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NASA NTRS1976

Use of LANDSAT-1 data for the detection and mapping of saline seeps in Montana

The author has identified the following significant results. April, May, and August are the best times to detect saline seeps. Specific times within these months would be dependent upon weather, phenology, and growth conditions. Saline seeps can be efficiently and accurately mapped, within resolution capabilities, from merged May and August LANDSAT 1 data. Seeps were mapped by detecting salt crusts in the spring and indicator plants in the fall. These indicator plants were kochia, inkweed, and foxtail barley. The total hectares of the mapped saline seeps were calculated and tabulated. Saline seeps less than two hectares in size or that have linear configurations less than 200 meters in width were not mapped using the LANDSAT 1 data. Saline seep signatures developed in the Coffee Creek test site were extended to map saline seeps located outside this area.

May, G. A.↗

NASA NTRS1979

A graphical summary of solar wind electron content observations by Pioneer 6, 8, and 9

Between 1965 and 1972, solar wind density measurements were obtained by Mariner 5 and Pioneer 6, 7, 8, and 9 using a radio science method called the 'dual-frequency experiment'. The measurements and data processing are now terminated. A graphical summary is presented of the best of the resulting data, showing the average electron number density of the solar wind across radio paths that vary in length up to 1.8 AU and that lie broadside to the flow direction. These graphs summarize 3.4 years of observations with a time resolution of 1 hour; the measurements provided 1-min resolution and spanned 7 years. Because the orientation and length of the paths change continually and have a major influence on the interpretation of these data, a spacecraft trajectory chart and a plot of the corotation interval are provided.

Croft, T. A.↗

NASA NTRS2003

The Aura Mission and Its Application to Climate and Air Quality

NASA's Aura satellite is scheduled to launch in the second quarter of 2004 into a polar orbit. The Aura mission is designed to collect data to address three high priority environmental science questions: (1) Is the ozone layer recovering as expected? (2) What are the sources and processes that control tropospheric pollutants? And (3) what is the quantitative impact of constituents on climate change? Aura will answer these questions by globally measuring a comprehensive set of trace gases and aerosols in the troposphere and stratosphere. Aura data will also have applications for monitoring and predicting climate and air quality parameters. Aura s observations will continue the TOMS ozone trend record and provide an assessment as to whether the Montreal Protocol is achieving its objective. Aura will measure gases and aerosols in the upper troposphere and lower stratosphere that contribute to climate forcing. These data will be of sufficient coverage, vertical resolution, and accuracy to help constrain climate models. In addition, Aura observations of tropospheric ozone and its precursors will have regional as well as intercontinental coverage, which could improve emission inventories. Near real time data will tested for local air quality forecasts in collaboration with the US's Environmental Protection UV-B forecasts from Aura ozone and cloud cover data. An overview of Aura s instruments, data products, validation, and examples of data applications will be presented.

Hilsenrath, Ernest↗

NASA NTRS2014

Application of Spectral Analysis Techniques in the Intercomparison of Aerosol Data: Part III. Using Combined PCA to Compare Spatiotemporal Variability of MODIS, MISR and OMI Aerosol Optical Depth

Satellite measurements of global aerosol properties are very useful in constraining aerosol parameterization in climate models. The reliability of different data sets in representing global and regional aerosol variability becomes an essential question. In this study, we present the results of a comparison using combined principal component analysis (CPCA), applied to monthly mean, mapped (Level 3) aerosol optical depth (AOD) product from Moderate Resolution Imaging Spectroradiometer (MODIS), Multiangle Imaging Spectroradiometer (MISR), and Ozone Monitoring Instrument (OMI). This technique effectively finds the common space-time variability in the multiple data sets by decomposing the combined AOD field. The results suggest that all of the sensors capture the globally important aerosol regimes, including dust, biomass burning, pollution, and mixed aerosol types. Nonetheless, differences are also noted. Specifically, compared with MISR and OMI, MODIS variability is significantly higher over South America, India, and the Sahel. MODIS deep blue AOD has a lower seasonal variability in North Africa, accompanied by a decreasing trend that is not found in either MISR or OMI AOD data. The narrow swath of MISR results in an underestimation of dust variability over the Taklamakan Desert. The MISR AOD data also exhibit overall lower variability in South America and the Sahel. OMI does not capture the Russian wild fire in 2010 nor the phase shift in biomass burning over East South America compared to Central South America, likely due to cloud contamination and the OMI row anomaly. OMI also indicates a much stronger (boreal) winter peak in South Africa compared with MODIS and MISR.

satellite observation↗

NASA NTRS1990

A global view of lander-to-orbiter communications accessibility for a Mars Global Network Mission

Given the mission objective to deploy a number of small landers to the surface of Mars at various latitude/longitude locations, it is of interest to obtain a global perspective of the communications link geometry between the landers and a data relay orbiter. Specifically, the question to be answered is what is the total time interval over one Martian day (1 sol) that a lander at any given latitude and longitude can communicate data to the orbiter. Results should be obtained for more than one elevation angle constraint (lander antenna design issue), and also for several time points into the mission since the orbiter's periapsis location moves under the influence of Mars' oblateness perturbation. Such information is presented in terms of global contour maps of available communications time per sol summed over all orbiter pases on that day. Global data of this type complements more detailed local site data such as communications range and elevation vs time per pass. Communications time contour maps are included here for sol grids of 180, 232, 318, 361, and 404 corresponding to orbiter periapsis latitudes of 35 S, 90 S, equatorial, 45 N, and 90 N. For each of these days, there is a map for both a 15 deg and 45 deg minimum elevation constraint on the lander-to-orbiter line of sight. The jagged appearance of the contour lines is due to computational resolution in interpolating between a finite number of latitude/longitude grid points. Although the contours should really be smooth, the general information content is represented by the lower resolution maps shown here. An example of the tabulated, finite-grid data points is also given. Communication with all sites is possible at the 15 deg elevation constraint, at times only for several minutes per sol but more generally for a much longer time up to 14 hours per sol. Significantly less time is available with a 45 deg elevation constraint, and at certain times in the mission some localized regions of the planet are inaccessible. Still, one may conclude that the reference orbit selection will support a more than adequate communications link through the mission timeline with landers emplaced at any desired location on Mars.

Friedlander, Alan L.↗

DOE OSTI2022

Gaussian process for calibration and control of GlueX Central Drift Chamber

We have developed and implemented a machine learning based system to calibrate and control the GlueX Central Drift Chamber at Jefferson Lab, VA, in near real-time. The system monitors environmental and experimental conditions during data taking and uses those as inputs to a Gaussian process (GP) with learned prior. The GP predicts calibration constants in order to recommend a high voltage (HV) setting for the detector that maintains consistent detector performance (gain and resolution) throughout data taking. This approach is in stark contrast to traditional detector operations in which the detector operates at fixed HV and its calibration parameters vary quite considerably with time. Additionally, the ML based system utilizes uncertainty quantification to correct the recommended control parameters when appropriate. We will present results from the ML system autonomously during the Charged Pion Polarizability (CPP) experiment conducted in Hall D at Jefferson Lab.

McSpadden, Helen↗

DOE OSTI2022

Gaussian process for calibration and control of GlueX Central Drift Chamber

McSpadden, Helen↗

DOE OSTI2021

Efficient prediction of concentrating solar power plant productivity using data clustering

Concentrating solar power (CSP) plants convert solar energy to electricity and can be deployed with a thermal storage capability to shift electricity generation from time periods with available solar resource to those with high electricity demand or electricity price. Rigorous optimization of plant design and operational strategies can improve the market-competitiveness and commercial viability; however, such optimization may require hundreds of annual performance simulations, each of which can be computationally expensive when including considerations such as optimization of dispatch scheduling, sub-hourly time resolution, and stochastic effects due to uncertain weather or electricity price forecasts. This paper proposes a methodology to reduce the computational burden associated with simulation of electricity yield and revenue for CSP plants over a single- or multi-year period. Data-clustering techniques are employed to select a small number of limited-duration time blocks for simulation that, when appropriately weighted, can reproduce generation and revenue over a single year or within each year of a multi-year period. After selection of appropriate data features and weighting factors defining similarity between time-series profiles, the methodology captured annual revenue within 2.3%, 1.7%, or 1.2% using simulation of 10, 30, or 50 three-day exemplar time blocks, respectively, for each of three single-year location/weather/market scenarios and five plant configurations ranging from low to high solar multiple and storage capacity. When applied to multi-year datasets, the proposed methodology can capture inter-year variability that is unavailable from typical meteorological year (TMY) datasets while simultaneously requiring simulation of less than a single year of data.

14 SOLAR ENERGY↗

NASA NTRS2024

Scenario Setup and Forcing Data for Impact Model Evaluation and Impact Attribution Within the Third Round of the Inter-Sectoral Model Intercomparison Project (ISIMIP3a)

This paper describes the rationale and the protocol of the first component of the third simulation round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a, http://www.isimip.org, last access: 2 November 2023) and the associated set of climate-related and direct human forcing data (CRF and DHF, respectively). The observation-based climate-related forcings for the first time include high-resolution observational climate forcings derived by orographic downscaling, monthly to hourly coastal water levels, and wind fields associated with historical tropical cyclones. The DHFs include land use patterns, population densities, information about water and agricultural management, and fishing intensities. The ISIMIP3a impact model simulations driven by these observation-based climate-related and direct human forcings are designed to test to what degree the impact models can explain observed changes in natural and human systems. In a second set of ISIMIP3a experiments the participating impact models are forced by the same DHFs but a counterfactual set of atmospheric forcings and coastal water levels where observed trends have been removed. These experiments are designed to allow for the attribution of observed changes in natural, human, and managed systems to climate change, rising CH 4 and CO 2 concentrations, and sea level rise according to the definition of the Working Group II contribution to the IPCC AR6.

Inter Sectoral Impact Model Intercomparison Projec↗

DOE OSTI2022

Sensor-Equipped Unmanned Surface Vehicle for High-Resolution Mapping of Water Quality in Low- to Mid-Order Streams

Longitudinal profiling of water quality via the deployment of sensors from watercraft has advanced the understanding of spatial patterns in large rivers and lakes; however, a similar approach in low- to mid-order streams is lacking. To fill this gap, we developed an unmanned surface vehicle (USV)-water quality measurement platform (the “AquaBOT”). The components of the AquaBOT included a nitrate sensor, multiparameter sonde (temperature, conductivity, turbidity, dissolved oxygen, chlorophyll), quantum sensor, and global positioning system (GPS) mounted to a small pontoon-style USV. The AquaBOT was tested in four streams and rivers in Iowa and Tennessee. All measured water quality parameters varied longitudinally, and greater ranges were generally observed along the low-order, agriculturally influenced streams in Iowa. Nitrate, in particular, was spatially heterogeneous. For example, during one run in early June, concentrations ranged from 10.5 to 12.5 mg N L –1 along a 2.3 km reach and hotspots were observed directly downstream of some tile drains. The spatial resolution of AquaBOT data collected in June was 10× higher than grab sampling data, and measurements were collected in less time and at a comparable cost. Here, the AquaBOT can complement existing measurement approaches and will lead to advancements in understanding the processes driving water quality along the stream-to-river continuum.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

A New Crater Near InSight: Implications for Seismic Impact Detectability on Mars

A new 1.5 m diameter impact crater was discovered on Mars only ~40 km from the InSight lander. Context camera images constrained its formation between 21 February and 6 April 2019; follow-up High Resolution Imaging Science Experiment images resolved the crater. During this time period, three seismic events were identified in InSight data. In this paper, we derive expected seismic signal characteristics and use them to evaluate each of the seismic events. However, none of them can definitively be associated with this source. Atmospheric perturbations are generally expected to be generated during impacts; however, in this case, no signal could be identified as related to the known impact. Using scaling relationships based on the terrestrial and lunar analogs and numerical modeling, we predict the amplitude, peak frequency, and duration of the seismic signal that would have emanated from this impact. The predicted amplitude falls near the lowest levels of the measured seismometer noise for the predicted frequency. Hence, it is not surprising this impact event was not positively identified in the seismic data. Finding this crater was a lucky event as its formation this close to InSight has a probability of only ~0.2, and the odds of capturing it in before and after images are extremely low. We revisit impact-seismic discriminators in light of real experience with a seismometer on the Martian surface. Using measured noise of the instrument, we revise our previous prediction of seismic impact detections downward, from ~a few to tens, to just ~2 per Earth year, still with an order of magnitude uncertainty.

58 GEOSCIENCES↗

DOE OSTI2020

Femtosecond laser produced periodic plasma in a colloidal crystal probed by XFEL radiation

With the rapid development of short-pulse intense laser sources, studies of matter under extreme irradiation conditions enter further unexplored regimes. In addition, an application of X-ray Free-Electron Lasers (XFELs) delivering intense femtosecond X-ray pulses, allows to investigate sample evolution in IR pump - X-ray probe experiments with an unprecedented time resolution. Here we present a detailed study of the periodic plasma created from the colloidal crystal. Both experimental data and theory modeling show that the periodicity in the sample survives to a large extent the extreme excitation and shock wave propagation inside the colloidal crystal. This feature enables probing the excited crystal, using the powerful Bragg peak analysis, in contrast to the conventional studies of dense plasma created from bulk samples for which probing with Bragg diffraction technique is not possible. X-ray diffraction measurements of excited colloidal crystals may then lead towards a better understanding of matter phase transitions under extreme irradiation conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Travelling wave‐based fault detection and location in a real low‐voltage DC microgrid

Abstract This paper discusses a device‐level implementation of a travelling wave (TW) protection device (PD) designed for a real low‐voltage DC microgrid. The TWPD fault detection and location algorithm is executed on a commercial digital signal processor (DSP) board, involving signal sampling at 1 MHz via the DSP board's analog‐to‐digital converter (ADC). The analogue input card measures positive pole, negative pole and pole‐to‐pole voltages at the TWPD location. Upon a successful fault detection using a second‐order high‐pass filter, the voltage data is normalised and multi‐resolution analysis (MRA) is performed on a 128‐sample buffer around the TW arrival time. MRA employs the discrete wavelet transform (DWT) to capture high‐frequency voltage patterns, and then the Parseval's energy theorem quantifies these TW characteristics by computing the energy of reconstructed wavelet coefficients. These energy values per decomposed frequency band are the basis for training a random forest classifier that predicts fault location and type. The TWPD is fully implemented and connected to a real DC microgrid in Albuquerque, NM, USA, for validation, and results are shown for field tests verifying the performance under faults.

Paruthiyil, Sajay Krishnan [Department of Electric↗

DOE OSTI2023

Pacific Coast High-Frequency Radar 2 km

High frequency (HF) radar systems measure the speed and direction of ocean surface currents in near real time provided by Integrated Ocean Observing System (IOOS). The HFR data along the Pacific Coast are at a spatial resolution of 2 km. These data are collected as part of an observational database developed to support the floating offshore wind energy research within the ORACLE project funded by DOE WETO.

17 WIND ENERGY↗

DOE OSTI2023

Pacific Coast High Frequency Radar 6 km

High frequency (HF) radar systems measure the speed and direction of ocean surface currents in near real time provided by Integrated Ocean Observing System (IOOS). The HFR data along the Pacific Coast are at a spatial resolution of 6 km. These data are collected as part of an observational database developed to support the floating offshore wind energy research within the ORACLE project funded by DOE WETO.

17 WIND ENERGY↗

DOE OSTI2020

Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated. Predictive models are developed using classical regression-based machine learning methods for nine experiments conducted during the 2017 and 2018 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University, Indiana, USA. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and the number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Geometry-based features derived from the LiDAR point cloud to characterize plant structure and chemistry-based features extracted from hyperspectral data provided the most accurate predictions. Evaluation of the impact of the time of data acquisition during the growing season on the prediction results indicated that although the most accurate and reliable predictions of final biomass were achieved using remotely sensed data from mid-season to end-of-season, predictions in mid-season provided adequate results to differentiate between promising varieties for selection. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method.

09 BIOMASS FUELS↗

NASA NTRS1972

High-time resolution of the low-latitude asymmetric disturbance in the geomagnetic field.

The low-latitude asymmetric disturbance field is investigated by means of a Fourier harmonic analysis performed on 2.5-min interspaced values of magnetic records from eight stations, in lieu of the hourly values used in Crooker and Siscoe's (1971) study. The major advantage of the higher time resolution is that substorm activity, which has a time scale of 1-2 hr, can be studied in detail. Data covering four active periods in 1965 and five active periods in 1967 are used.

Crooker, N. U.↗

NASA NTRS1975

Statistical properties of the interplanetary microscale fluctuations

Results are reported for a statistical study of short-period fluctuations in the interplanetary plasma velocity and magnetic field. The data base used consists of measurements of the interplanetary plasma and magnetic field by Pioneer 6 with a time resolution of 72 sec and by Mariner 5 with a resolution of 5 min. The analysis is conducted to characterize the parent population from which all individual microscale events on these time scales are drawn. The microscale changes are grouped according to their energy densities relative to the energy density of the background magnetic field, and it is found that each grouping exhibits certain statistical properties within the limits of observational uncertainty. It is noted that these statistical properties are purely observational and independent of any physical model which may be used to interpret them. The results are discussed in the context of MHD discontinuity theory for a thermally anisotropic plasma.

Belcher, J. W.↗