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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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DOE OSTI2018

Developing a landscape of urban building energy use with improved spatiotemporal representations in a cool-humid climate

Urban buildings account for up to 75% of total energy use in the United States (U.S.). Understanding urban building energy use is important for developing feasible options to mitigate energy use and greenhouse gas emissions. In this study, an improved bottom-up building energy use model, named City Building Energy Use Model (City-BEUM), was developed to estimate building energy use for all buildings in Polk County, Iowa. First, 28 commercial and 6 residential building prototypes were designed by combing Assessor's parcel data and building footprint data. Then, the EnergyPlus in the CityBEUM was calibrated for all building prototypes using the U.S. Energy Information Administration's survey data, monthly utility meter data, and actual weather data. Finally, spatial and temporal variations of building energy use in the study area were estimated using the CityBEUM. Results indicate that the spatial variation of building energy use in the study area can be captured using the CityBEUM. With the monthly-calibrated model, the temporal pattern of urban building energy use can be well represented. The comparison of building energy use using the Typical Meteorological Year and actual weather data demonstrates the importance of using actual weather data in building energy modeling for an improved temporal representation.

CityBEUM, Urban building energy use, High spatiote↗
DOE OSTI2018

Tonopah Test Range Air Monitoring: CY2017 Meteorological, Radiological, and Wind Transported Particulate Observations

In 1963, the U.S. Department of Energy (DOE) (formerly the Atomic Energy Commission [AEC]), implemented Operation Roller Coaster on the Tonopah Test Range (TTR) and an adjacent area of the Nevada Test and Training Range (NTTR) (formerly the Nellis Air Force Range). This operation resulted in radionuclide-contaminated soils at the Double Tracks site and Clean Slate I, II, and III sites. This report documents observations made during ongoing monitoring of radiological, meteorological, and dust conditions at stations installed adjacent to the Clean Slate sites and at the TTR Sandia National Laboratories (SNL) Range Operations Center (ROC). The primary objective of the monitoring effort is to support the DOE Environmental Management Nevada Program (EM NV) in the safe cleanup of the environmental legacy of nuclear device development and testing by determining if wind blowing across the Clean Slate sites is transporting particles of radionuclide-contaminated soil beyond the physical and administrative boundaries of the sites. The monitoring program underwent a significant change during 2017 with the decommissioning of one monitoring station and the establishment of three new stations. Station 400, located within TTR Area 3 and near the ROC, monitors conditions near the local workforce center and was in operation the full calendar year. Stations 401 and 403 are located on the northwest and southeast perimeter fence lines, respectively, of the Clean Slate III site. Station 401 operated throughout 2017, whereas Station 403 began operation at the end of April 2017. Stations 404 and 405 are along the northern and eastern boundary fence, respectively, at Clean Slate II and both began data collection at the end of April 2017. Also at the end of April, the Clean Slate I monitoring site, Station 402, was decommissioned. The stations are located to be generally downwind of the contaminated areas during either north-northwesterly or south-southeasterly winds, which are the predominant wind directions. The stations—which are similar in design to the Community Environmental Monitoring Program (CEMP) stations operating at locations surrounding the Nevada National Security Site and TTR—include meteorological instruments and continuous-flow, low-volume air samplers. Three pressurized ionization chambers for measuring gamma energy are in use at Stations 400, 401, and 403 (formerly at 402). Saltation sensors and saltation traps are deployed at the stations adjacent to the Clean Slate sites. Detailed meteorological data are recorded on data loggers, with periodic uploads via a satellite system to the Western Regional Climate Center (WRCC) at Desert Research Institute (DRI), to monitor instrument and site conditions. Air filter samples are collected every two weeks and the deposits in the saltation traps are collected when a sufficient sample mass for analysis has accumulated (generally a six- to eight-month interval). Dust transport by suspension and saltation is strongly dependent on wind speed. Concentrations of PM10 (particulate matter of aerodynamic diameter ≤10 micrometers [μm], a size fraction of small particles that are suspended in the air and can be easily inhaled) remain low until winds exceed approximately 32 km/hr (20 mph). Saltation particle counts also increase above the same general threshold wind speed. Wind speeds in excess of 32 km/hr (20 mph) occur less than three percent of the time. High winds are associated with two predominant directions: north-northwest and south. In 2017, the very highest winds were from the north, but all winds above 40 km/hr (25 mph) were approximately evenly split between south and northwest directions. The low-volume air samplers collect total suspended dust, and saltating particles are captured by the passive saltation traps, both for radiological analysis. Radionuclide monitoring at the ROC in TTR Area 3 (Station 400) found natural background levels of gross alpha and gross beta radiation. Similar background levels were found on filter samples analyzed using gamma and alpha spectroscopy. The gamma exposure rate data from pressurized ionization chambers (PIC) are comparable to those from the regional CEMP stations. Plutonium concentrations above background values were measured during 2017 from soil samples collected in passive saltation traps and on select air filters from the monitoring stations adjacent to the Clean Slate sites. The background for soils in the region is assumed to be 0.014 pCi/g plutonium-239+240 ( 239+240 Pu) as measured by Turner et al. (2003), whereas the background concentration of suspended 239+240Pu is assumed to be 9.5 x 10 -19 μCi/ml, which was determined by continuous air monitoring at the TTR airport in 1996 and 1997 (SNL, 1998). Background concentrations are exceeded by one to four orders of magnitude by the majority of samples from Clean Slate I, II, and III. The gross alpha and gross beta measurements of the air filters are also generally higher than those measured at the regional CEMP stations, although the gamma exposure rates determined by the PICs located at Clean Slate I and III are similar to CEMP measurements. The highest radionuclide concentrations of suspended dust are associated with individual filter samples from Clean Slate II Station 404 and Clean Slate III Station 401. Both of the stations are downwind of their respective sites during southerly winds, but they are also geographically closer to the highly contaminated detonation areas than the other monitoring stations. Considering all the analyzed samples, the 239+240Pu concentrations are generally higher for the Clean Slate II samples compared with those from Clean Slate III. The highest gross alpha and gross beta results are also observed for the Clean Slate II stations. The highest radionuclide concentrations are measured on the smaller (≤63 μm) saltation-sized particles and particulate matter collected downwind of each site. Nonetheless, traps oriented to collect dust moving toward the contamination areas also had plutonium concentrations significantly above background values, which is consistent with observations of low-level contamination dispersed beyond the fenced area. All the observed 239+240 Pu concentrations are below the risk-based action level (25 millirem per year, equivalent to a 239+240 Pu concentration of 4,120 pCi/g for an industrial worker scenario; U.S. DOE, 2014). The meteorological and particulate matter monitoring indicate that conditions for wind-borne contaminant movement exist at the Clean Slate sites, and that transport of radionuclide-contaminated soil by both suspension and saltation is occurring. The Corrective Action Unit (CAU) closure strategy uses a risk-based approach, whereby acceptable contaminant concentrations are determined as a function of anticipated human exposure. This strategy is consistent with the fence lines at the Clean Slate sites encircling areas of higher concentration and the observed presence of lower concentrations of contamination outside the fences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

An ML-based terrestrial data fusion and augmentation framework to enable advanced understanding of the terrestrial carbon and water interactions

Soil moisture is essential to the terrestrial carbon and water cycles and land–atmosphere interactions. There are various types of soil moisture data, and each type has the distinct spatiotemporal strengths and limitations, depending on the diverse applications and retrieval methodologies of different data types (Li et al., in review; The PNNL-82151 FY23 Report). However, the limitations of different soil moisture data in terms of accuracy and spatiotemporal coverage hinder our ability to further understand the soil moisture dynamics across scales. To have a gap free soil moisture data product with a fine spatiotemporal coverage and vertical profiles, we train extreme gradient boosting (XGBoost) models by using (1) in-situ soil moisture measurements from the International Soil Moisture Network (ISMN), (2) soil moisture from the ECMWF reanalysis (ERA) at the 9 km and sub-daily spatiotemporal resolution, (3) the Daymet meteorological fields, and (4) data products that characterize surface conditions, including soil texture, organic content, topography, vegetation type, and rooting depth. We use the trained XGBoost models that have consistent performance across seven soil layers, i.e., 0–5 cm, 5–10 cm, 10–20 cm, 20–40 cm, 40–60 cm, 60–100 cm, and 100–200 cm, and the gridded model predictors to generate a soil moisture data at the 1 km and daily spatiotemporal resolution for the Continental United States (CONUS) from 2001–2020. This dataset can be broadly used for Earth system model benchmark, monitoring extreme weathers, making informed decisions regarding agriculture, water resource management, climate change mitigation, and ecosystem preservation.

58 GEOSCIENCES↗
DOE OSTI2025

Advancing stream temperature prediction with a generalizable large-sample framework across CONUS river reaches

Accurately predicting stream temperature in ungauged basins remains a critical challenge for water resource management, thermoelectric power plant cooling, and ecosystem conservation. Large-sample machine learning models trained on hundreds of well-monitored river basins have shown remarkable performance; however, such models have yet to be developed solely using forcing data that can be readily extracted to simulate stream temperatures anywhere in the contiguous United States (CONUS). In this study, we present a scalable, large-sample deep learning framework using Long Short-Term Memory (LSTM) networks to simulate daily stream temperatures in ungauged basins across the CONUS. The framework leverages both modeled reanalysis of meteorological and streamflow inputs as well as static attributes available for all 2.7 million CONUS river reaches in the National Hydrography Dataset Plus (NHDPlusV2). By generating dynamical inputs from predefined thermally relevant upstream contributing areas, rather than the entire upstream basin, the model also offers improvements in very large basins where full-basin averaging can dilute the most important influences on stream temperature. Evaluated across 300 basins, the model achieves a median Mean Absolute Error (MAE) of 1.1 °C and a Nash-Sutcliffe Efficiency (NSE) of 0.95 on temporally and spatially distinct test folds—comparable to models trained exclusively using meteorological and streamflow observational data. The flexible, high-performing framework generalizes to any unmonitored river reach without significant regulation or unnatural thermal input immediately upstream, substantially expanding predictive capabilities in data-scarce regions.

Hydrology↗
DOE OSTI2023

Architecture of a Data Portal for Publishing and Delivering Open Data for Atmospheric Measurement

Atmospheric data are collected by researchers every day. Campaigns such as GOAmazon 2014/2015 and the Amazon Tall Tower Observatory collect essential data on aerosols, gases, cloud properties, and meteorological parameters in the Brazilian Amazon basin. These data products provide insights and essential information for analyzing and predicting natural processes. However, in Brazil, it is estimated that more than 80% of the scientific data collected are not published due to the lack of web portals that collect and store these data. This makes it difficult, or even impossible, to access and integrate the data, which can result in the loss of significant amounts of information and significantly affect the understanding of the overall data. To address this problem, we propose a data portal architecture and open data deployment that enable Big Data processing, human interaction, and download-oriented approaches with tools that help users catalog, publish and visualize atmospheric data. Thus, we describe the architecture developed, based on the experience of the Atmospheric Radiation Measurement Data Center, which incorporates the principles of FAIR, the infrastructure and content management system for managing scientific data. The portal partial results were tested with environmental data from contaminated areas at the University of São Paulo. Overall, this data portal creates more shared knowledge about atmospheric processes by providing users with access to open environmental data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Data and scripts from: “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”

This data package includes data and scripts from the manuscript “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”.The study addressed common challenges faced in environmental sensing and modeling, including uncertain input data, missing sensor observations, and high-dimensional datasets with interrelated but redundant variables. Point-scaled meteorological and soil sensor observations were perturbed with noises and missing values, and denoising autoencoder (DAE) neural networks were developed to reconstruct the perturbed data and further predict evapotranspiration. This study concluded that (1) the reconstruction quality of each variable depends on its cross-correlation and alignment to the underlying data structure, (2) uncertainties from the models were overall stronger than those from the data corruption, and (3) there was a tradeoff between reducing bias and reducing variance when evaluating the uncertainty of the machine learning models.This package includes:(1) Four ipython scripts (.ipynb): “DAE_train.ipynb” trains and evaluates DAE neural networks, “DAE_predict.ipynb” makes predictions from the trained DAE models, “ET_train.ipynb” trains and evaluates ET prediction neural networks, and “ET_predict.ipynb” makes predictions from trained ET models.(2) One python file (.py): “methods.py” includes all user-defined functions and python codes used in the ipython scripts.(3) A “sub_models” folder that includes five trained DAE neural networks (in pytorch format, .pt), which could be used to ingest input data before being fed to the downstream ET models in ‘ET_train.ipynb” or ‘ET_predict.ipynb’.(4) Two data files (.csv). Daily meteorological, vegetation, and soil data is in “df_data.csv”, where “df_meta.csv” contains the location and time information of “df_data.csv”. Each row (index) in “df_meta.csv” corresponds to each row in “df_data.csv”. These data files are formatted to follow the data structure requirements and be directly used in the ipython scripts, and they have been shuffled chronologically to train machine learning models. The meteorological and soil data was collected using point sensors between 2019-2023 at(4.a) Three shrub-dominated field sites in East River, Colorado (named “ph1”, “ph2” and “sg5” in “df_meta.csv”, where “ph1” and “ph2” were located at PumpHouse Hillslopes, and “sg5” was at Snodgrass Mountain meadow) and(4.b) One outdoor, mesoscale, and herbaceous-dominated experiment in Berkeley, California (named “tb” in “df_meta.csv”, short for Smartsoils Testbed at Lawrence Berkeley National Lab).- See "df_data_dd.csv" and "df_meta_dd.csv" for variable descriptions and the Methods section for additional data processing steps. See "flmd.csv" and "README.txt" for brief file descriptions.- All ipython scripts and python files are written in and require PYTHON language software.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Design and application of a mobile ground-based observatory for continuous measurements of atmospheric trace gas and criteria pollutant species

Ground-based measurements of atmospheric trace gas species and criteria pollutants are essential for understanding emissions dynamics across space and time. Gas composition in the lower 50 m of the atmosphere has the greatest direct impacts on human health as well as ecosystem processes; hence data at this level are necessary for addressing carbon-cycle- and public-health-related questions. However, such surface data are generally associated with stationary measurement towers, where spatial representation is limited due to the high cost of establishing and maintaining an extensive network of measurement stations. We describe here a compact mobile laboratory equipped to provide high-precision, high-frequency, continuous, on-road synchronous measurements of CO 2 , CO, CH 4 , H 2 O, NO x , O 3 , aerosol, meteorological, and geospatial position data. The mobile laboratory has been deployed across the western USA. In addition to describing the vehicle and its capacity, we present data that illustrate the use of the laboratory as a powerful tool for investigating the spatial structure of urban trace gas emissions and criteria pollutants at spatial scales ranging from single streets to whole ecosystem and regional scales. We assess the magnitude of known point sources of CH 4 and also identify fugitive urban CH 4 emissions. We illustrate how such a mobile laboratory can be used to better understand emissions dynamics and quantify emissions ratios associated with trace gas emissions from wildfire incidents. Lastly, we discuss additional mobile laboratory applications in health and urban metabolism.

Bush, S. E.↗
DOE OSTI2022

Resolving temperature limitation on spring productivity in an evergreen conifer forest using a model–data fusion framework

Abstract. The flow of carbon through terrestrial ecosystems and the response to climate are critical but highly uncertain processes in the global carbon cycle. However, with a rapidly expanding array of in situ and satellite data, there is an opportunity to improve our mechanistic understanding of the carbon (C) cycle's response to land use and climate change. Uncertainty in temperature limitation on productivity poses a significant challenge to predicting the response of ecosystem carbon fluxes to a changing climate. Here we diagnose and quantitatively resolve environmental limitations on the growing-season onset of gross primary production (GPP) using nearly 2 decades of meteorological and C flux data (2000–2018) at a subalpine evergreen forest in Colorado, USA. We implement the CARbon DAta-MOdel fraMework (CARDAMOM) model–data fusion network to resolve the temperature sensitivity of spring GPP. To capture a GPP temperature limitation – a critical component of the integrated sensitivity of GPP to temperature – we introduced a cold-temperature scaling function in CARDAMOM to regulate photosynthetic productivity. We found that GPP was gradually inhibited at temperatures below 6.0 ∘C (±2.6 ∘C) and completely inhibited below −7.1 ∘C (±1.1 ∘C). The addition of this scaling factor improved the model's ability to replicate spring GPP at interannual and decadal timescales (r=0.88), relative to the nominal CARDAMOM configuration (r=0.47), and improved spring GPP model predictability outside of the data assimilation training period (r=0.88). While cold-temperature limitation has an important influence on spring GPP, it does not have a significant impact on integrated growing-season GPP, revealing that other environmental controls, such as precipitation, play a more important role in annual productivity. This study highlights growing-season onset temperature as a key limiting factor for spring growth in winter-dormant evergreen forests, which is critical in understanding future responses to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2010

Solar Resource & Meteorological Assessment Project (SOLRMAP): Observed Atmospheric and Solar Information System (OASIS); Tucson, Arizona (Data)

The U.S. Department of Energy's National Renewable Energy Laboratory collaborates with the solar industry to establish high quality solar and meteorological measurements. This Solar Resource and Meteorological Assessment Project (SOLRMAP) provides high quality measurements to support deployment of power projects in the United States. The no-funds-exchanged collaboration brings NREL solar resource assessment expertise together with industry needs for measurements. The end result is high quality data sets to support the financing, design, and monitoring of large scale solar power projects for industry in addition to research-quality data for NREL model development. NREL provides consultation for instrumentation and station deployment, along with instrument calibrations, data acquisition, quality assessment, data distribution, and summary reports. Industry participants provide equipment, infrastructure, and station maintenance.

14 SOLAR ENERGY↗
DOE OSTI2017

Spatial Analysis of Stover Moisture Content During Harvest Season in the U.S.

The moisture content of a maturing crop varies as the harvest season progresses. For crop residues such as corn stover, moisture content at the time of harvest can be as high as 75% (wet mass basis) to less than 20% depending on the geographic location (climate conditions) and stage of harvest. Biomass moisture content is critical for baling and extended storage. It is therefore essential to have an estimate of the quantities of corn stover available as wet or dry for various parts of the U.S. To this end, we analyzed hourly weather data (temperature, humidity, and rainfall) from the Typical Meteorological Year v.3 (TMY3) dataset developed by the National Renewable Energy Laboratory. A recently published set of equations for calculating the moisture content of stover as a function of hourly temperature, humidity, and rainfall were used. The annual start and end of corn grain harvest along with annual grain production (in bushels) for each state were extracted from USDA National Agricultural Statistics Service reports. Using these datasets and moisture sorption equations, the percentage of corn stover tonnage with moisture content less than 20%, between 20% and 40%, or greater than 40% was estimated from the length of time that the biomass was in these moisture content ranges. These calculations were carried out for several locations within each of the states for which TMY data were available. It was concluded that about 37.2% of corn stover is dry (<20% moisture content), whereas 36.5% is wet (>40% moisture content) nationwide. The remaining 27.0% of corn stover is between 20% and 40% moisture content. Keywords: Corn stover, Equilibrium moisture content, Field drying, Moisture content, Stover harvest, Typical Meteorological Year data.

09 BIOMASS FUELS↗
DOE OSTI2015

The Initial Atmospheric Transport (IAT) Code: Description and Validation

The Initial Atmospheric Transport (IAT) computer code was developed at Sandia National Laboratories as part of their nuclear launch accident consequences analysis suite of computer codes. The purpose of IAT is to predict the initial puff/plume rise resulting from either a solid rocket propellant or liquid rocket fuel fire. The code generates initial conditions for subsequent atmospheric transport calculations. The Initial Atmospheric Transfer (IAT) code has been compared to two data sets which are appropriate to the design space of space launch accident analyses. The primary model uncertainties are the entrainment coefficients for the extended Taylor model. The Titan 34D accident (1986) was used to calibrate these entrainment settings for a prototypic liquid propellant accident while the recent Johns Hopkins University Applied Physics Laboratory (JHU/APL, or simply APL) large propellant block tests (2012) were used to calibrate the entrainment settings for prototypic solid propellant accidents. North American Meteorology (NAM )formatted weather data profiles are used by IAT to determine the local buoyancy force balance. The IAT comparisons for the APL solid propellant tests illustrate the sensitivity of the plume elevation to the weather profiles; that is, the weather profile is a dominant factor in determining the plume elevation. The IAT code performed remarkably well and is considered validated for neutral weather conditions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Low-CCN concentration air masses over the eastern North Atlantic: Seasonality, meteorology, and drivers

In this paper, a 20 month cloud condensation nucleus concentration (N CCN ) data set from Graciosa Island (39°N, 28°W) in the remote North Atlantic is used to characterize air masses with low cloud condensation nuclei (CCN) concentrations. Low-CCN events are defined as 6 h periods with mean N CCN <20 cm -3 (0.1% supersaturation). A total of 47 low-CCN events are identified. Surface, satellite, and reanalysis data are used to explore the meteorological and cloud context for low-CCN air masses. Low-CCN events occur in all seasons, but their frequency was 3 times higher in December–May than during June–November. Composites show that many of the low-CCN events had a common meteorological basis that involves southerly low-level flow and rather low wind speeds at Graciosa. Anomalously low pressure is situated to the west of Graciosa during these events, but back trajectories and lagged SLP composites indicate that low-CCN air masses often originate as cold air outbreaks to the north and west of Graciosa. Finally, low-CCN events were associated with low cloud droplet concentrations (N d ) at Graciosa, but liquid water path (LWP) during low-CCN events was not systematically different from that at other times. Satellite N d and LWP estimates from MODIS collocated with Lagrangian back trajectories show systematically lower N d and higher LWP several days prior to arrival at Graciosa, consistent with the hypothesis that observed low-CCN air masses are often formed by coalescence scavenging in thick warm clouds, often in cold air outbreaks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A harmonized global nighttime light dataset 1992–2018

Nighttime light (NTL) data from the Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership satellite provide a great opportunity for monitoring human activities from regional to global scales. Despite the valuable records of nightscape from DMSP (1992–2013) and VIIRS (2012–2018), the potential of the historical archive of NTL observations has not been fully explored because of the severe inconsistency between DMSP and VIIRS. In this study, we generated an integrated and consistent NTL dataset at the global scale by harmonizing the inter-calibrated NTL observations from the DMSP data and the simulated DMSP-like NTL observations from the VIIRS data. The generated global DMSP NTL time-series data (1992–2018) show consistent temporal trends. This temporally extended DMSP NTL dataset provides valuable support for various studies related to human activities such as electricity consumption and urban extent dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Development of a Wind Plant Large-Eddy Simulation with Measurement-Driven Atmospheric Inflow: Preprint

This paper details the development of an aeroelastic wind plant model with large-eddy simulation (LES). The chosen LES solver is the Simulator for Wind Farm Applications (SOWFA) based on the OpenFOAM framework, coupled to NREL's comprehensive aeroelastic analysis tool, FAST. An atmospheric boundary layer (ABL) precursor simulation was constructed based on assessments of meteorological tower, lidar, and radar data over a 3-hour window. This precursor was tuned to the specific atmospheric conditions that occurred both prior to and during the measurement campaign, enabling capture of a night-to-day transition in the turbulent ABL. In the absence of height-varying temperature measurements, spatially averaged radar data were sufficient to characterize the atmospheric stability of the wind plant in terms of the shear profile, and near-ground temperature sensors provided a reasonable estimate of the ground heating rate describing the morning transition. A full aeroelastic simulation was then performed for a subset of turbines within the wind plant, driven by the precursor. Analysis of two turbines within the array, one directly waked by the other, demonstrated good agreement with measured time-averaged loads.

17 WIND ENERGY↗
DOE OSTI2018

Anomalous cluster detection in spatiotemporal meteorological fields

Finding anomalous regions in spatiotemporal climate data is an important problem with a need for greater accuracy. The collective and contextual nature of anomalies (e.g., heat waves) coupled with the real‐valued, seasonal, multimodal, highly correlated, and gridded nature of climate variable observations poses a multitude of challenges. Existing anomaly detection methods have limitations in the specific setting of real‐valued areal spatiotemporal data. In this paper, we develop a method for extreme event detection in meteorological datasets that follows from well known distribution‐based anomaly detection approaches. The method models spatial and temporal correlations explicitly through a piecewise parametric assumption and generalizes the Mahalanobis distance across distributions of different dimensionalities. The result is an effective method to mine contiguous spatiotemporal anomalous regions from meteorological fields which improves upon the current standard approach in climatology. The proposed method has been evaluated on a real global surface temperature dataset and validated using historical records of extreme events.

Ramachandra, Bharathkumar↗
DOE OSTI2013

Probabilistic Hazard Assessment for Tornadoes, Straight-line Wind, and Extreme Precipitation at the Savannah River Site

Recent data sets for three meteorological phenomena with the potential to inflict damage on SRS facilities - tornadoes, straight winds, and heavy precipitation - are analyzed using appropriate statistical techniques to estimate occurrence probabilities for these events in the future. Summaries of the results for DOE-mandated return periods and comparisons to similar calculations performed in 1998 by Weber, et al., are given. Using tornado statistics for the states of Georgia and South Carolina, we calculated the probability per year of any location within a 2⁰ square area surrounding SRS being struck by a tornado (the ‘strike’ probability) and the probability that any point will experience winds above set thresholds. The strike probability was calculated to be 1.15E-3 (1 chance in 870) per year and wind speeds for DOE mandated return periods of 50,000 years, 125,000 years, and 1E+7 years (USDOE, 2012) were estimated to be 136 mph, 151 mph and 221 mph, respectively. In 1998 the strike probability for SRS was estimated to be 3.53 E-4 and the return period wind speeds were 148 mph every 50,000 years and 180 mph every 125,000 years. A 1E+7 year tornado wind speed was not calculated in 1998; however a 3E+6 year wind speed was 260 mph. The lower wind speeds resulting from this most recent analysis are largely due to new data since 1998, and to a lesser degree differences in the models used. By contrast, default tornado wind speeds taken from ANSI/ANS-2.3-2011 are somewhat higher: 161 mph for return periods of 50,000 years, 173 mph every 125,000 years, and 230 mph every 1E+7 years (ANS, 2011). Although the ANS model and the SRS models are very similar, the region defined in ANS 2.3 that encompasses the SRS also includes areas of the Great Plains and lower Midwest, regions with much higher occurrence frequencies of strong tornadoes. The SRS straight wind values associated with various return periods were calculated by fitting existing wind data to a Gumbel distribution, and extrapolating the values for any return period from the tail of that function. For the DOE mandated return periods, we expect straight winds of 123 mph every 2500 years, and 132mph every 6250 years at any point within the SRS. These values are similar to those from the W98 report (which also used the Gumbel distribution for wind speeds) which gave wind speeds of 115mph and 122 mph for return periods of 2500 years and 6250 years, respectively. For extreme precipitation accumulation periods, we compared the fits of three different theoretical extreme-value distributions, and in the end decided to maintain the use of the Gumbel distribution for each period. The DOE mandated 6-hr accumulated rainfall for return periods of 2500 years and 6250 years was estimated as 7.8 inches and 8.4 inches, respectively. For the 24-hr rainfall return periods of 10,000 years and 25,000 years, total rainfall estimates were 10.4 inches and 11.1 inches, respectively. These values are substantially lower than comparable values provided in the W98 report. This is largely a consequence of the W98 use of a different extreme value distribution with its corresponding higher extreme probabilities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Probabilistic Hazard Assessment for Tornadoes, Straight-Line Wind, and Extreme Precipitation at the Savannah River Site

Recent data sets for three meteorological phenomena with the potential to inflict damage on SRS facilities – tornadoes, straight-line winds, and heavy precipitation – are analyzed using appropriate statistical techniques to estimate the occurrence probabilities for these events in the future. Summaries of the results for DOE-mandated return periods and comparisons to similar calculations performed in 2013 by Werth et al. (W2013) are given. Using tornado statistics for i) the combined states of Georgia and South Carolina, and ii) a 2⁰ square area surrounding SRS, we calculated the probability per year of any location at SRS being struck by a tornado (the ‘strike’ probability) and the probability that any point will experience winds above set thresholds. The strike probability was calculated to be 7.04E-4 (1 chance in 1420) per year and tornadic wind speeds for DOE mandated return periods of 50,000 years (corresponding to wind design category 3 (WDC-3), and 125,000 years (meeting WDC-4) (USDOE, 2016) were estimated to be 132 mph and 147 mph, respectively. By contrast, default tornado wind speeds taken from ANSI/ANS-2.3-2011 are somewhat higher: 161 mph for return periods of 50,000 years and 173 mph every 125,000 years (ANS, 2011). Although the ANS and the SRS evaluation used the same basic model (Ramsdell and Rishel, 2007), the region defined in ANS 2.3 that encompasses the SRS also includes areas of the Great Plains and lower Midwest, regions with much higher occurrence frequencies of strong tornadoes. The SRS straight-line wind values associated with various return periods were calculated by fitting existing wind data to a GEV1 distribution and extrapolating the values for any return period from the tail of that function. For the DOE mandated return periods, we expect straight-line winds of 117 mph every 2500 years (the required WDC-3 standard) and 125 mph every 6250 years (WDC-4) at any point within the SRS. These values are similar to those from the ANS-2.3-2011 report, which has wind speeds of 125mph and 133 mph for return periods of 2500 years and 6250 years, respectively. For extreme precipitation, we compared the fits of two different theoretical extreme-value distributions and applied the one that fit the data best for each of several accumulation periods. The DOE mandated 6-hr accumulated rainfall for return periods of 10,000 years (corresponding to precipitation design category 3 (PDC-3) and 25,000 years (PDC-4) were estimated as 9.1 inches and 10.1 inches, respectively. For the 24-hr rainfall return periods of 10,000 years and 25,000 years, total rainfall estimates were 12.02 inches and 13.17 inches, respectively, higher than comparable values provided in the W2013 report.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Robustness of energy performance of Zero-Net-Energy (ZNE) homes

Zero-net-energy (ZNE) homes produce an adequate amount of energy on-site to meet their energy demand based on source energy for an entire year. California building energy efficiency standards require new residential buildings started in 2020 to be ZNE. For various reasons, a home designed as ZNE may not achieve ZNE performance in real operation. Here we aimed to quantify the robustness of the energy performance of ZNE homes due to weather variability, climate change, and the uncertainty of occupant behavior. A single-family ZNE house, based on the optimal cost-effective design in three California climate zones, was used to develop the EnergyPlus simulation models. Weather variations were considered from a combination of the historical 30 years’ actual meteorological year (AMY) weather data, typical year weather data in TMY3, and future weather data based on Intergovernmental Panel on Climate Change scenarios. Three scenarios of occupant behavior from the energy perspective were defined to represent the uncertainty about occupants’ activities, comfort requirements, and their adaptive interactions with buildings and systems. In terms of annual source energy, the simulation results of the ZNE homes showed: (1) a decrease of 23–38 percent for occupants with energy austerity behavior and an increase of 120–130 percent for occupants with energy wasteful behavior, compared with the baseline assumption of normal occupants; (2) a variation range of –15 percent to +14 percent for the results using 30-year AMY weather data compared with the baseline results using TMY3 weather data; (3) an increase of 10–13 percent with future weather in Fresno and Riverside and a decrease of 15 percent with San Francisco; and (4) climate change can reduce the gap between the austerity and wasteful consumption. These findings provide insights into how ZNE homes may perform in reality and inform architects, engineers, occupants, and policymakers to pay more attention to occupant behavior on design, operation, and regulations of ZNE homes to ensure energy performance robustness.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗