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@Article{AfonsoViGaQuBaChPa:2020:PrDiCy,
               author = "Afonso, Jo{\~a}o Maria de Sousa and Vila, Daniel Alejandro and 
                         Gan, Manoel Alonso and Quispe, David Pareja and Barreto, Naurinete 
                         de Jesus da Costa and Chinchay, Joao Henry Huam{\'a}n and 
                         Palharini, Rayana Santos Araujo",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Servicio Nacional de Meteorolog{\'{\i}}a e 
                         Hidrolog{\'{\i}}a del Per{\'u}} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Precipitation diurnal cycle assessment of satellite-based 
                         estimates over Brazil",
              journal = "Remote Sensing",
                 year = "2020",
               volume = "12",
               number = "14",
                pages = "e2339",
                month = "July",
             keywords = "precipitation, GSMaP, IMERG, CMORPH.",
             abstract = "The main objective of this study is to assess the ability of 
                         several high-resolution satellite-based precipitation estimates to 
                         represent the Precipitation Diurnal Cycle (PDC) over Brazil during 
                         the 20142018 period, after the launch of the Global Precipitation 
                         Measurement satellite (GPM). The selected algorithms are the 
                         Global Satellite Mapping of Precipitation (GSMaP), The Integrated 
                         Multi-satellitE Retrievals for GPM (IMERG) and Climate Prediction 
                         Center (CPC) MORPHing technique (CMORPH). Hourly rain gauge data 
                         from different national and regional networks were used as the 
                         reference dataset after going through rigid quality control tests. 
                         All datasets were interpolated to a common 0.1\◦  
                         0.1\◦ grid every 3 h for comparison. After a hierarchical 
                         cluster analysis, seven regions with different PDC characteristics 
                         (amplitude and phase) were selected for this study. The main 
                         results of this research could be summarized as follow: (i) Those 
                         regions where thermal heating produce deep convective clouds, the 
                         PDC is better represented by all algorithms (in term of amplitude 
                         and phase) than those regions driven by shallow convection or 
                         low-level circulation; (ii) the GSMaP suite (GSMaP-Gauge (G) and 
                         GSMaP-Motion Vector Kalman (MVK)), in general terms, outperforms 
                         the rest of the algorithms with lower bias and less dispersion. In 
                         this case, the gauge-adjusted version improves the satellite-only 
                         retrievals of the same algorithm suggesting that daily 
                         gauge-analysis is useful to reduce the bias in a sub-daily scale; 
                         (iii) IMERG suite (IMERG-Late (L) and IMERG-Final (F)) 
                         overestimates rainfall for almost all times and all the regions, 
                         while the satellite-only version provide better results than the 
                         final version; (iv) CMORPH has the better performance for a 
                         transitional regime between a coastal land-sea breeze and a 
                         continental amazonian regime. Further research should be performed 
                         to understand how shallow clouds processes and 
                         convective/stratiform classification is performed in each 
                         algorithm to improve the representativity of diurnal cycle.",
                  doi = "10.3390/rs12142339",
                  url = "http://dx.doi.org/10.3390/rs12142339",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-12-02339.pdf",
        urlaccessdate = "26 jan. 2021"
}


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