Daytime Variability of Cloud Fraction From DSCOVR/EPIC Observations

Delgado-Bonal, Alfonso; Marshak, Alexander; Yang, Yuekui; Oreopoulos, Lazaros

doi:10.1029/2019jd031488

Cited by 10 publications

(11 citation statements)

References 21 publications

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“…It demonstrates that the instrument is a legitimate source for determining the liquid cloud fraction, capturing persistent low‐clouds regions such as those identified by CloudSat/CALIPSO in the Northeast and Southeast Pacific, Northeast and Southeast Atlantic, Southeast Indian Ocean, North Atlantic, North Pacific and Southern Ocean (Muhlbauer et al., 2014). A comparison between EPIC's seasonal zonally averaged cloud fractions with those obtained by the operational MODIS cloud mask (MOD35) (King et al., 2013) showed that EPIC properly captures the seasonal dependence of liquid cloud fraction (Delgado‐Bonal et al., 2020).…”

Section: Resultsmentioning

confidence: 99%

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Global Daytime Variability of Clouds From DSCOVR/EPIC Observations

Delgado-Bonal

Marshak

Yang

et al. 2021

Geophysical Research Letters

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Knowledge of the daytime variability of cloud fraction is pivotal for the accurate determination of the atmosphere's energy balance. Yet, polar orbiting satellites are unable to fully capture daytime variability and individual geostationary satellites only provide measurements for portions of the Earth. In addition, diurnal cloud fraction variations analyzed with General Circulation Models provide results that cannot be reconciled with observations in many situations. Here, we show that the Earth Polychromatic Imaging Camera (EPIC) aboard the Deep Space Climate Observatory (DSCOVR) satellite presents a unique opportunity to examine changes in daytime global cloud fraction resolved at one‐hour frequency. For the first time, we characterize the global daytime variability of clouds with a single sensor, presenting evidence that the fraction of liquid clouds reaches its maximum over continental areas and its minimum over ocean around noon. We compare our results with previous global and regional studies and examine implications for the Earth's energy budget.

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Section: Resultsmentioning

confidence: 99%

“…EPIC cloud products have been previously used to quantify daytime variability for specific regions with well‐known daytime cycles of marine stratocumulus and convective clouds (Delgado‐Bonal et al., 2020). Here we aim to focus on the daytime cloud fraction variability for the entire globe.…”

Section: Resultsmentioning

confidence: 99%

Global Daytime Variability of Clouds From DSCOVR/EPIC Observations

Delgado-Bonal

Marshak

Yang

et al. 2021

Geophysical Research Letters

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“…This agreement might be purely coincidental and treated with caution, since the Cumberland Plain site was the least spatially representative site at the EPIC nominal resolution. The actual EPIC measurements used in this study may in fact come even from an area twice as large due to the large oblique angles around the hinge region (Delgado-Bonal et al, 2020). The two sites from Victoria, Whroo and Wombat, were found to be spatially non-representative at the EPIC nominal resolution.…”

Section: Discussionmentioning

confidence: 97%

Exploring the Potential of DSCOVR EPIC Data to Retrieve Clumping Index in Australian Terrestrial Ecosystem Research Network Observing Sites

Písek

Arndt

Erb

et al. 2021

Front. Remote Sens.

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Vegetation foliage clumping significantly alters the radiation environment and affects vegetation growth as well as water, carbon cycles. The clumping index (CI) is useful in ecological and meteorological models because it provides new structural information in addition to the effective leaf area index. Previously generated CI maps using a diverse set of Earth Observation multi-angle datasets across a wide range of scales have all relied on the single approach of using the normalized difference hotspot and darkspot (NDHD) method. We explore an alternative approach to estimate CI from space using the unique observing configuration of the Deep Space Climate Observatory Earth Polychromatic Imaging Camera (DSCOVR EPIC) and associated products at 10 km resolution. The performance was evaluated with in situ measurements in five sites of the Australian Terrestrial Ecosystem Research Network comprising a diverse range of canopy structure from short and sparse to dense and tall forest. The DSCOVR EPIC data can provide meaningful CI retrievals at the given spatial resolution. Independent but comparable CI retrievals obtained with a completely different sensor and new approach were encouraging for the general validity and compatibility of the foliage clumping information retrievals from space. We also assessed the spatial representativeness of the five TERN sites with respect to a particular point in time (field campaigns) for satellite retrieval validation. Our results improve our understanding of product uncertainty both in terms of the representativeness of the field data collected over the TERN sites and its relationship to Earth Observation data at different spatial resolutions.

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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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Daytime Variability of Cloud Fraction From DSCOVR/EPIC Observations

Cited by 10 publications

References 21 publications

Global Daytime Variability of Clouds From DSCOVR/EPIC Observations

Global Daytime Variability of Clouds From DSCOVR/EPIC Observations

Exploring the Potential of DSCOVR EPIC Data to Retrieve Clumping Index in Australian Terrestrial Ecosystem Research Network Observing Sites

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

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