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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS2003

ICESat Laser Altimeter Pointing, Ranging and Timing Calibration from Integrated Residual Analysis: A Summary of Early Mission Results

On January 12, 2003 the Ice, Cloud and land Elevation Satellite (ICESat) was successfully placed into orbit. The ICESat mission carries the Geoscience Laser Altimeter System (GLAS), which consists of three near-infrared lasers that operate at 40 short pulses per second. The instrument has collected precise elevation measurements of the ice sheets, sea ice roughness and thickness, ocean and land surface elevations and surface reflectivity. The accurate geolocation of GLAS's surface returns, the spots from which the laser energy reflects on the Earth's surface, is a critical issue in the scientific application of these data Pointing, ranging, timing and orbit errors must be compensated to accurately geolocate the laser altimeter surface returns. Towards this end, the laser range observations can be fully exploited in an integrated residual analysis to accurately calibrate these geolocation/instrument parameters. Early mission ICESat data have been simultaneously processed as direct altimetry from ocean sweeps along with dynamic crossovers resulting in a preliminary calibration of laser pointing, ranging and timing. The calibration methodology and early mission analysis results are summarized in this paper along with future calibration activities.

Scott B Luthcke↗
NASA NTRSDate not supplied

Artificial Neural Network (ANN) Surface Longwave and Shortwave Fluxes Trained on CERES Observations

The Clouds and Earth’s Radiant Energy System (CERES) project provides satellite-based observations of the radiative fluxes and clouds systems. CERES climate quality data products typically take several months of calibration and validation before release to the public. The Fast Longwave and Shortwave Radiative Flux (FLASHFlux) data product was developed to provide key data for the applied sciences and educational users within a week of observation. FLASHFlux achieves this by using simplified calibration, an operational meteorological product from Global Modeling and Assimilation Office (GMAO), and its own surface parameterizations model. The CERES FLASHFlux provides two data products: 1) an hourly Level 2 Single Scanner Footprint (SSF) data separately for Terra and NOAA-20 observations, and 2) a daily Level 3 Time Interpolated and Spatially Averaged (TISA) 1o x 1o gridded data that combines Terra and NOAA-20 observations. Currently, FLASHFlux uses the Langley Parameterized Shortwave Algorithm (LPSA) and Langley Parameterized Longwave Algorithm (LPLA) to derive its surface fluxes (Kratz et al., 2010; Gupta et al, 2001). A new Machine Learning (ML) based approach using Artificial Neural Networks to derive Surface Longwave (LW) & Shortwave (SW) fluxes based on training data from the CERES Clouds Radiative Swath (CRS) product is being investigated to replace LPSA and LPLA in the SSF surface flux products. One of the biggest hurdles in training ML model is model fitting. To overcome the problem of overfitting we use feature engineering that helps in finding the important feature and remove features that are irrelevant to the model. In our training we employed the Leave-One-Feature-Out Importance (LOFO) to evaluate the significance of each feature in our training. We intercompare ANN fluxes against surface fluxes produced from the Fu-Liou model in CRS and the LPSA/LPLA in FLASHFlux SSF. Furthermore, we validated ANN derived fluxes to the Baseline Surface Radiation Network (BSRN).

P C Sawaengphokhai↗
NASA NTRS1994

Application of airborne field mill data for use in launch support

An airborne field mill (ABFM) system was implemented on a Learjet 36A and was used to collect electric-field and microphysical data for summertime convective clouds near Cape Canaveral and the Kennedy Space Center. This system is described and the method used for calibrating it is outlined. Data from this ABFM system were used to investigate the relationship between the strengths of electric fields at the boundaries of summer maritime convective clouds and the distances from these cloud where the fields could first be detected by this system. The relationship between the electric-field strengths at the boundaries of convective clouds and selected radar-measurable parameters of these clouds were also investigated. This study shows promise that the radar volume of these clouds can be used to estimate the order of magnitude electric-field intensity. It is possible that selected remote radar measurements could be incorporated into the launch commit criteria as a means of providing greater confidence and flexibility for determining safe stand-off distances for launch vehicles from convective clouds.

Harris-Hobbs, Ray↗
NASA NTRS2022

Cloud Mask Intercomparison eXercise (CMIX): An evaluation of cloud masking algorithms for Landsat 8 and Sentinel-2

Cloud cover is a major limiting factor in exploiting time-series data acquired by optical spaceborne remote sensing sensors. Multiple methods have been developed to address the problem of cloud detection in satellite imagery and a number of cloud masking algorithms have been developed for optical sensors but very few studies have carried out quantitative intercomparison of state-of-the-art methods in this domain. This paper summarizes results of the first Cloud Masking Intercomparison eXercise (CMIX) conducted within the Committee Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). CEOS is the forum for space agency coordination and cooperation on Earth observations, with activities organized under working groups. CMIX, as one such activity, is an international collaborative effort aimed at intercomparing cloud detection algorithms for moderate-spatial resolution (10–30 m) spaceborne optical sensors. The focus of CMIX is on open and free imagery acquired by the Landsat 8 (NASA/USGS) and Sentinel-2 (ESA) missions. Ten algorithms developed by nine teams from fourteen different organizations representing universities, research centers and industry, as well as space agencies (CNES, ESA, DLR, and NASA), are evaluated within the CMIX. Those algorithms vary in their approach and concepts utilized which were based on various spectral properties, spatial and temporal features, as well as machine learning methods. Algorithm outputs are evaluated against existing reference cloud mask datasets. Those datasets vary in sampling methods, geographical distribution, sample unit (points, polygons, full image labels), and generation approaches (experts, machine learning, sky images). Overall, the performance of algorithms varied depending on the reference dataset, which can be attributed to differences in how the reference datasets were produced. The algorithms were in good agreement for thick cloud detection, which were opaque and had lower uncertainties in their identification, in contrast to thin/semi-transparent clouds detection. Not only did CMIX allow identification of strengths and weaknesses of existing algorithms and potential areas of improvements, but also the problems associated with the existing reference datasets. The paper concludes with recommendations on generating new reference datasets, metrics, and an analysis framework to be further exploited and additional input datasets to be considered by future CMIX activities.

Sergii Skakun↗
DOE OSTI2022

Adaptive stabilization of quantum circuits executed on unstable devices

Conventional computers have evolved to device components that demonstrate failure rates of 10 −17 or less, while current quantum computing devices typically exhibit error rates of 10 −2 or greater. This raises concerns about the reliability and reproducibility of the results obtained from quantum computers. The problem is highlighted by experimental observation that today’s NISQ devices are inherently unstable. Remote quantum cloud servers typically do not provide users with an ability to calibrate the device themselves. Using inaccurate characterization data for error mitigation can have devastating impact on reproducibility. In this study, we investigate if one can infer the critical channel parameters dynamically from the noisy binary output of the executed quantum circuit and use it to improve program stability. An open question however is how well does this methodology scale. We discuss the efficacy and efficiency of our adaptive algorithm using canonical quantum circuits such as the uniform superposition circuit. Our metric of performance is the Hellinger distance between the post-stabilization observations and the reference (ideal) distribution.

Dasgupta, Samudra↗
DOE OSTI2022

High temporal resolution estimates of Arctic snowfall rates emphasizing gauge and radar-based retrievals from the MOSAiC expedition

This article presents the results of snowfall rate and accumulation estimates from a vertically pointing 35-GHz radar and other sensors deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The radar-based retrievals are the most consistent in terms of data availability and are largely immune to blowing snow. The total liquid-equivalent accumulation during the snow accumulation season is around 110 mm, with more abundant precipitation during spring months. About half of the total accumulation came from weak snowfall with rates less than approximately 0.2 mmh–1. The total snowfall estimates from a Vaisala optical sensor aboard the icebreaker are similar to those from radar retrievals, though their daily and monthly accumulations and instantaneous rates varied significantly. Compared to radar retrievals and the icebreaker optical sensor data, measurements from an identical optical sensor at an ice camp are biased high. Blowing snow effects, in part, explain differences. Weighing gauge measurements significantly overestimate snowfall during February–April 2020 as compared to other sensors and are not well suited for estimating instantaneous snowfall rates. The icebreaker optical disdrometer estimates of snowfall rates are, on average, relatively little biased compared to radar retrievals when raw particle counts are available and appropriate snowflake mass-size relations are used. These counts, however, are not available during periods that produced more than a third of the total snowfall. While there are uncertainties in the radar-based retrievals due to the choice of reflectivity-snowfall rate relations, the major error contributor is the uncertainty in the radar absolute calibration. The MOSAiC radar calibration is evaluated using comparisons with other radars and liquid water cloud–drizzle processes observed during summer. Overall, this study describes a consistent, radar-based snowfall rate product for MOSAiC that provides significant insight into Central Arctic snowfall and can be used for many other purposes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Impact of Stochastic Perturbations in Physics Variables for Predicting Surface Solar Irradiance

We present a probabilistic framework tailored for solar energy applications referred to as the Weather Research and Forecasting-Solar ensemble prediction system (WRF-Solar EPS). WRF-Solar EPS has been developed by introducing stochastic perturbations into the most relevant physical variables for solar irradiance predictions. In this study, we comprehensively discuss the impact of the stochastic perturbations of WRF-Solar EPS on solar irradiance forecasting compared to a deterministic WRF-Solar prediction (WRF-Solar DET), a stochastic ensemble using the stochastic kinetic energy backscatter scheme (SKEBS), and a WRF-Solar multi-physics ensemble (WRF-Solar PHYS). The performances of the four forecasts are evaluated using irradiance retrievals from the National Solar Radiation Database (NSRDB) over the contiguous United States. We focus on the predictability of the day-ahead solar irradiance forecasts during the year of 2018. The results show that the ensemble forecasts improve the quality of the forecasts, compared to the deterministic prediction system, by accounting for the uncertainty derived by the ensemble members. However, the three ensemble systems are under-dispersive, producing unreliable and overconfident forecasts due to a lack of calibration. In particular, WRF-Solar EPS produces less optically thick clouds than the other forecasts, which explains the larger positive bias in WRF-Solar EPS (31.7 W/m 2 ) than in the other models (22.7–23.6 W/m 2 ). This study confirms that the WRF-Solar EPS reduced the forecast error by 7.5% in terms of the mean absolute error (MAE) compared to WRF-Solar DET, and provides in-depth comparisons of forecast abilities with the conventional scientific probabilistic approaches (i.e., SKEBS and a multi-physics ensemble). Guidelines for improving the performance of WRF-Solar EPS in the future are provided.

14 SOLAR ENERGY↗
NASA NTRS1993

Investigation of passive atmospheric sounding using millimeter and submillimeter wavelength channels

Activities within the period from July 1, 1992 through December 31, 1992 by Georgia Tech researchers in millimeter and submillimeter wavelength tropospheric remote sensing have been centered around the calibration of the Millimeter-wave Imaging Radiometer (MIR), preliminary flight data analysis, and preparation for TOGA/COARE. The MIR instrument is a joint project between NASA/GSFC and Georgia Tech. In the current configuration, the MIR has channels at 90, 150, 183(+/-1,3,7), and 220 GHz. Provisions for three additional channels at 325(+/-1,3) and 8 GHz have been made, and a 325-GHz receiver is currently being built by the ZAX Millimeter Wave Corporation for use in the MIR. Past Georgia Tech contributions to the MIR and its related scientific uses have included basic system design studies, performance analyses, and circuit and radiometric load design, in-flight software, and post-flight data display software. The combination of the above millimeter wave and submillimeter wave channels aboard a single well-calibrated instrument will provide unique radiometric data for radiative transfer and cloud and water vapor retrieval studies. A paper by the PI discussing the potential benefits of passive millimeter and submillimeter wave observations for cloud, water vapor and precipitation measurements has recently been published, and is included as an appendix.

Gasiewski, Albin J.↗
NASA NTRS1994

The two period-luminosity relations for population I Cepheids

We summarize the evidence that most Population I Cepheids with periods less than approximately 8 days pulsate in the first overtone mode. Fundamental model and first overtone pulsators must follow different period-luminosity (P - L) relations. We demonstrate these different relations for different stellar systems, especially for the calibrating Cepheids in clusters and for Cepheids in the Large Magellanic Cloud (LMC), the Small Magellanic Cloud (SMC), M31, and IC 4182.

Bohm-Vitense, Erika↗
NASA NTRS2000

Absorption of Solar Radiation by Clouds: An Overview

This talk provides an overview of the subject of absorption of solar radiation by clouds in the earth's atmosphere. The paper summarizes the available evidence which points to disagreements between theoretical and observed values of cloud absorption (and reflections). The importance of these discrepancies, particularly to remote sensing of clouds as well as to studies of cloud physics and earth radiation budgets, is emphasized. Existing cloud absorption and reflection measurements are reviewed and the persistent differences that exist between calculated and measured near-infrared cloud albedos are highlighted. Various explanations for these reflection and absorption discrepancies are discussed under two separate paths: a theoretician's approach and an experimentalist's approach. Examples for the former approach include model accuracy tests, large-droplet hypothesis, excess absorbing aerosol, enhanced water vapor continuum absorption, and effects of cloud inhomogeneity. The latter approach focuses on discussions of instrumental device, calibration, operational strategy, and signal/noise separation. A recommendation for future activities on this subject will be given.

Tsay, Si-Chee↗
NASA NTRS2000

A Plan for Measuring Climatic Scale Global Precipitation Variability: The Global Precipitation Mission

The outstanding success of the Tropical Rainfall Measuring Mission (TRMM) stemmed from a near flawless launch and deployment, a highly successful measurement campaign, achievement of all original scientific objectives before the mission life had ended, and the accomplishment of a number of unanticipated but important additional scientific advances. This success and the realization that satellite rainfall datasets are now a foremost tool in the understanding of decadal climate variability has helped motivate a comprehensive global rainfall measuring mission, called 'The Global Precipitation Mission' (GPM). The intent of this mission is to address looming scientific questions arising in the context of global climate-water cycle interactions, hydrometeorology, weather prediction, the global carbon budget, and atmosphere-biosphere-cryosphere chemistry. This paper addresses the status of that mission currently planed for launch in the early 2007 time frame. The GPM design involves a nine-member satellite constellation, one of which will be an advanced TRMM-like 'core' satellite carrying a dual-frequency Ku-Ka band radar (df-PR) and a TMI-like radiometer. The other eight members of the constellation can be considered drones to the core satellite, each carrying some type of passive microwave radiometer measuring across the 10.7-85 GHz frequency range, likely based on both real and synthetic aperture antenna technology and to include a combination of new lightweight dedicated GPM drones and both co-existing operational and experimental satellites carrying passive microwave radiometers (i.e., SSM/l, AMSR, etc.). The constellation is designed to provide a minimum of three-hour sampling at any spot on the globe using sun-synchronous orbit architecture, with the core satellite providing relevant measurements on internal cloud precipitation microphysical processes. The core satellite also enables 'training' and 'calibration' of the drone retrieval process. Additional information is contained in the original extended abstract.

Smith, Eric A.↗
NASA NTRS2004

Application of Atmospheric Infrared Sounder (AIRS) Data to Climate Research

The application of hyper spectral radiometric data to climate research requires very high absolute radiometric accuracy and stability. We use cloud-free tropical ocean data from the Atmospheric InfraRed Sounder (AIR) Calibration Data Subset (ADCS) to show that the radiometric precision and stability required climate applications has been achieved. The sea surface skin temperatures derived from the AIRS 2616cm-1 super window channel are stable relative to the RTG.SST at the better than 8 mK/year level, and the spectral calibration is stable at the 1 ppm/year level. The excellent stability and accuracy are the result of the implementation of AIRS as a grating array spectrometer, which is cooled and stabilized within 10 mK at 155 K. Analysis of daily measurements of the temperature gradient between the surface and 7 km altitude show that the AIRS Calibration Data Subset has applications which extend its original intent for calibration support to climate research. The Atmospheric Infrared Sounder (AIRS) on the EOS Aqua satellite was launched into polar orbit in May 2002. AIRS covers the spectral region from 640 to 2700 cm-1 with 2378 independent channels and represents the first of a new generation of hyper spectral resolution sounders in support of global sounding data for weather forecasting and climate research.

infrared↗
NASA NTRS2011

TPSAS-NF1676L-13349-DND

Progress on the effort to calibrate historical AVHRR solar channels and derive a CERES-consistent cloud climatology form AVHRR data is presented.

Patrick Minnis↗
NASA NTRSDate not supplied

TPSAS-NF1676L-29931-DND

The ACE science working group satellite concept calls for a combination of high spectral resolution aerosol lidar and a polarimeter able to determine vertical profiles of aerosol extinction and clouds. Here we discuss why an HSRL lidar is required to achieve these requirements. The primary advantage of an HSRL over an elastic backscatter lidar is that the HSRL channel allows for the independent retrieval of vertically resolved aerosol extinction without constraint. Not requiring a constraint means greater availability of retrievals in polar regions, on the night side, and in broken cloud situations, plus greater accuracy particularly in the lowest part of the atmosphere and in scenes with aerosol heterogeneity. Advantages go beyond the extinction products. Unlike attenuated backscatter, which is an integrated quantity, the aerosol backscatter coefficient depends only on the aerosol at a specific level in the atmosphere, making it preferable for use in quantitative applications. The aerosol backscatter coefficient is more accurate from an HSRL system compared to the retrieval from a single-channel system. This is because errors in both extinction and backscatter from a single-channel system increase with decreasing altitude, due to the need to accurately characterize the overlying attenuation. This means that an HSRL will also have more valid data in and near the boundary layer where aerosol is of primary interest for air quality and weather applications and for many climate objectives including aerosol-cloud interaction studies. Even cloud properties that do not directly use the HSRL capability (cloud phase and thin cirrus retrievals) benefit from the more accurate calibration of an HSRL system. The improved accuracy of backscatter and extinction also lead to better capabilities for aerosol typing, improved retrievals of aerosol radiative effect, and increased information content for combined lidar + polarimeter retrievals of profiles of microphysical properties such as vertically resolved effective radius.

S P Burton↗
NASA NTRSDate not supplied

On the use of DSCOVR EPIC to monitor the visible calibration stability of polar orbiting imagers to improve the next edition of the CERES climate data record.

The NASA CERES project has provided the climate quality observed TOA and computed surface fluxes to the scientific community. CERES instruments are onboard the Terra, Aqua, NPP, and NOAA-20 spacecraft. CERES uses MODIS and VIIRS imagers to retrieve cloud properties needed to convert CERES footprint radiance observations into fluxes using empirically derived angular directional models obtained during early CERES record. CERES utilizes geostationary sensors to infer the regional diurnal fluxes in between Terra and Aqua CERES observations. 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 Aqua-MODIS, SNPP-VIIRS, and NOAA-20 VIIRS afternoon imagers will also need to be radiometrically scaled to the same common calibration reference. Although all three imagers employ onboard solar diffusers, the calibration is not consistent over time due to instrument anomalies and ageing of the onboard calibrators. The CERES imager and GEO calibration group (IGCG) has been tasked to remove the imager channel calibration drifts for the next CERES reprocessing effort. The team will rely primarily on deep convective clouds, desert, and polar ice invariant targets to monitor the imager channel stability. The team uses ray-matched radiance pairs to radiometrically scale the SNPP and NOAA-20 VIIRS sensors to Aqua-MODIS. The radiometric scaling is further validated using geostationary imagers as transfer radiometers. 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 instrument onboard DSCOVR employs a CCD array to image the Earth approximately every 2-hours. The EPIC sensor contains no onboard calibration systems. However, multiple inter-calibration studies have shown that the EPIC imager is very stable in time. This is likely due to the DSCOVR satellite being located about a 1.5M km from the Earth, where very little Earth reflected solar radiation degrades the optics. The excellent radiometric stability of EPIC allows the CERES IGCG to utilize the EPIC observations as a stable reference for monitoring the calibration stability of the three afternoon 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 NTRS2024

CLARREO Pathfinder Solar Diffuser Calibration Progress

Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder (CPF) mission’s Hyperspectral Imager for Climate Science (HySICS) instrument’s transmissive flight diffuser calibration is presented. The absolute Bidirectional Transmittance Distribution Function (BTDF) measurement of the transmissive diffuser is needed to calculate the instrument’s absolute efficiency. Along with a known solar irradiance source such as Total Solar Irradiance Sensor (TSIS), it can provide an absolute irradiance measurement path on orbit, with NIST traceability. This provides an additional path for CPF to cross compare with other on orbit sensors’ measurement such as Visible-Infrared Imaging Radiometer Suite (VIIRS), Clouds and the Earth’s Radiant Energy System (CERES). The flight diffuser was calibrated at NASA’s Goddard Space Flight Center (GSFC) using the Facility’s Optical Scatterometer.

Bidirectional Transmittance Distribution Function↗
NASA NTRSDate not supplied

TPSAS-NF1676L-10735-DND

The Clouds and the Earth's Radiant Energy System (CERES) project has now surpassed the 10-year mark, has combined rigorous calibration, improved scene identification, and multi-satellite data fusion to produce a climate-accuracy data set of global 1° gridded radiative surface and TOA fluxes, and cloud properties. CERES provides the climate community with four observed TOA flux products. The ERBE-like product uses the same algorithms as ERBE in order to be compared with the ERBE time period. The SSF product uses the improved scene identification and radiance to flux conversion. The SYN product uses GEO derived clouds and fluxes to provide diurnally complete TOA fluxes. The EBAF product provides net balanced fluxes tied to the ocean heat storage term. This product is suited for climate modelers estimating the global mean energy budget. Each successive CERES flux product applies the improvements over the previous product. This presentation will focus on the diurnal averaging improvements by the inclusion of imager radiances from 5 geostationary (GEO) satellites at 3-hourly intervals to estimate the flux in between CERES observations. The regional monthly mean flux differences based on SYN (CERES/GEO) and SSF (CERES-only) can exceed 20 Wm^-2. CERES shortwave and longwave fluxes have been rigorously validated and are key elements in evaluating the ability of climate models to predict both past and future climate states. The CERES normalization of GEO derived fluxes in order to maintain the CERES instrument calibration and are consistent globally within 0.1 Wm^-2, even if the GEO calibration is artificially altered by ±5%, twice the anticipated calibration error. The predicted GEO TOA fluxes normalized to Terra have been compared at Aqua times to Aqua flux observations, as well as with GERB hourly fluxes over the METEOSAT domain. Similarly ground based radiometers have been used to validate the CERES radiative transfer computed surface fluxes and have confirmed that the CERES/GEO fluxes have improved the computed surface flux product. Although the GEO based fluxes are of inferior quality then the CERES observed fluxes, they capture the diurnal signal more effectively than using constant meteorology between CERES measurements. The validation of the GEO derived fluxes and clouds will be shown.

D. R. Doelling↗
NASA NTRS2019

The Inter-Calibration of the DSCOVR EPIC Imager 2 with Aqua-MODIS and NPP-VIIRS

The Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) continuously observes the illuminated disk from the Lagrange-1 point. The EPIC sensor was designed to monitor the diurnal variation of ozone, clouds, aerosols, and vegetation, especially those features that benefit from observation near-backscatter conditions. The EPIC sensor does not contain any onboard calibration systems. This study describes the inter-calibration of EPIC channels 5 (0.44 μm), 6 (0.55 μm), 7 (0.68 μm), and 10 (0.78 μm) with respect to Aqua-MODIS and NPP-VIIRS. The calibration is transferred using coincident ray-matched reflectance pairs over all-sky tropical ocean (ATO) and deep convective cloud (DCC) targets. A robust and automated image-alignment technique based on feature matching was formulated to improve the navigation quality of the EPIC images. The EPIC V02 dataset exhibits improved navigation over V01. Because the visible channels display similar spatial features, a single visible channel can be used to co-register the remaining visible bands. The VIIRS-referenced EPIC ATO and DCC ray-matched calibration coefficients are within 0.3%. The EPIC four-year calibration trends based on VIIRS are within 0.15%/year. The MODIS-based EPIC calibration coefficients were compared against the Geogdzhayev and Marshak 2018 published calibration coefficients and are found to be within 1.6%.

DSCOVR↗