Dual-field-of-view high-spectral-resolution lidar: Simultaneous profiling of aerosol and water cloud to study aerosol-cloud interaction

Constraining effects of aerosol-cloud interaction by accounting for coupling between cloud and land surface

Aerosol-cloud interactions (ACIs) are vital for regulating Earth's climate by influencing energy and water cycles. Yet, effects of ACI bear large uncertainties, evidenced by systematic discrepancies between observed and modeled estimates. This study quantifies a major bias in ACI determinations, stemming from conventional surface or space measurements that fail to capture aerosol at the cloud level unless the cloud is coupled with land surface. We introduce an advanced approach to determine radiative forcing of ACI by accounting for cloud-surface coupling. By integrating field observations, satellite data, and model simulations, this approach reveals a drastic alteration in aerosol vertical transport and ACI effects caused by cloud coupling. In coupled regimes, aerosols enhance cloud droplet number concentration across the boundary layer more homogeneously than in decoupled conditions, under which aerosols from the free atmosphere predominantly affect cloud properties, leading to marked cooling effects. Our findings spotlight cloud-surface coupling as a key factor for ACI quantification, hinting at potential underassessments in traditional estimates.

Synergistic monitoring of PM2.5 and CO2 based on active and passive remote sensing fusion during the 2022 Beijing Winter Olympics

Green and low-carbon are the keywords of the 2022 Beijing Winter Olympic Games (WOG) and the core of sustainable development. Beijing's P M 2.5 and C O 2 emissions attracted worldwide attention during WOG. However, the complex emission sources and frequently changing weather patterns make it impossible for a single monitoring approach to meet the high-resolution, full-coverage monitoring requirements. Therefore, we proposed an active-passive remote sensing fusion method to address this issue. The haze layer height (HLH) was first retrieved from vertical aerosol profiles measured by our high-spectral-resolution lidar located near Olympic venues, which provides new insights into the nonuniform boundary layer and the residual aerosol aloft above it. Second, we developed a bootstrap aggregating (bagging) method that assimilates the lidar-based HLH, satellite-based AOD, and meteorological data to estimate the hourly P M 2.5 with 1 km resolution. The P M 2.5 at Beijing region, Bird's Nest, and Yanqing venues during WOG was 23.00±18.33, 22.91±19.48, and 16.33±10.49µg/m 3, respectively. Third, we also derived the C O 2 enhancements, C O 2 spatial gradients resulting from human activities, and annual growth rate (AGR) to estimate the performance of carbon emission management in Beijing. Based on the top-down method, the results showed an average C O 2 enhancement of 1.62 ppm with an annual decline rate of 2.92 ppm. Finally, we compared the monitoring data with six other international cities. The results demonstrated that Beijing has the largest P M 2.5 annual decline rate of 7.43µg/m 3, while the C O 2 AGR is 1.46 ppm and keeps rising, indicating Beijing is still on its way to carbon peaking and needs to strive for carbon neutrality.

Potential of Mie-Fluorescence-Raman Lidar to Profile Chlorophyll a Concentration in Inland Waters

Vertical distribution of phytoplankton is crucial for assessing the trophic status and primary production in inland waters. However, there is sparse information about phytoplankton vertical distribution due to the lack of sufficient measurements. Here, we report, to the best of our knowledge, the first Mie-fluorescence-Raman lidar (MFRL) measurements of continuous chlorophyll a (Chl-a) profiles as well as their parametrization in inland water. The lidar-measured Chl-a during several experiments showed good agreement with the in situ data. A case study verified that MFRL had the potential to profile the Chl-a concentration. The results revealed that the maintenance of subsurface chlorophyll maxima (SCM) was influenced by light and nutrient inputs. Furthermore, inspired by the observations from MFRL, an SCM model built upon surface Chl-a concentration and euphotic layer depth was proposed with root mean square relative difference of 16.5% compared to MFRL observations, providing the possibility to map 3D Chl-a distribution in aquatic ecosystems by integrated active-passive remote sensing technology. Profiling and modeling Chl-a concentration with MFRL are expected to be of paramount importance for monitoring inland water ecosystems and environments.

Comprehensive, Continuous, and Vertical Measurements of Seawater Constituents with Triple-Field-of-View High-Spectral-Resolution Lidar

Measuring the characteristics of seawater constituent is in great demand for studies of marine ecosystems and biogeochemistry. However, existing techniques based on remote sensing or in situ samplings present various tradeoffs with regard to the diversity, synchronism, temporal-spatial resolution, and depth-resolved capacity of their data products. Here, we demonstrate a novel oceanic triple-field-of-view (FOV) high-spectral-resolution lidar (HSRL) with an iterative retrieval approach. This technique provides, for the first time, comprehensive, continuous, and vertical measurements of seawater absorption coefficient, scattering coefficient, and slope of particle size distribution, which are validated by simulations and field experiments. Furthermore, it depicts valuable application potentials in the accuracy improvement of seawater classification and the continuous estimation of depth-resolved particulate organic carbon export. The triple-FOV HSRL with high performance could greatly increase the knowledge of seawater constituents and promote the understanding of marine ecosystems and biogeochemistry.

Joint multiscale cloud detection algorithm for ground-based lidar

A ground-based lidar is a powerful tool for studying the vertical structure and optical properties of clouds. A layer detection algorithm is important to determine the presence and spatial position of clouds from vast lidar signals. However, current detection algorithms for ground-based lidar still involve substantial missing and false detections for tenuous layers and layer edges. Here, a joint multiscale cloud layer detection algorithm is proposed. The algorithm can effectively capture the tenuous layers and layer edges by using joint multiscale detection methods based on a trend function and the Bernoulli distribution assumption. Results show that the proposed algorithm detects 10.45% more cloud layers than the official cloud product of Micro Pulse Lidar Network (MPLNET) does. Specifically, 7.93% and 12.57% more cloud layers are detected at daytime and nighttime, respectively. The evaluation based on depolarization properties proves that the additional cloud layers detected by the joint multiscale algorithm are reliable. These additional detected clouds have important implications for cloud climatology and climate change research. The new algorithm remarkably enhances the cloud detection capability of ground-based lidar and potentially be widely used by the community.

Validation of the dual field-of-view polarization LIDAR technique for the retrieval of homogeneous water cloud microphysical properties: a study based on a polarimetric Monte Carlo simulation

This article highlights the validation of the dual fields-of-view (FOVs) polarization lidar technique for the retrieval of cloud droplet effective radius in conjunction with cloud extinction coefficient of homogeneous water cloud via simulation approach. The simulation is based on polarimetric Monte Carlo method incorporated with semianalytic features under multiple-scattering conditions. The simulation results show that the depolarization ratio measured at dual-FOVs is a function of the cloud droplet effective radius and cloud extinction coefficient. Using the method of standard deviation on extensive simulation results and then by applying the polynomial regression, two polynomial relationships are obtained expressing the retrieval of the cloud droplet effective radius and cloud extinction coefficient from the layer integrated depolarization ratio at low optical depths close to the cloud bottom. Eventually, the results those presented by Ref.[1] are validated. The water cloud microphysical properties, liquid water content and cloud droplet number concentration are the functions of these two parameters and thus can be found numerically.

High-spectral-resolution LIDAR based on a few-longitudinal mode laser for aerosol and cloud characteristics detection

A novel implementation of high-spectral-resolution LIDAR based on a passively Q-switched few-longitudinal mode laser (PQFLM-HSRL) is proposed, and the prototype is built for detecting aerosol and cloud characteristics. The spatial-temporal distributions of the aerosol and cloud are continuously observed by the PQFLM-HSRL for the first time, to the best of our knowledge. Based on observation, we present the retrieval results of backscatter coefficient, particle linear depolarization ratio, and LIDAR ratio, and these intensive parameters are used to classify the aerosol and cloud into different types. Particularly, we have observed mix-phased clouds. The resulting aerosol optical depths (AODs) are highly consistent with CE-318, the Sun photometer measurements of the local National Meteorological Station (NMS), which verify the retrieval accuracy and the system stability. In addition, the retrieved AODs also characterize the ambient air quality, which show a high correlation with the measured PM_2.5 concentrations. The implementation of the PQFLM-HSRL provides a new method for atmospheric feature detection, which shows superior scientific potential for further study on climate change and environmental health.

Shipborne oceanic high-spectral-resolution lidar for accurate estimation of seawater depth-resolved optical properties

Lidar techniques present a distinctive ability to resolve vertical structure of optical properties within the upper water column at both day- and night-time. However, accuracy challenges remain for existing lidar instruments due to the ill-posed nature of elastic backscatter lidar retrievals and multiple scattering. Here we demonstrate the high performance of, to the best of our knowledge, the first shipborne oceanic high-spectral-resolution lidar (HSRL) and illustrate a multiple scattering correction algorithm to rigorously address the above challenges in estimating the depth-resolved diffuse attenuation coefficient K_d and the particulate backscattering coefficient b_bp at 532 nm. HSRL data were collected during day- and night-time within the coastal areas of East China Sea and South China Sea, which are connected by the Taiwan Strait. Results include vertical profiles from open ocean waters to moderate turbid waters and first lidar continuous observation of diel vertical distribution of thin layers at a fixed station. The root-mean-square relative differences between the HSRL and coincident in situ measurements are 5.6% and 9.1% for K_d and b_bp, respectively, corresponding to an improvement of 2.7-13.5 and 4.9-44.1 times, respectively, with respect to elastic backscatter lidar methods. Shipborne oceanic HSRLs with high performance are expected to be of paramount importance for the construction of 3D map of ocean ecosystem.