为解决雾霾天气条件下激光雷达探测温度高度偏低的难题,研究了一套数据拼接方法。该方法引入高分辨WRF(weather research and forecasting model)模式模拟温度,与激光雷达测温数据进行拼接,通过拟合区域选取、分析高度层确定、拼接数据校正、最佳拼接区域选取、拼接效果评估等关键技术设计,将雾霾天激光雷达探测数据高度由2 km向上拓展至整个对流层并到达平流层中低部区域,即20 km左右高度。对拼接结果的综合质量评估表明,激光雷达与模式数据拼接效果较好,拼接廓线与标准廓线的吻合度较高,且最大相对误差为1.5%。特别是在最佳拼接区域内,激光雷达数据与模式数据具有很高的拟合度。本方法利用模式数据与雷达数据的互补优势,实现了温度廓线大量层、高质量数据重构。本文拼接方法同样适用于其它复杂天气条件。

In this study, a novel method for splicing LiDAR temperatures was proposed to solve the problem of low LiDAR detection heights when fog-haze conditions were encountered. Accordingly, a typical fog-haze case was selected as the research sample. High-resolution weather research and forecasting (WRF) model temperatures were specifically used to splice LiDAR temperatures. The splicing method focused on key technologies, including a fitting region selection technique, a coordinate height layer analysis method, a correction method between model data and LiDAR data, an optimal splicing region selection method, and an evaluation method for splicing results. The maximum height of splicing data was extended to approximately 20 km, including the entire troposphere and the lower-middle stratosphere. This was especially larger than the original height of the LiDAR data (2 km). According to a series of detailed quality assessments, the splicing data were very reliable, with a perfect match trend between the splicing profile and the standard profile and a maximum error of less than 1.5%. There was a better fit between LiDAR data and model data in the optimal splicing region. The advantages of both model data and LiDAR data were fully exploited in the proposed splicing method. Based on this, the data with a larger detection layer and high-quality temperature profile was reconstructed. Moreover, the proposed splicing method was also suitable for other complex weather conditions.