Xinqian Guo

Open‐path tunable diode laser absorption spectroscopy (TDLAS) analyzers are increasingly deployed for Eddy covariance flux measurements due to their high spectral selectivity. However, high‐frequency photoelectric background white noise inherent in these long‐path optical sensors often causes non‐physical upward curling in the power spectrum during field observations, compromising the measurement of small‐scale turbulent eddies. Traditional digital filtering methods introduce nonlinear phase delays or over‐smoothing, distorting high‐frequency transient features and causing systematic underestimation of mass transfer fluxes. To address this hardware limitation, this study introduces a high‐fidelity data preprocessing pipeline incorporating a dynamic segment‐adaptive wavelet noise suppression method. Validated against a commercial reference instrument (LI‐7500RS) over a 3‐day field deployment, the proposed framework effectively eliminates broadband thermal noise with zero phase distortion. Notably, the frequency‐domain evaluation demonstrates that the pipeline successfully suppresses the high‐frequency spectral baseline upturn, restoring the power spectral density to strictly adhere to the theoretical Kolmogorov −5/3 inertial subrange decay law. Furthermore, the 30‐min macroscopic flux estimates show high consistency with the reference, confirming that the noise removal process preserves lossless turbulent energy. This framework provides a robust and practical signal conditioning solution for enhancing the high‐frequency dynamic response of open‐path TDLAS sensors in complex field environments.

Atmospheric water vapor is a crucial factor in the Earth's water cycle. As an important greenhouse gas, changes in the spatio‐temporal distribution of atmospheric water vapor can contribute to the occurrence of various extreme weather phenomena. Lidar, with its high spatial and temporal resolutions, has great potential for applications in water vapor profile detection. Raman lidar and differential absorption lidar (DIAL) have been successfully used to detect atmospheric water vapor. System calibration is crucial to ensure that the measured profile accurately represents the concentration profile of atmospheric water vapor. Choosing an effective system calibration method can ensure the accuracy of long‐term lidar measurements. This paper reviews the latest progress and applications of atmospheric water vapor lidar calibration in recent years. The basic principles of Raman lidar and DIAL calibration are introduced. Various methods and benefits of system calibration are discussed. Raman lidar has three commonly used calibration methods: external calibration, internal calibration, and hybrid calibration methods. The most commonly used method is external calibration based on radiosondes. DIAL is usually implemented with an advantageous self‐calibration method. Finally, potential development directions for atmospheric water vapor lidar and calibration technology are discussed.This article is categorized under: Science of Water > Methods Engineering Water > Methods Human Water > Value of Water