author = "Kirstetter, Pierre-Emmanuel and Viltard, Nicolas and Gosset, 
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and Univ 
                         Versailles St Quentin, CNRS INSU, LATMOS IPSL, Guyancourt, France. 
                         and Univ Toulouse 3, IRD, GET OMP, CNRS, F-31062 Toulouse, 
                title = "An error model for instantaneous satellite rainfall estimates: 
                         evaluation of BRAIN-TMI over West Africa",
              journal = "Quarterly Journal of the Royal Meteorological Society",
                 year = "2013",
               volume = "139",
               number = "673, B, SI",
                pages = "894--911",
                month = "Apr.",
             keywords = "satellite-based rain estimation, QPE, conditional bias, 
                         geostatistics, rain-gauge, radiometry, West African Monsoon.",
             abstract = "Characterising the error associated with satellite rainfall 
                         estimates based on space-borne passive and active microwave 
                         measurements is a major issue for many applications, such as water 
                         budget studies or assessment of natural hazards caused by extreme 
                         rainfall events. We focus here on the error structure of the 
                         Bayesian Rain retrieval Algorithm Including Neural Network 
                         (BRAIN), the algorithm that provides instantaneous quantitative 
                         precipitation estimates at the surface based on the MADRAS 
                         radiometer on board the Megha-Tropiques satellite. A version of 
                         BRAIN using data from the Tropical Rainfall Measuring Mission 
                         (TRMM) Microwave Imager (TMI) has been compared to reference 
                         values derived either from TRMM Precipitation Radar (PR) or from a 
                         ground validation (GV) dataset. The ground-based measurements were 
                         provided by two densified rain-gauge networks in West Africa, 
                         using a geostatistical framework. The comparisons were carried out 
                         at the BRAIN retrieval scale for TMI (instantaneous and 12.5 km) 
                         and over a ten-year-long period. The primary contribution of this 
                         study is to provide some insight into the most significant error 
                         sources of satellite rainfall retrieval. This involves comparisons 
                         of rainfall detectability, distributions and spatial 
                         representativeness, as well as separation of systematic biases and 
                         random errors using Generalized Additive Models for Location, 
                         Scale and Shape. In spite of their different sampling properties, 
                         the three rain estimates were found to detect rainfall 
                         consistently. The most important BRAIN-TMI error is due to the 
                         rain/no-rain delimitation which causes about 20\\% of volume 
                         rainfall loss relative to PR and GV. BRAIN-TMI presents a narrow 
                         PDF relative to GV and catches the spatial structure of the most 
                         active part of rain fields. The conditional bias is significant 
                         (e.g. +2 mm h-1 for light-moderate rain rates, -2 mm h-1 for rain 
                         rates greater than 8 mm h-1) and the overall bias is within 
                         10\\%. The PR shows a significant underestimation for high rain 
                         rates with respect to GV. The proposed framework could be applied 
                         to the evaluation of other passive microwave sensors (SSMI, AMSR-E 
                         or MADRAS) or rainfall satellite products.",
                  doi = "10.1002/qj.1964",
                  url = "http://dx.doi.org/10.1002/qj.1964",
                 issn = "0035-9009",
                label = "isi 2013-11",
             language = "en",
           targetfile = "1964_ftp.pdf",
        urlaccessdate = "24 jan. 2021"