首页|Research on all-day water vapour profile retrieval method based on lidar data and machine learning algorithm
Research on all-day water vapour profile retrieval method based on lidar data and machine learning algorithm
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NETL
NSTL
Taylor & Francis
ABSTRACT Raman lidar can achieve high spatial and temporal resolution retrieval of atmospheric water vapour vertical profiles. However, it is difficult to effectively solve the problem of limited daytime water vapour retrieval distances owing to the influence of the solar background light. To enhance the daytime water vapour retrieval capability of lidar, this paper proposes a technique for retrieving the water vapour vertical profile by integrating lidar and ground meteorological parameters based on the backpropagation neural network algorithm. This study constructed a neural network training model, maximized its retrieval accuracy, and achieve the retrieval of daytime water vapour profiles. Under strong background light conditions at noon in summer, the proposed method increases the maximum retrieval height by 2.5 km compared to traditional lidar retrieval methods. A regression analysis was conducted between the neural network retrieval method proposed in this study and traditional lidar retrieval methods within the effective daytime detection height. The results demonstrate that the proposed method exhibits high accuracy, achieving a correlation coefficient, a coefficient of determination, and a root mean square error of 0.951, 0.904, and 0.889 g·kg− 1, respectively.
NETL
NSTL
Taylor & Francis
