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Physical-Optical Properties of Marine Aerosols over the South China Sea: Shipboard Measurements and MERRA-2 Reanalysis. REMOTE SENSING 2022. [DOI: 10.3390/rs14102453] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Aerosols play an important role in the Earth–atmosphere system. Their impacts on the weather and climate are highly dependent on spatiotemporal distributions as well as physical-optical properties. Physical-optical properties of the aerosols over the Asian continent have been widely investigated, but there are relatively few observations in maritime locations, especially the South China Sea (SCS). Here, with the combination of in situ ship-based observations from June and July 2019 as well as long-term MERRA-2 reanalysis datasets from January 2012 to December 2021, the physical and optical properties of marine aerosols in the SCS are explored. The impacts of meteorological factors, particularly frontal systems, on the aerosol properties are further analyzed based on detailed observations. The observed results show that aerosols are vertically concentrated below 3 km and the extinction coefficient reaches the maximum value of 0.055 km−1 near 480 m. Moreover, the particles are composed of an accumulation and a coarse particle mode, and they conform to the lognormal distribution. The synoptic-scale case study demonstrates that both the cold front and stationary front lead to an increase in aerosol optical thickness (AOD), which is due to the enhanced wind speed and the hygroscopic growth of fine particles, respectively. The long-term analysis indicates that AOD decreases from northwest to southeast with the increasing distance away from the continent, and it reflects higher values in spring and winter than in summer and autumn. Sulfate and sea salt dominate AOD in this region when compared with other components. The overall AOD shows a significant negative trend of −0.0027 year−1. This work will help us further understand the physical and optical properties of marine aerosols over the SCS and then contribute to quantifying the aerosol radiative forcing in the future.
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Variation and Driving Factor of Aerosol Optical Depth over the South China Sea from 1980 to 2020. ATMOSPHERE 2022. [DOI: 10.3390/atmos13030372] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Spatial and temporal variation of aerosol optical depth (AOD) and optical depth of different aerosol types derived from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) over the South China Sea (SCS) between 1980 and 2020 were studied. AOD distribution showed different characteristics throughout the entire SCS. Sulfate Aerosol Optical Depth (SO4AOD) and Sea Salt Aerosol Optical Depth (SSAOD) mainly contributed to the spatial and temporal variation of AOD over the SCS. A significant increasing trend followed by a decreasing trend of AOD could be observed in the north of the SCS from 1980 to 2020. Mean MERRA-2 AOD between 1980 and 2020 showed that AOD was high in the north and low in the south and that AOD gradually decreased from north to south over the SCS. AOD after 2000 was obviously higher than that of the 1980s and 1990s. Higher AOD appeared in the spring and winter, and low AOD appeared in the summer. The spatial distribution of scattering aerosol optical depth (SAOD) was similar to AOD distribution over the SCS. SO4AOD and SSAOD were obviously higher than black carbon aerosol optical depth (BCAOD), organic carbon aerosol optical depth (OCAOD), and dust aerosol optical depth (DUAOD) over the SCS. SO4AOD accounted for over 50% of total AOD (TAOD) over the north of the SCS, while BCAOD and DUAOD accounted for less than 10% of TAOD over the entire SCS. An obvious annual mean TAOD increase between 1980 and 2007 could be observed over the northern part of the SCS (NSCS), while a TAOD decrease happened from 2008 to 2020 in this region. The correlation coefficient between TAOD and SO4AOD over NSCS from 1980 to 2020 was about 0.93, indicating SO4AOD was the driving factor of TAOD variation in this area. Different AOD variation trends over the different areas of the SCS could be observed during the two periods including 1980–2007 and 2008–2020. AOD increase appeared over most of the SCS during the period from 1980 to 2007, while AOD decrease could be observed over most of the SCS from 2008 to 2020.
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Development of ZJU High-Spectral-Resolution Lidar for Aerosol and Cloud: Extinction Retrieval. REMOTE SENSING 2020. [DOI: 10.3390/rs12183047] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
The retrieval of the extinction coefficients of aerosols and clouds without assumptions is the most important advantage of the high-spectral-resolution lidar (HSRL). The standard method to retrieve the extinction coefficient from HSRL signals depends heavily on the signal-to-noise ratio (SNR). In this work, an iterative image reconstruction (IIR) method is proposed for the retrieval of the aerosol extinction coefficient based on HSRL data, this proposed method manages to minimize the difference between the reconstructed and raw signals based on reasonable estimates of the lidar ratio. To avoid the ill-posed solution, a regularization method is adopted to reconstruct the lidar signals in the IIR method. The results from Monte-Carlo (MC) simulations applying both standard and IIR methods are compared and these comparisons demonstrate that the extinction coefficient and the lidar ratio retrieved by the IIR method have smaller root mean square error (RMSE) and relative bias values than the standard method. A case study of measurements made by Zhejiang University (ZJU) HSRL is presented, and their results show that the IIR method not only obtains a finer structure of the aerosol layer under the condition of low SNR, but it is also able to retrieve more reasonable values of the lidar ratio.
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