Clouds cover about 2/3 of the earth's area and are an important part of the atmosphere. As one of the main factors affecting the global climate, clouds not only have thermal radiation but are also capable of absorbing and scattering solar short-wave radiation and surface long-wave radiation, thus influencing the net radiative forcing of the earth-atmosphere system, global energy balance, and water cycle [[1], [2], [3]]. For warm clouds, the mechanism of aerosol-cloud interaction was first proposed by Twomey in 1974. When the concentration of aerosol particles in the atmosphere increases, the number concentration of cloud droplets will increase. Under the premise that the LWC in the cloud remains unchanged, the effective radius of cloud droplets will decrease, and the albedo of the cloud will increase. It follows the route of “human activity-aerosol cloud microphysical properties-radiative forcing”, which is called the first indirect effect of aerosol [4,5]. Aerosols act as condensation centers for cloud droplets and ice crystals, thus changing the nature of the cloud. If more aerosol particles compete for water vapor uptake, the cloud droplets that form do not get larger. For the same amount of cloud water, more small cloud droplets have a larger surface area than fewer large cloud droplets. As a result, polluted clouds reflect more solar radiation back into space, leading to negative radiative forcing by cloud albedo effect. In addition, these more numerous but smaller cloud droplets collide with each other less efficiently, which reduces the precipitation efficiency of the polluted clouds and may prolong their lifetimes (Albrecht effect). It follows the route of “human activity-aerosol microphysical properties of clouds macro physical properties of clouds-radiation forcing” [6]. Aerosols have a semi-direct effect on clouds. Aerosols such as soot and black carbon have a strong ability to absorb solar radiation and re-release thermal radiation, which can heat the atmosphere and cloud masses to evaporate cloud droplets, reducing cloud volume, shortening cloud lifetimes and reducing the mean albedo of cloud bodies [7,8]. In turn, clouds can remove aerosols from the atmosphere through precipitation, thereby affecting the effect of aerosols on clouds [9,10]. Therefore, the detection of the macro and microscopic properties of clouds is the basis of studying cloud physical processes and fine structure within clouds and has important scientific research value for revealing the transmission of atmospheric radiative power [[11], [12], [13]].

The lidar measurement technique can provide high spatial and temporal resolution profiles of macro and micro parameters of water clouds and has become an effective tool for continuous observation of the vertical distribution, evolution, and life cycle of cloud properties [[14], [15], [16], [17]]. Affected by the strong absorption and attenuation of light waves by clouds, the current researchers mainly focus on the cloud phase identification of thin clouds and the detection of cloud base height [[18], [19], [20], [21]]. Since the thin cloud signal is often lower than the radar detection threshold, the lidar has good detection advantages and application potential in this case. Especially in the detection of macro-and microstructures of the clouds, the lidar has higher sensitivity for smaller droplets and can detect the cloud bottom area where droplet formation and aerosol particles are mixed with high accuracy. The main microphysical properties of water clouds measured by lidar are the cloud droplet extinction coefficient, the effective radius of the cloud droplet, and the LWC in the cloud. The presence of multiple scattering effects enables the acquisition of the microphysical properties of clouds [22,23]. The multiple scattering lidar signal is a function of the medium extinction coefficient and the angular scattering properties or particle size distribution, and for multiple scattering greater than order 2, there is no straightforward mathematical relationship between these localized properties and the observed values. The observed values are the result of many combined interactions, as demonstrated by the multiple integrals of the phenomenological model [24]. This makes it particularly difficult to obtain inversions of local extinction coefficients and particle sizes.

The multiple scattering in lidar manifests itself as greater signal strength and alteration of polarization state, with both the signal strength and the receding bias ratio being related to the received FOV. The first batch of multiple field-of-view (MFOV) elastic lidar aimed to study the multiple scattering effect and proposed the idea of measuring the effective particle radius of cloud droplets [25], which was successfully realized [[26], [27], [28]]. Among them, Roy et al. introduced a robust approach based on cross-polarized returns at MFOV that allows the evaluation of droplet size distributions [29]. Bissonnette et al. proposed a MFOV approach based on the measurement of total elastic backscattered echoes in conjunction with Monte Carlo simulations. However, the complex nature of the Mie scattering phase function complicates the acquisition of particle radii from MFOV elastic lidar measurements [30]. Since the phase function of Raman scattering by nitrogen molecules is almost isotropic in the backward direction, this naturally leads to the use of Raman scattering by atmospheric nitrogen, thus permitting the development of a viable Raman Lidar system to invert the microphysical properties of hydrometeors to overcome the drawbacks of the complex measurement setups resulting from the MFOV elastic lidar [31]. A dual-FOV Raman lidar technique has been experimentally demonstrated [32] for inverting cloud microphysical properties by simultaneously detecting Raman-scattered light from two FOV. The sensitivity of this dual-FOV Raman lidar system to cloud microphysical properties is dependent on the selected field-of-view pairs and the height of the target. Raman lidar has the advantage that the measured multiple scattering contribution is clearly related to the effective radius of the cloud droplets. However, the nitrogen Raman signal is weak, so observations are limited to nighttime, and it typically takes 10 min or more of signal averaging time to reduce the effect of signal noise on the lidar product to an acceptable level. This problem is extensively addressed in dual-FOV polarized lidar technology [33]. The new dual-FOV polarization lidar technique is applied to cloud measurements in pristine marine conditions at Punta Arenas in southern Chile. A multiwavelength polarization Raman lidar, upgraded by integrating a second polarization-sensitive channel to permit depolarization ratio observations at two FOVs, was used for these measurements at the southernmost tip of South America. Under these pristine conditions, measurements based on an upgraded aerosol-cloud lidar with a Doppler lidar for the vertical wind component allow for successful aerosol-cloud interaction (ACI) studies at 1-min time resolution. Thus, lidar technology has new potential for atmospheric and climate studies in the ACI field.

To address the complexity of the inversion algorithm and optical system for elastic lidar detection of water clouds, our research group is also constructing a Raman lidar system for water cloud observation and conducting preliminary detection experiments on the microphysical properties of low-altitude water clouds in Liupan Mountain area. This lidar system uses a variable dual-FOV technique to detect the multiple-scattered signals from cloud droplets by receiving backscattered light from nitrogen molecules in different FOV. According to the analytical model of the multiple scattering Raman lidar, the inversion methods of the extinction coefficient, effective radius and liquid water content of water clouds are given. Section 2 focuses on the lidar system and the inversion scheme. Section 3 analyzes the experimental results. Concluding remarks and an outlook are given in Section 4.