The impact of assimilating radiance data from the advanced satellite sensor GMI (GPM microwave imager) for typhoon analyses and forecasts was investigated using both a three-dimensional variational (3DVAR) and a hybrid ensemble-3DVAR method. The interface of assimilating the radiance for the sensor GMI was established in the Weather Research and Forecasting (WRF) model. The GMI radiance data are assimilated for Typhoon Matmo (2014), Typhoon Chan-hom (2015), Typhoon Meranti (2016), and Typhoon Mangkhut (2018) in the Pacific before their landing. The results show that after assimilating the GMI radiance data under clear sky condition with the 3DVAR method, the wind, temperature, and humidity fields are effectively adjusted, leading to improved forecast skills of the typhoon track with GMI radiance assimilation. The hybrid DA method is able to further adjust the location of the typhoon systematically. The improvement of the track forecast is even more obvious for later forecast periods. In addition, water vapor and hydrometeors are enhanced to some extent, especially with the hybrid method.

使用三维变分（3DVAR）和混合集成3DVAR方法研究了来自先进卫星传感器GMI（GPM微波成像仪）的辐射数据对台风分析和预报的影响。在天气研究和预报（WRF）模型中建立了为传感器GMI吸收辐射的接口。登陆前，太平洋的台风“马特莫”（2014年），“台风陈洪”（2015年），“台风梅兰蒂”（2016年）和“台风山竹”（2018年）均吸收了GMI辐射数据。结果表明，利用3DVAR方法将晴空条件下的GMI辐射数据同化后，可以有效地调整风场，温度场和湿度场，从而提高了GMI辐射同化对台风航迹的预报能力。混合DA方法能够进一步系统地调节台风的位置。跟踪预报的改进在以后的预报期内更为明显。此外，水蒸气和水凝物在某种程度上得到了增强，尤其是采用混合方法时。