Results for “evaluations” — Page 45

NASA NTRSDate not supplied

Evaluation of TAO Observation System on ENSO Predictions from the GMAO S2S Forecast System

Since the early 1990’s, the Tropical Atmosphere Ocean (TAO) mooring array has been instrumental for observing Kelvin and Rossby wave propagation which is key for El Nino/Southern Oscillation (ENSO) prediction. However, recent funding and programmatical issues have highlighted the need to rigorously assess the impact of the TAO observing system on ENSO predictions. Therefore, we evaluate the TAO using data assimilation observation denial experiments (also known as Observing System Evaluation or OSE experiments). This presentation will evaluate the TAO observing impact on both reanalyses and prediction of the big 2015 El Nino. We have completed reanalyses for July 2014-Dec 2015 for both the CONTROL experiment (that assimilates all available data) and the NOTAO experiment (that is identical to the CONTROL but withholds all TAO observations) using the best available NASA GEOS-S2S V3 seasonal prediction system. Validation of these reanalyses shows that TAO assimilation generally improves comparisons of temperature and salinity versus gridded in situ observations. Temperature is universally improved above the thermocline near the equator, but the biggest improvement is found in the eastern Pacific, just below the thermocline, where the variation of the thermocline defines ENSO events. In addition, a surprising result is that even with relatively few observations, salinity is improved throughout the equatorial region except near 120oW near the surface. ENSO forecasts were performed that were initialized from these CONTROL and NOTAO reanalyses. For the 9-month forecasts which were initialized in January, July, and October 2015, the NINO3.4 SST shows that the CONTROL forecasts are warmer and closer to observations than the NOTAO forecasts. We will show that upwelling and shoaling of the mixed layer amplifies the ENSO signal due to TAO assimilation. Prior to (after) April 2015, this upwelling is caused by relatively stronger Rossby (Kelvin) waves in the CONTROL than in the NOTAO experiments.

E. Hackert↗

NASA NTRSDate not supplied

Kinematic Sensors Evaluation for Spaceflight Exercise Data Collections

INTRODUCTION: On the International Space Station (ISS), exercise feedback from astronauts is very important to diagnose and mitigate any form-related injuries and ensure efficacious exercise prescriptions and systems. Going forward, exploration exercise efforts seek to gain further quantitative data of human and system performance. Currently, methods of collecting in-flight exercise data on the ISS are limited to marker-based motion capture (MoCap) where astronauts must wear reflective markers over their clothes and specialized cameras are used. The main objective of this work was to investigate the following alternative tracking options: markerless video-based MoCap and inertial measurement units (IMUs). These were compared against traditional marker-based MoCap to evaluate kinematic accuracy and inform feasible methods for future exercise data collections on the ISS, especially in support of future Vibration Isolation and Stabilization (VIS) system development. METHODS: Three test subjects performed a variety of flight-like resistance and aerobic exercises using the Miniature Exercise Device (MED-2), Concept-2 rowing ergometer, barbell mockup, bench (e.g., for bench press, hip thruster, and cycling), and a custom structure for dips. These were intended also to represent exercises which could be performed on the multi-modality European Enhanced Exploration Exercise Device (E4D) [1]. The marker-based MoCap data, collected through a 16-camera OptiTrack MoCap system, was regarded as the gold standard to compare the data against. Passive markers were affixed to each subject according to a modified full body Plug-in Gait marker set [2] with 46 total markers. The markerless MoCap data was collected using two GoPro Hero7 cameras and one GoPro Hero11 camera. For the IMU data, a full body set of 17 Xsens DOTs were placed on the subject: 10 upper body and 7 lower body IMUs. Biomechanical modeling and evaluation was conducted through OpenSim [3] (MoCap), OpenSense [4] (IMU), OpenCap [5] (markerless), ENABLE [6] (markerless), and other modeling software. Secondary objectives included comparing the volume of equipment, reducing mass and crew set-up time. RESULTS AND DISCUSSION: While there were issues with initial processing for the IMUs and markerless MoCap, the results aided in the understanding of each sensor, developing end-to-end processes, and identifying future needs. Some observed concerns with the markerless MoCap approaches included being cognizant of a cluttered background, number of people in field of view, camera number and placement. Some challenges with the IMUs included possible sliding, early deactivation possibly due to exercise pose, and large quantity sensor synchronization. Overall, the markerless MoCap option may be the preferred method of data collection and processing as it provides a solution for certain IMU shortcomings and may be least in equipment volume, upmass, and crew setup time. CONCLUSIONS: While this work was mainly focused on ISS data collection, these sensor data along with continued evaluation and development efforts will help to establish best methods for exercise data collection on Gateway, for other Artemis missions, and beyond. Details on the latest end-to-end processing of the data and results will be presented, along with lessons learned and recommended sensor selection and methods.

S. Faragalla↗

NASA NTRSDate not supplied

A Global Agroclimatology Solar Insolation and Meteorological Parameter Data Base: Meteorological Evaluation and Application Updates to NASA POWER

NASA’s Prediction of Worldwide Energy Resource (POWER) project facilitates the use of NASA Earth Science data holdings within the energy, agricultural, and architectural industries. POWER packages solar and meteorological data at various temporal levels from several NASA projects in a user friendly GIS-enabled web services system (https://power.larc.nasa.gov). Among other possible formats, data users can request POWER data in a column formatted DSSAT ASCII format by entering single site-specific coordinates or from an area by entering the appropriate area coordinate bounds. We present here recent evaluations of the POWER meteorological data through comparisons with global surface site measurements. While most meteorological parameters are taken from the NASA Modern Era Retrospective-analysis for Research and Applications (MERRA-2) data set, an alternate precipitation source from Integrated Multi-satellitE Retrievals for GPM (IMERG) is available and included in this evaluation. Expected updates to both IMERG and solar insolation sources will be evaluated as they become available.

J. Colleen Mikovitz↗

NASA NTRSDate not supplied

In-Depth Analysis of Subject Functional Performance and Subjective Feedback Data for Evaluating Argos Simulation Quality

Mechanical offloading systems such as the Active Response Gravity Offload System (ARGOS) at NASA Johnson Space Center (JSC) are used to simulate various partial gravity environments. ARGOS offloads pressurized suited subjects through a gimbaling pivot point system attached to a volumetric Portable Life Support System (PLSS). The pivot point can be configured to multiple locations with respect to the human-suit system center of gravity (CG). Previous work has tested and documented the interaction between different pivot point locations and measured CG. Observations indicated that small changes in pivot point location substantially affect the stability and functional performance (simulation quality) of a subject. This led to the development of a standardized gimbal assessment protocol to assess the functional performance of a pressurized suited subject as a function of the pivot point location, using the data obtained from a variety of planetary suit mobility tasks. As a result, an expansive repository of CG-related task metrics was generated from numerous ARGOS spacesuit test events, for different gimbal configurations and different test subjects. Based on the evaluation metrics and observed trends, several pivot point locations were iteratively identified to determine an “appropriate/optimal” configuration. This study thus describes the in-depth analysis of the CG evaluation metrics to better quantify trends and determine if specific factors are strongly associated with appropriate/optimal configurations. Quantitative and qualitative variables relating to subject task performance, subjective feedback, and anthropometry will be evaluated via probabilistic methods. The results from this study are expected to improve our understanding of optimal ARGOS gimbal settings, which will better inform the gimbal configuration identification process and improve simulation quality for extravehicular activity (EVA) training.

Joseph Yao↗

NASA NTRSDate not supplied

Progress Towards a Common CERES Cloud Mask Algorithm for MODIS and VIIRS: Evaluation using CALIOP Data

For over two decades The Clouds and the Earth’s Radiant Energy System (CERES) project has endeavored to produce a long-term global climate data record for detecting changes in the Earth’s radiation budget and to improve understanding of how clouds contribute to those changes. CERES incorporates cloud information derived from passive narrowband satellite imaging radiometers, and over the course of the project different instruments have contributed to this effort, namely the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS).Cloud properties have been derived from MODIS on the Aqua satellite for CERES since 2002,and VIIRS on the NOAA-20 satellite launched in 2017 will continue the cloud record once Aqua-MODIS reaches the end of its operational lifetime. However, MODIS and VIIRS have different spectral capabilities and characteristics which complicates our ability to seamlessly transition the record from MODIS to VIIRS. This study evaluates some of the recent algorithm modifications incorporated by the CERES Cloud Working Group towards developing a unified cloud mask algorithm for MODIS and VIIRS that utilizes a set of spectral bands common to both instruments. Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data products are used to evaluate MODIS and VIIRS regional cloud fraction estimates and identify areas where improvements can be made. The cloud mask algorithm relies on computed estimates of cloud-free top-of-atmosphere radiances to differentiate cloudy and cloud-free imager pixels. The unified cloud mask algorithm currently under development uses a reduced number of spectral bands common to both MODIS and VIIRS and so relies on the accuracy of these simulated cloud-free radiances more heavily than predecessor algorithms, i.e., the CERES Edition 4 cloud mask, which use as much spectral information as available on each imager with less consideration for cross-platform consistency. Our validation strategy using CALIOP data is also described. The CALIOP observations are critical for assessing cloud detection accuracies and for independently confirming cloud-free conditions which enables a more robust evaluation of the simulated cloud-free radiances for biases due to factors such as view angle and the spectral dependence of water vapor absorption. These effects are known to differ for the two satellite instruments. Comparisons of MODIS and VIIRS cloud fractions are presented in context with estimates from CALIOP and from the CERES Edition 4 cloud mask algorithm to gauge current progress in developing accurate and consistent MODIS and VIIRS cloud properties for CERES.

CERES↗

NASA NTRS2024

Kinematic Sensors Evaluation for Spaceflight Exercise Data Collections

INTRODUCTION: On the International Space Station (ISS), exercise feedback from astronauts is very important to diagnose and mitigate any form-related injuries and ensure efficacious exercise prescriptions and systems. Going forward, exploration exercise efforts seek to gain further quantitative data of human and system performance. Currently, methods of collecting in-flight exercise data on the ISS are limited to marker-based motion capture (MoCap) where astronauts must wear reflective markers over their clothes and specialized cameras are used. The main objective of this work was to investigate the following alternative tracking options: markerless video-based MoCap and inertial measurement units (IMUs). These were compared against traditional marker-based MoCap to evaluate kinematic accuracy and inform feasible methods for future exercise data collections on the ISS, especially in support of future Vibration Isolation and Stabilization (VIS) system development. METHODS: Three test subjects performed a variety of flight-like resistance and aerobic exercises using the Miniature Exercise Device (MED-2), Concept-2 rowing ergometer, barbell mockup, bench (e.g., for bench press, hip thruster, and cycling), and a custom structure for dips. These were intended also to represent exercises which could be performed on the multi-modality European Enhanced Exploration Exercise Device (E4D) [1]. The marker-based MoCap data, collected through a 16-camera OptiTrack MoCap system, was regarded as the gold standard to compare the data against. Passive markers were affixed to each subject according to a modified full body Plug-in Gait marker set [2] with 46 total markers. The markerless MoCap data was collected using two GoPro Hero7 cameras and one GoPro Hero11 camera. For the IMU data, a full body set of 17 Xsens DOTs were placed on the subject: 10 upper body and 7 lower body IMUs. Biomechanical modeling and evaluation was conducted through OpenSim [3] (MoCap), OpenSense [4] (IMU), OpenCap [5] (markerless), ENABLE [6] (markerless), and other modeling software. Secondary objectives included comparing the volume of equipment, reducing mass and crew set-up time. RESULTS AND DISCUSSION: While there were issues with initial processing for the IMUs and markerless MoCap, the results aided in the understanding of each sensor, developing end-to-end processes, and identifying future needs. Some observed concerns with the markerless MoCap approaches included being cognizant of a cluttered background, number of people in field of view, camera number and placement. Some challenges with the IMUs included possible sliding, early deactivation possibly due to exercise pose, and large quantity sensor synchronization. Overall, the markerless MoCap option may be the preferred method of data collection and processing as it provides a solution for certain IMU shortcomings and may be least in equipment volume, upmass, and crew setup time. CONCLUSIONS: While this work was mainly focused on ISS data collection, these sensor data along with continued evaluation and development efforts will help to establish best methods for exercise data collection on Gateway, for other Artemis missions, and beyond. Details on the latest end-to-end processing of the data and results will be presented, along with lessons learned and recommended sensor selection and methods.

S Faragalla↗

NASA NTRS2022

SoMoGym: A Toolkit for Developing and Evaluating Controllers and Reinforcement Learning Algorithms for Soft Robots

Soft robotsoffer a host of benefits over traditional rigid robots, including inherent compliance that lets them passively adapt to variable environments and operate safely around humans and fragile objects. However, that same compliance makes it hard to use model-based methods in planning tasks requiring high precision or complex actuation sequences. Reinforcement learning (RL) can potentially find effective control policies, but training RL using physical soft robots is often infeasible, and training using simulations has had a high barrier to adoption. To accelerate research in control and RL for soft robotic systems, we introduce SoMoGym ( So ft Mo tion Gym ), a software toolkit that facilitates training and evaluating controllers for continuum robots. SoMoGym provides a set of benchmark tasks in which soft robots interact with various objects and environments. It allows evaluation of performance on these tasks for controllers of interest, and enables the use of RL to generate new controllers. Custom environments and robots can likewise be added easily. We provide and evaluate baseline RL policies for each of the benchmark tasks. These results show that SoMoGym enables the use of RL for continuum robots, a class of robots not covered by existing benchmarks, giving them the capability to autonomously solve tasks that were previously unattainable.

Moritz A. Graule↗

NASA NTRSDate not supplied

Flight Test Evaluation of Automation-Induced Oscillations

Flight-test systems analysis techniques are applied to the case of aircraft automation-induced oscillations(AIO). Emerging technological advancements in flight control automation and artificial intelligence have created a need for evaluating the age-old aerial phenomenon of undesired aircraft oscillations. This paper is an overview on the study of AIO as interactions between human pilot, software engineer, and vehicle dynamics(e.g., latency) with the presence of winds aloft. Flight-test evaluation included a broad, non-comprehensive theory on cyclical and non-cyclical oscillations and a preliminary evaluation of the ecosystem from software engineer, airspace architect, and human test pilot.

David Zahn↗

NASA NTRS2024

Usability Evaluation of NASA TOPS Open Science 101

This report presents a usability evaluation of NASA’s Open Science 101 (OS101) interface. It offers design recommendations for improving the user experience (UX) and user interface (UI) of the initial self-paced OS101 release. The author initially conducted an independent heuristic evaluation, followed by an analysis of written user feedback to corroborate findings. Overall, the usability evaluation found that the OS101 interface offers a linear and interactive UX, with areas for improvement across five major, three minor, and two cosmetic aspects of usability.

Human-Computer Interaction↗

NASA NTRSDate not supplied

OCI Geolocation Evaluation and Refinement Using Landsat Control Points

The Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission is NASA’s next investment in ocean biology, clouds, and aerosol data records. A key feature of PACE is the inclusion of an advanced satellite radiometer, Ocean Color Instrument (OCI), a global mapping radiometer that combines multispectral and hyperspectral remote sensing. The geolocation processing is performed for OCI using spacecraft navigation data and an instrument geometry model. To evaluate the geolocation accuracy for OCI and develop refinements to the processing methods, control point matching using Landsat data has been implemented as a step in the operational processing of OCI data at the Science Data Segment. This processing provides between 200 and 300 high-quality matchups per day with good global and geometric distribution, allowing rapid evaluation of the OCI geolocation accuracy. The results provided an early indication of the overall quality of the geolocation processing and of specific aspects needing improvement. A standard set of granules was identified to support rapid implementation and testing of geolocation refinements, and this approach has been highly successful in improving the geolocation processing accuracy to meet the science requirements. The evaluation will continue throughout the mission to ensure the ongoing accuracy of geolocation. This paper describes the control point matching methodology, the approach to development of the geolocation processing refinements, and the recent results.

PACE↗

NASA NTRSDate not supplied

OCI Geolocation Evaluation and Refinement Using Landsat Control Points

The Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission is NASA’s next investment in ocean biology, clouds, and aerosol data records. A key feature of PACE is the inclusion of an advanced satellite radiometer, Ocean Color Instrument (OCI), a global mapping radiometer that combines multispectral and hyperspectral remote sensing. The geolocation processing is performed for OCI using spacecraft navigation data and an instrument geometry model. To evaluate the geolocation accuracy for OCI and develop refinements to the processing methods, control point matching using Landsat data has been implemented as a step in the operational processing of OCI data at the Science Data Segment. This processing provides between 200 and 300 high-quality matchups per day with good global and geometric distribution, allowing rapid evaluation of the OCI geolocation accuracy. The results provided an early indication of the overall quality of the geolocation processing and of specific aspects needing improvement. A standard set of granules was identified to support rapid implementation and testing of geolocation refinements, and this approach has been highly successful in improving the geolocation processing accuracy to meet the science requirements. The evaluation will continue throughout the mission to ensure the ongoing accuracy of geolocation. This paper describes the control point matching methodology, the approach to development of the geolocation processing refinements, and the recent results.

PACE↗

NASA NTRSDate not supplied

Evaluation of Markerless Motion Capture for Monitoring Sensorimotor Performance

BACKGROUND Astronauts returning from long-duration exposure to microgravity frequently exhibit alterations in sensorimotor function leading to postural imbalance, impaired locomotion, and operational challenges to manual control. Mission duration and individual responses often influence both the severity of performance decrements and the variability in adaptation timelines. Postflight disruptions during functional tasks are often detected through body-worn inertial measurement unit (IMU) devices. While IMU sensors are relatively compact, the long-term wear may lead to discomfort, displacement of the sensors on the body, and restrictions in movement or crew behavior. Although IMU data offers valuable insights from a research standpoint, interpreting changes in pre- and post-flight measures can be difficult for crew support personnel beyond the research domain, which can hinder the application for medical assessments and rehabilitation. Finally, the availability of inertial sensors in-flight is limited. There is a need for unobtrusive monitoring tools to improve our ability to monitor adaptation following gravitational transitions in various postflight evaluations and rehabilitation settings. Markerless motion capture (MMC) is an evolving unobtrusive technology that builds upon decades of research with marker-based motion capture systems to provide 3D human pose estimation from multiple synchronized 2D camera views using deep learning algorithms. Markerless technology can revolutionize how data is captured pre- and post-flight and potentially in-flight during intravehicular activity by enabling pose estimation of multiple crew members from onboard camera hardware. METHODS The following presents the initial evaluation of a state-of-the-art commercial-off-the-shelf MMC system, Theia Markerless, compared to IMU devices during various ground-based functional tasks and environmental conditions. The featured functional tasks include assessments from Human Research Program (HRP) funded studies such as Sensorimotor Standard Measures and Sensorimotor Assessments. Synchronous data collected using both motion capture and IMUs are analyzed for six male and female subjects of varying anthropometry. The analysis includes limited assessments of clothing, capture volume configurations, and the tool's sensitivity to detecting performance changes after a spaceflight analog centrifuge exposure. The development of visualization tools to enhance the application of the pose estimation output is also presented. RESULTS Initial results demonstrate comparable root mean square error (RMSE) to existing literature evaluating markerless and marker-based motion capture systems. Considering the relative functional range of motion of the cervical spine, normalized error values for the markerless system’s accuracy of the head was 0.032 in pitch, 0.025 in roll, and 0.018 in yaw plane of motion across a subset of functional tasks. The raw RMSE values were 3.49, 2.25, and 2.81 degrees respectively. For the torso, results suggest normalized errors of 0.469 in pitch, 0.191 in roll, and 0.307 in yaw planes of motion and raw RMSE values of 3.52, 1.53, and 3.07 degrees respectively. The data suggests the functional demands of a particular task influences the estimation accuracy of the MMC system where more dynamic motion and cases where subjects are not upright may introduce diminished tracking accuracy. DISCUSSION The following work lays the foundation for future implementations leveraging markerless motion capture to assess the time course of recovery and provide insight for rehabilitation protocols to enhance crew readiness for the resumption of daily activities. These tools offer effective methods for anonymizing sensitive crew data, facilitating numerous applications across research, medical, and rehabilitation groups. Collaborations with the Anthropometry and Biomechanics Facility will provide further comparisons of the Markerless system to a marker-based system. ACKNOWLEDGEMENT</ This work is supported by NASA’s Exploration Systems Development Mission Directorate Mars Campaign Office Crew Health Countermeasures.

Hannah M. Weiss↗

NASA NTRS2025

Evaluating the Viability of Compact and Portable X-Ray Systems for an Exploration Medical System in a Ground Demonstration

MOTIVATION FOR INCLUDING X-RAY CAPABILITIES For upcoming exploration missions, the need for enhanced medical care becomes critical due to extended mission durations, significant communication delays, and minimal evacuation opportunities. Previous evidence by our team has revealed that among the 119 medical conditions targeted for management during spaceflight within NASA Exploration Medical Capability’s IMPACT Condition List, at least 36 could benefit from radiography (XR). Utilizing XR for diagnosis and management is hypothesized to significantly improve management of crew health by enabling the immediate evaluation and confirmation of potential injuries or illnesses. Beyond clinical applications, XR also holds potential for non-destructive testing (NDT). This includes applications such as assessing the structural integrity of the spacecraft, analyzing surface and meteorite samples, and inspecting onboard electronics. THREE CANDIDATE X-RAY SYSTEMS CHOSEN FOR GROUND DEMONSTRATION The Exploration Medical Capability Element (ExMC) and the Exploration Medical Integrated Product Team (XMIPT) of the Mars Campaign Office initiated early background work for ground demonstrations. In FY21, ExMC published a Concept of Operations to guide requirements development. By FY23, XMIPT and yet2, a technology scouting and open innovation consulting firm, had completed a market survey and trade study to identify potential miniature XR systems. Selection criteria included commercial-off-the-shelf availability, low mass and volume, and regulatory compliance. The top three candidate devices—Remedi REMEX-KA6, MinXray Impact, and FujiFilm Xair—were acquired to characterize the requirements and capabilities of each device. To facilitate testing, phantoms, and radiographic personal protective equipment (PPE) were purchased, and a dedicated space was designated for XRS usage at Glenn Research Center. During this presentation, the mass, volume, and power requirements for each of the three piloted devices are revealed, as well as information regarding the detector, mA, and kV of the devices. GOAL AND OBJECTIVES OF A MINI XRS GROUND DEMONSTRATION The primary goal of ExMC/XMIPT technology demonstrations is to bridge the gap in available, flight-ready medical device technology by flight-testing diagnostic and treatment technologies essential for managing medical conditions during exploration missions. These technologies must adhere to vehicle constraints such as mass, volume, power, and data requirements, integrate seamlessly with medical decision-support tools, and support increasingly Earth-independent operations. There are three main objectives for the future ground demonstration of these three devices. First, we aim to determine the full capabilities of these three miniature XR systems within the context of the spaceflight environment. While medical applications are the primary focus for the miniature XR, a comprehensive exploration of non-medical uses has been initiated by an XMIPT-sponsored NASA SPARK campaign to identify collaborators. Second, we plan to establish criteria and to use insights gained from evaluating each miniature XR against those criteria to select the most suitable system among the three candidates. Third, we intend to evaluate their suitability for flight certification, which includes assessing its durability for launch, reentry, and exposure to high background radiation, as well as its compatibility with existing data architecture systems. Numerous subject matter experts from NASA and partner institutions will support these objectives.

C A Haddix↗

NASA NTRS2024

Evaluation of Initial and Midterm Air Traffic Procedures for Urban Air Mobility Operations

This paper describes a follow-on joint human-in-the loop (HITL) simulation effort by NASA and Joby Aviation that builds on the results of these previous studies. It extends that work by comparing the LOA condition for Initial UAM operations to Midterm operations that includes corridor airspace structures and some other changes to the operating environment. This is the first reported air traffic controller-in-the-loop evaluation of the corridor condition in a high-fidelity simulation environment. The following sections describe the routes, scenarios, and operating conditions along with metrics used to evaluate feasibility from the air traffic controllers’ perspectives. The results section presents the traffic levels that were feasible in each condition and concludes with recommendations for extending the scope and applicability of evaluations like this one in the future.

Savvy Verma↗

NASA NTRSDate not supplied

Evaluating the Global Water and Energy Cycles in Reanalyses Using Ensemble Spread

Reanalyses provide continuous observation based global data over weather and climate time scales through observational assimilation. Ensemble forecasts are a component of modern data assimilation systems, providing information to the analysis process. Storing this spread over the duration of a climate reanalysis will provide insight to the variations of the model that underlies the reanalysis data. Global water and energy cycles (WECs) are a critical aspect of both weather and climate, yet there are some large gaps in the observation of key components as well as their representation in models and reanalyses. NASA’s Water and Energy cycle Studies (NEWS) program is developing a corrected and balanced budget data set that is closed at monthly and regional scales during the 21st century, so far. In this paper, we will evaluate reanalysis WEC components against the initial NEWS collection of observation, but integrate the analysis spread from the reanalysis data assimilation as a new perspective on the evaluation. The initial evaluation will cover a few more recent years using ERA5 10 member spread. As the GMAO’s production of the Modern-Era Retrospective analysis for Research and Applications 21st Century (MERRA-21C) progresses, the effort will include the MERRA-21C 32 member spread. Ultimately, we will explore the potential of the ensemble spread as a measure of uncertainty in the reanalysis.

Michael Bosilovich↗

NASA NTRS1975

CARETS: A prototype regional environmental information system. Volume 13: Utility of CARETS products to local planners; an evaluation

The Metropolitan Washington Council of Governments (MWCOG) in cooperation with and under contract to the U.S. Geological Survey, conducted an evaluation of the utility of remote-sensor derived land use data produced by the USGS Central Atlantic Regional Ecological Test Site (CARETS) project. Investigators invited representatives of Washington, D.C. metropolitan area planning agencies to a workshop, introduced them to the CARETS products, and asked them to evaluate the products. In follow-up interviews, planners from 12 participating agencies reported general support for the fulI spectrum of CARETS products but gave more positive responses towards products with which they had some familiarity. Planners considered some products of limited utility because of (1) insufficient detail, (2) too small a scale, or (3) lack of technical capability to incorporate the information and products into the current planning process. Some planners expressed doubt about the application of CARETS products in most day-to-day planning activities , which involve specific rezoning requests or site development plans requiring highly localized data. The greatest potential of the CARETS products was in the identification of broad development patterns at the county or regional level. An appendix documents the results of an inventory of local government decisions relating to land use.

Land use planning↗

NASA NTRS2002

Rationally designed fluorescently labeled sulfate-binding protein mutants: evaluation in the development of a sensing system for sulfate

Periplasmic binding proteins from E. coli undergo large conformational changes upon binding their respective ligands. By attaching a fluorescent probe at rationally selected unique sites on the protein, these conformational changes in the protein can be monitored by measuring the changes in fluorescence intensity of the probe which allow the development of reagentless sensing systems for their corresponding ligands. In this work, we evaluated several sites on bacterial periplasmic sulfate-binding protein (SBP) for attachment of a fluorescent probe and rationally designed a reagentless sensing system for sulfate. Eight different mutants of SBP were prepared by employing the polymerase chain reaction (PCR) to introduce a unique cysteine residue at a specific location on the protein. The sites Gly55, Ser90, Ser129, Ala140, Leu145, Ser171, Val181, and Gly186 were chosen for mutagenesis by studying the three-dimensional X-ray crystal structure of SBP. An environment-sensitive fluorescent probe (MDCC) was then attached site-specifically to the protein through the sulfhydryl group of the unique cysteine residue introduced. Each fluorescent probe-conjugated SBP mutant was characterized in terms of its fluorescence properties and Ser171 was determined to be the best site for the attachment of the fluorescent probe that would allow for the development of a reagentless sensing system for sulfate. Three different environment-sensitive fluorescent probes (1,5-IAEDANS, MDCC, and acylodan) were studied with the SBP171 mutant protein. A calibration curve for sulfate was constructed using the labeled protein and relating the change in the fluorescence intensity with the amount of sulfate present in the sample. The detection limit for sulfate was found to be in the submicromolar range using this system. The selectivity of the sensing system was demonstrated by evaluating its response to other anions. A fast and selective sensing system with detection limits for sulfate in the submicromolar range was developed. Copyright 2002 Wiley Periodicals, Inc. Biotechnol Bioeng 78: 517-526, 2002.

Evaluation Studies↗

NASA NTRS1995

Evaluation of nearest-neighbor methods for detection of chimeric small-subunit rRNA sequences

Detection of chimeric artifacts formed when PCR is used to retrieve naturally occurring small-subunit (SSU) rRNA sequences may rely on demonstrating that different sequence domains have different phylogenetic affiliations. We evaluated the CHECK_CHIMERA method of the Ribosomal Database Project and another method which we developed, both based on determining nearest neighbors of different sequence domains, for their ability to discern artificially generated SSU rRNA chimeras from authentic Ribosomal Database Project sequences. The reliability of both methods decreases when the parental sequences which contribute to chimera formation are more than 82 to 84% similar. Detection is also complicated by the occurrence of authentic SSU rRNA sequences that behave like chimeras. We developed a naive statistical test based on CHECK_CHIMERA output and used it to evaluate previously reported SSU rRNA chimeras. Application of this test also suggests that chimeras might be formed by retrieving SSU rRNAs as cDNA. The amount of uncertainty associated with nearest-neighbor analyses indicates that such tests alone are insufficient and that better methods are needed.

NASA Discipline Exobiology↗