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.

中文翻译：

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集合变分数据同化对巴西东北部暴雨预报的影响

巴西东北部 (BNE) 位于巴西的热带地区。它以大西洋为界，气候和植被受大陆高原的影响很大。高原将潮湿的气团保持在东部，是降雨事件的原因，而在西部则观察到东北腹地和干燥的气团。这项工作是一个案例研究，旨在评估在与中尺度对流系统 (MCS) 相关的大雨事件中使用 3DEnVar（三维集合变分）系统更新模型初始条件的影响。将结果与 3DVar（三维变分）和 EnSRF（集合平方根滤波器）系统进行比较，并且没有数据同化。该研究包含了 2017 年 1 月 14 日和 24 日发生的两个 MCS 案例。为此，由天气预报和气候研究中心 (CPTEC) 维护的 RMS（区域建模系统）3.0.0 版使用了两个组件：天气研究和预报 (WRF) 中尺度模型和 GSI（网格点统计插值）数据同化系统。目前，RMS 通过使用 3DVar 数据同化方法提供 WRF 初始条件。3DVar 使用气候协方差矩阵来最小化模型误差。在这项工作中，3DEnVar 根据当天的误差通过预测成员更新 RMS 气候协方差矩阵。这项工作评估了 MCS 事件中 24 小时累积降水的检测和估计方面的改进。统计指标RMSE（均方根误差）表明，混合数据同化系统（3DEnVar）在再现2017年1月14日发生的MCS降水方面表现更好。 2017年1月24日，EnSRF是改善WRF的最佳系统预报。总体而言，BIAS 显示 WRF 以不同的初始条件初始化，高估了 24 h 累积降水。因此，使用混合系统的可行性可能取决于混合算法，该算法可以在同化周期内修改 GSI 中归于 EnSRF 和 3DVar 矩阵的权重。BIAS 显示 WRF 以不同的初始条件初始化高估了 24 h 累积降水。因此，使用混合系统的可行性可能取决于混合算法，该算法可以在同化周期内修改 GSI 中归于 EnSRF 和 3DVar 矩阵的权重。BIAS 显示 WRF 以不同的初始条件初始化高估了 24 h 累积降水。因此，使用混合系统的可行性可能取决于混合算法，该算法可以在同化周期内修改 GSI 中归于 EnSRF 和 3DVar 矩阵的权重。