Moderate Resolution Imaging Spectroradiometer Aerosol Optical Depth Products Research Articles

Air Quality Interpretation of Four Geographically Distinct Hot Spot States of India-An Appraisal Using Aerosol Optical Depth and Particulate Matter

The application of Moderate Resolution Imaging Spectroradiometer-Aerosol Optical Depth (MODIS-AOD) for Air Quality (AQ) assessment has contributed significantly by making use of a spatiotemporal relationship between ground-level particulate matter and aerosol optical density at both city and state levels. Traditional ground-based site-specific particulate matter monitoring method has found a profound gap in their spatial coverage. Such challenging situations prevail over the progressive development of the combined usage of ground monitoring technique along with the MODIS-AOD product (with 10 km spatial resolution). This was achieved and derived by assembling the satellite sensor, Terra as a tool for the regional-based specific studies for monitoring airborne particles. Their analytical linear association was evaluated and explored their feasibility for monitoring either Respirable Suspended organic Matter (RSPM) or AQI in spatiotemporally distinct geographical sites situated in four States of India. Examined the interrelationship between RSPM-AOD against meteorological drivers through Principal Component Analysis (PCA) and Quantitative Risk Assessment (QRA) of the focussed four zones. Elevated Air Pollution was noticed in the urban and industrial cities of Pune, Kochi, and Chennai. They were flanked by various industrial clusters and the substantial personal handling of vehicles. A region-wise RSPM-AOD correlation was recorded for the categorized areas: WS1, WS2, SW, and ES. Each zone was subsequently judged and a noteworthy association was linked between the ground-monitored RSPM data and the retrieved AOD output. This means of records could represent the aerosol component; consequently, infer their sources of origin and in later days could come up with reasonable emission control channels. States of India Maharashtra, Karnataka, Kerala, and Tamil Nadu, are categorized as WS1, WS2, SW, and ES respectively.

Improving MODIS Aerosol Estimates Over Land With the Surface BRDF Reflectances Using the 3-D Discrete Cosine Transform and RossThick-LiSparse Models

The retrieval of aerosol properties over land from satellite sensors has always been a challenge. At present, several different algorithms for retrieving aerosol optical depth (AOD) have been developed from different satellite sensors. While each algorithm has its own advantages, the accuracy of AOD retrieval still needs to be further improved. To improve the retrieval accuracy of aerosol algorithms, it is necessary to provide a better method to describe the surface properties. In the current study, a new aerosol retrieval algorithm for Moderate Resolution Imaging Spectroradiometer (MODIS) images at a high spatial resolution of 500 m is proposed based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$a$ </tex-math></inline-formula> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">priori</i> bidirectional reflectance distribution function (BRDF) shape parameters database, which is reconstructed via the 3-D discrete cosine transform (DCT-PLS) method. Then, the surface reflectances are calculated from BRDF model (i.e., RossThick-LiSparse), and a non-Lambertian forward model used to describe the surface anisotropy. The new algorithm is used for processing the MODIS over the Beijing–Tianjin–Hebei of China, and Southeastern United States of America regions, and results are validated against AERONET AOD measurements as well as compared with the MODIS AOD products. The comparison showed that the estimation scheme of surface reflectance in this new algorithm significantly improved the AOD retrievals accuracy, with average correlation coefficient ~0.965 and root-mean-square error ~0.125; the number of AOD retrievals falling within expected error has increased to ~80.1%, and the overestimation uncertainty has been reduced compared with MODIS products. Due to the high spatial resolution and continuous spatial distributions of the AOD retrievals by the new algorithm, therefore, it can well-captured aerosol details over mixed surfaces and better useful for air pollution studies than the MODIS products at local and urban scales.

Evaluation and possible uncertainty source analysis of JAXA Himawari-8 aerosol optical depth product over China

The latest version of JAXA aerosol optical depth (AOD) product over China in 2018 from Himawari-8 Advanced Himawari Imager (AHI) was evaluated, including validation with global AErosol RObotic NETwork (AERONET) and China Aerosol Research Network (CARSNET) ground-based observations, and comparison against the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD product. Considering both the quality and quantity of the AHI AOD product, the Quality Assurance Flags (QA_Flags) with “good” and “very good” are chosen for evaluation and analysis. AHI and MODIS AOD products are well correlated and the spatial distributions are similar, even though underestimation from AHI AOD in the morning and overestimation in the afternoon are found when compared with MODIS AOD. Validation with ground-based observations also indicates a good correlation (R > 0.75) when sparsely vegetated surfaces are excluded. It is found that 52.74% fall below the expected error (EE) at 10:30 BJT when compared with CARSNET which has more vegetation sites and 56.91% fall above the EE at 13:30 BJT when compared with AERONET which has more urban sites. The slope of the fit line is around 1.0 for all matched sites, while the scatter plots have divided into two parts as the performance of AHI AOD depends on the observation times and surface types. The temporal variations of AHI and AERONET AODs show that the overestimation from AHI AOD occurs in the afternoon for barren surface while the underestimation occurs in the morning for well-vegetation surface. For monthly variation, the overestimation of AHI AOD is found over Northern China in winter and those seasons when the plants are growing or disappearing rapidly.The assumption that the surface reflectance remains unchanged and the solar geometries are nearly identical within a month causes the underestimation of surface reflectance and thus contributes to the AHI AOD overestimation for low vegetation and bare surfaces in autumn and winter. While over the vegetated surfaces, neglecting the background aerosols causes overestimation of the surface reflectance, and thus leads to underestimation of AHI AOD. The difference between backward and forward scattering contributes to the larger JAXA AHI AOD error in the afternoon, which aggravates positive deviation for barren land sites and alleviates the underestimation for vegetation surface types in the afternoon. In addition, aerosol model selection and cloud contamination also affect the accuracy of aerosol products, especially when AOD is large.Overall, the JAXA AHI AOD provides a high temporal resolution and relative reliable aerosol product for environmental research. The analysis on characteristics and uncertainty sources in this study are to further enhance the understanding of the geostationary satellite aerosol products for applications, and provide useful information for improving the aerosol retrieval algorithm in future.

Assessment of the Representativeness of MODIS Aerosol Optical Depth Products at Different Temporal Scales Using Global AERONET Measurements

Assessments of long-term changes of air quality and global radiative forcing at a large scale heavily rely on satellite aerosol optical depth (AOD) datasets, particularly their temporal binning products. Although some attempts focusing on the validation of long-term satellite AOD have been conducted, there is still a lack of comprehensive quantification and understanding of the representativeness of satellite AOD at different temporal binning scales. Here, we evaluated the performances of the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products at various temporal scales by comparing the MODIS AOD datasets from both the Terra and Aqua satellites with the entire global AErosol RObotic NETwork (AERONET) observation archive between 2000 and 2017. The uncertainty levels of the MODIS hourly and daily AOD products were similarly high, indicating that MODIS AOD retrievals could be used to represent daily aerosol conditions. The MODIS data showed the reduced quality when integrated from the daily to monthly scale, where the relative mean bias (RMB) changed from 1.09 to 1.21 for MODIS Terra and from 1.04 to 1.17 for MODIS Aqua, respectively. The limitation of valid data availability within a month appeared to be the primary reason for the increased uncertainties in the monthly binning products, and the monthly data associated uncertainties could be reduced when the number of valid AOD retrievals reached 15 times in one month. At all three temporal scales, the uncertainty levels of satellite AOD products decreased with increasing AOD values. The results of this study could provide crucial information for satellite AOD users to better understand the reliability of different temporal AOD binning products and associated uncertainties in their derived long-term trends.

Improvement of snow/haze confusion data gaps in MODIS Dark Target aerosol retrievals in East China

The MODerate resolution Imaging Spectroradiometer (MODIS) is one of the most widely used meteorological remote sensing instruments. Its Dark Target aerosol optical depth (AOD) product has been widely used in environment and meteorology researches, such as model evaluation and data assimilation. However, this product has a low coverage under conditions of heavy haze in China. This is because the haze can be misidentified as snow under some circumstances by the algorithm and therefore rejected, leading to large-scale data omission. In the most polluted regions, misidentified snow cover exceeded 8%. Regarding this issue, a new method combining the snow mask derived from the MODIS cloud mask product and Fisher discrimination analysis was developed to give a more accurate identification of snow and ice cover. Applying this new method increases the AOD data coverage significantly. Comparisons with AOD values from ground-based observations showed that the newly produced data under haze conditions had a similar accuracy with the original data in the MODIS AOD product. Because the newly supplemented data are more distributed at the seriously polluted regions, the average AOD increased significantly after data filling in many regions. In winter, AOD in the most severely polluted regions (average air quality index >130) increased by 0.2–0.3 after improvement, about 30–50% of the original value. In 49 haze cases with large-scale pollution, the increase reached 0.3–0.6, about 50%–70% of the original value. During the haze episodes, the data omission led to an underestimation of the regional average AOD by 19–40%. The improvement in AOD coverage helps to provide a better reflection of the air pollution condition in East China through the perspective of AOD.

Evaluation of satellite-based aerosol datasets and the CAMS reanalysis over the ocean utilizing shipborne reference observations

Abstract. Reliable reference measurements over the ocean are essential for the evaluation and improvement of satellite- and model-based aerosol datasets. Within the framework of the Maritime Aerosol Network, shipborne reference datasets have been collected over the Atlantic Ocean since 2004 with Microtops Sun photometers. These were recently complemented by measurements with the multi-spectral GUVis-3511 shadowband radiometer during five cruises with the research vessel Polarstern. The aerosol optical depth (AOD) uncertainty estimate of both shipborne instruments of ±0.02 can be confirmed if the GUVis instrument is cross calibrated to the Microtops instrument to account for differences in calibration, and if an empirical correction to account for the broad shadowband as well as the effects of forward scattering is introduced. Based on these two datasets, a comprehensive evaluation of aerosol products from the Moderate Resolution Imaging Spectroradiometer (MODIS) flown on NASA's Earth Observing System satellites, the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the geostationary Meteosat satellite, and the Copernicus Atmosphere Monitoring Service reanalysis (CAMS RA) is presented. For this purpose, focus is given to the accuracy of the AOD at 630 nm in combination with the Ångström exponent (AE), discussed in the context of the ambient aerosol type. In general, the evaluation of MODIS AOD from the official level-2 aerosol products of C6.1 against the Microtops AOD product confirms that 76 % of data points fall into the expected error limits given by previous validation studies. The SEVIRI-based AOD product exhibits a 25 % larger scatter than the MODIS AOD products at the instrument's native spectral channels. Further, the comparison of CAMS RA and MODIS AOD versus the shipborne reference shows similar performance for both datasets, with some differences arising from the assimilation and model assumptions. When considering aerosol conditions, an overestimation of AE is found for scenes dominated by desert dust for MODIS and SEVIRI products versus the shipborne reference dataset. As the composition of the mixture of aerosol in satellite products is constrained by model assumptions, this highlights the importance of considering the aerosol type in evaluation studies for identifying problematic aspects.

Evaluation and modification of SARA high-resolution AOD retrieval algorithm during high dust loading conditions over bright desert surfaces

Many studies have been carried out for the validation of Aerosol Optical Depth (AOD) retrieved using MODerate resolution Imaging Spectroradiometer (MODIS) satellite data at the spatial resolution of several to several tens of km. Most of the validation efforts have focused on urban areas with fewer studies focused on deserts with high amounts of dust. On the other hand, calculating AOD at a high spatial resolution over desert surfaces is of high interest to identify local and accurate dust source regions. In this study, two different high-resolution MODIS AOD retrieval algorithms were evaluated to investigate the nature of high dust loading events over the Middle East region. These algorithms include the Simplified Aerosol Retrieval Algorithm (SARA) at 500 m resolution based on the MOD09 Level 2 daily (original SARAMOD09) and MCD43A4 16 day (modified SARAMCD43) surface reflectance products. These algorithms were validated using AERONET AOD observations at Kuwait University, and accuracy was evaluated using descriptive statistics and Expected Error (EE) over land. Also, the results of these algorithms were evaluated with the Collection 6 (C6) Deep Blue algorithm at 10 km spatial resolution (DB10km). Results show that the SARAMCD43 and SARAMOD09 AOD retrievals are well correlated (r) with AERONET data (SARAMCD43 = 0.89 and SARAMOD09 = 0.84), with a larger number of retrievals falling within the Expected Error (SARAMCD43 = 86% and SARAMOD09 = 77%) than the DB (DB10km = 60%) AOD product. The SARAMCD43 AOD exhibited good agreement with DB10km AOD over desert surfaces areas during high aerosol loading events, with an average correlation coefficient of 0.92, RMSE of 0.09, and mean absolute error (MAE) of 0.08. While SARAMOD09 AOD over background desert areas is similar to SARAMCD43, significant differences were observed over high aerosol loading areas and SARAMOD09 dramatically underestimates AOD. The results demonstrate that: (i) the SARA algorithm (regardless of surface reflectance data used) is more robust than the MODIS AOD products (both DT and DB), and is better able to represent spatial dust aerosol conditions with an accurate AOD over desert areas over a range of low to high dust pollutant levels; (ii) SARAMCD43 is well correlated with the DB AOD observations and provides AOD with much greater accuracy and reliability than SARAMOD09 over desert areas during severe dust loading events.

Synergy of AERONET and MODIS AOD products in the estimation of PM2.5 concentrations in Beijing

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.

Evaluation of the MODIS C6 Aerosol Optical Depth Products over Chongqing, China

The Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 (C6) aerosol optical depth (AOD) products from the 10/3 km Dark Target (DT) and Deep Blue (DB) algorithms are firstly evaluated using ground observed AODs by the sun photometer in Chongqing, a mountainous mega-city in southwest China. The validation results show that MODIS AODs from 10/3 km DT algorithm are comparable with those of the sun photometer, although there are slight overestimations. However, the DB algorithm substantially underestimates MODIS AODs when comparing with those of the sun photometer. Error analyses imply that the bias of surface reflectance estimation is the main error source for both algorithms. The cloud screening scheme of the DT algorithm is more effective than the DB algorithm. The cloud vicinity effect should be considered in the quality control processes for both of the algorithms. A sensitivity test suggests that in complex terrain area, like Chongqing, the collocation method in the validation of satellite products should be carefully selected according to local circumstances. When comparing the monthly mean AODs of MODIS products with sun photometer observations, it shows that the Terra MODIS AOD products are valid to represent the mean statuses in summer and autumn, but the monthly mean of Aqua MODIS AODs are limited in Chongqing.

Daily estimation of ground-level PM2.5 concentrations at 4km resolution over Beijing-Tianjin-Hebei by fusing MODIS AOD and ground observations.

The satellite-borne Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) is widely used to estimate ground-level fine ambient particulate matter (PM2.5) concentrations to evaluate their health effects. The associated estimation accuracy is often reduced by AOD missing values and by insufficiently accounting for the spatio-temporal PM2.5 variations. In this study, we aim to estimate ground-level PM2.5 concentrations at a fine resolution with improved accuracy by fusing fine-scale satellite and ground observations in the populated and polluted Beijing-Tianjin-Hebei (BTH) area of China in 2014. We employed a Bayesian-based statistical downscaler to model the spatio-temporal linear AOD-PM2.5 relationships. We used a 3km MODIS AOD product, which was resampled to a 4km resolution in a Lambert conic conformal projection, to assist comparison and fusion with predictions by atmospheric chemistry models. A two-step method was used to fill the missing AOD values to obtain a full AOD dataset with complete spatial coverage. The downscaler has a good performance in the fitting procedure (R2=0.75) and in the cross validation procedure (R2=0.58 by random method and R2=0.47 by city-specific method). The number of missing AOD values was serious and related to elevated PM2.5 concentrations. The gap-filled AOD values corresponded well with our understanding of PM2.5 pollution conditions in BTH. The prediction accuracy of PM2.5 concentrations were improved in terms of their annual and seasonal mean. As a result of its fine spatio-temporal resolution and complete spatial coverage, the daily PM2.5 estimation dataset could provide extensive and insightful benefits to related studies in the BTH area. This may include understanding the formation processes of regional PM2.5 pollution episodes, evaluating daily human exposure, and establishing pollution controlling measures.

Retrieval and Validation of Atmospheric Aerosol Optical Depth From AVHRR Over China

As Advanced Very High Resolution Radiometer (AVHRR) lacks a 2.1- $\mu\text{m}$ band, the widely used “dark target” algorithm cannot be used to retrieve aerosol optical depth (AOD) from AVHRR over land. Instead, a multiple regression algorithm has been developed to process a time series of AVHRR Level_1b measurements over China (15°–45° N, 75°–135° E) for AOD retrieval. As the apparent reflectance of AVHRR is closely related to AOD, which can be provided by Moderate Resolution Imaging Spectroradiometer (MODIS), spatially and temporally collocated Aqua/MODIS AOD and AVHRR Level_1b measurements from four years (January 2008–December 2011) were used to generate the regression coefficients. Angle information, normalized difference vegetation index, water vapor, and surface elevation are chosen in addition to the apparent reflectance as predictors for different surface types. By applying the regression coefficients to AVHRR, the AOD product from independent AVHRR Level_1b measurements (May 2003–December 2007) was generated. Validation with AErosol RObotic NETwork (AERONET) AOD and comparison with MODIS AOD products have been conducted to evaluate the uncertainty of the AVHRR AOD from May 2003 to December 2007. The distribution pattern of the seasonal mean AOD from AVHRR is consistent with that of the MODIS AOD. Taking regions with rapid economic development, for example, the regional monthly mean AOD for these two data sets agrees well, with a consistent tendency and high correlation coefficients. When compared with AERONET from four sites in China, the results are also encouraging. Results show that the multiple regression method offers the potential to generate an AOD climatology data record from a long-term AVHRR Level_1b data set over land.

ESTIMATING PM2.5 IN THE BEIJING-TIANJIN-HEBEI REGION USING MODIS AOD PRODUCTS FROM 2014 TO 2015

Fine particulate matter with a diameter less than 2.5 μm (PM2.5) has harmful impacts on regional climate, economic development and public health. The high PM2.5 concentrations in China’s urban areas are mainly caused by the combustion of coal and gasoline, industrial pollution and unknown/uncertain sources. The Beijing-Tianjin-Hebei (BTH) region with a land area of 218,000 km&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt;, which contains 13 cities, is the biggest urbanized region in northern China. The huge population (110 million, 8% of the China’s population), local heavy industries and vehicle emissions have resulted in severe air pollution. Traditional models have used 10 km Moderate-resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) products and proved the statistical relationship between AOD and PM2.5. In 2014, the 3 km MODIS AOD product was released which made PM2.5 estimations with a higher resolution became possible. This study presents an estimation on PM2.5 distributions in the BTH region from September 2014 to August 2015 by combining the MODIS satellite data, ground measurements of PM2.5, meteorological parameters and social-economic factors based on the geographically weighted regression model. The results demonstrated that the 10 km AOD product provided results with a slightly higher accuracy although the 3 km AOD product could provide more information about the spatial variations of PM2.5 estimations. Additionally, compared with the global regression, the geographically weighed model was able to improve the estimation results.

ESTIMATING PM2.5 IN THE BEIJING-TIANJIN-HEBEI REGION USING MODIS AOD PRODUCTS FROM 2014 TO 2015

Abstract. Fine particulate matter with a diameter less than 2.5 μm (PM2.5) has harmful impacts on regional climate, economic development and public health. The high PM2.5 concentrations in China’s urban areas are mainly caused by the combustion of coal and gasoline, industrial pollution and unknown/uncertain sources. The Beijing-Tianjin-Hebei (BTH) region with a land area of 218,000 km2, which contains 13 cities, is the biggest urbanized region in northern China. The huge population (110 million, 8% of the China’s population), local heavy industries and vehicle emissions have resulted in severe air pollution. Traditional models have used 10 km Moderate-resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) products and proved the statistical relationship between AOD and PM2.5. In 2014, the 3 km MODIS AOD product was released which made PM2.5 estimations with a higher resolution became possible. This study presents an estimation on PM2.5 distributions in the BTH region from September 2014 to August 2015 by combining the MODIS satellite data, ground measurements of PM2.5, meteorological parameters and social-economic factors based on the geographically weighted regression model. The results demonstrated that the 10 km AOD product provided results with a slightly higher accuracy although the 3 km AOD product could provide more information about the spatial variations of PM2.5 estimations. Additionally, compared with the global regression, the geographically weighed model was able to improve the estimation results.

Aerosol Optical Depth Retrieval over Bright Areas Using Landsat 8 OLI Images

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.

AOD–PM 2.5 Association: Paciorek and Liu Respond

AOD–PM _2.5 Association: Paciorek and Liu Respond

Estimating trace gas and aerosol emissions over South America: Relationship between fire radiative energy released and aerosol optical depth observations

Contemporary human activities such as tropical deforestation, land clearing for agriculture, pest control and grassland management lead to biomass burning, which in turn leads to land-cover changes. However, biomass burning emissions are not correctly measured and the methods to assess these emissions form a part of current research area. The traditional methods for estimating aerosols and trace gases released into the atmosphere generally use emission factors associated with fuel loading and moisture characteristics and other parameters that are hard to estimate in near real-time applications. In this paper, fire radiative power (FRP) products were extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) and from the Geostationary Operational Environmental Satellites (GOES) fire products and new South America generic biomes FRE-based smoke aerosol emission coefficients were derived and applied in 2002 South America fire season. The inventory estimated by MODIS and GOES FRP measurements were included in Coupled Aerosol-Tracer Transport model coupled to the Brazilian developments on the Regional Atmospheric Modeling System (CATT-BRAMS) and evaluated with ground truth collected in Large Scale Biosphere–Atmosphere Smoke, Aerosols, Clouds, rainfall, and Climate (SMOCC) and Radiation, Cloud, and Climate Interactions (RaCCI). Although the linear regression showed that GOES FRP overestimates MODIS FRP observations, the use of a common external parameter such as MODIS aerosol optical depth product could minimize the difference between sensors. The relationship between the PM 2.5μm (Particulate Matter with diameter less than 2.5 μm) and CO (Carbon Monoxide) model shows a good agreement with SMOCC/RaCCI data in the general pattern of temporal evolution. The results showed high correlations, with values between 0.80 and 0.95 (significant at 0.5 level by student t test), for the CATT-BRAMS simulations with PM 2.5μm and CO.

An investigation into seasonal and regional aerosol characteristics in East Asia using model-predicted and remotely-sensed aerosol properties

Abstract. In this study, the spatio-temporal and seasonal distributions of EOS/Terra Moderate Resolution Imaging Spectroradiometer (MODIS)-derived aerosol optical depth (AOD) over East Asia were analyzed in conjunction with US EPA Models-3/CMAQ v4.3 modeling. In this study, two MODIS AOD products (τMODIS: τM-BAER and τNASA) retrieved through a modified Bremen Aerosol Retrieval (M-BAER) algorithm and NASA collection 5 (C005) algorithm were compared with the AOD (τCMAQ) that was calculated from the US EPA Models-3/CMAQ model simulations. In general, the CMAQ-predicted AOD values captured the spatial and temporal variations of the two MODIS AOD products over East Asia reasonably well. Since τMODIS cannot provide information on the aerosol chemical composition in the atmosphere, different aerosol formation characteristics in different regions and different seasons in East Asia cannot be described or identified by τMODIS itself. Therefore, the seasonally and regionally varying aerosol formation and distribution characteristics were investigated by the US EPA Models-3/CMAQ v4.3 model simulations. The contribution of each particulate chemical species to τMODIS and τCMAQ showed strong spatial, temporal and seasonal variations. For example, during the summer episode, τMODIS and τCMAQ were mainly raised due to high concentrations of (NH4)2SO4 over Chinese urban and industrial centers and secondary organic aerosols (SOAs) over the southern parts of China, whereas during the late fall and winter episodes, τMODIS and τCMAQ were higher due largely to high levels of NH4NO3 formed over the urban and industrial centers, as well as in areas with high NH3 emissions. τCMAQ was in general larger than τMODIS during the year, except for spring. The high biases (τCMAQ&gt;τMODIS) may be due to the excessive formation of both (NH4)2SO4 (summer episode) and NH4NO3 (fall and winter episodes) over China, possibly from the use of overestimated values for NH3 emissions in the CMAQ modeling. According to CMAQ modeling, particulate NH4NO3 made a 14% (summer) to 54% (winter) contribution to σext and τCMAQ. Therefore, the importance of NH4NO3 in estimating τ should not be ignored, particularly in studies of the East Asian air quality. In addition, the accuracy of τM-BAER and τNASA was evaluated by a comparison with the AOD (τAERONET) from the AERONET sites in East Asia. Both τM-BAER and τNASA showed a strong correlation with τAERONET around the 1:1 line (R=0.79), indicating promising potential for the application of both the M-BAER and NASA aerosol retrieval algorithms to satellite-based air quality monitoring studies in East Asia.