Abstract. Extending the temperature measurement range and sustaining the system performance are important requirements in Rayleigh lidars. While replacing hardware and increasing the power aperture product are ways of achieving these objectives, the data analysis approach is another means for achieving the same. A method to retrieve atmospheric temperatures using the penalized maximum likelihood method after denoising the backscattered signal using the dictionary learning technique is presented and compared with the conventional method. The proposed combination has the advantage of improving the measurement range and reducing the standard error (SE) in temperatures. The penalized maximum likelihood function is solved using the method of successive approximations, and the SE in temperature is calculated using Monte Carlo simulations. Observations from the Rayleigh lidar at the National Atmospheric Research Laboratory, India, are used for testing the approach. When compared with the conventional method, the SE in temperatures improved by 5K at 84 km, and the average height improvement was about 6 km.

中文翻译：

---

用于扩展瑞利激光雷达测量范围的字典学习技术和惩罚最大似然

摘要。扩展温度测量范围和维持系统性能是瑞利激光雷达的重要要求。虽然更换硬件和增加功率孔径产品是实现这些目标的方法，但数据分析方法是另一种实现方法。提出了一种在使用字典学习技术对背向散射信号进行去噪后使用惩罚最大似然法检索大气温度的方法，并与传统方法进行了比较。建议的组合具有提高测量范围和降低温度标准误差 (SE) 的优点。惩罚最大似然函数使用逐次逼近的方法求解，温度的 SE 使用蒙特卡罗模拟计算。来自印度国家大气研究实验室瑞利激光雷达的观测结果用于测试该方法。与传统方法相比，84km处温度SE提高5K，平均高度提高约6km。