Ground-based Aerosol Optical Depth Research Articles

Inter-comparison of multi-satellites and Aeronet AOD over Indian Region

Aerosols affect global as well as regional climate by interacting with solar radiations and by altering the cloud properties. Due to the scarcity of ground observations over entire India, especially over Indo-Gangetic Plains (IGP), satellite-based measurements provide better spatial and temporal coverage for the evaluation of aerosol optical properties. This study presents the inter-comparison of multiple satellites (MISR, MODIS, and OMI) and ground-based aerosol optical depth (AOD) for the 2010–2017 (8 years) period over IGP (Gandhi College, Jaipur, and Kanpur) region. Satellite observations are spatially and temporally collocated by an average of 50 × 50 km square box and ±30 minutes, respectively. Results indicate a higher correlation with MODIS ( R 2 = 0.7 at Gandhi College) followed by MISR and OMI AOD. Irrespective of few observations (219) at Jaipur, MISR shows the highest percentage (58%) of data points within the error envelope because of multiple angle viewing and spectral capability gives the accurate information in high reflecting desert areas. MISR and OMI have a systematic negative bias trend for higher AOD at all test sites, while MODIS has a negative bias over the Jaipur region only. The temporal analysis reveals the higher concentration of fine mode aerosols captured by MISR and MODIS in December month over Gandhi College and Kanpur region, whereas Jaipur is over-loading with coarser aerosols during April to June month from the Thar Desert and Middle East dust storms. Seasonal distribution over the IGP region indicates higher AOD in summer owing to dust storms. Inter-comparison shows that MODIS has higher seasonal AOD as compared to MISR. The spatial correlation between MODIS and MISR is high during the summer and winter seasons. The OMI sensor performance over the IGP region has not been found to match with existing patterns. These findings are useful in the context of selecting the most reliable satellite AOD products for future studies. • MODIS, as compare to other sensors have high correlation with Aeronet. • OMI AOD underestimates over IGP region. • Seasonal AOD are positively bias by MODIS and negatively by MISR and OMI. • Spatial correlation between MODIS and MISR is high over IGP region.

Long-term aerosol optical depth trend over Iran and identification of dominant aerosol types

The paper focuses on analysis of long-term changes of aerosol optical depth (AOD) over Iran. It describes contributions of dominant aerosol in the aerosol load over Iran covering the period 1980–2018. For this purpose, a long-term AOD dataset from the reanalysis-based Modern Era Retrospective Analysis for Research and Applications (MERRA-2), the satellite-based Moderate Resolution Imaging Spectroradiometer (the new version of MODIS/Terra and Aqua) as well as a new AOD product (MERRA-2 MODIS merged) were used. The ground-based AOD measurements of the five Aerosol Robotic Network (AERONET) sites used for validation demonstrated better consistency of the MERRA-2 MODIS merged (MMM) product. Analysis of these datasets demonstrated high AOD in the southwest of Iran because of the proximity to the major source areas of natural mineral dust in spring and summer. In contrast, low AOD was mostly observed along the high elevation lands in the northern and western highlands. The trend analysis of AODs revealed differences between the AOD datasets, but agree on the positive trends over southwestern Iran and negative trend in northern Iran. Classification of major aerosol types indicated that the clean marine and mixed aerosols were the dominant aerosol types during the cold and hot seasons, respectively, and the increase of desert dust around 2010 was another obvious result in spring and summer. Our results indicate that the variation in dust aerosol has a key role in determining the AOD long-term changes in Iran which has contributed in regional climate change and environmental evolutions.

Assessment of aerosol types on improving the estimation of surface PM2.5 concentrations by using ground-based aerosol optical depth dataset

In this study, the influence of aerosol type classification on estimating surface PM2.5 concentrations was assessed by using ground-based aerosol optical depth (AOD) at 22 ground-based observation points in the United States. To clarify the influence, a two-stage model consisting of a multiple regression model (MRM) and an aerosol classification model (ACM) was proposed to estimate surface PM2.5 concentrations, and results from a traditional MRM and the new ACM-MRM were compared. Results show that the average determination coefficient (R2) of the ACM-MRM (0.52) was greater than that of the MRM (0.44), while the root mean square error (RMSE) and mean absolute percent error (MAPE) of the ACM-MRM (3.74 μg/m3 and 34.91%, respectively) were lower than the values obtained with the MRM (4.09 μg/m3 and 37.64%, respectively). The use of ACM improved the estimation of daily PM2.5 concentrations in different regions and different seasons by reducing the deviations caused by aerosol type changes. Further analysis demonstrated that aerosol type changes had adverse influences on the estimation of short-term PM2.5 concentrations and the introduction of ACM can effectively restrain the adverse influences when aerosols change frequently.

Investigations into the development of a satellite-based aerosol climate data record using ATSR-2, AATSR and AVHRR data over north-eastern China from 1987 to 2012

Abstract. Satellites provide information on the temporal and spatial distributions of aerosols on regional and global scales. With the same method applied to a single sensor all over the world, a consistent data set is to be expected. However, the application of different retrieval algorithms to the same sensor and the use of a series of different sensors may lead to substantial differences, and no single sensor or algorithm is better than any other everywhere and at all times. For the production of long-term climate data records, the use of multiple sensors cannot be avoided. The Along Track Scanning Radiometer (ATSR-2) and the Advanced ATSR (AATSR) aerosol optical depth (AOD) data sets have been used to provide a global AOD data record over land and ocean of 17 years (1995–2012), which is planned to be extended with AOD retrieved from a similar sensor. To investigate the possibility of extending the ATSR data record to earlier years, the use of an AOD data set from the Advanced Very High Resolution Radiometer (AVHRR) is investigated. AOD data sets used in this study were retrieved from the ATSR sensors using the ATSR Dual View algorithm ADV version 2.31, developed by Finnish Meteorological Institute (FMI), and from the AVHRR sensors using the aerosol optical depth over land (ADL) algorithm developed by RADI/CAS. Together, these data sets cover a multi-decadal period (1987–2012). The study area includes two contrasting areas, both in regards to aerosol content and composition and surface properties, i.e. a region over north-eastern China, encompassing a highly populated urban/industrialized area (Beijing–Tianjin–Hebei) and a sparsely populated mountainous area. Ground-based AOD observations available from ground-based sun photometer AOD data in AERONET and CARSNET are used as a reference, together with broadband extinction method (BEM) data at Beijing to cover the time before sun photometer observations became available in the early 2000s. In addition, MODIS-Terra C6.1 AOD data are used as a reference data set over the wide area where no ground-based data are available. All satellite data over the study area were validated against the reference data, showing the qualification of MODIS for comparison with ATSR and AVHRR. The comparison with MODIS shows that AVHRR performs better than ATSR in the north of the study area (40∘ N), whereas further south ATSR provides better results. The validation against sun photometer AOD shows that both AVHRR and ATSR underestimate the AOD, with ATSR failing to provide reliable results in the wintertime. This is likely due to the highly reflecting surface in the dry season, when AVHRR-retrieved AOD traces both MODIS and reference AOD data well. However, AVHRR does not provide AOD larger than about 0.6 and hence is not reliable when high AOD values have been observed over the last decade. In these cases, ATSR performs much better for AOD up to about 1.3. AVHRR-retrieved AOD compares favourably with BEM AOD, except for AOD higher than about 0.6. These comparisons lead to the conclusion that AVHRR and ATSR AOD data records each have their strengths and weaknesses that need to be accounted for when combining them in a single multi-decadal climate data record.

Evaluation of the Aqua-MODIS C6 and C6.1 Aerosol Optical Depth Products in the Yellow River Basin, China

In this study, Aqua-Moderate Resolution Imaging Spectroradiometer (MODIS) Collection (C6) and C6.1 Dark Target aerosol optical depth (AOD) retrievals at 3 km (DT3K) and 10 km (DT10K), Deep Blue AOD retrievals at 10 km (DB10K), and combined DT and DB (DTB) AOD retrievals at 10 km resolutions were validated from 2002 to 2014 against ground-based sunphotometer AOD measurements obtained from the Chinese aerosol remote sensing network (CARSNET). The CARSNET AOD data were obtained for sites at Mt. Waliguan (MW), Lanzhou (LZ), Ulate (UL), and Zhengzhou (ZZ) located in the Yellow River basin (YERB) region, China. Errors and agreement between satellite and ground data were reported using Pearson’s correlation (R) and relative mean bias (RMB). Results showed that the DT3K C6.1 highest quality flag (QF = 3) AOD retrievals were well correlated with the sunphotometer AOD data, with an R of 0.82 and an RMB of 1.01. Overestimation and underestimation in DT AOD retrievals were observed for AOD > 1.1 and AOD < 1.1, respectively. A significant underestimation of 37% in DB10K AOD retrievals was observed across all the sites except ZZ, which was indicated by a low-value RMB (0.63). Spatial distribution maps showed high AOD values (>0.8) over the lower part of the YERB and low AOD values (<0.4) across the upstream part of the YERB. This might be due to a large number of aerosol emissions over the lower developed areas and a scarcity of aerosols over the upstream mountain areas. Overall, this study supports the use of DT10K C6.1 AOD retrievals over the western semi-arid and arid regions of the YERB and DTB10K AOD retrievals over the north-central water system and eastern plain regions of the YERB.

Intercomparison and Validation of Satellite and Ground-Based Aerosol Optical Depth (AOD) Retrievals over Six AERONET Sites in West Africa

The geographical and climatic conditions of West Africa make the region an essential area for the description of atmospheric aerosol optical properties across the globe. This study provides an intercomparison of aerosol optical depth (AOD440nm) retrievals from satellite-based CALIPSO, MISR, MODIS and OMI sensors against ground-based sun photometer (AERONET) measurements between 2004 and 2014 from six West African sites, which are Agoufou, Banizoumbou, Ouagadougou, Dakar, Djougou and Ilorin during the period 2004–2014. The analysis revealed that MODISSTD performed better with a high degree of correlation for the six study sites, the CALIPSO and MISR–AERONET comparisons showed strong correlations, and the MODISDB also indicated better correlations, as did the OMI–AERONET comparisons. The root mean square error, mean absolute error and root mean bias error were also computed. The CALIPSO instrument has the lowest RMSE and MAE values over Dakar, while the highest RMSE and MAE values were indicated by the CALIPSO and MODISDB sensors, respectively, over Ilorin. The MISR instrument showed good agreement over Dakar than the other instruments, while CALIPSO AOD retrievals were better than those from the other sensors in Banizoumbou and Ouagadogou. The expected error bounds computed for both MODIS retrievals showed that MODISSTD consistently outperformed MODISDB in all the study sites. High AOD values were averagely observed by the satellite sensors during the local dry months (December–February), due to high concentrations of dust aerosols. High AOD values were observed during March–May, due to the condensation of water vapor on aerosol leading to increase in size and optical depth.

Assessing uncertainties of a geophysical approach to estimate surface fine particulate matter distributions from satellite-observed aerosol optical depth

Abstract. Health impact analyses are increasingly tapping the broad spatial coverage of satellite aerosol optical depth (AOD) products to estimate human exposure to fine particulate matter (PM2.5). We use a forward geophysical approach to derive ground-level PM2.5 distributions from satellite AOD at 1 km2 resolution for 2011 over the northeastern US by applying relationships between surface PM2.5 and column AOD (calculated offline from speciated mass distributions) from a regional air quality model (CMAQ; 12×12 km2 horizontal resolution). Seasonal average satellite-derived PM2.5 reveals more spatial detail and best captures observed surface PM2.5 levels during summer. At the daily scale, however, satellite-derived PM2.5 is not only subject to measurement uncertainties from satellite instruments, but more importantly to uncertainties in the relationship between surface PM2.5 and column AOD. Using 11 ground-based AOD measurements within 10 km of surface PM2.5 monitors, we show that uncertainties in modeled PM2.5∕AOD can explain more than 70 % of the spatial and temporal variance in the total uncertainty in daily satellite-derived PM2.5 evaluated at PM2.5 monitors. This finding implies that a successful geophysical approach to deriving daily PM2.5 from satellite AOD requires model skill at capturing day-to-day variations in PM2.5∕AOD relationships. Overall, we estimate that uncertainties in the modeled PM2.5∕AOD lead to an error of 11 µg m−3 in daily satellite-derived PM2.5, and uncertainties in satellite AOD lead to an error of 8 µg m−3. Using multi-platform ground, airborne, and radiosonde measurements, we show that uncertainties of modeled PM2.5∕AOD are mainly driven by model uncertainties in aerosol column mass and speciation, while model representation of relative humidity and aerosol vertical profile shape contributes some systematic biases. The parameterization of aerosol optical properties, which determines the mass extinction efficiency, also contributes to random uncertainty, with the size distribution being the largest source of uncertainty and hygroscopicity of inorganic salt the second largest. Future efforts to reduce uncertainty in geophysical approaches to derive surface PM2.5 from satellite AOD would thus benefit from improving model representation of aerosol vertical distribution and aerosol optical properties, to narrow uncertainty in satellite-derived PM2.5.

MODIS Collection 6.1 aerosol optical depth products over land and ocean: validation and comparison

Recently, the newest Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) aerosol optical depth (AOD) products were available with various refinements and improvements made to both the radiation calibration and Dark Target (DT) and Deep Blue (DB) algorithms. A combined DT and DB dataset (DTB) was also added based on piecewise fixed thresholds using the Normalized Difference Vegetation Index (NDVI) for taking advantage of one's merits. This study provides a cross-comparison and evaluation of these Terra MODIS aerosol products with reference to the enhanced ground-based AOD measurements by the Aerosol Robotic Network (AERONET) Level 3 Version 2.0 data at 384 ground stations. Their absolute and relative performance are evaluated in the period of 2013–2017 among the products, as well as between the current (C6.1) and previous (C6) releases. In general, the C6.1 aerosol products are found to be superior over the C6 products for three datasets from all scales, but the differences and improvements are rather non-uniform that varies with region. Overall, the DB AOD products show the best performance in most regions at about half of the sites, especially in Europe and North America. Meanwhile, besides bright surfaces (i.e., deserts and arid/semi-arid areas), DB products match more closely with the AERONET AODs than that of DT over medium or densely vegetated areas. The dependences of retrieval errors illustrate that the performance of three datasets deteriorates as surface reflectance, elevation and aerosol loading increase. However, the DB algorithm remains relatively more stable and less affected by changes in atmospheric and surface conditions. While the merged product using NDVI has some improvements over individual ones in general, worse performance is also shown in many cases. A more optimal method is thus wanting.

Evaluation of the AVHRR DeepBlue aerosol optical depth dataset over mainland China

Advanced Very High Resolution Radiometer (AVHRR) on-board NOAA series satellites have been used to observe the Earth’s surface and clouds for almost 40 years. Limited by bands and problematic instrument calibration, aerosol studies using AVHRR data have focused on retrieving data over the ocean. However, continuous developments have made it possible to retrieve aerosol over land as well. The newly developed AVHRR Deep Blue (DB) technique has been applied to process global aerosol datasets over both land and the ocean during 1989–1990, 1995–1999 and 2006–2011. This paper aims to evaluate, in detail, the performance of the AVHRR DB aerosol optical depth (AOD) dataset over mainland China by comparison with both ground-based data and satellite aerosol products. The ground-based validation results show that DB AOD is close to ground-based AOD when AOD is moderate during winter, while DB underestimates AOD when AOD increases over 1.0 during summer over vegetated surfaces. AVHRR DB underestimates dry, urban and transitional surfaces in Western China due to the high uncertainty in low retrievals over bright surfaces. Cross-comparison with the Moderate-resolution imaging spectrometer (MODIS) DB aerosol dataset shows that the disadvantages of the single longer visible channel are greatly increased over bright surfaces. Together with problematic instrument calibration, the differences between the two datasets over most of mainland China are significant. Meanwhile, the differences show strong seasonal variation characteristics.

Diurnal and seasonal variability of PM2.5 and AOD in North China plain: Comparison of MERRA-2 products and ground measurements

North China Plain (NCP) is one of heavily polluted regions that is characterized by a mixture of a myriad of anthropogenic and natural aerosols. A substantial spatial and temporal variations of aerosols and their compositions there poses a good testbed for the validation of model simulations. Aerosol optical depth (AOD) and PM2.5 (particulate matter with aerodynamic diameter < 2.5 μm) concentration products from the Modern Era Retrospective-Analysis for Research and Applications, version 2 (MERRA-2) are evaluated using available independent ground-based in situ and remote sensing products in the NCP. The comparison of MERRA-2 aerosol species to the observations is also performed. Although several satellite and ground-based AOD products are assimilated into the MERRA-2, MERRA-2 AOD is systematically smaller than independent sunphotometer measurements. The biases range from 0.09 (13%) in the summer to 0.17 (33%) in the spring and show little spatial dependence. Daytime AOD variations are captured by the MERRA-2, although MERRA-2 has relatively lower AOD. MERRA-2 produces lower PM2.5 concentration relative to surface measurements in all seasons except in summer. The largest bias is found in the winter (44 μgm−3). On the contrary, summer MERRA-2 PM2.5 is close to surface-measured PM2.5 (with bias of 0.4 μgm−3). MERRA-2 was unable to reproduce diurnal PM2.5 variation. Evaluation of MERRA-2 aerosol species in the winter of 2014 suggests that MERRA-2 could not keep track of dramatic day-to-day variation of aerosols and their species. Potential causes for this deficiency may include a lack of nitrate aerosols (accounting for 20% of PM2.5 concentrations during heavily polluted days). This fault cannot be remedied by assimilation of satellite AODs because they are often missing.

Characteristics of long-lasting haze episodes observed in Seoul, South Korea, for 2009–2014

The meteorological, physical, chemical, and optical characteristics of long-lasting haze in Seoul were studied. Four episodes were observed between 2009 and 2014, all in winter. PM10 mass concentration (PM10), chemical species, and aerosol optical depth (AOD) were analyzed along with the synoptic meteorological conditions. During the episodes, the temporal variations of the PM10 generally proceeded from the west along the Yellow Sea. The ground-based AOD was also high in Seoul and other sites. High AOD (MODIS) distributions were observed to move from China to Korea. The high sulfate concentration, along with the high sulfur oxidation ratio value of the third and fourth episodes (Eps. 3 and 4), respectively, corroborated the possible long-range transport of air pollutants. Stagnant meteorological conditions were a reason for the occurrence of long-lasting hazes. An anticyclone system had a dominant influence on the Korean peninsula during all episodes. The air mass over China was able to rise, and that over Korea was more stagnant in terms of climatology except Ep. 2. In addition to transport from outside, locally emitted air pollutants contributed to the PM10 partly due to the stagnant conditions, during which diurnal variations in NO2 and nitrates showed similar peak times during Eps. 3 and 4. Analysis of the episodes consistently showed that the long-lasting haze episodes were influenced by both the long-range transport of air pollutants from outside Seoul, mostly from China, and the accumulation of air pollutants that were locally emitted and transformed.

Spatiotemporal Distribution of Satellite-Retrieved Ground-Level PM2.5 and Near Real-Time Daily Retrieval Algorithm Development in Sichuan Basin, China

Satellite-based monitoring can retrieve ground-level PM2.5 concentrations with higher-resolution and continuous spatial coverage to assist in making management strategies and estimating health exposures. The Sichuan Basin has a complex terrain and several city clusters that differ from other regions in China: it has an enclosed air basin with a unique planetary boundary layer dynamic which accumulates air pollution. The spatiotemporal distribution of 1-km resolution Aerosol Optical Depth (AOD) in the Sichuan Basin was retrieved using the improved dark pixel method and Moderate Resolution Imaging Spectroradiometer (MODIS) data in this study. The retrieved seasonal AOD reached its highest values in spring and had the lowest values in autumn. The higher correlation (r = 0.84, N = 171) between the ground-based Lidar AOD and 1-km resolution MODIS AOD indicated that the high-resolution MODIS AOD could be used to retrieve the ground-level PM2.5 concentration. The Lidar-measured annual average extinction coefficient increased linearly with the Planetary Boundary Layer Height (PBLH) in the range of 100~670 m, but exponentially decreased between the heights of 670~1800 m. Both the correlation and the variation tendency of simulated PBLH from the Weather Research and Forecasting (WRF) model &amp; Shin-Hong (SHIN)/California Meteorological (CALMET) model (WRF_SHIN/CALMET) were closer to the Lidar observation than that of three other Planetary Boundary Layer (PBL) schemes (the Grenier-Bretherton-McCaa (GBM) scheme, the Total Energy-Mass Flux (TEMF) scheme and the University of Washington (UW) scheme), which suggested that the simulated the Planetary Boundary Layer Height (PBLH) could be used in the vertical correction of retrieval PM2.5. Four seasonal fitting functions were also obtained for further humidity correction. The correlation coefficient between the aerosol extinction coefficient and the fitted surface-level PM2.5 concentration at the benchmark station of Southwest Jiao-tong University was enhanced significantly from 0.62 to 0.76 after vertical and humidity corrections during a whole year. During the evaluation of the retrieved ground-level PM2.5 with observed values from three cities, Yibin (YB), Dazhou (DZ), and Deyang (DY), our algorithm performed well, resulting in higher correlation coefficients of 0.78 (N = 177), 0.77 (N = 178), and 0.81 (N = 181), respectively.

Retrieval of Aerosol Optical Depth in the Arid or Semiarid Region of Northern Xinjiang, China

Satellite remote sensing has been widely used to retrieve aerosol optical depth (AOD), which is an indicator of air quality as well as radiative forcing. The dark target (DT) algorithm is applied to low reflectance areas, such as dense vegetation, and the deep blue (DB) algorithm is adopted for bright-reflecting regions. However, both DT and DB algorithms ignore the effect of surface bidirectional reflectance. This paper provides a method for AOD retrieval in arid or semiarid areas, in which the key points are the accurate estimation of surface reflectance and reasonable assumptions of the aerosol model. To reduce the uncertainty in surface reflectance, a minimum land surface reflectance database at the spatial resolution of 500 m for each month was constructed based on the moderate-resolution imaging spectroradiometer (MODIS) surface reflectance product. Furthermore, a bidirectional reflectance distribution function (BRDF) correction model was adopted to compensate for the effect of surface reflectance anisotropy. The aerosol parameters, including AOD, single scattering albedo, asymmetric factor, Ångström exponent and complex refractive index, are determined based on the observation of two sunphotometers installed in northern Xinjiang from July to August 2014. The AOD retrieved from the MODIS images was validated with ground-based measurements and the Terra-MODIS aerosol product (MOD04). The 500 m AOD retrieved from the MODIS showed high consistency with ground-based AOD measurements, with an average correlation coefficient of ~0.928, root mean square error (RMSE) of ~0.042, mean absolute error (MAE) of ~0.032, and the percentage falling within the expected error (EE) of the collocations is higher than that for the MOD04 DB product. The results demonstrate that the new AOD algorithm is more suitable to represent aerosol conditions over Xinjiang than the DB standard product.

Evaluation and utilization of MODIS and CALIPSO aerosol retrievals over a complex terrain in Himalaya

The study elucidate upon the evaluation of satellite retrievals with ground based aerosol optical depth (AOD) measurements, their utilization in LiDAR ratio (LR) estimation, boundary layer (BL) height determination and the case studies on aerosol transport over Himalayan region. The AOD retrievals from the latest level-2 data collections (C5.1 and C6.0) of MODerate resolution Imaging Spectroradiometer (MODIS) onboard Aqua and Terra satellites and Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observations (CALIPSO) versions (4.10 and 3) are subjected for quantitative analysis to assess the level of agreement with the quality assured level-2 ground based AErosol RObotic NETwork (AERONET) measurements over Manora peak (29.36° N, 79.46°E), a high altitude site in the Himalayas. Analysis revealed that the AOD from the latest MODIS Terra C6.0 deep blue (DB) 30km×30km and CALIPSO ver. 4.10 (overpass within ~100km distance) are in a very good agreement (R≥0.9) with that from coincident AERONET measurements averaged over the span of ±30min. About 77% of the AOD retrieved using MODIS and ~87% from CALIPSO were found to be within the expected error (EE) limits. The AOD comparison between MODIS Terra C6.0DB and CALIPSO ver. 4.10, suggested their synergic use for aerosol characterization over Himalayas. In comparison to the ver. 3, CALIPSO ver. 4.10 is found to have undergone substantial changes, and their long term inter-comparison in the grid 28.86°-29.86° N and 78.96°–79.96° E revealed that their vertical feature and aerosol sub-types are in agreement of ~94.6% and ~68.6%, respectively. Utilizing the AOD retrievals from AERONET and MODIS collections, the iteratively computed LR for three LiDAR systems was found to be lower (<16) during winter and higher (>43) during summer. Study on the BL height estimations suggested that the wavelet covariance transform (WCT) method for CALIPSO could be the best choice as compared to the threshold method, and complements well with the specific humidity gradient method used with the radiosonde observation. Case studies on the continental transport of smoke plumes emanating from crop-residue burning in post-monsoon, and long range transport of aerosols and dust over the region in summer are also discussed using the collocated measurements from ground-based AERONET and LiDAR, in conjunction with MODIS, CALIPSO, reanalysis data and trajectory modeling.

The retrieval of aerosol over land surface from GF-1 16m camera with Deep Blue algorithm

As Chinese new satellite of high resolution for earth observation, the application of air pollution monitoring for GF-1 data is a key problem which need to be solved. In the paper, based on Hsu et al (2004) Deep Blue algorithm, by taking count of the characteristic of GF-1 16m camera, the contribution of land surface was removed by MODIS surface reflectance product, and aerosol optical depth (AOD) was retrieved from apparent reflectance in blue band. So, Deep Blue algorithm was applied to GF-1 16m camera successfully. Then, we collected GF-1 16m camera data over Beijing area between August and November, 2014, and the experiment of AOD was processed. It is obvious that the retrieved image showed the distribution of AOD well. At last, the AOD was validated by ground-based AOD data of AERONET/PHOTONS Beijing site. It is showed that there are good agreement between GF-1 AOD and AERONET/PHOTONS AOD, (R>0.7). But GF-1 AOD is obviously larger than ground-based AOD which may be brought by the difference of filter response function between MODIS and GF-1 camera.

Understanding dust emission in the Bodélé region by extracting locally mobilized dust aerosols from satellite Aerosol Optical Depth data using principal component analysis

Abstract Despite the increasing availability of satellite and ground-based Aerosol Optical Depth (AOD) data, their application in dust modeling is limited because these data do not differentiate locally mobilized dust from remotely advected dust and other aerosols. In this work, we extract the locally mobilized Dust Optical Depth (DOD) in the Bodele region from historical AOD data through a principal component analysis of wind speed and AOD time series (2003–2012). Principal component analysis effectively identifies the correlated signature between wind speed and AOD making it possible to separate the dust component from AOD data. Using the reconstructed DOD, we then study the effect of key environmental variables, namely wind speed, soil moisture, soil temperature, vegetation, and boundary layer height on dust emission. Results show that all of these environmental variables are significantly correlated with the reconstructed DOD indicating their association with the dust emission process. The extraction technique described in this study can be extended to regional and global scales to identify the dust sources which are not adequately represented in regional and global dust models.

The empirical relationship between PM2.5 and AOD in Nanjing of the Yangtze River Delta

Ground-based Aerosol optical properties and PM2.5 concentrations in an industrial area in Nanjing were measured and analyzed from May 2013 to May 2015. The annual mean of the aerosol optical depth (AOD) at 550 nm was 1.04 ± 0.63. The annual mean of the Angstrom exponent (α) was 1.03 ± 0.23. The annual PM2.5 concentration was 62 ± 13 μg m−3. There were significant correlations between the PM2.5 concentration and the AOD. The linear regression functions of the daily PM2.5 concentration (y) with the ground-based AOD (x) and the MODIS AOD (x) were y = 55.70x + 25.60 (R2 = 0.53) and y = 63.84x + 39.33 (R2 = 0.56), respectively. The results demonstrated significant differences in the slopes and intercepts of the linear regression functions for different seasons. The distribution of PM2.5 was retrieved by the seasonal functions between the ground-based PM2.5 and the MODIS AOD on sunny days in the region. The MODIS-derived PM2.5 concentrations in the north of Jiangsu and parts of Shandong and Henan were generally higher than those in the hilly lands of the south of Anhui and the northwest of Zhejiang. There was a high-PM2.5 area anchored over the industrial area. The particle pollution was more serious in spring and winter than in autumn and summer. The MODIS-derived PM2.5 concentration in urban and industrial areas was higher than 80 μg m−3 during spring and winter. The retrieved PM2.5 values had the highest absolute error (+17 μg m−3) in summer and the lowest (+2 μg m−3) in winter, and the average relative error of the seasonal PM2.5 concentration was 15% ± 10%.

The Variations and Trends of MODIS C5 &amp; C6 Products’ Errors in the Recent Decade over the Background and Urban Areas of North China

With ten-year (2004–2013) ground-based observations of Beijing Forest (BJF) and Beijing City (BJC) sites in North China, we validated the high-quality MODerate resolution Imaging Spectroradiometer (MODIS) Collection 5 (C5) and Collection 6 (C6) Aerosol Optical Depth (AOD) products’ precision and discussed the sensors degradation issues. The annual mean AOD and Angstrom exponent (α) were 0.20 ± 0.02 and 0.83 ± 0.15 in the background over the past ten years, and they were 0.59 ± 0.07 and 1.13 ± 0.08 in the urban, respectively. Ground-based AOD had both slightly declining trends, with variations of 0.023 and 0.057 over the past decade in the background and urban, respectively. There were large differences among the eight kinds of MODIS AOD products (Terra vs. Aqua, C5 vs. C6, DT (Deep Target) vs. DB (Deep Blue), and DTDB in the background and urban areas), but all the products’ monthly errors had larger variations in the spring and summer, and smaller ones in the autumn and winter. In the background, more than 62% of DT matchups for C5 and C6 products were within NASA’s expected error (EE) envelope. In the urban, 69%~72% of C6 DB retrievals were falling within EE envelope. The new dataset named C6 DTDB had better performance in the background, whereas it overestimated by 37%~41% in the urban caused by surface reflectivity estimation error. The range of monthly average error varied from −0.21 to 0.28 in the background and from −0.63 to 0.48 in the urban. From the background to the urban areas, the retrieval errors of Terra and Aqua had slightly increased by 0.0023~0.0158 and 0.0011~0.0124 per year, respectively, which implied that the two MODIS instruments had degraded slowly.

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.

Application of spectral analysis techniques to the intercomparison of aerosol data – Part 4: Synthesized analysis of multisensor satellite and ground-based AOD measurements using combined maximum covariance analysis

Abstract. In this paper, we introduce the usage of a newly developed spectral decomposition technique – combined maximum covariance analysis (CMCA) – in the spatiotemporal comparison of four satellite data sets and ground-based observations of aerosol optical depth (AOD). This technique is based on commonly used principal component analysis (PCA) and maximum covariance analysis (MCA). By decomposing the cross-covariance matrix between the joint satellite data field and Aerosol Robotic Network (AERONET) station data, both parallel comparison across different satellite data sets and the evaluation of the satellite data against the AERONET measurements are simultaneously realized. We show that this new method not only confirms the seasonal and interannual variability of aerosol optical depth, aerosol-source regions and events represented by different satellite data sets, but also identifies the strengths and weaknesses of each data set in capturing the variability associated with sources, events or aerosol types. Furthermore, by examining the spread of the spatial modes of different satellite fields, regions with the largest uncertainties in aerosol observation are identified. We also present two regional case studies that respectively demonstrate the capability of the CMCA technique in assessing the representation of an extreme event in different data sets, and in evaluating the performance of different data sets on seasonal and interannual timescales. Global results indicate that different data sets agree qualitatively for major aerosol-source regions. Discrepancies are mostly found over the Sahel, India, eastern and southeastern Asia. Results for eastern Europe suggest that the intense wildfire event in Russia during summer 2010 was less well-represented by SeaWiFS (Sea-viewing Wide Field-of-view Sensor) and OMI (Ozone Monitoring Instrument), which might be due to misclassification of smoke plumes as clouds. Analysis for the Indian subcontinent shows that here SeaWiFS agrees best with AERONET in terms of seasonality for both the Gangetic Basin and southern India, while on interannual timescales it has the poorest agreement.