Retrievals of Aerosol Optical Depth and Spectral Absorption From DSCOVR EPIC

Lyapustin, Alexei; Go, Sujung; Korkin, Sergey; Wang, Yujie; Torres, Omar; Jethva, Hiren; Marshak, Alexander

doi:10.3389/frsen.2021.645794

Cited by 24 publications

(30 citation statements)

References 52 publications

(65 reference statements)

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“…Additionally, the oxygen A and B bands are employed to retrieve smoke and dust aerosol layer heights with an RMSE ∼0.5 km (Xu et al, 2017(Xu et al, , 2019. Recently, Lyapustin et al (2021) have combined the retrievals of UV and VIS/NIR aerosol optical depths (AOD) and single scattering albedos (SSA) using an innovative retrieval algorithm that utilizes sequences of EPIC images. The initial results are quite promising for improved estimates of those parameters at all wavelengths and development of the algorithms continues (e.g., Sasi et al, 2020).…”

Section: Surface and Atmospheric Propertiesmentioning

confidence: 99%

Lagrange Point Missions: The Key to next Generation Integrated Earth Observations. DSCOVR Innovation

Valero

Marshak

Minnis

2021

Front. Remote Sens.

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A new perspective for studying Earth processes has been soundly demonstrated by the Deep Space Climate Observatory (DSCOVR) mission. For the past 6 years, the first Earth-observing satellite orbiting at the Lagrange 1 (L1) point, the DSCOVR satellite has been viewing the planet in a fundamentally different way compared to all other satellites. It is providing unique simultaneous observations of nearly the entire sunlit face of the Earth at a relatively high temporal resolution. This capability enables detailed coverage of evolving atmospheric and surface systems over meso- and large-scale domains, both individually and as a whole, from sunrise to sunset, under continuously changing illumination and viewing conditions. DSCOVR’s view also contains polar regions that are only partially seen from geostationary satellites (GEOs). To exploit this unique perspective, DSCOVR instruments provide multispectral imagery and measurements of the Earth’s reflected and emitted radiances from 0.2 to 100 µm. Data from these sensors have been and continue to be utilized for a great variety of research involving retrievals of atmospheric composition, aerosols, clouds, ocean, and vegetation properties; estimates of surface radiation and the top-of-atmosphere radiation budget; and determining exoplanet signatures. DSCOVR’s synoptic and high temporal resolution data encompass the areas observed during the day from low Earth orbiting satellites (LEOs) and GEOs along with occasional views of the Moon. Because the LEO and GEO measurements can be easily matched with simultaneous DSCOVR data, multiangle, multispectral datasets can be developed by integrating DSCOVR, LEO, and GEO data along with surface and airborne observations, when available. Such datasets can open the door for global application of algorithms heretofore limited to specific LEO satellites and development of new scientific tools for Earth sciences. The utility of the integrated datasets relies on accurate intercalibration of the observations, a process that can be facilitated by the DSCOVR views of the Moon, which serves as a stable reference. Because of their full-disc views, observatories at one or more Lagrange points can play a key role in next-generation integrated Earth observing systems.

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Section: Surface and Atmospheric Propertiesmentioning

confidence: 99%

Lagrange Point Missions: The Key to next Generation Integrated Earth Observations. DSCOVR Innovation

Valero

Marshak

Minnis

2021

Front. Remote Sens.

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“…Recently, we expanded the MAIAC EPIC algorithm to include simultaneous retrieval of AOD and spectral imaginary refractive index for the detected absorbing aerosols, e.g. biomass burning smoke and mineral dust (Lyapustin et al, 2021). Spectral absorption in MAIAC is represented by a power-law expression,…”

Section: V2 Maiac Epic Algorithmmentioning

confidence: 99%

“…MAIAC retrievals are reported for the effective aerosol heights of 1 km and 4 km, representing the typical boundary layer and free troposphere transported aerosol. (Lyapustin et al, 2021). The global AOD and SSA accuracy analysis of EPIC MAIAC for the entire mission period 2015-2021 will be published elsewhere.…”

Section: V2 Maiac Epic Algorithmmentioning

confidence: 99%

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Inferring iron oxides species content in atmospheric mineral dust from DSCOVR EPIC observations

Lyapustin

Schuster

et al. 2021

Preprint

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Abstract. The iron-oxide content of dust in the atmosphere and most notably its apportionment between hematite (α-Fe2O3) and goethite (α-FeOOH) are key determinants in quantifying dust's light absorption, its top of atmosphere UV radiances used for dust monitoring, and ultimately shortwave dust direct radiative effects (DRE). Hematite and goethite column mass concentrations and iron-oxide mass fractions of total dust mass concentration were retrieved from the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) measurements in the ultraviolet–visible (UV–Vis) channels. The retrievals were performed for dust-identified aerosol plumes using aerosol optical depth (AOD) and spectral imaginary refractive index provided by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm over six continental regions (North America, North Africa, West Asia, Central Asia, East Asia, and Australia). The dust particles are represented as an internal mixture of non-absorbing host and absorbing hematite and goethite. We use the Maxwell–Garnett effective medium approximation with carefully selected complex refractive indices of hematite and goethite that produce mass fractions of iron oxides species consistent with in situ values found in the literature to derive the hematite and goethite volumetric/mass concentrations from MAIAC EPIC products. We compared the retrieved hematite and goethite concentrations with in situ dust aerosol mineralogical content measurements, as well as with published data. Our data display variations within the published range of hematite, goethite, and iron-oxide mass fractions for pure mineral dust cases. A specific analysis is presented for 15 sites over the main dust source regions. Sites in the central Sahara, Sahel, and Middle East exhibit a greater temporal variability of iron oxides relative to other sites. Niger site (13.52° N, 2.63° E) is dominated by goethite over Harmattan season with median of ~2 weight percentage (wt.%) of iron-oxide. Saudi Arabia site (27.49° N, 41.98° E) over Middle East also exhibited surge of goethite content with the beginning of Shamal season. The Sahel dust is richer in iron-oxide than Saharan and northern China dust except in Summer. The Bodélé Depression area shows a distinctively lower iron-oxide concentration (~1 wt. %) throughout the year. Finally, we show that EPIC data allow to constrain the hematite refractive index. Specifically, we select 5 out of 13 different number of hematite refractive indices widely variable in published laboratory studies by constraining the iron-oxide mass ratio to the known measured values. Provided climatology of hematite and goethite mass fractions across main dust regions of the Earth will be useful for dust shortwave DRE studies and climate modeling.

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“…It measures the solar radiance backscattered from the sunlit portion of the Earth using 10 narrow-band wavelength filters, from the ultraviolet (UV) to the near-infrared (NIR). The science products (L2 data, see also Table 1) derived from these observations include total column ozone (Herman et al, 2018;Yang and Liu, 2019;Herman et al, 2020) and sulfur dioxide (SO 2 ) (Carn et al, 2018), aerosol information (Christian et al, 2019;Sasi et al, 2020;Xu et al, 2019;Torres et al, 2020;Lyapustin et al, 2021), cloud (Meyer et al, 2016;Molina García et al, 2018;Davis et al, 2018;, Yin et al, 2020Zhou et al, 2020) and vegetation (Marshak and Knyazikhin, 2017;Weber et al, 2020;Pisek et al, 2021) properties, reflectivity (Song et al, 2018;Yang et al, 2018;Wen et al, 2019), and atmospheric correction (Herman et al, 2020;Lyapustin et al, 2021). The sequence from raw data to final products is a 3-step process:…”

Section: Introductionmentioning

confidence: 99%

Raw EPIC Data Calibration

Cede

Huang

McCauley

et al. 2021

Front. Remote Sens.

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Earth Polychromatic Imaging Camera (EPIC) raw level-0 (L0) data in one channel is a 12-bit 2,048 × 2,048 pixels image array plus auxiliary data such as telemetry, temperature, etc. The EPIC L1a processor applies a series of correction steps on the L0 data to convert them into corrected count rates (level-1a or L1a data): Dark correction, Enhanced pixel detection, Read wave correction, Latency correction, Non-linearity correction, Temperature correction, Conversion to count rates, Flat fielding, and Stray light correction. L1a images should have all instrumental effects removed and only need to be multiplied by one single number for each wavelength to convert counts to radiances, which are the basis for all higher-level EPIC products, such as ozone and sulfur dioxide total column amounts, vegetation index, cloud, aerosol, ocean surface, and vegetation properties, etc. This paper gives an overview of the mathematics and the pre-launch and on-orbit calibration behind each correction step.

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Retrievals of Aerosol Optical Depth and Spectral Absorption From DSCOVR EPIC

Cited by 24 publications

References 52 publications

Lagrange Point Missions: The Key to next Generation Integrated Earth Observations. DSCOVR Innovation

Lagrange Point Missions: The Key to next Generation Integrated Earth Observations. DSCOVR Innovation

Inferring iron oxides species content in atmospheric mineral dust from DSCOVR EPIC observations

Raw EPIC Data Calibration

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