Abstract The impact of assimilating radiances from the global satellite precipitation program (GPM) Microwave Imager (GMI) on the track prediction of typhoon Chan-hom was studied with a hybrid three-dimensional ensemble-variational (3DEnVar) data assimilation (DA) system. It is found that the data assimilation of clear-sky GMI radiance is able to depict the structure of typhoon well by warming the typhoon inner core area. The bias correction coefficients obtained by the off-line model along with the variational bias correction method can reduce the bias. GMI radiance data assimilation experiment can effectively adjust the core area of Typhoon “Chan-hom” and systematically correct the typhoon position in the background of the model. The sensitivity of the hybrid 3DEnVar DA behaviors on the choice of the ensemble members is also investigated. Results show that the 3DEnVar Global Ensemble Forecast System (3DEnVar_GEFS)-based ensemble spread is essentially dominated in the vicinity of Typhoon Chan-hom, which is less disturbed by random errors. In general, the track forecasts from 3DEnVar_RCV match better with the best track by adding Gaussian noise randomly based on the background error than those from the 3DVAR, and 3DEnVar_GEFS.

中文翻译：

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使用 WRF 混合 3DEnVar 系统同化 GPM 微波成像仪辐射数据以预测台风 Chan-hom (2015)

摘要 采用混合三维集合变分（3DEnVar）数据同化（DA）系统，研究了全球卫星降水计划（GPM）微波成像仪（GMI）的同化辐射对台风灿鸿轨迹预测的影响。发现晴空GMI辐照度数据同化能够很好地刻画台风核心区的增温结构。离线模型得到的偏差校正系数和变分偏差校正方法可以减少偏差。GMI辐射数据同化实验可以有效调整台风“灿鸿”核心区域，系统校正模型背景中的台风位置。还研究了混合 3DEnVar DA 行为对合奏成员选择的敏感性。结果表明，基于 3DEnVar 全球集合预报系统 (3DEnVar_GEFS) 的集合扩展在台风 Chan-hom 附近基本上占主导地位，受随机误差的干扰较小。一般来说，通过基于背景误差随机添加高斯噪声，3DEnVar_RCV 的轨迹预测与最佳轨迹的匹配度比 3DVAR 和 3DEnVar_GEFS 的轨迹预测更好。