Earth Polychromatic Imaging Camera Research Articles

Local and Regional Diurnal Variability of Aerosol Properties Retrieved by DSCOVR/EPIC UV Algorithm

AbstractThe hour‐to‐hour variability of 388 nm aerosol optical depth (AOD) and single scattering albedo (SSA) derived from near UV observations by the Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory has been evaluated at multiple locations around the world. AOD retrievals by the EPIC near UV algorithm (EPICAERUV) have been compared to ground based AOD measurements at 16 Aerosol Robotic Network (AERONET) stations representative of the most commonly observed aerosol types over geographic regions in three continents. Obtained results show that, in general, the EPICAERUV algorithm reproduces closely the hour‐to‐hour AOD variability reported by AERONET ground‐truth observations. Although most sites in the analysis show high correlation between the AOD hourly measurements by the ground‐based and space‐borne measuring techniques. Best algorithm performance is observed in the presence of carbonaceous and desert dust aerosols. The diurnal cycle of the retrieved SSA product was also analyzed. Although, a direct comparison of hourly EPICAERUV retrievals to equivalent ground‐based observations was not possible, the satellite result shows that diurnal SSA variability as large as 0.05 can be observed mostly associated with carbonaceous aerosols. EPICAERUV observed diurnal cycle of retrieved AOD on a regional basis was examined for the unusually active seasons of aerosol production of Saharan desert dust aerosols in 2020, and during the 2023 Canadian wildfires. Results presented in this study confirm the EPIC near UV aerosol product is well suited for observing diurnal variability of aerosols and, therefore, it is an important resource for climate and air quality studies.

Evaluation of EPIC oxygen bands stability with radiative transfer simulations over the South Pole

The Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) satellite orbiting the Sun at the Lagrange-1 point was launched without onboard calibration systems. Vicarious calibration is conducted for 8 of the 10 UV/VIS/NIR channels using other low earth orbiting satellite instruments, while its two O2 bands (688 nm and 764 nm) rely on indirect moon-view calibrations because the same narrow-band O2 bands are not readily available from other in-flight instruments. This study compares EPIC measurements from the four O2 bands aiming at examining sensor stability over a uniquely suited location, i.e., the permanently snow-covered South Pole. The study utilizes radiative transfer model simulations with in-situ atmospheric soundings taken at South Pole during months of December and January from 2015 to 2022. The absolute discrepancy between the model simulations and observations is less than 1.0% for the two reference bands, but 5.75% and 15.63% for the 688 nm, and 764 nm absorption bands, respectively. The simulated A-band and B-band ratios are 16.09% and 4.74% higher than that from the observations. Various sensitivities are conducted to estimate possible contributions to the discrepancies from input atmospheric profiles, spectral surface albedos and surface BRDF. While none of the input uncertainties is likely to account for the large discrepancies in the oxygen absorption bands, a small shift in the instrument response function could be the main reason for these biases. On the other hand, the model simulations are able to capture systematic variations with observed angular measurements and explain the multi-year trends found in observed O2 band ratios due to satellite orbit shifting. When model simulated contributions from the angle variations are deducted from the observed O2 band ratios, the residual O2 band ratios are found to be stable since 2015.

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.

First Mapping of Monthly and Diurnal Climatology of Saharan Dust Layer Height Over the Atlantic Ocean From EPIC/DSCOVR in Deep Space

AbstractThe monthly and hourly climatology of Saharan dust layer height over the Atlantic, at a spatial resolution of ∼10 km, is obtained for the first time, via a passive remote sensing technique. The technique is applied to multiple years of Earth Polychromatic Imaging Camera (EPIC) data collected at the Lagrange‐1 point, generating a climate data record (CDR) of aerosol optical centroid height (AOCH). This CDR offers unprecedented spatial coverage and diurnal sampling compared to spaceborne lidars (CALIOP and CATS). Our results show high correspondence with CALIOP data in domain‐averaged monthly variations and with CATS data in diurnal variations, respectively. A principal component analysis (PCA) reveals the dominant role of dust transport in regulating AOCH variation, whereas the impact of the boundary layer is less significant. MERRA‐2 and satellite retrievals respectively display zero and 200–1,000 m of diurnal variation of AOCH, highlighting the uniqueness of EPIC AOCH CDR in constraining climate models.

First validation of Earth Reflector Type Index (p) parameter from DSCOVR EPIC VESDR data product using Terrestrial Ecosystem Research Network observing sites in Australia

The spectral invariants theory predicts that amount of radiation scattered by a canopy in a given wavelength is a simple function of the leaf albedo and a spectrally invariant parameter p. This study provides the first assessment of p parameter obtained from the Earth Reflector Type Index (ERTI) provided in Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) Vegetation Earth System Data Record (VESDR) product. Our results suggest that ERTI parameter can possibly deliver information about photon recollision probability, albeit with a risk of lower accuracy, even for conditions that differ from the original assumptions in the spectral invariants theory (non-reflecting soil, dense canopies). We verified the expected link between ERTI p parameter in the EPIC VESDR product, foliage clumping, and canopy cover with empirical data. Importantly, this was done with study sites in Australia that included types of forests that are still relatively unexplored with spectral invariants theory. Our findings contribute to understanding product uncertainty and spatial representativeness of the in situ measurements from the Terrestrial Ecosystem Research Network (TERN) SuperSites in Australia.

Algorithm theoretical basis for ozone and sulfur dioxide retrievals from DSCOVR EPIC

Abstract. On board the Deep Space Climate Observatory (DSCOVR), the first Earth-observing satellite at the L1 point (the first Lagrangian point in the Earth–Sun system), the Earth Polychromatic Imaging Camera (EPIC) continuously observes the entire sunlit face of the Earth. EPIC measures the solar backscattered and reflected radiances in 10 discrete spectral channels, four of which are in the ultraviolet (UV) range. These UV bands are selected primarily for total ozone (O3) and aerosol retrievals based on heritage algorithms developed for the series of Total Ozone Mapping Spectrometers (TOMS). These UV measurements also provide sensitive detection of sulfur dioxide (SO2) and volcanic ash, both of which may be episodically injected into the atmosphere during explosive volcanic eruptions. This paper presents the theoretical basis and mathematical procedures for the direct vertical column fitting (DVCF) algorithm used for retrieving total vertical columns of O3 and SO2 from DSCOVR EPIC. This paper describes algorithm advances, including an improved O3 profile representation that enables profile adjustments from multiple spectral measurements and the spatial optimal estimation (SOE) scheme that reduces O3 artifacts resulting from EPIC's band-to-band misregistrations. Furthermore, this paper discusses detailed error analyses and presents intercomparisons with correlative data to validate O3 and SO2 retrievals from EPIC.

Out of the blue: Volcanic SO2 emissions during the 2021–2022 eruptions of Hunga Tonga—Hunga Ha’apai (Tonga)

Most volcanism on Earth is submarine, but volcanic gas emissions by submarine eruptions are rarely observed and hence largely unquantified. On 15 January 2022 a submarine eruption of Hunga Tonga-Hunga Ha’apai (HTHH) volcano (Tonga) generated an explosion of historic magnitude, and was preceded by ∼1 month of Surtseyan eruptive activity and two precursory explosive eruptions. We present an analysis of ultraviolet (UV) satellite measurements of volcanic sulfur dioxide (SO2) between December 2021 and the climactic 15 January 2022 eruption, comprising an unprecedented record of Surtseyan eruptive emissions. UV measurements from the Ozone Monitoring Instrument (OMI) on NASA’s Aura satellite, the Ozone Mapping and Profiler Suite (OMPS) on Suomi-NPP, the Tropospheric Monitoring Instrument (TROPOMI) on ESA’s Sentinel-5P, and the Earth Polychromatic Imaging Camera (EPIC) aboard the Deep Space Climate Observatory (DSCOVR) are combined to yield a consistent multi-sensor record of eruptive degassing. We estimate SO2 emissions during the eruption’s key phases: the initial 19 December 2021 eruption (∼0.01 Tg SO2); continuous SO2 emissions from 20 December 2021—early January 2022 (∼0.12 Tg SO2); the 13 January 2022 stratospheric eruption (0.06 Tg SO2); and the paroxysmal 15 January 2022 eruption (∼0.4–0.5 Tg SO2); yielding a total SO2 emission of ∼0.6–0.7 Tg SO2 for the eruptive episode. We interpret the vigorous SO2 emissions observed prior to the January 2022 eruptions, which were significantly higher than measured in the 2009 and 2014 HTHH eruptions, as strong evidence for a rejuvenated magmatic system. High cadence DSCOVR/EPIC SO2 imagery permits the first UV-based analysis of umbrella cloud spreading and volume flux in the 13 January 2022 eruption, and also tracks early dispersion of the stratospheric SO2 cloud injected on January 15. The ∼0.4–0.5 Tg SO2 discharged by the paroxysmal 15 January 2022 HTHH eruption is low relative to other eruptions of similar magnitude, and a review of other submarine eruptions in the satellite era indicates that modest SO2 yields may be characteristic of submarine volcanism, with the emissions and atmospheric impacts likely dominated by water vapor. The origin of the low SO2 loading awaits further investigation but scrubbing of SO2 in the water-rich eruption plumes and rapid conversion to sulfate aerosol are plausible, given the exceptional water emission by the 15 January 2022 HTHH eruption.

Worldwide validation of an Earth Polychromatic Imaging Camera (EPIC) derived radiation product and comparison with recent reanalyses

A very recent gridded product for the hourly global horizontal irradiance (GHI), derived from the measurements of the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) launched by a NOAA/NASA/USAF consortium, is validated at 31 locations worldwide, from January, 2017 to June, 2019. In contrast to those traditional methods that leverage (simplified) radiative transfer, this EPIC-derived product uses machine learning – a random forest model – to map out the connection between satellite-observed variables of various kinds and GHI. Nonetheless, the detailed validation conducted here shows that the quality of this EPIC-derived GHI dataset not only does not outperform those traditional gridded solar radiation datasets, but also contains undesirable artifacts that can be possibly attributed to inadequacies in the machine-learning procedure. For these reasons, it is not recommended to use this EPIC-derived dataset in its current form for solar resource assessment purposes.

Earth as a Proxy Exoplanet: Simulating DSCOVR/EPIC Observations Using the Earth Spectrum Simulator

Analyzing time-resolved disk-integrated spectral images of the Earth can provide a baseline for future exoplanet characterization. The Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) provides ∼5000 full-disk sunlit Earth images each year in ten wavelengths from the ultraviolet to the near-infrared. A whole-disk radiative transfer model can improve our understanding of the temporal variation of Earth’s disk-integrated reflected radiance (“light curves”) at different wavelengths and create a pool of possible observations of Earth-like exoplanets. We use the two-stream-exact-single-scattering line-by-line radiative transfer model to build the Earth Spectrum Simulator (ESS) and reconstruct DSCOVR/EPIC spectral observations. Atmospheric effects, such as scattering by air molecules, clouds, aerosols, and gaseous absorption, are included. Surface contributions are treated using appropriate bidirectional reflectance distribution functions. We simulate ∼300 images in each channel for observations collected in 2016, with a spatial resolution of ∼2000 pixels over the visible disk. ESS provides a simultaneous fit to the observed light curves, with time-averaged reflectance differences typically less than 7% and root-mean-square errors less than 1%. The only exceptions are in the oxygen absorption channels, where reflectance biases can be as large as 19.55%; this is a consequence of simplified assumptions about clouds; especially their vertical placement. We also recover principal components of the spectrophotometric light curves and correlate them with atmospheric and surface features.

Reduction of Spectral Radiance Reflectance During the Annular Solar Eclipse of 21 June 2020 Observed by EPIC

The annular solar eclipse on 21 June 2020 passed over desert areas (parts of Central and Eastern Africa, the southern Arabian Peninsula), partly cloudy regions (parts of South Asia and the Himalayas), and the mostly cloudy region in East Asia. Moving around the Earth-Sun Lagrange point 1 (L1), the Earth Polychromatic Imaging Camera (EPIC) instrument on the Deep Space Climate Observatory (DSCOVR) spacecraft captured three sets of images of the sunlit Earth during the eclipse, allowing us to study the impact of the solar eclipse on reflected solar radiation when the underlying surface and/or cloudy conditions in the Moon’s shadow are quite different. We analyzed EPIC images acquired during the 21 June 2020 and 21 August 2017 eclipses. We found that (1) EPIC-observed average spectral as well as spectrally averaged reflectance reductions of the entire sunlit Earth during the 21 June 2020 solar eclipse are distinctly different from those during the total solar eclipse of 21 August 2017; (2) the reduction of spectral reflectance depends strongly on underlying reflector properties, including the brightness, the area coverage of each reflector in the penumbra and the average distance to the center of the Moon’s shadow.

Unique Observational Constraints on the Seasonal and Longitudinal Variability of the Earth’s Planetary Albedo and Cloud Distribution Inferred From EPIC Measurements

Thorough comparison to observations is key to developing a credible climate model forecasting capability. Deep Space Climate Observatory (DSCOVR) measurements of Earth’s reflected solar and emitted thermal radiation provide a unique observational perspective that permits a more reliable model/data comparison than is possible with the otherwise available satellite data. The uniqueness is in the DSCOVR satellite’s viewing geometry, which enables continuous viewing of the Earth’s sunlit hemisphere from its Lissajous orbit around the Lagrangian L1 point. The key instrument is the Earth Polychromatic Imaging Camera (EPIC), which views the Earth’s sunlit hemisphere with 1024-by-1024-pixel imagery in 10 narrow spectral bands from 317 to 780 nm, acquiring up to 22 high spatial resolution images per day. The additional feature is that the frequency of EPIC image acquisition is nearly identical to that of the climate GCM data generation scheme where climate data for the entire globe are ‘instantaneously’ calculated at 1-h radiation time-step intervals. Implementation of the SHS (Sunlit Hemisphere Sampling) EPIC-view geometry for the in-line GCM output data sampling establishes a precise self-consistency in the space-time data sampling between EPIC observational and GCM output data generation and sampling. The remaining problem is that the GCM generated data are radiative fluxes, while the EPIC measurements are backscatter-dependent radiances. Radiance to flux conversion is a complex problem with no simple way to convert GCM radiative fluxes into spectral radiances. The more expedient approach is to convert the EPIC spectral radiances into broadband radiances by MODIS/CERES-based regression relationships and then into solar radiative fluxes using the CERES angular distribution models. Averaging over the sunlit hemisphere suppresses the meteorological weather noise, but preserves the intra-seasonal larger scale variability. Longitudinal slicing by the Earth’s rotation permits a self-consistent model/data comparison of the longitudinal model/data differences in the variability of the reflected solar radiation. Ancillary EPIC Composite data provide additional cloud property information for climate model diagnostics. Comparison of EPIC-derived seasonal and longitudinal variability of the Earth’s planetary albedo with the GISS ModelE2 results shows systematic overestimate of cloud reflectivity over the Pacific Ocean with corresponding underestimates over continental land areas.

EPIC/DSCOVR as a Pathfinder in Cloud Remote Sensing Using Differential Oxygen Absorption Spectroscopy

We argue that the Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate ObserVatoRy (DSCOVR) platform has blazed new pathways in observational technology, starting with its ∼ 1.5 × 106 km stand-off distance, but also in remote sensing science. We focus here on EPIC’s two oxygen absorption channels that 1) are unique in their spectral sampling and 2) have stimulated deep innovation in cloud remote sensing using Differential Oxygen Absorption Spectroscopy (DO2AS). Although first formulated 6 decades ago, DO2AS-based cloud probing from overhead assets is still an emerging observational technique. It is indeed somewhat paradoxical that one should use absorption by a gas to assay scattering by particles. After surveying the history of space-based DO2AS, and looking into its future, we see that EPIC/DSCOVR marks an inflection point in this important development. EPIC’s unique DO2AS capability motivated a notable sequence of papers revisited here. This research indeed spawned a rare occurrence of information content analysis coming from radically different—yet complementary—perspectives. First, we adopted the increasingly popular machinery of optimal estimation (OE) that is grounded in Bayesian statistics and uses a somehow linearized radiative transfer (RT) model. Nonetheless, OE feels like a black-box algorithm that outputs a number of “degrees of freedom” (a.k.a. independent pieces of information about clouds under observation). However, the very same conclusions are reached using fully transparent physics-based modeling for the RT, with a few approximations that enable closed-form analytical formulation. Lastly, we preview a novel DO2AS technique for regaining shortwave sensitivity to cloud optical thickness past the threshold where cloud reflectivity flattens off.

Temporal Variation of the Shortwave Spherical Albedo of the Earth

The Earth’s spherical albedo describes the ratio of light reflected from the Earth to that incident from the Sun, an important variable for the Earth’s radiation balance. The spherical albedo has been previously estimated from satellites in low-Earth orbits, and from light reflected from the Moon. We developed a method to derive the Earth’s spherical shortwave albedo using the images from the Earth Polychromatic Imaging Camera (EPIC) on board National Oceanic and Atmospheric Administration’s (NOAA) Deep Space Climate Observatory (DSCOVR). The satellite is located in the Lagrange 1 point between the Earth and the Sun and observes the complete illuminated part of the Earth at once. The method allows us to provide continuously updated spherical albedo time series data starting from 2015. This time series shows a systematic seasonal variation with the mean annual albedo estimated as 0.295±0.008 and an exceptional albedo maximum in 2020, attributed to unusually abundant cloudiness over the Southern Oceans.

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

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 ultraviolet (UV) radiances used for dust monitoring, and ultimately shortwave dust direct radiative effects (DREs). 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 over land using aerosol optical depth (AOD) and the 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-oxide 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. The Niger site (13.52∘ N, 2.63∘ E) is dominated by goethite over the Harmattan season with a median of ∼ 2 weight percentage (wt %) of iron oxide. The Saudi Arabia site (27.49∘ N, 41.98∘ E) over the Middle East also exhibited a surge of goethite content with the beginning of the 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 the constraining of the hematite refractive index. Specifically, we select 5 out of 13 different hematite refractive indices that are widely variable in published laboratory studies by constraining the iron-oxide mass ratio to the known measured values. The provided climatology of hematite and goethite mass fractions across the main dust regions of Earth will be useful for dust shortwave DRE studies and climate modeling.

An Overview of Neural Network Methods for Predicting Uncertainty in Atmospheric Remote Sensing

In this paper, we present neural network methods for predicting uncertainty in atmospheric remote sensing. These include methods for solving the direct and the inverse problem in a Bayesian framework. In the first case, a method based on a neural network for simulating the radiative transfer model and a Bayesian approach for solving the inverse problem is proposed. In the second case, (i) a neural network, in which the output is the convolution of the output for a noise-free input with the input noise distribution; and (ii) a Bayesian deep learning framework that predicts input aleatoric and model uncertainties, are designed. In addition, a neural network that uses assumed density filtering and interval arithmetic to compute uncertainty is employed for testing purposes. The accuracy and the precision of the methods are analyzed by considering the retrieval of cloud parameters from radiances measured by the Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR).

Global quasi-daily fractional vegetation cover estimated from the DSCOVR EPIC directional hotspot dataset

Fractional Vegetation Cover (FVC) represents the planar fraction of the land-surface covered by green foliage, and its dynamics are important for an enhanced understanding of ecosystems especially how they respond to climate change. The lack of global near-real-time satellite-based products restricts the application of FVC in ecosystem modeling, climate change, and vegetation phenology studies. Earth Polychromatic Imaging Camera (EPIC) onboard Deep Space Climate Observatory (DSCOVR) spacecraft provides daily spectral reflectance of the entire sunlit Earth in the near Hotspot directions. Hotspot observations (i.e., observation in Hotspot direction which has the peak backscattering reflected radiation) with only sunlit vegetation and sunlit soil components are more suitable for FVC estimation with a two-endmember mixture model as such observations exclude contributions from shaded vegetation and soil components. In this study, an algorithm for retrieving quasi-daily FVC from EPIC based on two-endmember mixture and gap fraction models is developed. Analyses of its performance predict that the average Root-Mean-Square Deviations (RMSDs) of retrievals in FVC units is below 0.050 when compared with reference values. The RMSD is 0.043 when compared to field-based Landsat reference FVC, which confirms lower retrieval uncertainty than FVC retrieved from Low-Earth-Orbit (LEO) satellite products such as MODIS, VIIRS, and GEOV2 with RMSDs 0.049– 0.087. The comparison analyses suggest a good consistency between EPIC FVC and FVC products from LEO and geostationary (GEO) satellites sensor, SEVIRI, with RMSD values less than 0.129. EPIC allows for quasi-daily FVC estimation across the global terrestrial surface at 10 km resolution, which is an important development for numerous biophysical applications.

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 understand their differences in spatial and temporal variations (seasonal and diurnal cycle timescale) with respect to the observations from the Earth Polychromatic Imaging Camera (EPIC) on the 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 observations, 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 it in the afternoon, while the opposite is the case in the reanalyses.

Earth Polychromatic Imaging Camera Geolocation; Strategies to Reduce Uncertainty

Earth Polychromatic Imaging Camera occupies a unique point of view for an Earth imager by being located approximately 1.5 million km from the planet at Earth-Sun Lagrange point, L1. This creates a number of unique challenges in geolocation, some of which are distance and mission specific. To solve these problems, algorithmic adaptations need to be made for calculations used for standard geolocation solutions, as well as artificial intelligence-based corrections for star tracker attitude and optical issues. This paper discusses methods for resolving these issues and bringing the geolocation solution to within requirements.

High-spatiotemporal-resolution estimation of solar energy component in the United States using a new satellite-based model

Diffuse solar radiation (Rd), known as an important component of global solar radiation (Rg), is a key parameter for solar energy related applications and ecosystem photosynthesis. Some meteorological models have been developed to estimate Rd with acceptable accuracy, but their spatial scales are often small due to the limited meteorological station number. Satellite-based models provide accurate and large-scale Rg estimates. However, remote sensing estimations of Rd are often with low spatial resolutions and large uncertainties, because their methods were based on inaccurate surface and atmospheric parameters. To address these challenges, the high-spatiotemporal-resolution (half-hourly and 1-km) Rd in the United States was estimated using the top-of-atmosphere (TOA) data from the new-generation geostationary satellite Geostationary Operational Environmental Satellites (GOES-16) and the method iterative random forest (RF). The results showed that the iterative RF model had higher accuracy than the simple RF and artificial neural network (ANN) models, and using TIR (thermal infrared) bands can improve models’ accuracy. The best model can estimate half-hourly Rd with the accuracy R2 = 0.88, RMSE = 37.81 W/m2, and MBE = 0.01 W/m2. Compared with the previous 0.01-degree (∼11-km) Rd product Earth Polychromatic Imaging Camera (EPIC), the GOES-16 estimated 1-km Rd had similar spatial patterns. Moreover, based on the GOES-16 estimated half-hourly and 1-km Rd in the United States, the spatiotemporal heterogeneity of Rd was quantitatively observed. The proposed approach can be used to produce more high-spatiotemporal-resolution Rd products, and these products are very helpful for many solar-related research topics and industrial applications.

Vegetation Angular Signatures of Equatorial Forests From DSCOVR EPIC and Terra MISR Observations

In vegetation canopies cross-shading between finite dimensional leaves leads to a peak in reflectance in the retro-illumination direction. This effect is called the hot spot in optical remote sensing. The hotspot region in reflectance of vegetated surfaces represents the most information-rich directions in the angular distribution of canopy reflected radiation. This paper presents a new approach for generating hot spot signatures of equatorial forests from synergistic analyses of multiangle observations from the Multiangle Imaging SpectroRadiometer (MISR) on Terra platform and near backscattering reflectance data from the Earth Polychromatic Imaging Camera (EPIC) onboard NOAA’s Deep Space Climate Observatory (DSCOVR). A canopy radiation model parameterized in terms of canopy spectral invariants underlies the theoretical basis for joining Terra MISR and DSCOVR EPIC data. The proposed model can accurately reproduce both MISR angular signatures acquired at 10:30 local solar time and diurnal courses of EPIC reflectance (NRMSE < 9%, R2 > 0.8). Analyses of time series of the hot spot signature suggest its ability to unambiguously detect seasonal changes of equatorial forests.