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@InProceedings{VendrascoHerdAnge:2015:Co3DRa,
               author = "Vendrasco, Eder Paulo and Herdies, Dirceu Luis and Angelis, Carlos 
                         Frederico",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Constraining a 3D-Var radar data assimilation system with 
                         large-scale analysis to improve short-range precipitation 
                         forecast",
                 year = "2015",
         organization = "Conference on Radar Meteorology, 37.",
             abstract = "It is known from previous studies that radar data assimilation can 
                         improve the short-range forecast of precipitation, mainly when 
                         radial wind and reflectivity are available. However, from our 
                         experience the radar data assimilation, when using the 3D-Var 
                         technique, can produce spurious precipitation and large errors on 
                         the position and amount of precipitation. One possible reason for 
                         the problem is attributed to the lack of proper balance in the 
                         dynamical and microphysical fields. This work attempts to minimize 
                         this problem by adding a large-scale analysis constraint in the 
                         cost function. The large-scale analysis constraint is defined by 
                         the departure of the high resolution 3D-Var analysis from a 
                         coarser resolution large-scale analysis. It is found that this 
                         constraint is able to guide the assimilation process in such a way 
                         that the final result still maintains the large-scale pattern, 
                         while adding the convective characteristics where radar data are 
                         available. As a result, the 3D-Var analysis with the constraint is 
                         more accurate when verified against an independent dataset. The 
                         performance of this new constraint on improving precipitation 
                         forecast is tested using six convective cases and verified against 
                         radar-derived precipitation by four skill indices. All skill 
                         indices show improved forecast when using the methodology 
                         presented in this study.",
  conference-location = "Norman, OK",
      conference-year = "14-18 Sept.",
        urlaccessdate = "01 dez. 2020"
}


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