author = "Vendrasco, Eder Paulo and Sun, Juanzhen and Herdies, Dirceu Luis 
                         and Angelis, Carlos Frederico de",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {National 
                         Center for Atmospheric Research} 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 
              journal = "Journal of Applied Meteorology and Climatology",
                 year = "2016",
               volume = "55",
               number = "3",
                pages = "673--690",
             keywords = "Data assimilation, Nowcasting, Numerical weather 
                         prediction/forecasting, Short-range prediction.",
             abstract = "It is known from previous studies that radar data assimilation can 
                         improve short-range forecasts of precipitation, mainly when radial 
                         wind and reflectivity are available. However, from the authors' 
                         experience radar data assimilation, when using the 
                         three-dimensional variational data assimilation (3DVAR) technique, 
                         can produce spurious precipitation results and large errors in 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 3DVAR 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 3DVAR analysis with the constraint is 
                         more accurate when verified against an independent dataset. The 
                         performance of this new constraint on improving precipitation 
                         forecasts is tested using six convective cases and verified 
                         against radar-derived precipitation by employing four skill 
                         indices. All of the skill indices show improved forecasts when 
                         using the methodology presented in this paper.",
                  doi = "10.1175/JAMC-D-15-0010.1",
                  url = "http://dx.doi.org/10.1175/JAMC-D-15-0010.1",
                 issn = "1558-8432 and 1558-8424",
                label = "lattes: 3752951275341381 3 VendrascoSunHerdAnge:2015:Co3DRa",
             language = "en",
        urlaccessdate = "23 jan. 2021"