# Science Systems and Applications, Inc.

• Lanham, Maryland, United States
Recent publications
At NASA Goddard Space Flight Center, we have previously demonstrated a kilo-pixel array of transition-edge sensor (TES) microcalorimeters capable of meeting the energy resolution requirements of the future X-ray Integral Field Unit (X-IFU) instrument that is being developed for the Advanced Telescope for High ENergy Astrophysics (ATHENA) observatory satellite. The TES design in this array was a square device with side length of $50 \,\mathrm{\upmu }\mathrm{m}$ . Here, we describe studies of TES designs with small variations of the dimensions, exploring lengths, parallel to the current direction, ranging from $75 \,\mathrm{\upmu }\mathrm{m}$ to $50 \,\mathrm{\upmu }\mathrm{m}$ and widths, perpendicular to the current direction, ranging from $50 \,\mathrm{\upmu }\mathrm{m}$ to $15 \,\mathrm{\upmu }\mathrm{m}$ . We describe how these changes impact transition properties, thermal conductance and magnetic field sensitivity. In particular, we show that using a TES with a length of $50 \,\mathrm{\upmu }\mathrm{m}$ and width of $30 \,\mathrm{\upmu }\mathrm{m}$ may be a promising route to reduce the maximum time-derivative of the TES current in an X-ray pulse and reduce the sensitivity of the TES to magnetic field.
The radiative effects of the large‐scale air traffic slowdown during April and May 2020 due to the international response to the COVID‐19 pandemic are estimated by comparing the coverage (CC), optical properties, and radiative forcing of persistent linear contrails over the conterminous United States and two surrounding oceanic air corridors during the slowdown period and a similar baseline period during 2018 and 2019 when air traffic was unrestricted. The detected CC during the slowdown period decreased by an area‐averaged mean of 41% for the three analysis boxes. The retrieved contrail optical properties were mostly similar for both periods. Total shortwave contrail radiative forcings (CRFs) during the slowdown were 34% and 42% smaller for Terra and Aqua, respectively. The corresponding differences for longwave CRF were 33% for Terra and 40% for Aqua. To account for the impact of any changes in the atmospheric environment between baseline and slowdown periods on detected CC amounts, the contrail formation potential (CFP) was computed from reanalysis data. In addition, a filtered CFP (fCFP) was also developed to account for factors that may affect contrail formation and visibility of persistent contrails in satellite imagery. The CFP and fCFP were combined with air traffic data to create empirical models that estimated CC during the baseline and slowdown periods and were compared to the detected CC. The models confirm that decreases in CC and radiative forcing during the slowdown period were mostly due to the reduction in air traffic, and partly due to environmental changes.
This work describes the extension of the Global Modeling and Assimilation Office (GMAO) Observing System Simulation Experiment (OSSE) framework to use a hybrid 4D-EnVar scheme instead of 3D-Var. The original 3D-Var and hybrid 4D-EnVar OSSEs use the same version of the data assimilation system (DAS) so that a direct comparison is possible in terms of the validation with respect to their corresponding real cases. Rather than quantifying the differences between the two data assimilation methodologies, a short inter-comparison of upgrading from a 3D- to a 4D-OSSE is provided to highlight aspects where this change matters to the OSSE community and to identify particular features of data assimilation that can only be explored in a four-dimensional OSSE framework. A short validation of the hybrid 4D-EnVar OSSE shows that conclusions from previous assessments of the 3D-Var OSSE in its ability to mimic the behavior of the real system still hold with the same caveats. Furthermore, some aspects of the ensemble configuration and behavior are discussed along with forecast sensitivity to observation impacts (FSOI). Estimates of error standard deviations are shown to be smaller in the hybrid 4D-EnVar OSSE but with little impact on the character of the error. A discussion on future work directions focuses on exploring the four-dimensional aspect such as the error distribution within the assimilation window or four-dimensional handling of high-temporal density observations.
The NASA Cloud, Aerosol, and Monsoon Processes Philippines Experiment (CAMP ² Ex) employed the NASA P-3, Stratton Park Engineering Company (SPEC) Learjet 35, and a host of satellites and surface sensors to characterize the coupling of aerosol processes, cloud physics, and atmospheric radiation within the Maritime Continent’s complex southwest monsoonal environment. Conducted in the late summer of 2019 from Luzon Philippines in conjunction with the Office of Naval Research Propagation of Intraseasonal Tropical OscillatioNs (PISTON) experiment with its R/V Sally Ride stationed in the North Western Tropical Pacific, CAMP ² Ex documented diverse biomass burning, industrial and natural aerosol populations and their interactions with small to congestus convection. The 2019 season exhibited El Nino and associated drought, high biomass burning emissions, and an early monsoon transition allowing for observation of pristine to massively polluted environments as they advected through intricate diurnal mesoscale and radiative environments into the monsoonal trough. CAMP ² Ex’s preliminary results indicate 1) increasing aerosol loadings tend to invigorate congestus convection in height and increase liquid water paths; 2) lidar, polarimetry, and geostationary Advanced Himawari Imager remote sensing sensors have skill in quantifying diverse aerosol and cloud properties and their interaction; and 3) high resolution remote sensing technologies are able to greatly improve our ability to evaluate the radiation budget in complex cloud systems. Through the development of innovative informatics technologies, CAMP ² Ex provides a benchmark dataset of an environment of extremes for the study of aerosol, cloud and radiation processes as well as a crucible for the design of future observing systems.
Plain Language Summary Instruments on Earth orbiting satellites offer the opportunity to detect long‐term changes in atmospheric temperature. Many factors may affect the ability to identify actual long‐term changes in the temperature and to distinguish these from changes in the instrument or from unintended changes in the algorithm that produces the temperature data from the instrument observations. SABER is an instrument on a NASA satellite that has been observing temperatures from 15 to 110 km (10–68 miles) in altitude for over 20 years. An “instability” in the scientific algorithm used to derive temperature from the instrument observations was recently discovered, beginning in late 2019. An unintended change was made in a parameter central to the derivation of temperature from SABER measurements. The consequence was that the atmospheric temperatures between 85 and 110 km (51–68 miles) from 2020 onward were several degrees colder than they would have been without the unintended change. This has been corrected and an updated version of the SABER temperatures and all other SABER data products, called Version 2.08, is now publicly available.
The objectives, instrumentation, methods and data leading up to launch of the NASA Living With a Star (LWS) Space Environment Testbed (SET) payload onboard the Air Force Research Laboratory Demonstration and Science Experiments (DSX) spacecraft are described. The experiments characterize the space radiation environment and how it affects hardware performance. The payload consists of a compact space weather instrument and a carrier containing four board experiments.
Lidar profiling of the atmosphere provides information on existence of cloud and aerosol layers and the height and structure of those layers. Knowledge of feature boundaries is a key input to assimilation models. Moreover, identifying feature boundaries with minimal latency is essential to impact operational assimilation and real-time decision making. Using advanced convolution neural network algorithms, we demonstrate real-time determination of atmospheric feature boundaries using an airborne backscatter lidar. Results are shown to agree well with traditional processing methods and are produced with higher horizontal resolution than the traditional method. Demonstrated using airborne lidar, the algorithms and process are extendable to real-time generation of data products from a future spaceborne sensor.
Hydrologic extremes often involve a complex interplay of several processes. For example, flood events can have a cascade of impacts, such as saturated soils and suppressed vegetation growth. Accurate representation of such interconnected processes while accounting for associated triggering factors and subsequent impacts of flood events is difficult to achieve with conceptual hydrological models alone. In this study, we use the 2019 flood in the Northern Mississippi and Missouri Basins, which caused a series of hydrologic disturbances, as an example of such a flood event. This event began with above-average precipitation combined with anomalously high snowmelt in spring 2019. This series of anomalies resulted in above normal soil moisture that prevented crops from being planted over much of the corn belt region. In the present study, we demonstrate that incorporating remote sensing information within a hydrologic modeling system adds substantial value in representing the processes that lead to the 2019 flood event and the resulting agricultural disturbances. This remote sensing data infusion improves the accuracy of soil moisture and snowmelt estimates by up to 16% and 24%, respectively, and it also improves the representation of vegetation anomalies relative to the reference crop fraction anomalies.
The decades-long Clouds and Earth’s Radiant Energy System (CERES) Project includes both cloud and radiation measurements from instruments on the Aqua, Terra, and Suomi National Polar-orbiting Partnership (SNPP) satellites. To build a reliable long-term climate data record, it is important to determine the accuracies of the parameters retrieved from the sensors on each satellite. Cloud amount, phase, and top height derived from radiances taken by the Visible Infrared Imaging Radiometer Suite (VIIRS) on the SNPP are evaluated relative to the same quantities determined from measurements by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) spacecraft. The accuracies of the VIIRS cloud fractions are found to be as good as or better than those for the CERES amounts determined from Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) data and for cloud fractions estimated by two other operational algorithms. Sensitivities of cloud fraction bias to CALIOP resolution, matching time window, and viewing zenith angle are examined. VIIRS cloud phase biases are slightly greater than their CERES MODIS counterparts. A majority of cloud phase errors are due to multilayer clouds during the daytime and supercooled liquid water clouds at night. CERES VIIRS cloud-top height biases are similar to those from CERES MODIS, except for ice clouds, which are smaller than those from CERES MODIS. CERES VIIRS cloud phase and top height uncertainties overall are very similar to or better than several operational algorithms, but fail to match the accuracies of experimental machine learning techniques. The greatest errors occur for multilayered clouds and clouds with phase misclassification. Cloud top heights can be improved by relaxing tropopause constraints, improving lapse-rate to model temperature profiles, and accounting for multilayer clouds. Other suggestions for improving the retrievals are also discussed.
The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a, total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.
The Surface ALbedo VALidation (SALVAL) online platform is designed to allow producers of satellite-based albedo products to move to operational validation systems. The SALVAL tool integrates long-term satellite products, global in situ datasets, and community-agreed-upon validation protocols into an online and interactive platform. The SALVAL tool, available on the ESA Cal/Val portal, was developed by EOLAB under the framework outlined by the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) Land Product Validation (LPV) subgroup, and provides transparency, consistency, and traceability to the validation process. In this demonstration, three satellite-based albedo climate data records from different operational services were validated and intercompared using the SALVAL platform: (1) the Climate Change Service (C3S) multi-sensor product, (2) the NASA MODIS MCD43A3 product (C6.1) and (3) Beijing Normal University’s Global LAnd Surface Satellites (GLASS) version 4 products. This work demonstrates that the three satellite albedo datasets enable long-term reliable and consistent retrievals at the global scale, with some discrepancies between them associated with the retrieval processing chain. The three satellite albedo products show similar uncertainties (RMSD = 0.03) when comparing the best quality retrievals with ground measurements. The SALVAL platform has proven to be a useful tool to validate and intercompare albedo datasets, allowing them to reach stage 4 of the CEOS LPV validation hierarchy.
Traditionally, heliophysics is characterized as the study of the near-Earth space environment, where plasmas and neutral gases originating from the Earth, the Sun, and other solar system bodies interact in ways that are detectable only through in-situ or close-range (usually within ∼10 AU) remote sensing. As a result, heliophysics has data from the space environment around a handful of solar system objects, in particular the Sun and Earth. Comparatively, astrophysics has data from an extensive array of objects, but is more limited in temporal, spatial, and wavelength information from any individual object. Thus, our understanding of planetary space environments as a complex, multi-dimensional network of specific interacting systems may in the past have seemed to have little to do with the highly diverse space environments detected through astrophysical methods. Recent technological advances have begun to bridge this divide. Exoplanetary studies are opening up avenues to study planetary environments beyond our solar system, with missions like Kepler, TESS, and JWST, along with increasing capabilities of ground-based observations. At the same time, heliophysics studies are pushing beyond the boundaries of our heliosphere with Voyager, IBEX, and the future IMAP mission. The interdisciplinary field of star-exoplanet interactions is a critical, growing area of study that enriches heliophysics. A multidisciplinary approach to heliophysics enables us to better understand universal processes that operate in diverse environments, as well as the evolution of our solar system and extreme space weather. The expertise, data, theory, and modeling tools developed by heliophysicists are crucial in understanding the space environments of exoplanets, their host stars, and their potential habitability. The mutual benefit that heliophysics and exoplanetary studies offer each other depends on strong, continuing solar system-focused and Earth-focused heliophysics studies. The heliophysics discipline requires new targeted funding to support inter-divisional opportunities, including small multi-disciplinary research projects, large collaborative research teams, and observations targeting the heliophysics of planetary and exoplanet systems. Here we discuss areas of heliophysics-relevant exoplanetary research, observational opportunities and challenges, and ways to promote the inclusion of heliophysics within the wider exoplanetary community.
This study simulates V-band sea surface reflectance and normalized radar cross-section (NRCS) for sea surface air pressure barometry using a differential absorption radar operating at three spectrally even spaced close frequency bands (65.5, 67.75 and 70.0 GHz) with ± 15° cross-track scanning angle. The reflectance ratios of two neighboring frequency pairs and the ratio of the two ratios or three-channel approach are the focus of this study. Impacts of major sea surface geophysical variables such as sea surface temperature, wind, salinity, whitecap, and incidence angle on these reflection properties are analyzed. The reflection simulation is essentially based on geometric optics of rough sea surface. Simulation shows that NRCS values are sufficiently strong within the scanning angle and sea surface salinity would only introduce minimal variations in the surface reflection. The impact of sea surface reflection variations with sea surface temperature, wind, and whitecaps on sea surface barometry are mitigated when the ratios of frequency-paired radar signals are used. Furthermore, the ratios of a three-channel approach are very close to unity and calibration or compensation for the reflectance ratios may not be needed for sea level pressure retrievals. These results improve our understanding of sea surface reflection variations and would help the system design and development.
Plain Language Summary The production of marine aerosol in the Bellingshausen Sea of the western Antarctic is coupled to the environment. Processes driving marine aerosol include wind speed, which produces sea spray aerosol (SSA), sea surface temperature (SST), which can enhance the production of SSA, and seasonal dynamics of sea ice melt and phytoplankton blooms, which can contribute to production of biogenic sources of marine aerosol. To study these drivers in closer detail, we used two specialized proxies of marine aerosol concentration: coarse‐mode Aerosol Optical Depth (AODC), a proxy for SSA, and Marine Aerosol Optical Depth (MAOD) a proxy for low‐altitude marine aerosol. We examined MAOD and AODC from 2007 to 2018 and found that wind speed was a driver of day‐to‐day fluxes in marine aerosol. In contrast to the tropical Pacific in which enhanced biological activity suppresses SSA particle production, we did not observe lessening of MAOD and AODC magnitudes during the biologically productive austral summertime. In fact, summertime MAOD exhibited a weak significant correlation to daily wind speed in the coastal ocean despite a lack of significance in wintertime. This work enriches our knowledge of biotic and abiotic drivers of marine aerosol in high‐latitude environments.
Since the early 2000s, sea ice has experienced an increased rate of decline in thickness, extent and age. This new regime, coined the ‘New Arctic’, is accompanied by a reshuffling of energy flows at the surface. Understanding of the magnitude and nature of this reshuffling and the feedbacks therein remains limited. A novel database is presented that combines satellite observations, model output, and reanalysis data with sea ice parcel drift tracks in a Lagrangian framework. This dataset consists of daily time series of sea ice parcel locations, sea ice and snow conditions, and atmospheric states, including remotely sensed surface energy budget terms. Additionally, flags indicate when sea ice parcels travel within cyclones, recording cyclone intensity and distance from the cyclone center. The quality of the ice parcel database was evaluated by comparison with sea ice mass balance buoys and correlations are high, which highlights the reliability of this database in capturing the seasonal changes and evolution of sea ice. This database has multiple applications for the scientific community; it can be used to study the processes that influence individual sea ice parcel time series, or to explore generalized summary statistics and trends across the Arctic.
Here we shed light on two mechanisms that stimulate deep particle export via upper-ocean iron fertilization in the Southern Ocean: deep frontal mixing and melting of sea ice. We present data collected a decade apart in the Pacific sector of the Southern Ocean when, serendipitously, seasonal Antarctic ice melt was anomalously low (2008) and anomalously high (2017). In 2008, the low ice melt year, we concluded that vertical mixing of iron into the euphotic zone via deep-mixing fronts was the primary stimulant of export that reached depths of ~1500 meters. This process was evidenced by localized enhancements of dissolved organic carbon (DOC) concentrations up to 4 µmol C kg-1 beneath seven branches of fronts embedded within the Antarctic Circumpolar Current (ACC). We used these enhanced DOC concentrations in the bathypelagic as primary indications of the depths and locations of recent export, as it is a logical residue of such. In 2017, the year in which sea ice melt was anomalously high, we concluded that the main driver of a widespread export event to the seafloor was the lateral influx of iron within the melt. Indications of this event included substantial enhancements of DOC concentrations (2 - 6 µmol C kg-1), elevated beam attenuation, and enhanced surface iron concentrations associated with a layer of low salinity water at a nearby station. Further, significant deficits of upper ocean silicic acid during the 2017 occupation indicated that deep export was likely stimulated by an iron-fueled diatom bloom. This analysis highlights the impact of iron supplied from frontal vertical mixing and sea ice melt on export and ultimately for long-term carbon sequestration in the Southern Ocean, as well as the utility of deep DOC enrichments as signatures of particle export. Understanding the impact that ice melt events have on carbon export is crucial given that anomalous events are occurring more often as our climate changes.
For aerosol, cloud, land, and ocean remote sensing, the development of accurate cloud detection methods, or cloud masks, is extremely important. For airborne passive remotesensing, it is also important to identify when clouds are above the aircraft since their presence contaminates the measurements of nadir-viewing passive sensors. We describe the development of a camera-based approach to detecting clouds above the aircraft via a convolutional neural network called the cloud detection neural network (CDNN). We quantify the performance of this CDNN using human-labeled validation data where we report 96% accuracy in detecting clouds in testing datasets for both zenith viewing and forward-viewing models. We present results from the CDNN basedon airborne imagery from the NASA Aerosol Cloud meteorology Interactions oVer the western Atlantic Experiment (ACTIVATE) and the Clouds, Aerosol, and Monsoon Processes Philippines Experiment (CAMP2Ex). We quantify the ability of the CDNN to identify the presence of clouds above the aircraft using a forward-looking camera mounted inside the aircraft cockpit compared to the use of an all-sky upward-looking camera that is mounted outside the fuselage on top of the aircraft. We assess our performance by comparing the flight-averaged cloud fraction of zenith and forward CDNN retrievals with that of the prototype hyperspectral total-diffuse Sunshine Pyranometer (SPN-S) instrument’s cloud optical depth data. A comparison of the CDNN with the SPN-S on time-specific intervals resulted in 93% accuracy for the zenith viewing CDNN and 84% for the forward-viewing CDNN. The comparison of the CDNNs with the SPN-S on flight-averaged cloud fraction resulted in an agreement of .15 for the forward CDNN and .07 for the zenith CDNN. For CAMP2Ex, 53% of flight dates had above-aircraft cloud fraction above 50%, while for ACTIVATE, 52% and 54% of flight dates observed above-aircraft cloud fraction above 50% for 2020 and 2021, respectively. The CDNN enables cost-effective detection of clouds above the aircraft using an inexpensive camera installed in the cockpit for airborne science research flights where there are no dedicated upward-looking instruments for cloud detection, the installation of which requires time-consuming and expensive aircraft modifications, in addition to added mission cost and complexity of operating additional instruments.
Deep convection associated with tropical cyclones leads to stratosphere‐troposphere exchange (STE), which affects the upper‐tropospheric ozone concentrations in the vicinity of the cyclones. This study estimates the ozone enhancements over India due to the North Indian Ocean (NIO) cyclones‐driven STE. Indicators such as stratospheric fraction and potential vorticity calculated using the reanalysis data sets suggest that roughly 70% of the cyclones show anomalously high stratospheric intrusions. Aircraft observations over different locations across India also show elevated ozone concentrations in the mid‐to‐upper troposphere on cyclone days. Further, ozone and stratospheric ozone tracer concentrations from Goddard Earth Observing System‐Chemistry simulations and the Copernicus Atmosphere Monitoring Service reanalysis data sets show up to 40 ppb of excess upper tropospheric ozone over India, of which stratospheric ozone accounts for roughly 60%. Stratospheric intrusion due to the Bay of Bengal and the Arabian Sea cyclones affected the upper tropospheric ozone amounts over North and South India, respectively. The stratospheric ozone was observed to propagate downwards into the troposphere, often reaching ∼600 hPa and, in some cases, even the surface.
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