Sea fog represents a significant risk for safe navigation of sea vessels. Visibility LiDAR systems might offer a striking way to reduce the risks associated with sea fog, but they should be appropriately designed to provide a proper level of detection for reliable forewarning of sea fog. Here we analyze the performances of a visibility LiDAR system with the aim of achieving optimal detection operation. A series of echo signals are simulated under different visibility conditions addressing the influence of the various hardware parameters on the final system performances and defining an optimal visibility LiDAR configuration. Using the optimized parameters, a visibility LiDAR system was realized and tested in a field campaign on Hengsha Island (Shanghai). The experimental findings obtained by the visibility LiDAR are compared with results of a forward scattering visibility meter showing good consistency in homogeneous atmosphere, while even superior performances are observed for inhomogeneous atmospheric conditions. Our experimental results indicate that an optimized visibility LiDAR can provide an early warning for light fog located at a distance of 5 km, i.e. about 3.5 hours in advance to the spreading of the fog to the shore. These findings demonstrate the good performances of the visibility LiDAR developed in the present study in performing visibility measurements and its capability of providing sea-fog warning.

It is well-known that coastal low-level jets (CLLJs) play an essential role in transporting heat, water vapor and pollutants. However, the CLLJ characteristics in the Pearl River Estuary have not been deeply revealed due to the lack of long-term observations. Based on the long-term observations from a wind lidar, we analyze the primary climatic characteristics of the CLLJs in the Pearl River Estuary and investigate their relationships with large-scale and local-scale synoptic systems. The results show that the CLLJs mainly appear during the flood season, with the most occurrence in May. The CLLJ occurrence during the flood season is mainly influenced by the large-scale north–south pressure gradient driven by the western Pacific subtropical high and terrestrial low-pressure systems. The occurrence of the CLLJs exhibits a distinct diurnal cycle with two different peaks in different seasons. One peak appears at nighttime, mainly during non-flood seasons. The other appears in the afternoon, mainly during the flood season. In the non-flood seasons, under the influence of cold air, the inertial oscillations triggered by the land–sea thermal contrast lead to CLLJ onset at nighttime in the Pearl River Estuary. During the flood season, the strong near-surface pressure gradient contributes to CLLJ onset in the afternoon, while the topography (blocking and passing) is more conducive to the occurrence of the CLLJs in the Pearl River Estuary. These findings reveal the formation mechanisms of the CLLJs over the Pearl River Estuary, thus providing a basis for further understanding the precipitation in the Pearl River Estuary and the occurrence of the CLLJs in other coastal areas with complex mountain ranges.

Sea fog changes widely and rapidly, and existing Lidar scanning speeds are insufficient to detect such changes. Therefore, we developed a Lidar system with a fast scanning speed and long detection distance. Experimental results show that at high scanning speeds, the maximum correlation between the Lidar’s visibility results and those from two forward scattering visibility meters reaches 0.9537, with a minimum relative error less than 15.31%. The results also show that the visibility of the proposed Lidar system has high accuracy when fast scanning. During the tests, the Lidar system successfully captured sea fog many times and closely tracked the changes of sea visibility, which verifies the feasibility and reliability of the developed Lidar system for obtaining visibility measurements and sea fog detection.

As forest terrain is complex, containing leaves and other obstacles, it is difficult to distinguish the signal of forest-fire smoke when using single-channel lidar. To address this difficulty, a scanning micropulse polarization lidar system is developed, and a new method to detect forest fires is proposed in this study. Based on the characteristics of the depolarization ratio of in-scene obstacles, a matrix is constructed to remove obstacle signals, which in turn reduces the misidentification rate. Artificial forest-fire tests are carried out to verify the correctness of the proposed method and the feasibility of early forest-fire detection using the scanning polarization lidar system. In the working mode, the developed polarizing lidar system can locate a forest fire within three minutes with the proposed method. The experimental results show that forest fires can be accurately detected in real time when using scanning polarization lidar.

The height of the stable boundary layer is a key parameter in atmospheric transmission and diffusion, air quality, emergency response, wind energy, and numerical weather prediction models. Existing methods mainly determine the stable boundary layer height via a threshold or minimum value of the wind speed variance under a low-level jet. Based on multi-meteorological element data from a meteorological gradient observation tower, this paper revealed the limitations of existing methods from the perspective of dynamic and thermal effects. In this paper, it is demonstrated that there were four types of shapes of the wind speed variance profile under the low-level jet and a method for using the shape of the variance profile to retrieve the height of the stable boundary layer was proposed. This method distinguished different types of wind speed variance profiles and solved the problems of the misjudgment and omissions (about 34%) present in existing methods. Our experiment showed that the average absolute error of the proposed method was less than 18 m and the average relative error was less than 9%. The results showed that the proposed inversion method was extended to all kinds of wind field detection equipment for inversion of the stable boundary layer height and has very high universality.

Although polarization lidar technology has been widely used to detect depolarized particles, many difficulties still exist in measuring accurate values for the depolarization ratio. This ratio is basically related to the difference between the optical and the electronic gain of the lidar's channels, the polarization purity of the laser pulse, and the error in the alignment between the laser's polarization plane and the polarizer. We propose here an algorithm for the calibration and calculation of polarization lidars. Numerical simulations showed that the depolarization ratio profile retrieved from the proposed algorithm agrees well with the simulated profile. The proposed algorithm no longer requires the laser source to be linearly polarized. Instead, the light polarization can be elliptical or random. During the installation process, the polarization of the detector is no longer required to be parallel or perpendicular to the polarization direction of the laser source and can be done in any direction.