AERONET AOD Research Articles

Retrieving hourly seamless PM2.5 concentration across China with physically informed spatiotemporal connection

Seamless hourly surface fine particulate matters (PM2.5) datasets are urgently needed for environmental and epidemiologic studies. Most existing models rely on satellite AOD with spatial gaps and the time-geolocation information not directly physically related to ground-level PM2.5 as input, leading to several problems, e.g., for daytime only, spatial discontinuity. By exploring spatiotemporal connection, here we propose a novel framework for improving reanalysis AOD with satellite AOD and retrieving seamless PM2.5 concentration. With the wavelet decomposition on the meteorological / AOD time-series and the machine learning algorithm ‘CatBoost’, the grid cells are well connected based on the meteorological/AOD similarity (quasi-Lagrangian description), rather than the spatial distance (Eulerian description). Our new AOD dataset (compared with AERONET AOD, R2 = 0.60, RMSE = 0.19) combines the advantages of reanalysis AOD (all-day hourly; seamless) and satellite AOD (high accuracy and spatial resolution). The new PM2.5 estimation strategy performed excellent in cross-validation (spatial CV R2/RMSE = 0.84/14.57 μg/m3) and mapping (no spatial discontinuity). With the help of new AOD dataset, the PM2.5 estimation exhibits significant improvement (R2: increased from 0.60 to 0.65, RMSE: reduced from 21.52 to 20.10 μg/m3) on retrieving PM2.5 at ground monitoring site sparse area (the spatial distance to the closest site is over 20 km). Such high temporal-resolved PM2.5 prediction improved daily estimates of PM2.5 as it leveraged the additional hourly information to well capture the transport pattern, with the increased R2 (from 0.72 to 0.77) and decreased RMSE (from 15.54 to 14.30 μg/m3) at ground monitoring site sparse area. The annual averaged PM2.5 estimated by hourly model also exhibit noticeable differences (∼6%) from that estimated with daily model, implying the latter might suffer overestimation (∼3%) in population-weighted PM2.5 exposure across China. In addition, our new PM2.5 dataset shows the Chinese average PM2.5 is highest around 24:00, lowest around 17:00.Synopsis: By performing the wavelet decomposition on meteorological factors/AOD (independent variables), the hourly seamless PM2.5 concentration across China is well estimated with improved spatiotemporal connection.

Validation and diurnal variation evaluation of MERRA-2 multiple aerosol properties on a global scale

The Modern-Era Retrospective analysis for Research and Applications (MERRA-2) provides a spatiotemporal seamless simulation of global aerosol properties. This study focused on evaluating its diurnal AOD accuracy and variation and the performance of multi-aerosol properties (including Ångström exponent (AE), fine mode fraction (FMF), and single scattering albedo (SSA)) using AERONET measurements. The spatial distribution patterns of MERRA-2 AOD are compared with the MODIS (daytime) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO, nighttime) satellite products, respectively. The results show that MERRA-2 AOD exhibits good agreement with AERONET AOD during daytime and nighttime, with a Pearson correlation coefficient (R) exceeding 0.785, and the fraction of meeting the expected error (EE) exceeding 0.705. The accuracy of daytime AOD is comparable to that of the MODIS AOD product. Furthermore, MERRA-2 AE and FMF have a higher accuracy than MODIS products, with an R > 0.840 and an RMSE of 0.313 for AE and 0.150 for FMF. And it effectively captures the diurnal variation of aerosol properties, with an R of 0.911, 0.392, and 0.72 for AOD, AE, and FMF at different times of day compared to AERONET. Additionally, MERRA-2's ability to characterize the spatial patterns of daytime and nighttime is comparable to those of satellite products. Overall, this study provides the usage references for MERRA-2 aerosol product, highlighting its high accuracy and ability to capture diurnal variation and spatial patterns.

Estimation of Daily Seamless PM2.5 Concentrations with Climate Feature in Hubei Province, China

The urgent necessity for precise and uninterrupted PM2.5 datasets of high spatial–temporal resolution is underscored by the significant influence of PM2.5 on weather, climate, and human health. This study leverages the AOD reconstruction method to compensate for missing values in the MAIAC AOD throughout Hubei Province. The reconstructed AOD dataset, exhibiting an R2/RMSE of 0.76/0.18, compared to AERONET AOD, was subsequently used for PM2.5 estimation. Our research breaks from traditional methodologies that solely depend on latitude and longitude information. Instead, it emphasizes the use of climate feature as an input for estimating PM2.5 concentrations. This strategic approach prevents potential spatial discontinuities triggered by geolocation information (latitude and longitude), thus ensuring the precision of the PM2.5 estimation (sample/spatial CV R2 = 0.91/0.88). Moreover, we proposed a method for identifying the absolute feature importance of machine-learning models. Contrasted with the relative feature-importance property typical of machine-learning models (a minor difference in the order of top three between geolocation-based and climate-feature-based models, and the slight difference in the top three: 0.08%/0.17%), our method provides a more comprehensive explanation of the absolute significance of features to the model (maintaining the same order and a larger difference in the top three: 0.99%/0.72%). Crucially, our findings demonstrated that AOD reconstruction can mitigate the overestimation of annual mean PM2.5 concentrations (ranging from 0.52 to 9.28 µg/m3). In addition, the seamless PM2.5 dataset contributes to reducing the bias in exposure risk assessment (ranging from −0.11 to 9.81 µg/m3).

Retrieval uncertainty and consistency of Suomi-NPP VIIRS Deep Blue and Dark Target aerosol products under diverse aerosol loading scenarios over South Asia

Retrieval accuracy and stability of two operational aerosol retrieval algorithms, Deep Blue (DB) and Dark Target (DT), applied on Visible Infrared Imaging Radiometer Suite (VIIRS) on-board Suomi National Polar-orbiting Partnership (S-NPP) satellite were evaluated over South Asia. The region is reported to be highly challenging to accurate estimation of satellite-based aerosol optical properties due to variations in surface reflectance, complex aerosol system and regional meteorology. Performance of both algorithms were initially evaluated by comparing their ability to retrieve aerosol signal over the complex geographical region under specific air pollution emission scenario. Thereafter, retrieval accuracy was investigated against 10 AERONET sites across South Asia, selected based on their geography and predominance aerosol types, from year 2012–2021. Geo-spatial analysis indicates DB to efficiently retrieve fine aerosol features over bright arid surfaces, and for smoke/dust dominating events whereas DT was better to identify small fire events under dark vegetated surface. Both algorithms however, indicate unsatisfactory retrieval accuracy against AERONET having 56–59% of valid retrievals with high RMSE (0.30–0.33) and bias. Overall, DB slightly underpredicted AOD with −0.02 mean bias (MB) whereas DT overpredicted AOD (MB: 0.13), with seasonality in their retrieval efficiency against AERONET. Time-series analysis indicates stability in retrieving AOD and match-up number for both algorithms. Retrieval bias of DB and DT AOD against AERONET AOD under diverse aerosol loading, aerosol size, scattering/absorbing aerosol, and surface vegetation coverage scenarios revealed DT to be more influenced by these conditions. Error analysis indicates at low AOD (≤0.2), accuracy of both DB and DT were subject to underlying vegetation coverage. At AOD>0.2, DB performed well in retrieving coarse aerosols whereas DT was superior when fine aerosols dominated. Overall, accuracy of both VIIRS algorithms require further refinement to continue MODIS AOD legacy over South Asia.

Improvement of aerosol optical depth data for localized solar resource assessment

Solar irradiance, especially for direct normal irradiance (DNI), is sensitive to the atmospheric aerosols, which can strongly extinguish DNI over areas with elevated aerosol loadings. The National Solar Radiation Database (NSRDB), developed by the National Renewable Energy Laboratory (NREL), uses AOD from MERRA-2 reanalysis and is further downscaled from 0.5° to 2-km based on elevation. However, the elevation-based downscaling may not accurately represent AOD, especially over the areas with large AOD gradients. This study examined whether the 1-km MODIS MAIAC satellite-retrieved AOD product can better represent the AOD distribution and be used to improve solar irradiance assessment. We focused on areas with relatively high AOD over North America (California, and New York City, US, and Mexico City in Mexico, in particular). The evaluation of MAIAC AOD and MERRA-2 AOD against ground-truth AERONET AOD shows that MAIAC AOD exhibits smaller RMSE by about 0.05 and higher correlation coefficient by 0.1–0.6 than MERRA-2 AOD. In addition, MERRA-2 AOD shows larger negative bias. The simulated DNI using MAIAC AOD (DNIMAIAC) and using MERRA-2 AOD (DNINSRDB) were evaluated with DNI observations. The results show the performance of DNIMAIAC is better than that of DNINSRDB with smaller RMSE by 0.5 - 1.5%, smaller positive mean bias by 0.8 - 3.1 % and comparable correlation for the 3 sites analyzed. Overall, 1-km MAIAC AOD shows higher accuracy than 2-km elevation-based MERRA-2 AOD, leading to better performance of the simulated DNI using MAIAC AOD. Therefore, 1-km MAIAC AOD can be used to improve the accuracy of solar resource assessment.

Saharan Dust Storm Aerosol Characterization of the Event (9 to 13 May 2020) over European AERONET Sites

This research was aimed at investigating the Saharan dust cloud recorded on 11 and 12 May 2020, by AERONET AOD stations in Italy, Austria, Slovakia, Poland, Ukraine, and Romania and determining whether it affected the area of the Republic of Moldova. During this period, the Chisinau AERONET monitoring site was not operational. The incentive for the investigation was the discovery of a high sediment load in rainwater collected on 12 May 2020 in Pelinia, a village in the Dochia district of the Republic of Moldova, in the southeastern part of Europe (47.8780 latitude, 27.8344 longitude), which could have originated from the Saharan dust storm. Backward trajectory analysis with NOAA’s HYSPLIT model confirmed that the Saharan dust storm impacted the village of Pelinia. Scanning electron microscopy coupled with electron dispersive X-ray spectroscopy (SEM-EDS) and Fourier transform infrared spectroscopy (FTIR) analysis of Pelinia rainwater sediments confirmed the chemical composition and morphological structure of Saharan dust particles. The particle size of the sediments matched the measurements at the AOD stations at Timisoara and Magurele, supporting the suggestion that Saharan dust probably entered the Republic of Moldova from Romania. FTIR analysis identified chemical compounds such as carbon dioxide, carbonates, sulfates, ferrocyanides, and organics (amines, amides, polypeptides, imines, oximes, pyrroles, aldehydes, sulfoxides, sulfones, nitro-derivatives) that were adsorbed and/or absorbed from the atmosphere, consistent with Saharan dust aerosols. Bio-allergens such as pollen were detected in the SEM images, showing the role of Saharan dust in transporting and spreading this kind of biological material. This study highlights the risk of Saharan dust clouds to humans, animals, and plants, but also its potential benefits for agriculture when suitable conditions are met in this regard.

Aerosol characteristics from earth observation systems: A comprehensive investigation over South Asia (2000–2019)

The present study summarizes two decades (2000–2019) of climatology and trends in aerosol loading and optical properties using a high spatial resolution data obtained from NASA's MODIS MAIAC and MISR aerosol products supplemented by moderate resolution aerosol data from OMI sensor over South Asia (SA). MISR AOD showed good agreement against AERONET AOD with 68.68% of the retrievals falling within the expected error and high Pearson's correlation coefficient (R = 0.83). The 20 years geometric mean of MAIAC and MISR AOD revealed higher loading of aerosols over the Indo-Gangetic Plain (IGP) and Eastern coast of India by 30% to 44% compared to the mean AOD over the entire SA. The highest mean AOD under cloud-free conditions was noted during monsoon season, followed by pre-monsoon, post-monsoon, and winter. The high contribution of coarse-mode AOD (cAOD) mainly from natural aerosol emission and small-mode AOD (sAOD) from local anthropogenic emissions are the main driver to high AOD in monsoon and pre-monsoon seasons. Besides, the presence of high humidity during the monsoon season favors the hygroscopic growth of the particles and leads to higher AOD values over SA. The high spatial resolutions of MODIS/MAIAC and MISR aerosol products enabled the identification of previously unobserved aerosol hotspots over Bihar, West Bengal, and the eastern Indian coastal state of Odisha, which is mainly dominated by small aerosol particles. The contributions of smaller aerosol particles to the total aerosol loading were found to be higher during post-monsoon and winter over most states in India, Nepal, and Bangladesh. In contrast, the contribution of coarser particles was higher over Pakistan during pre-monsoon and monsoon seasons. Smaller particles were predominantly retrieved over the Indian states dominated by mining industries, including Jharkhand and Odisha. A typical dominance of absorbing carbonaceous aerosols was also noted over the northwestern region of IGP during post-monsoon, which otherwise was mainly affected by mixed dust aerosols and carbonaceous aerosols in pre-monsoon and monsoon seasons. A statistically significant positive temporal trend in AOD was observed for the whole study period, over most of the SA region, which was influenced by the increase in small particles over India and Bangladesh. Urban/industrial weakly absorbing aerosols were found to be the main contributor to a similarly positive trend over Central India and East coast Indian states. Overall, recent advancements in high spatial resolution satellite-based aerosol optical properties showed good potential to identify the aerosol hotspots and constrain aerosol types across a highly polluted SA region.

Spatio-temporal and synoptic changes in dust at the three islands in the Persian Gulf region

The occurrence of severe and inclusive dust storms has caused a lot of damage every year in the Persian Gulf region and it has destructive environmental effects in this area. The aim of this study is to investigate the monthly changes in the frequency of dust storms over Khark, Kish and Qeshm islands located in the Persian Gulf region during the 10-year period (2007–2017) using satellite and model observations. The results show that the highest dust frequency was observed over Qeshm Island followed by Kish and Khark Islands. The highest number of dusty days was reported in all three stations in 2008 and most of the dust events occurred in May and July as compared to other months. The average wind speed in these two months (May and July) shows that the wind directions are mostly from northern and northwestern areas. The dust AOD evaluation of eight dust prediction models shows that the models simulated AOD patterns vary differently from each other for a severe dust storm on July 14, 2014 in the study area. Among the eight models, DREAM8-MACC model has a better performance and has good agreement with satellite observations. The analysis showed a high correlation (R = 0.74) between AERONET and DREAM8-MACC model AOD at the observation sites.

Comparison of three models to estimate the slant path atmospheric attenuation in central receiver solar thermal plants under Indian climatic conditions

This paper compares three attenuation models for the distinct meteorological conditions of India and assesses the feasibility of using the satellite data to calculate the slant path extinction coefficient and the slant path transmissivity of the lower atmosphere. The attenuation models are compared for the sites of Pune, Kanpur, and Jaipur. The AERONET data is used with REST2 to model the direct normal irradiance and the total optical depth. The results indicate that the three models give similar transmissivity values for low aerosol concentrations. As the aerosol concentrations increase, the difference in the slant path transmissivity as calculated by two of the models increases with respect to the third reference model.The slant path transmissivity comparison shows that for AERONET AOD at 550 nm (AOD550) less than 0.3, the relative error in the transmissivity values estimated by two of the compared models with respect to the third model is not higher than 10 % for Pune, 18 % for Kanpur, and 12 % for Jaipur. The mean relative error in the slant path transmissivity values obtained from the reference model using MODIS data with respect to the values obtained using AERONET data is 4.8 %, 10.2 %, and 9.2 % for Pune, Kanpur, and Jaipur, respectively. A difference of 0.2 between AERONET AOD550 and MODIS AOD550 values resulted in a difference of 0.05–0.08 between the transmissivity values calculated by the reference model using AERONET AOD550 and using MODIS AOD550 for the three sites.

Climatology of aerosol optical depth over Eastern Europe based on 19 years (2000–2018) MODIS TERRA data

AbstractThis study investigates the spatial–temporal evolution of aerosols, their optical properties (aerosol optical depth [AOD] and ångström exponent [AE]) and the trend over a period of 19 years in the Eastern European countries. The data used for the study are from moderate‐resolution imaging spectroradiometer (MODIS) Terra Collection 6.1 aerosol products with the use of Dark Target algorithm for the period from 2000 to 2018. The satellite‐based AOD products were validated against the AERONET AOD measurements in four stations, located in different regions. The results demonstrate high coherence in the setting of high values of correlation (R = 0.8–0.9) and low values of root‐mean‐square error (RMSE [0.066–0.130]) and mean absolute error (MAE [0.048–0.067]). However, of the total amount of data from AERONET stations, only 68.9% of available data fall within expected error (EE) over land. Generally, all countries recorded a low AOD (83.2% less than 0.2) and high AE (82.5% higher than 1). Mean AOD and AE values varied from 0.17 ± 0.12 and 1.31 ± 0.27 (Russia) to 0.24 ± 0.14 and 1.31 ± 0.33 (Czech Republic). There was a gradual decrease in aerosol load in all countries, the highest tendencies of AOD reduction are observed in Czech Republic, Bulgaria, Slovakia and Hungary (by −0.0028, −0.0027, −0.0026 and −0.0025 per year), while the lowest tendencies of reduction are observed in Russia and Moldova (by −0.001 and −0.0006 per year). Seasonal averaged AOD values have maximum values in summer and minimum values in winter. We studied predominant aerosol types, which are based on AOD and AE interrelation, the results showed the predominance of clean continental and mixed aerosols over all countries of the region.

Exploring the trend, prediction and driving forces of aerosols using satellite and ground data, and implications for climate change mitigation

Human activities-related aerosol emissions and CO2 emissions originate from many of the common sources. Identifying the aerosol variations and the underling determinates can provide insights into united mitigation policy controls targeting on both aerosol pollution and climate change. Long-term trend analysis and modeling offers an effective way to fully appreciate how aerosols interlink with carbon cycle and climate change. This study analyzes the current trends, models the future predictions, and investigates potential driving forces of aerosol loading at six sites across North America and East Asia during 2003–2015. Satellite-retrieved MODIS Collection 6 retrievals and ground measurements derived from AERONET are used. Results show that there is a persistent decreasing trend in AOD for both MODIS data and AERONET data at three sites. Monthly and seasonal AOD variations reveal consistent aerosol patterns at sites along mid-latitudes. Regional differences caused by impacts of climatology and land cover types are observed for the selected sites. Statistical validation of time series ARIMA models indicates that the non-seasonal ARIMA model performs better for AERONET AOD data than for MODIS AOD data at most sites, suggesting the method works better for data with higher quality. The seasonal ARIMA model reproduces time series with distinct seasonal variations much more precisely. The reasonably predicted AOD values could provide reliable estimates to better inform the decision-making for sustainable environmental management. Drawn from aerosol pollution control strategies, it is suggested that the enforcement of regulations on emission sources and the initiative of reforestation on emission sinks could have potential implications for climate change mitigation.

Assesment and comparison of modis aod and ae product over Ilorin Aeronet station using the statistical analysis of empirical orthogonal function (EOF)

In this Paper, Empirical Orthogonal Function (EOF) was used to assess the MODIS C006 LV2 aerosol AOD and AE products, and compared the data with AERONET AOD and AE observations. The data were taken from an AERONET station at Ilorin, Nigeria which were obtained from (MAPSS) and were averaged monthly using Microsoft excel spread sheets. The data period for the two satellite data were from Dec 2004 to May 2015 It was observed from the graphical representation that the seasonal variation of AOD peaks during the dry season from Dec to Feb and reaches minimum during summer in August 2008.The comparisons showed both underestimations in MODIS AOD and considerable overestimations in the AE. On the EOF analysis it was observed that a good correlation between MODIS & AERONET AOD are observed on the correlation matrices in all the data. Lower correlation is only observed at Ilorin AERONET wavelength at 470nm (ILA470) with Ilorin 660nm (ILM660).On the total variance explained table for MODIS and AERONET AOD, it was observed that out of the 6 components put into the EOF, only one component was extracted with a very high percentage of 81.57% For MODIS and AERONET AE,two components were observed with a percentage of 83.95% These show that the level of similarities is very high for MODIS and AERONET AOD,and little variation was observed for MODIS and AERONET AE.Keywords: Aerosol optical depth, Angstrom exponent, Empirical orthogonal function,AERONET nd MODIS Satellite data.

Long term analysis of the columnar and surface aerosol relationship at an urban European coastal site

Simultaneous PM2.5, PM10 and columnar (ground and satellite based) AOD measurements have been analyzed at Burjassot site in the metropolitan area of Valencia (Spain) during the period 2007–2016. The site is representative of a south European city in the Western Mediterranean coastal area, influenced by local urban pollution but also from frequent Saharan dust events. First, multiannual statistics were performed to characterize the main aerosol burden characteristics. The averages and standard deviations resulted 18 ± 9 μg m−3, 25 ± 19 μg m−3, 0.15 ± 0.11, 0.23 ± 0.17, 0.19 ± 0.15 and 1.2 ± 0.3 for PM2.5, PM10, AERONET AOD, MODIS Terra AOD, MODIS Aqua AOD, and AERONET Ångström exponent AE, respectively. The AOD is always referred to 550 nm. PM10 and AOD showed seasonal patterns with maxima in summer and minima in winter. However, PM2.5 and AE did not show such an evident seasonality. The relationship between surface and columnar measurements show a poor correlation (r down to 0.30) for daily values, although the correlation increases to r up to 0.90 for yearly averages. The relationship between PM and AOD becomes more consistent when the databases are binned in intervals of 0.05 AOD. Results for AERONET and MODIS AOD are very similar, although for daily averages is slightly worse for satellite than ground based measurements. In order to explain some seasonality effects found, the mixing layer height has been included in the analysis. Results show that the correlation is maximum when PM2.5 is used and the mixing layer height is greater than 1 000 m (r > 0.90).

Aerosol pollution and its potential impacts on outdoor human thermal sensation: East Asian perspectives

Aerosols affect the insolation at ground and thus the Aerosol Optical Depth (AOD, a measure of aerosol pollution) plays an important role on the variation of the Physiological Equivalent Temperature (PET) at locations with different aerosol climatology. The aerosol effects upon PET were studied for the first time at four East Asian cities by coupling a radiative transfer model and a human thermal comfort model which were previously well evaluated. Evident with the MODIS and AERONET AOD observations, the aerosol pollution at Beijing and Seoul was higher than at Chiayi (Taiwan) and Hong Kong. Based on the AERONET data, with background AOD levels the selected temperate cities had similar clear-sky PET values especially during summertime, due to their locations at similar latitudes. This also applied to the sub-tropical cities. Increase in the AOD level to the seasonal average one led to an increase in diffuse solar radiation and in turn an increase in PET for people living in all the cities. However, the heavy aerosol loading environment in Beijing and Seoul in summertime (AODs > 3.0 in episodic situations) reduced the total radiative flux and thus PET values in the cities. On the contrary, relatively lower episodic AOD levels in Chiayi and Hong Kong led to strong diffuse and still strong direct radiative fluxes and resulted in higher PET values, relative to those with seasonal averaged AOD levels. People tended to feel from “hot” to “very hot” during summertime when the AOD reached their average levels from the background level. This implies that in future aerosol effects add further burden to the thermal environment apart from the effects of greenhouse gas-induced global warming. Understanding the interaction between ambient aerosols and outdoor thermal environment is an important first step for effective mitigation measures such as urban greening to reduce the risk of human heat stress. It is also critical to make cities more attractive and enhancing to human well-being to achieve enhancing sustainable urbanization as one of the principal goals for the Nature-based Solutions.

New customized methods for improvement of the MODIS C6 Dark Target and Deep Blue merged aerosol product

The Moderate resolution and Imaging Spectroradiometer (MODIS) level 2 operational aerosol products (MOD04/MYD04), based on the Dark Target (DT) and the enhanced Deep Blue (DB) algorithms, have been providing daily global aerosol information. The MOD04/MYD04 has different data coverage for the DT and the DB algorithms due to differences in their retrieval methods. Recently in the collection 6 (C6), a DT and DB merged (DTBC6) AOD product has been introduced based on different thresholds of the Normalized Difference Vegetation Index (NDVI) to improve the data coverage. The main objective of this study is to increase the data coverage and reduce the error in the merged DTBC6 AOD product by introducing three new customized methods (DTBMX): (i) DTBM1: “use an average of the DT and DB AOD retrievals or the available one for all the NDVI values ”, as it is independent of the NDVI values, (ii) DTBM2: “use an average of the DT and DB AOD retrievals or the available one for NDVI≥0.2, and use the DB retrievals for NDVI<0.2”, and (iii) DTBM3: “use AOD retrievals from the DB algorithm for NDVI>0.3, and use an average of the DT and DB retrievals or the available one for NDVI≤0.3”. Validation of the DTBMX is conducted at a global scale from 2004 to 2014 using AERONET AOD measurements from 68 sites located in Asia (9), Europe (22), Southern Africa (8), and North (23) and South America (8), and for comparison purpose, the DTBC6 is evaluated for the same period. Results showed that the number of coincident observations of the DTBMX methods compared to the DTBC6 is increased by 31%, 41% 30%, and 108% for Asian, European, Southern African, and North and South American sites, respectively. At global scale, the number of coincident observations are increased for the DTBM1, DTBM2, and DTBM3 from 29,088 for DTBC6, to 45,937, 45,028, 37,393 which are 58%, 57%, and 29%, respectively more than the for DTBC6 observations. For an equal number of coincident observations, the percentage of retrievals within the EE is increased by between 17% to 20% and the RMSE is decreased by up to 15% for the DTBMX methods, but R is the same as for the DTBC6. The percentage above the EE is also decreased by between 43% to 55% due to greater contribution of the DB retrievals. Overall, the performance of all the DTBMX methods is much better than the DTBC6, but the DTBM1 is the most robust as it is independent of NDVI values, and significantly increases the data coverage. Therefore, it can be used operationally for global merged AOD retrievals.

Vertical mass impact and features of Saharan dust intrusions derived from ground-based remote sensing in synergy with airborne in-situ measurements

A study of the vertical mass impact of Saharan dust intrusions is presented in this work. Simultaneous ground-based remote-sensing and airborne in-situ measurements performed during the AMISOC-TNF campaign over the Tenerife area (Canary Islands) in summertime from 01 July to 11 August 2013 were used for that purpose. A particular dusty (DD) case, associated to a progressively arriving dust intrusion lasting for two days on 31 July (weak incidence) and 01 August (strong incidence), is especially investigated. AERONET AOD and AEx values were ranging, respectively, from 0.2 to 1.4 and 0.35 to 0.05 along these two days. Vertical particle size distributions within fine and coarse modes (0.16–2.8 μm range) were obtained from aircraft aerosol spectrometer measurements. Extinction profiles and Lidar Ratio (LR) values were derived from MPLNET/Micro Pulse Lidar observations. MAXDOAS measurements were also used to retrieve the height-resolved aerosol extinction for evaluation purposes in comparison to Lidar-derived profiles. The synergy between Lidar observations and airborne measurements is established in terms of the Mass Extinction Efficiency (MEE) to calculate the vertical mass concentration of Saharan dust particles. Both the optical and microphysical profilings show dust particles mostly confined in a layer of 4.3 km thickness from 1.7 to 6 km height. LR ranged between 50 and 55 sr, typical values for Saharan dust particles. In addition, this 2-day dust event mostly affected the Free Troposphere (FT), being less intense in the Boundary Layer (BL). In particular, rather high Total Mass Concentrations (TMC) were found on the stronger DD day (01 August 2013): 124, 70 and 21 μg m−3 were estimated, respectively, at FT and BL altitudes and on the near-surface level. This dust impact was enhanced due to the increase of large particles affecting the FT, but also the BL, likely due to their gravitational settling. However, the use of an assumed averaged MEE value can be especially critical for estimating the mass concentration of particular layers. Moreover, the potential of MAXDOAS retrieval for aerosol extinction profiling is also evidenced by showing a relatively good agreement with the Lidar-derived extinction profiles, once a particular smoothing procedure is applied to Lidar measurements.

Validation and accuracy assessment of a Simplified Aerosol Retrieval Algorithm (SARA) over Beijing under low and high aerosol loadings and dust storms

An effective Simplified Aerosol Retrieval Algorithm (SARA) based on real viewing geometry and encompassing a wide range of aerosol conditions and types (ωo=0.30–1.0) was tested over Beijing, a city under the influence of both anthropogenic emissions and dust storms. The SARA AOD was retrieved at seven resolutions (500m, 1km, 2km, 4km, 6km, 8km, and 10km) using MODIS level 1 and 2 data products (MOD02, MOD03, and MOD09) for the years 2012 and 2013. For comparison purposes, the MODIS dark-target (DT) AOD observations at 10km resolution were obtained for the same time period. Both algorithms, SARA and MODIS DT were validated using Beijing_RADI and Beijing_CAMS AERONET AOD measurements and accuracy was evaluated using the Confidence Envelope of Expected Error (CEEE). The SARA AOD achieved very high correlation (0.972–0.994) with low Root Mean Square Error (RMSE~0.067–0.133) and very high accuracy (87–95%). In comparison, the MODIS DT algorithm overestimated AOD for both low and high AOD observations with large RMSE (0.115–0.342) and very low accuracy (20–52%). The robustness and accuracy of SARA and the MODIS DT algorithms for low (AOD<0.40) and high (AOD>0.40) aerosol loadings was evaluated using the Fraction of Expected Error (FEE). The SARA algorithm achieved 46–60% higher average accuracy than the MODIS DT algorithm indicating SARA's greater accuracy and reliability in AOD retrieval over Beijing under low and high aerosol loadings, including severe dust storms. The SARA algorithm is unique among satellite based AOD retrievals, in its ability to depict extreme dust storms (AOD~5.0) at fine spatial resolution (500m) over urban and suburban areas such as Beijing, XiangHe and the Bohai Sea, as is demonstrated by the dust storm of 17th April 2006.

Inter-comparison of seasonal variability and nonlinear trend between AERONET aerosol optical depth and PM10 mass concentrations in Hong Kong

Here we used Empirical Mode Decomposition (EMD) method to study seasonal variability and nonlinear trend of corrected AERONET Aerosol Optical Depth (AOD/Hi) and corrected PM10 mass concentrations (PM10×f(RH)) in Hong Kong during 2005–2011. AOD/Hi is highly correlated with PM10×f(RH) in semi-annual and annual time scales (with correlation coefficient 0.67 for semi-annual and 0.79 for annual components, 95% confidence interval). On the semi-annual scale, both AOD/Hi and PM10×f(RH) can capture the two maxima in March and October, respectively, with much stronger amplitude in March probably due to the long-range transport of dust storm. On the annual cycle, the AOD/Hi and PM10×f(RH), which are negatively correlated with the precipitation and solar radiation, vary coherently with the maxima in February. This annual peak occurs about one month earlier than the first peak of the semi-annual variability in March, but with only half amplitude. During 2005–2011, both AOD/Hi and PM10×f(RH) exhibit the pronounced decreasing trend with the mean rate of 14 μg m−3 per year for PM10×f(RH), which reflects the significant effects of the air pollution control policy in Hong Kong during the past decade. The nonlinear trend analysis indicates that the decreasing of PM10×f(RH) is slower than that of AOD/Hi when the AOD/Hi is less than 0.44 but becomes faster when the AOD/Hi exceeds 0.44. These results illustrate that the AERONET AOD can be used quantitatively to estimate local air-quality variability on the semi-annual, annual, and long-term trend time scales.

Trend analysis of the aerosol optical depth from fusion of MISR and MODIS retrievals over China

Atmospheric aerosol plays an important role in the climate change though direct and indirect processes. In order to evaluate the effects of aerosols on climate, it is necessary to have a research on their spatial and temporal distributions. Satellite aerosol remote sensing is a developing technology that may provide good temporal sampling and superior spatial coverage to study aerosols. The Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging Spectroradiometer (MISR) have provided aerosol observations since 2000, with large coverage and high accuracy. However, due to the complex surface, cloud contamination, and aerosol models used in the retrieving process, the uncertainties still exist in current satellite aerosol products. There are several observed differences in comparing the MISR and MODIS AOD data with the AERONET AOD. Combing multiple sensors could reduce uncertainties and improve observational accuracy. The validation results reveal that a better agreement between fusion AOD and AERONET AOD. The results confirm that the fusion AOD values are more accurate than single sensor. We have researched the trend analysis of the aerosol properties over China based on nine-year (2002-2010) fusion data. Compared with trend analysis in Jingjintang and Yangtze River Delta, the accuracy has increased by 5% and 3%, respectively. It is obvious that the increasing trend of the AOD occurred in Yangtze River Delta, where human activities may be the main source of the increasing AOD.

Application of spectral analysis techniques in the intercomparison of aerosol data. Part II: Using maximum covariance analysis to effectively compare spatiotemporal variability of satellite and AERONET measured aerosol optical depth

AbstractModerate Resolution Imaging SpectroRadiometer (MODIS) and Multi‐angle Imaging Spectroradiomater (MISR) provide regular aerosol observations with global coverage. It is essential to examine the coherency between space‐ and ground‐measured aerosol parameters in representing aerosol spatial and temporal variability, especially in the climate forcing and model validation context. In this paper, we introduce Maximum Covariance Analysis (MCA), also known as Singular Value Decomposition analysis as an effective way to compare correlated aerosol spatial and temporal patterns between satellite measurements and AERONET data. This technique not only successfully extracts the variability of major aerosol regimes but also allows the simultaneous examination of the aerosol variability both spatially and temporally. More importantly, it well accommodates the sparsely distributed AERONET data, for which other spectral decomposition methods, such as Principal Component Analysis, do not yield satisfactory results. The comparison shows overall good agreement between MODIS/MISR and AERONET AOD variability. The correlations between the first three modes of MCA results for both MODIS/AERONET and MISR/AERONET are above 0.8 for the full data set and above 0.75 for the AOD anomaly data. The correlations between MODIS and MISR modes are also quite high (&gt; 0.9). We also examine the extent of spatial agreement between satellite and AERONET AOD data at the selected stations. Some sites with disagreements in the MCA results, such as Kanpur, also have low spatial coherency. This should be associated partly with high AOD spatial variability and partly with uncertainties in satellite retrievals due to the seasonally varying aerosol types and surface properties.