Cloud information content in EPIC/DSCOVR’s oxygen A- and B-band channels: A physics-based approach

Davis, Anthony B.; Ferlay, Nicolas; Libois, Quentin; Marshak, Alexander; Yang, Yuekui; Min, Qilong

doi:10.1016/j.jqsrt.2018.09.006

Cited by 12 publications

(9 citation statements)

References 32 publications

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“…The selection of the O 2 A-band ratio as a cloud mask test is based on the fact that everything being equal, the ratio is higher with the presence of clouds compared to clear sky cases because O 2 absorption is proportional to the air mass above the reflecting layer. Thresholds for this test are a function of surface elevation, which is based on the Global Gridded Elevation and Bathymetric (ETOPO5) dataset (Edwards, 1989). While the O 2 B-band ratio is also useful for cloud detection for the same reason (and is used over snow/ice as discussed below), the A-band ratio is selected for use over land because it provides better skill than that of the B band due to its higher sensitivity to the photon path length change (Y.…”

Section: Epic Cloud Mask Algorithmmentioning

confidence: 99%

Cloud products from the Earth Polychromatic Imaging Camera (EPIC): algorithms and initial evaluation

et al. 2019

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Abstract. This paper presents the physical basis of the Earth Polychromatic Imaging Camera (EPIC) cloud product algorithms and an initial evaluation of their performance. Since June 2015, EPIC has been providing observations of the sunlit side of the Earth with its 10 spectral channels ranging from the UV to the near-infrared. A suite of algorithms has been developed to generate the standard EPIC Level 2 cloud products that include cloud mask, cloud effective pressure/height, and cloud optical thickness. The EPIC cloud mask adopts the threshold method and utilizes multichannel observations and ratios as tests. Cloud effective pressure/height is derived with observations from the O2 A-band (780 and 764 nm) and B-band (680 and 688 nm) pairs. The EPIC cloud optical thickness retrieval adopts a single-channel approach in which the 780 and 680 nm channels are used for retrievals over ocean and over land, respectively. Comparison with co-located cloud retrievals from geosynchronous earth orbit (GEO) and low earth orbit (LEO) satellites shows that the EPIC cloud product algorithms are performing well and are consistent with theoretical expectations. These products are publicly available at the Atmospheric Science Data Center at the NASA Langley Research Center for climate studies and for generating other geophysical products that require cloud properties as input.

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Section: Epic Cloud Mask Algorithmmentioning

confidence: 99%

Cloud products from the Earth Polychromatic Imaging Camera (EPIC): algorithms and initial evaluation

et al. 2019

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“…The EPIC UV channels are used to retrieve ozone amount (Herman, Huang, et al, ) and estimate SO 2 amounts from volcanic eruptions (Carn et al, ). The near UV at 340 and 388 nm and the visible and the NIR channels allow us to derive aerosol and cloud properties in particular, while the oxygen A and B bands enable us to derive the altitude of cloud and dust plumes (e.g., Davis et al, ; Xu et al, ; Yang et al, ). EPIC images were used to quantify the reduction of reflected sunlight during the 2017 solar eclipse in North America (Herman, Wen, et al, ).…”

Section: Introductionmentioning

confidence: 99%

A Relationship Between Blue and Near‐IR Global Spectral Reflectance and the Response of Global Average Reflectance to Change in Cloud Cover Observed From EPIC

Wen

Marshak

Song

et al. 2019

Earth and Space Science

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We performed a detailed analysis of Earth Polychromatic Imaging Camera (EPIC) spectral data. We found that the vector composed of blue and near‐infrared (NIR) reflectance follows a counterclockwise closed‐loop trajectory from 0 to 24 UTC as Earth rotates. This nonlinear relationship was not observed by any other satellites due to limited spatial or temporal coverage of either low Earth orbit or geostationary satellites. We found that clouds play an important role in determining the nonlinear relationship in addition to the well‐known cloud‐free land‐ocean reflectance contrast in the two bands. The nonlinear relationship is the result of three factors: (1) a much larger cloud‐free land‐ocean contrast in the NIR band compared to the blue band, (2) significantly larger difference between cloudy land and cloudy ocean reflectance in the NIR band compared to the blue band, and (3) the periodic variation of fractions of clear land, clear ocean, cloudy land, and cloudy ocean in the sunlit hemisphere as Earth rotates. We found that the green vegetation contributes significantly to the NIR global average reflectance when the South and North Americas appear and disappear in the EPIC's field of view. The blue and NIR relationship can be useful for exoplanet research. Clouds impose a strong impact on global spectral reflectance, and the reflectance response to a change in cloud cover depends on whether the change is over land or over the ocean. On average, an increase of 0.1 in cloud coverage will lead to a 7% increase in spectrally integrated global average reflectance.

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“…That change mitigated the inability to determine cloud top height/pressure from EPIC. These channels along with other VIS/NIR channels have been used to determine cloud fraction Delgado-Bonal et al, 2020), cloud phase and optical depth (Meyer et al, 2016) and cloud-top height/pressure (Yang et al, 2013Davis et al, 2018a;Yin et al, 2020). An example of the cloud fraction retrieved from EPIC is compared with the higher resolution cloud retrieval composite from Khlopenkov et al (2017) in Figure 8.…”

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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Cloud information content in EPIC/DSCOVR’s oxygen A- and B-band channels: A physics-based approach

Cited by 12 publications

References 32 publications

Cloud products from the Earth Polychromatic Imaging Camera (EPIC): algorithms and initial evaluation

Cloud products from the Earth Polychromatic Imaging Camera (EPIC): algorithms and initial evaluation

A Relationship Between Blue and Near‐IR Global Spectral Reflectance and the Response of Global Average Reflectance to Change in Cloud Cover Observed From EPIC

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

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