期刊
REMOTE SENSING
卷 14, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/rs14071597
关键词
planetary boundary layer height; machine learning; bagged tree model
类别
资金
- National Natural Science Foundation of China [41901295, 41904032, 72088101]
- Natural Science Foundation of Hunan Province, China [2020JJ5708]
- Key Program of the National Natural Science Foundation of China [41930108]
In this study, a new method based on a bagged tree model and micro-lidar backscatter profiles is proposed for retrieving the planetary boundary layer height (PBLH). Compared with traditional methods, the proposed method shows better performance in relevant indicators, is almost unaffected by cloud and aerosol layers, and can be used for retrieving shallow PBL.
The planetary boundary layer (PBL) is the part of the troposphere in which the soil's influence is noticeable. It plays an important role in the fields of air pollution, meteorology, weather forecasting, and climate. Continuous observation of lidar makes obtaining the day-night PBL height (PBLH) with a high temporal resolution possible. A high-precision PBLH retrieval method is the key to achieving this goal. In this study, we propose a new method based on a bagged tree model to retrieve the PBLH from micro-lidar backscatter profiles. With the radiosonde measurements taken as the true reference, lidar features (the ten maximum slopes identified by the maximum gradient method) and four meteorological parameters (atmospheric pressure, temperature, relative humidity, and wind speed) serve as characteristic variables. The PBLH retrieval model is evaluated using a 10-fold cross-validation (CV) method and then compared with the four traditional methods (i.e., maximum gradient, maximum standard deviation, wavelet covariance, and the ideal profile method). The correlation coefficient (R) between the retrieved PBLHs and the radiosonde measurements is 0.89, which is much bigger than the R (0.2-0.48) from the four traditional methods. Moreover, the root mean square error and mean absolute error for the retrieved PBLH are 0.3 km and 0.2 km, respectively, which are lower than those of the four traditional methods (0.5 similar to 0.6 km for RMSE and 0.4-0.5 for MAE). Cases with different conditions show that this new method is almost undisturbed by cloud and suspended/thick aerosol layers. It can also be used to retrieve shallow PBL in cases in which using traditional methods would be difficult. Long-term analysis of averaged PBLHs retrieved by the proposed model from 2013 to 2016 shows that the hourly PBLH rises at sunrise and sets at sunset, and that the monthly PBLH in summer is higher than that in winter. The results suggest that the proposed method is better than the four traditional methods and available for use in conditions such as existing cloud layers and multiple-layers.
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