@Article{NobreVendBast:2021:ImEnDa,
               author = "Nobre, Jo{\~a}o Pedro Gon{\c{c}}alves and Vendrasco, {\'E}der 
                         Paulo and Bastarz, Carlos Frederico",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Impact of ensemble-variational data assimilation in heavy rain 
                         forecast over brazilian northeast",
              journal = "Atmosphere",
                 year = "2021",
               volume = "12",
               number = "9",
                pages = "e1201",
                month = "Sept.",
             keywords = "3DEnVar, GSI, Mesoscale convective systems, WRF.",
             abstract = "The Brazilian Northeast (BNE) is located in the tropical region of 
                         Brazil. It is bounded by the Atlantic Ocean, and its climate and 
                         vegetation are strongly affected by continental plateaus. The 
                         plateaus keep the humid air masses to the east and are responsible 
                         for the rain episodes, and at the west side the northeastern 
                         hinterland and dry air masses are observed. This work is a case 
                         study that aims to evaluate the impact of updating the model 
                         initial condition using the 3DEnVar (Three-Dimensional Ensemble 
                         Variational) system in heavy rain episodes associated with 
                         Mesoscale Convective Systems (MCS). The results were compared to 
                         3DVar (Three-Dimensional Variational) and EnSRF (Ensemble Square 
                         Root Filter) systems and with no data assimilation. The study 
                         enclosed two MCS cases occurring on 14 and 24 January 2017. For 
                         that purpose, the RMS (Regional Modeling System) version 3.0.0, 
                         maintained by the Center for Weather Forecasting and Climate 
                         Studies (CPTEC), used two components: the Weather Research and 
                         Forecasting (WRF) mesoscale model and the GSI (Gridpoint 
                         Statistical Interpolation) data assimilation system. Currently, 
                         the RMS provides the WRF initial conditions by using 3DVar data 
                         assimilation methodology. The 3DVar uses a climatological 
                         covariance matrix to minimize model errors. In this work, the 
                         3DEnVar updates the RMS climatological covariance matrix through 
                         the forecast members based on the errors of the day. This work 
                         evaluated the improvements in the detection and estimation of 24 h 
                         accumulated precipitation in MCS events. The statistic index RMSE 
                         (Root Mean Square Error) showed that the hybrid data assimilation 
                         system (3DEnVar) performed better in reproducing the precipitation 
                         in the MCS occurred on 14 January 2017. On 24 January 2017, the 
                         EnSRF was the best system for improving the WRF forecast. In 
                         general, the BIAS showed that the WRF initialized with different 
                         initial conditions overestimated the 24 h accumulated 
                         precipitation. Therefore, the viability of using a hybrid system 
                         may depend on the hybrid algorithm that can modify the weights 
                         attributed to the EnSRF and 3DVar matrix in the GSI over the 
                         assimilation cycles.",
                  doi = "10.3390/atmos12091201",
                  url = "http://dx.doi.org/10.3390/atmos12091201",
                 issn = "2073-4433",
             language = "en",
           targetfile = "nobre_impact.pdf",
        urlaccessdate = "19 maio 2024"
}