Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “ARGOS”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7

Collaborative Autonomous Logistics

This video shows a demonstration of collaborative autonomous logistics. Turtlebot, a 1-g stand-in for Astrobee, carried REALM sensors around a mockup of ISS in ARGOS to autonomously survey for a cargo bag stowed in a drawer. It provided the location of the bag to Robonaut, which then autonomously climbed across the mockup to the drawer. Robonaut then used the Affordance Template manipulation framework to localize and open the drawer and then again localize and retrieve the cargo bag.

Badger, Julia M.↗

Development of an Inflatable Airlock for a Deep Space Gateway

Inflatable structures technology utilizes high-strength fabric materials and internal pressure to create a stiffened pressure vessel that can replace traditional metallic primary structure in a habitable spacecraft. The flexibility of fabric structures allows them to be compactly stowed for launch and expanded in space, providing significant launch volume savings. The unique construction and design flexibility of these structures can be customized for a variety of uses in space including landing bags, decelerators, long duration in-space and planetary surface habitats, and even airlocks. An airlock is often a required component of a crewed spacecraft to allow for maintenance and human exploration outside of the vehicle. Airlock designs in use today rely on complex hatches and seals connected by metallic walls. Recent developments towards the design of an inflatable airlock structure show feasibility and a significant launch volume savings over a traditional metallic design. This paper will provide a high-level summary of these projects and the current state-of-the-art in inflatable airlock development with additional references and detail about previous and on-going research, providing guidance for the design of a softgoods airlock system. The use of inflatables in space has been in development since the 1960’s for both habitats and airlocks. The first ever EVA was conducted by the USSR in 1965 using an inflatable airlock known as the Volga. This airlock was attached to the Voskhod 2 spacecraft and turned the vehicle into a dual chamber airlock. The airlock was successfully deployed, used and jettisoned after Alexey Leonov’s historic spacewalk. Additional work on human-rated inflatable structures was not continued until the late 1990’s when NASA-JSC led an effort to demonstrate these structures as feasible long-term pressurized elements with the TransHab project. The technology developed and pioneered during this project led to multiple patents and proven feasibility that inflatables could be used for large habitable structures. Following TransHab, Bigelow Aerospace continued the development of inflatable structures with technical support from NASA. This partnership eventually led to the successful flight certification, launch, attachment and deployment of the Bigelow Expandable Activities Module (BEAM) on the ISS in 2016. Inflatable and expandable airlock structures have undergone various detailed feasibility studies and testing for over 15 years, most notably with the Advanced Inflatable Airlock (AIA), Dual-Chamber Hybrid Inflatable Suitlock (DCIS), Minimalistic Advanced Soft Hatch (MASH), and Lightweight External Inflatable Airlock (LEIA). During this time, full-scale articles have been built and pressure-tested, and mock-ups and demonstrators have been constructed and evaluated. During the 2001-2003 timeframe, the AIA concept was matured through requirements development, conceptual design, subscale and full-scale engineering breadboards subjecting various test articles to deployment and pressure testing up to four times operating pressure. These tests proved the feasibility of successful deployment and structural integrity of an inflatable crewlock. Additional testing was performed in the ensuing years, as funding permitted, to further refine additional structural and deployment concepts and to understand the EVA crewmember interfaces, hatches and EVA support equipment interfaces that would be required for a fully functioning airlock. This work resulted in a refinement of the structural requirements and an accounting of the systems needed in an inflatable airlock. In 2014, the MASH project developed an ultra-lightweight airlock concept with a fabric hatch that utilized a unique pressure vessel shape to minimize structural loads around a linear seal. The concept uses an automated zipper-like seal that allows for crew egress/ingress. Most of the development work on the project thus far has focused on the design, analysis and testing of the primary structure and the zipper-like seal system is in the preliminary stages of development with a successful proof-of-concept test. As part of the 2017 LEIA effort, studies were conducted on EVA crewmember interfaces on the inside of an inflatable airlock. These efforts included the design of an internal secondary structure and placement of handholds and foot restraints to enable hatch opening, closing and translation through the airlock. Structural design, analysis and testing was completed on several secondary structure candidates. Crew interface testing was also completed using an inflatable crewlock mockup and the JSC Active Response Gravity Offload System (ARGOS) to simulate the movement of an EVA crewmember through an inflatable crewlock in microgravity. The results of these tests helped demonstrate the feasibility of utilizing an inflatable structure as an airlock and informed the required volume, hatch size, and configuration and location of translation aids for crewmembers in a microgravity crewlock. The ISS Quest airlock uses a dual-chamber design with isolated compartments known as the equipment-lock and the crewlock. The equipment-lock houses the Servicing, Performance and Checkout Equipment (SPCE) items (suit batteries, consumables, etc.) while the crewlock has limited internal hardware and is the nominally depressurized compartment during US EVAs. While inflatable dual chamber airlocks have been studied, the current state of the art emphasizes an inflatable crewlock-type structure attached to a rigid equipment-lock type or habitat structure. Since a large portion of the hardware in the equipment-lock are rigid components and connectors that are installed on the ground - and an inflatable structure does not achieve full structural capabilities until pressurized in space - a depressurized fabric structure cannot provide the capabilities of a full equipment-lock. The use of an inflatable as a crewlock, however, provides all the required capabilities for EVA operations in a small launch package that offers significant volume savings over a metallic crewlock. The functions of a traditional equipment lock, including the SPCE, could be provided by a spacecraft’s habitat module or node and not necessarily in a separate equipment lock. An inflatable crewlock would be attached to the vehicle and launched in a packed and compressed state, saving volume under the launch shroud and mass for the overall airlock element compared to a rigid crewlock. Work is currently underway to continue development of an inflatable airlock with a variety of focus areas including the consideration of crew-induced loads and interfaces, the design and development of an internal sub-structure to provide translation aids and restraints, the thermal considerations of a fabric shell depressurized during an EVA, the micrometeorite environment in deep space, and the packaging and deployment of an inflatable airlock.

Deep Space Exploration↗

Impact of Satellite Sea Surface Salinity Observations on ENSO Predictions from the GMAO S2S Forecast System

El Nino/Southern Oscillation (ENSO) has far reaching global climatic impacts and so extending useful ENSO forecasts would be of great benefit for society. However, one key variable that has yet to be fully exploited within coupled forecast systems is accurate estimation of near-surface ocean density. Satellite Sea Surface Salinity (SSS), combined with temperature, help to identify ocean density changes and associated mixing near the ocean surface. We assess the impact of satellite SSS observations for improving near-surface dynamics within ocean analyses and how these impact dynamical ENSO forecasts using the NASA GMAO (Global Modeling and Assimilation Office) Sub-seasonal to Seasonal (S2S_v2.1) coupled forecast system (Molod et al. 2018 - i.e. NASA's contribution to the NMME (National Multi-Model Ensemble) project). For all initialization experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF ( Local Ensemble Transform Kalman Filter) scheme similar to Penny et al., 2013. A separate reanalysis additionally assimilates Aquarius V5 (September 2011 to June 2015) and SMAP (Soil Moisture Active Passive) V4 (March 2015 to present) along-track data.We highlight the impact of satellite SSS on ocean reanalyses by comparing validation statistics of experiments that assimilate SSS versus our current prediction system that withholds SSS. We find that near-surface validation versus observed statistics for salinity are slightly degraded when assimilating SSS. This is an expected result due to known biases between SSS (measured by the satellite at approximately 1 centimeter) and in situ measurements (typically measured by Argo floats at 3 meters). On the other hand, a very encouraging result is that both temperature, absolute dynamic topography, and mixed layer statistics are improved with SSS assimilation. Previous work has shown that correcting near-surface density structure via gridded SSS assimilation can improve coupled forecasts. Here we present results of coupled forecasts that are initialized from the GMAO S2S reanalyses that assimilates/withholds along-track (L2) SSS. In particular, we contrast forecasts of the overestimated 2014 El Nino, the big 2015 El Nino, and the minor 2016 La Nina. For each of these ENSO scenarios, assimilation of satellite SSS improves the forecast validation. Improved SSS and density upgrades the mixed layer depth leading to more accurate coupled air/sea interaction.

Hackert, E.↗

Lunar Environment Simulation for a High Performance Motor

NASA and the Canadian Space Agency (CSA) through a CSA contractor, Argo Space/Robotics Division, partnered to perform environmental performance tests on a high torque producing motor. CSA provided the motor and NASA provided a thermal vacuum chamber capable of achieving high vacuum (P < 1 E-5 torr) and temperatures between 25 and 400 K. NASA also provided a dynamometer system capable of measuring and or applying break torque between 0 and 28 Nm. The two primary goals of the test were to simulate sun exposed and shadow condition expected on the lunar surface in order to determine survivability of the motor at extreme temperature conditions and to operate the motor under a constant break load of 6.8 Nm in the temperature range of 30K to 415K. A secondary objective of the test was to operate the motor for 15 km under 6.8 Nm of load. The primary goals of the test were fully achieved. The secondary goal was partially achieved.

O'Connell, Joe↗

Regularization and Error Characterization of GRACE Mascons

We present a new global time-variable gravity mascon solution derived from Gravity Recovery and Climate Experiment (GRACE) Level 1B data. The new product from the NASA Goddard Space Flight Center (GSFC) results from a novel approach that combines an iterative solution strategy with geographical binning of inter-satellite range-acceleration residuals in the construction of time-dependent regularization matrices applied in the inversion of mascon parameters. This estimation strategy is intentionally conservative as it seeks to maximize the role of the GRACE measurements on the final solution while minimizing the influence of the regularization design process. We fully reprocess the Level 1B data in the presence of the final mascon solution to generate true post-fit inter-satellite residuals, which are utilized to confirm solution convergence and to validate the mascon noise uncertainties. We also present the mathematical case that regularized mascon solutions are biased, and that this bias, or leakage, must be combined with the estimated noise variance to accurately assess total mascon uncertainties. The estimated leakage errors are determined from the monthly resolution operators. We present a simple approach to compute the total uncertainty for both individual mascon and regional analysis of the GSFC mascon product, and validate the results in comparison with independent mascon solutions and calibrated Stokes uncertainties. Lastly, we present the new solution and uncertainties with global analyses of the mass trends and annual amplitudes, and compute updated trends for the global ocean, and the respective contributions of the Greenland Ice Sheet, Antarctic Ice Sheet, Gulf of Alaska, and terrestrial water storage. This analysis highlights the successful closure of the global mean sea level budget, that is, the sum of global ocean mass from the GSFC mascons and the steric component from Argo floats agrees well with the total determined from sea surface altimetry.

B. D. Loomis↗

Impact of Satellite Sea Surface Salinity Observations on ENSO Predictions from the GMAO Seasonal Forecast System

El Nino/Southern Oscillation (ENSO) has far reaching global climatic impacts and so extending useful ENSO forecasts would be of great benefit for society. However, one key variable that has yet to be fully exploited within coupled forecast systems is accurate estimation of near-surface ocean density. Satellite sea surface salinity (SSS), combined with temperature, help to identify ocean density changes and associated mixing near the ocean surface. We assess the impact of satellite SSS observations for improving near-surface dynamics within ocean analyses and how these impact dynamical ENSO forecasts using the NASA GMAO (Global Modeling and Assimilation Office) Sub-seasonal to Seasonal (S2S_v2.1) coupled forecast system (Molod et al. 2018 - i.e. NASA's contribution to the NMME (North American Multi-Model Ensemble) project). For all initialization experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF (Local Ensemble Transform Kalman Filter) scheme similar to Penny et al., 2013. A separate reanalysis additionally assimilates Aquarius V5 (September 2011 to June 2015) and SMAP (Soil Moisture Active Passive satellite) V4.1 (March 2015 to present) along-track data.We highlight the impact of satellite SSS on ocean reanalyses by comparing validation statistics of experiments that assimilate SSS versus our current prediction system that withholds SSS. We find that near-surface validation versus observed statistics for salinity are slightly degraded when assimilating SSS. This is an expected result due to known biases between SSS (measured by satellite at approximately 1-centimeter depth) and in situ measurements (typically measured by Argo floats at 3-meters depth). On the other hand, a very encouraging result is that both temperature, absolute dynamic topography, and mixed layer statistics are improved with SSS assimilation. Previous work has shown that correcting near-surface density structure via gridded SSS assimilation can improve coupled forecasts. Here we present results of coupled forecasts that are initialized from GMAO S2S spring reanalyses that assimilate/withhold along-track (L2) SSS. In particular, we contrast forecasts of the big 2015 El Nino, the 2017 La Nina and the 2018 weak El Nino. For each of these ENSO scenarios, assimilation of satellite SSS improves the forecast validation. Improved SSS and density upgrade the mixed layer depth leading to more accurate coupled air/sea interaction. From March to June 2015, the availability of two overlapping satellite SSS instruments, Aquarius and SMAP, allows a unique opportunity to compare and contrast forecasts initialized with the benefit of these two satellite SSS observation types. We assess the impact of gridded satellite sea surface salinity observations on dynamical ENSO forecasts for the big 2015 El Nino.

Hackert, E.↗

Satellite Salinity Observing System: Recent Discoveries and the Way Forward

Advances in L-band microwave satellite radiometry in the past decade, pioneered by ESA's SMOS and NASA's Aquarius and SMAP missions, have demonstrated an unprecedented capability to observe global sea surface salinity (SSS) from space. Measurements from these missions are the only means to probe the very-near surface salinity (top cm), providing a unique monitoring capability for the interfacial exchanges of water between the atmosphere and the upper-ocean, and delivering a wealth of information on various salinity processes in the ocean, linkages with the climate and water cycle, including land-sea connections, and providing constraints for ocean prediction models. The satellite SSS data are complimentary to the existing in situ systems such as Argo that provide accurate depiction of large-scale salinity variability in the open ocean but under-sample mesoscale variability, coastal oceans and marginal seas, and energetic regions such as boundary currents and fronts. In particular, salinity remote sensing has proven valuable to systematically monitor the open oceans as well as coastal regions up to approximately 40 km from the coasts. This is critical to addressing societally relevant topics, such as land-sea linkages, coastal-open ocean exchanges, research in the carbon cycle, near-surface mixing, and air-sea exchange of gas and mass. In this paper, we provide a community perspective on the major achievements of satellite SSS for the aforementioned topics, the unique capability of satellite salinity observing system and its complementarity with other platforms, uncertainty characteristics of satellite SSS, and measurement versus sampling errors in relation to in situ salinity measurements. We also discuss the need for technological innovations to improve the accuracy, resolution, and coverage of satellite SSS, and the way forward to both continue and enhance salinity remote sensing as part of the integrated Earth Observing System in order to address societal needs.

Vinogradova, Nadya↗

MERRA-2 Ocean: The NASA Global Modeling and Assimilation Office's Weakly Coupled Atmosphere-Ocean Reanalysis Using GEOS-S2S Version 3

The NASA Modern Era Reanalysis for Research and Applications (MERRA2) has been a respected and widely used reanalysis that has so far been restricted to the atmosphere. Now a newly released version of the atmosphere/ocean coupled data assimilation system (AODAS) has been developed by the NASA/Goddard Global Modeling and Assimilation Office to perform a retrospective ocean reanalysis from 1982 to present. In addition to assimilating all available in situ data (e.g. Argo, mooring, XBT and CTD data) and altimetry information into the ocean, the new version (GEOS-S2S Version 3) model includes a higher resolution, eddy-permitting ocean model than previous versions, a more realistic implementation of the atmosphere-ocean interface layer, and an improved coupling between glacier and ocean (among other improvements). In addition, this ocean data assimilation was expanded to include the assimilation of satellite sea surface salinity. The MERRA-2 AODAS will be described, and preliminary results will be shown from the assimilation reanalysis and from retrospective forecasts issued using a new ensemble strategy. Following the Global Ocean Data Assimilation Experiment (GODAE) protocols, we will present Class 1 through Class 4 validation results from the ocean reanalysis. Results indicate an improved ocean mixed layer depth, improved salinity near Greenland, an improved diurnal cycle of the sea surface skin temperature, an improved estimate of ocean evaporation, and better representation of western boundary currents (e.g. Gulf Stream) from our new ocean reanalysis. One of the motivations of this project is to provide optimal initial states for ENSO forecasting. Therefore, we will also present some preliminary results of retrospective ENSO forecasts. After thorough testing, it is expected that the GEOS-S2S Version 3 will replace our contributions to North American Multi-Model Ensemble (NMME), WCRP Subseasonal to Seasonal (S2S), and IRI seasonal prediction forecast projects.

Molod, Andrea↗

Evaluating Multi-Sensor Agreement of Satellite Particulate Backscatter Retrievals by Validatin Against In-Water Measurement

Biogeochemical-Argo profiling floats have increased in situ data density across multiple water types, creating new opportunities to evaluate satellite instrument-to-instrument differences in particulate back scattering coefficient(bbp). Retrievals of bbp from identical GIOP algorithm configurations differ between satellite instruments due to1)algorithm input differences and 2) radiometric differences. 3.Instrument-to-instrument differences must be considered before creating a merged timeseries of satellite ocean color products,in order to distinguish real, environmental contributions from spurious algorithmic or radiometricones

Scott, Joel P.↗

Regularization and Error Characterization of GRACE Mascons

We present a new global time-variable gravity mascon solution derived from Gravity Recovery and Climate Experiment (GRACE) Level 1B data. The new product from the NASA Goddard Space Flight Center (GSFC) results from a novel approach that combines an iterative solution strategy with geographical binning of inter-satellite range-acceleration residuals in the construction of time-dependent regularization matrices applied in the inversion of mascon parameters. This estimation strategy is intentionally conservative as it seeks to maximize the role of the GRACE measurements on the final solution while minimizing the influence of the regularization design process. We fully reprocess the Level 1B data in the presence of the final mascon solution to generate true post-fit inter-satellite residuals, which are utilized to confirm solution convergence and to validate the mascon noise uncertainties. We also present the mathematical case that regularized mascon solutions are biased, and that this bias, or leakage, must be combined with the estimated noise variance to accurately assess total mascon uncertainties. The estimated leakage errors are determined from the monthly resolution operators. We present a simple approach to compute the total uncertainty for both individual mascon and regional analysis of the GSFC mascon product, and validate the results in comparison with independent mascon solutions and calibrated Stokes uncertainties. Lastly, we present the new solution and uncertainties with global analyses of the mass trends and annual amplitudes, and compute updated trends for the global ocean, and the respective contributions of the Greenland Ice Sheet, Antarctic Ice Sheet, Gulf of Alaska, and terrestrial water storage. This analysis highlights the successful closure of the global mean sea level budget, that is, the sum of global ocean mass from the GSFC mascons and the steric component from Argo floats agrees well with the total determined from sea surface altimetry.

Loomis, B. D.↗

Overview and Initial Results of SAND-E: Semi-Autonomous Navigation for Detrital Environments

Unmanned aerial systems (UAS) and automated terrain analysis for science and navigation are new technologies for planetary exploration. The Mars Helicopter will fly with the Mars2020 rover, the Dragonfly quadcopter will explore Titan, and Soil Properties and Object Classification (SPOC) software will be used for path planning and navigation on the Mars2020 rover. Using an Argo J5 rover instrumented with stereo cameras and Autonomous Soil Assessment System (ASAS) software, and an off the shelf quadcopter, SAND-E tested the use of automated terrain analysis and UAS data for science operations in a Mars-analog environment in Iceland during July of 2019. Scientifically, we sought to determine changes in the physical and chemical properties of sediments along a glacial-fluvial-aeolian transport pathway. Operationally, we tested rover mission-like scenarios that included UAS images and classified terrain images. Here, we present the initial results for both the operations and science elements of the study. Site Selection: A goal of SAND-E is examine sorting and alteration of sediments in fluvial and aeolian environments in both mineral-dominated and glass-dominated basaltic settings. During the first year of the project we focused on a mineral-dominated environment. Selection of the location was based on prior publications that indicated our selected region had a greater abundance of crystalline sediments than other areas fluvial-aeolian settings in Iceland. Other criteria included the presence of both fluvial and aeolian landforms along a transport pathway such that the sediments in transport could be linked to their source rocks. We chose the Skjaldbreidauhraun glacial outwash plain, which sits at the base of Thórisjökull glacier. The site is 30 km north of Thingvellir National Park and ~2 hours from Reykjavik. The outwash plain is fed by two small catchments that drain from the base of the glacier and cut through hyaloclastite and shield volcano bedrock. The drainage progresses from steep alluvial fans near the glacier into a low-sloping fluvial braidplain that becomes confined by the Skjaldbreidur shield volcano and creates a shallow canyon cut into lava bedrock. The fluvial system was a typical braided alluvial environment composed pebble- and cobble-bedded longitudinal bars and sandy channel beds. The river remained active and fluctuated in response to diurnal runoff cycles near the glacier before disappearing into the sandy substrate downstream. The high concentration of suspended sediment in the river was evident by the cloudy water and the silt and clay-sized sediments that draped the channel beds after abandonment and created playas in the lowest sloping areas of the catchment. The entire fluvial system was affected by the winds generated by frontal systems and katabatic flows descending the glacier. This resulted in the formation of aeolian lag deposits and a wind-deflation plain where the fluvial system was not active. Wind ripples and drifts formed in abandoned fluvial channels from aeolian reworking of the sand-sized fluvial sediments. The silt- and clay-sized sediments found in fluvial channels, bar tops, and playas generated dust plumes during high wind events. Our operation sought to capture the variability in this system by sampling from the range of fluvial and aeolian features 6.3 km (proximal), 11.3 km (medial), and 14.4 km (distal) along the river from its origin at the base of glacier.

Ewing, R. C.↗

Toward an Integrated Product of Higher Accuracy and Longer Time Span: Improvement of the Upcoming NASA GEWEX SRB Data Release 4 over Release 3 Based on Validation Against Surface-Based Measurements

The NASA Global Energy and Water Exchanges (GEWEX) Surface Radiation Budget (SRB) is moving toward its next generation, the Release 4 Integrated Product (Rel. 4-IP), and its shortwave algorithm preliminary results as of this writing span 34 years continuously from July 1983 to June 2017 on a quasi-equal-area grid system with 44,016 gridboxes ranging from 1 longitude times 1 latitude around the Equator to 120 longitude times 1 latitude. The data are in the form of 3-hourly, 3-hourly-monthly, daily and monthly means. The Baseline Surface Radiation Network (BSRN) is a project of the Data and Assessments Panel from the GEWEX under the umbrella of the World Climate Research Programme (WCRP). Currently in the archive there are data from 67 sites located in all seven continents spanning from 1992 to the very present. The original BSRN records are at 1-, 2-, 3- or 5-minute intervals, and sophisticated quality-control procedures have been developed to eliminate erroneous records before hourly, daily and monthly means are computed. The Pacific Marine Environmental Laboratory (PMEL) arrays of buoys in the tropical oceans, TAO/TRITON (Pacific Ocean), RAMA (Indian Ocean) and PITARA (Atlantic Ocean), measure shortwave downward fluxes using pyranometers. These buoys transmit measured data to shore in real-time via the Argos satellite system. And these buoys are major components of the CLIVAR climate analysis project and the GOOS, GCOS, and GEOSS observing systems. So far there are data from 64 buoys and the earliest data start from 2000. We have extensively validated the Rel. 4-IP preliminary results against the BSRN and PMEL 3-hourly, daily and monthly means. In this paper, we will show that the new results not only temporally span 10 years longer than its predecessor, Release 3, but agree significantly better with the BSRN data over the land and PMEL data over the ocean.

Taiping Zhang↗

Particulate backscattering in the global ocean: A comparison of independent assessements

How well do we know the particulate backscattering coefficient (bbp) in the global ocean? Satellite lidar bbp has never been validated globally and few studies have compared lidar bbp to bbp derived from reflectances (via ocean color) or in-situ observations. Here we validate lidar bbp with autonomous biogeochemical Argo floats using a decorrelation analysis to identify relevant spatiotemporal matchup scales inspired by geographical variability in the Rossby radius of deformation. We compare lidar, float, and ocean color bbp at the same locations and times to assess performance. Lidar bbp outperforms ocean color, with a median percent error of 18% compared to 24% in the best case and a relative bias of -11% compared to -21%, respectively. Phytoplankton carbon calculated from ocean color and lidar exhibits basin-scale differences that can reach ± 50%.

ocean color↗

Using Earth-based Operational Field Tests as High-Fidelity Analogs for Planetary Surface Exploration

NASA is preparing to land the first woman and first person of color on the Moon within the next decade and establish a permanent sustainable human presence before sending humans onto Mars. To ensure the success of these missions, NASA has performed operational testing in terrestrial, aquatic, and laboratory analog environments that simulate Lunar and Martian environmental characteristics to evaluate exploration concepts of operations (ConOps), engineering design requirements, science support needs, mission operations techniques, and crew training. Terrestrial analogs include Desert Research and Technology Studies (D-RATS), Biologic Analog Science Associated with Lava Terrains (BASALT), and Next Space Technologies for Exploration Partnerships (NextSTEP)Habitat Ground Testing. Aquatic analogs include NASA Extreme Environment Mission Operations (NEEMO) and Pavilion Lake Research Project (PLRP).Laboratory analogs include the Neutral Buoyancy Laboratory (NBL), Active Response Gravity Offload System(ARGOS), rock yards, and virtual and hybrid reality simulation environments. While no single Earth-based analog environment is perfect for simulating all characteristics of other planetary surfaces, testing across multiple locations leverages the strengths of each to provide an integrated understanding of how to best conduct real spaceflight surface exploration missions.

B A Janoiko↗

High School Aerospace Scholars Virtual Robotics Tour

Tour of the Johnson Space Center Robotic Technologies: ARGOS (Active Response Gravity Offload System), Rover (Featuring the Small Pressurized Rover), mSTAR/STAR, Six-Degree-of-Freedom Dynamic Test System (SDTS docking test system). Video has a 19:19 run time, color, audio and must be downloaded to view.

Reeve David Lambert↗

Seawater Debye Model Function at L-Band and Its Impact on Salinity Retrieval From Aquarius Satellite Data

A model function of seawater, which specifies the dielectric constant of seawater as a function of salinity, temperature, and frequency, is important for the retrieval of sea surface salinity using satellite data. In 2017, a model function has been developed based on measurement data at 1.4134 GHz using a third-order polynomial expression in salinity ( S ) and temperature ( T ). Although the model showed improvements in salinity retrieval, it had an inconsistent behavior between partitioned salinities. To improve the stability of the model, new dielectric measurements of seawater have been made recently over a broad range of salinities and temperatures to expand the data set used for developing the model function. The structure of the model function has been changed from a polynomial expansion in S and T to a physics-based model consisting of a Debye molecular resonance term plus a conductivity term. Each unknown parameter is expressed in S and T based on the expanded measurement data set. Physical arguments have been used to limit the number of unknown coefficients in these expressions to improve the stability of the model function. The new model function has been employed in the retrieval algorithm of the Aquarius satellite mission to obtain a global salinity map. The retrieved salinity using a different model function is compared with in situ data collected by Argo floats to evaluate the impact and the performance of model functions. The results indicate that the new model function has significant improvements in salinity retrieval compared with other existing models.

Seawater↗

Debye Dielectric Model Function for Seawater Based on Expanded L-Band Measurement Data Set

New seawater dielectric measurements have recently been made over a broad range of salinities and temperatures at the George Washington University (GW). These measurements have been used to refine the existing dielectric model function of seawater for salinity retrieval. The mathematical structure of the new model function has been investigated to: (a) look for a physical basis for the mathematical form; and (b) to optimize the accuracy of data fitting. The Debye model has been chosen to represent the dielectric constant of seawater as a function of salinity (S), temperature (T) and frequency (f). The retrieved salinity using the Debye model has good agreement with the in-situ data collected by Argo floats. The global differences in retrieved salinity and in-situ data will be presented at the meeting.

ocean salinity↗

Seasonal Bias in Global Ocean Color Observations

In this study we identify a seasonal bias in the ocean color satellite derived remote sensing reflectances (Rrs(λ); sr-1) at the ocean color validation site, MOBY (Marine Optical BuoY). The seasonal bias in Rrs(λ) is present to varying degrees in all ocean color satellites examined, including VIIRS (Visible Infrared Imaging Radiometer Suite), SeaWiFS (Sea-viewing Wide Field-of-view Sensor), and MODIS (Moderate Resolution Imaging Spectrometer). The relative bias in Rrs has spectral dependence. Products derived from Rrs(λ) are affected by the bias to varying degrees, with particulate backscattering varying up to 50% over a year, chlorophyll varying up to 25% over a year, and absorption from phytoplankton or dissolved material varying by up to 15%. The propagation of Rrs(λ) bias into derived products is broadly confirmed on regional and global scales using Argo floats and data from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) instrument aboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder) satellite. The artifactual seasonality in ocean color is prominent in areas of low biomass (i.e., subtropical gyres) and is not easily discerned in areas of high biomass. While we have eliminated several candidates that could cause the biases in Rrs(λ), there are still outstanding questions regarding potential contributions from atmospheric corrections. Specifically, we provide evidence that the aquatic bidirectional reflectance distribution function may in part cause the observed seasonal bias, but this does not preclude an additional effect of the aerosol estimation. Our investigation highlights the contributions that atmospheric correction schemes can make in introducing biases in Rrs (λ) and we recommend more simulations to discern these influence Rrs (λ) biases. Community efforts are needed to find the root cause of the seasonal bias because all past, present, and future data are or will be affected until a solution is implemented.

satellite ocean color↗