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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.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1998

Spectral Characterizations of the Clouds and the Earth's Radiant Energy System (CERES) Thermistor Bolometers using Fourier Transform Spectrometer (FTS) Techniques

Fourier Transform Spectrometer (FTS) techniques are being used to characterize the relative spectral response, or sensitivity, of scanning thermistor bolometers in the infrared (IR) region (2 - >= 100-micrometers). The bolometers are being used in the Clouds and the Earth's Radiant Energy System (CERES) program. The CERES measurements are designed to provide precise, long term monitoring of the Earth's atmospheric radiation energy budget. The CERES instrument houses three bolometric radiometers, a total wavelength (0.3- >= 150-micrometers) sensor, a shortwave (0.3-5-micrometers) sensor, and an atmospheric window (8-12-micrometers) sensor. Accurate spectral characterization is necessary for determining filtered radiances for longwave radiometric calibrations. The CERES bolometers spectral response's are measured in the TRW FTS Vacuum Chamber Facility (FTS - VCF), which uses a FTS as the source and a cavity pyroelectric trap detector as the reference. The CERES bolometers and the cavity detector are contained in a vacuum chamber, while the FTS source is housed in a GN2 purged chamber. Due to the thermal time constant of the CERES bolometers, the FTS must be operated in a step mode. Data are acquired in 6 IR spectral bands covering the entire longwave IR region. In this paper, the TRW spectral calibration facility design and data measurement techniques are described. Two approaches are presented which convert the total channel FTS data into the final CERES spectral characterizations, producing the same calibration coefficients (within 0.1 percent). The resulting spectral response curves are shown, along with error sources in the two procedures. Finally, the impact of each spectral response curve on CERES data validation will be examined through analysis of filtered radiance values from various typical scene types.

Thornhill, K. Lee↗
NASA NTRS2004

Robotic Subsurface Analyzer and Sample Handler for Resource Reconnaissance and Preliminary Site Assessment for ISRU Activities at the Lunar Cold Traps

Since the 1960s, claims have been made that water ice deposits should exist in permanently shadowed craters near both lunar poles. Recent interpretations of data from the Lunar Prospector-Neutron Spectrometer (LP- NS) confirm that significant concentrations of hydrogen exist, probably in the form of water ice, in the permanently shadowed polar cold traps. Yet, due to the large spatial resolution (45-60 Ian) of the LP-NS measurements relative to these shadowed craters (approx.5-25 km), these data offer little certainty regarding the precise location, form or distribution of these deposits. Even less is known about how such deposits of water ice might effect lunar regolith physical properties relevant to mining, excavation, water extraction and construction. These uncertainties will need to be addressed in order to validate fundamental lunar In Situ Resource Utilization (ISRU) precepts by 2011. Given the importance of the in situ utilization of water and other resources to the future of space exploration a need arises for the advanced deployment of a robotic and reconfigurable system for physical properties and resource reconnaissance. Based on a collection of high-TRL. designs, the Subsurface Analyzer and Sample Handler (SASH) addresses these needs, particularly determining the location and form of water ice and the physical properties of regolith. SASH would be capable of: (1) subsurface access via drilling, on the order of 3-10 meters into both competent targets (ice, rock) and regolith, (2) down-hole analysis through drill string embedded instrumentation and sensors (Neutron Spectrometer and Microscopic Imager), enabling water ice identification and physical properties measurements; (3) core and unconsolidated sample acquisition from rock and regolith; (4) sample handling and processing, with minimized contamination, sample containerization and delivery to a modular instrument payload. This system would be designed with three mission enabling goals, including: (1) a self-contained, low power, low mass, "black box'' configuration for operations from a lander, various classes of rovers or a surface-based platform with human assistance or robotic anchoring mechanisms; (2) reconfigurable and scalable sample handling for delivery to various types of instrumentation, depending on mission requirements; and (3) the use of advanced automation control and diagnostic techniques that will afford local human deployed, remote teleoperation and fully autonomous intelligent operations. Though a great deal of technology has been advanced toward these objectives, the SASH system faces significant design challenges, including the low gravity environment, various levels of autonomy in operations, radiation exposure, dust contamination, and temperature extremes and deltas. Significant input from the scientific and engineering communities, as well as a significant environmental testing program, will be required to guide the design process.

Gorevan, S. P.↗
NASA NTRS2005

Lessons Learned in the Livingstone 2 on Earth Observing One Flight Experiment

The Livingstone 2 (L2) model-based diagnosis software is a reusable diagnostic tool for monitoring complex systems. In 2004, L2 was integrated with the JPL Autonomous Sciencecraft Experiment (ASE) and deployed on-board Goddard's Earth Observing One (EO-1) remote sensing satellite, to monitor and diagnose the EO-1 space science instruments and imaging sequence. This paper reports on lessons learned from this flight experiment. The goals for this experiment, including validation of minimum success criteria and of a series of diagnostic scenarios, have all been successfully net. Long-term operations in space are on-going, as a test of the maturity of the system, with L2 performance remaining flawless. L2 has demonstrated the ability to track the state of the system during nominal operations, detect simulated abnormalities in operations and isolate failures to their root cause fault. Specific advances demonstrated include diagnosis of ambiguity groups rather than a single fault candidate; hypothesis revision given new sensor evidence about the state of the system; and the capability to check for faults in a dynamic system without having to wait until the system is quiescent. The major benefits of this advanced health management technology are to increase mission duration and reliability through intelligent fault protection, and robust autonomous operations with reduced dependency on supervisory operations from Earth. The work-load for operators will be reduced by telemetry of processed state-of-health information rather than raw data. The long-term vision is that of making diagnosis available to the onboard planner or executive, allowing autonomy software to re-plan in order to work around known component failures. For a system that is expected to evolve substantially over its lifetime, as for the International Space Station, the model-based approach has definite advantages over rule-based expert systems and limit-checking fault protection systems, as these do not scale well. The model-based approach facilitates reuse of the L2 diagnostic software; only the model of the system to be diagnosed and telemetry monitoring software has to be rebuilt for a new system or expanded for a growing system. The hierarchical L2 model supports modularity and expendability, and as such is suitable solution for integrated system health management as envisioned for systems-of-systems.

Hayden, Sandra C.↗
NASA NTRS2006

Space-Based Near-Infrared CO2 Measurements: Testing the Orbiting Carbon Observatory Retrieval Algorithm and Validation Concept Using SCIAMACHY Observations over Park Falls, Wisconsin

Space-based measurements of reflected sunlight in the near-infrared (NIR) region promise to yield accurate and precise observations of the global distribution of atmospheric CO2. The Orbiting Carbon Observatory (OCO) is a future NASA mission, which will use this technique to measure the column-averaged dry air mole fraction of CO2 (XCO2) with the precision and accuracy needed to quantify CO2 sources and sinks on regional scales (approx.1000 x 1000 sq km and to characterize their variability on seasonal timescales. Here, we have used the OCO retrieval algorithm to retrieve XCO2 and surface pressure from space-based Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) measurements and from coincident ground-based Fourier transform spectrometer (FTS) measurements of the O2 A band at 0.76 mm and the 1.58 mm CO2 band for Park Falls,Wisconsin. Even after accounting for a systematic error in our representation of the O2 absorption cross sections, we still obtained a positive bias between SCIAMACHY and FTS XCO2 retrievals of approx.3.5%. Additionally, the retrieved surface pressures from SCIAMACHY systematically underestimate measurements of a calibrated pressure sensor at the FTS site. These findings lead us to speculate about inadequacies in the forward model of our retrieval algorithm. By assuming a 1% intensity offset in the O2 A band region for the SCIAMACHY XCO2 retrieval, we significantly improved the spectral fit and achieved better consistency between SCIAMACHY and FTS XCO2 retrievals. We compared the seasonal cycle of XCO2 at Park Falls from SCIAMACHY and FTS retrievals with calculations of the Model of Atmospheric Transport and Chemistry/Carnegie-Ames-Stanford Approach (MATCH/CASA) and found a good qualitative agreement but with MATCH/CASA underestimating the measured seasonal amplitude. Furthermore, since SCIAMACHY observations are similar in viewing geometry and spectral range to those of OCO, this study represents an important test of the OCO retrieval algorithm and validation concept using NIR spectra measured from space. Finally, we argue that significant improvements in precision and accuracy could be obtained from a dedicated CO2 instrument such as OCO, which has much higher spectral and spatial resolutions than SCIAMACHY. These measurements would then provide critical data for improving our understanding of the carbon cycle and carbon sources and sinks.

carbon dioxide↗
NASA NTRS2008

Classification of Small Negative Lightning Reports at the KSC-ER

The NASA Kennedy Space Center (KSC) and Air Force Eastern Range (ER) operate an extensive suite of lightning sensors because Florida experiences the highest area density of ground strikes in the United States, with area densities approaching 16 fl/sq km/yr when accumulated in 10x10 km (100 sq km) grids. The KSC-ER use data derived from two cloud-to-ground (CG) lightning detection networks, the "Cloud-to-Ground Lightning Surveillance System" (CGLSS) and the U.S. National Lightning Detection Network (TradeMark) (NLDN) plus a 3-dimensional lightning mapping system, the Lightning Detection and Ranging (LDAR) system, to provide warnings for ground operations and to insure mission safety during space launches. For operational applications at the KSC-ER it is important to understand the performance of each lightning detection system in considerable detail. In this work we examine a specific subset of the CGLSS stroke reports that have low values of the negative inferred peak current, Ip, i.e. values between 0 and -7 kA, and were thought to produce a new ground contact (NGC). When possible, the NLDN and LDAR systems were used to validate the CGLSS classification and to determine how many of these reported strokes were first strokes, subsequent strokes in a pre-existing channel (PEC), or cloud pulses that the CGLSS misclassified as CG strokes. It is scientifically important to determine the smallest current that can reach the ground either in the form of a first stroke or by way of a subsequent stroke that creates a new ground contact. In Biagi et al (2007), 52 low amplitude, negative return strokes ([Ip] < or = 10 kA) were evaluated in southern Arizona, northern Texas, and southern Oklahoma. The authors found that 50-87% of the small NLDN reports could be classified as CG (either first or subsequent strokes) on the basis of video and waveform recordings. Low amplitude return strokes are interesting because they are usually difficult to detect, and they are thought to bypass conventional lightning protection that relies on a sufficient attractive radius to prevent "shielding failure" (Golde, 1977). They also have larger location errors compared to the larger current events. In this study, we use the estimated peak current provided by the CGLSS and the results of our classification to determine the minimum Ip for each category of CG stroke and its probability of occurrence. Where possible, these results are compared to the findings in the literature.

Ward, Jennifer G.↗
NASA NTRS2002

Global Aerosol Remote Sensing from MODIS

The physical characteristics, composition, abundance, spatial distribution and dynamics of global aerosols are still very poorly known, and new data from satellite sensors have long been awaited to improve current understanding and to give a boost to the effort in future climate predictions. The derivation of aerosol parameters from the MODerate resolution Imaging Spectro-radiometer (MODIS) sensors aboard the Earth Observing System (EOS) Terra and Aqua polar-orbiting satellites ushers in a new era in aerosol remote sensing from space. Terra and Aqua were launched on December 18, 1999 and May 4, 2002 respectively, with daytime equator crossing times of approximately 10:30 am and 1:30 pm respectively. Several aerosol parameters are retrieved at 10-km spatial resolution (level 2) from MODIS daytime data. The MODIS aerosol algorithm employs different approaches to retrieve parameters over land and ocean surfaces, because of the inherent differences in the solar spectral radiance interaction with these surfaces. The parameters retrieved include: aerosol optical thickness (AOT) at 0.47, 0.55 and 0.66 micron wavelengths over land, and at 0.47, 0.55, 0.66, 0.87, 1.2, 1.6, and 2.1 micron over ocean; Angstrom exponent over land and ocean; and effective radii, and the proportion of AOT contributed by the small mode aerosols over ocean. To ensure the quality of these parameters, a substantial part of the Terra-MODIS aerosol products were validated globally and regionally, based on cross correlation with corresponding parameters derived from ground-based measurements from AERONET (AErosol RObotic NETwork) sun photometers. Similar validation efforts are planned for the Aqua-MODIS aerosol products. The MODIS level 2 aerosol products are operationally aggregated to generate global daily, eight-day (weekly), and monthly products at one-degree spatial resolution (level 3). MODIS aerosol data are used for the detailed study of local, regional, and global aerosol concentration, distribution, and temporal dynamics, as well as for radiative forcing calculations. We show several examples of these results and comparisons with model output.

Ichoku, Charles↗
DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy's National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real-time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI's Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: Run in real-time on the edge (≥ 12 Hz), Classification: Achieve less than 5% false positive detection, Classification: Achieve ≥ 95% methane plume detection rate, Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time. In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗
NASA NTRS2003

Intercomparison of Latent Heat Fluxes Over Global Oceans

Turbulent fluxes of momentum, moisture, and heat at the air-sea interface are essential for climate studies. Version 2 Goddard Satellite-based Surface Turbulent Fluxes (GSSTF2) has been derived from the Special Sensor Microwave/Imager (SSM/I) radiance measurements. This dataset, covering the period July 1987-December 2000 over global oceans, has a spatial resolution of 1 deg x 1 deg lat-long and a temporal resolution of 1 day. Turbulent fluxes are derived from the SSM/I surface winds and surface air humidity, as well as the 2-m air and sea surface temperatures (SST) of the NCEP/NCAR reanalysis, using a bulk aerodynamic algorithm based on the surface layer similarity theory. The GSSTF2 bulk flux model, and retrieved daily wind stress, latent heat flux, wind speed, and surface air humidity validate well with ship observations of ten field experiments over the tropical and midlatitude oceans during 1991-99. The global distributions of 1988-2000 annual- and seasonal-mean turbulent fluxes show reasonable patterns related to the atmospheric general circulation and seasonal variations. Latent heat fluxes and related input parameters over global oceans during 1992-93 have been compared among GSSTF1 (version 1), GSSTF2, HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data), NCEP/NCAR reanalysis, and one based on COADS (Comprehensive Ocean-Atmosphere Data Set). Our analyses suggest that the GSSTF2 latent heat flux, surface air humidity, surface wind, and SST are quite realistic compared to the other four flux datasets examined. However, significant differences are found among these five flux datasets. The GSSTF2, available at http://daac.gsfc.nasa.gov/CAMPAIGN_DOCS/hydrology/hd_gsstf2.O.html, is useful for climate studies.

Chou, Shu-Hsien↗
NASA NTRS2006

MicroMAPS CO Measurements over North America and Europe during Summer-Fall 2004

The MicroMAPS instrument is a nadir-viewing, gas filter-correlated radiometer which operating in the 4.67 micrometer fundamental band of carbon monoxide. Originally designed and built for a space mission, this CO remote sensor is being flown in support of satellite validation and science instrument demonstrations for potential UAV applications. The MicroMAPS instrument system, as flown on Proteus, was designed by a senior student design project in the Aerospace Engineering Department, Virginia Tech, in Blacksburg, VA. and then revised by Systems Engineers at NASA Langley. The final instrument system was integrated and tested at NASA LaRC, in partnership with Scaled Composites and Virginia Space Grant Consortium (VSGC). VSGC supervised the fabrication of the nacelle that houses the instrument system on the right rear tail boom of Proteus. Full system integration and flight testing was performed at Scaled Composites, in Mojave, in June 2004. Its successful performance enabled participation in four international science missions on Proteus: in 2004, INTEX -NA over eastern North America in July, ADRIEX over the Mediterranean region and EAQUATE over the United Kingdom region in September,and TWP-ICE over Darwin, Australia and the surrounding oceans in Jan-Feb 2006. These flights resulted in nearly 300 hours of data. In parallel with the engineering developments, theoretical radiative transfer models were developed specifically for the MicroMAPS instrument system at the University of Virginia, Mechanical Engineering Department by a combined undergraduate and graduate student team. With technical support from Resonance Ltd. in June 2005, the MicroMAPS instrument was calibrated for the conditions under which the Summer-Fall 2004 flights occurred. The analyses of the calibration data, combined with the theoretical radiative transfer models, provide the first data reduction for the science flights reported here. These early results and comparisons with profile data from the NASA DC-8, the coincident AIRS CO retrievals, and selected CO measurements from the MOZAIC program will be presented.

Connors, Vickie S.↗
NASA NTRSDate not supplied

The West-Coast Hyperspectral Microwave Sensor Intensive Experiment (WHyMSIE): A Prototype for A PBL Mission of Missions

We present an overview of the 2024 West-Coast Hyperspectral Microwave Sensor Intensive Experiment(WHyMSIE). WHyMSIE is a joint NASA-NOAA multi-sensor airborne experiment, embracing passive and active sensors from the Program of Record (PoR) along with novel technology funded through the NASA ESTO Instrument Incubation Program. At the core of this effort is the demonstration of the Conical Scanning Millimeter-wave Imaging Radiometer Hyperspectral (CoSMIR-H) instrument, a PBL DSI funded effort to develop hyperspectral sounding capability in the thermal microwave domain finalized to improved temperature and water vapor soundings in the Earth’s Planetary Boundary Layer (PBL). An overview of the field campaign design, instrument payload and validation plan is presented here.

Planetary Boundary Layer↗
DOE OSTI2014

Design Report for the ½ Scale Air-Cooled RCCS Tests in the Natural convection Shutdown heat removal Test Facility (NSTF)

The Natural convection Shutdown heat removal Test Facility (NSTF) is a large scale thermal hydraulics test facility that has been built at Argonne National Laboratory (ANL). The facility was constructed in order to carry out highly instrumented experiments that can be used to validate the performance of passive safety systems for advanced reactor designs. The facility has principally been designed for testing of Reactor Cavity Cooling System (RCCS) concepts that rely on natural convection cooling for either air or water-based systems. Standing 25-m in height, the facility is able to supply up to 220 kW at 21 kW/m 2 to accurately simulate the heat fluxes at the walls of a reactor pressure vessel. A suite of nearly 400 data acquisition channels, including a sophisticated fiber optic system for high density temperature measurements, guides test operations and provides data to support scaling analysis and modeling efforts. Measurements of system mass flow rate, air and surface temperatures, heat flux, humidity, and pressure differentials, among others; are part of this total generated data set. The following report provides an introduction to the top level-objectives of the program related to passively safe decay heat removal, a detailed description of the engineering specifications, design features, and dimensions of the test facility at Argonne. Specifications of the sensors and their placement on the test facility will be provided, along with a complete channel listing of the data acquisition system.

42 ENGINEERING↗
NASA NTRS2016

Extending "Deep Blue" Aerosol Retrieval Coverage to Cases of Absorbing Aerosols Above Clouds: Sensitivity Analysis and First Case Studies

Cases of absorbing aerosols above clouds (AACs), such as smoke or mineral dust, are omitted from most routinely processed space-based aerosol optical depth (AOD) data products, including those from the Moderate Resolution Imaging Spectroradiometer (MODIS). This study presents a sensitivity analysis and preliminary algorithm to retrieve above-cloud AOD and liquid cloud optical depth (COD) for AAC cases from MODIS or similar sensors, for incorporation into a future version of the "Deep Blue" AOD data product. Detailed retrieval simulations suggest that these sensors should be able to determine AAC AOD with a typical level of uncertainty approximately 25-50 percent (with lower uncertainties for more strongly absorbing aerosol types) and COD with an uncertainty approximately10-20 percent, if an appropriate aerosol optical model is known beforehand. Errors are larger, particularly if the aerosols are only weakly absorbing, if the aerosol optical properties are not known, and the appropriate model to use must also be retrieved. Actual retrieval errors are also compared to uncertainty envelopes obtained through the optimal estimation (OE) technique; OE-based uncertainties are found to be generally reasonable for COD but larger than actual retrieval errors for AOD, due in part to difficulties in quantifying the degree of spectral correlation of forward model error. The algorithm is also applied to two MODIS scenes (one smoke and one dust) for which near-coincident NASA Ames Airborne Tracking Sun photometer (AATS) data were available to use as a ground truth AOD data source, and found to be in good agreement, demonstrating the validity of the technique with real observations.

aerosols above clouds↗
DOE OSTI2024

Assessing Spatial Representativeness of Global Flux Tower Eddy-Covariance Measurements Using Data from FLUXNET2015

Large datasets of carbon dioxide, energy, and water fluxes were measured with the eddy-covariance (EC) technique, such as FLUXNET2015. These datasets are widely used to validate remote-sensing products and benchmark models. One of the major challenges in utilizing EC-flux data is determining the spatial extent to which measurements taken at individual EC towers reflect model-grid or remote sensing pixels. To minimize the potential biases caused by the footprint-to-target area mismatch, it is important to use flux datasets with awareness of the footprint. This study analyze the spatial representativeness of global EC measurements based on the open-source FLUXNET2015 data, using the published flux footprint model (SAFE-f). The calculated annual cumulative footprint climatology (ACFC) was overlaid on land cover and vegetation index maps to create a spatial representativeness dataset of global flux towers. The dataset includes the following components: (1) the ACFC contour (ACFCC) data and areas representing 50%, 60%, 70%, and 80% ACFCC of each site, (2) the proportion of each land cover type weighted by the 80% ACFC (ACFCW), (3) the semivariogram calculated using Normalized Difference Vegetation Index (NDVI) considering the 80% ACFCW, and (4) the sensor location bias (SLB) between the 80% ACFCW and designated areas (e.g. 80% ACFCC and window sizes) proxied by NDVI. Finally, we conducted a comprehensive evaluation of the representativeness of each site from three aspects: (1) the underlying surface cover, (2) the semivariogram, and (3) the SLB between 80% ACFCW and 80% ACFCC, and categorized them into 3 levels. The goal of creating this dataset is to provide data quality guidance for international researchers to effectively utilize the FLUXNET2015 dataset in the future.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2015

Cloud-to-Ground Lightning Estimates Derived from SSMI Microwave Remote Sensing and NLDN

Lightning observations are collected using ground-based and satellite-based sensors. The National Lightning Detection Network (NLDN) in the United States uses multiple ground sensors to triangulate the electromagnetic signals created when lightning strikes the Earth's surface. Satellite-based lightning observations have been made from 1998 to present using the Lightning Imaging Sensor (LIS) on the NASA Tropical Rainfall Measuring Mission (TRMM) satellite, and from 1995 to 2000 using the Optical Transient Detector (OTD) on the Microlab-1 satellite. Both LIS and OTD are staring imagers that detect lightning as momentary changes in an optical scene. Passive microwave remote sensing (85 and 37 GHz brightness temperatures) from the TRMM Microwave Imager (TMI) has also been used to quantify characteristics of thunderstorms related to lightning. Each lightning detection system has fundamental limitations. TRMM satellite coverage is limited to the tropics and subtropics between 38 deg N and 38 deg S, so lightning at the higher latitudes of the northern and southern hemispheres is not observed. The detection efficiency of NLDN sensors exceeds 95%, but the sensors are only located in the USA. Even if data from other ground-based lightning sensors (World Wide Lightning Location Network, the European Cooperation for Lightning Detection, and Canadian Lightning Detection Network) were combined with TRMM and NLDN, there would be enormous spatial gaps in present-day coverage of lightning. In addition, a globally-complete time history of observed lightning activity is currently not available either, with network coverage and detection efficiencies varying through the years. Previous research using the TRMM LIS and Microwave Imager (TMI) showed that there is a statistically significant correlation between lightning flash rates and passive microwave brightness temperatures. The physical basis for this correlation emerges because lightning in a thunderstorm occurs where ice is first present in the cloud and electric charge separation occurs. These ice particles efficiently scatter the microwave radiation at the 85 and 37 GHz frequencies, thus leading to large brightness temperature depressions. Lightning flash rate is related to the total amount of ice passing through the convective updraft regions of thunderstorms. Confirmation of this relationship using TRMM LIS and TMI data, however, remains constrained to TRMM observational limits of the tropics and subtropics. Satellites from the Defense Meteorology Satellite Program (DMSP) have global coverage and are equipped with passive microwave imagers that, like TMI, observe brightness temperatures at 85 and 37 GHz. Unlike the TRMM satellite, however, DMSP satellites do not have a lightning sensor, and the DMSP microwave data has never been used to derive global lightning. In this presentation, a relationship between DMSP Special Sensor Microwave Imager (SSMI) data and ground-based cloud-to-ground (CG) lightning data from NLDN is investigated to derive a spatially complete time history of CG lightning for the USA study area. This relationship is analogous to the established using TRMM LIS and TMI data. NLDN has the most spatially and temporally complete CG lightning data for the USA, and therefore provides the best opportunity to find geospatially coincident observations with SSMI sensors. The strongest thunderstorms generally have minimum 85 GHz Polarized Corrected brightness Temperatures (PCT) less than 150 K. Archived radar data was used to resolve the spatial extent of the individual storms. NLDN data for that storm spatial extent defined by radar data was used to calculate the CG flash rate for the storm. Similar to results using TRMM sensors, a linear model best explained the relationship between storm-specific CG flash rates and minimum 85 GHz PCT. However, the results in this study apply only to CG lightning. To extend the results to weaker storms, the probability of CG lightning (instead of the flash rate) was calculated for storms having 85 GHz PCT greater than 150 K. NLDN data was used to determine if a CG strike occurred for a storm. This probability of CG lightning was plotted as a function of minimum 85 GHz PCT and minimum 37 GHz PCT. These probabilities were used in conjunction with the linear model to estimate the CG flash rate for weaker storms with minimum 85 GHz PCTs greater than 150 K. Results from the investigation of CG lightning and passive microwave radiation signals agree with the previous research investigating total lightning and brightness temperature. Future work will take the established relationships and apply them to the decades of available DMSP data for the USA to derive a map of CG lightning flash rates. Validation of this method and uncertainty analysis will be done by comparing the derived maps of CG lightning flash rates against existing NLDN maps of CG lightning flash rates.

Winesett, Thomas↗
DOE OSTI2023

Artificial intelligence models, photos, and data associated with the manuscript “Quantifying Streambed Grain Size, Uncertainty, and Hydrobiogeochemical Parameters Using Machine Learning Model YOLO” (v2)

This data package is associated with the manuscript “Quantifying Streambed Grain Size, Uncertainty, and Hydrobiogeochemical Parameters Using Machine Learning Model YOLO” published in Water Resources Research (Chen et al., 2024). This data package includes the training, validation, testing, and prediction data used by the artificial intelligence (AI) model for automated grain size and hydro-biogeochemistry quantification using streambed photos. The grain size data are extracted for each photo using You Look Only Once (YOLO), a pre-trained object detection model. This data package was originally published in October 2023. It was updated August 2025 (v2; new and modified files). File and folder names were not revised to indicate changes. See the change history section in the readme for more details. Please see flmd.csv for a list of all files contained in this data package and descriptions for each. Please see dd.csv for a data dictionary that defines the column headers of .csv files in the data package. This dataset is comprised of one data folder containing (1) file-level metadata; (2) data dictionary; (3) readme; and (4) six subfolders. Subfolders 1 to 4 include the training, validation, testing, and prediction data. Subfolder 5_Summary includes the summary results of different combinations of training, validation, testing, and prediction data. Subfolder 6_SupplementalData includes additional data downloaded from public sources (Kaufman et al., 2023a; Kaufman et al., 2023b; Garefalakis et al., 2023; Mair et al., 2024; https://github.com/river-corridors-sfa/Geospatial_variables). In total, the data package includes 110 folders and 44,283 files. These files include 9,047 .jpg photos, 1 .png photo, 3 .tif photos; 26,639 photo labels and individual grain sizes and probability from AI (.txt); 8,447 grain size distribution data (.dat); and 126 CSV files for results summary, and 14 required metadata files (.xlsx). The summary CSV files contain 68 columns and approximately 2,200 rows that represent photo names, site locations, recording time, GPS coordinates, grains sizes (D10, D50, D60, and D84), number of grains, and additional hydro-biogeochemical data such as water depth, flow velocity, Manning’s coefficient, friction factor, hydraulic conductivity, permeability, streambed interstitial velocity magnitude, mass transfer rate, and nitrate uptake velocity. The photos were obtained from 75 sites in the Yakima River Basin and the Columbia River shorelines, and other associated data from samples and sensors obtained when the photos were taken are publicly available (Fulton et al. 2022; Grieger et al. 2023). All files are .csv, .txt, .dat, .jpg, or .pdf. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected some of these data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Remote Monitoring of the Structural Health of Hydrokinetic Composite Turbine Blades (Final Report)

A health monitoring approach is investigated for hydrokinetic turbine blade applications. In-service monitoring is critical due to the difficult environment for blade inspection and the cost of inspection downtime. Composite blade designs have advantages that include long life in marine environments and great control over mechanical properties. Experimental strain characteristics are determined for static loads and free-vibration loads. These experiments are designed to simulate the dynamic characteristics of hydrokinetic turbine blades. Carbon/epoxy symmetric composite laminates are manufactured using an autoclave process. Four-layer composite beams, eight-layer composite beams, and two-dimensional eight-layer composite blades are instrumented for strain. Experimental results for strain measurements from electrical resistance gages are validated with theoretical characteristics obtained from in-house finite-element analysis for all sample cases. These preliminary tests on the composite samples show good correlation between experimental and finite-element strain results. A health monitoring system is proposed in which damage to a composite structure, e.g. delamination and fiber breakage, causes changes in the strain signature behavior. The system is based on embedded strain sensors and embedded motes in which strain information is demodulated for wireless transmission. In-service monitoring is critical due to the difficult environment for blade inspection and the cost of inspection downtime. Composite blade designs provide a medium for embedding sensors into the blades for in-situ health monitoring. The major challenge with in-situ health monitoring is transmission of sensor signals from the remote rotating reference frame of the blade to the system monitoring station. In the presented work, a novel system for relaying in-situ blade health measurements in hydrokinetic systems is described and demonstrated. An ultrasonic communication system is used to transmit sensor data underwater from the rotating frame of the blade to a fixed relay station. Data are then broadcast via radio waves to a remote monitoring station. Results indicate that the assembled system can transmit simulated sensor data with an accuracy of ±5% at a maximum sampling rate of 500 samples/sec. A power investigation of the transmitter within the blade shows that continuous max-sampling operation is only possible for short durations (~days), and is limited due to the capacity of the battery power source. However, intermittent sampling, with long periods between samples, allows for the system to last for very long durations (~years). Finally, because the data transmission system can operate at a high sampling rate for short durations or at a lower sampling rate/higher duty cycle for long durations, it is well-suited for short-term prototype and environmental testing, as well as long-term commercially-deployed hydrokinetic machines.

13 HYDRO ENERGY↗
NASA NTRS2012

Instrumentation for the Characterization of Inflatable Structures

Current entry, descent, and landing technologies are not practical for heavy payloads due to mass and volume constraints dictated by limitations imposed by launch vehicle fairings. Therefore, new technologies are now being explored to provide a mass- and volume-efficient solution for heavy payload capabilities, including Inflatable Aerodynamic Decelerators (IAD) [1]. Consideration of IADs for space applications has prompted the development of instrumentation systems for integration with flexible structures to characterize system response to flight-like environment testing. This development opportunity faces many challenges specific to inflatable structures in extreme environments, including but not limited to physical flexibility, packaging, temperature, structural integration and data acquisition [2]. In the spring of 2012, two large scale Hypersonic Inflatable Aerodynamic Decelerators (HIAD) will be tested in the National Full-Scale Aerodynamics Complex s 40 by 80 wind tunnel at NASA Ames Research Center. The test series will characterize the performance of a 3.0 m and 6.0 m HIAD at various angles of attack and levels of inflation during flight-like loading. To analyze the performance of these inflatable test articles as they undergo aerodynamic loading, many instrumentation systems have been researched and developed. These systems will utilize new experimental sensing systems developed by the HIAD ground test campaign instrumentation team, in addition to traditional wind tunnel sensing techniques in an effort to improve test article characterization and model validation. During the 2012 test series the instrumentation systems will target inflatable aeroshell static and dynamic deformation, structural strap loading, surface pressure distribution, localized skin deflection, and torus inflation pressure. This paper will offer an overview of inflatable structure instrumentation, and provide detail into the design and implementation of the sensors systems that will be utilized during the 2012 HIAD ground test campaign.

Swanson, Gregory T.↗
DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy’s National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real- time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI’s Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: 1. Run in real-time on the edge (≥ 12 Hz) 2. Classification: Achieve less than 5% false positive detection 3. Classification: Achieve ≥ 95% methane plume detection rate 4. Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗