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.

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 ten-fold cross validation. The optimal model is selected according to the models' results on test data. In the second scheme, retrieval models are built without regarding cloud types. Training data of the eight cloud types are combined together. Validation data and test data are processed similarly. The three data sets are used to obtain the RF model and GBT model, and to select the optimal retrieval model. Finally, the optimal scheme and model of CBH retrieval for FY-4A are selected according to the models' performance.

Root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R), and mean relative error (MRE) are used to evaluate models' performance. In the first scheme, the GBT model is the optimal retrieval model for Cirrus (Ci), Altostratus (As), and Altostratus (Ac). RF model is the optimal retrieval model for Stratus/Stratocumulus (St/Sc), Cumulus (Cu), Nimbostratus (Ns), deep convective cloud (Dc), and multilayer cloud (Multi). In the second scheme, the GBT model is the optimal retrieval model. The models of the two schemes are compared on test data with 129515 samples. Overall, the retrieval model of the first scheme outperforms that of the second scheme. Specifically, RMSE of the model in the first scheme is 1304.7 m. MAE is 898.3 m, R is 0.9214, and MRE is 63.93%. For the eight types of clouds, RMSE, MAE, R, and MRE of the model in the first scheme are also superior to those of the model in the second scheme. Although the first scheme can obtain better results, the retrieval model of the first scheme still needs to be improved in the future. For example, the performance of the retrieval model for Dc is not a patch on that of other types of clouds. Additionally, the paper discusses how to apply the proposed method to practice. First, level 1 data (i.e. reflectance and brightness temperature) and level 2 data (i.e. CTH, COT, and CER) of FY-4A can be used to acquire the eight cloud types according to a cloud type classification model proposed by Yu et al. Second, according to the cloud type classification results, the retrieval models of the first scheme can be adopted to retrieve CBH for the eight types of clouds respectively.

CBH is a critical cloud parameter, but there are no CBH products of geostationary meteorological satellites currently. Thus, a CBH retrieval method for FY-4A based on ensemble learning is introduced in this paper. Two schemes of CBH retrieval are designed, and corresponding CBH retrieval models are built based on two ensemble learning algorithms, namely, RF and GBT. Data of CTH, COT, and CER from FY-4A are used in this paper. The first scheme employs eight independent models to retrieve CBH for eight types of clouds (i.e. Ci, As, Ac, St/Sc, Cu, Ns, Dc, and Multi) respectively. Specifically, for Ci, As, and Ac, the GBT model is used to retrieve CBH. For the other five types of cloud, the RF model is used to retrieve CBH. The second scheme uses a GBT model to retrieve CBH without regarding cloud types. CBH from CloudSat is used to evaluate the results of the two schemes, and the retrieval model of the first scheme outperforms that of the second scheme. For the eight types of clouds, the retrieval model of the first scheme also obtains better results.