The Deep Blue (DB) algorithm has been used to retrieve aerosol optical depth (AOD) and Ångström exponent (AE) over land from multiple satellite instruments, including the Moderate Resolution Imaging ...Spectroradiometers (MODIS) aboard the Terra and Aqua platforms and the Visible Infrared Imaging Radiometer Suite (VIIRS). This study first validates the latest MODIS (Collection 6.1) and VIIRS (Version 1) DB data products against Aerosol Robotic Network observations. On global average, the typical level of uncertainty in AOD is slightly better than ±(0.05 + 20%) relative to Aerosol Robotic Network. AE is quantitatively more uncertain but qualitatively shows skill at distinguishing between fine‐mode and coarse‐mode dominated aerosol columns. Results are also compared with the previous MODIS Collection 6. The stability of the three DB data sets ranges from 0.005–0.01 AOD per decade. Second, spatial and temporal patterns in AOD and AE are compared between the three data sets. It is found that they all show similar patterns of spatial coverage, which is predominantly linked to cloud cover, snow, and polar night. Regional time series of AOD also show highly consistent seasonal and interannual variations and are strongly correlated, although have offsets in some regions due to a combination of algorithmic and sensor‐related differences.
Plain Language Summary
Aerosols are small particles in the atmosphere like desert dust, volcanic ash, smoke, industrial haze, and sea spray. Understanding them is important for applications such as hazard avoidance, air quality and human health, and climate studies. Satellite instruments provide an important tool to study aerosol loading over the world. However, individual satellites do not last forever, and newer satellites often have improved capabilities compared to older ones. This paper evaluates the latest version of the Deep Blue algorithm for monitoring aerosols as applied to the Moderate Resolution Imaging Spectroradiometers (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) satellite instruments. The two MODIS sensors provide data from 2000 and 2002 onward, while the first VIIRS was launched in late 2011, and VIIRS will carry on the MODIS data records into the future. The evaluation is performed by comparing to ground‐truth data which are part of (National Aeronautics and Space Administration) NASA's global Aerosol Robotic Network. The stability in time and consistency between the MODIS and VIIRS data sets are also examined.
Key Points
VIIRS and MODIS Deep Blue show very similar validation results against AERONET
Decadal stability in retrieved AOD is about 0.01 per decade or better
The data sets show consistent seasonal and interannual variations in regional AOD
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
A unified satellite algorithm is presented to simultaneously retrieve aerosol properties (aerosol optical depth; AOD and aerosol type) and clear-sky shortwave direct radiative effect (hereafter, ...DREA) over ocean. The algorithm is applied to Moderate Resolution Imaging spectroradiometer (MODIS) observations for a period from 2003 to 2010 to assess the DREA over the global ocean. The simultaneous retrieval utilizes lookup table (LUT) containing both spectral reflectances and solar irradiances calculated using a single radiative transfer model with the same aerosol input data. This study finds that aerosols cool the top-of-atmosphere (TOA) and bottom-of-atmosphere (BOA) by 5.2 ± 0.5 W/m2 and 8.3 W/m2, respectively, and correspondingly warm the atmosphere (hereafter, ATM) by 3.1 W/m2. These quantities, solely based on the MODIS observations, are consistent with those of previous studies incorporating chemical transport model simulations and satellite observations. However, the DREAs at BOA and ATM are expected to be less accurate compared to that of TOA due to low sensitivity in retrieving aerosol type information, which is related with the atmospheric heating by aerosols, particularly in low AOD conditions; consequently, the uncertainties could not be quantified. Despite the issue in the aerosol type information, the present method allows us to confine the DREA attributed only to fine-mode dominant aerosols, which are expected to be mostly anthropogenic origin, in the range from −1.1 W/m2 to −1.3 W/m2 at TOA. Improvements in size-resolved AOD and SSA retrievals from current and upcoming satellite instruments are suggested to better assess the DREA, particularly at BOA and ATM, where aerosol absorptivity induces substantial uncertainty.
•Clear-sky direct radiative effects (DREAs) of different aerosol types are quantified from satellite data.•Unified algorithm is developed to simultaneously retrieve aerosol properties and DREA.•New DREA values of different aerosol types provide valuable dataset for model comparisons.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
A new dust detection algorithm is developed by combining the results of multiple dust detection methods using IR channels onboard the MODerate resolution Imaging Spectroradiometer (MODIS). Brightness ...Temperature Difference (BTD) between two wavelength channels has been used widely in previous dust detection methods. However, BTD methods have limitations in identifying the offset values of the BTD to discriminate clear-sky areas. The current algorithm overcomes the disadvantages of previous dust detection methods by considering the Brightness Temperature Ratio (BTR) values of the dual wavelength channels with 30-day composite, the optical properties of the dust particles, the variability of surface properties, and the cloud contamination. Therefore, the current algorithm shows improvements in detecting the dust loaded region over land during daytime. Finally, the confidence index of the current dust algorithm is shown in 10×10pixels of the MODIS observations. From January to June, 2006, the results of the current algorithm are within 64 to 81% of those found using the fine mode fraction (FMF) and aerosol index (AI) from the MODIS and Ozone Monitoring Instrument (OMI). The agreement between the results of the current algorithm and the OMI AI over the non-polluted land also ranges from 60 to 67% to avoid errors due to the anthropogenic aerosol. In addition, the developed algorithm shows statistically significant results at four AErosol RObotic NETwork (AERONET) sites in East Asia.
•An improved algorithm to detect dust from satellite remote sensing using IR•Combines new and existing algorithm to improve the accuracy of dust detection•Thorough evaluation of results with previous methods•New cloud masking algorithm using the correlation between two different IR channels
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
The Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean, and Meteorological Satellite (COMS) is the first multi-channel ocean color imager in geostationary orbit. Hourly GOCI ...top-of-atmosphere radiance has been available for the retrieval of aerosol optical properties over East Asia since March 2011. This study presents improvements made to the GOCI Yonsei Aerosol Retrieval (YAER) algorithm together with validation results during the Distributed Regional Aerosol Gridded Observation Networks - Northeast Asia 2012 campaign (DRAGONNE Asia 2012 campaign). The evaluation during the spring season over East Asia is important because of high aerosol concentrations and diverse types of Asian dust and haze. Optical properties of aerosol are retrieved from the GOCI YAER algorithm including aerosol optical depth (AOD) at 550 nm, fine-mode fraction (FMF) at 550 nm, single-scattering albedo (SSA) at 440 nm, Angstrom exponent (AE) between 440 and 860 nm, and aerosol type. The aerosol models are created based on a global analysis of the Aerosol Robotic Networks (AERONET) inversion data, and covers a broad range of size distribution and absorptivity, including nonspherical dust properties. The Cox-Munk ocean bidirectional reflectance distribution function (BRDF) model is used over ocean, and an improved minimum reflectance technique is used over land. Because turbid water is persistent over the Yellow Sea, the land algorithm is used for such cases. The aerosol products are evaluated against AERONET observations and MODIS Collection 6 aerosol products retrieved from Dark Target (DT) and Deep Blue (DB) algorithms during the DRAGON-NE Asia 2012 campaign conducted from March to May 2012. Comparison of AOD from GOCI and AERONET resulted in a Pearson correlation coefficient of 0.881 and a linear regression equation with GOCI AOD = 1.083 x AERONET AOD - 0.042. The correlation between GOCI and MODIS AODs is higher over ocean than land. GOCI AOD shows better agreement with MODIS DB than MODIS DT. The other GOCI YAER products (AE, FMF, and SSA) show lower correlation with AERONET than AOD, but still show some skills for qualitative use.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
The Atmospheric Brown Cloud–East Asia Regional Experiment (ABC‐EAREX) was conducted under the UNEP/ABC‐Asia project to intercompare the aerosol and gas measurements in springtime from various ...instruments from late February to April 2005 at the Gosan Supersite on Jeju Island, Korea. Satellite instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Ozone Monitoring Instrument (OMI) provide a large‐scale regional view of the aerosol during the ABC‐EAREX period. This study shows the temporal and spatial distribution of four major aerosol types (dust, carbonaceous, sea salt and sulfate) retrieved by MODIS‐OMI Algorithm and Four‐Channel Algorithm utilizing data from MODIS and OMI over east Asia during the ABC‐EAREX campaign. Results from two different retrieval show that a complexity of aerosol types and sources exist over east Asia: Some aerosols are emitted while others are transported. Nevertheless, the results show reasonable consistency in the distribution according to aerosol type. The agreement of aerosol type classification for each aerosol type ranges from 32% to 81% depending on the type. These results were compared with the results from a three‐dimensional aerosol transport radiation model, SPRINTARS. Dust type aerosol is usually found to be mixed with carbonaceous type aerosol. It implies that the dust type aerosol is loaded and transported with polluted air mass. The evidence that polluted air masses in the continent can be transported long distance is also captured; that is, sea salt type mixed with the sulfate aerosol is detected over a remote ocean.
Despite continuous efforts to retrieve aerosol optical depth (AOD) using a conventional 5-channel meteorological imager in geostationary orbit, the accuracy in urban areas has been poorer than other ...areas primarily due to complex urban surface properties and mixed aerosol types from different emission sources. The two largest error sources in aerosol retrieval have been aerosol type selection and surface reflectance. In selecting the aerosol type from a single visible channel, the season-dependent aerosol optical properties were adopted from long-term measurements of Aerosol Robotic Network (AERONET) sun-photometers. With the aerosol optical properties obtained from the AERONET inversion data, look-up tables were calculated by using a radiative transfer code: the Second Simulation of the Satellite Signal in the Solar Spectrum (6S). Surface reflectance was estimated using the clear sky composite method, a widely used technique for geostationary retrievals. Over East Asia, the AOD retrieved from the Meteorological Imager showed good agreement, although the values were affected by cloud contamination errors. However, the conventional retrieval of the AOD over Hong Kong was largely underestimated due to the lack of information on the aerosol type and surface properties. To detect spatial and temporal variation of aerosol type over the area, the critical reflectance method, a technique to retrieve single scattering albedo (SSA), was applied. Additionally, the background aerosol effect was corrected to improve the accuracy of the surface reflectance over Hong Kong. The AOD retrieved from a modified algorithm was compared to the collocated data measured by AERONET in Hong Kong. The comparison showed that the new aerosol type selection using the critical reflectance and the corrected surface reflectance significantly improved the accuracy of AODs in Hong Kong areas, with a correlation coefficient increase from 0.65 to 0.76 and a regression line change from τMI basic algorithm=0.41τAERONET+0.16 to τMI new algorithm=0.70τAERONET+0.01.
•New adaptive dynamic aerosol models optimized over Asia from the AERONET•Improved aerosol algorithm from a single visible channel of geostationary satellite•Combines techniques to improve the accuracy of aerosol retrieval over urban area
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
An aerosol retrieval algorithm for the first Geostationary Ocean Color Imager (GOCI) to be launched in March 2010 onboard the Communication, Ocean, and Meteorological Satellite (COMS) is presented. ...The algorithm retrieves aerosol optical depth (AOD), fine-mode fraction (FMF), and aerosol type in 500
m
×
500
m resolution. All the products are retrieved over clear water which is defined by surface reflectance ratio between 640
nm and 860
nm (SRR) less or equal to 2.5, while only AOD is retrieved over turbid water (SRR
>
2.5) due to high surface reflectance. To develop optimized algorithm for the target area of GOCI, optical properties of aerosol are analyzed from extensive observation of AERONET sunphotometers to generate lookup table. Surface reflectance of turbid water is determined from 30-day composite of Rayleigh- and gas corrected reflectance. By applying the present algorithm to MODIS top-of-the atmosphere reflectance, three different aerosol cases dominated by anthropogenic aerosol contains black carbon (BC), dust, and non-absorbing aerosol are analyzed to test the algorithm. The algorithm retrieves AOD, and size information together with aerosol type which are consistent with results inferred by RGB image in a qualitative way. The comparison of the retrieved AOD with those of MODIS collection 5 and AERONET sunphotometer observations shows reliable results. Especially, the application of turbid water algorithm significantly increases the accuracy in retrieving AOD at Anmyon station. The sensitivity study between MODIS and GOCI instruments in terms of relative sensitivity and scattering angle shows promising applicability of the present algorithm to future GOCI measurements.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Gas flaring is a common practice in the oil industry that can have significant environmental impacts, but has until recently been largely overlooked in terms of relevance to climate change. We ...utilize data from various satellite sensors to examine pollutant emissions from oil exploitation activities in four areas near the Arctic. Despite the remoteness of these sparsely populated areas, tropospheric NO2 retrieved from the Ozone Monitoring Instrument (OMI) is substantial at ∼1 × 1015 molecules cm−2, suggesting sizeable emissions from these industrial activities. Statistically significant (at the 95% confidence level, corresponding uncertainties in parentheses) increasing trends of 0.017 (±0.01) × 1015 and 0.015 (±0.006) × 1015 molecules cm−2 year−1 over 2004–2015 were found for Bakken (USA) and Athabasca (Canada), two areas having recently experienced fast expansion in the oil industry. This rapid change has implications for emission inventories, which are updated less frequently. No significant trend was found for the North Sea (Europe), where oil production has been declining since the 1990s. For northern Russia, the trend was just under the 95% significance threshold at 0.0057 (±0.006) × 1015 molecules cm−2 year−1. This raises an interesting inconsistency as prior studies have suggested that, in contrast to the continued, albeit slow, expansion of Russian oil/gas production, gas flaring in Russia has decreased in recent years. However, only a fraction of oil fields in Russia were covered in our analysis. Satellite aerosol optical depth (AOD) data revealed similar tendencies, albeit at a weaker level of statistical significance, due to the longer lifetime of aerosols and contributions from other sources. This study demonstrates that synergetic use of data from multiple satellite sensors can provide valuable information on pollutant emission sources that is otherwise difficult to acquire.
•Gas flaring emissions from oil/gas fields near the Arctic studied with satellite data.•OMI NO2 data show evidence of enhanced NOx emissions due to gas flaring.•Growth of NO2 over two North American regions associated with boom in oil industry.•Lack of significant AOD trend highlights need for longer, high-quality measurements.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Aerosol Robotic Network (AERONET)‐based nonspherical dust optical models are developed and applied to the Satellite Ocean Aerosol Retrieval (SOAR) algorithm as part of the Version 1 Visible Infrared ...Imaging Radiometer Suite (VIIRS) NASA “Deep Blue” aerosol data product suite. The optical models are created using Version 2 AERONET inversion data at six distinct sites influenced frequently by dust aerosols from different source regions. The same spheroid shape distribution as used in the AERONET inversion algorithm is assumed to account for the nonspherical characteristics of mineral dust, which ensures the consistency between the bulk scattering properties of the developed optical models and the AERONET‐retrieved microphysical and optical properties. For the Version 1 SOAR aerosol product, the dust optical model representative for Capo Verde site is used, considering the strong influence of Saharan dust over the global ocean in terms of amount and spatial coverage. Comparisons of the VIIRS‐retrieved aerosol optical properties against AERONET direct‐Sun observations at five island/coastal sites suggest that the use of nonspherical dust optical models significantly improves the retrievals of aerosol optical depth (AOD) and Ångström exponent by mitigating the well‐known artifact of scattering angle dependence of the variables, which is observed when incorrectly assuming spherical dust. The resulting removal of these artifacts results in a more natural spatial pattern of AOD along the transport path of Saharan dust to the Atlantic Ocean; that is, AOD decreases with increasing distance transported, whereas the spherical assumption leads to a strong wave pattern due to the spurious scattering angle dependence of AOD.
Key Points
AERONET‐based nonspherical dust optical property models are developed for use in NASA's Deep Blue/SOAR aerosol algorithm
New dust optical model significantly improves SOAR aerosol product over dust layers
VIIRS Deep Blue algorithm suite will provide an enhanced view of global aerosol properties
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
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