静止卫星的高时空分辨率对高影响灾害性天气的监测和预报有显著优势。开展了基于风云四号A星多通道扫描成像辐射计（Advanced Geosynchronous Radiation Imager，AGRI）红外光谱亮温的台风降水反演研究。探讨了正则化反问题方法中K-最邻近（K-Nearest Neighbor，KNN）不同距离度量对降水反演精度的影响。降水反演共分两步：一是降水视场点识别，主要基于训练字典样本，利用KNN识别待反演的亮温“降水”和“非降水”信号；二是降水视场点反演，即在判识视场点有降水的基础上采用正则化反问题方法进行红外亮温降水反演。KNN距离度量分别采用欧氏、标准化欧氏、马氏和布洛克距离。以台风“安比（2018）”为例，开展了降水反演试验。试验表明，反演结果与GPM的相似度较高，且不同距离度量在反演“极端降水”时各有优势。

The high spatial and temporal resolution of geostationary satellite has significant advantages in monitoring and forecasting disastrous weather with high impact. A typhoon precipitation retrieval study based on infrared spectrum bright temperatures of the advanced geosynchronous radiation imager (AGRI) in FY-4A satellite is conducted. The influences on precipitation retrieval accuracy of different distance measurements of K-nearest neighbor (KNN) in the inverse problem of regularization method are discussed. The precipitation retrieval consists of two steps: （1） the identification of precipitation field of view, which uses KNN to identify the “precipitation” and “non-precipitation” signals of brightness temperatures to be retrieved mainly based on the training dictionary samples；（2） the retrieval of the precipitation field of view, which uses inverse problem of regularization method to retrieve the infrared brightness temperatures precipitation on the basis of identifying the precipitation field of view. KNN distance measurement adopts Euclidean, normalized Euclidean, Mahalanobis and Cityblock respectively. Taking typhoon “Ampil (2018)”as an example, the precipitation retrieval experiment is carried out. The experiment shows that the retrieval result has a high similarity with GPM, and different distance measurements have their own advantages in the retrieval of "extreme precipitation".