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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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At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

A THEMIS Survey of Flux Ropes and Traveling Compression Regions: Location of the Near-Earth Reconnection Site During Solar Minimum

A statistical study of flux ropes and traveling compression regions (TCRs) during the Time History of Events and Macroscale Interactions during Substorms (THEMIS) second tail season has been performed. A combined total of 135 flux ropes and TCRs in the range GSM X approx -14 to -31 R(sub E) were identified, many of these occurring in series of two or more events separated by a few tens of seconds. Those occurring within 10 min of each other were combined into aggregated reconnection events. For the purposes of this survey, these are most likely the products of reconnect ion occurring simultaneously at multiple, closely spaced x-lines as opposed to statistically independent episodes of reconnection. The 135 flux ropes and TCRs were grouped into 87 reconnection events; of these, 28 were moving tailward and 59 were moving Earthward. The average location of the near-Earth x-line determined from statistical analysis of these reconnection events is (X(sub GSM), Y*(sub GSM)) = (-30R(sub E), 5R(sub E)), where Y* includes a correction for the solar aberration angle. A strong east-west asymmetry is present in the tailward events, with >80% being observed at GSM Y* > O. Our results indicate that the Earthward flows are similarly asymmetric in the midtail region, becoming more symmetric inside - 18 R(sub E). Superposed epoch analyses indicate that the occurrence of reconnection closer to the Earth, i.e., X > -20 R(sub E), is associated with elevated solar wind velocity and enhanced negative interplanetary magnetic field B(sub z). Reconnection events taking place closer to the Earth are also far more effective in producing geomagnetic activity, judged by the AL index, than reconnection initiated beyond X approx -25 R(sub E).

Imber, S. M.↗
DOE OSTI2021

Leaf gas exchange and fitted parameters, San Lorenzo, Panama, 2020

This data package contains photosynthetic CO2 response curves (ACi curves), light response curves (AQ curves), dark adapted dark respiration, conductance curves, and survey measurements for leaves measured at the San Lorenzo forest canopy crane site, Panama (PA-SLZ) from January to March 2020. Leaves were sampled from both the top of the canopy and multiple heights within the canopy from 10 vertical profiles and included around 50 species. In addition, six core species were also measured, from the top of the canopy. Fitted maximum carboxylation rate (Vcmax), maximum electron transport rate (Jmax), and triose phosphate utilization rate (TPU) were derived from measured data as well as the convexity, light use efficiency, apparent quantum yield, and Rdark. The measurements can also be used to derive the conductance parameters g0 and g1 of stomatal model. All the gas exchange data and metadata are presented in .csv files and complete instrument output are included in .zip folders. Data and metadata meet the ESS-DIVE leaf-level gas exchange reporting format requirements. The protocol details are provided as pdf documents. In addition to gas exchange data described here these samples were also used for measurement of leaf optical properties, carbon and nitrogen content, and leaf mass per unit leaf area (LMA) these data can be cross linked using the unique sample ID and are provided in separate related data packages. Sample information including canopy elevation and leaf area index (LAI) can be found in the related “Leaf and canopy traits” data package.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Nexus of thermal resilience and energy efficiency in buildings: A case study of a nursing home

Extreme weather events become more frequent and severe due to climate change. Although energy efficiency technologies can influence thermal resilience of buildings, they are traditionally studied separately, and their interconnections are rarely quantified. This study developed a methodology of modeling and analysis to provide insights into the nexus of thermal resilience and energy efficiency of buildings. In this work, we conducted a case study of a real nursing home in Florida, where 12 patients died during Hurricane Irma in 2017 due to HVAC system power loss, to understand and quantify how passive and active energy efficiency measures (EEMs) can improve thermal resilience to reduce heat-exposure risk of patients. Results show that passive measures of opening windows and doors for natural ventilation, as well as miscellaneous load reduction, are very effective in eliminating the extreme dangerous occasions. However, to maintain safe conditions, active measures such as on-site power generators and thermal storage are also needed. The nursing home was further studied by changing its location to two other cities: San Francisco (mild climate) and Chicago (cold winter and hot summer). Results revealed that the EEMs' impacts on thermal resilience vary significantly by climate and building characteristics. The study also estimated the costs of EEMs to help stakeholders prioritize the measures. Passive measures that may not save energy may greatly improve thermal resilience, and thus should be considered in building design or retrofit. Findings from this study indicate energy efficiency technologies should be evaluated not only by their energy savings performance but also by their influence on a building's resilience to extreme weather events.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1999

Real-Time Assimilation of Goes-Derived Products into A Mesoscale Model and It's Impact on Short-Term (06-36hr) Forecasts from 17 October 1998 through the Present

As the parameterizations of surface energy budgets in regional models have become more complete physically, models have the potential to be much more realistic in simulations of coupling between surface radiation, hydrology, and surface energy transfer. Realizing the importance of properly specifying the surface energy budget, many institutions are using land-surface models to represent the lower boundary forcing associated with biophysical processes and soil hydrology. However, the added degrees of freedom due to inclusion of such land-surface schemes require the specification of additional parameters within the model system such as vegetative resistances, green vegetation fraction, leaf area index, soil physical and hydraulic characteristics, stream flow, runoff, and the vertical distribution of soil moisture. Spatial heterogeneity of these parameters makes correct specification problematic since measurements are not routinely available. A technique has been developed for assimilating GOES-IR skin temperature tendencies, solar insolation, and surface albedo into the surface energy budget equation of a mesoscale model so that the simulated rate of temperature change closely agrees with the satellite observations. The technique has been successfully employed in a number of mesoscale models in case-study mode. We have taken the next step and developed a study to determine if assimilating these types of data into mesoscale models in real-time can improve short-term (648h) forecasts of temperature, relative humidity, and QPF on a daily basis over relatively large regions. Therefore, an operational modeling/assimilation system has been developed at the GHCC during the past summer that allows us to produce simulations out to 48 hours in a timely manor. The PSU/NCAR MM5 is used in a nested configuration with a 25 km grid covering the southeastern third of the US. The model has been on-line since 1 July 1998 and forecast products are posted on our web site. The satellite algorithms that generate data to be assimilated came on-line 17 October 1998. Quantitative assessment of the forecast quality is performed via traditional verification statistics. In addition, invaluable qualitative information is obtained through close collaboration with several NWSFO's who are using the MM5 products in real-time on a daily basis. The assimilation technique has been applied in an off-line mode since 17 October. Results based on bulk statistical verification of surface meteorology over the entire Southeastern US show that assimilating the GOES-derived land surface tendencies and solar radiation results in a significant reduction of the shelter air temperature and RH bias on a daily basis. In fact, the assimilation technique has produced improved temperature and RH forecasts for 97% of the 100 simulations performed to date. Work is currently underway to determine the sensitivity of the assimilation procedure to the availability of satellite data, length of assimilation period, model initialization, and synoptic-scale meteorological conditions. In addition, results from a detailed energy budget analysis using the Early Eta, our operational MM5, and the assimilation runs will help us to better understand the satellite assimilation the land-surface energy budge. Research during the spring-summer of 1999 will focus on the impact of the assimilation technique during the warm season where it is hypothesized that it can have a positive impact on QPF during conditions of weak synoptic-scale forcing.

Lapenta, William M.↗
DOE OSTI2024

Estimating snow cover from high-resolution satellite imagery by thresholding blue wavelengths: Supporting Data

The extent and duration of snow cover is predicted to be altered as the climate changes. Developing high-resolution estimates of snow cover change is crucial for estimating changes in snow cover and the effects of these changes on watershed and ecosystems processes. Remote sensing tools have been a common method for rapidly mapping snow covered area (SCA) across a landscape. The most common remote sensing method for estimating SCA uses satellite-based calculations of the normalized difference snow index (NDSI), which relies on spectral measurements in the shortwave-infrared wavelengths (SWIR). NDSI is effective at catchment- to regional-scale estimates of SCA, but due to spatial resolution limitations of SWIR measurements, NDSI cannot be used to assess fine-scale SCA. In this work, we develop a new algorithm, called the Blue Snow Threshold (BST) algorithm, that maps high-resolution SCA by calculating a threshold on the blue wavelengths from high-resolution satellite imagery. This data package includes Orthorectified IKONOS-2 imagery (IkonosTestImage.tif) from August 14, 2004 at 1.00 meters Ground Sample Distance for Cook Inlet, Alaska (59.966414 , -152.982975). The Blue Snow Threshold algorithm (BST.py) was then used to produce a snow cover estimate (IkonosTestImage_BST.tif) for this study area. Additional imagery metadata is included in the ImageInfo.txt file. See Thaler et al., 2023 (https://doi.org/10.1016/j.rse.2022.113403) for more information about the BST algorithm.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2003

HEASARC Software Archive

(1) Chandra Archive: SAO has maintained the interfaces through which HEASARC gains access to the Chandra Data Archive. At HEASARC's request, we have implemented an anonymous ftp copy of a major part of the public archive and we keep that archive up-to- date. SAO has participated in the ADEC interoperability working group, establishing guidelines or interoperability standards and prototyping such interfaces. We have provided an NVO-based prototype interface, intending to serve the HEASARC-led NVO demo project. HEASARC's Astrobrowse interface was maintained and updated. In addition, we have participated in design discussions surrounding HEASARC's Caldb project. We have attended the HEASARC Users Group meeting and presented CDA status and developments. (2) Chandra CALDB: SA0 has maintained and expanded the Chandra CALDB by including four new data file types, defining the corresponding CALDB keyword/identification structures. We have provided CALDB upgrades for the public (CIAO) and for Standard Data Processing. Approximately 40 new files have been added to the CALDB in these version releases. There have been in the past year ten of these CALDB upgrades, each with unique index configurations. In addition, with the inputs from software, archive, and calibration scientists, as well as CIAO/SDP software developers, we have defined a generalized expansion of the existing CALDB interface and indexing structure. The purpose of this is to make the CALDB more generally applicable and useful in new and future missions that will be supported archivally by HEASARC. The generalized interface will identify additional configurational keywords and permit more extensive calibration parameter and boundary condition specifications for unique file selection. HEASARC scientists and developers from SAO and GSFC have become involved in this work, which is expected to produce a new interface for general use within the current year. (3) DS9: One of the decisions that came from last year's HEADCC meeting was to make the ds9 image display program the primary vehicle for displaying line graphics (as well as images). The first step required to make this possible was to enhance the line graphics capabilities of ds9. SAO therefore spent considerable effort upgrading ds9 to use Tcl 8.4 so that the BLT line graphics package could be built and imported into ds9 from source code, rather than from a pre-built (and generally outdated) shared library. This task, which is nearly complete, allows us to extend BLT as needed for the HEAD community. Following HEADCC discussion concerning archiving and the display of archived data, we extended ds9 to support full access to many astronomical Web-based archives sites, including HEASARC, MAST, CHANDRA, SKYVIEW, ADS, NED, SIMBAD, IRAS, NVRO, SAO TDC, and FIRST. Using ds9's new internal Web access capabilities, these archives can be accessed via their Web page. FITS images, plots, spectra, and journal abstracts can be referenced, down-loaded, and displayed directly and easily in ds9. For more information, see: http://hea-www.harvard.edu/saord/ds9. Also after the HEADCC discussion concerning region filtering, we extended the Funtools sample implementation of region filtering as described in: http://hea-www.harvard.edu/saord/funtools/regions.html. In particular, we added several new composite regions for event and image filtering, including elliptical and box annuli. We also extended the panda (Pie AND Annulus) region support to include box pandas and elliptical pandas. These new composite regions are especially useful in programs that need to count photons in each separate region using only a single pass through the data. Support for these new regions was added to ds9. In the same vein, we developed new region support for filtering images using simple FITS image masks, i.e. 8-bit or 16-bit FITS images where the value of a pixel is the region id number for that pixel. Other important enhancements to DS9 this year, include supporor multiple world coordinate systems, three dimensional event file binning, image smoothing, region groups and tags, the ability to save images in a number of image formats (such as JPEG, TIFF, PNG, FITS), improvements in support for integrating external analysis tools, and support for the virtual observatory. In particular, a full-featured web browser has been implemented within D S 9 . This provides support for full access to HEASARC archive sites such as SKYVIEW and W3BROWSE, in addition to other astronomical archives sites such as MAST, CHANDRA, ADS, NED, SIMBAD, IRAS, NVRO, SA0 TDC, and FIRST. From within DS9, the archives can be searched, and FITS images, plots, spectra, and journal abstracts can be referenced, downloaded and displayed The web browser provides the basis for the built-in help facility. All DS9 documentation, including the reference manual, FAQ, Know Features, and contact information is now available to the user without the need for external display applications. New versions of DS9 maybe downloaded and installed using this facility. Two important features used in the analysis of high energy astronomical data have been implemented in the past year. The first is support for binning photon event data in three dimensions. By binning the third dimension in time or energy, users are easily able to detect variable x-ray sources and identify other physical properties of their data. Second, a number of fast smoothing algorithms have been implemented in DS9, which allow users to smooth their data in real time. Algorithms for boxcar, tophat, and gaussian smoothing are supported.

White, Nicholas↗
DOE OSTI2020

Revised Kimberlina Groundwater Simulations

Kimberlina Groundwater Qaulity Simulations (Version 3: 2015 October) by Lawrence Livermore National Labratory. Submitted to the EDX team at the National Energy Technology Laboratory for research use. This dataset contains 562 updated groundwater quality simulations based on the Kimberlina site. It is a six-layer model including the aquifers and aquitards inbetween the ground surface and the reservoir. The input files and associated leakage information for these simulations are included in the zip file "nrap_kim_well31_fluxes and inputs.tar.gz". These 562 simulations are the successful results out of 1000 monte carlo simulations, with time length greater than 5 years. Each simulation consists 6 output files, therefore, there are 3372 files (6*562) in total. One single example simulation of full time length (200 year) is also provided in the zip file "single_simulation_200yr_sim0012.tar.gz". These 6 groundwater quality parameters are: - pH (use absolute value) - TDS (mg/kg) - Pressure (Pa) - CO2 Liquid Saturation (fraction range 0.0-1.0) - CO2 Gas Concentration (mol/L) - CO2 Liquid Concentration (mol/kg) Their correponding file names, take the first simulation for example, are: ph0001.ntab (for pH) , tds0001.ntab (for TDS), usnt0001.p.ntab (for pressure), usnt0001.s_liquid.ntab (for liquid saturation), usnt0001.c_co2_gas.ntab (for CO2 gas concentration) and usnt0001.c_co2_liquid.ntab (for CO2 liquid concentration). Each output file contains at least 19 columns (variables). The total number of columns depends on the simulation time length. The column data include the information of numerical records, geologic location and sizes and the simulated parameter values over time. The first 13 variables are about numerical records and relative geologic information for a simulation grid: 1. index: simulation index 2. i: the ith grid of x-axis 3. j: the jth grid of y-axis 4. k: the kth grid of z-axis 5. element_ref: element type reference 6. nuft_ind: nuft index 7. x: grid location in the x axis direction 8. y: grid location in the y axis direction 9. z: grid location in the z axis direction 10. dx: grid length in the x axis direction 11. dy: grid length in the y axis direction 12. dz: grid length in the z axis direction 13. volume: volume of the simulation grid The rest (14, 15, 16...) variables are the simulated parameter values over time, take pH as an example, are: 14. ph000y: initial pH value. 15. ph001y: simulated pH value at the end of the 1st year. 16. ph002y: simulated pH value at the end of the 2nd year. ... (and so on)... The zip input files have the input fluxes of brine and CO2 (in kg/s) allocated to model layers 1,2,3 and 5 (layers 4 and 6 are assumed to be no-flow aquitards) through the following nodes: (src1 WELL 7 7 25 25 10 32) #Etchegoin (src2 WELL 7 7 25 25 33 72) #Macoma Chanac (src3 WELL 7 7 25 25 73 83) #Santa Margarita Mclure (src4 WELL 7 7 25 25 84 89) #Fruitvale Round Mountain (aquitard) (src5 WELL 7 7 25 25 90 96) #Olcese Sand (thief zone) (src6 WELL 7 7 25 25 97 99) #tremblor Freeman (aquitard) ex: brn1 is assigned to src 1 from i=(7,7), j=(25,25), k=(10,32) Edited by Ya-Mei Yang at National Energy Technology Laboratory

brine leakage↗
DOE OSTI2024

The influence of burn severity on dissolved organic carbon concentrations across a stream network differs based on seasonal wetness conditions

Large, high-severity wildfires in many regions across the globe have increased concerns about their impacts on carbon cycling in watersheds. Altered sources of carbon and changes in catchment hydrology after wildfire can lead to shifts in dissolved organic carbon (DOC) concentrations in streams, which can have negative impacts on aquatic ecosystem health and downstream drinking-water treatment. Despite its importance, post-fire DOC responses remain relatively unconstrained in the literature, and we lack critical knowledge of how burn severity, landscape elements, and climate interact to affect DOC concentrations. To improve our understanding of the impact of burn severity on DOC concentrations, we measured DOC at 129 sites across a stream network extending upstream, within, and downstream of a large, high-severity wildfire in Oregon, USA. We collected samples across the study sub-basin during four distinct seasonal wetness conditions. We used our high-spatial-resolution data to develop spatial stream network (SSN) models to predict DOC across the stream network and to improve our understanding of the controls on DOC concentrations. Spatially, we found no obvious wildfire signal – instead, we observed a pattern of increasing DOC concentrations from the high-elevation headwaters to the sub-basin outlet, while the mainstem maintained consistently low DOC concentrations. This suggests that effects from large wildfires may be “averaged” out at higher stream orders and larger spatial scales. When we grouped DOC concentrations by burn severity group, we observed a significant decrease in the variability of DOC concentrations in the moderate and high burn severity sub-catchments. However, our SSN models were able to predict decreases in DOC concentrations with increases in burn severity across the stream network. Decreases in DOC concentrations were also highly variable across seasonal wetness conditions, with the greatest (–1.40 to –1.64 mg L –1 ) decrease occurring in the high-severity group during the wetting season. Additionally, our models indicated that in all seasons, baseflow index was more influential in predicting DOC concentrations than burn severity was, indicating that groundwater discharge can obscure the impacts of wildfire in a stream network. Overall, our results suggested that landscape characteristics can regulate the DOC response to wildfire. Moreover, our results also indicated that the seasonal timing of sampling can influence the observed response of DOC concentrations to wildfire.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2016

Transgenic Mouse Model for Reducing Oxidative Damage in Bone

Bone loss can occur due to many challenges such age, radiation, microgravity, and Reactive Oxygen Species (ROS) play a critical role in bone resorption by osteoclasts (Bartell et al. 2014). We hypothesize that suppression of excess ROS in skeletal cells, both osteoblasts and osteoclasts, regulates skeletal growth and remodeling. To test our hypothesis, we used transgenic mCAT mice which overexpress the human anti-oxidant catalase gene targeted to the mitochondria, the main site for endogenous ROS production. mCAT mice have a longer life-span than wildtype controls and have been used to study various age-related disorders. To stimulate remodeling, 16 week old mCAT mice or wildtype mice were exposed to treatment (hindlimb-unloading and total body-irradiation) or sham treatment conditions (control). Tissues were harvested 2 weeks later for skeletal analysis (microcomputed tomography), biochemical analysis (gene expression and oxidative damage measurements), and ex vivo bone marrow derived cell culture (osteoblastogenesis and osteoclastogenesis). mCAT mice expressed the transgene and displayed elevated catalase activity in skeletal tissue and marrow-derived osteoblasts and osteoclasts grown ex vivo. In addition, when challenged with treatment, bone tissues from wildtype mice showed elevated levels of malondialdehyde (MDA), indicating oxidative damage) whereas mCAT mice did not. Correlation analysis revealed that increased catalase activity significantly correlated with decreased MDA levels and that increased oxidative damage correlated with decreased percent bone volume (BVTV). In addition, ex-vivo cultured osteoblast colony growth correlated with catalase activity in the osteoblasts. Thus, we showed that these transgenic mice can be used as a model to study the relationship between markers of oxidative damage and skeletal properties. mCAT mice displayed reduced BVTV and trabecular number relative to wildtype mice, as well as increased structural model index in the cancellous tibia. Treatment caused bone loss in wildtype mice, as expected. Treatment also caused deficits in microarchitecture of mCAT mice, although less severe than wildtype mice in some parameters (percent bone volume, structural model index and cortical area). In conclusion, our results indicate that endogenous ROS signaling in both osteoblast and osteoclast lineage cells contributes to skeletal growth and remodeling, and quenching oxidative damage could play a role in bone loss prevention.

transgenic mice↗
NASA NTRS2022

Using airborne and DESIS imaging spectroscopy to map plant diversity across the largest contiguous tract of tallgrass prairie on earth

Grassland ecosystems are under threat globally, primarily due to land-use and land-cover changes that have adversely affected their biodiversity. Given the negative ecological impacts of biodiversity loss in grasslands, there is an urgent need for developing an operational biodiversity monitoring system that functions in these ecosystems. In this paper, we assessed the capability of airborne and spaceborne imaging spectroscopy (also known as hyperspectral imaging) to capture plant α-diversity in a large naturally-assembled grassland while considering the impact of common management practices, specifically prescribed fire. We collected a robust in-situ plant diversity data set, including species composition and percent cover from 2500 sampling points with different burn ages, from recently-burned to transitional and pre-prescribed fire at the Joseph H. Williams Tallgrass Prairie Preserve in Oklahoma, USA. We expressed in-situ plant α-diversity using the first three Hill numbers, including species richness (number of observed species in a plant community), exponential Shannon entropy index (hereafter Shannon diversity; effective number of common species, where species are weighed proportional to their percent cover), and inverse Simpson concentration index (hereafter Simpson diversity; effective number of dominant species, where more weight is given to dominant species) at four different plot sizes, including 60 m × 60 m, 120 m × 120 m, 180 m × 180 m, and 240 m × 240 m. We collected full-range airborne hyperspectral data with fine spatial resolution (1 m) and visible and near-infrared spaceborne hyperspectral data from DESIS sensor with coarse spatial resolution (30 m), and used the spectral diversity hypothesis—i.e., that the variability in spectral data is largely driven by plant diversity—to estimate α-diversity remotely. In recently-burned plots and those at the transitional stage, both airborne and spaceborne data were capable of capturing Simpson diversity—a metric that calculates the effective number of dominant species by emphasizing abundant species and discounting rare species—but not species richness or Shannon diversity. Further, neither airborne nor spaceborne hyperspectral data sets were capable of capturing plant α-diversity of 60 m × 60 m or 120 m × 120 m plots. Based on these results, three main findings emerged: (1) management practices influence grassland biodiversity patterns that can be remotely detected, (2) both fine- and coarse-resolution remotely-sensed data can detect the effective number of dominant species (e.g., Simpson diversity), and (3) attention should be given to site-specific plant diversity field data collection to appropriately interpret remote sensing results. Findings of this study indicate the feasibility of estimating Simpson diversity in naturally-assembled grasslands using forthcoming spaceborne imagers such as National Aeronautics and Space Administration's Surface Biology and Geology mission.

Hamed Gholizadeh↗
DOE OSTI2025

A topological approach to magnetic nulls

Magnetic nulls are locations where the magnetic field vanishes. They determine to a large degree the magnetic connectivity in a system, and are sites of magnetic reconnection. We describe a novel approach to understanding movement, appearance, and disappearance of nulls in magnetic fields. This approach is based on the concept of isotropes, or lines where the field direction is constant. These lines are streamlines of a vector field whose flux is sourced by the topological indices of nulls, and can be conceptualized as corresponding to ‘lines of force’ between nulls. We show how this topological approach can be used to generate analytical expressions for the location of nulls in the presence of external fields for dipoles and for a field defined by the Hopf fibration.

magnetic null↗
DOE OSTI2020

NGEE Arctic CO2, CH4 and Energy Eddy-Covariance (EC) Flux Tower Auxiliary Measurements, Council Road Mile Marker 71, Seward Peninsula, Alaska, 2017 - 2019

Measurements began in July 2017 and are made year-round (2017 seasonal only) on the tussock tundra with discontinuous permafrost at the Council Road Site on the Seward Peninsula. CO2, CH4 and energy fluxes using the Eddy Covariance (EC) technique (Baldocchi 2003) and meteorological measurements are reported as 30-minute averages. The tower site is registered with AmeriFlux as US-NGC. See http://ameriflux.lbl.gov/sites/siteinfo/US-NGC for more information. Reported data include: (1) additional/extra meteorological and eddy covariance CO2, CH4 and energy flux data, (2) binned spectra for the three wind components, the sonic temperature and gas densities together with the binned cospectra for covariances of w (vertical wind component) and gas densities, (3) binned ogives (cumulative (co)spectra) for the three wind components, the sonic temperature and gas densities, and (4) tower site footprint matrices. The majority of the files are *.csv with paired visualization footprint files as *.jpg. Meteorological and eddy covariance CO2, CH4 and energy flux data (AmeriFlux BASE data product) are available from AmeriFlux BASE US-NGC NGEE Arctic Council (Torn, 2020) https://doi.org/10.17190/AMF/1634883. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) near Utqiagvik (Barrow), Alaska and 2) multiple areas in the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2014

Comparison of SMOS and SMAP Soil Moisture Retrieval Approaches Using Tower-based Radiometer Data over a Vineyard Field

The objective of this study was to compare several approaches to soil moisture (SM) retrieval using L-band microwave radiometry. The comparison was based on a brightness temperature (TB) data set acquired since 2010 by the L-band radiometer ELBARA-II over a vineyard field at the Valencia Anchor Station (VAS) site. ELBARA-II, provided by the European Space Agency (ESA) within the scientific program of the SMOS (Soil Moisture and Ocean Salinity) mission, measures multiangular TB data at horizontal and vertical polarization for a range of incidence angles (30-60). Based on a three year data set (2010-2012), several SM retrieval approaches developed for spaceborne missions including AMSR-E (Advanced Microwave Scanning Radiometer for EOS), SMAP (Soil Moisture Active Passive) and SMOS were compared. The approaches include: the Single Channel Algorithm (SCA) for horizontal (SCA-H) and vertical (SCA-V) polarizations, the Dual Channel Algorithm (DCA), the Land Parameter Retrieval Model (LPRM) and two simplified approaches based on statistical regressions (referred to as 'Mattar' and 'Saleh'). Time series of vegetation indices required for three of the algorithms (SCA-H, SCA-V and Mattar) were obtained from MODIS observations. The SM retrievals were evaluated against reference SM values estimated from a multiangular 2-Parameter inversion approach. The results obtained with the current base line algorithms developed for SMAP (SCA-H and -V) are in very good agreement with the reference SM data set derived from the multi-angular observations (R2 around 0.90, RMSE varying between 0.035 and 0.056 m3m3 for several retrieval configurations). This result showed that, provided the relationship between vegetation optical depth and a remotely-sensed vegetation index can be calibrated, the SCA algorithms can provide results very close to those obtained from multi-angular observations in this study area. The approaches based on statistical regressions provided similar results and the best accuracy was obtained with the Saleh methods based on either bi-angular or bipolarization observations (R2 around 0.93, RMSE around 0.035 m3m3). The LPRM and DCA algorithms were found to be slightly less successful in retrieving the 'reference' SM time series (R2 around 0.75, RMSE around 0.055 m3m3). However, the two above approaches have the great advantage of not requiring any model calibrations previous to the SM retrievals.

Soil Moisture↗
DOE OSTI2026

Solar Resource Measurements in Eugene, OR: Cooperative Research and Development Final Report, CRADA Number CRD-07-00252

Site-specific, long-term, continuous, and high-resolution measurements of solar irradiance are important for developing renewable resource data. These data are used for several research and development activities consistent with the NLR mission: establish a national 3-year climatological database of measured solar irradiances; provide high quality ground-truth data for satellite remote sensing validation; support development of radiative transfer models for estimating solar irradiance from available meteorological observations; provide solar resource information needed for technology deployment and operations. Data acquired under this agreement will be available to the public through NLR's Measurement & Instrumentation Data Center – MIDC (http://www.nlr.gov/midc) Or the Renewable Resource Data Center - RReDC (http://rredc.nlr.gov). The MIDC offers a variety of standard data display, access, and analysis tools designed to address the needs of a wide user audience (e.g., industry, academia, and government interests).

14 SOLAR ENERGY↗
DOE OSTI2022

Lithological controls on soil geochemistry and clay mineralogy across Spodosols in the coastal temperate rainforest of southeast Alaska

The interaction of similar soil forming factors with different parent materials determines soil chemical weathering, influencing soil processes and properties. In the coastal temperate rainforests of southeast Alaska, Spodosols is the dominant soil order in well-drained parent materials regardless of the lithology. Yet, the role of lithology on chemical weathering, base cation depletion, and mineral transformation during pedogenesis remains elusive. Here, we established a lithosequence comprised of soils derived from tonalite, phyllite, slate, and metavolcanic rocks to test the hypothesis that despite the soils in southeast Alaska presenting similar taxonomy and morphology, the influence of lithology in chemical weathering intensity and mineralogy can be detected. We evaluated physicochemical properties, clay mineralogy, Fe-Al oxides, chemical weathering, and elemental mass balance on eleven Spodosols sampled along the lithosequence. We also propose a new weathering index, the Weathering Index for Spodosols (WISP), suitable to evaluate the weathering degree of soils with Al mobility and base-rich parent materials. This index fills a gap in traditional weathering indices that uses Al as an immobile element and/or does not evaluate the leaching of main base cations. We found the pedons across the lithosequence expressed similar physicochemical properties, with predominantly thick profiles, presenting andic properties, relatively thick spodic horizons, and thin E horizons. Podzolization imposed similar mineral transformation trends, mainly dissolution of chlorite, mica alteration to interstratified mica-vermiculite, and formation of smectite in E horizons. Kaolinite was detected in the soils from all lithologies, but only in trace amounts in phyllite soils. Fe oxides depth distribution was similar but with a higher concentration in the slate (44.5 ± 4.2 g/kg), followed by metavolcanic (31.6 ± 4.0 g/kg), phyllite (27.9 ± 3.8 g/kg), and tonalite soils (19.9 ± 5.7 g/kg) and predominantly as organometallic complexes (extracted by Na-pyrophosphate). The stronger weathering of chlorite and mica in the E horizons was reflected in larger losses of Mg and K, while Ca and Na were more depleted in the spodic horizons, suggesting plagioclase weathering. Slate soils were the most depleted in base cations, followed by phyllite, tonalite, and metavolcanic soils. The more depleted status of slate soils was supported by the higher WISP values (WISP = 62), followed by phyllite (WISP = 55), tonalite (WISP = 53), and metavolcanic soils (WISP = 45). Our results demonstrated that lithology controlled elemental depletion intensities, Fe oxide concentrations, and partially the clay fraction mineralogy in addition to podzolization acting as a dominant pedogenic process across all sites. These findings advance our understanding of the role of lithology on soil mineralogy/geochemistry that impacts critical soil functions, such as the soil carbon cycle, elemental fluxes, organo-mineral interactions, and solid-solution reactions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

3D Deep Learning Joint Inversion of Active Seismic Full Waveform and Passive Seismic Traveltime Data for Reservoir Imaging and Uncertainty Quantification

Here, we present deep learning (DL) networks for three-dimensional (3D) joint inversion of active seismic full waveform and passive seismic traveltime data to image reservoirs and their properties and quantify imaging uncertainties. Active seismic full-waveform data can provide high-resolution monitoring images but are collected only intermittently because of their high acquisition cost. In contrast, passive seismic data can be gathered at relatively low cost between regular active surveys, although their imaging quality can be compromised by factors such as low signal-to-noise ratios and limited ray coverage of the target. Although these datasets are routinely acquired together at CO 2 storage sites, their combined inversion within a 3D DL framework has not been previously demonstrated. To our knowledge, this is the first study to address this gap, combining the strength of both data types. For efficient data storage and DL training with large 3D seismic datasets, we use a 3D data matrix in which a random number of passive seismic traveltime data are stored as parabolic envelopes using one-hot encoding and a 3D full-waveform data matrix in which multiple shot gathers are summed. Two network architectures are evaluated: a single-encoder U-Net for single-data type inversion and a dual-encoder U-Net for joint inversion of active and passive seismic data. We also evaluate the single-encoder U-Net for joint inversion by concatenating full-waveform data and traveltime data. We propose a systematic approach for selecting an optimal dropout rate that balances regularization during training and Monte Carlo dropout-based uncertainty quantification during prediction by examining the correlation coefficient between standard deviation and prediction error, along with the training misfit, across a range of dropout rates. 3D DL inversion experiments include five different network configurations, with evaluations under ideal, noisy and dropout-enabled conditions. Both model and data uncertainties are assessed, as well as their combined effects. Across all conditions, the networks consistently predict accurate CO 2 saturation models with low prediction errors, such as a structural similarity index of 0.993 and CO 2 difference of 1.1%. Uncertainty estimates show strong spatial correlation with prediction errors, confirming the effectiveness of the proposed dropout selection approach. The results demonstrate that our DL approach, utilizing compact data representations and appropriate uncertainty quantification, yields accurate subsurface images under various inversion conditions and provides valuable insights into the reliability of predictions.

Um, Evan Schankee [Lawrence Berkeley National Labo↗
NASA NTRS2006

Evaluation of Daytime Measurements of Aerosols and Water Vapor made by an Operational Raman Lidar over the Southern Great Plains

Raman lidar water vapor and aerosol extinction profiles acquired during the daytime over the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in northern Oklahoma (36.606 N, 97.50 W, 315 m) are evaluated using profiles measured by in situ and remote sensing instruments deployed during the May 2003 Aerosol Intensive Operations Period (IOP). The automated algorithms used to derive these profiles from the Raman lidar data were first modified to reduce the adverse effects associated with a general loss of sensitivity of the Raman lidar since early 2002. The Raman lidar water vapor measurements, which are calibrated to match precipitable water vapor (PWV) derived from coincident microwave radiometer (MWR) measurements were, on average, 5-10% (0.3-0.6 g/m(exp 3) higher than the other measurements. Some of this difference is due to out-of-date line parameters that were subsequently updated in the MWR PWV retrievals. The Raman lidar aerosol extinction measurements were, on average, about 0.03 km(exp -1) higher than aerosol measurements derived from airborne Sun photometer measurements of aerosol optical thickness and in situ measurements of aerosol scattering and absorption. This bias, which was about 50% of the mean aerosol extinction measured during this IOP, decreased to about 10% when aerosol extinction comparisons were restricted to aerosol extinction values larger than 0.15 km(exp -1). The lidar measurements of the aerosol extinction/backscatter ratio and airborne Sun photometer measurements of the aerosol optical thickness were used along with in situ measurements of the aerosol size distribution to retrieve estimates of the aerosol single scattering albedo (omega(sub o)) and the effective complex refractive index. Retrieved values of omega(sub o) ranged from (0.91-0.98) and were in generally good agreement with omega(sub o) derived from airborne in situ measurements of scattering and absorption. Elevated aerosol layers located between about 2.6 and 3.6 km were observed by the Raman lidar on May 25 and May 27. The airborne measurements and lidar retrievals indicated that these layers, which were likely smoke produced by Siberian forest fires, were primarily composed of relatively large particles (r(sub eff) approximately 0.23 micrometers), and that the layers were relatively nonabsorbing (omega(sub o) approximately 0.96-0.98). Preliminary results show that major modifications that were made to the Raman lidar system during 2004 have dramatically improved the sensitivity in the aerosol and water vapor channels and reduced random errors in the aerosol scattering ratio and water vapor retrievals by an order of magnitude.

Ferrare, Richard↗
DOE OSTI2023

DEEPEN Global Standardized Categorical Exploration Datasets for Magmatic Plays

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), weights needed to be developed for use in the weighted sum of the different favorability index models produced from geoscientific exploration datasets. This was done using two different approaches: one based on expert opinions, and one based on statistical learning. This GDR submission includes the datasets used to produce the statistical learning-based weights. While expert opinions allow us to include more nuanced information in the weights, expert opinions are subject to human bias. Data-centric or statistical approaches help to overcome these potential human biases by focusing on and drawing conclusions from the data alone. The drawback is that, to apply these types of approaches, a dataset is needed. Therefore, we attempted to build comprehensive standardized datasets mapping anomalies in each exploration dataset to each component of each play. This data was gathered through a literature review focused on magmatic hydrothermal plays along with well-characterized areas where superhot or supercritical conditions are thought to exist. Datasets were assembled for all three play types, but the hydrothermal dataset is the least complete due to its relatively low priority. For each known or assumed resource, the dataset states what anomaly in each exploration dataset is associated with each component of the system. The data is only a semi-quantitative, where values are either high, medium, or low, relative to background levels. In addition, the dataset has significant gaps, as not every possible exploration dataset has been collected and analyzed at every known or suspected geothermal resource area, in the context of all possible play types. The following training sites were used to assemble this dataset: - Conventional magmatic hydrothermal: Akutan (from AK PFA), Oregon Cascades PFA, Glass Buttes OR, Mauna Kea (from HI PFA), Lanai (from HI PFA), Mt St Helens Shear Zone (from WA PFA), Wind River Valley (From WA PFA), Mount Baker (from WA PFA). - Superhot EGS: Newberry (EGS demonstration project), Coso (EGS demonstration project), Geysers (EGS demonstration project), Eastern Snake River Plain (EGS demonstration project), Utah FORGE, Larderello, Kakkonda, Taupo Volcanic Zone, Acoculco, Krafla. - Supercritical: Coso, Geysers, Salton Sea, Larderello, Los Humeros, Taupo Volcanic Zone, Krafla, Reyjanes, Hengill. **Disclaimer: Treat the supercritical fluid anomalies with skepticism. They are based on assumptions due to the general lack of confirmed supercritical fluid encounters and samples at the sites included in this dataset, at the time of assembling the dataset. The main assumption was that the supercritical fluid in a given geothermal system has shared properties with the hydrothermal fluid, which may not be the case in reality. Once the datasets were assembled, principal component analysis (PCA) was applied to each. PCA is an unsupervised statistical learning technique, meaning that labels are not required on the data, that summarized the directions of variance in the data. This approach was chosen because our labels are not certain, i.e., we do not know with 100% confidence that superhot resources exist at all the assumed positive areas. We also do not have data for any known non-geothermal areas, meaning that it would be challenging to apply a supervised learning technique. In order to generate weights from the PCA, an analysis of the PCA loading values was conducted. PCA loading values represent how much a feature is contributing to each principal component, and therefore the overall variance in the data.

15 GEOTHERMAL ENERGY↗