Surface Meteorological Station - ANL 10m tower, Walla Walla - Raw Data
Basic meteorological measurements
17 WIND ENERGY↗
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Surface Meteorological Station - ANL 10m tower, Walla Walla - Reviewed Data
Basic meteorological measurements
17 WIND ENERGY↗
Simulated Real-World Energy Impacts of a Thermally Sensitive Powertrain Considering Viscous Losses and Enrichment: Preprint
It is widely understood that cold ambient temperatures increase vehicle fuel consumption due to heat transfer losses, increased friction (increased viscosity lubricants), and enrichment strategies (accelerated catalyst heating). However, relatively little effort has been dedicated to thoroughly quantifying these impacts across a large set of real world drive cycle data and ambient conditions. This work leverages experimental dynamometer vehicle data collected under various drive cycles and ambient conditions to develop a simplified modeling framework for quantifying thermal effects on vehicle energy consumption. These models are applied over a wide array of real-world usage profiles and typical meteorological data to develop estimates of in-use fuel economy. The paper concludes with a discussion of how this integrated testing/modeling approach may be applied to quantify real-world, off-cycle fuel economy benefits of various technologies.
33 ADVANCED PROPULSION SYSTEMS↗
Preliminary Thermal Modeling of HI-STORM 100 Storage Modules at Diablo Canyon Power Plant ISFSI
Thermal analysis is being undertaken at Pacific Northwest National Laboratory (PNNL) in support of inspections of selected storage modules at various locations around the United States, as part of the Used Fuel Disposition Campaign of the U.S. Department of Energy, Office of Nuclear Energy (DOE-NE) Fuel Cycle Research and Development. This report documents pre-inspection predictions of temperatures for two modules at the Diablo Canyon Power Plant ISFSI identified as candidates for inspection. These are HI-STORM 100 modules of a site-specific design for storing PWR 17x17 fuel in MPC-32 canisters. The temperature predictions reported in this document were obtained with detailed COBRA-SFS models of these storage systems, with the following boundary conditions and assumptions. • storage module overpack configuration based on FSAR documentation of HI-STORM100S-218, Version B; due to unavailability of site-specific design data for Diablo Canyon ISFSI modules • Individual assembly and total decay heat loadings for each canister, based on at-loading values provided by PG&E, “aged” to time of inspection using ORIGEN modeling - Special Note: there is an inherent conservatism of unquantified magnitude – informally estimated as up to approximately 20% -- in the utility-supplied values for at-loading assembly decay heat values • Axial decay heat distributions based on a bounding generic profile for PWR fuel. • Axial location of beginning of fuel assumed same as WE 17x17 OFA fuel, due to unavailability of specific data for WE17x17 STD and WE 17x17 Vantage 5 fuel designs • Ambient conditions of still air at 50°F (10°C) assumed for base-case evaluations - Wind conditions at the Diablo Canyon site are unquantified, due to unavailability of site meteorological data - additional still-air evaluations performed at 70°F (21°C), 60°F (16°C), and 40°F (4°C), to cover a range of possible conditions at the time of the inspection. (Calculations were also performed at 80°F (27°C), for comparison with design basis assumptions.) All calculations are for steady-state conditions, on the assumption that the surfaces of the module that are accessible for temperature measurements during the inspection will tend to follow ambient temperature changes relatively closely. Comparisons to the results of the inspections, and post-inspection evaluations of temperature measurements obtained in the specific modules, will be documented in a separate follow-on report, to be issued in a timely manner after the inspection has been performed.
Site Inspection↗
Simulating the temperature and precipitation signal in an Alpine ice core
Accumulation and δ 18 O data from Alpine ice cores provide information on past temperature and precipitation. However, their correlation with seasonal or annual mean temperature and precipitation at nearby sites is often low. This is partly due to the irregular sampling of the atmosphere by the ice core (i.e. ice cores almost only record precipitation events and not dry periods) and the possible incongruity between annual layers and calendar years. Using daily meteorological data from a nearby station and reanalyses, we replicate the ice core from the Grenzgletscher (Switzerland, 4200 m a.s.l.) on a sample-by-sample basis by calculating precipitation-weighted temperature (PWT) over short intervals. Over the last 15 yr of the ice core record, accumulation and δ 18 O variations can be well reproduced on a sub-seasonal scale. This allows a wiggle-matching approach for defining quasi-annual layers, resulting in high correlations between measured quasi-annual δ 18 O and PWT. Further back in time, the agreement deteriorates. Nevertheless, we find significant correlations over the entire length of the record (1938–1993) of ice core δ 18 O with PWT, but not with annual mean temperature. This is due to the low correlations between PWT and annual mean temperature, a characteristic which in ERA-Interim reanalysis is also found for many other continental mid-to-high-latitude regions. The fact that meteorologically very different years can lead to similar combinations of PWT and accumulation poses limitations to the use of δ 18 O from Alpine ice cores for temperature reconstructions. Rather than for reconstructing annual mean temperature, δ 18 O from Alpine ice cores should be used to reconstruct PWT over quasi-annual periods. This variable is reproducible in reanalysis or climate model data and could thus be assimilated into conventional climate models.
54 ENVIRONMENTAL SCIENCES↗
Seismic Noise Induced by Wind Turbine Operation and Wind Gusts
Improved seismic noise characterization, due to varied sources, may benefit traditional applications. Some examples are earthquake detection, Earth structure research, and nuclear testing. This improvement could also permit use of seismic data in transdisciplinary research such as wind gust detection and wind turbine (WT) condition monitoring. However, it is a challenging task to unambiguously quantify relationships between potential sources of seismic noise and the actual seismic response. In this article, we analyze seismic and meteorological data (wind speed and pressure), measured at a remote site in a complex terrain area in eastern Portugal, to examine seismic signatures from WT operational status and wind gusts. Results presented herein show the following: (1) WT signatures in seismic data can be used to accurately determine WT-operational status. Attenuation of WT signatures in seismic data exhibits an exponential decay with distance, with attenuation coefficients that scale with frequency. (2) After WT signatures are removed from seismic power spectra, wind gust signatures remain. Analyses of these data further indicate that it may be possible to extract quantitative wind gust estimates from seismic data and decompose them into pressure and shear stress drivers of this coupling.
Geochemistry & Geophysics↗
Assessment of Mechanical Draft Cooling Tower Thermal Emissions from Visual Images of Plumes
Using a one-dimensional code, we computed the power (enthalpy discharge rate) of a twelve-cell mechanical draft cooling tower (MDCT) using over two hundred visible condensed water vapor plume volume measurements derived from images, weather data, and tower operating conditions. The plume images were simultaneously captured by multiple stationary digital cameras surrounding the cooling tower. An analysis technique combining structure from motion (SfM), a neural-network-based image segmentation algorithm, and space carving was used to quantify the volumes. Afterwards, the power output was computed using novel techniques in the one-dimensional code that included cooling tower exhaust plume adjacency effects implemented with a modified version of the entrainment function, weather data averaged from eleven stations, and fan operations at the times when plume volumes were measured. The model was then compared with the averaged observed power output, and it validated well with an average error ranging from 6 to 12%, depending on the meteorological data used in the simulations. This methodology can possibly determine power plant fuel consumption rates by applying visible imagery.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Characterization of Speciated Aerosol Direct Radiative Forcing Over California
A fully coupled meteorology-chemistry model (WRF-Chem) with added capability of diagnosing the spatial and seasonal distribution of radiative forcings for individual aerosol species over California is used to characterize the radiative forcing of speciated aerosols in California. Model simulations for the year of 2005 are evaluated with various observations including meteorological data from California Irrigation Management Information System (CIMIS), aerosol mass concentrations from US EPA Chemical Speciation Network (CSN) and Interagency Monitoring of Protected Visual Environments (IMPROVE), and aerosol optical depth from AErosol RObotic NETwork (AERONET) and satellites. The model well captures the observed seasonal meteorological conditions over California. Overall, the simulation is able to reproduce the observed spatial and seasonal distribution of mass concentration of total PM2.5 and the relative contribution from individual aerosol species, except the model significantly underestimates the surface concentrations of organic matter (OM) and elemental carbon (EC), potentially due to uncertainty in the anthropogenic emissions of OM and EC and the outdated secondary organic aerosol mechanism used in the model. A sensitivity simulation with anthropogenic EC emission doubled significantly reduces the model low bias of EC. The simulation reveals high anthropogenic aerosol loading over the Central Valley and the Los Angeles metropolitan regions and high natural aerosol (dust) loading over southeastern California. The seasonality of aerosol surface concentration is mainly determined by vertical turbulent mixing, ventilation, and photochemical activity, with distinct characteristics for individual aerosol species and between urban and rural areas. The simulations show that anthropogenic aerosols dominate the aerosol optical depth (AOD). The ratio of AOD to AAOD (aerosol absorption optical depth) shows distinct seasonality with a winter maximum and a summer minimum. Aerosol radiative forcing is presented along with the contribution from individual aerosol species from the simulation with anthropogenic EC emission doubled. On statewide average over California, aerosol reduces the seasonal-average surface radiation fluxes by about 3 W m-2 with a maximum of 10 W m-2 in summer. In the atmosphere, aerosol introduces a warming effect of about 2 W m-2 with a maximum of 10 W m-2 also in summer. EC and dust contribute about 75-95% and 1-10% of the total warming through the seasons, respectively. At the top of atmosphere (TOA), the overall aerosol radiative effect is cooling with a maximum of -3.5 W m-2. EC contributes exclusively to the TOA warming of up to about 0.7 W m-2. The encouraging performance of WRF-Chem in simulating aerosols and their radiative forcing suggests that the model is suitable for further investigation of the impact of emission control on radiative forcing and regional climate over California.
aerosol↗
Spectral and Microclimate Measurements in Relation to Gold-Standard Methods Field Campaign Report
Arable is an agricultural data and analytics company offering an Internet of things (IOT)-enabled irrigation management tool, weather station, and crop monitor: the Arable Mark. The device synthesizes both climate and plant data to produce actionable insights on natural resource management, mainly commercial agriculture at any scale. Arable collected 11 months of microclimate and radiometric data at the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observatory to compare measurements of the Arable Mark to the ARM-SGP gold-standard sensors, and for demonstrating the ease of installation and system value to stakeholders. A member from Arable labs installed the device on January 10, 2018, at the ARM facilities and the site managers were in charge of maintaining the sensors in a clean state until the unit’s removal on January 15, 2019. The sampling was positively uneventful, except for a period during the winter when the device stopped posting data for about one week. The verdict was that the equipment required an external solar panel to stay powered through the shorter days of winter. A solar panel was shipped to the SGP, and the unit came back to full charge to continue sending data for the rest of the sampling period. The main goal of the project is to use the data collected by the Arable Mark and build a model that can establish a close relationship with the current methodologies applied to measure ecosystem response to environmental changes. During the sampling period, our team discovered that there was a drift in some of the meteorological data and also in the radiometry, so all efforts are focused into the development of models that would reduce the errors in the reported values.
Southern Great Plains, evapotranspiration, photosy↗
Multi-seasonal measurements of the ground-level atmospheric ice-nucleating particle abundance on the North Slope of Alaska
Atmospheric ice-nucleating particles (INPs) are an important subset of aerosol particles that are responsible for the heterogeneous formation of ice crystals. INPs modulate the arctic cloud phase (liquid vs. ice), resulting in implications for radiative feedbacks. The number of arctic INP studies investigating specific INP episodes or sources increased recently. However, existing studies are based on short-duration field data, and long-term datasets are lacking. Continuous, long-term measurements are key to determining the abundance and variability of ambient arctic INPs and constraining aerosol–cloud interactions, e.g., to verify and/or improve simulations of mixed-phase clouds. Here, we present a new long-duration INP dataset from the Arctic: 2 years of predominantly immersion-mode INP concentrations (n INP ) measured continuously at the National Oceanic and Atmospheric Administration's Barrow Atmospheric Baseline Observatory (BRW) on the North Slope of Alaska. A portable ice nucleation experiment chamber (PINE-03), which simulates adiabatic expansion cooling, was used to directly measure the ground-level INP abundance with an approximately 12 min time resolution from October 2021 to December 2023. We document PINE-03 n INP measurements as well as estimated ice nucleation active surface site density (n s ) over a wide range of heterogeneous freezing temperatures from −16 to −31 °C from which we introduce new season-specific parameterizations suitable for modeling mixed-phase clouds. Collocated aerosol and meteorological data were analyzed to assess the correlation between ambient n INP , air mass origin region, and meteorological variability. Our findings suggest (1) very high freezing efficiency of INPs across the measured temperatures (n s ≈ 2×10 8 –10 10 m −2 for −16 to −31 °C), which is a factor of 10–1000 times greater efficiency as compared to that found in the previous mid-latitude INP measurements in fall using the same instrument; (2) surprisingly high n INP (≥ 1 L −1 at −25 °C) for the examined temperatures throughout the year that were not measured by PINE-03 at other sites; and (3) high n INP in spring, possibly related to arctic haze episodes. Relatively low concentrations of aerosol surface area and contrasting high-INP concentrations at BRW relative to mid-latitude sites are the possible reasons for the observed high freezing efficiency.
54 ENVIRONMENTAL SCIENCES↗
Timing and duration of hydrological transitions in Arctic polygonal ground from stable isotopes
Land surface models and Earth system models that include Arctic landscapes must capture the abrupt hydrological transitions that occur during the annual thaw and deepening of the active layer. In this work, stable water isotopes (δ 2 H and δ 18 O) are used to appraise hydrologically significant transitions during annual landscape thaw at the Barrow Environmental Observatory (Utqiaġvik, Alaska). These hydrologically significant periods are then linked to annual shifts in the landscape energy balance, deduced from meteorological data and described by the microclimatic periods: Winter, Pre-Melt, Melt, Post-Melt, Summer, and Freeze-Up. The tight coupling of the microclimatic periods with the hydrological transitions supports the use of microclimatic periods as a means of linking polygonal surface water hydrology to meteorological datasets, which provides a mechanism for improving the representation of polygonal surface water hydrology in process-based models. Rayleigh process reconstruction of the isotopic changes revealed that 19% of winter precipitation was lost to sublimation prior to melting and that 23% of surface water was lost to evaporation during the first 10 days post-melt. This agrees with evaporation rates reported in a separate study using an eddy covariance flux tower located nearby. An additional 17% was lost to evaporation during the next 33 days. Stable water isotopes are also used to identify the dominant sources of surface water to various hydrogeomorphological features prevalent in polygonal terrain (a lake, a low centre polygon centre, troughs within the rims of low centre polygons, flat centre polygon troughs, a high centre polygon trough, and drainages). Hydrogeomorphologies that retained significant old water or acted as snow drifts are isotopically distinct during the Melt Period and therefore are easily distinguished. Finally, biogeochemical changes related to the annual thaw are also reported and coupled to the hydrological transitions, which provides insight into the sources and sinks of these ions to and from the landscape.
stable water isotopes↗
Assessing Photovoltaic Capacity Factor Variability Using Long-Term Satellite Derived Solar Resource Data Under Brazilian Climate
Accurate estimation of photovoltaic (PV) energy yield and its variability is essential for reducing financial risk and supporting reliable system planning for rapidly expanding PV markets. In Brazil, high solar adoption and increasing levels of distributed energy resources are beginning to introduce operational challenges such as curtailment and evolving grid requirements. Understanding how natural variability in solar resource propagates into PV system performance is therefore increasingly important for both project design and grid integration. Modern PV yield assessments commonly rely on multi-year meteorological datasets and probabilistic exceedance metrics (e.g., P50/P90) to quantify energy yield uncertainty for project financing. However, the implications of long-term solar resource variability for PV system design choices and high-adoption grid conditions remain less well characterized for rapidly expanding markets such as Brazil. In particular, understanding how weather-driven variability propagates into PV production distributions and capacity factor expectations is important for evaluating curtailment exposure, deployment strategies, and storage requirements in regions experiencing rapid growth of distributed and utility-scale PV. Seasonal and interannual variability in atmospheric conditions can produce substantial fluctuations in monthly PV energy production, which propagate into uncertainty in annual energy yield and capacity factor expectations. Characterizing this variability using long-term meteorological datasets allows probabilistic estimation of PV system performance and provides improved insight into the range of expected PV energy outcomes. This study explores the use of long-term satellite-derived meteorological data from the National Solar Radiation Database (NSRDB) to evaluate the variability of photovoltaic system performance across multiple locations in Brazil. Using a 27-year dataset (1998-2024), PV system simulations are performed to characterize the distribution of annual and seasonal capacity factors and energy yield outcomes, while propagating key sources of meteorological variability and model uncertainty through the PV modeling chain. The analysis also investigates the sensitivity of PV performance outcomes to key system design assumptions within the PV modeling chain, including tracking configuration and system sizing parameters. The resulting probabilistic performance characterization provides insight into how weather-driven variability influences PV production expectations and capacity factor distributions. These results provide a foundation for evaluating how weather-driven variability interacts with high PV adoption and potential storage or curtailment mitigation strategies.
14 SOLAR ENERGY↗
U.S. Agrivoltaics Irradiance Database
This is a foundational data set for research and deployment of agrivoltaics, which is the co-location of agriculture and solar power plants on the same land. This irradiance and shading dataset can be utilized to determine the suitability of agrivoltaics configurations for a given region and crop-type. The data is hourly, 4x4 km resolution across the contiguous United States and Hawaii. It is calculated from the National Solar Radiation Database sites, using the System Advisor Model (SAM) to simulate the shading patterns for 10 common agrivoltaics configurations. Sunlight availability data is reported for 10 locations on the ground between adjacent rows of solar panels, as well as averaged across areas of interest such as the average irradiance in the edge-to-edge open area or across 3-6 planting beds. Other available metrics include the input meteorological data from the NSRDB (e.g. global horizontal irradiance, wind speed, etc.) and estimates for comparing energy and agricultural characteristic across the 10 configurations, including power output per acre or per kW installed capacity and farmable land area per acre.
14 SOLAR ENERGY↗
Watershed Workflow: A toolset for parameterizing data-intensive, integrated hydrologic models
Integrated, distributed hydrologic models leverage advances in computational power and data accessibility to improve predictive understanding of the water cycle. While impressive advances in this area of environmental modeling have been accomplished, such models are still rarely used, partially because of difficulty integrating model and data. This research describes the release of Watershed Workflow version 1.2, a new library aiming to automate and enable complex workflows defining inputs to high resolution, integrated, distributed hydrologic models. Watershed Workflow provides tools enabling the discovery, acquisition, mapping, and coordination of watershed geometry, land cover, soil properties, and meteorological data. It enables the construction of unstructured meshes that incorporate this data, and provides tools for automating a “first” simulation on any watershed in the United States. We present the design of the workflow tool, and describe best practices for its usage, culminating in a final example from watershed specification to simulation at the Coweeta Hydrologic Laboratory.
Integrated hydrologic modeling↗
Module Level Exposure and Evaluation Test (MLEET) for Real- world and Laboratory-based PV Modules: Common Data and Analytics for Quantitative Cross-correlation and Validation
Our objective is to develop a set of lifetime performance prediction models for PV modules. Models were developed based on common I-V, P mp datastreams from modules under outdoor (real-world) exposure and indoor (laboratory-based) accelerated exposures and temporal analytics. These common datastreams that span both indoor and outdoor exposures were supplemented with additional information such as meteorology data, climate and PV module make/model and module specifications. The predictive models were developed using a stressor/mechanism/response framework in which all data are categorized as stressor, mechanism or performance variables and are represented as time-series datastreams. We develop and validate these accelerated indoor exposures and evaluation tests and models and cross-correlate the temporal signatures of PV module degradation mechanisms in the I-V, P mp time-series between outdoor and accelerated indoor tests. The predictive test and model specify indoor and outdoor exposure and data acquisition criteria, variable selection, and temporal duration and variance.
14 SOLAR ENERGY↗
NDVI and Multispectral Images, UAS Flights, Council Road Site, Seward Peninsula, Alaska, June 2022
The dataset contains Normalized Difference Vegetation Index (NDVI) and multispectral images collected on June 18, 2022 using an UAS (Unmanned Aerial System) Yuneec H520, Mapir Survey 3, and Parrot Sequoia cameras. This data was created to accompany the following datasets:- NGEE Arctic CO2, CH4 and Energy Eddy-Covariance (EC) Flux Tower Auxiliary Measurements, Council Road Mile Marker 71, Seward Peninsula, Alaska (https://doi.org/10.5440/1526749)- Sun-induced chlorophyll fluorescence/eddy covariance measurements at NGEE Arctic Council site, Seward Peninsula, Alaska, 2022 (https://doi.org/10.15485/2466175)- NGEE Arctic Meteorological Data from Instrumented PCDC Stations, Council Site, Seward Peninsula, Alaska, 2018 - 2023 (https://doi.org/10.5440/1529604)Survey 3 is RGN (Red, Greeen, Near Infra Red) camera with 87° Horizontal Field of View (HFOV) (19mm) f/2.8 Aperture, 550nm/660nm/850nm (Red+Green+NIR) filter transmission and produced geo referenced jpg and raw format images. Parrot Sequoia is a camera with 16 megapixel RGB (Red, Green, Blue) sensor and 1.2 megapixel monochrome sensors in discrete spectral bands: Green (550nm - 40Nm Bandwidth), Red (660nm - 40nm Bandwidth), Red Edge (735nm - 10nm Bandwidth) and Near Infrared (790nm - 40nm Bandwidth).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) 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↗
The WACMOS-ET project – Part 1: Tower-scale evaluation of four remote-sensing-based evapotranspiration algorithms
The WAter Cycle Multi-mission Observation Strategy – EvapoTranspiration (WACMOS-ET) project has compiled a forcing data set covering the period 2005–2007 that aims to maximize the exploitation of European Earth Observations data sets for evapotranspiration (ET) estimation. The data set was used to run four established ET algorithms: the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL), the Penman–Monteith algorithm from the MODerate resolution Imaging Spectroradiometer (MODIS) evaporation product (PM-MOD), the Surface Energy Balance System (SEBS) and the Global Land Evaporation Amsterdam Model (GLEAM). In addition, in situ meteorological data from 24 FLUXNET towers were used to force the models, with results from both forcing sets compared to tower-based flux observations. Model performance was assessed on several timescales using both sub-daily and daily forcings. The PT-JPL model and GLEAM provide the best performance for both satellite- and tower-based forcing as well as for the considered temporal resolutions. Simulations using the PM-MOD were mostly underestimated, while the SEBS performance was characterized by a systematic overestimation. In general, all four algorithms produce the best results in wet and moderately wet climate regimes. In dry regimes, the correlation and the absolute agreement with the reference tower ET observations were consistently lower. While ET derived with in situ forcing data agrees best with the tower measurements ( R 2 = 0.67), the agreement of the satellite-based ET estimates is only marginally lower ( R 2 = 0.58). Results also show similar model performance at daily and sub-daily (3-hourly) resolutions. Overall, our validation experiments against in situ measurements indicate that there is no single best-performing algorithm across all biome and forcing types. In conclusion, an extension of the evaluation to a larger selection of 85 towers (model inputs resampled to a common grid to facilitate global estimates) confirmed the original findings.
54 ENVIRONMENTAL SCIENCES↗
Enhancing Risk Analysis of Accidental Release Using CFD Modeling and Machine Learning
This project report presents the results from the AU31 project that is entitled “Enhancing Risk Analysis of Accidental Release Using CFD Modeling and Machine Learning” performed between FY2021 and FY2022. The technical objective of this project is to enhance risk analysis of accidental release of radioactive materials and provide novel risk forecasting and capabilities for assessing unplanned hazardous chemical releases for the U.S. Department of Energy (DOE). The approach includes a cross-platform finite element analysis of airborne chemical release, machine learning (ML) for simulation and forecasting, and a big data approach to data management, transformation and analysis capable of handling petabyte-scale unstructured and structured data. The outcome of these findings were assembled and accessible as a final output via an intuitive web hosted geospatial-based user interface for demonstration and general dissemination. These new results also provide a mechanism of combining this knowledge in a risk analysis framework tool. The geospatial analysis and output can utilize 5 years of qualified meteorological data to illustrate projected plume footprints that are generated from computational aspects of this study. Specifically, we show the development of a geospatial risk analysis capabilities to respond to and plan for accidental release of radioactive materials and to provide a risk forecasting tool for assessing hazardous chemical releases for the DOE. The focus of this effort is germane to common practices in risk analysis; and it is highly relevant to the timely and transparent release of information related to materials at risk including hazardous materials and chemical vapors and rapid assessment of the potential impacts on the workforce at active sites across the DOE complex.
42 ENGINEERING↗