horizontal resolution (~. 50. km) conducive for studying rainfall in relatively small precipitation systems, and the Climate Forecast System Reanalysis and Reforecast, which features an improved horizontal resolution (~. 38. km). The third dataset was the seventh version of the Tropical Rainfall Measurement Mission 3B42 algorithm, which is conducive for studying rainfall at an ~. 25. km horizontal resolution. The fourth dataset utilizes a new technique known as the Combined Scheme, which successfully removes satellite bias. All four of these datasets were aggregated to a coarser resolution. Additionally, the daily totals were calculated to match the cumulative daily values of the ground observations. This research aimed to describe and compare precipitations in the two reanalysis datasets, the satellite-algorithm dataset, and the Combined Scheme with ground observations. Two seasons were selected for studying the precipitation estimates: the rainy season (December-February) and the dry season (June-August). The average, bias, standard deviation, correlation coefficient, and root mean square error were calculated. Moreover, a contingency table was generated to calculate the accuracy, bias frequency, probability of detection, false alarm ratio, and equitable threat score.All four datasets correctly depicted the spatial rainfall pattern. However, CFSR and MERRA overestimated precipitation along the Andes' eastern-facing slopes and exhibited a dry bias over the eastern Amazon; TRMM3B42 and the Combined Scheme depicted a more realistic rainfall distribution over both the Amazon and the Andes. When separating the precipitation into classes, MERRA and CFSR overestimated light to moderate precipitation (1-20. mm/day) and underestimated very heavy precipitation (>. 50. mm/day). TRMM3B42 and CoSch depicted behaviors similar to the surface observations; however, CoSch underestimated the precipitation in very intense systems (>. 50. mm/day).The statistical variables indicated that CoSch's correlation coefficient was highest for every season and basin. Additionally, the bias and RMSE values suggested that CoSch closely represented the surface observations.

Original languageEnglish
Pages (from-to)117-131
Number of pages15
JournalAtmospheric Research
Volume163
DOIs
StatePublished - 5 Sep 2015
Externally publishedYes

Bibliographical note

Funding Information:
The first author would like to thank Jesus Christ “In whom are hid all the treasures of wisdom and knowledge” (Colossians 2:3, KJV); also, wishes to acknowledge the Grupo de Desenvolvimento e Assimilação de Dados at Centro de Previsão do Tempo e Estudos Climáticos for their support and collaboration in working with the surface stations. The authors are grateful to the three anonymous reviewers and the editor who helped in improving the quality of the original manuscript. This work was carried out with the aid of a grant from the Inter-American Institute for Global Change Research ( IAI ) CRN3035 which is supported by the US National Science Foundation (Grant GEO-1128040 ). We also acknowledge the support of the funding agencies from Brazil ( CAPES , CNPq , and CAPES/PROEX-1613-2013 ) who made this research possible. Free Software packages like Ubuntu, CDO, GrADS, LibreOffice, and Gimp have been used for data analysis and graphics.

Publisher Copyright:
© 2015 The Authors.

Keywords

  • Data assimilation
  • Precipitation
  • Precipitation estimations
  • Statistical analysis

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