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NASA NTRS2008

Algorithms for Spectral Decomposition with Applications to Optical Plume Anomaly Detection

The analysis of spectral signals for features that represent physical phenomenon is ubiquitous in the science and engineering communities. There are two main approaches that can be taken to extract relevant features from these high-dimensional data streams. The first set of approaches relies on extracting features using a physics-based paradigm where the underlying physical mechanism that generates the spectra is used to infer the most important features in the data stream. We focus on a complementary methodology that uses a data-driven technique that is informed by the underlying physics but also has the ability to adapt to unmodeled system attributes and dynamics. We discuss the following four algorithms: Spectral Decomposition Algorithm (SDA), Non-Negative Matrix Factorization (NMF), Independent Component Analysis (ICA) and Principal Components Analysis (PCA) and compare their performance on a spectral emulator which we use to generate artificial data with known statistical properties. This spectral emulator mimics the real-world phenomena arising from the plume of the space shuttle main engine and can be used to validate the results that arise from various spectral decomposition algorithms and is very useful for situations where real-world systems have very low probabilities of fault or failure. Our results indicate that methods like SDA and NMF provide a straightforward way of incorporating prior physical knowledge while NMF with a tuning mechanism can give superior performance on some tests. We demonstrate these algorithms to detect potential system-health issues on data from a spectral emulator with tunable health parameters.

Srivastava, Askok N.↗

NASA NTRS1986

Narrow-angle tail radio sources and evidence for radial orbits in Abell clusters

Published observational data on the tail orientations (TOs) of 60 narrow-angle-tail (NAT) radio sources in Abell clusters of galaxies are analyzed statistically using a maximum-likelihood approach. The results are presented in a table, and it is found that the observed TO distributions in the whole sample and in subsamples of morphologically regular NATs and NATs with pericentric distances d greater than 500 kpc are consistent with isotropic orbits, whereas the TOs for NATs with d less than 500 kpc are consistent with highly radial orbits. If radial orbits were observed near the centers of other types of cluster galaxies as well, it could be inferred that violent relaxation during cluster formation was incomplete, and that clusters form by spherical collapse and secondary infall, as proposed by Gunn (1977).

O'Dea, Christopher P.↗

NASA NTRS1990

On the maximum rate of change in sunspot number growth and the size of the sunspot cycle

Statistically significant correlations exist between the size (maximum amplitude) of the sunspot cycle and, especially, the maximum value of the rate of rise during the ascending portion of the sunspot cycle, where the rate of rise is computed either as the difference in the month-to-month smoothed sunspot number values or as the 'average rate of growth' in smoothed sunspot number from sunspot minimum. Based on the observed values of these quantities (equal to 10.6 and 4.63, respectively) as of early 1989, it is inferred that cycle 22's maximum amplitude will be about 175 + or - 30 or 185 + or - 10, respectively, where the error bars represent approximately twice the average error found during cycles 10-21 from the two fits.

Wilson, Robert M.↗

NASA NTRS2004

Physical Characterization of the Near-Earth Object Population

Many pieces of the puzzle must be brought together in order to have a clear picture of the near-Earth object (NEO) population. Four of the pieces that can be described include: i) the taxonomic distribution of the population as measured by observational sampling, ii) the determination of albedos that can be associated with the taxonomic distribution, iii) discovery statistics for the NE0 population, and iv) the debiasing of the discovery statistics using the taxonomic and albedo information. Support from this grant enables us to address three of these four pieces. Binzel et al. (2004, submitted) presents the first piece, detailing the observations and observed characteristics of the NE0 and Mars-crossing (MC) population. For the second piece, a complementary program of albedo measurements is pursued at the Keck Observatory (Binzel, P. I.) with first results published in Delbo et al. (2003). For the third piece, the most extensive NE0 discovery statistics are provided by the LINEAR survey. Binzel has supervised the MIT Ph. D. thesis work of Stuart (2003) to bring the fourth piece, submitted for publication by Stuart and Binzel (2004). Our results provide new constraints for the NE0 population and progress for the Spaceguard Survey, illuminate asteroid and comet source regions for the NEOs, and provide new evidence for space weathering processes linking asteroids and meteorites. Further, we are identifying top priority near-Earth spacecraft mission candidates based on their spectral properties and inferred compositions.

Binzel, Richard P.↗

NASA NTRSDate not supplied

Determining the Most Influencing Medical Conditions in MEDPRAT’s SIN Directed Graph

INTRODUCTION: The Susceptibility Inference Network (SIN) is a network of medical conditions, part of the Medical Extensible Probabilistic Risk Assessment Tool (MEDPRAT) developed by NASA to assess human health and medical risk to space exploration missions. The SIN is subject matter expert informed and acts as a prototype that provides relationships and dependencies between events modeled by MEDPRAT. Each vertex in the SIN has a weight which evaluates the severity of having the condition regardless of the progression from or to that condition. In this presentation, we consider two statistics to measure that stand alone risk: Quality Time Lost (QTL) and Loss of Crew Life (LOCL). Our goal is to identify the medical conditions that contribute the most to crew members QTL and LOCL risks due to progression of conditions in the network. We investigate how different computation parameters result in different condition rankings and address the choice of parameters that allows appropriate interventions to ensure space mission success. METHODS: The Katz score, one of many centrality measures created for ranking purposes in network analysis, takes into account all possible walks through the network, penalizing each additional step in a walk by a factor α called the Katz parameter. The literature does not provide specific values for the choice of α. We derive an analytical relationship between α and the maximum path length which has influence on the Katz score and ranking. Based on the probability of progression of each condition in the SIN, we identify that maximum path length of interest and calculate α that is then used in the Katz formula to rank the conditions in the SIN. RESULTS AND CONCLUSION: The effective probabilities of the SIN matrix generally fall below 10−6, which is below the level of the least influencing condition in the set. This corresponds to the probability of at most six consecutive progressions of a condition. Consequently, we calculate the Katz Parameter α and get 0.32. We rank the medical conditions and find that Acute Radiation Symptom is the condition the most prone to contribute to quality time loss due to progression.

risk analysis↗

NASA NTRS2010

Predicting the Size and Timing of Sunspot Maximum for Cycle 24

For cycle 24, the minimum value of the 12-month moving average (12-mma) of the AA-geomagnetic index in the vicinity of sunspot minimum (AAm) appears to have occurred in September 2009, measuring about 8.4 nT and following sunspot minimum by 9 months. This is the lowest value of AAm ever recorded, falling below that of 8.9 nT, previously attributed to cycle 14, which also is the smallest maximum amplitude (RM) cycle of the modern era (RM = 64.2). Based on the method of Ohl (the preferential association between RM and AAm for an ongoing cycle), one expects cycle 24 to have RM = 55+/-17 (the +/-1 - sigma prediction interval). Instead, using a variation of Ohl's method, one based on using 2-cycle moving averages (2-cma), one expects cycle 23's 2-cma of RM to be about 115.5+/-8.7 (the +/-1 - sigma prediction interval), inferring an RM of about 62+/-35 for cycle 24. Hence, it seems clear that cycle 24 will be smaller in size than was seen in cycle 23 (RM = 120.8) and, likely, will be comparable in size to that of cycle 14. From the Waldmeier effect (the preferential association between the ascent duration (ASC) and RM for an ongoing cycle), one expects cycle 24 to be a slow-rising cycle (ASC > or equal to 48 months), having RM occurrence after December 2012, unless it turns out to be a statistical outlier.

Wilson, Robert M.↗

NASA NTRS2017

Statistical Assessment of Cetacean Stranding Events in Cape Cod Area

Cetacean (whales, dolphins and porpoises) mass strandings are a longstanding mystery in the field of marine biology that continue to be recorded in coastal environments around the world. For each of these events, anywhere from a few to several hundred otherwise healthy animals strand in onshore environments, often for no apparent reason. While the causes of these events remain unclear, anthropogenic and naturogenic mechanisms have been suggested. We present results of an inter-disciplinary study that draws expertise from space weather, marine mammal biology and ecology, and marine mammal stranding response. This study assessed 16 years of cetacean stranding events in the Cape Cod (Massachusetts, USA) area concurrently with a large dataset of meteorological, geophysical, biological, oceanographic and space weather data to produce inferences about possible causes for these unexplained events.

Dolphi↗

NASA NTRS1993

Small volcanoes in Tempe Terra, Mars: Their detailed morphometry and inferred geologic significance

The Tempe Terra province contains a variety of volcanic landforms that range in size from small vents (less than 10 km in diameter) to moderately sized volcanoes (150 km in diameter). The volcanoes are aligned along the dominant northerly and northeasterly trends of the faults in this region, and many of the volcanoes occur on grabens. Some workers have speculated on the nature of some of the volcanoes on the basis of their general morphology, shadow measurement height, lateral dimension, and geologic setting. As part of a larger study, we have obtained detailed photoclinometric profiles across five of the more conspicuous small volcanoes in the Tempe Terra region. For these data, we extracted for each volcano its flank width and edifice height and the width and depth of its summit crater. We statistically compared these dimensions for each volcano with a set of average dimensions for each type of terrestrial volcanic feature listed in Pike and Clow (1981). These comparisons indicate that the morphometries of the Martian volcanoes 1, 2, and 3 most closely match Earth's cratered basaltic lava shields, and the morphometries of volcanoes 4 and 5 are similar to those of terrestrial basaltic tuff rings.

Davis, P. A.↗

NASA NTRS1972

A review of some aspects of inferring the ozone profile by inversion of ultraviolet radiance measurements

The mathematical inversion of light-scattering observations to obtain the atmospheric ozone profile is discussed in terms of the filtering properties of the physical and mathematical processes for different spatial scales. Within this context, it is shown that the physical process of scattering acts as a low pass filter; which transfers large-scale profile information efficiently to radiance observations but very strongly attenuates small- or fine-scale profile information. To avoid domination of the mathematical inversion by the random error of radiance measurements, an equivalent spatial-scale filtering in the inversion procedure is essential. The available evidence suggests that mathematical inversion to obtain the low level ozone profile below 25 to 30 km is either inferior to or no better than the statistical estimation of the profile using total ozone as predictor. However, inversion profiles for high level ozone above 25 to 30 km appear to have moderately good accuracy.

Mateer, C. L.↗

NASA NTRS2021

Calibration of the DSCOVR EPIC Visible and NIR Channels using Multiple LEO Radiometers

The first five years of operation of the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) at the Lagrange one point have produced results that uniquely complement the data from currently operating low orbit Earth-observing instruments. In this paper we describe an updated unified approach to EPIC calibration. In this approach, calibration coefficients and their trends were obtained by comparing EPIC observations to the measurements from polar orbiting radiometers. In this study L1B reflectances from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua and Terra satellites, Multi-angle Imaging Spectroradiometer (MISR) onboard Terra and Visible Infrared Imaging Radiometer (VIIRS) onboard the Suomi National Polar-orbiting Partnership (Suomi NPP) spacecraft were used to infer calibration coefficients for four EPIC visible and near-infrared channels: 443 nm, 551 nm, 680 nm, and 780 nm. EPIC Version three measurements made between June 2015 and August 2020 were used for comparison. The calibration procedure identifies the most homogeneous low Earth orbit radiometer scenes matching scattering angles that are temporarily and spatially collocated with EPIC observations. These scenes are used to determine reflectance to count (R/C) ratios in spectrally analogous channels. Seasonal average R/C ratios were analyzed to obtain EPIC calibration gains and trends. The trends for the full dataset period are not statistically significant except in the 443 nm channel. No significant changes in calibration were found after the instrument’s exit from safe hold in March 2020. The R/C ratios were also used to determine the differences in EPIC gains resulting from separate calibrations: against MODIS Aqua or Terra, as well as against forward or aftward MISR cameras. Statistical tests indicate that the differences between the two datasets are not significant except in the 780 nm channels where Aqua-derived coefficients may be around 2% lower compared to Terra. The dependence of EPIC calibration gains on the instrument scattering angle and on DSCOVR-Earth distance were investigated. Lastly, model Low Earth Orbit (LEO) reflectances calculated to match the EPIC viewing geometry were employed to study how EPIC calibration coefficients depend on EPIC-LEO viewing geometry differences. The effect of LEO and EPIC angular mismatch on calibration was shown to be small.

deep space climate observatory/earth polychromatic↗

NASA NTRS2003

Adaptation of Mesoscale Weather Models to Local Forecasting

Methodologies have been developed for (1) configuring mesoscale numerical weather-prediction models for execution on high-performance computer workstations to make short-range weather forecasts for the vicinity of the Kennedy Space Center (KSC) and the Cape Canaveral Air Force Station (CCAFS) and (2) evaluating the performances of the models as configured. These methodologies have been implemented as part of a continuing effort to improve weather forecasting in support of operations of the U.S. space program. The models, methodologies, and results of the evaluations also have potential value for commercial users who could benefit from tailoring their operations and/or marketing strategies based on accurate predictions of local weather. More specifically, the purpose of developing the methodologies for configuring the models to run on computers at KSC and CCAFS is to provide accurate forecasts of winds, temperature, and such specific thunderstorm-related phenomena as lightning and precipitation. The purpose of developing the evaluation methodologies is to maximize the utility of the models by providing users with assessments of the capabilities and limitations of the models. The models used in this effort thus far include the Mesoscale Atmospheric Simulation System (MASS), the Regional Atmospheric Modeling System (RAMS), and the National Centers for Environmental Prediction Eta Model ( Eta for short). The configuration of the MASS and RAMS is designed to run the models at very high spatial resolution and incorporate local data to resolve fine-scale weather features. Model preprocessors were modified to incorporate surface, ship, buoy, and rawinsonde data as well as data from local wind towers, wind profilers, and conventional or Doppler radars. The overall evaluation of the MASS, Eta, and RAMS was designed to assess the utility of these mesoscale models for satisfying the weather-forecasting needs of the U.S. space program. The evaluation methodology includes objective and subjective verification methodologies. Objective (e.g., statistical) verification of point forecasts is a stringent measure of model performance, but when used alone, it is not usually sufficient for quantifying the value of the overall contribution of the model to the weather-forecasting process. This is especially true for mesoscale models with enhanced spatial and temporal resolution that may be capable of predicting meteorologically consistent, though not necessarily accurate, fine-scale weather phenomena. Therefore, subjective (phenomenological) evaluation, focusing on selected case studies and specific weather features, such as sea breezes and precipitation, has been performed to help quantify the added value that cannot be inferred solely from objective evaluation.

Manobianco, John T.↗

NASA NTRS1981

PKS 2155-304 relativistically beamed synchrotron radiation from BL LAC object

The newly discovered BL Lacertae object, PKS 2155-304, was observed with the medium and high intensity energy detectors of the HEAO-1 A2 experiment. The variability by a factor of two in less than a day reported by Snyder, et al (1979) is confirmed. Two spectra, obtained a year apart, while the satellite was in scanning mode, are well fit by simple power laws with energy spectral index alpha sub 1 equals approximately 1.4. A third spectrum, of higher statistical quality, obtained while the satellite was pointed at its source, has has two components. An acceptable fit was obtained using a two power law model, with indices alpha sub 1 equals 2.0 (+1.2, -0.6) and alpha sub 2 equals -1.5 (+1.5, -2.3). An interpretation of the overall spectrum from radio through X-rays in terms of a synchrotron self-Compton model gives a good description of the data if allowance is made for relativistic beaming. Thus, from a consideration of the spectrum, combined with an estimate of the size of the source, the presence of jets is inferred without their observation.

Urry, C. M.↗

NASA NTRS2003

Automated Monitoring with a BCP Fault-Decision Test

The Bayesian conditional probability (BCP) technique is a statistical fault-decision technique that is suitable as the mathematical basis of the fault-manager module in the automated-monitoring system and method described in the immediately preceding article. Within the automated-monitoring system, the fault-manager module operates in conjunction with the fault-detector module, which can be based on any one of several fault-detection techniques; examples include a threshold-limit-comparison technique or the BSP or SPRT technique mentioned in the preceding article. The present BCP technique is used to evaluate a series of one or more fault-detection events for the purpose of filtering out occasional false alarms produced by many types of statistical fault-detection procedures. The BCP technique increases the probability that an automated monitoring system produces a correct decision regarding the presence or absence of a fault. Because occasional false alarms are an inevitable consequence of the SPRT, BSP, or any other statistically based fault-detection test, there is a need for a logical procedure to distinguish between true and false alarms. Heretofore, it has been common practice to make a fault decision on an ad hoc basis for example by following a multiple-observation voting strategy in which a signal is declared to be indicative of a fault if m of the last n observations produced a fault-detection alarm. The BCP technique was developed to obtain results more reliable than those afforded by a voting strategy. The BCP technique involves a test in which one applies Bayesian inference techniques to a series of one or more single-observation alarms produced by a fault-detection test. One considers the last n decisions generated by a fault-detection test in order to evaluate the conditional probability that a failure is indicated (see figure). Each new decision reached by a fault-detection test is treated as a new piece of evidence about the state of the monitored asset, and the conditional probability of failure for the system is updated on the basis of this new evidence. The conditional probability of failure is compared with a predefined limit. For a probability below the limit, the asset is declared to be healthy. For a probability above the limit, the asset is declared to be faulty.

Bickford, Randall L.↗

NASA NTRS1999

The Second Most Distant Cluster of Galaxies in the Extended Medium Sensitivity Survey

We report on our ASCA, Keck, and ROSAT observations of MS 1137.5+6625, the second most distant cluster of galaxies in the Einstein Extended Medium Sensitivity Survey (EMSS), at redshift 0.78. We now have a full set of X-ray temperatures, optical velocity dispersions, and X-ray images for a complete, high-redshift sample of clusters of galaxies drawn from the EMSS. Our ASCA observations of MS 1137.5 +6625 yield a temperature of 5.7 (+2.1)(-1.1) keV and a metallicity of 0.43 (+40)(-3.7) solar, with 90% confidence limits. Keck II spectroscopy of 22 cluster members reveals a velocity dispersion of 884 (+185)(-124) km 24/s. This cluster is the most distant in the sample with a detected iron line. We also derive a mean abundance at z = 0.8 by simultaneously fitting X-ray data for the two z = 0.8 clusters, and obtain an abundance of Z(sub Fe) = 0.33 (+.26)(-.23). Our ROSAT observations show that MS 1137.5+6625 is regular and highly centrally concentrated. Fitting of a Beta model to the X-ray surface brightness yields a core radius of only 71/h kpc (q(sub o) = 0.1) with Beta = 0.70(+.45)(-.15) The gas mass interior to 0.5/h Mpc is thus 1.2 (+0.2)(-0.3) X 10(exp 13) h(exp - 5/2) Solar Mass (q(sub o) = 0.1). If the cluster's gas is nearly isothermal and in hydrostatic equilibrium with the cluster potential, the total mass of the cluster within this same region is 2.1(+1.5)(-0.8) X 10exp 14)/h Solar Mass, giving a gas fraction of 0.06 +/-0.04 h (exp -3/2). This cluster is the highest redshift EMSS cluster showing evidence for a possible cooling flow (about 20-400 Solar Mass/yr). The velocity dispersion, temperature, gas fraction, and iron abundance of MS 1137.5+6625 are all statistically the same as those properties in lower red- shift clusters of similar luminosity. With this cluster's temperature now in hand, we derive a high-redshift temperature function for EMSS clusters at 0.5 < z < 0.9 and compare it with temperature functions at lower redshifts, showing that the evolution of the temperature function is relatively modest. Supplementing our high-redshift sample with other data from the literature, we demonstrate that neither the cluster luminosity-temperature relation, nor cluster metallicities, nor the cluster gas evolved with redshift. The very modest degree of evolution in the luminosity-temperature relation inferred from these data is inconsistent with the absence of evolution in the X-ray luminosity functions derived from ROSAT cluster surveys if a critical density structure formation model is assumed.

Donahue, Megan↗

NASA NTRS2012

Adaptive Sampling of Time Series During Remote Exploration

This work deals with the challenge of online adaptive data collection in a time series. A remote sensor or explorer agent adapts its rate of data collection in order to track anomalous events while obeying constraints on time and power. This problem is challenging because the agent has limited visibility (all its datapoints lie in the past) and limited control (it can only decide when to collect its next datapoint). This problem is treated from an information-theoretic perspective, fitting a probabilistic model to collected data and optimizing the future sampling strategy to maximize information gain. The performance characteristics of stationary and nonstationary Gaussian process models are compared. Self-throttling sensors could benefit environmental sensor networks and monitoring as well as robotic exploration. Explorer agents can improve performance by adjusting their data collection rate, preserving scarce power or bandwidth resources during uninteresting times while fully covering anomalous events of interest. For example, a remote earthquake sensor could conserve power by limiting its measurements during normal conditions and increasing its cadence during rare earthquake events. A similar capability could improve sensor platforms traversing a fixed trajectory, such as an exploration rover transect or a deep space flyby. These agents can adapt observation times to improve sample coverage during moments of rapid change. An adaptive sampling approach couples sensor autonomy, instrument interpretation, and sampling. The challenge is addressed as an active learning problem, which already has extensive theoretical treatment in the statistics and machine learning literature. A statistical Gaussian process (GP) model is employed to guide sample decisions that maximize information gain. Nonsta tion - ary (e.g., time-varying) covariance relationships permit the system to represent and track local anomalies, in contrast with current GP approaches. Most common GP models are stationary, e.g., the covariance relationships are time-invariant. In such cases, information gain is independent of previously collected data, and the optimal solution can always be computed in advance. Information-optimal sampling of a stationary GP time series thus reduces to even spacing, and such models are not appropriate for tracking localized anomalies. Additionally, GP model inference can be computationally expensive.

Thompson, David R.↗

NASA NTRS2013

Comparing Two Approaches for Assessing Observation Impact

Langland and Baker introduced an approach to assess the impact of observations on the forecasts. In that approach, a state-space aspect of the forecast is defined and a procedure is derived ultimately relating changes in the aspect with changes in the observing system. Some features of the state-space approach are to be noted: the typical choice of forecast aspect is rather subjective and leads to incomplete assessment of the observing system, it requires availability of a verification state that is in practice correlated with the forecast, and it involves the adjoint operator of the entire data assimilation system and is thus constrained by the validity of this operator. This article revisits the topic of observation impacts from the perspective of estimation theory. An observation-space metric is used to allow inferring observation impact on the forecasts without the limitations just mentioned. Using differences of observation-minus-forecast residuals obtained from consecutive forecasts leads to the following advantages: (i) it suggests a rather natural choice of forecast aspect that directly links to the data assimilation procedure, (ii) it avoids introducing undesirable correlations in the forecast aspect since verification is done against the observations, and (iii) it does not involve linearization and use of adjoints. The observation-space approach has the additional advantage of being nearly cost free and very simple to implement. In its simplest form it reduces to evaluating the statistics of observationminus- background and observation-minus-analysis residuals with traditional methods. Illustrations comparing the approaches are given using the NASA Goddard Earth Observing System.

Variational Analysis↗

NASA NTRS1995

Estimating the urban bias of surface shelter temperatures using upper-air and satellite data. Part 1: Development of models predicting surface shelter temperatures

Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986-89 using upper-air data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to represent the background climate and site-specific data to represent the local landscape. Global monthly mean temperature models were developed using data from over 5000 stations available in the Global Historical Climate Network (GHCN). Monthly maximum, mean, and minimum temperature models for the United States were also developed using data from over 1000 stations available in the U.S. Cooperative (COOP) Network and comparative monthly mean temperature models were developed using over 1150 U.S. stations in the GHCN. Three-, six-, and full-variable models were developed for comparative purposes. Inferences about the variables selected for the various models were easier for the GHCN models, which displayed month-to-month consistency in which variables were selected, than for the COOP models, which were assigned a different list of variables for nearly every month. These and other results suggest that global calibration is preferred because data from the global spectrum of physical processes that control surface temperatures are incorporated in a global model. All of the models that were developed in this study validated relatively well, especially the global models. Recalibration of the models with validation data resulted in only slightly poorer regression statistics, indicating that the calibration list of variables was valid. Predictions using data from the validation dataset in the calibrated equation were better for the GHCN models, and the globally calibrated GHCN models generally provided better U.S. predictions than the U.S.-calibrated COOP models. Overall, the GHCN and COOP models explained approximately 64%-95% of the total variance of surface shelter temperatures, depending on the month and the number of model variables. In addition, root-mean-square errors (rmse's) were over 3 C for GHCN models and over 2 C for COOP models for winter months, and near 2 C for GHCN models and near 1.5 C for COOP models for summer months.

Epperson, David L.↗

NASA NTRS2021

Operational Detection of Sun Glints in DSCOVR EPIC Images

Satellite images often feature sun glints caused by the specular reflection of sunlight from water surfaces or from horizontally oriented ice crystals occurring in clouds. Such glints can prevent accurate retrievals of atmospheric and surface properties using existing algorithms, but the glints can also be used to infer more about the glint-causing objects—for example about the microphysical properties and radiative effects of ice clouds. This paper introduces the recently released operational glint product of the Earth Polychromatic Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) spacecraft. Most importantly, the paper describes the algorithm used for generating the key component of the new product: a glint mask indicating the presence of sun glint caused by the specular reflection of sunlight from ice clouds and smooth water surfaces. After describing the glint detection algorithm and glint product, the paper shows some examples of the detected glints and discusses some basic statistics of the glint population in a yearlong dataset of EPIC images. These statistics provide insights into the performance of glint detection and point toward possibilities for using the glint product to gain scientific insights about ice clouds and water surfaces.

DSCOVR↗