基于集成学习的FY-4A云底高度反演方法  

Cloud Base Height Retrieval Methods for FY-4A Based on Ensemble Learning

作　　者：余茁夫 王雅 马烁 艾未华 严卫 Yu Zhuofu;Wang Ya;Ma Shuo;Ai Weihua;Yan Wei(College of Meteorology and Oceanography,National University of Defense Technology,Changsha 410000,Hunan,China;National Satellite Meteorological Center,China Meteorological Administration,Beijing 100081,China)

机构地区：[1]国防科技大学气象海洋学院,湖南长沙410000 [2]中国气象局国家卫星气象中心,北京100081

出　　处：《光学学报》2023年第6期41-54,共14页Acta Optica Sinica

基　　金：国家自然科学基金(41705007)。

摘　　要：云底高度是地气系统辐射收支以及飞行安全的重要影响因素。介绍了利用FY-4A卫星的数据产品反演云底高度的方法,设计了两种云底高度反演方案:第一种方案先将云划分为卷云(Ci)、高层云(As)、高积云(Ac)、层云/层积云(St/Sc)、积云(Cu)、雨层云(Ns)、深对流云(Dc)和多层云(Multi)等8种云类型,再分别采用独立的集成学习模型反演这8类云的云底高度;第二种方案不区分云的类型,采用统一的集成学习模型反演云底高度。将CloudSat探测的云底高度作为参考值,以129515个样本对两种方案进行评估,结果表明方案一的反演模型效果更好,均方根误差(RMSE)为1304.7 m,平均绝对误差(MAE)为898.4 m,相关系数(R)为0.9214。Objective Cloud base height(CBH)is a crucial cloud parameter affecting the water cycle and radiation budget of the earth-atmosphere system.Additionally,CBH has a great impact on aviation safety.Low CBH often leads to a decrease in visibility,which poses a great threat to flight safety.Therefore,it is meaningful to acquire accurate CBH for related scientific research and meteorological services.It is valuable but challenging to use satellite passive remote sensing data to retrieve CBH.Some cloud products such as cloud top height(CTH)and cloud optical thickness(COT)are often used in previous research,related to CBH retrieval,from which two ideas to retrieve CBH can be summarized.The first idea employed independent methods to obtain CBH of different types of clouds respectively,and the second one directly retrieves CBH using cloud products of satellites without regarding cloud types.At present,there is no CBH products of FY-4A.Therefore,a CBH retrieval method for FY-4A is introduced in this paper.According to the two ideas mentioned above,two schemes of CBH retrieval are designed,which are compared to find more suitable ideas to retrieve CBH for FY-4A and to provide reference for subsequent development of FY-4A CBH products.Methods A CBH retrieval method based on ensemble learning is proposed in this paper.CTH,COT,and cloud effective radius(CER)from FY-4A are used.Additionally,CBH and cloud types from CloudSat are employed for their widely recognized data quality.First,data of FY-4A and CloudSat are matched spatiotemporally and are divided into training data,validation data,and test data.Second,CBH retrieval models are built based on two ensemble learning algorithms,random forest(RF),and gradient boosting tree(GBT).Two schemes of CBH retrieval are designed in this paper.In the first scheme,matched data are divided into eight types according to the eight cloud types of CloudSat.For each type of cloud,two retrieval models are built based on RF and GBT using training data and validation data through tenfold cross valid

关 键 词：大气光学 云底高度反演 FY-4A 云顶高度 云光学厚度 云粒子有效半径 集成学习 

分 类 号：P412[天文地球—大气科学及气象学]