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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Global Scale Attribution of Anthropogenic and Natural Dust Sources and their Emission Rates Based on MODIS Deep Blue Aerosol Products

Our understanding of the global dust cycle is limited by a dearth of information about dust sources, especially small-scale features which could account for a large fraction of global emissions. Here we present a global-scale high-resolution (0.1 deg) mapping of sources based on Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue estimates of dust optical depth in conjunction with other data sets including land use. We ascribe dust sources to natural and anthropogenic (primarily agricultural) origins, calculate their respective contributions to emissions, and extensively compare these products against literature. Natural dust sources globally account for 75% of emissions; anthropogenic sources account for 25%. North Africa accounts for 55% of global dust emissions with only 8% being anthropogenic, mostly from the Sahel. Elsewhere, anthropogenic dust emissions can be much higher (75% in Australia). Hydrologic dust sources (e.g., ephemeral water bodies) account for 31% worldwide; 15% of them are natural while 85% are anthropogenic. Globally, 20% of emissions are from vegetated surfaces, primarily desert shrublands and agricultural lands. Since anthropogenic dust sources are associated with land use and ephemeral water bodies, both in turn linked to the hydrological cycle, their emissions are affected by climate variability. Such changes in dust emissions can impact climate, air quality, and human health. Improved dust emission estimates will require a better mapping of threshold wind velocities, vegetation dynamics, and surface conditions (soil moisture and land use) especially in the sensitive regions identified here, as well as improved ability to address small-scale convective processes producing dust via cold pool (haboob) events frequent in monsoon regimes.

aerosols↗
NASA NTRS2012

Impacts of Cross-Platform Vicarious Calibration on the Deep Blue Aerosol Retrievals for Moderate Resolution Imaging Spectroradiometer Aboard Terra

The retrieval of aerosol properties from spaceborne sensors requires highly accurate and precise radiometric measurements, thus placing stringent requirements on sensor calibration and characterization. For the Terra/Moderate Resolution Imaging Spedroradiometer (MODIS), the characteristics of the detectors of certain bands, particularly band 8 [(B8); 412 nm], have changed significantly over time, leading to increased calibration uncertainty. In this paper, we explore a possibility of utilizing a cross-calibration method developed for characterizing the Terral MODIS detectors in the ocean bands by the National Aeronautics and Space Administration Ocean Biology Processing Group to improve aerosol retrieval over bright land surfaces. We found that the Terra/MODIS B8 reflectance corrected using the cross calibration method resulted in significant improvements for the retrieved aerosol optical thickness when compared with that from the Multi-angle Imaging Spectroradiometer, Aqua/MODIS, and the Aerosol Robotic Network. The method reported in this paper is implemented for the operational processing of the Terra/MODIS Deep Blue aerosol products.

Jeong, Myeong-Jae↗
NASA NTRS2007

SMART-COMMIT Observations and Deep-Blue Retrievals of Saharan Dust Properties during NAMMA

Monsoon rainfalls sustain the livelihood of more than half of the world's population. The interaction between natural/anthropogenic aerosols, clouds, and precipitation is a critical mechanism that drives the water cycle and fresh water distribution. Analyses of the longterm trend of July-August precipitation anomaly for the last 50 years in the 20" century depict that the largest regional precipitation deficit occurs over the Sahel, where the monsoon water cycle plays an important role. Thus, it is of paramount importance to study how dust aerosols, as well as air pollution and smoke, influence monsoon variability. The NASA African Monsoon Multidisciplinary Activities (NAMMA) was conducted during the international AMMA Special Observation Period (SOP-3) of September 2006 to better comprehend the key attributes of the Saharan Air Layer (SAL) and how they evolve from the source regions to the Atlantic Ocean. The SAL occurs during the late spring through early fall and originates as a result of low-level convergence induced by heat lows over the Sahara that lifts hot, dry, dust laden air aloft into a well mixed layer that extends up to 500mb. This is crucial for understanding the impact of SAL on the key atmospheric processes that determine precipitation over West Africa and tropical cyclogenesis. Results obtained from the synergy of satellite (Deep- Blue) and surface (SMART-COMMIT) observations will be presented and discussed how the physical, optical and radiative properties of the dust in the SAL evolve from the continental to the marine environment.

Tsay, Si-Chee↗
NASA NTRS2017

Seasonal Assessment and Classification of Aerosols Transported to Lahore Using AERONET and MODIS Deep Blue Retrievals

Daily measurements of aerosol optical depth (𝜏) and Ångstrom wavelength exponent (𝛼) acquired from Aerosol Robotic Network (AERONET) and Moderate Resolution Imaging Spectrometer (MODIS) are analysed over Lahore - an urban city of Pakistan (period: 2010-2014) to investigate contribution of different types of aerosols originating from both local and regional source locations. The obtained annual averages (mean+/-standard deviation) for AERONET retrievals of 𝜏 (500 nm) and 𝛼 (440-870 nm) are 0.68+/-0.37 (0.08-2.91) and 0.99+/-0.33 (0-1.8), respectively. Of all the sources, 61% are found within Pakistan, 11% in India, 19% in Afghanistan, 6% in Iran and 2% in Saudi Arabia with seasonal contributions of 35, 25, 23 and 17% in pre-monsoon, monsoon, winter and post-monsoon, respectively. The bimodal distributions of 𝛼 show dominance of coarse-mode particles during pre-monsoon, fine-mode particles during post-monsoon and presence of both coarse-mode and fine-mode particles during winter and monsoon with winter showing more fine-mode particles. Two broad classes of aerosols namely desert dust (DD) and biomass burning/urban industrial (BU) are identified with criteria, e.g. 𝜏 ≥0.3 and 𝛼 ≤0.75 indicating presence of DD while 𝜏 ≥0.2 and 𝛼 ≥1.15 indicating BU. The frequency of occurrence (FOO) of DD and BU aerosols is further identified by applying classification criteria over Aqua-MODIS deep blue retrievals. The FOO identifies anthropogenic activity on-going throughout the year, disrupted with DD aerosols only during pre-monsoon and monsoon. The maximum dust activity is seen over Indo-Gangetic plains (IGP) (localized maxima: 35-45%) and the Arabian peninsula (>55%) during pre-monsoon, while maximum BU aerosols are found over IGP, central and south-eastern plains of India and the state of Gujarat (localized maxima: >70%) in winter and post-monsoon.

Zafar, Qudsia↗
NASA NTRS2019

Two Decades Observing Smoke Above Clouds in the South-Eastern Atlantic Ocean: Deep Blue Algorithm Updates and Validation with ORACLES Field Campaign Data

This study presents and evaluates an updated algorithmfor quantification of absorbing aerosols above clouds(AACs) from passive satellite measurements. The focus isbiomass burning in the south-eastern Atlantic Ocean duringthe 2016 and 2017 ObseRvations of Aerosols above CLoudsand their intEractionS (ORACLES) field campaign deployments.The algorithm retrieves the above-cloud aerosoloptical depth (AOD) and underlying liquid cloud opticaldepth and is applied to measurements from the Sea-viewingWide Field-of-view Sensor (SeaWiFS), Moderate ResolutionImaging Spectroradiometer (MODIS), and Visible InfraredImaging Radiometer Suite (VIIRS) from 1997 to 2017. AirborneNASA Ames Spectrometers for Sky-Scanning, Sun-Tracking Atmospheric Research (4STAR) and NASA LangleyHigh Spectral Resolution Lidar 2 (HSRL2) data collectedduring ORACLES provide important validation for spectralAOD for MODIS and VIIRS; as the SeaWiFS missionended in 2010, it cannot be evaluated directly. The 4STARand HSRL2 comparisons are complementary and reveal performancegenerally in line with uncertainty estimates providedby the optimal estimation retrieval framework used. Atpresent the two MODIS-based data records seem the mostreliable, although there are differences between the deployments,which may indicate that the available data are not yetsufficient to provide a robust regional validation. Spatiotemporalpatterns in the data sets are similar, and the time seriesare very strongly correlated with each other (correlationcoefficients from 0.95 to 0.99). Offsets between the satellitedata sets are thought to be chiefly due to differences in absolutecalibration between the sensors. The available validationdata for this type of algorithm are limited to a small numberof field campaigns, and it is strongly recommended that suchairborne measurements continue to be made, both over thesouthern Atlantic Ocean and elsewhere.

Sayer, Andrew M.↗
NASA NTRS2020

Assessment of Natural and Anthropogenic Aerosol Air Pollution In the Middle East Using MERRA-2, CAMS Data Assimilation Products, and High-Resolution WRF-Chem Model Simulations

Modern-Era Retrospective analysis for Research and Applications v.2 (MERRA-2), Copernicus Atmosphere Monitoring Service Operational Analysis (CAMS-OA), and a high-resolution regional Weather Research and Forecasting model coupled with chemistry (WRF-Chem) were used to evaluate natural and anthropogenic particulate matter (PM) air pollution in the Middle East (ME) during 2015–2016. Two Moderate Resolution Imaging Spectrometer (MODIS) retrievals – combined product Deep Blue and Deep Target (MODIS-DB&DT) and Multi-Angle Implementation of Atmospheric Correction (MAIAC) – and Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) observations as well as in situ PM measurements for 2016 were used for validation of the WRF-Chem output and both assimilation products. MERRA-2 and CAMS-OA assimilate AOD observations. WRF-Chem is a free-running model, but dust emission in WRF-Chem is tuned to fit AOD and aerosol volume size distributions obtained from AERONET. MERRA-2 was used to construct WRF-Chem initial and boundary conditions both for meteorology and chemical and aerosol species. SO2 emissions in WRF-Chem are based on the novel OMI-HTAP SO2 emission dataset. The correlation with the AERONET AOD is highest for MERRA-2 (0.72–0.91), MAIAC (0.63–0.96), and CAMSOA (0.65–0.87), followed by MODIS-DB&DT (0.56–0.84) and WRF-Chem (0.43–0.85). However, CAMS-OA has a relatively high positive mean bias with respect to AERONET AOD. The spatial distributions of seasonally averaged AODs from WRF-Chem, assimilation products, and MAIAC are well correlated with MODIS-DB&DT AOD product. MAIAC has the highest correlation (R = 0.8), followed by MERRA-2 (R = 0.66), CAMS-OA (R = 0.65), and WRF-Chem (R = 0.61). WRF-Chem, MERRA-2, and MAIAC underestimate and CAMS-OA overestimates MODIS-DB&DT AOD. The simulated and observed PM concentrations might differ by a factor of 2 because it is more challenging for the model and the assimilation products to reproduce PM concentration measured within the city. Although aerosol fields in WRF-Chem and assimilation products are entirely consistent, WRF-Chem is preferable for analysis of regional air quality over the ME due to its higher spatial resolution and better SO2 emissions. The WRF-Chem’s PM background concentrations exceed the World Health Organization (WHO) guidelines over the entire ME. Mineral dust is the major contributor to PM (≈ 75%–95%) compared to other aerosol types. Near and downwind from the SO2 emission sources, non-dust aerosols (primarily sulfate) contribute up to 30% to PM(sub 2.5). The contribution of sea salt to PM in coastal regions can reach 5%. The contributions of organic matter, black carbon and organic carbon to PM over the Middle East are insignificant. In the major cities over the Arabian Peninsula, the 90th percentile of PM(sub 10) and PM(sub 2.5) (particles with diameters less than 10 and 2.5 μm, respectively) daily mean surface concentrations exceed the corresponding Kingdom of Saudi Arabia air quality limits. The contribution of the non-dust component to PM(sub 2.5) is < 25%, which limits the emission control effect on air quality. The mitigation of the dust effect on air quality requires the development of environment-based approaches like growing tree belts around the cities and enhancing in-city vegetation cover. The WRF-Chem configuration presented in this study could be a prototype of a future air quality forecast system that warns the population against air pollution hazards.

Alexander Ukhov↗
NASA NTRS2008

Long-term Satellite Observations of Asian Dust Storm: Source, Pathway, and Interannual Variability

Among the many components that contribute to air pollution, airborne mineral dust plays an important role due to its biogeochemical impact on the ecosystem and its radiative-forcing effect on the climate system. In East Asia, dust storms frequently accompany the cold and dry air masses that occur as part of springtime cold front systems. Outbreaks of Asian dust storms occur often in the arid and semi-arid areas of northwestern China -about 1.6x10(exp 6) square kilometers including the Gobi and Taklimakan deserts- with continuous expanding of spatial coverage. These airborne dust particles, originating in desert areas far from polluted regions, interact with anthropogenic sulfate and soot aerosols emitted from Chinese megacities during their transport over the mainland. Adding the intricate effects of clouds and marine aerosols, dust particles reaching the marine environment can have drastically different properties than those from their sources. Furthermore, these aerosols, once generated over the source regions, can be transported out of the boundary layer into the free troposphere and can travel thousands of kilometers across the Pacific into the United States and beyond. In this paper, we will demonstrate the capability of a new satellite algorithm to retrieve aerosol properties (e.g., optical thickness, single scattering albedo) over bright-reflecting surfaces such as urban areas and deserts. Such retrievals have been difficult to perform using previously available algorithms that use wavelengths from the mid-visible to the near IR because they have trouble separating the aerosol signal from the contribution due to the bright surface reflectance. This new algorithm, called Deep Blue, utilizes blue-wavelength measurements from instruments such as SeaWiFS and MODIS to infer the properties of aerosols, since the surface reflectance over land in the blue part of the spectrum is much lower than for longer wavelength channels. Reasonable agreements have been achieved between Deep Blue retrievals of aerosol optical thickness and those directly from AERONET sunphotometers over desert and semi-desert regions. New Deep Blue products will allow scientists to determine quantitatively the aerosol properties near sources using high spatial resolution measurements from SeaWiFS and MODIS-like instruments. Long-term satellite measurements (1998 - 2007) from SeaWiFS will be utilized to investigate the interannual variability of source, pathway, and dust loading associated with the Asian dust storm outbreaks. In addition, monthly averaged aerosol optical thickness during the springtime from SeaWiFS will also be compared with the MODIS Deep Blue products.

Hsu, N. Christina↗
NASA NTRS2008

Recent Progress in Characterization of Dust over Land Surfaces with Space-borne Passive Remote Sensing

Among the many components that contribute to air pollution, airborne mineral dust plays an important role due to its biogeochemical impact on the ecosystem and its radiative-forcing effect on the climate system. In East Asia, dust storms frequently accompany the cold and dry air masses that occur as part of springtime cold front systems. Outbreaks of Asian dust storms occur often in the arid and semi-arid areas of northwestern China -about 1.6x10(exp 6) square kilometers including the Gobi and Taklimakan deserts- with continuous expanding of spatial coverage. These airborne dust particles, originating in desert areas far from polluted regions, interact with anthropogenic sulfate and soot aerosols emitted from Chinese mega-cities during their transport over the mainland. Adding the intricate effects of clouds and marine aerosols, dust particles reaching the marine environment can have drastically different properties than those from their sources. Furthermore, these aerosols, once generated over the source regions, can be transported out of the boundary layer into the free troposphere and can travel thousands of kilometers across the Pacific into the United States and beyond. In this paper, we will demonstrate the capability of a new satellite algorithm to retrieve aerosol properties (e.g., optical thickness, single scattering albedo) over bright-reflecting surfaces such as urban areas and deserts. Such retrievals have been difficult to perform using previously available algorithms that use wavelengths from the mid-visible to the near IR because they have trouble separating the aerosol signal from the contribution due to the bright surface reflectance. This new algorithm, called Deep Blue, utilizes blue-wavelength measurements from instruments such as SeaWiFS and MODIS to infer the properties of aerosols, since the surface reflectance over land in the blue part of the spectrum is much lower than for longer wavelength channels. Reasonable agreements have been achieved between Deep Blue retrievals of aerosol optical thickness and those directly from AERONET sunphotometers over desert and semi-desert regions. New Deep Blue products will allow scientists to determine quantitatively the aerosol properties near sources using high spatial resolution measurements from SeaWiFS and MODIS-like instruments. Long-term satellite measurements (1998 - 2007) from SeaWiFS will be utilized to investigate the interannual variability of source, pathway, and dust loading associated with the Asian dust storm outbreaks. In addition, monthly averaged aerosol optical thickness during the springtime from SeaWiFS will also be compared with the MODIS Deep Blue products.

Hsu, N. Christina↗
NASA NTRS2011

Validating and Improving Long-Term Aerosol Data Records from SeaWiFS

Natural and anthropogenic aerosols influence the radiative balance of the Earth through direct and indirect interactions with incoming solar radiation. However, the quantification of these interactions and their ultimate effect on the Earth's climate still have large uncertainties. This is partly due to the limitations of current satellite data records which include short satellite lifetimes, retrieval algorithm uncertainty, or insufficient calibration accuracy. We have taken the first steps in overcoming this hurdle with the production and public release of an aerosol data record using the radiances from the Sea-viewing Wide Field-of-View Sensor (Sea WiFS). Sea WiFS was launched in late 1997 and provided exceptionally well-calibrated top-of-atmosphere radiance data until December 2010, more than 13 years. We have partnered this data with an expanded Deep Blue aerosol retrieval algorithm. In accordance with Deep Blue's original focus, the latest algorithm retrieves aerosol properties not only over bright desert surfaces, but also over oceans and vegetated surfaces. With this combination of a long time series and global algorithm, we can finally identify the changing patterns of regional aerosol loading and provide insight into longterm variability and trends of aerosols on regional and global scales. In this work, we provide an introduction to Sea WiFS, the current algorithms, and our aerosol data records. We have validated the data over land and ocean with ground measurements from the Aerosol Robotic Network (AERONET) and compared them with other satellites such as MODIS and MISR. Looking ahead to the next data release, we will also provide details on the implemented and planned algorithm improvements, and subsequent validation results.

Bettenhausen, Corey↗
NASA NTRS2020

Aerosol Layer Height with Enhanced Spectral Coverage Achieved by Synergy between VIIRS and OMPS-NM Measurements

This letter presents a near production-ready algorithm to retrieve the height of biomass burning smoke and mineral dust aerosols as part of National Aeronautics and Space Administration (NASA)’s Deep Blue aerosol data product suite. It utilizes the enhanced spectral coverage achieved by using colocated data from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM), both aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite. In particular, the 412-nm top-of-atmosphere (TOA) reflectance from VIIRS and the ultraviolet aerosol index from OMPS-NM are used to determine the height and single-scattering albedo of the absorbing aerosols simultaneously. Constraints on aerosol optical depth at 550 nm and surface reflectance for the 412-nm band are provided by the operational VIIRS Deep Blue aerosol product. Wildfire smoke layer heights obtained from the algorithm over North America, where smoke plumes often stretched thousands of kilometers, are shown to agree with those from Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements, with an uncertainty generally within 1–1.5 km. This new height data set will be included in the upcoming Version 2 VIIRS Deep Blue aerosol product.

aerosol↗
NASA NTRS2020

Aerosol Layer Height With Enhanced Spectral Coverage Achieved by Synergy Between VIIRS and OMPS-NM Measurements

This study presents a near production-ready algorithm to retrieve the height of biomass burning smoke and mineral dust aerosols as part of NASA’s Deep Blue aerosol data product suite. It utilizes the enhanced spectral coverage achieved by using co-located data from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Ozone Mapping and Profiler Suite Nadir Mapper (OMPS-NM), both aboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite. Specifically, the 412 nm top-of-atmosphere reflectance from VIIRS and the ultraviolet aerosol index from OMPS-NM are used to determine the height and single-scattering albedo of the absorbing aerosols simultaneously. Constraints on aerosol optical depth at 550 nm and surface reflectance for the 412 nm band are provided by the operational VIIRS Deep Blue aerosol product. Wildfire smoke layer heights obtained from the algorithm over North America, where smoke plumes often stretched thousands of kilometers, are shown to agree with those from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) measurements, with an uncertainty generally within 1-1.5 km. This new height data set will be included in the upcoming Version 2 VIIRS Deep Blue aerosol product.

Aerosol↗
NASA NTRS2011

Comparative Analysis of Aerosol Retrievals from MODIS, OMI and MISR Over Sahara Region

MODIS is a wide field-of-view sensor providing daily global observations of the Earth. Currently, global MODIS aerosol retrievals over land are performed with the main Dark Target algorithm complimented with the Deep Blue (DB) Algorithm over bright deserts. The Dark Target algorithm relies on surface parameterization which relates reflectance in MODIS visible bands with the 2.1 micrometer region, whereas the Deep Blue algorithm uses an ancillary angular distribution model of surface reflectance developed from the time series of clear-sky MODIS observations. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm has been developed for MODIS. MAIAC uses a time series and an image based processing to perform simultaneous retrievals of aerosol properties and surface bidirectional reflectance. It is a generic algorithm which works over both dark vegetative surfaces and bright deserts and performs retrievals at 1 km resolution. In this work, we will provide a comparative analysis of DB, MAIAC, MISR and OMI aerosol products over bright deserts of northern Africa.

Lyapustin, A.↗
NASA NTRS2014

Assessment of OMI Near-UV Aerosol Optical Depth over Land

This is the first comprehensive assessment of the aerosol optical depth (AOD) product retrieved from the near-UV observations by the Ozone Monitoring Instrument (OMI) onboard the Aura satellite. The OMI-retrieved AOD by the ultraviolet (UV) aerosol algorithm (OMAERUV version 1.4.2) was evaluated using collocated Aerosol Robotic Network (AERONET) level 2.0 direct Sun AOD measurements over 8 years (2005-2012). A time series analysis of collocated satellite and ground-based AOD observations over 8 years shows no discernible drift in OMI's calibration. A rigorous validation analysis over 4 years (2005-2008) was carried out at 44 globally distributed AERONET land sites. The chosen locations are representative of major aerosol types such as smoke from biomass burning or wildfires, desert mineral dust, and urban/industrial pollutants. Correlation coefficient (p) values of 0.75 or better were obtained at 50 percent of the sites with about 33 percent of the sites in the analysis reporting regression line slope values larger than 0.70 but always less than unity. The combined AERONET-OMAERUV analysis of the 44 sites yielded a p of 0.81, slope of 0.79, Y intercept of 0.10, and 65 percent OMAERUV AOD falling within the expected uncertainty range (largest of 30 percent or 0.1) at 440 nanometers. The most accurate OMAERUV retrievals are reported over northern Africa locations where the predominant aerosol type is desert dust and cloud presence is less frequent. Reliable retrievals were documented at many sites characterized by urban-type aerosols with low to moderate AOD values, concentrated in the boundary layer. These results confirm that the near-ultraviolet observations are sensitive to the entire aerosol column. A simultaneous comparison of OMAERUV, Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue, and Multiangle Imaging Spectroradiometer (MISR) AOD retrievals to AERONET measurements was also carried out to evaluate the OMAERUV accuracy in relation to those of the standard aerosol satellite products. The outcome of the comparison indicates that OMAERUV, MODIS Deep Blue, and MISR retrieval accuracies in arid and semiarid environments are statistically comparable.

aerosol optical depth↗