@Article{RozanteRamiFernVila:2020:PePrPr,
               author = "Rozante, Jos{\'e} Roberto and Ramirez Gutierrez, Enver and 
                         Fernandes, Alex de Almeida and Vila, Daniel Alejandro",
          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)}",
                title = "Performance of precipitation products obtained from combinations 
                         of satellite and surface observations",
              journal = "International Journal of Remote Sensing",
                 year = "2020",
               volume = "41",
               number = "19",
                pages = "7585--7604",
             abstract = "Knowing the spatiotemporal distribution of precipitation is 
                         undoubtedly important for planning various economic/social 
                         activities, such as agriculture, livestock, and energy production. 
                         The coarse observation density over certain regions may 
                         significantly compromise the quality of precipitation products 
                         interpolated by only surface observations. To minimize the lack of 
                         observations over certain regions, the Centre for Weather Forecast 
                         and Climate Studies (CPTEC) of National Institute for Space 
                         Research (INPE) developed two types of blended precipitation 
                         products, namely, the Combined Scheme (CoSch) and MERGE, which 
                         combine observed precipitation data with satellite estimates on a 
                         daily scale. To understand how different blending methodologies 
                         impact the final results, a comparison of each algorithm with 
                         independent rain gauges was performed with a focus over the 
                         Brazilian territory. Both products were generated at a 10-km 
                         horizontal resolution using input data from the Global 
                         Precipitation Measurement (GPM) Integrated Multi-satellitE 
                         Retrievals for GPM (IMERG-Early) for product (Version 5) in 
                         conjunction with surface observations from Surface Synoptic 
                         Observations (SYNOP), data collection platforms (DCPs) and data 
                         from regional meteorological centres. The cumulative 24-hour 
                         precipitation was evaluated for the period from June 2014 to June 
                         2017. The results show that both products reliably characterize 
                         the precipitation regimes over most of the study regions, although 
                         MERGE and CoSch tend to over- and underestimate the amount of 
                         precipitation, respectively. However, the magnitude of the Bias 
                         achieved by MERGE is smaller than that achieved by CoSch. Overall, 
                         MERGE outperforms CoSch when analysing rain/no rain and light to 
                         moderate rainfall (0.5 to 20.0 mm). For heavy precipitation (>35.0 
                         mm), the performance of both products is similar. The most 
                         significant differences between the two products occur over the 
                         Northeast Region of Brazil (R3 and R4), where CoSch tends to 
                         encounter difficulties characterizing the precipitation regime 
                         during the northeastern wet period (April November). In R3 and R4, 
                         MERGE relies more on surface observations, whereas CoSch relies on 
                         GPM-IMERG-Early, which could be associated with the deficiency of 
                         GPM-IMERG-Early in estimating the amount of precipitation 
                         associated with warm clouds.",
                  doi = "10.1080/01431161.2020.1763504",
                  url = "http://dx.doi.org/10.1080/01431161.2020.1763504",
                 issn = "0143-1161",
             language = "en",
           targetfile = "Performance of precipitation products obtained from combinations 
                         of satellite and surface observations.pdf",
        urlaccessdate = "27 abr. 2024"
}