Processing of Rainfall Time Series Data in the State of Rio de Janeiro

Authors

  • Givanildo de Gois Federal University of Rondônia Foundation (UNIR), Porto Velho, Rondônia, 76801-059, Brazil
  • José Francisco de Oliveira-Júnior Applied Meteorology and Environment Laboratory (LAMMA), Institute of Atmospheric Sciences (ICAT), Federal University of Alagoas (UFAL), Maceió, Alagoas, 57072-900, Brazil

DOI:

https://doi.org/10.30564/jasr.v4i4.3603

Abstract

The goal was to perform the filling, consistency and processing of the rainfall time series data from 1943 to 2013 in five regions of the state. Data were obtained from several sources (ANA, CPRM, INMET, SERLA and LIGHT), totaling 23 stations. The time series (raw data) showed failures that were filled with data from TRMM satellite via 3B43 product, and with the climatological normal from INMET. The 3B43 product was used from 1998 to 2013 and the climatological normal over the 1947- 1997 period. Data were submitted to descriptive and exploratory analysis, parametric tests (Shapiro-Wilks and Bartlett), cluster analysis (CA), and data processing (Box Cox) in the 23 stations. Descriptive analysis of the raw data consistency showed a probability of occurrence above 75% (high time variability). Through the CA, two homogeneous rainfall groups (G1 and G2) were defined. The group G1 and G2 represent 77.01% and 22.99% of the rainfall occurring in SRJ, respectively. Box Cox Processing was effective in stabilizing the normality of the residuals and homogeneity of variance of the monthly rainfall time series of the five regions of the state. Data from 3B43 product and the climatological normal can be used as an alternative source of quality data for gap filling.

Keywords:

Data validation； Parametric tests； Cluster analysis

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How to Cite

de Gois, G., & de Oliveira-Júnior, J. F. (2021). Processing of Rainfall Time Series Data in the State of Rio de Janeiro. Journal of Atmospheric Science Research, 4(4), 19–35. https://doi.org/10.30564/jasr.v4i4.3603

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Vol. 4 , Iss. 4 (October 2021)

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Article

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This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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