同化IASI资料对台风“红霞”和“莫兰蒂”预报的影响研究

红外高光谱大气探测仪IASI可提供高精度的大气垂直温度和湿度信息，能够探测台风结构特征，有效弥补台风影响区域观测资料稀缺的不足。以WRFDA三维变分同化系统为基础构建IASI同化试验平台，实现McNally提出的MW云检测方法，并调整参数形成大阈值的LMW云检测方法，以超强台风“红霞”（1506）和“莫兰蒂”（1614）为试验个例，对IASI观测资料进行同化对比试验。对于台风“红霞”，MW云检测方案对于高层通道299保留的观测数目仅为大阈值LMW云检测的16.2%和WRFDA系统默认的MMR云检测的9.2%，对于底层通道921分别为3.3%和2.6%。但是MW试验分析场强度最强，获得的72 h台风路径预报最接近真实路径，路径误差最小。两个台风个例试验结果相似，表明有效的云检测过程能提高IASI资料同化分析场的准确性，同化IASI资料有利于改善台风预报技巧。 

IMPACT OF DATA ASSIMILATION FOR THE IASI OBSERVATIONS ON THE FORECAST OF TYOHOON HONGXIA AND MOLANDI

The Infrared Atmospheric Sounder Interferometer (IASI) provides the temperature and humidity information with high precision about the atmosphere in the vertical direction. The IASI instrument can detect the characteristics of typhoon structure and make up for the shortage of the observation data distributed sparsely in typhoon-affected areas. In this study, a three-dimensional variational data assimilation for Weather Research and Forecasts (WRFDA) system was chosen as the basic assimilation system, and the MW cloud detection put forward by McNally was implemented in the WRFDA system for IASI cloud contamination detection and the cloud parameters were tuned for the research. All the IASI observations are assimilated after quality control and variational bias correction and the impact of the data assimilation on the forecasts of the super typhoon Hongxia (1506) and Molandi (1614) are assessed. The results from both the typhoon experiments are similar and indicate that the cloud detection influences the assimilation of the IASI observations very much. For the super typhoon Hongxia, the MW cloud detection scheme retained just 16.2% the number of observations by the large-threshold LMW cloud detection scheme and 9.2% of that by the MMR cloud detection scheme for the upper-level channel 299, and 3.3% and 2.6% respectively for the lower-level channel 921, but the analysis affected by the MW cloud detection scheme reduces the track error of typhoon Hongxia for the first 72 hours most remarkably and improves the path forecast most accurately. Generally the assimilation of IASI observations improves the skills of typhoon forecast.