Observations from a wintertime and summertime field campaign are used to assess the relationship between black and brown carbon (BC and BrC, respectively) optical properties and particle composition and coating state. The wintertime campaign, in Fresno, CA, was impacted by primary emissions from residential wood burning, secondary organic and inorganic particle formation, and BC from motor vehicles. Two major types of BrC were observed in wintertime. One occurred primarily at night—the result of primary biomass burning emissions. The second was enhanced in daytime and strongly associated with particulate nitrate and the occurrence of fog. The biomass‐burning‐derived BrC absorbed more strongly than the nitrate‐associated BrC but had a weaker wavelength dependence. The wintertime BC‐specific mass absorption coefficient (MAC_BC) exhibited limited dependence on the ensemble‐average coating‐to‐BC mass ratio (R_coat‐rBC) at all wavelengths, even up to R_coat‐rBC of ~5. For the summertime campaign, in Fontana, CA, BC dominated the light absorption, with negligible BrC contribution even after substantial photochemical processing. The summertime MAC_BC exhibited limited dependence on R_coat‐rBC, even up to ratios of >10. Based on the four classes of BC‐containing particles identified by Lee et al. (2017, https://doi.org/10.5194/acp‐17‐15055‐2017) for the summertime measurements, the general lack of an absorption enhancement can be partly—although not entirely—attributed to an unequal distribution of coating materials between the BC‐containing particle types. These observations demonstrate that in relatively near‐source environments, even those impacted by strong secondary aerosol production, the ensemble‐average, mixing‐induced absorption enhancement for BC due to coatings can be quite small.

Cloud and aerosol contribute with great uncertainty in Earth’s radiative budget. There is an urgent need for global 3-D observation of these atmospheric constituents. High-spectral-resolution Lidar (HSRL) can obtain vertical atmosphere profile with high accuracy, hence several space-borne HSRLs are planned to launch in few years. However, as far as we know, the performance evaluation of space-borne HSRL has not been reported yet. In this paper, we present the characteristics of a new designed space-borne HSRL for aerosol and cloud optical property profiling (ACHSRL), which is part of the Aerosol & Carbon Detection Lidar (ACDL) developed in China. The ACHSRL is essentially similar to the famous Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), which is on board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO). Moreover, the ACHSRL employs an iodine absorption filter as the spectral discriminator. The atmospheric optical properties data observed by CALIOP is used to estimate the performance of ACHSRL. We chose the level 2 profile data (version 4.10) in South Japan in June 2015 to compare the detection uncertainty of ACHSRL and CALIOP. The simulation calculates the uncertainties of ACHSRL and makes a statistic analysis. The analysis result demonstrates that 73.63% of the backscatter coefficient uncertainties are below 40% for ACHSRL. By contrast, the number is 30.72% for CALIOP. As for absolute extinction coefficient errors, the statistics shows that 76.01% of the extinction coefficient uncertainties are lower than 0.2 km⁻¹ for ACHSRL, while that for CALIOP are 56.97%. The assessment shows that ACHSRL could measure the particulate optical properties with better accuracy and compared with CALIOP. The estimation in this study reveals that the next generation space-borne HSRLs have a promising future.

Detecting the vertical profile of optical properties is an important task in the remote sensing of the upper ocean, especially for 3-D reconstruction. Ocean color remote sensing can only provide surface information, while the light detection and ranging (lidar) technique can provide depth-resolved data. Lidar can provide global-scale observations of the upper ocean for days and nights with minimal atmospheric correction errors. Unfortunately, due to the strong multiple scattering effects that occur when light propagates in seawater, the simple lidar equation may cause some deviations between the actual measurements and the simulation of the lidar signals. In this paper, we present a shipborne oceanic lidar, which was developed to detect the optical properties of seawater. For evaluating the performance of the lidar system, a Monte Carlo (MC) model was established to simulate lidar signals based on the simultaneous in situ inherent optical properties of seawater. The lidar measurements and the MC simulation can provide both the lidar signals and the retrieved lidar attenuation coefficient α . The results of the comparison indicate that the lidar-measured signals correspond well with the MC-simulated signals at different experiment stations in the Yellow Sea and at various receiving fields of view (FOVs). We also observed strong correlations between the lidar-measured α and MC-simulated α at different stations (r = 0.95) and at various FOVs (r = 0.96). The results indicate the reliability of the developed lidar system.

Multiple scattering is an inevitable effect in spaceborne oceanic lidar because of the large footprint size and the high optical density of seawater. The effective attenuation coefficient k_lidar in the oceanic lidar equation, which indicates the influence of the multiple scattering effect on the formation of lidar returns, is an important parameter in the retrieval of inherent optical properties (IOPs) of seawater. In this paper, the relationships between k_lidar of the spaceborne lidar signal and the IOPs of seawater are investigated by solving the radiative transfer equation with an improved semianalytic Monte Carlo model. Apart from the geometric loss factors, k_lidar is found to decrease exponentially with the increase of depth in homogeneous waters. k_lidar is given as an exponential function of depth and IOPs of seawater. The mean percentage errors between k_lidar calculated by the exponential function and the simulated ones in three typical stratified waters are within 0.5%, proving the effectiveness and applicability of this k_lidar-IOPs function. The results in this paper can help researchers have a better understanding of the multiple scattering effect of spaceborne lidar and improve the retrieval accuracy of the IOPs and the chlorophyll concentration of case 1 water from spaceborne lidar measurements.

As one of the most influential greenhouse gases, carbon dioxide (CO₂) has a profound impact on the global climate. The spaceborne integrated path differential absorption (IPDA) lidar will be a great sensor to obtain the columnar concentration of CO₂ with high precision. This paper analyzes the performance of a spaceborne IPDA lidar, which is part of the Aerosol and Carbon Detection Lidar (ACDL) developed in China. The line-by-bine radiative transfer model was used to calculate the absorption spectra of CO₂ and H₂O. The laser transmission process was simulated and analyzed. The sources of random and systematic errors of IPDA lidar were quantitatively analyzed. The total systematic errors are 0.589 ppm. Monthly mean global distribution of relative random errors (RREs) was mapped based on the dataset in September 2016. Afterwards, the seasonal variations of the global distribution of RREs were studied. The global distribution of pseudo satellite measurements for a 16-day orbit repeat cycle showed relatively uniform distribution over the land of the northern hemisphere. The results demonstrated that 61.24% of the global RREs were smaller than 0.25%, or about 1 ppm, while 2.76% of the results were larger than 0.75%. The statistics reveal the future performance of the spaceborne IPDA lidar.