@Article{LoaizaCerónMoRiAnKaCa:2020:PrCoAn,
               author = "Loaiza Cer{\'o}n, Wilmar and Molina-Carpio, Jorge and Rivera, 
                         Irma Ayes and Andreoli, Rita Val{\'e}ria and Kayano, Mary Toshie 
                         and Canchala, Teresita",
          affiliation = "{Universidad del Valle} and {Universidad Mayor de San Andr{\'e}s} 
                         and {Instituto Nacional de Pesquisas da Amazonia (INPA)} and 
                         {Universidade do Estado do Amazonas (UEA)} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)} and Universidad del Valle, Cali",
                title = "A principal component analysis approach to assess CHIRPS 
                         precipitation dataset for the study of climate variability of the 
                         La Plata Basin, Southern South America",
              journal = "Natural Hazards",
                 year = "2020",
               volume = "103",
               number = "1",
                pages = "767--783",
                month = "Aug.",
             keywords = "CHIRPS, Satellite precipitation estimate, Performance metrics, 
                         Principal component analysis, La Plata Basin.",
             abstract = "This article assesses the consistency of the satellite 
                         precipitation estimate CHIRPS v.2 to describe the spatiotemporal 
                         rainfall variability in the La Plata Basin (LPB), the second 
                         largest hydrographic basin in South America, by (a) pixel-to-point 
                         comparison of CHIRPS data with 167 observed monthly precipitation 
                         time series using three pairwise metrics (coefficient of 
                         correlation, bias and root mean square error) and (b) principal 
                         component analysis (PCA) to evaluate the large-scale coherence 
                         between CHIRPS and rain gauge data. The pairwise metrics indicate 
                         that CHIRPS better represents the rainfall in the coastal, 
                         northeastern and southeastern parts of the basin than in the 
                         Andean region to the west. The PCA shows that CHIRPS describes 
                         most of the observed rainfall variability in the LPB, but contains 
                         more variability, especially during December-February and 
                         March-May seasons. The two major modes observed are highly 
                         correlated spatially (empirical orthogonal functions-EOFs) and 
                         temporally (principal components-PCs) with the corresponding 
                         CHIRPS modes. The PCA allows the determination of the main 
                         rainfall variability modes and their possible relations with 
                         climate variability modes. Besides, the analyses of the 
                         precipitation anomaly modes show that the El Nino Southern 
                         Oscillation explains the first EOF modes of datasets. The PCA 
                         provides an alternative and effective means of assessing the 
                         consistency of CHIRPS data in representing spatial and temporal 
                         rainfall variability in the LPB.",
                  doi = "10.1007/s11069-020-04011-x",
                  url = "http://dx.doi.org/10.1007/s11069-020-04011-x",
                 issn = "0921-030X",
             language = "en",
           targetfile = "ceron_a principal.pdf",
        urlaccessdate = "29 jun. 2024"
}