China Aerosol Remote Sensing Network Research Articles

Long-term validation and error analysis of DB and MAIAC aerosol products over bright surface of China

Two distinct algorithms, namely Deep Blue (DB) and Multiangle Implementation of Atmospheric Correction (MAIAC), based on the Moderate Resolution Imaging Spectroradiometer (MODIS), can provide aerosol optical depth (AOD) products over bright surfaces. Recently, the newest MAIAC Collection 6.1 (C6.1) product was available, which corrected many known deficiencies of C6. Due to the significant impact of high-reflectivity surfaces on the accuracy of aerosol retrieval, long-term validation and comprehensive assessment of AOD over bright surfaces are required at a regional scale. Firstly, this study conducted a systematic evaluation of three products against ground-based Sunphotometer data obtained from 21 sites of the China Aerosol Remote Sensing Network (CARSNET) and 2 sites of the Aerosol Robotic Network (AERONET) from the year 2002 to 2014. The validation results showed a significant underestimation in DB and MAIAC AOD retrievals as indicated by the large percent of retrievals below the expected error (EE) envelope, and significant negative biases. In comparison, both versions of MAIAC products performed better than DB product, which was also revealed by the site scale validation. Besides, the performance of the C6.1 MAIAC was slightly improved over the study region compared to the C6 MAIAC. Finally, this study made an in-depth investigation of the underestimation of AOD products affected by various factors, i.e., aerosol loading, land type, NDVI, surface albedo, and scattering angle. The results indicated that the large negative bias of AOD retrievals for the three products tended to occur in the case of large aerosol loadings and high bright surfaces, which could be attributed to the inaccurate assumption of aerosol models and the insensitivity of top of atmosphere (TOA) reflectance to AOD variations caused by the high reflectivity of the underlying surface. The findings of this study are expected to provide some reference for the improvement of aerosol retrieval algorithms over bright surfaces.

Retrieval and validation of long-term aerosol optical depth from AVHRR data over China

ABSTRACT Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration (NOAA) satellites can provide over 40 years of global remote sensing observations, which can be used to retrieve long-term aerosol optical depth (AOD). This is of great significance to the study of global climate change. In this paper, we proposed an algorithm to jointly calculate AOD and land surface properties from AVHRR observations. With assumptions that AOD doesn’t vary in adjacent space and earth surface property doesn’t vary in two days, the algorithm considered non-Lambertian surface reflection based on the shape of bidirectional reflectance distribution function (BRDF shape) and obtained AOD and surface property by optimal estimation (OE) method. The algorithm has been applied to NOAA-7, 9, 11, 14, 16, 18, and 19 satellites and AVHRR-retrieved AOD with 5 × 10 km over China (15°–60° N, 70°–140°E) has been obtained from 1982 to 2016. Comparisons of AVHRR-retrieved AOD against AErosol RObotic NETwork (AERONET) (in and around China) and China Aerosol Remote Sensing Network (CARSNET) AOD show good consistency with 62.62% points within the uncertainty of Δτ = ± (0.05 + 0.25τ) and root-mean-square error (RMSE) of 0.26. Further comparison of the monthly mean AOD of multiple AOD datasets in the ‘Beijing’, ‘Dalanzadgad’, ‘NCU_Taiwan’ and ‘Kanpur’ stations shows that the results of the algorithm are stable. The yearly averaged AOD data also has similar agreements with MERRA-2 (The Modern-Era Retrospective analysis for Research and Applications, Version 2) and AVHRRDB data (AVHRR ‘Deep Blue’ aerosol data set). The multi-year mean correlation coefficient is 0.70 and 0.61 and the percentages within the uncertainty are 80.01% and 67.29% compared with MERRA-2 AOD and AVHRRDB AOD respectively.

Evaluation and Comparison of MODIS C6 and C6.1 Deep Blue Aerosol Products in Arid and Semi-Arid Areas of Northwestern China

The Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) algorithm was developed for aerosol retrieval on bright surfaces. Although the global validation accuracy of the DB product is satisfactory, there are still some regions found to have very low accuracy. To this end, DB has updated the surface database in the latest version of the Collection 6.1 (C6.1) algorithm. Some studies have shown that DB aerosol optical depth (AOD) of the old version Collection 6 (C6) has been seriously underestimated in Northwestern China. However, the status of the new version of the C6.1 product in this region is still unknown. This study aims to comprehensively evaluate the performance of the MODIS DB product in Northwestern China. The DB AOD with high quality (Quality Flag = 2 or 3) was selected to validate against the 23 sites from the China Aerosol Remote Sensing Network (CARSNET) and Aerosol Robotic Network (AERONET) during the period 2002–2014. By the overall analysis, the results indicate that both C6 and C6.1 show significant underestimation with a large fraction of more than 54% of collocations falling below the Expected Error (EE = ±(0.05 + 20% AODground)) envelope and with a large negative Mean Bias (MB) of less than −0.14. Furthermore, the new C6.1 products failed to achieve reasonable improvements in the region of Northwestern China. Besides, C6.1 has slightly fewer collocations than C6 due that some pixels with systematic biases have been removed from the new surface reflectance database. From the analysis of the site scale, the scatter plot of C6.1 is similar to that of C6 in most sites. Furthermore, a significant underestimation of DB AOD was observed at most sites, with the most severe underestimation at two sites located in the Taklimakan Desert region. Among 23 sites in Northwestern China, there are only two sites where C6.1 has largely improved the underestimation of C6. Furthermore, it is interesting to note that there are also two sites where the accuracy of the new C6.1 has declined. Moreover, it is surprising that there is one site where a large overestimation was observed in C6 and improved in C6.1. Additionally, we found a constant value of about 0.05 for both C6 and C6.1 at several sites with low aerosol loading, which is an obvious artifact. The significant improvements of C6.1 were observed in the Middle East and Central Asia but not in most sites of Northwestern China. The results of this study will be beneficial to further improvements in the MODIS DB algorithm.

A satellite-measured view of aerosol component content and optical property in a haze-polluted case over North China Plain

The Directional Polarimetric Camera (DPC) is the first Chinese satellite-borne multi-angle polarimetric sensor onboard the GaoFen-5 (GF-5) satellite, which was launched on May 9th 2018. In this paper, we present the spatiotemporal variability of satellite-measured aerosol components (such as, black carbon, brown carbon, iron oxides contained in mineral dust etc.) over North China Plain (36–41°N, 113–123°E) for a heavily haze-polluted case, to demonstrate the first successful retrievals of aerosol component content and optical property directly from the DPC/GF-5 instrument measurements. We also reveal the relationship between absorbing/scattering component mass ratio and aerosol absorption based on the multi-angle polarimetric satellite measurements, which presents comparable agreement with previous laboratory studies and field studies for an indirect validation of aerosol component retrievals. In addition, the feasibility, stability and uncertainty of the aerosol optical property and component content retrievals from DPC multi-angle polarimetric observations were evaluated and assessed by sensitivity tests. The distributions of DPC/GF-5 aerosol optical depth (AOD) are in line with satellite-observed MODIS (Moderate-resolution Imaging Spectroradiometer) AOD and ground-based CARSNET (China Aerosol Remote Sensing Network) AOD products. Therefore, the multi-angle polarimetric satellite measurements show an encouraging potential for the comprehensive inversion of aerosol optical, microphysical and chemical properties. The results and further applications of this component algorithm in the future are helpful to reduce the uncertainty of climate change assessment by providing the measurements of aerosol component, in particular for black carbon, brown carbon, and iron oxides contained in dust, with large and extensive spatiotemporal scale.

A global-scale analysis of the MISR Level-3 aerosol optical depth (AOD) product: Comparison with multi-platform AOD data sources

This study analyses the applicability of the recently released Level-3 (L3) daily and monthly aerosol optical depth (AOD) products (version F15_0032) over land retrieved from the Multiangle Imaging Spectroradiometer (MISR) instrument. For this purpose, daily AOD data from 427 Aerosol Robotic Network (AERONET) sites worldwide during 2001–2018 and 39 China Aerosol Remote Sensing Network (CARSNET) sites across China during 2002–2014 were selected for comparison. Also, MISR-based size-segregated AODs were collected to compare with the coarse- and fine-mode AODs retrieved from AERONET to reveal the particles size modes that mainly contribute to the offset of MISR total AOD relative to observations. By comparing with five other monthly AOD datasets, including two MODIS (Moderate resolution Imaging Spectroradiometer) retrieval products, a multi-satellite merged product, and two aerosol reanalysis, MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications) and CAMS (Copernicus Atmosphere Monitoring Service), we then explored the applicability of MISR for characterizing the climatology, interannual variations, and long-term (2003–2017) trends in regional aerosol loadings over the 12 regions of interest. Overall, about 80% (60%) of the daily AOD values retrieved by MISR fall within expected error bounds of ± [0.05 + 0.2 × AOD] (±[0.03 + 0.1 × AOD]), with a high correlation (R = 0.87). Our comparison results show that although the V23 algorithm addresses several issues in L2 AOD retrieval relative to the previous version, the L3 data aggregated from L2 data tends to on average overestimate low AOD values and underestimate high AOD values. These offset relative to observations are mainly attributed to the overestimation of coarse-mode AOD and the underestimation of fine-mode AOD by MISR. Intercomparison of 2003–2017 AOD trends from multiple data sources indicate that MISR can capture well the increases occurring in South Asia, as well as the decreases occurring in Eastern China, Eastern United States, and Western Europe. Our study confirmed that MISR L3 product performs reliably at regional scales, particularly in typically polluted areas, but caution is still needed when applied to areas where sparse sampling is encountered.

Extracting Taklimakan Dust Parameters from AIRS with Artificial Neural Network Method

Two back-propagation artificial neural network retrieval models have been developed for obtaining the dust aerosol optical depth (AOD) and dust-top height (DTH), respectively, from Atmospheric InfraRed Sounder (AIRS) brightness temperature (BT) measurements over Taklimakan Desert area. China Aerosol Remote Sensing Network (CARSNET) measurements at Tazhong station were used for dust AOD validation. Results show that the correlation coefficient of dust AODs between AIRS and CARSNET reaches 0.88 with a deviation of −0.21, which is the same correlation coefficient as the AIRS dust AOD and the Moderate-Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product. In the AIRS DTH retrieval model, there is an option to include the collocated MODIS deep blue (DB) AOD as additional input for daytime retrieval; the independent dust heights from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) are used for AIRS DTH validation, and results show that the DTHs derived from the combined AIRS BT measurements and MODIS DB AOD product have better accuracy than those from AIRS BT measurements alone. The correlation coefficient of DTHs between AIRS and independent CALIOP dust heights is 0.79 with a standard deviation of 0.41 km when MODIS DB AOD product is included in the retrieval model. A series of case studies from different seasons were examined to demonstrate the feasibility of retrieving dust parameters from AIRS and potential applications. The method and approaches can be applied to process measurements from advanced infrared (IR) sounder and high-resolution imager onboard the same platform.

Spatial distribution of aerosol microphysical and optical properties and direct radiative effect from the China Aerosol Remote Sensing Network

Abstract. Multi-year observations of aerosol microphysical and optical properties, obtained through ground-based remote sensing at 50 China Aerosol Remote Sensing Network (CARSNET) sites, were used to characterize the aerosol climatology for representative remote, rural, and urban areas over China to assess effects on climate. The annual mean effective radii for total particles (ReffT) decreased from north to south and from rural to urban sites, and high total particle volumes were found at the urban sites. The aerosol optical depth at 440 nm (AOD440 nm) increased from remote and rural sites (0.12) to urban sites (0.79), and the extinction Ångström exponent (EAE440–870 nm) increased from 0.71 at the arid and semi-arid sites to 1.15 at the urban sites, presumably due to anthropogenic emissions. Single-scattering albedo (SSA440 nm) ranged from 0.88 to 0.92, indicating slightly to strongly absorbing aerosols. Absorption AOD440 nm values were 0.01 at the remote sites versus 0.07 at the urban sites. The average direct aerosol radiative effect (DARE) at the bottom of atmosphere increased from the sites in the remote areas (−24.40 W m−2) to the urban areas (−103.28 W m−2), indicating increased cooling at the latter. The DARE for the top of the atmosphere increased from −4.79 W m−2 at the remote sites to −30.05 W m−2 at the urban sites, indicating overall cooling effects for the Earth–atmosphere system. A classification method based on SSA440 nm, fine-mode fraction (FMF), and EAE440–870 nm showed that coarse-mode particles (mainly dust) were dominant at the rural sites near the northwestern deserts, while light-absorbing, fine-mode particles were important at most urban sites. This study will be important for understanding aerosol climate effects and regional environmental pollution, and the results will provide useful information for satellite validation and the improvement of climate modelling.

Large contribution of meteorological factors to inter-decadal changes in regional aerosol optical depth

Abstract. Aerosol optical depth (AOD) has become a crucial metric for assessing global climate change. Although global and regional AOD trends have been studied extensively, it remains unclear what factors are driving the inter-decadal variations in regional AOD and how to quantify the relative contribution of each dominant factor. This study used a long-term (1980–2016) aerosol dataset from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) reanalysis, along with two satellite-based AOD datasets (MODIS/Terra and MISR) from 2001 to 2016, to investigate the long-term trends in global and regional aerosol loading. Statistical models based on emission factors and meteorological parameters were developed to identify the main factors driving the inter-decadal changes of regional AOD and to quantify their contribution. Evaluation of the MERRA-2 AOD with the ground-based measurements of AERONET indicated significant spatial agreement on the global scale (r= 0.85, root-mean-square error = 0.12, mean fractional error = 38.7 %, fractional gross error = 9.86 % and index of agreement = 0.94). However, when AOD observations from the China Aerosol Remote Sensing Network (CARSNET) were employed for independent verification, the results showed that MERRA-2 AODs generally underestimated CARSNET AODs in China (relative mean bias = 0.72 and fractional gross error =-34.3 %). In general, MERRA-2 was able to quantitatively reproduce the annual and seasonal AOD trends on both regional and global scales, as observed by MODIS/Terra, although some differences were found when compared to MISR. Over the 37-year period in this study, significant decreasing trends were observed over Europe and the eastern United States. In contrast, eastern China and southern Asia showed AOD increases, but the increasing trend of the former reversed sharply in the most recent decade. The statistical analyses suggested that the meteorological parameters explained a larger proportion of the AOD variability (20.4 %–72.8 %) over almost all regions of interest (ROIs) during 1980–2014 when compared with emission factors (0 %–56 %). Further analysis also showed that SO2 was the dominant emission factor, explaining 12.7 %–32.6 % of the variation in AOD over anthropogenic-aerosol-dominant regions, while black carbon or organic carbon was the leading factor over the biomass-burning-dominant (BBD) regions, contributing 24.0 %–27.7 % of the variation. Additionally, wind speed was found to be the leading meteorological parameter, explaining 11.8 %–30.3 % of the variance over the mineral-dust-dominant regions, while ambient humidity (including soil moisture and relative humidity) was the top meteorological parameter over the BBD regions, accounting for 11.7 %–35.5 % of the variation. The results of this study indicate that the variation in meteorological parameters is a key factor in determining the inter-decadal change in regional AOD.

Long-term validation of MODIS C6 and C6.1 Dark Target aerosol products over China using CARSNET and AERONET

This study provided a comprehensive evaluation of the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 006 (C6) and 061 (C6.1) Dark Target (DT) 10 km aerosol optical depth (AOD) over China during 2002–2014. Considering that sparse Aerosol Robotic Network (AERONET) sites are available in China, 18 sites from China Aerosol Remote Sensing Network (CARSNET) were also used to conduct this validation. The results showed that C6.1 DT outperform C6 with 59.03% of the retrievals falling within the expected error (EE) compared to C6 (54.94%). Meanwhile, C6.1 DT achieved a reduced RMSE of 0.171, a higher R of 0.901 and a bias closer to 0 relative to C6 (RMSE: 0.185; R: 0.890). When the validation was conducted over different underlying surfaces, C6 DT overestimated AOD by 19.8%, with only 45.01% of the retrievals within the EE over urban sites, whereas C6.1 showed clear improvements, with 11.8% more data falling within the EE. Hardly any improvement was observed in C6.1 over forest, cropland, and grassland sites. The C6.1 DT exhibited more significant improvements over Beijing area and northern China than southern China. The highest retrieval accuracy of 61.05% among the four Beijing sites was achieved at Beijing_CARSNET, but the improvements were lower than other Beijing sites. The extent of the improvements was positively correlated with the percentage of urban pixels over the sites in Beijing and northern China in terms of the retrieval accuracy. Moreover, C6.1 DT had a little effect on improvements over southern China and showed reduced collocation over coastal cities.

Understanding MODIS dark-target collection 5 and 6 aerosol data over China: Effect of surface type, aerosol loading and aerosol absorption

For regions where limited in-situ observations are available for use as a reference, such as in China, the understanding of how changes in the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol retrieval algorithm impact the data quality is limited. In this paper, the MODIS-Aqua Aerosol Optical Thickness (AOT) Collection 5 (C5) and 6 (C6) Dark-Target (DT) products are evaluated by comparison with similar products from the China Aerosol Remote Sensing NETwork (CARSNET) and the Aerosol Robotic Network (AERONET) in China for the year 2008. The validation data are analyzed for key factors affecting the results of the MODIS-DT aerosol retrieval algorithm, including surface type, aerosol loading and aerosol absorption. The impacts of the change from the C5 to the C6 versions of the MODIS AOT retrieval algorithm are discussed in detail. The main findings are: (1) The agreement from retrieval aspects between both MODIS C5/C6 and CARSNET-AERONET for most AOT values over distinct regions indicates that the conclusions obtained from C5 hold for C6; (2) MODIS-DT has an extended coverage (more retrievals) with good quality due to the change of upstream data (calibration, cloud masking, etc); (3) About 64% of the MODISDT C6 AOT data over China fall within the (±0.15 AOT ± 0.05) error envelope; (4) positive AOT differences between C6 and C5 over relatively dark surfaces and negative differences over relatively bright surfaces due to the change of surface parameterization have been observed; (5) the AOT difference between MODIS and CARSNET-AERONET relating to AOT loading is not significant, especially for large AOT; (6) MODIS-DT performs well for absorbing aerosol over relatively bright surfaces.

The value of satellite observations in the analysis and short-range prediction of Asian dust

Abstract. Asian dust is a seasonal meteorological phenomenon which affects east Asia, and has severe consequences on the air quality of China, North and South Korea and Japan. Despite the continental extent, the prediction of severe episodes and the anticipation of their consequences is challenging. Three 1-year experiments were run to assess the skill of the model of the European Centre for Medium-Range Weather Forecasts (ECMWF) in monitoring Asian dust and understand its relative contribution to the aerosol load over China. Data used were the Moderate Resolution Imaging Spectroradiometer (MODIS) Dark Target and the Deep Blue aerosol optical depth (AOD). In particular the experiments aimed at understanding the added value of data assimilation runs over a model run without any aerosol data. The year 2013 was chosen as representative of the availability of independent AOD data from two established ground-based networks (AERONET, Aerosol Robotic Network, and CARSNET, China Aerosol Remote Sensing Network), which could be used to evaluate experiments. Particulate matter (PM) data from the China Environmental Protection Agency were also used in the evaluation. Results show that the assimilation of satellite AOD data is beneficial to predict the extent and magnitude of desert dust events and to improve the short-range forecast of such events. The availability of observations from the MODIS Deep Blue algorithm over bright surfaces is an asset, allowing for a better localization of the sources and definition of the dust events. In general both experiments constrained by data assimilation perform better than the unconstrained experiment, generally showing smaller normalized mean bias and fractional gross error with respect to the independent verification datasets. The impact of the assimilated satellite observations is larger at analysis time, but lasts into the forecast up to 48 h. The performance of the global model in terms of particulate matter does not show the same degree of skill as the performance in terms of optical depth. Despite this, the global model is able to capture some regional pollution patterns. This indicates that the global model analyses may be used as boundary conditions for regional air quality models at higher resolution, enhancing their performance in situations in which part of the pollution may have originated from large-scale mechanisms. While assimilation is not a substitute for model development and characterization of the emission sources, results indicate that it can play a role in delivering improved monitoring of Asian dust optical depth.

Improving the Estimation of Daily Aerosol Optical Depth and Aerosol Radiative Effect Using an Optimized Artificial Neural Network

Aerosols can absorb and scatter surface solar radiation (SSR), which is called the aerosol radiative forcing effect (ARF). Great efforts have been made for the estimation of the aerosol optical depth (AOD), SSR and ARF using meteorological measurements and satellite observations. However, the accuracy, and spatial and temporal resolutions of these existing AOD, SSR and ARF models should be improved to meet the application requirements, due to the uncertainties and gaps of input parameters. In this study, an optimized back propagation (BP) artificial neural network (Genetic_BP) was developed for improving the estimation of the AOD values. The retrieved AOD values using the Genetic_BP model and meteorological measurements at China Meteorological Administration (CMA) stations were used to calculate SSR and bottom of the atmosphere (BOA) ARF (ARFB) using Yang’s Hybrid model (YHM). The result show that the Genetic_BP could be used for estimating AOD values with high accuracy (R = 0.866 for CASNET (China Aerosol Remote Sensing Network) stations and R = 0.865 for AERONET (Aerosol Robotic Network) stations). The estimated SSR also showed a good agreement with SSR measurements at 96 CMA radiation stations, with RMSE, MAE, R and R2 of 29.27%, 23.77%, 0.948, and 0.899, respectively. The estimated ARFB values are also highly correlated with the AERONET ARFB ones with RMSE, MAE, R and R2 of −35.47%, −25.33%, 0.843, and 0.711, respectively. Finally, the spatial and temporal variations of AOD, SSR, and ARFB values over Mainland China were investigated. Both AOD and SSR values are generally higher in summer than in other seasons. The ARFB are generally stronger in spring and summer than in other seasons. The ranges for the monthly mean AOD, SSR and ARFB values over Mainland China are 0.183–0.333, 10.218–24.196 MJ m−2day−1 and −2.986 to −1.244 MJ m−2day−1, respectively. The Qinghai-Tibetan Plateau has always been an area with the highest SSR, the lowest AOD and the weakest ARFB. In contrast, the Sichuan Basin has always been an area with low SSR, high AOD, and strong ARFB. The newly proposed AOD model may be of vital importance for improving the accuracy and computational efficiency of AOD, SSR and ARFB estimations for solar energy applications, ecological modeling, and energy policy.

Daytime variation of aerosol optical depth in North China and its impact on aerosol direct radiative effects

Daytime cycles of aerosol optical depth (AOD) and Angstrom exponent (AE) climatology were analyzed based on long-term (5–15 years) measurements at 18 stations of the China Aerosol Remote Sensing Network (CARSNET) in North China. AOD in Northwest China (NWC) exhibits a daytime trend with negative departures in the early morning and later afternoon while positive departures at midday. Daytime AOD relative departure in different sites and seasons varies from −30.26% to 30.28%. AE in NWC shows an opposite pattern to daytime variation of AOD. Daytime variation of AE is negligible in North China Plain (NCP), AOD increases steadily throughout the day. This trend is consistent and repeatable in four seasons. Such pronounced variability in AOD and AE should be taken accounted for in the estimation of diurnal aerosol direct radiative effects (ADRE), as suggested by the radiative transfer model simulations. The replacement of the observed daytime variation of AOD and AE by daytime mean values in NWC results in ADRE differences of 0.31 Wm−2 at the surface and 0.05 Wm−2 at the top of the atmosphere (TOA). ADRE in NWC will be underestimated by −0.47 Wm-2 and -0.25 Wm-2 at the surface and TOA, respectively, if instantaneous AOD and AE during the overpass time of Terra and Aqua are taken as the daytime mean values. The annual mean ADRE at the surface and TOA will be underestimated by −0.17 Wm-2 and -0.03 Wm-2 if daytime variations of AOD and AE are replaced by daytime mean values in NCP. ADRE will be underestimated by −0.07 Wm-2 at the surface and −0.06 Wm-2 at the TOA if instantaneous AOD and AE during the overpass time of Terra and Aqua other than daytime variation of AOD and AE are used in the calculations.

Aerosol optical properties and direct radiative forcing based on measurements from the China Aerosol Remote Sensing Network (CARSNET) in eastern China

Abstract. Aerosol pollution in eastern China is an unfortunate consequence of the region's rapid economic and industrial growth. Here, sun photometer measurements from seven sites in the Yangtze River Delta (YRD) from 2011 to 2015 were used to characterize the climatology of aerosol microphysical and optical properties, calculate direct aerosol radiative forcing (DARF) and classify the aerosols based on size and absorption. Bimodal size distributions were found throughout the year, but larger volumes and effective radii of fine-mode particles occurred in June and September due to hygroscopic growth and/or cloud processing. Increases in the fine-mode particles in June and September caused AOD440 nm > 1.00 at most sites, and annual mean AOD440 nm values of 0.71–0.76 were found at the urban sites and 0.68 at the rural site. Unlike northern China, the AOD440 nm was lower in July and August (∼ 0.40–0.60) than in January and February (0.71–0.89) due to particle dispersion associated with subtropical anticyclones in summer. Low volumes and large bandwidths of both fine-mode and coarse-mode aerosol size distributions occurred in July and August because of biomass burning. Single-scattering albedos at 440 nm (SSA440 nm) from 0.91 to 0.94 indicated particles with relatively strong to moderate absorption. Strongly absorbing particles from biomass burning with a significant SSA wavelength dependence were found in July and August at most sites, while coarse particles in March to May were mineral dust. Absorbing aerosols were distributed more or less homogeneously throughout the region with absorption aerosol optical depths at 440 nm ∼ 0.04–0.06, but inter-site differences in the absorption Angström exponent indicate a degree of spatial heterogeneity in particle composition. The annual mean DARF was −93 ± 44 to −79 ± 39 W m−2 at the Earth's surface and ∼ −40 W m−2 at the top of the atmosphere (for the solar zenith angle range of 50 to 80∘) under cloud-free conditions. The fine mode composed a major contribution of the absorbing particles in the classification scheme based on SSA, fine-mode fraction and extinction Angström exponent. This study contributes to our understanding of aerosols and regional climate/air quality, and the results will be useful for validating satellite retrievals and for improving climate models and remote sensing algorithms.

Evaluation of MODIS Deep Blue Aerosol Algorithm in Desert Region of East Asia: Ground Validation and Intercomparison

AbstractThe abundant dust particles from widespread deserts in East Asia play a significant role in regional climate and air quality. In this study, we provide a comprehensive evaluation of the widely used Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) aerosol retrievals in desert regions of East Asia using ground‐based observations over eight sites of the China Aerosol Remote Sensing Network (CARSNET). Different from their well‐characterized performance in urban and cropland areas around the globe, DB aerosol optical depth (AOD) retrievals exhibit underestimation across the deserts in East Asia. We found that 38%–96% of satellite values fall out of an expected‐error envelope of ±(0.05 + 20%AODCARSNET), with the worst performance in Taklimakan Desert. In particular, DB retrievals erroneously give a nearly constant low values of 0.05 in Taklimakan Desert when AOD is below 0.5, which does not match with variation of moderate dust plumes. Comparison with Multi‐angle Imaging SpectroRadiometer AOD shows that a similar underestimation is prevalent over the extensive deserts. Inversion of sky light measurements show that single scattering albedos of the yellow dust in East Asia are mostly below 0.9 at 440 nm, much lower than the “whiter” and “redder” dust models applied in the DB algorithm. On the other hand, overestimation of surface reflectance dominantly contributes to the significant low constant AOD values in MODIS DB retrievals in Taklimakan Desert. These large biases, however, can be substantially reduced by considering unique characteristics of aerosols and surface over the arid regions in East Asia.

Technical note: Intercomparison of three AATSR Level 2 (L2) AOD products over China

Abstract. One of four main focus areas of the PEEX initiative is to establish and sustain long-term, continuous, and comprehensive ground-based, airborne, and seaborne observation infrastructure together with satellite data. The Advanced Along-Track Scanning Radiometer (AATSR) aboard ENVISAT is used to observe the Earth in dual view. The AATSR data can be used to retrieve aerosol optical depth (AOD) over both land and ocean, which is an important parameter in the characterization of aerosol properties. In recent years, aerosol retrieval algorithms have been developed both over land and ocean, taking advantage of the features of dual view, which can help eliminate the contribution of Earth's surface to top-of-atmosphere (TOA) reflectance. The Aerosol_cci project, as a part of the Climate Change Initiative (CCI), provides users with three AOD retrieval algorithms for AATSR data, including the Swansea algorithm (SU), the ATSR-2ATSR dual-view aerosol retrieval algorithm (ADV), and the Oxford-RAL Retrieval of Aerosol and Cloud algorithm (ORAC). The validation team of the Aerosol-CCI project has validated AOD (both Level 2 and Level 3 products) and AE (Ångström Exponent) (Level 2 product only) against the AERONET data in a round-robin evaluation using the validation tool of the AeroCOM (Aerosol Comparison between Observations and Models) project. For the purpose of evaluating different performances of these three algorithms in calculating AODs over mainland China, we introduce ground-based data from CARSNET (China Aerosol Remote Sensing Network), which was designed for aerosol observations in China. Because China is vast in territory and has great differences in terms of land surfaces, the combination of the AERONET and CARSNET data can validate the L2 AOD products more comprehensively. The validation results show different performances of these products in 2007, 2008, and 2010. The SU algorithm performs very well over sites with different surface conditions in mainland China from March to October, but it slightly underestimates AOD over barren or sparsely vegetated surfaces in western China, with mean bias error (MBE) ranging from 0.05 to 0.10. The ADV product has the same precision with a low root mean square error (RMSE) smaller than 0.2 over most sites and the same error distribution as the SU product. The main limits of the ADV algorithm are underestimation and applicability; underestimation is particularly obvious over the sites of Datong, Lanzhou, and Urumchi, where the dominant land cover is grassland, with an MBE larger than 0.2, and the main aerosol sources are coal combustion and dust. The ORAC algorithm has the ability to retrieve AOD at different ranges, including high AOD (larger than 1.0); however, the stability deceases significantly with increasing AOD, especially when AOD > 1.0. In addition, the ORAC product is consistent with the CARSNET product in winter (December, January, and February), whereas other validation results lack matches during winter.

Calibration of the 936 nm water-vapor channel for the China aerosol remote sensing NETwork (CARSNET) and the effect of the retrieval water-vapor on aerosol optical property over Beijing, China

Based on measurements of the 936 nm water-vapor channel and the 870 nm, and 1020 nm atmospheric window band for the CE-318 sunphotometer of CARSNET, clear-sky direct solar radiation observational data and relative optical air masses of between 2 and 5, and considering sensitivity parameters such as pressure, altitude, wavelength, and AOD936nm, which may affect the calibration result, a method to calibrate the 936 nm water-vapor channel using a modified Langley method is presented. The daily average columnar water-vapor (CWV) over urban Beijing was retrieved using three methods (Methods A, B and C), and compared with the AERONET water-vapor product. Finally, the seasonal relationships of CWV with AOD500nm and α440–870 were analyzed. The calibration results showed the calibration value to differ from the original value by about 4%. The CWV retrieval result showed that AERONET and the three methods of retrieval for CWV were highly correlated. The monthly average variation trends of AOD500nm and CWV were similar, with the maximum occurring in July, and the minimum in December. The seasonal average variation trends of AOD500nm and CWV were also similar, but could be placed in the following order: summer > autumn > spring > winter. The seasonal variation of α440–870 versus AOD500nm, α440–870 versus CWV, and AOD500nm versus CWV, showed increasing AOD500nm with increasing CWV, which reflected the hygroscopicity of aerosol fine particle growth characteristics. Summer was mainly characterized by fine particles; while spring, autumn and winter were a mix of coarse and fine particles, showing typical urban aerosol properties.

A consistent aerosol optical depth (AOD) dataset over mainland China by integration of several AOD products

The Moderate Resolution Imaging Spectroradiometer (MODIS), the Multiangle Imaging Spectroradiometer (MISR) and the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) provide validated aerosol optical depth (AOD) products over both land and ocean. However, the values of the AOD provided by each of these satellites may show spatial and temporal differences due to the instrument characteristics and aerosol retrieval algorithms used for each instrument. In this article we present a method to produce an AOD data set over Asia for the year 2007 based on fusion of the data provided by different instruments and/or algorithms. First, the bias of each satellite-derived AOD product was calculated by comparison with ground-based AOD data derived from the AErosol RObotic NETwork (AERONET) and the China Aerosol Remote Sensing NETwork (CARSNET) for different values of the surface albedo and the AOD. Then, these multiple AOD products were combined using the maximum likelihood estimate (MLE) method using weights derived from the root mean square error (RMSE) associated with the accuracies of the original AOD products. The original and merged AOD dataset has been validated by comparison with AOD data from the CARSNET. Results show that the mean bias error (MBE) and mean absolute error (MAE) of the merged AOD dataset are not larger than that of any of the original AOD products. In addition, for the merged AOD dataset the fraction of pixels with no data is significantly smaller than that of any of the original products, thus increasing the spatial coverage. The fraction of retrievable area is about 50% for the merged AOD dataset and between 5% and 20% for the MISR, SeaWiFS, MODIS-DT and MODIS-DB algorithms.

Mesoscale modeling study of the interactions between aerosols and PBL meteorology during a haze episode in Jing–Jin–Ji (China) and its nearby surrounding region – Part 1: Aerosol distributions and meteorological features

Abstract. The urbanized region ofJing(Beijing)-Jin(Tianjin)-Ji (alias of Hebei province) and its nearby surrounding region (3JNS) is becoming China's most polluted area by haze, exceeding even the Yangtze and Pearl river deltas. Aside from pollutant emission, the meteorology of the planetary boundary layer (PBL) is the most important factor affecting haze pollution. Focusing on July 2008, the aerosol optical properties and PBL meteorology features closely related to haze formation were simulated in the 3JNS region using an online atmospheric chemical transport model. The relationship between regional PBL meteorology, PM2.5, and haze is discussed. Model results accurately simulated the aerosol optical depth (AOD), single scattering albedo (SSA) and asymmetry parameter (ASY), validated by comparison with observations from the MODerate Resolution Imaging Spectroradiometer (MODIS), the China Aerosol Remote Sensing NETwork (CARSNET) and the Aerosol Robotic NETwork (AERONET). Modeled PBL wind speeds showed reasonable agreement with those from the National Centers for Environmental Prediction (NCEP) Reanalysis 2. A monthly mean AOD value as high as 1.2 was found from both model and observations, with a daily mean larger than 2.0 during haze episodes in the 3JNS region. Modeled and observed SSA values of 0.90–0.96 and ASY values of 0.72–0.74 demonstrated the high scattering characteristic of summer aerosols in this region. PBL wind speeds from modeled and NCEP data both showed a reversing trend of PM2.5 variation, illustrating the importance of the "PBL window shadow" in haze formation. Turbulence diffusion and PBL height had opposite phases to surface PM2.5, indicating that lower PBL height and weaker PBL turbulence diffusion are essential to haze formation. It is noted that homogeneous air pressure does not occur at the surface, but at an 850–950 hPa height during the haze episode. The momentum transmitting downward of the cold air from above the PBL to the low PBL and surface lead to an increase in surface wind speeds and haze dispersal.

Investigation of Aerosol Optical Depth (AOD) and Ångström Exponent over the Desert Region of Northwestern China Based on Measurements from the China Aerosol Remote Sensing Network (CARSNET)

Aerosols at ten sites in northwestern China are classified in this study: (1) by using the aerosol optical depth (AOD), the Ångström exponent (α) and the Ångström exponent difference (δα); and (2) by using the total means of AOD440nm and α. The seasonal variations of the AOD and α show that the maximum AODs occur in spring, except at Urumqi and Lanzhou. The seasonal mean α values are lower than 0.80 in all four seasons at Tazhong, Hotan, Hami, Ejina, Dunhuang, Minqin, and Jiuquan, but higher than 0.80 in winter at Urumqi, Lanzhou and Yinchuan. The first classification method shows that coarse mode particles are found at all ten sites, but that fine mode growth only happens at Urumqi, Lanzhou, and Yinchuan. The relationship between AOD440nm and α show that α smaller than 0.80 decrease with increasing AOD440nm at all ten sites. Aerosols are classified into four types (Type I–IV) according to the total mean τ440 (τ̅440) and total mean Ångström exponent (ᾱ) of each site. Aerosols with a τ440 smaller than τ̅440, but greater than or equal to ᾱ (τ440 < τ̅440; α ≥ ᾱ) are classified as Type I; aerosols with τ440 ≥ τ̅440 and α ≥ ᾱ are Type II; those with τ440 < and α < ᾱ are Type III; and those with τ440 ≥ τ̅440 and α < ᾱ are Type IV. The second aerosol classification method shows that Type I and Type III aerosols are the most common at all ten sites. Type II aerosols are the least at Tazhong and Hotan, but are the most common at Urumqi, Lanzhou, and Yinchuan. On the contrary, Type IV aerosols are the most common at Tazhong and Hotan, but are the least common at Urumqi, Lanzhou and Yinchuan.