Improvement of Aerosol Optical Depth Data for Localized Solar Forecasting
This presentation provides an overview of the improvement of aerosol optical depth data for localized solar forecasting.
14 SOLAR ENERGY↗
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Improvement of aerosol optical depth data for localized solar resource assessment
Solar irradiance, especially for direct normal irradiance (DNI), is sensitive to the atmospheric aerosols, which can strongly extinguish DNI over areas with elevated aerosol loadings. The National Solar Radiation Database (NSRDB), developed by the National Renewable Energy Laboratory (NREL), uses AOD from MERRA-2 reanalysis and is further downscaled from 0.5° to 2-km based on elevation. However, the elevation-based downscaling may not accurately represent AOD, especially over the areas with large AOD gradients. This study examined whether the 1-km MODIS MAIAC satellite-retrieved AOD product can better represent the AOD distribution and be used to improve solar irradiance assessment. We focused on areas with relatively high AOD over North America (California, and New York City, US, and Mexico City in Mexico, in particular). The evaluation of MAIAC AOD and MERRA-2 AOD against ground-truth AERONET AOD shows that MAIAC AOD exhibits smaller RMSE by about 0.05 and higher correlation coefficient by 0.1-0.6 than MERRA-2 AOD. In addition, MERRA-2 AOD shows larger negative bias. The simulated DNI using MAIAC AOD (DNIMAIAC) and using MERRA-2 AOD (DNINSRDB) were evaluated with DNI observations. The results show the performance of DNIMAIAC is better than that of DNINSRDB with smaller RMSE by 0.5-1.5%, smaller positive mean bias by 0.8-3.1% and comparable correlation for the 3 sites analyzed. Overall, 1-km MAIAC AOD shows higher accuracy than 2-km elevation-based MERRA-2 AOD, leading to better performance of the simulated DNI using MAIAC AOD. Therefore, 1-km MAIAC AOD can be used to improve the accuracy of solar resource assessment.
14 SOLAR ENERGY↗
Block Island Environmental Monitoring Data (Weather, Waves & Sensor Depth)
This dataset contains meteorological, oceanographic, and sensor-depth data collected near Block Island during the 2016 and 2017 RODEO field seasons to provide environmental context for concurrent acoustic and survey operations. It includes buoy-derived wind and wave time series, in-situ water temperature logger records, and depth (pressure) records from a sensor mounted on the vertical line array.
17 WIND ENERGY↗
Semi-automatic image annotation using 3D LiDAR projections and depth camera data
Efficient image annotation is necessary to utilize deep learning object recognition neural networks in nuclear safeguards, such as for the detection and localization of target objects like nuclear material containers (NMCs). This capability can help automate the inventory accounting of different types of NMCs within nuclear storage facilities. The conventional manual annotation process is labor-intensive and time-consuming, hindering the rapid deployment of deep learning models for NMC identifications. This paper introduces a novel semi-automatic method for annotating 2D images of nuclear material containers (NMCs) by combining 3D light detection and ranging (LiDAR) data with color and depth camera images collected from a handheld scan system. The annotation pipeline involves an operator manually marking new target objects on a LiDAR-generated map, and projecting these 3D locations to images, thereby automatically creating annotations from the projections. The semi-automatic approach significantly reduces manual efforts and the expertise in image annotation that is required to perform the task, allowing deep learning models to be trained on-site within a few hours. The paper compares the performance of models trained on datasets annotated through various methods, including semi-automatic, manual, and commercial annotation services. The evaluation demonstrates that the semi-automatic annotation method achieves comparable or superior results, with a mean average precision (mAP) above 0.9, showcasing its efficiency in training object recognition models. Additionally, the paper explores the application of the proposed method to instance segmentation, achieving promising results in detecting multiple types of NMCs in various formations.
98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
Data for "Depth of nutrient uptake by deep-rooted plants is regulated by water availability"
The data set consists of strontium (Sr) isotope ratios (87Sr/86Sr), water isotopes, soil cation concentrations, soil water potential sensor data, and results of 87Sr/86Sr mixing model. The plant canopy size files include the dataset of canopy dimension of sagebrush, lupine, and sunflower. The soil and plant ICPMS (Inductively Coupled Plasma Mass Spectrometry) data file includes both of 87Sr/86Sr, and cation concentration dataset from soil exchangeable pool, apatite pool, silicate extract, atmospheric rain deposition, and plant leaf and stem tissues. The plant dendrochronology file includes the dendrochronogical ring width of several sagebrush, and dendrochemical sample data includes the 87Sr/86Sr for each separated growth ring. The modeling result gives the proportion of nutrient sources of each plants (based on their 87Sr/86Sr in leaf tissues and growth rings) from atmospheric deposition and mineral weathering. Soil water potential data includes continuous collection of soil water potential dataset at 2 depths (30 cm and 60 cm, from Nov 24 - Jun 25) of the sampling site. All the samples were collected from 2 sampling campaign June and July 2023, and rain water is a separate sampling from Aug - Sept 2023, at north-facing hillslope near pumphouse site. The data showed that the depth of cation nutrient acquisition is thus tightly coupled with, and likely determined by, water availability in soil, saprolite and bedrock. The enhanced uptake of cations and water from regions of mineral weathering could confer plant and ecosystem resilience during low water years and may impact the rate of bedrock weathering and watershed chemistry during drought. This dataset includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type; a location metadata file (locations.csv); and a samples metadata file (samples.csv). All files are provided as comma-separated values (CSV) files (.csv). This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.
54 ENVIRONMENTAL SCIENCES↗
Soil temperature and soil moisture raw data, permafrost table depths, and accompanying environmental variable data, Kenai Wildlife Refuge, 2019-2022
Data package purpose: This data package was created to contain all data used in an upcoming article, "Canopy Cover and Microtopography Control Precipitation-Enhanced Thaw of Ecosystem-Protected Permafrost." In review.This data package includes: Raw output from 19 distributed temperature profilers with a thermistor every 10 cm along a 160 cm length at a measurement interval of 15 minutes (.CSV). Raw output from two soil moisture and temperature profilers (90 cm length and 120 cm length) that took composite soil moisture readings every 15 cm along the sensor length at a measurement interval of 30 minutes (.CSV). Permafrost depths were measured annually in mid-September at DTP sensor locations (.CSV) and along an across-site transect (.CSV). Environmental variables (snow depth, canopy closure, moss depth, and elevation) for all sensor locations. Real-time kinetic (RTK) GPS points showing site microtopography (.CSV).Analysis software: Our analysis was done in Matlab. File types can be used with any software.
54 ENVIRONMENTAL SCIENCES↗
Spatial Study 2022: Surface Water Samples, Cotton Strip Degradation, and Hydrologic Sensor Data across the Yakima River Basin, Washington, USA (v3)
This dataset supports a broader study examining the drivers of spatial variability in sediment respiration rates in the Yakima River Basin. The dataset provides data and photos generated from sample collection during the same one-week period at 48 sites within multiple rivers throughout the Yakima River Basin in Washington, USA. The contents include surface water geochemistry data; river substrate grain size photos; stream depth data; manual chamber open channel respiration data; and field metadata (including qualitative information on instream and river corridor characteristics). Grain size photos can be used to improve estimates of channel substrate D50 data. The dataset also includes tensile strength and photos from cotton strip field degradation experiments; five-week sensor time series temperature, dissolved oxygen, pressure, pH, specific conductance, chlorophyll A, and turbidity data; plots of the sensor data; and R scripts used to generate the plots. Samples collected during this study were labeled as “Second Spatial Study” or “SSS.” A subset of data from the SSS samples were published in the contiguous United States (CONUS)-Scale Model-Sample (CM) study data package available at https://data.ess-dive.lbl.gov/view/doi:10.15485/1923689 that presents data from across the CONUS. SSS data published in the CM data package were not included in this data package. They include dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC), total nitrogen (TN), grain size, aerobic sediment respiration, dissolved oxygen (DO), and temperature. Parent IDs and Site IDs are consistent between the SSS and CM data packages, and they can be mapped directly so data across packages can be used together. Additionally, sensor data from a similar 2021 spatial study can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1892052 and 2021 sample data can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1898914. The 2021 spatial study had some sites in common with this 2022 spatial study. This dataset is comprised of three photo folders and one main data folder with six subfolders. The photo folders contain photographs and videos of cotton strip retrieval and sediment quadrats. The main data folder consists of (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) total suspended solids (TSS) data and cotton strip tensile strength data and averages; (5) field protocol; (6) readme; (7) methods codes; (8) international generic sample number (IGSN) mapping file; (9) sensor installation methods summary; (10) stream depth and averages; and (11) Ultrameter data and averages. The Sonar subfolder consists of Sonar time-series depth data and a processing script. The BarotrollAtm, DepthHOBO, MantaRiver, miniDOT, and miniDOTManualChamber subfolders contain time-series data, plots, and summary files. All files are .csv, .pdf, .txt, .R, .Rmd, .jpg, .jpeg, .AVI, .mp4, or .mov. The data package was originally published in April 2023. It was updated in August 2023 (v2; modified files) and September 2024 (v3; modified files). See the change history section in the readme for details. We acknowledge the Yakama Nation as owners and caretakers of the lands where we collected these data. We thank the Confederated Tribes and Bands of the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate sample collection and optimization of data usage according to their values and worldview.
54 ENVIRONMENTAL SCIENCES↗
Five Years of Dissolved Oxygen, Temperature, Salinity, Depth, Weather Data from a Transitioning Wetland at Beaver Creek, Washington, USA
Groundwater dissolved oxygen (DO) variability in coastal system remains poorly understood despite its importance for biogeochemical cycling and ecosystem modeling. Here we investigate the temporal variability in groundwater DO and its hydro-climatic drivers across hourly to seasonal timescales in a transitioning wetland at Beaver Creek, Washington, USA. The site is transitioning from a freshwater forest to a brackish tidal wetland following removal of a barrier in 2014 that prevented tides from accessing the freshwater creek. By utilizing novel optical dissolved oxygen instrumentation (Opti O2, LLC) we obtained continuous, high-frequency (5-minute), in-situ measurements of DO from the flood-plain from June 26th, 2019 through September 30th, 2024. This 63 month dataset is comprised of groundwater dissolved oxygen, temperature, water level and salinity timeseries from the floodplain. This dataset also includes rainfall, air pressure, air temperature, and solar radiation data collected with a co-located Campbell ClimaVUE50 weather sensor. All data is contained within a single csv (2019-06-26 to 2024-09-30 Beaver Creek DO, saln, BGS, temp, weather.csv) that can easily be viewed either using software such as Excel or using any text editor.
54 ENVIRONMENTAL SCIENCES↗
Schneider Springs Fire Study 2023 for Ecosystem Respiration Rates: Surface Water Chemistry and Hydrologic Sensor Data across the Yakima River Basin, Washington, USA (v2)
This dataset supports a broader study examining the drivers of spatial variability in wildfire impacts across the Yakima River Basin. Data provided within this dataset were generated from sample collection across 17 total sites (8 sites affected by a recent wildfire, 9 sites unaffected by a recent wildfire) within multiple rivers throughout the Yakima River Basin in Washington, USA from May-July 2023. Fire affected sites are defined as those affected by the 2021 Schneider Springs Fire, based on the drainage area of the streams being within the 2021 Schneider Springs Fire burn perimeter or not (Figure 1, below). The contents include surface water geochemistry data (dissolved organic carbon; total dissolved nitrogen; total suspended solids); short-term sonde data (specific conductivity; turbidity; pH; chlorophyll A; temperature); stream depth data; stream velocity; manual chamber open channel respiration data; sensor time-series data (oxygen; water pressure; barometric pressure); field metadata (including qualitative information on in stream and river corridor characteristics); and environmental context photos taken in the field. The dataset also includes a summary file of the sensor data and plots of the sensor data. Sensors were only recovered at 15 out of the 17 sites, and not all sensors were recovered at all 15 sites (see Methods section for more details), therefore all data does not exist at all sites. Data from a 2022 study at the same sites, as well as additional sites, can be found at https://data.ess-dive.lbl.gov/view/doi:10.15485/1969566. The data package was originally published in November 2023. It was updated in June 2025 (v2; modified files). See the change history section in the readme for more details. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. This dataset is comprised of one folder with field photos and one main data folder with two subfolders. The main data folder consists of (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) field protocol; (5) readme; (6) international generic sample number (IGSN) mapping file; and (7) stream depth and averages. The sensor data subfolder consists of (1) sensor installation methods summary; (2) stream velocity; and (3) six subfolders. The BarotrollAtm (barometric pressure; temperature), DepthHOBO (water pressure; temperature), MantaRiver (specific conductivity; turbidity; pH; chlorophyll A; temperature), EXO (specific conductivity; pH; temperature), miniDOT (dissolved oxygen; temperature), and miniDOTManualChamber (dissolved oxygen; temperature) contain time-series data, plots, and summary files. The sample data subfolder consists of (1) total suspended solids (TSS) data; (2) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (3) total dissolved nitrogen (TN) data and averages; and (4) methods codes. All files are .csv, .pdf, .jpg, .jpeg, or .mov.
54 ENVIRONMENTAL SCIENCES↗
A method of determining ablation depth from free surface velocities in laser induced ablation experiments
The generation of laser ablation depth data in the ultrafast (100ps) time regime is important for the validation of radiation hydrodynamic codes in that time regime. We present a technique using data from a velocimetry diagnostic to determine the hot electron penetration depth into a metal sample.
47 OTHER INSTRUMENTATION↗
Pit depth analysis using automated software for three containers with complete imaging
The 3013 surveillance program is tasked with ensuring the long term safety of plutonium storage in 3013-compliant containers. One component of this task is to understand corrosion features found on the interior sidewall of the inner container closure weld region (ICCWR). To this end, three containers were selected to have laser confocal microscope (LCM) 20X imaging of the entire ICCWR. This report describes the analysis of the LCM pit depth data using the automated corrosion analysis software. The analysis included determining maximum pit depths and distributions of pit depths, volumes, and diameters for all pit features identified on the three containers. Results provided insight into possible corrosion mechanisms and were consistent with previous pit growth model predictions.
36 MATERIALS SCIENCE↗
Fast Semi-automated Filtration Method for Non-targeted LC-QTOF Data of Aged Nitroplasticizer Samples
A full dataset of aged nitroplasticizer (NP) is composed of more than 2000 unique mass-to-charges (m/z) when combining the non-targeted data obtained from both positive and negative electrospray ionization modes in time-of-flight mass spectrometry. Therefore, manual processing of these data often takes days, weeks, or even months to scrutinize for mechanistic insights. To effectively extract meaningful signals that represent vital degradation intermediates in the early NP degradation mechanism, a semi-automated postprocessing workflow for data filtering, tailored to the aging experiment of NP, has been developed. The automated portion of this workflow is written in a Python code (using pandas, numpy, and matplotlib libraries), which removes more than 65% of potential false signals within seconds via four threshold-based adjustable filters: signal sensitivity, coefficient of variation, number of measurements, and retention time variability. As for the manual portion, a pattern-based inspection method is employed to reduce another 23% or more false positives, which greatly simplifies data visualization and results in less than 3% of potential candidate m/z needing in-depth data interpretation. As a positive control, known compounds are verified. Using this semi-automated data reduction method, the amount of time required is reduced to a matter of hours for data filtering in the non-targeted datasets of aged NP, which saves more time and effort for compound identification.
36 MATERIALS SCIENCE↗
Impact of genotype-calling methodologies on genome-wide association and genomic prediction in polyploids
This dataset contains all data used in the paper "Impact of genotype-calling methodologies on genome-wide association and genomic prediction in polyploids". The dataset includes genotypes and phenotypic data from two autotetraploid species Miscanthus sacchariflorus and Vaccinium corymbosum that was used used for genome wide association studies and genomic prediction and the scripts used in the analysis. In this V2, 2 files have the raw data are added: "Miscanthus_sacchariflorus_RADSeq.vcf" is the VCF file with the raw SNP calls of the Miscanthus sacchariflorus data used for genotype calling using the 6 genotype calling methods. "Blueberry_data_read_depths.RData" is the a RData file with the read depth data that was used for genotype calling in the Blueberry dataset.
allelic dosage↗
Evaluating proxy influence in assimilated paleoclimate reconstructions – Testing the exchangeability of two ensembles of spatial processes
Climate field reconstructions (CFR) attempt to estimate spatiotemporal fields of climate variables in the past using climate proxies such as tree rings, ice cores, and corals. Data Assimilation (DA) methods are a recent and promising new means of deriving CFRs that optimally fuse climate proxies with climate model output. Despite the growing application of DA-based CFRs, little is understood about how much the assimilated proxies change the statistical properties of the climate model data. To address this question, we propose a robust and computationally efficient method, based on functional data depth, to evaluate differences in the distributions of two spatiotemporal processes. In this study, we apply our test to study global and regional proxy influence in DA-based CFRs by comparing the background and analysis states, which are treated as two samples of spatiotemporal fields. We find that the analysis states are significantly altered from the climate-model-based background states due to the assimilation of proxies. Moreover, the difference between the analysis and background states increases with the number of proxies, even in regions far beyond proxy collection sites. Our approach allows us to characterize the added value of proxies, indicating where and when the analysis states are distinct from the background states.
54 ENVIRONMENTAL SCIENCES↗
Reference Site Condition Datasets for Floating Wind Arrays in the United States
Floating offshore wind farm design is highly site-specific, requiring detailed information about the specific conditions of a project area for realistic design studies. Unfortunately, publicly available site condition data for potential floating offshore wind project sites in the United States is scarce. To support U.S. offshore wind research, we developed reference site condition datasets, including metocean and seabed information, for four potential floating wind project areas in the U.S.: Humboldt Bay, Morro Bay, the Gulf of Maine, and the Gulf of Mexico. These datasets were compiled using publicly available data. Our metocean analysis, covering wind, waves, and surface currents, utilized measurement data from 2000 to 2020. Sources included the National Renewable Energy Laboratory’s National Offshore Wind Dataset for wind data, National Data Buoy Center buoys for wave data, and the High Frequency Radar Network for surface currents. These data were integrated into hourly time series used to compute extreme return periods up to 500 years, monthly statistics, and joint probability clusters for fatigue analysis. Soil conditions were evaluated using the usSEABED database and bathymetry grids were interpolated from the NCEI Digital Elevation Model Global Mosaic. Further information on the datasets and how they were created can be found in: Biglu, M., M. Hall, E. Lozon, S. Housner. 2024. Reference Site Conditions for Floating Wind Arrays in the United States. Golden, CO: National Renewable Energy Laboratory (NREL). NREL/TP-5000-89897. The data are also available at: https://github.com/FloatingArrayDesign/SiteConditions The content of each dataset is as follows: _NOW23_wind.txt: Hourly NOW-23 wind data up to a height of 400 meter. _metocean_1hr.txt: Hourly time series including wind, wave, surface current and temperature data. _Summary.xlsx: Metocean data, including extreme values, joint probability distributions and monthly statistics. _usSEABED_soil.csv: Extract of the usSEABED database for this specific site. _bathymetry_200m.txt (and 500m, 1000m): Gridded seabed depth data.
16 TIDAL AND WAVE POWER↗
Utah FORGE: 16B(78)-32 RFS DSS Strain Change Rate vs. Depth During 16A(78)-32 Stimulation
This dataset contains strain change rate versus depth data acquired using a Rayleigh frequency shift (RFS) distributed strain sensing (DSS) system during hydraulic stimulation of well 16A(78)-32 at the Utah FORGE site in April 2024. The data were collected from an optical fiber installed in the annulus of production well 16B(78)-32, approximately 300 feet from the injection well. The dataset includes tabulated strain data and an explanation of the methodology used to generate the frac log, which integrates strain change rate signals over selected time windows to identify fracture events.
15 GEOTHERMAL ENERGY↗
Quarterly Soil Core and Root Analyses from the Missouri Ozarks AmeriFlux (MOFLUX) Site, Ashland, Missouri, 2017-2023
This dataset contains quarterly soil core measurements from the Missouri Ozarks AmeriFlux (MOFLUX) site located at the University of Missouri’s Thomas H. Baskett Wildlife Research and Education Area near Ashland, Missouri. These data will be used to parameterize an ensemble of MOFLUX-optimized soil carbon-nitrogen models, used to simulate carbon (C) and nitrogen (N) cycling responses to future hydroclimatic scenarios and the trajectory of soil C stocks with concomitant forest decline. Beginning in 2017, eight soil cores were collected approximately quarterly near plot 1 of the southeast transect, near the automated soil respiration flux chambers, from 0–15 cm depth. Data are currently available through 2023 (2017-06-14 to 2023-11-13); additional observations will be appended to this dataset as they become available. Cores were analyzed for gravimetric moisture content, pH, total carbon and nitrogen, texture, microbial biomass carbon and nitrogen, and extractable dissolved organic carbon and nitrogen. This dataset contains one data file in comma separate (*.csv) format. Additional metadata are provided: one data dictionary and a file-level metadata file in comma separate (*.csv) format and a user guide in PDF (*.pdf) format.
54 ENVIRONMENTAL SCIENCES↗
Scanning SQUID characterization of extremely overdoped La 2-x Sr x CuO 4
Recently, advances in film synthesis methods have enabled a study of extremely overdoped La 2 - x Sr x Cu O 4 . This has revealed a surprising behavior of the superfluid density as a function of doping and temperature, the explanation of which is vividly debated. One popular class of models posits electronic phase separation, where the superconducting phase fraction decreases with doping, while some competing phase (e.g., ferromagnetic) progressively takes over. A problem with this scenario is that all the way up to the dome edge the superconducting transition remains sharp, according to mutual inductance measurements. However, the physically relevant scale is the Pearl penetration depth Λ P , and this technique probes the sample on a length scale L that is much larger than Λ P . In the present paper, we use local scanning superconducting quantum interference device (SQUID) measurements that probe the susceptibility of the sample on the scale L << Λ P . Our SQUID maps show uniform landscapes of susceptibility and excellent overall agreement of the local penetration depth data with the bulk measurements. Overall, these results contribute an important piece to the puzzle of how high-temperature superconductivity vanishes on the overdoped side of the cuprate phase diagram.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗