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NASA NTRS2021

Detecting Layer Height of Smoke and Dust Aerosols Over Vegetated Land and Water Surfaces Via Oxygen Absorption Bands

We present an algorithm for retrieving aerosol layer height (ALH) and aerosol optical depth (AOD) for smoke and dust over vegetated land and water surfaces from measurements of the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR). Our algorithm uses EPIC atmospheric window bands to determine AOD and then takes advantage of oxygen A and B bands to derive ALH. We applied this algorithm on several dust and smoke events. Validation shows our results are of high accuracy.

Jing Zeng↗

NASA NTRS2021

An Investigation on Seasonal and Diurnal Cycles of TOA Shortwave Radiations from DSCOVR/EPIC, CERES, MERRA-2, and ERA5

Reflected shortwave (SW) solar radiations at the top of atmosphere from Clouds and the Earth’s Radiant Energy System (CERES), Modern Era-Retrospective analysis for Research and Applications version 2 (MERRA-2), and ECMWF Reanalysis 5th Generation (ERA5) are examined to better under-stand their differences in spatial and temporal variations (seasonal and diurnal cycle time-scale) with respect to the observations from the Earth Polychromatic Imaging Camera (EPIC) on Deep Space Climate Observatory (DSCOVR) satellite. Comparisons between two reanalyses (MERRA-2 and ERA5) and EPIC reveal that MERRA-2 has a generally larger deviation from EPIC than ERA5, in terms of the SW radiance and diurnal variability in all seasons, which can be attributed to larger cloud biases in MERRA-2. MERRA-2 produces more ice/liquid water content than ERA5 over the tropical warm pool, leading to positive SW biases in cloud and radiance, while both reanalyses underestimate the observed SW radiance from EPIC in the stratus-topped region off the western coast of US/Mexico in the boreal summer. Himalaya/Tibet region in the boreal spring/summer and the midlatitude Southern Hemisphere in the boreal winter are the regions where MERRA-2 and ERA5 deviate largely from EPIC but their deviations have the opposite sign. Vertical structures of cloud ice/liquid water content explain reasonably well these contrasting differences between the two reanalyses. As two independent observa-ions, CERES and EPIC agree well with each other in terms of the SW radiance maps, showing 2-3% mean absolute errors over the tropical-midlatitudes. The CERES-EPIC consistency further confirms that the reanalyses still have challenges in representing the SW flux and its global distribution. In the CERES-EPIC observation differences, CERES slightly overestimates the diurnal cycle (as a function of local solar time) of the observed EPIC irradiance in the morning and underestimates in the after-noon, while the opposite is the case in the reanalyses.

Reflected shortwave radiance↗

NASA NTRS2018

Deep Space Observations of Sun Glint from Oceans

Every hour or two, the Earth Polychromatic Imaging Camera (EPIC) onboard the DSCOVR satellite provides unique full-color images of the sunlit side of the Earth from the L1 Lagrangian point, which is four times farther the Moon. Casual glances at such images often reveal bright colorful spots that stand out markedly from their surroundings. Such spots often appear not only over ocean but also over land. Tracking the colorful spots using an automated image analysis algorithm reveals that they are caused by specular reflection of sunlight, sometimes from ocean surfaces and other times from clouds containing horizontally oriented ice crystals. The presented study characterizes these spots in terms of prevalence, location, color, and brightness, and provides insights into the factors that lead to their appearance.

ice crystals↗

NASA NTRS2016

Construction of a Matched Global Cloud and Radiance Product from LEO/GEO and EPIC Observations to Estimate Daytime Earth Radiation Budget from DSCOVR

With the launch of the Deep Space Climate Observatory (DSCOVR), new estimates of the daytime Earth radiation budget can be computed from a combination of measurements from the two Earth-observing sensors onboard the spacecraft, the Earth Polychromatic Imaging Camera (EPIC) and the National Institute of Standards and Technology Advanced Radiometer (NISTAR). Although these instruments can provide accurate top-of-atmosphere (TOA) radiance measurements, they lack sufficient resolution to provide details on small-scale surface and cloud properties. Previous studies have shown that these properties have a strong influence on the anisotropy of the radiation at the TOA, and ignoring such effects can result in large TOA-flux errors. To overcome these effects, high-resolution scene identification is needed for accurate Earth radiation budget estimation. Selected radiance and cloud property data measured and derived from several low earth orbit (LEO, including NASA Terra and Aqua MODIS, NOAA AVHRR) and geosynchronous (GEO, including GOES (east and west), METEOSAT, INSAT-3D, MTSAT-2, and HIMAWARI-8) satellite imagers were collected to create hourly 5-km resolution global composites of data necessary to compute angular distribution models (ADM) for reflected shortwave (SW) and longwave (LW) radiation. The satellite data provide an independent source of radiance measurements and scene identification information necessary to construct ADMs that are used to determine the daytime Earth radiation budget. To optimize spatial matching between EPIC measurements and the high-resolution composite cloud properties, LEO/GEO retrievals within the EPIC fields of view (FOV) are convolved to the EPIC point spread function (PSF) in a similar manner to the Clouds and the Earth's Radiant Energy System (CERES) Single Scanner Footprint TOA/Surface Fluxes and Clouds (SSF) product. Examples of the merged LEO/GEO/EPIC product will be presented, describing the chosen radiance and cloud properties and details of how data from the multi-satellite measurements are selected.

Duda, David P.↗

NASA NTRS2020

Cloud Detection over Snow and Ice with Oxygen A- and B-band Observations from the Earth Polychromatic Imaging Camera (EPIC)

Satellite cloud detection over snow and ice has been difficult for passive remote sensing instruments due to the lack of contrast between clouds and cold/bright surfaces; cloud mask algorithms often heavily rely on shortwave infrared (IR) channels over such surfaces. The Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) does not have infrared channels, which makes cloud detection over snow and ice surfaces even more challenging. This study investigates the methodology of applying EPIC's two oxygen absorption band pair ratios in the A band (764, 780 nm) and B band (688, 680 nm) for cloud detection over the snow and ice surfaces. We develop a novel elevation and zenith-angle-dependent threshold scheme based on radiative transfer model simulations that achieves significant improvements over the existing algorithm. When compared against a composite cloud mask based on geosynchronous Earth orbit (GEO) and low Earth orbit (LEO) sensors, the positive detection rate over snow and ice surfaces increased from around 36 % to 65 % while the false detection rate dropped from 50 % to 10 % for observations of January 2016 and 2017. The improvement in July is less substantial due to relatively better performance in the current algorithm. The new algorithm is applicable for all snow and ice surfaces including Antarctic, sea ice, high-latitude snow, and high-altitude glacier regions. This method is less reliable when clouds are optically thin or below 3 km because the sensitivity is low in oxygen band ratios for these cases.

EPIC↗

NASA NTRS2023

Effect of Scattering Angle on DSCOVR/EPIC Observations

The Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory (DSCOVR) routinely captures reflected radiation from the whole sunlit side of the Earth in the near backward direction to monitor the changing planet. The instrument had routinely operated until 27 June 2019, when the spacecraft was placed in an extended safe hold due to degradation of an inertial navigation unit. DSCOVR returned to full operations on 2 March 2020. Since then, the range of scattering angles between the incident sunlight and sensor direction has been larger than before and the largest scattering angle reaches ∼178°, only 2° from perfect backscattering, proving a unique opportunity to study the top-of-atmosphere (TOA) reflectance under such extreme conditions. In the paper, we compare EPIC global spectral reflectances in 2021–2016. We found that there are four occasions when the scattering angle reaches about 178° and associated with them enhanced global daily average spectral reflectances in 2021. The scattering angle related reflectance enhancements are not found in 2016 data when the maximum scattering angle is about 174.5°. CERES data do not show such occasions in global daily reflected shortwave flux. As a result, those enhanced reflectance occasions are primarily due to the change in scattering angle. The enhancement due to changes in scattering angle depends strongly on wavelength, primarily because of wavelength dependence of cloud scattering phase function. Radiative transfer calculations show that the change in scattering angles has the largest impact on reflectance in the red and NIR channels at 680 nm and 780 nm and the smallest influence on reflectance in the UV channel at 388 nm, consistent with EPIC observations. The change of global average cloud amount also plays an important role in the reflectance enhancement. The influence of the cloud effect depends on whether the change is in phase or not with the change of scattering angle.

DSCOVR EPIC↗

NASA NTRS2018

Synoptic Ozone, Cloud Reflectivity, and Erythemal Irradiance from Sunrise to Sunset for the Whole Earth as Viewed by the DSCOVR Spacecraft from the Earth-Sun Lagrange 1 Orbit

EPIC (Earth Polychromatic Imaging Camera) on board the DSCOVR (Deep Space Climate Observatory) spacecraft is the first earth science instrument located near the earth-sun gravitational plus centrifugal force balance point, Lagrange 1. EPIC measures earth-reflected radiances in 10 wavelength channels ranging from 317.5 to 779.5 nm. Of these channels, four are in the UV range 317.5, 325, 340, and 388 nm, which are used to retrieve O3, 388 nm scene reflectivity (LER: Lambert equivalent reflectivity), SO2, and aerosol properties. These new synoptic quantities are retrieved for the entire sunlit globe from sunrise to sunset multiple times per day as the earth rotates in EPIC's field of view. Retrieved ozone amounts agree with ground-based measurements and satellite data to within 3 %. The ozone amounts and LER are combined to derive the erythemal irradiance for the earth's entire sunlit surface at a nadir resolution of 1818 km2 using a computationally efficient approximation to a radiative transfer calculation of irradiance. The results show very high summertime values of the UV index (UVI) in the Andes and Himalayas (greater than 18), and high values of UVI near the Equator at equinox.

Cloud reflectivity↗

NASA NTRS2019

Detecting Layer Height of Smoke Aerosols over Vegetated Land and Water Surfaces via Oxygen Absorption Bands: Hourly Results from EPIC/DSCOVR in Deep Space

We present an algorithm for retrieving aerosol layer height (ALH) and aerosol optical depth (AOD) for smoke over vegetated land and water surfaces from measurements of the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR). The algorithm uses Earth-reflected radiances in six EPIC bands in the visible and near-infrared and incorporates flexible spectral fitting that accounts for specifics of land and water surface reflectivity. The fitting procedure first determines AOD using EPIC atmospheric window bands (443 nm, 551 nm, 680nm, and 780 nm), then uses oxygen (O2) A and B bands (688 nm and 764 nm) to derive ALH, which represents an optical centroid altitude. ALH retrieval over vegetated surface primarily takes advantage of measurements in the O2B band. We applied the algorithm to EPIC observations of several biomass burning events over the United States and Canada in August 2017. We found that the algorithm can be used to obtain AOD and ALH multiple times daily over water and vegetated land surface. Validation is performed against aerosol extinction profiles detected by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and against AOD observed at nine Aerosol Robotic Network (AERONET) sites, showing, on average, an error of 0.58 km and a bias of -0.13 km in retrieved ALH and an error of 0.05 and a bias of 0.03 in retrieved AOD. Additionally, we show that the aerosol height information retrieved by the present algorithm can potentially benefit the retrieval of aerosol properties from EPIC’s ultraviolet (UV) bands.

Cloud–Aerosol Lidar with Orthogonal Polarization (↗

NASA NTRS2021

Deep Space Observations of Terrestrial Glitter

Deep space climate observatory (DSCOVR) spacecraft drifts about the Lagrangian point ≈1.4–1.6 × 106 km from Earth, where its Earth polychromatic imaging camera (EPIC) observes the sun-lit face of the Earth every 1 to 2 hours. At any instance, there is a preferred (specular) spot on the globe, where a glint may be observed by EPIC. While monitoring reflectance at these spots (terrestrial glitter), we observe occasional intense glints originating from neither ocean surface nor cloud ice and we argue that mountain lakes high in the Andes are among the causes. We also examine time-averaged reflectance at the spots and find it exceeding that of neighbors, with the excess monotonically increasing with separation distance. This specular excess is found in all channels and is more pronounced in the latest and best-calibrated version of EPIC data, thus opening the possibility of testing geometric calibration by monitoring distant glitter.

DSCOVR↗

DOE OSTI2020

Exploring the Use of DSCOVR/EPIC Satellite Observations to Monitor Vegetation Phenology

Vegetation phenology plays a pivotal role in regulating several ecological processes and has profound impacts on global carbon exchange. Large-scale vegetation phenology monitoring mostly relies on Low-Earth-Orbit satellite observations with low temporal resolutions, leaving gaps in data that are important for monitoring seasonal vegetation phenology. High temporal resolution satellite observations have the potential to fill this gap by frequently collecting observations on a global scale, making it easier to study change over time. This study explored the potential of using the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) satellite, which captures images of the entire sunlit face of the Earth at a temporal resolution of once every 1–2 h, to observe vegetation phenology cycles in North America. We assessed the strengths and shortcomings of EPIC-based phenology information in comparison with the Moderate-resolution Imaging Spectroradiometer (MODIS), Enhanced Thematic Mapper (ETM+) onboard Landsat 7, and PhenoCam ground-based observations across six different plant functional types. Our results indicated that EPIC could capture and characterize seasonal changes of vegetation across different plant functional types and is particularly consistent in the estimated growing season length. Our results also provided new insights into the complementary features and benefits of the four datasets, which is valuable for improving our understanding of the complex response of vegetation to global climate variability and other disturbances and the impact of phenology changes on ecosystem productivity and global carbon exchange.

Weber, Maridee A.↗

NASA NTRSDate not supplied

June 2023 Canadian Wildfire Effects on Hampton Roads VA using ASDC Data

In June of 2023 thousands of acres of wildfires spread across northwestern Canada. This event emitted huge amounts of smoke, and pollutants that travelled east across the North America, the Atlantic ocean and eventually all the way to Continental Europe. This smoke created a dangerous air quality event throughout the Atlantic Coast. As climate change increases the prevalence of wildfires more cities and states will be affected by the dangerous smoke and pollutants produced by these events. In this poster we documented and investigated the June 06th Air Quality Event over Hampton Roads, VA. To perform a comparative analysis, we utilized two data sources: NASA’s Atmospheric Science Data Center we used the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), the Deep Space Climate ObserVatoRy (DSCOVR) Enhanced Polychromatic Imaging Camera (EPIC), data product, and the EPA’s ground-based Air Quality System (AQS).

Air Quality↗

NASA NTRS2019

Deep Space Observations of Sun Glints from Marine Ice Clouds

The Earth Polychromatic Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) spacecraft takes images of the sunlit face of Earth from a million miles away. Earlier work showed that EPIC detected the specular reflection of sunlight (that is, sun glint) from ice crystals floating in cold clouds over land; here we show that this phenomenon can also be detected over oceans. Furthermore, the results show that - using its observations at Oxygen A-band absorption bands - EPIC can distinguish glints off marine ice clouds from those off the ocean surface. The analysis of more than two years of EPIC data reveals that the two kinds of glints are detected with comparable frequency. Glints off clouds are shown to be generally brighter but smaller in spatial extent. It is also demonstrated that glints off ice clouds have a discernible effect on the regional mean reflectance and that EPIC observations can help constrain the radiative contribution of oriented ice crystals.

Atmosphere↗

NASA NTRSDate not supplied

The Radiometric Scaling of the MODIS and VIIRS Imagers to a Common Reference and Stability Analysis for the Next Edition of CERES Products

The NASA CERES project has provided the climate quality observed TOA and computed surface fluxes to the scientific community. CERES uses MODIS and VIIRS imagers to retrieve cloud properties needed to convert Terra, Aqua, NPP, and NOAA-20 CERES footprint radiance observations into fluxes. The imagers are also used to radiometrically scale the geostationary sensors (GEO) radiances to the imager calibration reference to ensure that the GEO derived cloud properties and broadband TOA fluxes are consistent in both space and time. Both the imager and GEO retrieved cloud properties are used to compute the surface fluxes. The CERES imager and GEO calibration team uses ray-matched radiance pairs to radiometrically scale the SNPP and NOAA-20 VIIRS sensors to the Aqua-MODIS calibration reference. The radiometric scaling is further validated using geostationary imagers as transfer radiometers. The team will rely primarily on deep convective clouds to monitor the imager channel calibration stability and to correct for short term calibration drifts. The DSCOVR satellite was launched on February 25, 2015 and orbits around the L1 Lagrange point directly between the Earth and the sun. The EPIC sensor contains no onboard calibration systems. However, multiple inter-calibration studies have shown that the EPIC imager is very stable in time. The excellent radiometric stability of EPIC allows the use of EPIC observations as a stable reference to validate both the short term drift corrections of the imagers, as well as to validate the radiometric scaling factors between them. Examples of the imager relative calibration using EPIC before and after radiometric scaling will be shown along with the results from the use of invariant targets to remove imager calibration drifts.

DSCOVR-EPIC↗

NASA NTRS2018

EPIC Spectral Observations of Variability in Earth's Global Reflectance

NASA’s Earth Polychromatic Imaging Camera (EPIC) onboard NOAA’s Deep Space Climate Observatory (DSCOVR) satellite observes the entire sunlit Earth every 65 to 110 min from the Sun–Earth Lagrangian L1 point. This paper presents initial EPIC shortwave spectral observations of the sunlit Earth reflectance and analyses of its diurnal and seasonal variations. The results show that the reflectance depends mostly on (1) the ratio between land and ocean areas exposed to the Sun and (2) cloud spatial and temporal distributions over the sunlit side of Earth. In particular, the paper shows that (a) diurnal variations of the Earth’s reflectance are determined mostly by periodic changes in the land–ocean fraction of its the sunlit side; (b) the daily reflectance displays clear seasonal variations that are significant even without including the contributions from snow and ice in the polar regions (which can enhance daily mean reflectances by up to 2 to 6% in winter and up to 1 to 4% in summer); (c) the seasonal variations of the sunlit Earth reflectance are mostly determined by the latitudinal distribution of oceanic clouds.

Spectral Reflectance↗

NASA NTRS2019

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

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 non-linear relationship was not observed by any other satellites due to limited spatial or temporal coverage of either low earth orbit (LEO) or geostationary (GEO) satellites. We found that clouds play an important role in determining the non-linear relationship in addition to the well-known cloud free land-ocean reflectance contrast in the two bands. The non-linear 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 theNIR band compared to the blue band; (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.

EPIC↗

NASA NTRS2019

A New Discrete Wavelength BUV Algorithm for Consistent Volcanic SO2 Retrievals from Multiple Satellite Missions

This paper describes a new discrete wavelength algorithm developed for retrieving volcanic sulfur dioxide (SO2) vertical column density (VCD) from UV observing satellites. The Multi-Satellite SO2 algorithm (MS_SO2) simultaneously retrieves column densities of sulfur dioxide, ozone, and Lambertian effective reflectivity (LER) and its spectral dependence. It is used operationally to process measurements from the heritage Total Ozone Mapping Spectrometer (TOMS) onboard NASA's Nimbus-7 satellite (N7/TOMS: 1978-1993) and from the current Earth Polychromatic Imaging Camera (EPIC) onboard Deep Space Climate Observatory (DSCOVR: 2015-) from the Earth-Sun Lagrange (L1) orbit. Results from MS_SO2 algorithm for several volcanic cases were assessed using the more sensitive principal component analysis (PCA) algorithm. The PCA is an operational algorithm used by NASA to retrieve SO2 from hyperspectral UV spectrometers, such as the Ozone Monitoring Instrument (OMI) onboard NASA's Earth Observing System Aura satellite and Ozone Mapping and Profiling Suite (OMPS) onboard NASA-NOAA Suomi National Polar Partnership (SNPP) satellite. For this comparative study, the PCA algorithm was modified to use the discrete wavelengths of the Nimbus-7/TOMS instrument, described in Sect. S1 of the Supplement. Our results demonstrate good agreement between the two retrievals for the largest volcanic eruptions of the satellite era, such as the 1991 Pinatubo eruption. To estimate SO2 retrieval systematic uncertainties, we use radiative transfer simulations explicitly accounting for volcanic sulfate and ash aerosols. Our results suggest that the discrete-wavelength MS_SO2 algorithm, although less sensitive than hyperspectral PCA algorithm, can be adapted to retrieve volcanic SO2 VCDs from contemporary hyperspectral UV instruments, such as OMI and OMPS, to create consistent, multi-satellite, long-term volcanic SO2 climate data records.

Bradford L Fisher↗

NASA NTRSDate not supplied

EPIC Cloud Observations with Oxygen Bands Over Snow, Ice and Sunglint Regions

Cloud detections over snow/ice surfaces and sunglint regions are challenging with passive remote sensing instrument due to lack of contract between cold/bright surfaces in the former and glint reflectance that could exceed that of cloudy sky in the latter. We developed novel cloud detection algorithms for these regions utilizing EPIC’s unique oxygen-bands. Over the snow and ice surfaces, a dynamic threshold scheme based on the ratios of two pairs of EPIC’s oxygen bands are developed to significantly improve the existing algorithm in these regions. Over the ocean, we improved the EPIC’s ocean cloud mask algorithm by implementing a dynamic reflectance threshold and supplemental A-band ratio test for cloud detection in the sun glint regions. The new ocean cloud mask algorithm improves the diurnal cycles of cloud fraction over ocean by reducing the artificial peak at local noon time in the glint center latitudes and reducing early morning and afternoon cloud fraction in most oceanic regions.

EPIC↗

NASA NTRS2019

Cloud Remote Sensing with EPIC/DSCOVR Observations: A Sensitivity Study with Radiative Transfer Simulations

The Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) views nearly the whole sunlit face of the Earth with 10 spectral bands ranging from the UV to the near-infrared, including two oxygen absorbing bands centered at 764 nm (A-band) and 687.75 nm (B-band). Clouds are among the primary remote sensing targets using EPIC images because of their important impacts on the Earth’s radiation budget. In order to facilitate the EPIC cloud data product development, we have built a radiative transfer simulator featuring flexible cloud microphysical parameters, gas absorptions, and the instrument line shape functions for each EPIC band. The radiative transfer simulator is used to explore the sensitivity of EPIC observations on liquid-phase cloud microphysical parameters, including optical depth, geometric thickness, and cloud top height. We found that the ratios of the reflectances in the oxygen A and B bands to their respective continuum measurements can be used to increase the confidence level of cloud masking over scenes with sun-glint. In addition, the 388 nm band can be used to differentiate low and high clouds with the uncertainty of roughly 2–3 km. Combining this information with the oxygen absorption bands, the cloud geometric thickness can be obtained with the rough uncertainty of 3–4 km.

atmospheric and ocean optics↗