Dust aerosol index (DAI) algorithm for MODIS

Ciren, Pubu; Kondragunta, Shobha

doi:10.1002/2013jd020855

Cited by 47 publications

(57 citation statements)

References 56 publications

(76 reference statements)

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“…We refer to AEt = 0.5 and 0.7 as Criteria 1 and 2, respectively. The value 1.0 is the largest AEt found in the publication [42] to our best knowledge to screen out fine-mode aerosols together with a second condition of AOD > 0.2. We refer to this method as Criterion 3.…”

Section: Methodsmentioning

confidence: 90%

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Analysis of Dust Aerosol Retrievals Using Satellite Data in Central Asia

Sokolik

2018

Atmosphere

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Several long-term monitoring of aerosol datasets from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board Terra/Aqua, Multi-angle Imaging SpectroRadiometer (MISR), Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) were used to derive the dust aerosol optical depth (DOD) in Central Asia based on the Angstrom exponent parameter and/or the particle shape. All sensors agree very well on the interannual variability of DOD. The seasonal analysis of DOD and dust occurrences identified the largest dust loading and the most frequent dust occurrence in the spring and summer, respectively. No significant trend was found during the research period in terms of both DOD and the dust occurrence. Further analysis of Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) aerosol products on a case-by-case basis in most dust months of 2007 suggested that the vertical structure is varying in terms of the extension and the dust loading from one event to another, although dust particles of most episodes have similar physical characteristics (particle shape and size). Our analysis on the vertical structure of dust plumes, the layer-integrated color ratio and depolarization ratio indicates a varied climate effect (e.g., the direct radiative impact) by mineral dust, dependent on the event being observed in Central Asia.

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Section: Methodsmentioning

confidence: 90%

“…For Central Asia, no previous studies are available as our reference. Therefore, a series of AEt values (0.5, 0.7, and 1.0) were used to make conclusions more robust, following Ciren et al [42]. We refer to AEt = 0.5 and 0.7 as Criteria 1 and 2, respectively.…”

Section: Methodsmentioning

confidence: 99%

Analysis of Dust Aerosol Retrievals Using Satellite Data in Central Asia

Sokolik

2018

Atmosphere

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“…The aerosol detection algorithm used in the case study was initially developed for the Visible Infrared Imaging Radiometer Suite (VIIRS), a key instrument on-board the Suomi National Polar-Orbiting Partnership (SNPP) spacecraft [58]. In the algorithm, the presence of absorbing aerosol is detected by a decrease in the spectral contrast between light reflected at two neighboring deep-blue wavelengths observed by VIIRS (412 nm and 440 nm) relative to a Rayleigh scattering only atmosphere.…”

Section: Overview Of the Viirs Aerosol Detection Algorithmmentioning

confidence: 99%

A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments

et al. 2018

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In the near future, there will be several new instruments measuring atmospheric composition from geostationary orbit over North America, East Asia, and Europe. This constellation of satellites will provide high resolution, time resolved measurements of trace gases and aerosols for monitoring air quality and tracking pollution sources. This paper describes a detailed, fast, and accurate (less than 1.0% uncertainty) method for calculating synthetic top of the atmosphere (TOA) radiances from a global simulation with a mesoscale free running model, the GEOS-5 Nature Run, for remote sensing instruments in geostationary orbit that measure in the ultraviolet-visible spectral range (UV-Vis). Generating these synthetic observations is the first step of an Observing System Simulation Experiment (OSSE), a framework for evaluating the impact of a new observation or algorithm. This paper provides details of the model sampling, aerosol and cloud optical properties, surface reflectance modeling, Rayleigh scattering calculations, and a discussion of the uncertainties of the simulated TOA radiance. An application for the simulated TOA radiance observations is demonstrated in the manuscript. Simulated TEMPO (Tropospheric Emissions: Monitoring of Pollution) and GOES-R (Geostationary Operational Environmental Satellites) observations were used to show how observations from the two instruments could be combined to facilitate aerosol type discrimination. The results demonstrate the viability of a detailed instrument simulator for radiance measurements in the UV-Vis that is capable of accurately simulating high resolution, time-resolved measurements with reasonable computational efficiency.Atmosphere 2019, 10, 2 2 of 36 spectral range (UV-Vis). The large-scale, time-resolved measurements of these pollutants provided by the geostationary constellation are important for managing air quality, and understanding how human activity impacts local, regional, and global distributions of pollution, as well as global climate.Aerosols are one of the most important forcing agents in the climate system. They can be transported around the globe [4], and interact with weather systems [5], scatter and absorb radiation [6][7][8], and modify cloud properties [9,10]. Routine measurements of various aerosol parameters, such as aerosol optical depth (AOD), absorbing aerosol optical depth (AAOD), aerosol index (AI), and single scattering albedo (SSA), are currently available from several sensors in low earth orbit (LEO). These sensors include the twin MODIS (Moderate Resolution Imaging Spectroradiometer) instruments (on-board Terra and Aqua), MISR (Multi-angle Imaging SpectroRadiometer), OMI (Ozone Monitoring Instrument), and POLDER (Polarization and Directionality of the Earth Reflectances) (see Table 1 in [11]). In addition, CALIOP, a spaceborne LiDAR, measures the vertical distribution of aerosol backscattering.Instruments in LEO provide one or two measurements per day, but as aerosol parameters cannot be retrieved under cloudy conditions there can be a l...

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“…One is a clean atmosphere case (26 April 2012), and another case involves dust over the northern part of the Yellow Sea (27 April 2012). To compare the sensitivity between pixels over turbid water and those with absorbing aerosol, the Deep Blue Aerosol Index (DAI) is calculated using GOCI TOA reflectance at 412 and 443 nm (Hsu et al, 2004(Hsu et al, , 2006Ciren and Kondragunta, 2014). Note that DAI and ρ 660 are plotted over cloud-free pixels, and only positive DAI pixels are presented to check the existence of absorbing aerosol such as dust in Fig.…”

Section: Turbid Water Detectionmentioning

confidence: 99%

“…Note that DAI and ρ 660 are plotted over cloud-free pixels, and only positive DAI pixels are presented to check the existence of absorbing aerosol such as dust in Fig. 4e and f, because absorbing aerosol such as dust or smoke shows a DAI greater than 4 over ocean (Ciren and Kondragunta, 2014). The true color image for the clean case shows severe turbidity in the ocean along the coast of eastern China and the western Korean Peninsula.…”

Section: Turbid Water Detectionmentioning

confidence: 99%

GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during the DRAGON-NE Asia 2012 campaign

et al. 2016

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Abstract. 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 (DRAGON-NE 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, Ångström 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 × 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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Dust aerosol index (DAI) algorithm for MODIS

Cited by 47 publications

References 56 publications

Analysis of Dust Aerosol Retrievals Using Satellite Data in Central Asia

Analysis of Dust Aerosol Retrievals Using Satellite Data in Central Asia

A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments

GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during the DRAGON-NE Asia 2012 campaign

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