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NASA NTRSDate not supplied

New VIIRS DNB Stray Light Correction Method

The Day/Night Band (DNB) broadband (500-900nm) imager is one of the sensors in the Visible Infrared Imaging Radiometer Suite (VIIRS) on-board the Suomi National Polar-orbiting Partnership (SNPP) and the NOAA-20 satellites, which are polar-orbiting and fly at about 830km above the surface of the Earth. Depending on the satellite location and the sensor viewing geometry, for a short period just before (or after) the satellite enters into (or emerges from) the shadow of the Earth, unwanted stray light, seemingly caused by the reflection of solar beam on the slightly exposed inner side of the leading (or trailing) nadir door, can enter the instrument and impose noticeable impacts to a narrow portion of DNB satellite images in every orbit. The impacted images can be modified to look normal, and the current DNB stray light correction algorithm empirically assigns corrections. There is also an alternative approach using image-processing techniques. Both methods rely mainly on the stray light impacted measurements. To simplify the approach and for better correction results, the new method presented in this paper incorporates the observed asymptotic nature of measurements surrounding the stray light impacted region by fitting to the drop off in sun light from day to night. The new method can be applied to the raw measurements in real-time, and it can help to advance our understanding of this special type of DNB stray light that happens over the polar regions

VIIRS↗
NASA NTRS2020

Continuity of MODIS and VIIRS Snow Cover Extent Data Products for Development of an Earth Science Data Record

An Earth Observing System global snow cover extent data products record at moderate spatial resolution (375–500 m) began in February 2000 with the Moderate-resolution Imaging Spectroradiometer (MODIS) instrument onboard the Terra satellite. The record continued with the Aqua MODIS in July 2002, the Suomi-National Polar Platform (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) in January 2012 and continues with the Joint Polar Satellite System-1 (JPSS-1) VIIRS, launched in November of 2017. The objective of this work is to develop a snow cover extent Earth Science Data Record (ESDR) using different satellites, sensors and algorithms. There are many issues to understand when data from different algorithms and sensors are used over a decade-scale time period to create a continuous dataset. Issues may also arise with sensor degradation and even differences in sensor band locations. In this paper we describe development of an ESDR derived from existing MODIS and VIIRS data products and demonstrate continuity among the products. The MODIS and VIIRS snow cover detection algorithms produce very similar daily snow cover maps, with 90–97% agreement in snow cover extent (SCE) in different landscapes. Differences in SCE between products ranged from 2–15% and are attributable to convolved factors of viewing geometry, pixel spread across a scan and time of observation. Compared at a common grid size of 1 km, there is a mean of 95% agreement in SCE and a difference range of 1–10% between the MODIS and VIIRS SCE maps. Mapping sensor observations to a coarser resolution grid reduces the effect of the factors convolved in the 500 m tile to tile comparisons. We conclude that the MODIS and VIIRS SCE data products are reliable constituents of a moderate-resolution ESDR.

snow cover extent↗
NASA NTRS2000

A comparison of water vapor derived from GPS occultations and global weather analyses

Occultation measurements from the Global Positioning System (GPS) should improve upon this situation. Individual occultations yield profiles of specific humidity accurate to 0.2 to 0.5 g/kg providing sensitive measurements of lower and middle tropospheric water vapor with global coverage in a unique, all-weather, limb-viewing geometry with several hundred metres to a kilometre vertical resolution. The authors have derived water vapor profiles from June 21 to July 4, 1995

global↗
NASA NTRS2019

First Provisional Land Surface Reflectance Product from Geostationary Satellite Himawari-8 AHI

A provisional surface reflectance (SR) product from the Advanced Himawari Imager (AHI) on-board the new generation geostationary satellite (Himawari-8) covering the period between July 2015 and December 2018 is made available to the scientific community. The Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm is used in conjunction with time series Himawari-8 AHI observations to generate 1-km gridded and tiled land SR every 10 minutes during day time. This Himawari-8 AHI SR product includes retrieved atmospheric properties (e.g., aerosol optical depth at 0.47μm and 0.51μm), spectral surface reflectance (AHI bands 1–6), parameters of the RTLS BRDF model, and quality assurance flags. Product evaluation shows that Himawari-8 AHI data on average yielded 35% more cloud-free, valid pixels in a single day when compared to available data from the low earth orbit (LEO) satellites Terra/Aqua with MODIS sensor. Comparisons of Himawari-8 AHI SR against corresponding MODIS SR products (MCD19A1) over a variety of land cover types with the similar viewing geometry show high consistency between them, with correlation coefficients (r) being 0.94 and 0.99 for red and NIR bands, respectively. The high-frequency geostationary data are expected to facilitate studies of ecosystems on daily to diurnal time scales, complementing observations from networks such as the FLUXNET.

Himawari-8 AHI↗
NASA NTRSDate not supplied

Development of the GeoNEX Level 2G Products: Exploiting the Diurnal Variability of TOA Reflectance in Atmospheric Correction

This study develops a new atmospheric correction algorithm to generate the Level 2G products, in particular the gap-filled Surface Reflectance at 10-minute time steps, for the Geostationary-NASA Earth Exchange (GeoNEX) project. The algorithm is based on the MODIS MAIAC (Multi-Angle Implementation of Atmospheric Correction) framework but with significant modifications to exploit angular/temporal information from the diurnal variability of the GeoNEX L1G TOA (Top-of-Atmosphere) reflectance. The algorithm starts by evaluating the roughness/smoothness of the diurnal time series of the TOA reflectance. Because rapid changes in TOA reflectance are generally caused by passing clouds or shadows, rough segments of the time series are automatically filtered out while the smooth segments are further tested for brightness and temperature to identify clear-sky and snow-free observations. Next the algorithm runs the MAIAC RTM (Radiative Transfer Model) to retrieve the Ross-Thick-Li-Sparse (RTLS) BRDF model parameters and the daily-mean atmospheric optical depth (AOD) that allow the RTM to optimally simulate the observed diurnal variability of clear-sky TOA reflectance. Once the initial RTLS parameters are retrieved after the algorithm’s burn-in period, they are used as the prior information to predict the AOD level for the next days, while the subsequent clear-sky observations are used to make necessary adjustments to the RTLS parameters in an continuous fashion. This “prediction-analysis” cycle is then iterated to process the full time series of the L1G data, skipping only total-cloudy days or when surface snow is detected. We tested the algorithm over a list of selected AERONET sites. The retrieved results (the daily mean AOD and the RTLS parameters) reasonably agree with the ground-based measurements. Importantly, the results indicate that the diurnal cycles of surface reflectance are continuous functions of the illumination-view geometry. Thus we can use the retrieved RTLS model to accurately fill in data gaps on partial cloudy days. Also, our algorithm is totally independent from the traditional approaches based on the use of spectral band ratios between the shortwave infrared (e.g., 2.2µm) and the visible (e.g., 0.47µm and 0.64µm) bands. Our results thus demonstrate that the high-frequent diurnal geostationary observations contain unique information that helps us improve atmospheric correction of remote sensing data.

GeoNEX↗
NASA NTRSDate not supplied

Detection of Drizzle and Light Snowfall Over the Southern Ocean Using the GPM Combined Algorithm

Following the 2014 launch of the Global Precipitation Measurement Mission (GPM), an unprecedented combination of coincident active and passive microwave observations are available for state of the art precipitation retrieval. The GPM Combined Algorithm forms the backbone of this effort, optimizing geophysical variables for agreement with the multi-spectral information content. Combined retrievals are then utilized, along with a radiative transfer model, as a database applied for retrievals across a constellation of passive microwave radiometers of varying frequencies and viewing geometries. By keeping such retrievals related through the transfer standard of the combined algorithm, level 3 products such as the Integrated Multi-satellitE Retrievals for GPM (IMERG) are able to provide consistent global products for users at the higher temporal resolution required for hydrological applications on the global scale. In the current version of the combined product, precipitation retrievals are carried out only in the presence of a signal from the active radar. As a result, light precipitation and drizzle below the threshold of radar sensitivity are not included in any of the products down the chain from the constellation to IMERG. In this work, the effects of enhancing the retrievals with an optimal estimation-type (OE) water vapor and cloud retrieval using the passive observations are explored over the Southern Ocean on a regional scale. Non-convergence of the OE in areas with no detectable radar signal is used to identify areas with high probability of light precipitation and drizzle. Microphysical scale characterization of the light precipitation will be explored using information derived from the higher sensitivity CloudSat mission along with model information to identify and associate the related atmospheric state and dynamics. This is a physically-based approach requiring radiometric consistency with all available multi-spectral observations. Retrieved drizzle can then be included in the constellation databases continuing across all scales through to the level 3 products. The technique, successfully demonstrated for the Southern Ocean, can be easily adapted for light precipitation retrieval over other areas and surfaces leading to a global climatology of light precipitation.

Sarah Rinegerud↗
NASA NTRS2021

Compact Thermal Imager (CTI) for Atmospheric Remote Sensing

The demonstration of a newly developed compact thermal imager (CTI) on the International Space Station (ISS) has provided not only a technology advancement but a rich high-resolution dataset on global clouds, atmospheric and land emissions. This study showed that the free-running CTI instrument could be calibrated to produce scientifically useful radiance imagery of the atmosphere, clouds, and surfaces with a vertical resolution of ~460 m at limb and a horizontal resolution of ~80 m at nadir. The new detector demonstrated an excellent sensitivity to detect the weak limb radiance perturbations modulated by small-scale atmospheric gravity waves. The CTI’s high-resolution imaging was used to infer vertical cloud temperature profiles from a side-viewing geometry. For nadir imaging, the combined high-resolution and high-sensitivity capabilities allowed the CTI to better separate cloud and surface emissions, including those in the planetary boundary layer (PBL) that had small contrast against the background surface. Finally, based on the ISS’s orbit, the stable detector performance and robust calibration algorithm produced valuable diurnal observations of cloud and surface emissions with respect to solar local time during May–October 2019, when the CTI had nearly continuous operation.

thermal imager↗
NASA NTRS2022

A Radiative Transfer Simulator for PACE: Theory and Applications

A radiative transfer simulator was developed to compute the synthetic data of all three instruments onboard NASA’s Plankton Aerosol, Cloud, ocean Ecosystem (PACE) observatory, at the top of the atmosphere (TOA). The instrument suite includes the ocean color instrument (OCI), the HyperAngular Rainbow Polarimeter 2 (HARP2), and the Spectro-Polarimeter for Planetary Exploration 1 (SPEXone). The PACE simulator is wrapped around a monochromatic radiative transfer model based on the successive order of scattering (RTSOS), which accounts for atmosphere and ocean coupling, polarization, and gas absorption. Inelastic scattering, including Raman scattering from pure ocean water, fluorescence due to chlorophyll, and colored dissolved organic matter (CDOM), is also simulated. This PACE simulator can be used to explore the sensitivity of the hyperspectral and polarized reflectance of the Earth system with tunable atmosphere and ocean parameters, which include aerosol and cloud number concentration, refractive indices, and size distribution, ocean particle microphysical parameters, and solar and sensor-viewing geometry. The PACE simulator is used to study two important case studies. One is the impact of the significant uncertainty in pure ocean water absorption coefficient to the radiance field in the ultraviolet (UV) spectral region, which can be as much as 6%. The other is the influence of different amounts of brown carbon aerosols and CDOM on the polarized radiance field at TOA. The percentage variation of the radiance field due to CDOM is mostly for wavelengths smaller than 600 nm, while brown aerosols affect the whole spectrum from 350 to 890 nm, primarily due to covaried soot aerosols. Both case studies are important for aerosol and ocean color remote sensing and have not been previously reported in the literature.

PACE↗
NASA NTRS2022

Special Sensor Microwave Imager/Sounder Updates for the Global Precipitation Measurement V07 Data Suite

Observations from the Special Sensor Microwave Imager/Sounder (SSMIS) onboard the Defense Meteorological Satellite Program F16, F17, F18, and F19 spacecrafts provide a significant portion of the microwave radiometer data within the Global Precipitation Measurement (GPM) mission constellation. In preparation for the GPM Version 7 (V07) data release, SSMIS corrections developed over a decade ago and incorporated in GPM Version 5 (V05) are reexamined and updated. The calibration updates presented here include pointing parameters affecting geolocation and viewing geometry, along-scan bias adjustments to account for scan edge falloffs, and sun angle corrections to account for heating anomalies including an emissive reflector. To address errors in the V05 geolocation, the sensor roll, pitch, yaw, half cone angle, and timing offsets are reanalyzed and updated. The along-scan bias adjustment is updated in a manner consistent with recent Special Sensor Microwave Imager updates to account for variations in scene temperature. Finally, significant improvements to the SSMIS sun angle correction are made by using data from the entire mission, extending the corrections to include the higher frequency channels, and deriving a more consistent channel-to-channel correction. From V05 to V07, the geolocation adjustment is approximately 5-10 km, the earth incidence angle difference is 0-0.4°, and the average brightness temperature change is 0-2 K, but individual pixels may be up to several Kelvin difference depending on sensor and channel. The result of these updates is a significant improvement in the quality and long-term consistency of the SSMIS data that are included in the GPM V07 dataset.

Rachael A Kroodsma↗
NASA NTRS2021

Tracking aerosols and SO2 clouds from the Raikoke eruption: 3D view from satellite observations

The 21 June 2019 eruption of the Raikoke volcano (Kuril Islands, Russia; 48° N, 153° E) produced significant amounts of volcanic aerosols (sulfate and ash) and sulfur dioxide (SO2) gas that penetrated into the lower stratosphere. The dispersed SO2 and sulfate aerosols in the stratosphere were still detectable by multiple satellite sensors for many months after the eruption. For this study of SO2 and aerosol clouds we use data obtained from two of the Ozone Mapping and Profiler Suite sensors on the Suomi National Polar-orbiting Partnership satellite: total column SO2 from the Nadir Mapper and aerosol extinction profiles from the Limb Profiler as well as other satellite data sets. We evaluated the limb viewing geometry effect (the “arch effect”) in the retrieval of the LP standard aerosol extinction product at 674 nm. It was shown that the amount of SO2 decreases with a characteristic period of 8–18 d and the peak of stratospheric aerosol optical depth recorded at a wavelength of 674 nm lags the initial peak of SO2 mass by 1.5 months. Using satellite observations and a trajectory model, we examined the dynamics of an unusual atmospheric feature that was observed, a stratospheric coherent circular cloud of SO2 and aerosol from 18 July to 22 September 2019.

Eruption of the Raikoke volcano↗
NASA NTRSDate not supplied

Development of a Global Reference Surface Reflectance and BRDF Datasets from Geostationary Satellite Observations and AERONET Measurements

Surface reflectances and their dependency on illumination-view geometries (i.e., BRDF) are the foundation of many high-level satellite products for land and water monitoring. Yet it is difficult to evaluate the quality of satellite-based surface reflectances with ground-based measurements due to the spatial scale differences. In order to fill the gap, here we develop a reference dataset of surface reflectance and BRDF at the global AERONET sites with data streams from operational geostationary sensors including Himawari 8/9 AHI, GK-2A AMI, and GOES 16/17 ABI. Taking the top-of-atmosphere (TOA) reflectance and the site measured atmospheric aerosol optical depth (AOD) as the main inputs, we apply the GeoNEX-AC algorithm to performance accurate atmospheric correction and derive 10-minute surface reflectance and daily Ross-Thick-Li-Sparse (RTLS) BRDF parameters at AERONET sites where coincident AOD measurements and TOA observations are available from 2016 (for Himawari) or 2018 (for GOES) onwards. The algorithm ensures that the retrieved surface BRDF parameters, along with the site-measured AOD, allow the atmospheric radiative transfer model, SHARM, accurately simulate the observed TOA reflectance at diurnal and longer time scales. They are our best estimates of the surface optical properties and thus can serve as the “reference” to evaluate the performance of operational atmospheric correction algorithms (where AOD is assumed unknown and needs to be retrieved). The reference BRDF also allow us to evaluate the spectral band ratios between the SWIR (e.g., 2200 nm) and the visible (e.g., 650 nm) regions, which are commonly used in operational atmospheric correction algorithms. Finally, we demonstrate that the reference dataset can be used to develop potential data synergies between different GEO satellites as well as GEO-LEO sensors.

Weile Wang↗
NASA NTRSDate not supplied

ARCSTONE: Calibration of Lunar Spectral Reflectance from Space ESTO InVEST 2020

Long-term Objective: - Enable on-orbit high-accuracy absolute calibration for the past, current, and future reflected solar sensors in LEO and GEO by providing lunar spectral irradiance as function of satellite viewing geometry and specified wavelength. IIP Objective (complete): - Design, build, and characterize a prototype instrument demonstrating form-fit-function for a 6U CubeSat. InVEST Objective: - Demonstrate high-accuracy measurements of lunar spectral reflectance, < 0.5% (k=1), via 6U CubeSat on-orbit in LEO for 6 months, with validation and data analysis

ARCSTONE↗
NASA NTRSDate not supplied

A Modeling and Simulation Study for the GeoXO Atmospheric Composition Instrument (ACX): System Level SO2 and O3 Retrieval Performance as a Function of SNR

NOAA’s Geostationary Extended Observations (GeoXO) program is planning to include a hyperspectral UV/visible atmospheric composition instrument in geostationary orbit slated for operations by the early 2030s. Trace gas retrievals provide critical information about our Earth’s system as it relates to both natural and anthropogenic activity. This work focused on the impacts of the signal-to-noise ratio (SNR) on the retrieval uncertainty of SO2 and O3. An end-to-end physics-based imaging system and scene modeling and simulation framework was developed to assess instrument performance considerations in support of the planned GeoXO atmospheric composition instrument (ACX). The framework is composed of three primary components: 1) simulated at-sensor radiance via radiative transfer models of a known atmospheric composition, 2) a noise model driven by previous and planned sensor specifications, and 3) a physical retrieval algorithm. In this analysis, the tropospheric concentrations of SO2 and O3 in the atmospheric column were varied in the boundary layer (0-3 km) and at an elevation of 5 km with a thickness of 1-2 km; the uncertainty in retrieval was studied for various SNR levels for multiple viewing geometries and solar zenith angles. This effort supports the development of the GeoXO ACX instrument by providing a simulated ACX performance assessment of the system-level retrieval performance as a function of SNR, particularly with respect to the wavelength range of interest (approximately 305 – 340 nm) for SO2 and O3 trace gas retrievals.

Monica Cook↗
NASA NTRSDate not supplied

GeoXO Ocean Color Instrument (OCX) Spatial and Temporal Coverage Assessment

NOAA’s next generation Geostationary Extended Observations (GeoXO) satellite system will advance Earth observations from geostationary orbit. GeoXO will supply vital information supporting the U.S. weather, ocean, and climate operations. The recommended three-satellite constellation includes spacecrafts at the current GOES-East and GOES-West positions that will carry an imager, lightning mapper, and an ocean color (OCX) instrument. In this poster we describe an assessment of U.S. Exclusive Economic Zone (EEZ) availability for OCX observations, based on EEZ view geometry, solar angle range over the year, sun glint, as well as cloud climatology. The availability for observations for each EEZ region is estimated per day of the year, as function of atmospheric mass factor (AMF) and sun glint. In addition, cloud climatology – based on statistics derived from three years of GOES-16 ABI cloud mask – is considered to determine the fraction of cloud-free OCX observations for each region as function of time of day and season. The results of such assessments can be used to optimize OCX collections and potentially explore regions outside the EEZ.

Boryana Efremova↗
NASA NTRSDate not supplied

NASA’s Near-Earth Object Observations Program: Abbreviated History and Update

In the wake of Comet Shoemaker-Levy 9’s (D/1993 F2) collision with Jupiter, NASA’s Near-Earth Object (NEO) Observations Program was established more than two decades ago. The NEO Observations Program is responsible for finding, tracking, and characterizing NEOs. Since the Program’s inception in 1998, NASA-funded efforts have discovered more than 98% of the more than 30,000 NEOs currently known. This unique NASA Program supports several NEO surveys that contribute to a sustained and productive campaign to find and track NEOs; collecting data of sufficient precision to allow accurate predictions of the future trajectories of discovered objects. It is a key element of the Planetary Defense Coordination Office (PDCO). The Program also sponsors applied planetary science research conducted at NASA field centers, astronomical observatories, and other locations around the United States. The PDCO relies on data from projects supported by the NEO Observations Program which, in turn, also coordinates NEO observation efforts conducted at ground-based observatories sponsored by the National Science Foundation (NSF) and the space domain awareness facilities of the United States Space Force (USSF). PDCO’s current priority is the NEO Surveyor Mission (NEOSM) , launched away from the vicinity of Earth to SEL1 and working in coordination with large ground-based surveys could complete the NEO survey within a decade following launch. NEOSM’s has three primary mission objectives: - assess the current risk to the Earth of asteroid impact(s); - examine the origin and ultimate fate of the asteroid population (within our solar system); - to find suitable, low-Δv targets for future robotic and piloted missions of exploration Operating at SEL1, NEOSM will also find NEOs with long synodic periods (i.e., several decades or longer) and unfavorable orbital viewing geometries. NEOSM will obviate the inherent Earth-based geometric observing handicap.

NEO↗
NASA NTRSDate not supplied

CLARREO Pathfinder as a SI-traceable Reference for Satellite Intercalibration

The Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder (CPF) consists of an Earthviewing reflected solar (RS) spectrometer that will measure the Earth-reflected solar radiation from International Space Station with an SI-traceable radiometric uncertainty of 0.3% (1-sigma). The high-accuracy CPF measurements will provide an in-orbit reference for intercalibrating other spaceflight RS instruments. The CPF intercalibration team has been tasked to develop a state-of-the-art approach to calibrate the shortwave channel (300-5000 nm) of the Clouds and the Earth’s Radiant Energy System (CERES) instrument and the reflective solar bands (RSB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument, both onboard the NOAA-20 satellite, against the CPF benchmark measurements. The aimed intercalibration methodology uncertainty for both the target instruments is also 0.3%. To meet this stringent intercalibration accuracy, the CPF team has developed methods for mitigating the impacts of spatial, spectral, and angular differences between the intercalibration footprints from the CPF and target instruments. To further alleviate uncertainty, the CPF team will employ Polarization Distribution Models (PDMs) to characterize the polarization state of the Earth-reflected radiance as a function of the intercalibration footprint scene type, solar and viewing geometry, and wavelength. The PDMs will assist in identifying low-polarized scene radiances for meticulously intercalibrating the polarization sensitive VIIRS instrument against the significantly-less polarization-sensitive CPF instrument. This paper will highlight the CPF mission overview, the details of the CPF intercalibration approach, and additional outcomes of the CPF intercalibration studies that may benefit the broader remote sensing community.community.

Hyperspectral↗
NASA NTRSDate not supplied

GeoXO Atmospheric Composition Instrument (AC) Measurements Conditions Assessment

NOAA’s next generation Geostationary Extended Observations (GeoXO) satellite system will advance Earth observations from geostationary orbit. GeoXO will supply vital information supporting the U.S. weather, ocean, and climate operations. The recommended three-satellite constellation includes spacecraft at GOES-Central position, that will carry an Atmospheric Composition Instrument (ACX) among its payloads; the requirement on the ACX is to scan its region of interest in one hour. An assessment of ACX observational conditions is presented, based on view geometry and solar angle range over the year. The availability for observations of each pixel is estimated per day of the year, as function of the geometric air mass factor (AMF), which can be related to signal to noise ratio using a model of the ACX instrument performance, and ultimately to expected column error for specific atmospheric species. In addition, cloud climatology – based on statistics derived from GOES-16 ABI cloud product data – is considered to determine typical fractions of cloud-free ACX observations and typical cloud optical depths, for each pixel as function of time of day and season. The results of such assessments can be used to prepare for GeoXO ACX data and further the understanding and development of the future instrument operations.

Boryana Efremova↗
NASA NTRSDate not supplied

Seasonal and Dayurnal Planetary Albedo Variability from Six Years of DSCOVR EPIC Data

Deep Space Climate Observatory (DSCOVR) measurements of Earth’s reflected solar radiation from the Lissajous orbital position near the Lagrangian L1 point provide continuous monitoring of the Earth’s sunlit hemisphere. CERES-based angle models were used to convert the near-hourly reflected radiances of the EPIC images into a climate-style planetary albedo data-point over the sunlit hemisphere. Integration over the sunlit hemispheres averages out the meteorological weather noise, but retains the seasonal and planetary-scale variability. As the Earth rotates, this generates variations in the Earth’s planetary albedo that are precisely aligned in longitude, which constitutes the dayurnal cycle. This dayurnal variability in the planetary albedo arises from planetary-scale changes in cloud radiative properties that can be directly compared to similarly sampled climate GCM output data. Six years of EPIC data have been analyzed, showing characteristic patterns in the seasonal and dayurnal variability of the Earth’s planetary albedo. Much of the seasonal change in planetary albedo is associated with the changing DSCOVR viewing geometry and the change in solar declination. But throughout the year, the highest planetary albedos are observed over the Central Asia (Iraq) longitude, while the lowest planetary albedos occur over the Central Pacific longitude. For these longitudes, the relative seasonal changes in the planetary albedo are slowly varying and anti-correlated. Dayurnal amplitude maxima tend to occur during the July-September timeframe, with April-May and December exhibit distinct minima in the dayurnal amplitude. West Africa and the West Pacific longitudes likewise exhibit anti-correlated seasonal variability, while they also undergo anti-correlated short period oscillations. On the other hand, in nearby longitudes, there are short period spikes in planetary albedo of a few-days duration, as well as longer period oscillations that may range from a week to several months, that generally tend to be correlated.

Planetary Albedo↗