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All sourcesNASA NTRSDOE OSTI
NASA NTRS2006

Snow Depth and Ice Thickness Measurements From the Beaufort and Chukchi Seas Collected During the AMSR-Ice03 Campaign

In March 2003, a field validation campaign was conducted on the sea ice near Barrow, AK. The goal of this campaign was to produce an extensive dataset of sea ice thickness and snow properties (depth and stratigraphy) against which remote sensing products collected by aircraft and satellite could be compared. Chief among these were products from the Polarimetric Scanning Radiometer (PSR) flown aboard a NASA P-3B aircraft and the Aqua Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). The data were collected in four field areas: three on the coastal sea ice near Barrow, AK, and the fourth out on the open ice pack 175 km northeast of Barrow. The snow depth ranged from 9.4-20.8 cm in coastal areas (n = 9881 for three areas) with the thinnest snow on ice that had formed late in the winter. Out in the main pack ice, the snow was 20.6 cm deep (n = 1906). The ice in all four areas ranged from 138-219 cm thick (n = 1952), with the lower value again where the ice had formed late in the winter. Snow layer and grain characteristics observed in 118 snow pits indicated that 44% of observed snow layers were depth hoar; 46% were wind slab. Snow and ice measurements were keyed to photomosaics produced from low-altitude vertical aerial photographs. Using these, and a distinctive three-way relationship between ice roughness, snow surface characteristics, and snow depth, strip maps of snow depth, each about 2 km wide, were produced bracketing the traverse lines. These maps contain an unprecedented level of snow depth detail against which to compare remote sensing products. The maps are used in other papers in this special issue to examine the retrieval of snow properties from the PSR and AMSR-E sensors.

Sturm, M.↗
NASA NTRS2018

Radar Measurements of the LEO Orbital Debris Environment

Access to space and the preservation of the near-Earth space environment is of critical significance. Increased interest in issues surrounding space traffic management and the continued assessment and discussion of orbital debris at the United Nations Committee on the Peaceful Uses of Outer Space (COPUOS) illustrates the significance of the topic of orbital debris. There are currently over 20,000 tracked objects in the publicly available satellite catalog on Space-Track.org. The catalog is maintained by the US Air Force Space Command using a network of optical and radar ground-based sensors and is believed to be complete for a characteristic size of 10 cm or larger in low Earth orbit (LEO). Based on the work of the NASA Orbital Debris Program Office (ODPO) over approximately the past 40 years it is understood that the small debris population (< 10 cm) increases by orders of magnitude as characteristic size decreases. With population estimates ranging from 500,000 to 1,000,000 small debris objects (> 5 mm) in orbit, it is currently not practical to track and maintain precision orbits on every object. Instead the NASA ODPO uses powerful ground-based radars to sample the low Earth Orbit (LEO) environment and assign approximate orbits to each detection. This poses an interesting signal processing challenge as we are trying to detect the smallest objects possible on the edge of the radar's sensitivity. For approximately the last 30 years, NASA ODPO has partnered with the Massachusetts Institute of Technology Lincoln Laboratory (MIT/LL) to utilize the Haystack Ultra-wideband Satellite Imaging Radar (HUSIR - formerly the Long-Range Imaging Radar or simply Haystack) and the Haystack Auxiliary (HAX) radar to collect orbital debris radar data. Additionally, the ODPO collaborates with the NASA Jet Propulsion Laboratory (JPL) to use the Goldstone Solar System Radar. The orbital debris detections from these radars serve as inputs for statistical risk models used by the human spaceflight and satellite communities to assess risk to spacecraft posed by orbital debris. In this paper, we will describe the history of orbital debris radar measurements conducted by NASA, provide an overview of current radar measurements techniques and facilities, discuss the signal processing software used for orbital debris measurements and the inference of debris size and orbital parameters from these measurements, and discuss how orbital debris radar measurements are validated for use in models that are used throughout the aerospace industry.

Blackwell, Chris↗
DOE OSTI2022

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants (3 rd Annual Report)

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This report primarily focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 ± 0.014, 0.0026 ± 0, and 0.063 ± 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure. This report summarizes the Fiscal Year 2021 research progress encompassing the (1) data cleaning and feature selection necessary for ML applications; (2) development of short-term forecasting models to predict future plant process parameters for both single and multiple time steps ahead; and (3) validation of the feature selection methods and short-term forecasting models given new data from different systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

The EGS Collab Project: Status and Accomplishments

The EGS Collab project, supported by the US Department of Energy, is addressing challenges in implementing enhanced geothermal systems (EGS). This includes improving understanding of the stimulation of crystalline rock to create appropriate flow pathways, and the ability to effectively simulate both the stimulation and the flow and transport processes in the resulting fracture network. The project is performing intensively monitored rock stimulation and flow tests at the 10-m scale in an underground research laboratory. Data and observations from the field test are compared to simulations to understand processes and to build confidence in numerical modeling of the processes. In Experiment 1, we examined hydraulic fracturing an underground test bed at the Sanford Underground Research Facility (SURF) in Lead, South Dakota, at a depth of approximately 1.5 km. We drilled eight sub-horizontal boreholes in a well-characterized phyllite. Six of the boreholes were instrumented with many sensor types to allow careful monitoring of stimulation events and flow tests, and the other two boreholes were used for water injection and production. We performed a number of stimulations and flow tests in the testbed. Our monitoring systems allowed detailed observations and collection of numerous data sets of processes occurring during stimulation and during dynamic flow tests. Long-term ambient temperature and chilled water flow tests were performed in addition to many tracer tests to examine system behavior. Data were rapidly analyzed, allowing adaptive control of the tests. Numerical simulation was used to answer key experimental design questions, to forecast fracture propagation trajectories and extents, and to analyze and evaluate results. Many simulations were performed in near-real-time in conjunction with the field experiments, with more detailed process study simulations performed on a longer timeframe. Experiment 2 will examine hydraulic shearing in a test bed being built at the SURF at a depth of about 1.25 km in amphibolite under a different set of stress and fracture conditions than Experiment 1. Five sets of fracture orientations were considered in design, and three orientations seem to be consistently observed.

EGS Collab↗
DOE OSTI2026

Intelligent Experiments through Real-Time AI: Fast Data Processing and Autonomous Detector Control for High-Energy Nuclear Experiments

The aim of this project is to develop software and hardware for fast real-time data processing and autonomous detector control and calibration for the sPHENIX and the future EIC experiments. Below summarizes Georgia Tech team efforts in the past year: 1. We developed a real-time clustering algorithm and FPGA-based pipeline architecture for processing fired pixel data from ALPIDE sensors in sPHENIX experiments. Our Columnar Clustering Co-Design introduces a hardware-aware, stream-friendly approach that segments pixel data by column pairs using a Column Pair Clustering (CPC) strategy, followed by Cluster Stitching to merge adjacent subclusters. Implemented in Vitis HLS, the pipeline comprises five stages—read-in, subclustering, stitching, analysis, and write-out—connected by tagged HLS streams with custom end-of-event signaling for robust synchronization. We designed a pipelined dataflow model optimized for throughput, low latency, and minimal buffering, enabling scalable clustering across events of arbitrary size. Our system maintains spatial precision via center-of-mass and shape key extraction and efficiently handles edge cases such as fragmented or nested clusters. Compared against DBSCAN in both software and hardware, our approach demonstrates competitive performance under FPGA constraints. 2. We also conducted a comprehensive algorithm-to-hardware co-design of connected component analysis tailored for sPHENIX experiments, focusing on real-time, low-latency processing using FPGAs and High-Level Synthesis (HLS). Starting from a Python-based particle tracking pipeline, the team translated the core logic—graph traversal via DFS and Union-Find—into an HLS-compatible C++ model, replacing dynamic memory and recursion with static arrays and pipelined control flow. The final design includes a fully streamed and dataflow-compatible Union-Find kernel optimized across five iterations, incorporating loop pipelining, array partitioning, AXI/FIFO interface tuning, and function flattening. Experimental results show up to 14.8× speedup over the CPU baseline, reducing per-graph latency to 1.58 μs and demonstrating strong resource efficiency with only ~7k LUTs and zero BRAM usage. The design maintains functional correctness against the Python reference using a Python-based C-simulation framework and Mean Squared Error metrics. This work validates the potential of HLS-driven FPGA designs for edge-level HEP data acquisition, laying a scalable foundation for future integration with real-time detector pipelines and multi-graph processing systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2008

Total Lightning Observations of Extreme Weather Events over the Contiguous United States

The overall objective is to investigate total lightning characteristics of extreme weather events over the contiguous United States (CONUS) using TRMM (Tropical Rainfall Measuring Mission) LIS (Lightning Image Sensor) and OTD (Optical Transient Detector) satellite observations. A large LIS (10+ years) and OTD (5 years) data base is available to study the instantaneous total or cloud-to-ground (CG) plus intracloud (IC) lightning characteristics of extreme weather events. More specifically, the LIS and OTD data are combined with National Lightning Detection Network (NLDN) observations to examine the total and CG lightning flash rate and density, the IC:CG ratio, and positive CG percentage. These instantaneous lightning characteristics can be used for basic science studies to better understand the physical and dynamical linkages between lightning and precipitation and their environmental controls. They can also provide a first-look of extreme weather events leading up to future satellite observations (e.g., NOAA GOES-R Geostationary Lightning Mapper [GLM]) for use in climate studies and the short-term prediction and warning process. Extreme weather events are defined by the NOAA Storm Data reports of tornadoes, large hail (at least 0.75 inch) and strong straight-line winds (at least 50 kts). Over CONUS, there are over 70,000 severe storm reports in the TRMM spatial domain (< 35 N) from 1998-2007 and over 100,000 storm reports in the OTD spatial domain (5/1995-4/2000). Temporal co-location is on the order of 1% (i.e., 1000 s of coincident overpasses), providing a statistically significant sample of instantaneous total lightning properties. This instantaneous behavior of lightning in extreme weather is then compared to that of typical thunderstorm events, or randomly sampled LIS/OTD events in which the extreme events have been eliminated from the population. Results describing the instantaneous behavior of total lightning within a large sample of extreme and typical thunderstorms over CONUS will be presented. When possible, coincident VHF lightning observations from the ground-based Northern Alabama Lightning Mapping Array (LMA) are compared to the LIS/OTD optical lightning signatures, providing a validation source for instantaneous space-based optical lightning properties and a means to extend lightning inferences over the life-cycle of extreme weather.

Carey, Lawrence D.↗
DOE OSTI2025

Root genetics in the field to understand drought adaptation and carbon sequestration (Final Scientific/Technical Report)

For all crop plants, roots play a critical role in growth. Roots anchor the plants, and are the primary site of nutrient and water uptake. Roots are also the main source of C to soil in the form of root tissues and exudates, and thus greatly influence SOM stocks. To perform these functions, primary roots extend into soil, producing a network of branching roots of characteristic form, known as its root system architecture (RSA). RSA varies among species, and among varieties within a species that are adapted to different environments. Root traits are major targets for the second green revolution because of their potential to improve crop productivity, increase drought tolerance and nutrient acquisition, and increase C capture of soil. Improving the quality of roots in maize will be particularly valuable, since this crop is planted on over 92 million acres annually in the US. The future sustainability of agricultural systems relies on their ability to enhance soil organic matter (SOM) storage and reduce GHG emissions, while maintaining or enhancing productivity. This program had two components, Sensors and Models. For the first component, we designed and built a high-throughput phenotyping platform for root pulling of maize plants. This eliminated the physical labor of manually pulling up plants and reduced the number of personnel required down to one. The standardized pulling mechanism allowed recording force curves during the pulling process, providing additional information. We validated that the maximum force for pulling the root system was well-correlated with the root system mass and provided root crowns for further RSA analysis. These root crowns identified significant correlations with 2D root area and root depth, along with 3D root volume, total root length and number of root tips. We then used this system for field-based studies in maize on the genetics of root system architecture and its relation to nitrogen-use efficiency (NUE), including using lines relevant to the Corteva breeding program. Varieties were also evaluated at Corteva sites in the cornbelt and Danforth farm in Missouri, to establish responses across sites. From these studies we have identified genetic loci associated with root traits and created mutant lines for these loci and correlations of root traits with NUE. For the Models component, we worked to incorporate root and soil characteristics into the MEMS 2.0 soil and ecosystem biogeochemical model. Existing soil C models, such as Century, are unable to represent specific root trait interactions with the soil environment and therefore to accurately forecast the potential C sequestration benefits of root breeding under different climatic and soil type conditions. We have developed the MEMS 2.0 ecosystem biogeochemical model to improve quantification of farm-scale soil carbon and greenhouse gas emissions. The new knowledge and large datasets produced by this project will be used to develop and drive an innovative model capable of forecasting the impacts on soil C stocks and nutrient dynamics. An innovation was to use the empirical data from the field studies (in 1, above) to model genetic variation in nitrogen use efficiencies and soil C input. Our work demonstrated that maize root-derived C rapidly replaces existing soil C and after 3 years of continuous maize, up to 20% of soil organic C in the topsoil (0-15cm) and 3% in the subsoil (15-30cm) was contributed by maize. However, this contribution did not entirely represent a net increase. Root C contribution to soil was affected by maize genetics. We have analyzed soils derived from the CSU field trials for C and N stocks, in the different soil physical fractions represented by the MEMS model, using both physical fractionation with elemental analyses, and Fourier transformed infrared spectroscopy. Data will be used to link crop nitrogen use efficiencies with soil C sequestration and provide data to bridge the field trials with the model development, for verification of model predictions. The project had a number of successful outcomes: we have used the new phenotyping platform to identify new genetic loci that can enhance root phenotypes; we have partnered with multiple maize seed companies phenotype varieties in their breeding programs; we have developed the MEMS model that can help inform industry on the potential for carbon sequestration in the agricultural sector, and which is now available at the CSU Soil Carbon Solutions Center for use.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Creating a Simulation Platform for Research and Development of Advanced Control Methods

Advanced nuclear reactors are essential to meet the changing energy requirements throughout both the United States and the rest of world. In addition to other features, they are designed to enable deployment in remote locations and operate in a fully (or near-fully) autonomous manner, which will require a new control paradigm. To realize autonomously operating reactors, the U.S. Department of Energy’s Nuclear Energy Enabling Technologies Advanced Sensors and Instrumentation (NEET ASI) program conducts research and development into the enabling technologies and methods needed, including digital twins, machine learning, and risk modeling, in addition to various types of control methods. These technologies and methods are the key foundations needed to achieve fully autonomous systems. To develop and evaluate the technologies and methods necessary for achieving autonomous operations, it is critical to identify a software tool capable of integrating all the required elements. In surveying the available solutions, no software platforms were identified that could accomplish what was needed without introducing drawbacks. This challenge was the motivation for the current effort: to develop a software platform that can seamlessly integrate autonomouscontrol-enabling technologies and methods, allowing for accelerated research and development and transfer of ideas. The resulting platform, known as the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND), is Python-based, and leverages open-source tools to provide flexibility and facilitate building upon prior research. It is designed to enable advanced reactor developers to deploy and test advanced control technologies and methods coupled with their own models, solutions, and hardware. Given the substantial undertaking of developing such a platform, the current effort focused on laying down scalable, flexible software foundations and infrastructure, then demonstrating the platform via a use case. These foundations included developing generic modules, which contain the base variable and system blocks (the information and functional building blocks, respectively, that can be used to design a simulation) and the data handling and storage blocks needed to exchange information between the various blocks; as well as enablingtechnology-specific modules. This platform was evaluated via a use case, which was to simulate and control a process for the Microreactor Automated Control System (MACS) test bed. While MACS is not currently directly coupled to any specific microreactor physics, it was initially developed in concert with the Microreactor Applications Research Validation and Evaluation (MARVEL) microreactor, and so the MARVEL physics are used here. As part of this use case, several enabling-technology-specific blocks within COMMAND were integrated, including a proportional integral derivative (PID) control block, a Reactor Excursion and Leak Analysis Program (RELAP5-3D) block, and an anomaly detection block. The COMMAND software platform was successfully demonstrated to achieve the scalability and flexibility objectives of this effort and will be leveraged by the program’s research efforts to advance state of the art control methodologies towards autonomous operations of advanced reactors. As new use cases are created and implemented, it is anticipated that COMMAND will continue to grow and evolve to meet new requirements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

A globally sampled high-resolution hand-labeled validation dataset for evaluating surface water extent maps

Effective monitoring of global water resources is increasingly critical due to climate change and population growth. Advancements in remote sensing technology, specifically in spatial, spectral, and temporal resolutions, are revolutionizing water resource monitoring, leading to more frequent and high-quality surface water extent maps using various techniques such as traditional image processing and machine learning algorithms. However, satellite imagery datasets contain trade-offs that result in inconsistencies in performance, such as disparities in measurement principles between optical (e.g., Sentinel-2) and radar (e.g., Sentinel-1) sensors and differences in spatial and spectral resolutions among optical sensors. Therefore, developing accurate and robust surface water mapping solutions requires independent validations from multiple datasets to identify potential biases within the imagery and algorithms. However, high-quality validation datasets are expensive to build, and few contain information on water resources. For this purpose, we introduce a globally sampled, high-spatial-resolution dataset labeled using 3 m PlanetScope imagery. Our surface water extent dataset comprises 100 images, each with a size of 1024×1024 pixels, which were sampled using a stratified random sampling strategy covering all 14 biomes. We highlighted urban and rural regions, lakes, and rivers, including braided rivers and coastal regions. We evaluated two surface water extent mapping methods using our dataset – Dynamic World, based on Sentinel-2, and the NASA IMPACT model, based on Sentinel-1. Dynamic World achieved a mean intersection over union (IoU) of 72.16 % and F1 score of 79.70 %, while the NASA IMPACT model had a mean IoU of 57.61 % and F1 score of 65.79 %. Performance varied substantially across biomes, highlighting the importance of evaluating models on diverse landscapes to assess their generalizability and robustness. Our dataset can be used to analyze satellite products and methods, providing insights into their advantages and drawbacks. Our dataset offers a unique tool for analyzing satellite products, aiding the development of more accurate and robust surface water monitoring solutions. The dataset can be accessed via https://doi.org/10.25739/03nt-4f29.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

Estimation of Smoothing Error in SBUV Profile and Total Ozone Retrieval

Data from the Nimbus-4, Nimbus-7 Solar Backscatter Ultra Violet (SBUV) and seven of the NOAA series of SBUV/2 instruments spanning 41 years are being reprocessed using V8.6 algorithm. The data are scheduled to be released by the end of August 2011. An important focus of the new algorithm is to estimate various sources of errors in the SBUV profiles and total ozone retrievals. We discuss here the smoothing errors that describe the components of the profile variability that the SBUV observing system can not measure. The SBUV(/2) instruments have a vertical resolution of 5 km in the middle stratosphere, decreasing to 8 to 10 km below the ozone peak and above 0.5 hPa. To estimate the smoothing effect of the SBUV algorithm, the actual statistics of the fine vertical structure of ozone profiles must be known. The covariance matrix of the ensemble of measured ozone profiles with the high vertical resolution would be a formal representation of the actual ozone variability. We merged the MLS (version 3) and sonde ozone profiles to calculate the covariance matrix, which in general case, for single profile retrieval, might be a function of the latitude and month. Using the averaging kernels of the SBUV(/2) measurements and calculated total covariance matrix one can estimate the smoothing errors for the SBUV ozone profiles. A method to estimate the smoothing effect of the SBUV algorithm is described and the covariance matrixes and averaging kernels are provided along with the SBUV(/2) ozone profiles. The magnitude of the smoothing error varies with altitude, latitude, season and solar zenith angle. The analysis of the smoothing errors, based on the SBUV(/2) monthly zonal mean time series, shows that the largest smoothing errors were detected in the troposphere and might be as large as 15-20% and rapidly decrease with the altitude. In the stratosphere above 40 hPa the smoothing errors are less than 5% and between 10 and 1 hPa the smoothing errors are on the order of 1%. We validate our estimated smoothing errors by comparing the SBUV ozone profiles with other ozone profiling sensors.

Kramarova, N. A.↗
NASA NTRS2003

Retrieval of Ozone Column Content from Airborne Sun Photometer Measurements During SOLVE II: Comparison with SAGE III, POAM III,THOMAS and GOME Measurements

During the Second SAGE 111 Ozone Loss and Validation Experiment (SOLVE II), the 14- channel NASA Ames Airborne Trackmg Sunphotometer (AATS-14) was mounted on the NASA DC-8 and successfully measured spectra of total and aerosol optical depth (TOD and AOD) during the sunlit portions of eight science flights. Values of ozone column content above the aircraft have been derived from the AATS-14 data by using a linear least squares method. For each AATS-14 measured TOD spectrum, this method iteratively finds the ozone column content that yields the best match between measured and calculated TOD. The calculations assume the known Chappuis ozone band shape and a three-parameter AOD shape (quadratic in log-log space). Seven of the AATS-14 channels (each employing an interference filter with a nominal full-width at half maximum bandpass of -5 nm) are within the Chappuis band, with center wavelengths between 452.9 nm and 864.5 nm. One channel (604.4 nm) is near the peak, and three channels (499.4, 519.4 and 675.1 nm) have ozone absorption within 30-40% of that at the peak. For the typical DC-8 SOLVE II cruising altitudes of approx. 8-12 km and the background stratospheric aerosol conditions that prevailed during SOLVE 11, absorption of incoming solar radiation by ozone comprised a significant fraction of the aerosol-plus-ozone optical depth measured in the four AATS-14 channels centered between 499.4 and 675.1 nm. Typical AODs above the DC-8 ranged from 0.003-0.008 in these channels. For comparison, an ozone overburden of 0.3 atm-cm (300 DU) translates to ozone optical depths of 0.009,0.014, 0.041, and 0.012, respectively, at these same wavelengths. In this paper, we compare AATS-14 values of ozone column content with temporally and spatially near-coincident values derived from measurements acquired by the Stratospheric Aerosol and Gas Experiment III (SAGE III) and the Polar Ozone and Aerosol Measurement 111 (POAM III) satellite sensors. We also compare AATS-14 ozone retrievals during selected DC-8 latitudinal and longitudinal transects with total column ozone data acquired by the Total Ozone Mapping Spectrometer (TOMS) and the Global Ozone Monitoring Experiment (GOME) satellite sensors. To enable this comparison, the amount of ozone in the column below the aircraft is estimated by combining SAGE and/or POAM data with high resolution, fast response in-situ ozone measurements acquired during the DC-8 ascent at the start of each science flight.

Livingston, J.↗
NASA NTRS2017

Nominal 30-M Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine

A satellite-derived cropland extent map at high spatial resolution (30-m or better) is a must for food and water security analysis. Precise and accurate global cropland extent maps, indicating cropland and non-cropland areas, is a starting point to develop high-level products such as crop watering methods (irrigated or rainfed), cropping intensities (e.g., single, double, or continuous cropping), crop types, cropland fallows, as well as assessment of cropland productivity (productivity per unit of land), and crop water productivity (productivity per unit of water). Uncertainties associated with the cropland extent map have cascading effects on all higher-level cropland products. However, precise and accurate cropland extent maps at high spatial resolution over large areas (e.g., continents or the globe) are challenging to produce due to the small-holder dominant agricultural systems like those found in most of Africa and Asia. Cloud-based Geospatial computing platforms and multi-date, multi-sensor satellite image inventories on Google Earth Engine offer opportunities for mapping croplands with precision and accuracy over large areas that satisfy the requirements of broad range of applications. Such maps are expected to provide highly significant improvements compared to existing products, which tend to be coarser in resolution, and often fail to capture fragmented small-holder farms especially in regions with high dynamic change within and across years. To overcome these limitations, in this research we present an approach for cropland extent mapping at high spatial resolution (30-m or better) using the 10-day, 10 to 20-m, Sentinel-2 data in combination with 16-day, 30-m, Landsat-8 data on Google Earth Engine (GEE). First, nominal 30-m resolution satellite imagery composites were created from 36,924 scenes of Sentinel-2 and Landsat-8 images for the entire African continent in 2015-2016. These composites were generated using a median-mosaic of five bands (blue, green, red, near-infrared, NDVI) during each of the two periods (period 1: January-June 2016 and period 2: July-December 2015) plus a 30-m slope layer derived from the Shuttle Radar Topographic Mission (SRTM) elevation dataset. Second, we selected Cropland/Non-cropland training samples (sample size 9791) from various sources in GEE to create pixel-based classifications. As supervised classification algorithm, Random Forest (RF) was used as the primary classifier because of its efficiency, and when over-fitting issues of RF happened due to the noise of input training data, Support Vector Machine (SVM) was applied to compensate for such defects in specific areas. Third, the Recursive Hierarchical Segmentation (RHSeg) algorithm was employed to generate an object-oriented segmentation layer based on spectral and spatial properties from the same input data. This layer was merged with the pixel-based classification to improve segmentation accuracy. Accuracies of the merged 30-m crop extent product were computed using an error matrix approach in which 1754 independent validation samples were used. In addition, a comparison was performed with other available cropland maps as well as with LULC maps to show spatial similarity. Finally, the cropland area results derived from the map were compared with UN FAO statistics. The independent accuracy assessment showed a weighted overall accuracy of 94, with a producers accuracy of 85.9 (or omission error of 14.1), and users accuracy of 68.5 (commission error of 31.5) for the cropland class. The total net cropland area (TNCA) of Africa was estimated as 313 Mha for the nominal year 2015.

Cropland mapping; cropland areas; 30-m; Landsat-8;↗
DOE OSTI2024

Visualization and Quantification of Wind Induced Variability in Hydrogen Clouds Following Releases of Liquid Hydrogen: Preprint

Well characterized experimental data for consequence model validation is important in progressing the use of liquid hydrogen as an energy carrier. In 2019, the Health and Safety Executive (HSE) undertook a series of liquid hydrogen dispersion and combustion experiments as a part of the Pre-normative Research into the Safe Use of Liquid Hydrogen (PRESLHY) project. In partnership between the National Renewable Energy Laboratory (NREL) and HSE, time and spatially varying hydrogen concentration measurements were made in 25 dispersion experiments and 23 congested ignition experiments associated with PRESLHY WP3 and WP5, respectively. These measurements were undertaken using the hydrogen wide area monitoring system developed by NREL. During the 23 congested ignition experiments, high variability was observed in the measured explosion severity during experiments with similar initial conditions. This led to the conclusion that wind, including localized gusts, had a large influence on the dispersion of the hydrogen, and therefore the quantity of hydrogen that was present in the congested region of the explosions. Using the hydrogen concentration measurements taken immediately prior to ignition, the hydrogen clouds were visualized in an attempt to rationalize the variability in overpressure between the tests. Gaussian process regression was applied to quantify the variability of the measured hydrogen concentrations. This analysis could also be used to guide modifications in experimental designs for future research on hydrogen combustion behavior.

HSR&D↗
NASA NTRS2002

Calibration Plans for the Global Precipitation Measurement (GPM)

The Global Precipitation Measurement (GPM) is an international effort led by the National Aeronautics and Space Administration (NASA) of the U.S.A. and the National Space Development Agency of Japan (NASDA) for the purpose of improving research into the global water and energy cycle. GPM will improve climate, weather, and hydrological forecasts through more frequent and more accurate measurement of precipitation world-wide. Comprised of U.S. domestic and international partners, GPM will incorporate and assimilate data streams from many spacecraft with varied orbital characteristics and instrument capabilities. Two of the satellites will be provided directly by GPM, the core satellite and a constellation member. The core satellite, at the heart of GPM, is scheduled for launch in November 2007. The core will carry a conical scanning microwave radiometer, the GPM Microwave Imager (GMI), and a two-frequency cross-track-scanning radar, the Dual-frequency Precipitation Radar (DPR). The passive microwave channels and the two radar frequencies of the core are carefully chosen for investigating the varying character of precipitation over ocean and land, and from the tropics to the high-latitudes. The DPR will enable microphysical characterization and three-dimensional profiling of precipitation. The GPM-provided constellation spacecraft will carry a GMI radiometer identical to that on the core spacecraft. This paper presents calibration plans for the GPM, including on-board instrument calibration, external calibration methods, and the role of ground validation. Particular emphasis is on plans for inter-satellite calibration of the GPM constellation. With its Unique instrument capabilities, the core spacecraft will serve as a calibration transfer standard to the GPM constellation. In particular the Dual-frequency Precipitation Radar aboard the core will check the accuracy of retrievals from the GMI radiometer and will enable improvement of the radiometer retrievals. Observational intersections of the core with the constellation spacecraft are essential in applying this technique to the member satellites. Information from core spacecraft retrievals during intersection events will be transferred to the constellation radiometer instruments in the form of improved calibration and, with experience, improved radiometric algorithms. In preparation for the transfer standard technique, comparisons using the Tropical Rainfall Measuring Mission (TRMM) with sun-synchronous radiometers have been conducted. Ongoing research involves study of critical variables in the inter-comparison, such as correlation with spatial-temporal separation of intersection events, frequency of intersection events, variable azimuth look angles, and variable resolution cells for the various sensors.

Bidwell, S. W.↗
DOE OSTI2017

EPIC Calibration/Validation Experiment Field Campaign Report

A field exercise involving several different kinds of Unmanned Aerial Systems (UAS) and supporting instrumentation systems provided by DOE/ARM and NOAA/NSSL was conducted at the ARM SGP site in Lamont, Oklahoma on 29-30 October 2016. This campaign was part of a larger National Oceanic and Atmospheric Administration (NOAA) UAS Program Office program awarded to the National Severe Storms Laboratory (NSSL). named Environmental Profiling and Initiation of Convection (EPIC). The EPIC Field Campaign (Test and Calibration/Validation) proposed to ARM was a test or “dry-run” for a follow-up campaign to be requested for spring/summer 2017. The EPIC project addresses NOAA’s objective to “evaluate options for UAS profiling of the lower atmosphere with applications for severe weather.” The project goal is to demonstrate that fixed-wing and rotary-wing small UAS have the combined potential to provide a unique observing system capable of providing detailed profiles of temperature, moisture, and winds within the atmospheric boundary layer (ABL) to help determine the potential for severe weather development. Specific project objectives are: 1) to develop small UAS capable of acquiring needed wind and thermodynamic profiles and transects of the ABL using one fixed-wing UAS operating in tandem with two different fixed rotary-wing UAS pairs; 2) adapt and test miniaturized, high-precision, and fast-response atmospheric sensors with high accuracy in strong winds characteristic of the pre-convective ABL in Oklahoma; 3) conduct targeted short-duration experiments at the ARM Southern Great Plains site in northern Oklahoma concurrently with a second site to be chosen in “real-time” from the Oklahoma Mesonet in coordination with the (National Weather Service (NWS)-Norman Forecast Office; and 4) gain valuable experience in pursuit of NOAA’s goals for determining the value of airborne, mobile observing systems for monitoring rapidly evolving high-impact severe weather conditions not observed with current operational systems. OU operated three UAS at the Lamont SGP site – the OU CopterSonde, the OU Iris, and the Meteomatics Meteodrone – under a blanket Federal Aviation Administration (FAA) Certificate of Authorization (COA) allowing flights up to 400 feet above ground level (AGL). The mission for the rotary-wing UAS involved four aircraft, three from the ARM Lamont site and one from an Oklahoma Mesonet site located at Medford, involving a vertical ascent to an altitude of 400 ft (130 m) AGL at ~ 30 minute intervals for ~ 5 hours duration on each of the two experiment days. This operation was conducted in close coordination with NSSL-launched rawinsonde balloons at the two sites, and operation of a fixed-wing UAS from the University of Colorado called the TTwistor that flew mission flight legs between the Lamont site and Medford. The NSSL operation at Medford (outside of ARM) also involved use of their Collaborative Lower Atmosphere Mobile Profiling System (CLAMPS) ground-based remote-sensing system for measuring atmospheric profiles of temperature, moisture, and winds with atmospheric emitted radiance interferometer (AERI), microwave radiometer, and Doppler wind lidar systems. The ARM Facility supported the project by providing access to their instrumented tower data at Lamont (at three levels), as well as AERI and Doppler wind lidar data obtained from systems quite similar to those used by CLAMPS. These non-UAS data from both ARM and NSSL provided the observations used to validate the experimental UAS observations.

atmospheric boundary layer↗
NASA NTRS2016

A Quasi-Global Approach to Improve Day-Time Satellite Surface Soil Moisture Anomalies through the Land Surface Temperature Input

Passive microwave observations from various spaceborne sensors have been linked to the soil moisture of the Earth's surface layer. A new generation of passive microwave sensors are dedicated to retrieving this variable and make observations in the single theoretically optimal L-band frequency (1-2 GHz). Previous generations of passive microwave sensors made observations in a range of higher frequencies, allowing for simultaneous estimation of additional variables required for solving the radiative transfer equation. One of these additional variables is land surface temperature, which plays a unique role in the radiative transfer equation and has an influence on the final quality of retrieved soil moisture anomalies. This study presents an optimization procedure for soil moisture retrievals through a quasi-global precipitation-based verification technique, the so-called Rvalue metric. Various land surface temperature scenarios were evaluated in which biases were added to an existing linear regression, specifically focusing on improving the skills to capture the temporal variability of soil moisture. We focus on the relative quality of the day-time (01:30 pm) observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), as these are theoretically most challenging due to the thermal equilibrium theory, and existing studies indicate that larger improvements are possible for these observations compared to their night-time (01:30 am) equivalent. Soil moisture data used in this study were retrieved through the Land Parameter Retrieval Model (LPRM), and in line with theory, both satellite paths show a unique and distinct degradation as a function of vegetation density. Both the ascending (01:30 pm) and descending (01:30 am) paths of the publicly available and widely used AMSR-E LPRM soil moisture products were used for benchmarking purposes. Several scenarios were employed in which the land surface temperature input for the radiative transfer was varied by imposing a bias on an existing regression. These scenarios were evaluated through the Rvalue technique, resulting in optimal bias values on top of this regression. In a next step, these optimal bias values were incorporated in order to re-calibrate the existing linear regression, resulting in a quasi-global uniform LST relation for day-time observations. In a final step, day-time soil moisture retrievals using the re-calibrated land surface temperature relation were again validated through the Rvalue technique. Results indicate an average increasing Rvalue of 16.5%, which indicates a better performance obtained through the re-calibration. This number was confirmed through an independent Triple Collocation verification over the same domain, demonstrating an average root mean square error reduction of 15.3%. Furthermore, a comparison against an extensive in situ database (679 stations) also indicates a generally higher quality for the re-calibrated dataset. Besides the improved day-time dataset, this study furthermore provides insights on the relative quality of soil moisture retrieved from AMSR-E's day- and night-time observations.

soil moisture↗
DOE OSTI2023

Sensor Development for Liquid Water Detection in Dry Storage Casks: FY 2023 Status

Modeling efforts were undertaken in fiscal year (FY) 2023 to evaluate the feasibility and capability for sensing of liquid water inside canister-based dry cask storage systems (DCSSs). The focus was on the development of full-scale finite element models (FEMs) of ultrasound propagation through DCSS canister components. For these initial investigations, simulation of the baseplate component was targeted, envisioning water collection at the bottom of a vertically oriented canister. The effects of internal canister components (i.e., fuel basket, fuel assemblies) on ultrasound propagation in the baseplate component were evaluated in these efforts. The environment inside a DCSS confinement is intended to be inert and free of water to prevent potential corrosion of used fuel cladding or other internal hardware. Spent fuel assemblies are dried, after storage in water pools, to make sure water has been removed from assembly cavities. However, there is some uncertainty about the amount of residual water potentially left behind in a DCSS after drying processes, because water can become trapped in cavities or other small crevices in the surfaces formed by the fuel cladding, fuel assemblies, and other internal hardware components. Considering the complex space- and time-dependent temperature profiles in DCSSs, water may be in a liquid or gas phase depending on its location in the cask and how long the cask has been in storage. Evacuating most water and oxidizing agents contained within a canister is recommended by NUREG-1536 (NRC 2010), which covers DCSSs. As summarized by Salazar et al. (2020), existing guidance typically relies on achievable vacuum pumping pressures sustained over a hold period as a signal of dryness and water removal. However, time series data about water removal from full-scale commercial drying procedures are lacking (Hanson and Alsaed 2019). A review of drying specifications from several vendors led to the conclusion that if the specifications are followed correctly, the residual moisture in DCSSs should present an insignificant risk of cladding degradation (Knoll and Gilbert 1987). A more recent analysis concluded that much larger quantities of residual water could remain in DCSSs, but the amount would still not be expected to lead to significant corrosion of fuel cladding or other internal components (Jung et al. 2013). Industry drying procedures are mostly prescriptive in nature, and operational issues arising during the process could result in incomplete drying. A summary of operational issues and potential negative effects of residual water is provided by Salazar et al. (2019). Experimental efforts are ongoing to validate the extent of water removal in a DCSS based on drying procedures used at nuclear power plants through well-designed investigations of drying process efficacy and water retention (Durbin et al. 2021; Pulido et al. 2022a, Pulido et al. 2022b). This has been approached by simulating limited portions of a DCSS internal volume and fuel assemblies. So far, these efforts have focused on the effects of potential water trapping in the dashpot region of control rod guide tubes but are expected to be expanded to include the effects of other internal hardware features and failed fuel rod cladding. A method for detection and measurement of liquid water (Meyer et al. 2022; Meyer et al. 2021), in tandem with the drying information gained through experimental investigations, provides comprehensive bases for understanding the internal conditions of DCSSs and provides technical information to support licensing decisions.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2024

Decade-Long Ozone Profile Record from Suomi NPP OMPS Limb Profiler: Assessment of Version 2.6 Data

We evaluate a decadal ozone profile record derived from the Suomi National Polar-orbiting Partnership (SNPP) Ozone Mapping and Profiler Suite (OMPS) Limb Profiler (LP) satellite instrument. In 2023, the OMPS LP data were re-processed with the new version 2.6 retrieval algorithm that combines measurements from the ultraviolet (UV) and visible (VIS) parts of the spectra and employs the second order Tikhonov regularization to retrieve a single vertical ozone profile between 12.5 km (or cloud tops) and 57.5 km with the vertical resolution of about 1.9 - 2.5 km between 20-55 km. The algorithm uses radiances measured at six UV ozone-sensitive wavelengths (295, 302, 306, 312, 317 and 322 nm) paired with 353 nm, and one VIS wavelength at 606 nm combined with 510 nm and 675 nm to form a triplet. Each wavelength pair or triplet is used over a limited range of tangent altitudes where the sensitivity to ozone changes are strongest. A new implemented aerosol correction scheme is based on a gamma-function particle size distribution. Numerous calibration changes that affected ozone retrievals were also applied to measured LP radiances, including updates in altitude registration, radiometric calibration, stray light, and spectral registration. The key version 2.6 improvement is the reduction in relative drifts between LP ozone and correlative measurements, linked previously to a drift in the version 2.5 LP altitude registration. We compare LP ozone profiles with those from Aura Microwave Limb Sensor (MLS) to quantify ozone changes in version 2.6.

Limb scattering↗