Reply on RC2

Abstract. Satellite-based aerosol optical depth (AOD) has gained popularity as a powerful data source for calibrating aerosol models and correcting model errors through data assimilation. However, simulated airborne particle mass concentrations are not directly comparable to satellite-based AODs. For this, an AOD operator needs to be developed that can convert the simulated mass concentrations into model AODs. The AOD operator is most sensitive to the input of the particle size and chemical composition of aerosols. Furthermore, assumptions regarding particle size vary significantly amongst model AOD operators. More importantly, satellite retrieval algorithms rely on different size assumptions. Consequently, the differences between the simulations and observations do not always reflect the actual difference in aerosol amount. In this study, the sensitivity of the AOD operator to aerosol properties has been explored. We conclude that, to avoid inconsistencies between the AOD operator and retrieved properties, a common understanding of the particle size is required. Accordingly, we designed a hybrid assimilation methodology (hybrid AOD assimilation) that includes two sequentially conducted procedures. First, aerosol size in the model operator has been brought closer to the assumption of the satellite retrieval algorithm via assimilation of Ångström exponents. This ensures that the model AOD operator is more consistent with the AOD retrieval. The second step in the methodology concerns optimization of aerosol mass concentrations through direct assimilation of AOD (standard AOD assimilation). The hybrid assimilation method is tested over the European domain using Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue products. The corrections made to the model aerosol size information are validated through a comparison with the ground-based Aerosol Robotic Network (AERONET) optical product. The increments in surface aerosol mass concentration that occur due to either the standard AOD assimilation analysis or the hybrid AOD assimilation analysis are evaluated against independent ground PM2.5 observations. The standard analysis always results in relatively accurate posterior AOD distributions; however, the corrections are hardly transferred into better aerosol mass concentrations due to the uncertainty in the AOD operator. In contrast, the model AOD and mass concentration states are considerably more accurate when using the hybrid methodology.

The minimum acceptable accuracy of the top of the atmosphere radiative flux of ΔF = 0.5 W/m2 leads to the required accuracy in satellite based aerosol optical depth (AOD) retrieval of about Δτ = 0.015 over the land and Δτ = 0.010 over the ocean. None of the current operational satellite based instruments for AOD retrieval has been able to achieve this accuracy. The RMSE (Root Mean Square Error) of the AVHRR (Advanced Very High Resolution Radiometer) is typically between 0.06 and 0.15, while the RMSE of the MODIS (Moderate resolution Imaging Spectroradiometer) over the land has been estimated to be Δτ = 0.05 + 0.2τ, which varies between Δτ = 0.07 and Δτ = 0.21 within the limit of usual aerosol optical depth between τ = 0.1 and τ = 0.8. The Department of Energy research satellite instrument, the Multispectral Thermal Imager (MTI), is capable of retrieving aerosol optical depth with an accuracy of Δτ = 0.03 using an off nadir view at medium scattering angles. Theoretical analysis suggests that the uncertainties in aerosol phase function (due to uncertainties in aerosol shape, size distribution and optical properties) are the major obstacles for accurate aerosol optical depth retrieval. These uncertainties lead to a much larger error in aerosol optical depth retrieval at large scattering angles (usually at close to nadir view) than at off nadir views at medium scattering angles. On the basis of our theoretical analysis and on MTI experience, we suggest that in order to achieve the required accuracy in AOD retrieval, future satellite instruments using a single or dual‐view AOD retrieval algorithm should use off‐nadir views at medium scattering angles (between 50° and 100°).

Conventional methods for Aerosol Optical Depth (AOD) retrieval are limited to areas with low reflectance such as water or vegetated areas because the satellite signals from the aerosols in these areas are more obvious than those in areas with higher reflectance such as urban and sandy areas. Land Surface Reflectance (LSR) is the key parameter that must be estimated accurately. Most current methods used to estimate AOD are applicable only in areas with low reflectance. It has historically been difficult to estimate the LSR for bright surfaces because of their complex structure and high reflectance. This paper provides a method for estimating LSR for AOD retrieval in bright areas, and the method is applied to AOD retrieval for Landsat 8 Operational Land Imager (OLI) images at 500 m spatial resolution. A LSR database was constructed with the MODerate-resolution Imaging Spectroradiometer (MODIS) surface reflectance product (MOD09A1), and this database was also used to estimate the LSR of Landsat 8 OLI images. The AOD retrieved from the Landsat 8 OLI images was validated using the AOD measurements from four AErosol RObotic NETwork (AERONET) stations located in areas with bright surfaces. The MODIS AOD product (MOD04) was also compared with the retrieved AOD. The results demonstrate that the AOD retrieved with the new algorithm is highly consistent with the AOD derived from ground measurements, and its precision is better than that of MOD04 AOD products over bright areas.

Simulation of aerosol direct radiative forcing with RAMS-CMAQ in East Asia

Satellite aerosol optical depth (AOD) is widely used to estimate particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5) mass concentrations. Polar orbiting satellite retrieval 1–2 times each day is frequently affected by cloud, snow cover or misclassification of heavy pollution. Novel methods are therefore required to improve AOD sampling. Sunphotometer provides much more AODs than satellite at a fixed point. Furthermore, much of the aerosol pollution is regional. Both factors indicate that sunphotometer has great potential for PM2.5 concentration estimation. The spatial representativeness of the Aerosol Robotic Network (AERONET) AOD at Beijing site is investigated by linear regression analysis of 13-year daily paired AODs at each grid from Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and Beijing AERONET. The result suggests a good correlation for the whole Beijing Administrative region, with regional mean correlation coefficient exceeding 0.73. Pixel AODs are then estimated from AERONET AOD using linear equations, which are verified to have the same accuracy as that of MODIS AOD. Either AOD from MODIS retrieval or estimation from AERONET AOD in the absence of MODIS pixel AOD is finally used to predict PM2.5 concentration. Daily AOD sampling in average is enhanced by 59% in winter when MODIS AODs are very limited. More importantly, synergy of AERONET and MODIS AOD is able to improve the estimation of regional mean PM2.5 concentrations, which indicates this method would play a significant role in monitoring regional aerosol pollution.

A surface reflectance correction model to improve the retrieval of MISR aerosol optical depth supported by MODIS data

We present the first extended validation of a new synergy global aerosol product (SY_2_AOD) which is based on synergistic use of data from the Ocean and Land Color Instrument (OLCI), and the Sea and Land Surface Temperature Radiometer (SLSTR) sensors onboard the Copernicus Sentinel-3A (S3A) and Sentinel-3B (S3B) satellites. Several approaches, including statistical analysis, time series analysis, comparison with similar aerosol products from the other spaceborne sensor Moderate Resolution Imaging Spectroradiometer (MODIS), were used for validation end evaluation of S3A and S3B SY_2 aerosol products, including Aerosol Optical Depth (AOD) provided at different wavelengths, AOD pixel level uncertainties, fine mode AOD and Angström exponent. Over ocean, the performance of SY_2 retrieved AOD is good: for S3A and S3B respectively, correlation coefficients with the Maritime Aerosol Network (MAN) component of the AErosol RObotic NETwork (AERONET) are 0.88 and 0.85; 88.6 % and 89.5 % of pixels fit into MODIS error envelope of ±0.05±0.2*AOD. Over land correlation coefficients with AERONET AOD are 0.60 and 0.63 for S3A and S3B respectively; 51.4 % and 57.9 % of pixels fit into MODIS error envelope. Reduced performance over land is expected since the surface reflectance and angular distribution of scattering is higher and more difficult to predict over land than over ocean. The results are affected by a large number of outliers. Evaluation of the per-retrieval uncertainty indicated good correlation with measured error distributions. The retrieval of Angstrom exponent, related to aerosol size distribution, shows good spatial correlation with expected sources but generally overestimates AE for cases where AERONET Angstrom is low, resulting in overall high bias. Differences between the annual and seasonal AOD values from SY_2 and MODIS Dark Target and Deep blue products are within 0.02 for the study area. The agreement is better over ocean. Over bright land surface (Saharan desert) the difference between two products is highest (up to 0.11), and the sign of the difference varies over time and space. For both S3A and S3B AOD products, validation statistics are often slightly better in the Southern hemisphere. In general, the performance of S3B is slightly better.

For several decades, satellite-based microwave sensors have provided valuable soil moisture monitoring in various surface conditions. We have first developed a modeled aerosol optical depth (AOD) dataset by utilizing Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), and the Global Land Data Assimilation System (GLDAS) soil moisture datasets in order to estimate dust outbreaks over desert areas of East Asia. Moderate Resolution Imaging Spectroradiometer- (MODIS-) based AOD products were used as reference datasets to validate the modeled AOD (MA). The SMOS-based MA (SMOS-MA) dataset showed good correspondence with observed AOD (R-value: 0.56) compared to AMSR2- and GLDAS-based MA datasets, and it overestimated AOD compared to observed AOD. The AMSR2-based MA dataset was found to underestimate AOD, and it showed a relatively lowR-value (0.35) with respect to observed AOD. Furthermore, SMOS-MA products were able to simulate the short-term AOD trends, having a highR-value (0.65). The results of this study may allow us to acknowledge the utilization of microwave-based soil moisture datasets for investigation of near-real time dust outbreak predictions and short-term dust outbreak trend analysis.

For most satellite aerosol retrieval algorithms even for multi-angle instrument, the simple forward model (FM) based on Lambertian surface assumption is employed to simulate top of the atmosphere (TOA) spectral reflectance, which does not fully consider the surface bi-directional reflectance functions (BRDF) effect. The approximating forward model largely simplifies the radiative transfer model, reduces the size of the look-up tables, and creates faster algorithm. At the same time, it creates systematic biases in the aerosol optical depth (AOD) retrieval. AOD product from the Moderate Resolution Imaging Spectro-radiometer (MODIS) data based on the dark target algorithm is considered as one of accurate satellite aerosol products at present. Though it performs well at a global scale, uncertainties are still found on regional in a lot of studies. The Lambertian surface assumpiton employed in the retrieving algorithm may be one of the uncertain factors. In this study, we first use radiative transfer simulations over dark target to assess the uncertainty to what extent is introduced from the Lambertian surface assumption. The result shows that the uncertainties of AOD retrieval could reach up to ±0.3. Then the Lambertian FM (L_FM) and the BRDF FM (BRDF_FM) are respectively employed in AOD retrieval using dark target algorithm from MODARNSS (MODIS/Terra and MODIS/Aqua Atmosphere Aeronet Subsetting Product) data over Beijing AERONET site. The validation shows that accuracy in AOD retrieval has been improved by employing the BRDF_FM accounting for the surface BRDF effect, the regression slope of scatter plots with retrieved AOD against AEROENET AOD increases from 0.7163 (for L_FM) to 0.7776 (for BRDF_FM) and the intercept decreases from 0.0778 (for L_FM) to 0.0627 (for BRDF_FM).

<p>Aerosol optical depth (AOD) is an important factor to estimate the effect of aerosol on light, and an accurate retrieval of it can make great contribution to monitor atmosphere. Therefore, retrieval of AOD has been a frontier topic and attracted much attention from researchers at home and abroad. However, the spatial resolution of AOD, based on Moderate-resolution Imaging Spectroradiometer (MODIS), is low, and hard to meet the needs of regional air quality fine monitoring. In 2018, China launched Gaofen-6 satellite, which set up a network with Gaofen-1 enabling two-day revisited observations in China's land area, improving the scale and timeliness of remote sensing data acquisition and making up for the shortcomings of lacking multi-spectral satellite with medium and high spatial resolution. Along with advancement of the Earth Observation System and the launch of high-resolution satellites, it is of profound significance to give full play to the active role of high-scoring satellites, in monitoring atmospheric environmental elements such as atmospheric aerosols and particulate matter concentrations, and achieve high-resolution retrieval of AOD through Gaofen satellites.</p><p>In this paper the data of Gaofen-6 and Gaofen-1 was used to retrieve the AOD. based on the Synergetic Retrieval of Aerosol Properties (SRAP) algorithm. This algorithm can retrieve the surface reflectance and AOD synchronously through constructing a closed equation based on double star observations. It can be applied to various types of surface reflectance which extends the coverage of the retrieval of AOD inversion effectively. Experimental data includes the satellite data of New South Wales and eastern Queensland on November 21, 2019, which have been suffered from unprecedented large-scale forest fires for over 2 months. The retrieval of AOD during the time with the satellite data is benefit for the prevention and monitoring of forest fire. The experimental results are compared with the AERONET ground observation data for preliminary validation. The correlation coefficient is about 0.7. The experimental results show that the method have higher accuracy, and further validation work is continuing.</p>

Gaofen 4 (GF-4) is a geostationary satellite, with a panchromatic and multispectral sensor (PMS) onboard, and has great potential in observing atmospheric aerosols. In this study, we developed an aerosol optical depth (AOD) retrieval algorithm for the GF-4 satellite. AOD retrieval was realized based on the pre-calculated surface reflectance database and 6S radiative transfer model. We customized the unique aerosol type according to the long time series aerosol parameters provided by the Aerosol Robotic Network (AERONET) site. The solar zenith angle, relative azimuth angle, and satellite zenith angle of the GF-4 panchromatic multispectral sensor image were calculated pixel-by-pixel. Our 1 km AOD retrievals were validated against AERONET Version 3 measurements and compared with MOD04 C6 AOD products at different resolutions. The results showed that our GF-4 AOD algorithm had a good robustness in both bright urban areas and dark rural areas. A total of 71.33% of the AOD retrievals fell within the expected errors of ±(0.05% + 20%); root-mean-square error (RMSE) and mean absolute error (MAE) were 0.922 and 0.122, respectively. The accuracy of GF-4 AOD in rural areas was slightly higher than that in urban areas. In comparison with MOD04 products, the accuracy of GF-4 AOD was much higher than that of MOD04 3 km and 10 km dark target AOD, but slightly worse than that of MOD04 10 km deep blue AOD. For different values of land surface reflectance (LSR), the accuracy of GF-4 AOD gradually deteriorated with an increase in the LSR. These results have theoretical and practical significance for aerosol research and can improve retrieval algorithms using the GF-4 satellite.

Thailand: Tokai – carbon black

Moderate resolution imaging spectroradiometer (MODIS) has been widely used in related aerosol studies because of its long data records. However, operational aerosol optical depth (AOD) products at coarse spatial resolutions limit their applications on small and medium scales. Thus, high-spatial-resolution AOD products are needed. In this paper, a regionally robust high-resolution aerosol retrieval algorithm is developed for MODIS images over Eastern China which has complex surfaces and severe air pollution. Several major challenges in aerosol retrieval are resolved including: 1) surface reflectance by correcting for the effects of surface bidirectional reflectance distribution function using the RossThick-LiSparse model; 2) aerosol models assumed by time-series data analysis with historical aerosol optical properties measurements from the Aerosol Robotic Network (AERONET) sites; and 3) cloud screening using the proposed universal dynamic threshold cloud detection algorithm. Moreover, gas (i.e., ozone and water vapor) absorption is also corrected. Finally, our AOD retrievals are compared with the newest AERONET Version 3 Level 2.0 AOD ground-based measurements, latest MODIS Collection 6.1 AOD products at 3- and 10-km resolutions, and multiangle implementation of the atmospheric correction (MAIAC) AOD product at a 1-km resolution. The results suggest that our algorithm performs well over dark vegetated and bright urban surfaces and that 78.56% of the retrievals meet the acceptable expected error of ±(0.05% + 20%) with a mean absolute error and a root-mean-square error of 0.074 and 0.125, respectively. Comparison results indicate that the newly generated 1-km AOD data set is much better than the routine MOD04 3- and 10-km dark target data sets, and slightly better than the 10-km deep blue (with lower resolution) and 1-km MAIAC (with narrower space coverage) AOD products. This attests to the robustness of our algorithm that generates an AOD product with a more continuous coverage and finer resolution over complex surfaces.

A parameterization is introduced for the derivation of columnar aerosol mass concentration (AMC) and cloud condensation nucleus concentration (CCNC) from the primary aerosol products of Moderate‐Resolution Imaging Spectroradiometer (MODIS). The method relies on the scaling between AMC and optical depth with a proportionality constant dependent on MODIS‐derived reff, η (contribution of the accumulation mode radiance to the total radiance), ambient RH, and an assumed constant aerosol composition. The CCNC is derived from a parameterization of concentration as a function of the retrieved aerosol volume. By comparing with in situ data, it is shown that retrievals in dry ambient conditions (dust) are improved when using a proportionality constant dependent on reff and η obtained in the same pixel. In high‐humidity environments, the improvement in the new method is inconclusive because of the difficulty in accounting for the uneven vertical distribution of relative humidity. Additionally, two detailed comparisons of AMC and CCNC retrieved by the MODIS Airborne Simulator (MAS) algorithm and the new method are shown. The new method and MAS retrievals of AMC are within the same order of magnitude with respect to the in situ measurements of aerosol mass. However, the proposed method is closer to the in situ measurements than the MODIS retrievals. The retrievals of CCNC are also within the same order of magnitude for both methods. Finally, the new method is applied to an actual MODIS retrieval. Although no in situ data is available for comparison, it is shown that the proposed method yields more credible values than the MODIS retrievals.

Agricultural biomass burning (ABB) in central and east China occurs every year from May to October and peaks in June. During the period from 26 May to 16 June 2007, one strong ABB procedure happened mainly in Anhui, Henan, Jiangsu and Shandong provinces. This article focuses on analysis of this ABB procedure using a comprehensive set of aerosol optical depth (AOD) data merged by using the optimal interpolation method from the Moderate Resolution Imaging Spectroradiometer, the Multi-angle Imaging Spectroradiometer (MIRS) as well as Sea-viewing Wide Field-of-view Sensor (SeaWiFS)-derived AOD products. In addition, the following additional data are used: fire data from the National Satellite Meteorological Centre of China Meteorological Administration, the mass trajectory analyses from hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model and ground-based AOD and Ångström data derived from the Aerosol Robotic Network and China Aerosol Remote Sensing Network. The results show that merged satellite AOD values can expand the spatial coverage of agricultural biomass aerosol distributions with good accuracy (R = 0.93, root mean square error = 0.37). Based on the merged AOD images, the highest AOD values were found concentrated in central China as well as in eastern China before 6 June and further extended to northeast China after 12 June. AODs from ground measurement show that eastern China always keeps high AOD values (>1.0), with a maximum exceeding 3.0 and extending as high as nearly 5.0 during this ABB event. With the help of the HYSPLIT model, we analysed the ABB sources and examined how transport paths affect the concentrations of air pollutants in some sites. The results show that Henan, Jiangsu and Anhui provinces are the three main sources in this ABB.