Atmospheric humidity, a fundamental meteorological parameter, serves as a critical factor in phase transition processes and spatial-temporal distribution, directly shaping cloud formation, precipitation dynamics, and various weather phenomena. Currently, coherent light detection and ranging (LiDAR) has demonstrated considerable success in wind field measurement by exploiting Doppler frequency shifts, but it remains limited in their capacity to simultaneously acquire high-resolution observations of both wind and humidity fields with comparable precision. Consequently, the development of coherent lidar technology enabling integrated, synergistic retrieval of wind and humidity parameters represents a pivotal advancement. Such innovation holds substantial applications in meteorological forecasting, environmental monitoring, and aviation safety.

To meet the requirements for rapid and simultaneous retrieval of atmospheric wind and humidity fields, a coherent lidar enabling integrated observation of both parameters was developed based on a conventional coherent Doppler wind LiDAR architecture. The lidar incorporates a coherent differential absorption detection technique, which is realized through a dual-wavelength all-fiber configuration. First, the differential absorption spectrum features of water vapor molecules were analyzed (Fig.1), leading to the selection of 1552 nm and 1550 nm as the on-line (absorption peak) and off-line (absorption valley) wavelengths, respectively. The analysis informed the design of the all-fiber coherent differential absorption detection system (Fig.2). Subsequently, an atmospheric stratification model (Fig.5) was employed to simulate and validate the feasibility of humidity retrieval using the dual-wavelength spectral differential method (Fig.6). Field experiments were conducted to compare the detection results of the system with hygrometer data (Fig.8), thereby confirming the reliability and effectiveness of the proposed integrated observation system.

Field experiments demonstrate that the data obtained from the coherent differential absorption LiDAR is highly consistent with those obtained from a reference humidity sensor. The tests conducted under various typical weather conditions, including fog, clear skies, haze, and overcast days (Table 2), consistently exhibited reliable detection performance, yielding a correlation coefficient of 0.91 and an average bias of 3.20% (Fig.9), thereby validating the measurement accuracy of the system. Simultaneous retrieval of radial wind speed and atmospheric humidity was achieved over a 400 m horizontal path (Fig.10), highlighting the effective detection capability of the dual-wavelength differential absorption technique at 1550 nm/1552 nm under near-surface, limited turbulence conditions. Although the current experiments confirm the fundamental principles and operational performance of the system over relatively short paths under limited turbulence, further investigation is required to evaluate its practical applicability under extended propagation paths and more turbulent atmospheric environments.

By integrating the dual-wavelength differential absorption technique at 1552 nm and 1550 nm with an all-fiber system architecture and dual-wavelength spectral differential processing methods, high-precision, rapid, and synchronized retrieval of atmospheric wind and humidity fields has been achieved. In experiments conducted over a 400 m horizontal path, the system successfully obtained simultaneous measurements of radial wind speed, carrier-to-noise ratio, and atmospheric humidity. The retrieved humidity exhibited a correlation coefficient of 0.91, with reference sensor data and an average bias of 3.20%, robustly validating the reliability and accuracy of the proposed detection approach. The methodology offers an effective technical pathway for collaborative observation of multiple atmospheric parameters, particularly suitable for meteorological and environmental monitoring applications that require high temporal and spatial resolution as well as rapid response capabilities.