Yanqing Xie

Abstract An overview of Sun–Sky Radiometer Observation Network (SONET) measurements in China is presented. Based on observations at 16 distributed SONET sites in China, atmospheric aerosol parameters are acquired via standardization processes of operational measurement, maintenance, calibration, inversion, and quality control implemented since 2010. A climatology study is performed focusing on total columnar atmospheric aerosol characteristics, including optical (aerosol optical depth, ÅngstrÖm exponent, fine-mode fraction, single-scattering albedo), physical (volume particle size distribution), chemical composition (black carbon; brown carbon; fine-mode scattering component, coarse-mode component; and aerosol water), and radiative properties (aerosol radiative forcing and efficiency). Data analyses show that aerosol optical depth is low in the west but high in the east of China. Aerosol composition also shows significant spatial and temporal variations, leading to noticeable diversities in optical and physical property patterns. In west and north China, aerosols are generally affected by dust particles, while monsoon climate and human activities impose remarkable influences on aerosols in east and south China. Aerosols in China exhibit strong light-scattering capability and result in significant radiative cooling effects.

Abstract Remote sensing provides aerosol loading information, but to address climate and air quality model validation, there are additional needs to acquire aerosol composition information. In this study, a comprehensive aerosol composition model is established to quantify black carbon (BC), brown carbon (BrC), mineral dust (DU), particulate organic matters, ammonium sulfate like (AS), sea salt, and aerosol water uptake. We develop forward modeling of aerosol components, including microphysical parameters (real and imaginary refractive indices, volume fraction ratio of fine to coarse mode, and sphericity) and hygroscopic growth models, and propose an optimization scheme to estimate the components. The uncertainties caused by input parameters are also assessed. Sun‐sky radiometer measurements and meteorological data obtained during a campaign in Huairou, Beijing, are processed to estimate aerosol components, which are further compared with synchronous in situ chemical measurements. The results show generally good consistencies between remotely estimated and measured components (e.g., correlation coefficients for BC, BrC, AS, and PM 2.5 lie in about 0.8–0.9). The comparisons between modeled and observed microphysical parameters also show good agreements, with the exception of sphericity, which is likely caused by high uncertainties of this parameter. Sensitivity studies show that BC and BrC are highly sensitive to imaginary refractive index, while DU is strongly correlated to both volume size and sphericity. The performance of composition retrieval is expected to be improved when the sphericity uncertainty is significantly reduced.

The advanced Himawari imager (AHI) aboard the Himawari-8 geostationary satellite provides high-frequency observations with broad coverage, multiple spectral channels, and high spatial resolution. In this paper, AHI data were used to develop an algorithm for joint retrieval of aerosol optical depth (AOD) over land and land surface bidirectional reflectance. Instead of performing surface reflectance estimation before calculating AOD, the AOD and surface bidirectional reflectance were retrieved simultaneously using an optimal estimation method. The algorithm uses an atmospheric radiative transfer model coupled with a surface bidirectional reflectance factor (BRF) model. Based on the assumption that the surface bidirectional reflective properties are invariant during a short time period (i.e., a day), multiple temporal AHI observations were combined to calculate the AOD and surface BRF. The algorithm was tested over East Asia for year 2016, and the AOD retrieval results were validated against the aerosol robotic network (AERONET) sites observation and compared with the Moderate Resolution Imaging Spectroradiometer Collection 6.0 AOD product. The validation of the retrieved AOD with AERONET measurements using 14 713 colocation points in 2016 over East Asia shows a high correlation coefficient: R = 0.88, root-mean-square error = 0.17, and approximately 69.9% AOD retrieval results within the expected error of ±0.2·AOD <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AERONET</sub> ±0.05. A brief comparison between our retrieval and AOD product provided by Japan Meteorological Agency is also presented. The comparison and validation demonstrates that the algorithm has the ability to estimate AOD with considerable accuracy over land.

In this study, simple dust detection and intensity estimation methods using Himawari-8 Advanced Himawari Imager (AHI) data are developed. Based on the differences of thermal radiation characteristics between dust and other typical objects, brightness temperature difference (BTD) among four channels (BT11–BT12, BT8–BT11, and BT3–BT11) are used together for dust detection. When considering the thermal radiation variation of dust particles over different land cover types, a dynamic threshold scheme for dust detection is adopted. An enhanced dust intensity index (EDII) is developed based on the reflectance of visible/near-infrared bands, BT of thermal-infrared bands, and aerosol optical depth (AOD), and is applied to the detected dust area. The AOD is retrieved using multiple temporal AHI observations by assuming little surface change in a short time period (i.e., 1–2 days) and proved with high accuracy using the Aerosol Robotic Network (AERONET) and cross-compared with MODIS AOD products. The dust detection results agree qualitatively with the dust locations that were revealed by AHI true color images. The results were also compared quantitatively with dust identification results from the AERONET AOD and Ångström exponent, achieving a total dust detection accuracy of 84%. A good agreement is obtained between EDII and the visibility data from National Climatic Data Center ground measurements, with a correlation coefficient of 0.81, indicating the effectiveness of EDII in dust monitoring.

As the successors of the overdue HuanjingJianzai-1 (HJ-1) satellites and new members in Chinese Environmental Protection and Disaster Monitoring Satellite Constellation, the first two of HuanjingJianZai-2 series satellites (HJ-2 A/B) have been launched on September 27, 2020. Each satellite carries four sensors, including the Polarized Scanning Atmospheric Corrector (PSAC), the charge-coupled device (CCD) camera, the hyperspectral imager (HSI) and the infrared spectroradiometer (IRS). Among them, PSAC is mainly used for the monitoring of atmospheric parameters to provide data support for atmospheric environmental monitoring and atmospheric correction of data from other sensors. To test the in-orbit performance of PSAC, we develop the &#x201C;day-1&#x201D; aerosol and water vapor retrieval algorithms. The preliminary validation results based on ground-based observations show that the aerosol optical depth (AOD) and columnar water vapor (CWV) datasets developed based on PSAC data have high accuracy and can effectively characterize the temporal trends of AOD and CWV. The accuracy of PSAC AOD dataset is better than the expected error &#x00B1;(0.05 + 0.2 * AODAERONET), and the accuracy of PSAC CWV dataset is better than the expected error &#x00B1;(0.5 + 0.15 * CWVAERONET). To eliminate the negative impact of the atmosphere on CCD data and expand its application range, aerosol and water vapor data developed based on PSAC are used for atmospheric correction of CCD data. Compared with L1 CCD data, the texture details and clarity of CCD data after atmospheric correction have been significantly improved.