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At least 343 records · Page 19
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DOE OSTI2024

Analysis of Ground Source Heat Pump ECMs: Implemented Under DOE ESPC IDIQ Contract

Several federal performance and decarbonization objectives are driving the U.S. government to transition to net-zero emissions across the federal building portfolio. The overarching purpose of this Federal Energy Management Program (FEMP)-Geothermal Technologies Office (GTO) work is to support federal facilities in accomplishing the administration’s federal building decarbonization goals. Creating awareness of existing geothermal (or ground-source) heat pump (GHP) systems deployed on federal property, implemented using performance contracts and evaluating the energy and environmental impacts of these systems, this work will assist federal facilities in considering inclusion of GHP technology energy conservation measures (ECMs) in future performance contracting project awards. This report summarizes analyses of data from GHP ECMs installed under the DOE ESPC IDIQ contract across various climate areas and geographic locations, agencies, and building use types. Through the analysis of proposed GHP ECMs and understanding key metrics on implemented GHP systems in a variety of applications, FEMP and GTO can further support federal agencies in meeting decarbonization and net-zero building goals by evaluating and incorporating GHP systems into performance contracts and communicate the benefits of this technology.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Machine Learning-Driven Quantification of CO2 Plume Dynamics at Illinois Basin Decatur Project Sites Using Microseismic Data

This study utilizes machine learning to quantify CO2 plume extents by analyzing microseismic data from the Illinois Basin Decatur Project (IBDP). Leveraging a unique dataset of well logs, microseismic records, and CO2 injection metrics, this work aims to predict the temporal evolution of subsurface CO2 saturation plumes. The findings illustrate that machine learning can predict plume dynamics, revealing vertical clustering of microseismic events over distinct time periods within certain proximities to the injection well, consistent with an invasion percolation model. The buoyant CO2 plume partially trapped within sandstone intervals periodically breaches localized barriers or baffles, which act as leaky seals and impede vertical migration until buoyancy overcomes gravity and capillary forces, leading to breakthroughs along vertical zones of weakness. Between different unsupervised clustering techniques, K-Means and DBSCAN were applied and analyzed in detail, where K-means outperformed DBSCAN in this specific study by indicating the combination of the highest Silhouette Score and the lowest Davies–Bouldin Index. The predictive capability of machine learning models in quantifying CO2 saturation plume extension is significant for real-time monitoring and management of CO2 sequestration sites. The models exhibit high accuracy, validated against physical models and injection data from the IBDP, reinforcing the viability of CO2 geological sequestration as a climate change mitigation strategy and enhancing advanced tools for safe management of these operations.

Iyegbekedo, Ikponmwosa↗
DOE OSTI2025

Remote Sensing and Fluxes Upscaling for Real-world Impact (Workshop Report)

The "Remote Sensing and Fluxes Upscaling for Real-world Impact" workshop, held on July 9-10, 2024, at Lawrence Berkeley National Lab, was a collaborative effort led by the AmeriFlux Management Project, NEON, and the Carbon Dew Community of Practice. The event brought together over 200 registrants and approximately 100 attendees each day, including leading experts, researchers, and practitioners. The primary focus was on bridging the gap between cutting-edge research and practical applications in environmental monitoring by integrating remote sensing and flux data. Key themes included the importance of site-level measurements for validating remote sensing products, providing nature-based climate solutions, and addressing challenges such as instrument costs and the need for standardized methods. At the regional scale, discussions centered on addressing spatial heterogeneity and using high-resolution remote sensing and machine learning methods to enhance data interpretation. Global scale challenges included data consistency, gap filling, and accurate emission source identification, with opportunities for international collaboration and standardized practices to improve global carbon budget assessments. The workshop emphasized the critical need for integrating data across local, regional, and global scales through explicit scale-matching and developed a workflow for scaling flux data using "straight shot" and "explicit nesting" approaches. The event highlighted the importance of connecting scientific research with real-world applications in carbon, energy, and water management, ensuring that advancements translate into tangible societal benefits. These insights will guide future research, technology transfer, and collaboration, maximizing the potential of environmental fluxes to address real-world challenges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Hydrology Outweighs Temperature in Driving Production and Export of Dissolved Carbon in a Snowy Mountain Catchment

Abstract Terrestrial production and export of dissolved organic and inorganic carbon (DOC and DIC) to streams depends on water flow and biogeochemical processes in and beneath soils. Yet, understanding of these processes in a rapidly changing climate is limited. Using the watershed‐scale reactive‐transport model BioRT‐HBV and stream data from a snow‐dominated catchment in the Rockies, we show deeper groundwater flow averaged about 20% of annual discharge, rising to ∼35% in drier years. DOC and DIC production and export peaked during snowmelt and wet years, driven more by hydrology than temperature. DOC was primarily produced in shallow soils (1.94 ± 1.45 gC/m 2 /year), stored via sorption, and flushed out during snowmelt. Some DOC was recharged to and further consumed in the deeper subsurface via respiration (−0.27 ± 0.02 gC/m 2 /year), therefore reducing concentrations in deeper groundwater and stream DOC concentrations at low discharge. Consequently, DOC was primarily exported from the shallow zone (1.62 ± 0.96 gC/m 2 /year, compared to 0.12 ± 0.02 gC/m 2 /year from the deeper zone). DIC was produced in both zones but at higher rates in shallow soils (1.34 ± 1.00 gC/m 2 /year) than in the deep subsurface (0.36 ± 0.02 gC/m 2 /year). Deep respiration elevated DIC concentrations in the deep zone and stream DIC concentrations at low discharge. In other words, deep respiration is responsible for the commonly‐observed increasing DOC concentrations (flushing) and decreasing DIC concentrations (dilution) with increasing discharge. DIC export from the shallow zone was ~66% of annual export but can drop to ∼53% in drier years. Numerical experiments suggest lower carbon production and export in a warmer, drier future, and a higher proportion from deeper flow and respiration processes. These results underscore the often‐overlooked but growing importance of deeper processes in a warming climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Refining seasonal performance metrics for room air-conditioning in emerging markets: Integrating building simulations with real-world equipment performance data

Buildings significantly impact worldwide energy consumption, emphasizing the need to reduce the cooling energy demand, especially in warm climates. Minimum energy performance standards (MEPS) and seasonal performance metrics such as the Cooling Seasonal Performance Factor (CSPF) are crucial for improving room air conditioning (RAC) efficiency. However, challenges remain, particularly in emerging markets like Brazil, where seasonal performance metrics have recently been introduced. This study assesses the factors influencing country-level seasonal efficiency metrics and proposes a framework to refine these calculations by considering local climates and expected RAC usage in real-world households via building simulations. Key considerations include outdoor air temperature binning for different climates, RAC usage patterns (i.e., daytime and nighttime usages), envelope thermal performance of households, and urban heat island (UHI) effects. The results reveal that CSPF values can vary significantly based on climate conditions, with observed CSPF ranging from 4.10 to 11.59 Wh/Wh across 577 Brazilian climates. The inclusion of UHI effects led to a reduction in CSPF values by up to 29% during nighttime operations in hot urban areas. Additionally, building envelope efficiency showed contrasting impacts on RAC performance, with CSPFs reaching up to 15.35 Wh/Wh under specific optimized conditions. These findings highlight the need for transparent policymaking in RAC performance databases, facilitating the application of approaches like those proposed in this study and supporting diverse stakeholders in decision-making.

Bavaresco, Mateus↗
DOE OSTI2024

Recommendations for an Applicant to Calculate Activity Data for Greenhouse Gases Estimates

In 2009, the U.S. Nuclear Regulatory Commission (NRC) directed the NRC staff to address climate change issues and consider the impacts of the emissions of carbon dioxide (CO 2 ) and other greenhouse gases (GHGs) in its environmental reviews for major licensing actions (NRC 2009b). To implement this direction from the Commission, the staff issued guidance in 2011 and updated guidance in 2014 in Attachment 1 to Interim Staff Guidance COL/ESP-ISG-026 (NRC 2011; NRC 2014). This guidance provides a simpler method than the method described in RG 4.2 Rev. 3, that an applicant can use to meet the plant parameter envelope (PPE) value from the Generic Environmental Impact Statement for Licensing of New Nuclear Reactors (NR GEIS). NRC staff estimated the 97-year lifecycle GHG emissions from a reference 1000 megawatt electrical (MWe) light-water reactor (LWR) for various activities associated with construction, operation (including uranium fuel cycle), and decommissioning of nuclear power plants and presented the results in Appendix H of the NR GEIS. Appendix H of the NR GEIS includes estimates of direct emissions from construction equipment and emergency diesel engines in a nuclear facility and indirect emissions from workforce vehicular traffic, fuel transportation and the uranium fuel cycle. The NR GEIS Section 3.3 extended the estimates in Appendix H for the installation of two 1000 MWe nuclear reactors on the same site. Scaling factors were used to extrapolate the GHG emissions of a reference 1000 MWe reactor to a two-unit nuclear reactor plant (each reactor unit generating 1000 MWe). GHG emission estimates for building, operation, decommissioning and safe storage (SAFSTOR) for a two-unit nuclear reactor plant would be based on the plant’s physical size, and therefore estimates for these source categories were assumed to be twice the value of the reference 1000 MWe reactor. However, GHG emissions from the fuel cycle (including fuel transportation) were scaled upward by a factor of 3, based on plant efficiencies greater than the 80 percent assumption in Appendix H. Table 1 below shows the PPE emissions for two 1000 MWe nuclear reactors as provided in NR GEIS. The total GHG emissions for two 1000 MWe reactors were calculated as 2,534,000 metric tons (MT) of CO 2 equivalent (CO 2 (e)) based on a 97 year GHG life cycle period. The GHG emissions lifetime of 97 years for a reference nuclear reactor includes a 7-year building phase, 40 years of operation, 10 years of active decommissioning, and 40 years of SAFSTOR operations (NRC 2024). Construction equipment and vehicular traffic from workers commute would contribute to the GHG emissions during a 7-year building phase. Uranium fuel cycle, vehicular traffic, fuel and waste transportation, and testing of standby diesel generators would contribute to GHG emissions during the 40-year operations phase. While NRC’s regulations allow up to 60 years of reactor facility decommissioning, Appendix H estimated that most of the GHGs would occur over an estimated 10-year period during which to the licensee would engage in significant demolition and earth-moving activities, as discussed in Supplement 1 to NUREG-0586 (NRC 2002). Vehicular traffic by the workforce during a 40-year SAFSTOR period would additionally contribute GHG emissions. The carbon footprint for a 40-year SAFSTOR period was separately analyzed from the decommissioning activities as provided in Table YYYY-2 of the staff issued guidance in 2011 (NRC 2011).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Simulating Hurricane Katrina in the Simple Cloud‐Resolving E3SM Atmosphere Model v1

Climate models are important tools for advancing understanding and prediction of tropical cyclones (TCs). Traditional global climate models, however, do not have the ability to properly simulate TC intensity due to their coarse horizontal resolution. Regional models can be run at convection‐permitting resolutions, but these models are often strongly influenced by the data used in the lateral boundary forcing, and domain choice can have a large impact on the simulation. Cloud‐resolving global climate models have demonstrated great potential for realism in TC simulations, and in this study we focus specifically on the Simple Cloud‐Resolving Energy Exascale Earth System Model (E3SM) Atmosphere Model (SCREAM) v1 configuration. We evaluate SCREAMv1 against the observational record and the Weather Research and Forecasting (WRF) model run at a convection‐permitting resolution with Hurricane Katrina as our case study. We found that both models produced realistic simulations of Hurricane Katrina. SCREAMv1 demonstrated skill in simulating TC track, size, and intensity, while the model produced an excessive amount of precipitation. In comparison, WRF more accurately simulated TC precipitation and intensity, although the TC wind extent was smaller than the observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Physical, socio-psychological, and behavioural determinants of household energy consumption in the UK

Determining which attitudes and behaviours predict household energy consumption can help accelerate the low-carbon energy transition. Conventional approaches in this domain are limited, often relying on survey methods that produce data on individuals’ motivations and self-reported activities without pairing these with actual energy consumption records, which are particularly hard to collect for large, nationally representative samples. This challenge precludes the development of empirical evidence on which attitudes and behaviours influence patterns of energy consumption, thus limiting the extent to which these can inform energy interventions or conservation programs. This study demonstrates a novel methodology for estimating energy consumption in the absence of actual energy records by using a large, publicly available data set of energy consumption in the UK. We develop a predictive model using the Smart Energy Research Laboratory (SERL) data portal (with records from nearly 13,000 UK households) and then use this model to predict energy consumption (both electric and gas) for a sample of 1,000 UK householders for which we separately collect over 200 variables relating to climate change attitudes and practices. Our approach uses a set of over 50 independent variables that are shared between the data sets, allowing us to train a model on the SERL data and use it to analyse the relationship between energy consumption and the opinions, motivations, and daily practices of survey respondents. Results show that electricity consumption is influenced by a broader range of factors compared to gas. Household energy use is best explained by physical dwelling characteristics, socio-demographic variables, and certain behavioural and attitudinal measures. Notably, pro-environmental attitudes, frugality, and conscientiousness correlate with lower energy use, while income and consumerism are linked to higher consumption. We discuss how these findings can inform efforts to decarbonise home energy use in the UK.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Global spatially explicit carbon emissions from land-use change over the past six decades (1961–2020)

Land-use change (LUC) is a major source of global anthropogenic carbon emissions that contribute to climate change. However, current estimates of LUC-induced emissions vary widely with differences in data and models, making it challenging to identify and manage emissions hotspots by location and LUC activity. Here, we estimated spatially explicit carbon fluxes associated with global major gross land-use transitions based on a new bookkeeping model (i.e., LUCE). Between 1961 and 2020, LUC induced 215 and 142 Pg C of global carbon emissions and removals, respectively, resulting in average net emissions (E LUC ) of 1.21 Pg C year −1 . Latin America, Southeast Asia, and Sub-Saharan Africa dominated global E LUC with 69% of 60-year cumulative emissions, or about 86% between 2001 and 2020. Forest-related LUC activities (e.g., deforestation, reforestation) contributed largely to both regional and global carbon fluxes. Our findings shed new light on identifying LUC-induced emissions hotspots and managing land for climate mitigation and conservation.

bookkeeping model↗
DOE OSTI2024

CROCUS Air Quality Data at Northeastern Illinois University Rooftop

This dataset is from the Department of Energy Office of Science funded project, Community Research on Urban and Climate Science (CROCUS) (https://crocus-urban.org/). The AQT (Vaisala AQT530) instrument provides observations on meteorological conditions, including particulate matter (PM2.5, PM10), gas species concentrations (NO, NO2, O3, CO), and environment temperature and moisture. These measurements are critical for understanding air quality. These measurements are useful for understanding changes in aerosol properties, air quality research, and comparing to model experiments especially in urban environments.Datasets are stored in the netCDF data format, and we we encourage users to make use the associated toolkits available from Unidata (https://www.unidata.ucar.edu/software/netcdf/), Project Pythia (https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html), and our “Instrument Cookbooks” (https://crocus-urban.github.io/instrument-cookbooks) for more information on how to process the metadata-rich datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Controls From Above and Below: Snow, Soil, and Steepness Drive Diverging Trends of Subsurface Water and Streamflow Dynamics

ABSTRACT The importance of subsurface water dynamics, such as water storage and flow partitioning, is well recognised. Yet, our understanding of their drivers and links to streamflow generation has remained elusive, especially in small headwater streams that are often data‐limited but crucial for downstream water quantity and quality. Large‐scale analyses have focused on streamflow characteristics across rivers with varying drainage areas, often overlooking the subsurface water dynamics that shape streamflow behaviour. Here we ask the question: What are the climate and landscape characteristics that regulate subsurface dynamic storage, flow path partitioning, and dynamics of streamflow generation in headwater streams? To answer this question, we used streamflow data and a widely‐used hydrological model (HBV) for 15 headwater catchments across the contiguous United States. Results show that climate characteristics such as aridity and precipitation phase (snow or rain) and land attributes such as topography and soil texture are key drivers of streamflow generation dynamics. In particular, steeper slopes generally promoted more streamflow, regardless of aridity. Streams in flat, rainy sites (< 30% precipitation as snow) with finer soils exhibited flashier regimes than those in snowy sites (> 30% precipitation as snow) or sites with coarse soils and deeper flow paths. In snowy sites, less weathered, thinner soils promoted shallower flow paths such that discharge was more sensitive to changes in storage, but snow dampened streamflow flashiness overall. Results here indicate that land characteristics such as steepness and soil texture modify subsurface water storage and shallow and deep flow partitioning, ultimately regulating streamflow response to climate forcing. As climate change increases uncertainty in water availability, understanding the interacting climate and landscape features that regulate streamflow will be essential to predict hydrological shifts in headwater catchments and improve water resources management.

Kerins, Devon [Department of Civil and Environment↗
DOE OSTI2024

Identifying atmospheric rivers and their poleward latent heat transport with generalizable neural networks: ARCNNv1

Abstract. Atmospheric rivers (ARs) are extreme weather events that can alleviate drought or cause billions of US dollars in flood damage. By transporting significant amounts of latent energy towards the poles, they are crucial to maintaining the climate system's energy balance. Since there is no first-principle definition of an AR grounded in geophysical fluid mechanics, AR identification is currently performed by a multitude of expert-defined, threshold-based algorithms. The variety of AR detection algorithms has introduced uncertainty into the study of ARs, and the thresholds of the algorithms may not generalize to new climate datasets and resolutions. We train convolutional neural networks (CNNs) to detect ARs while representing this uncertainty; we name these models ARCNNs. To detect ARs without requiring new labeled data and labor-intensive AR detection campaigns, we present a semi-supervised learning framework based on image style transfer. This framework generalizes ARCNNs across climate datasets and input fields. Using idealized and realistic numerical models, together with observations, we assess the performance of the ARCNNs. We test the ARCNNs in an idealized simulation of a shallow-water fluid in which nearly all the tracer transport can be attributed to AR-like filamentary structures. In reanalysis and a high-resolution climate model, we use ARCNNs to calculate the contribution of ARs to meridional latent heat transport, and we demonstrate that this quantity varies considerably due to AR detection uncertainty.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Quantum-Inspired Bayesian Sampling for Uncertainty Quantification and Machine Learning (Final Technical Report)

With increasing simulation and measurement data, machine learning and artificial intelligence have been widely used in computational decision-making of complex engineering systems. The resulting tools, such as uncertainty quantification solvers, reinforcement learning, and physics-informed machine learning, have achieved great success in critical DOE tasks such as material discovery and design, energy system modeling and control, and numerical weather and climate prediction. A core topic in scientific machine learning and artificial intelligence is Bayesian inference: given an observed data set, people want to estimate the posterior distribution of a (possibly large) number of hidden parameters. Due to the flexibility and weak assumptions, Bayesian sampling has been the mainstream Bayesian inference solvers despite the rapid progress of approximate Bayesian inference. Classical Bayesian sampling methods such as Markov-chain Monte Carlo suffer from a low-acceptance rate due to the random walk nature, therefore state-of-the-art techniques use Hamiltonian Monte Carlo and its variants to efficiently draw posterior samples in a high dimension. The key idea of Hamiltonian Monte Carlo and its variants is to simulate the Hamiltonian dynamics of a classical particle with a fixed mass, and their performance significantly degrades when the posterior distribution is highly spiky or has multiple modes. Leveraging the idea of quantum physics, this project has investigated new theory, algorithms and applications of Bayesian inference (especially Bayesian sampling). The main results include: (1) novel quantum-inspired Bayesian sampling methods that can lead to better accuracy for challenging multi-modal or spiky distributions, (2) more scalable machine learning framework leveraging tensor-compressed Bayesian inference, and (3) Bayesian and sampling approaches for verifying the robustness of continuous and binary neural networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Impacts of year-to-year weather variability and inter-panel spacing on agrivoltaic crop yields in Massachusetts

Deployment of utility-scale solar power plants could lead to agricultural land-use changes. Agrivoltaics, a dual land use combining solar and agriculture on the same land, can provide multiple environmental benefits, including improving soil quality and water use efficiency. The body of agrivoltaic field data is still growing, and crop responses to different solar configurations under different local climates are highly varied. We investigate the impact of adding spacing between adjacent solar panels in a fixed-tilt system to improve light diffusion to crops. For four crops (broccoli, peppers, kale, Swiss chard) grown across 3 years in an agrivoltaic system in Massachusetts, we found that only kale had a linearly increasing trend as the inter-panel spacing increased from 0.6 m to 1.5 m (2 ft to 5 ft). However, there were significant year-to-year differences in the yield of agrivoltaic versus control fields. Agrivoltaic and full sun fields produced equivalent yields in a hot, dry year, whereas the full-sun control beds produced more salable yield for all four crops in a warm, wet year. This demonstrates variability of agricultural outcomes and the need for more multi-year studies to ensure agrivoltaic impacts are not under- or overestimated.

14 SOLAR ENERGY↗
DOE OSTI2024

Hardware-in-the-loop Laboratory Performance Verification of Flexible Building Equipment in a Typical Commercial Building

This project aims to develop high-resolution equipment performance and occupant data that quantifies demand flexibility in typical commercial buildings. The dataset documented in this report includes comprehensive time-series measurements from hardware-in-the-loop (HIL) experiments conducted across multiple testbeds designed to simulate realistic operational environments for HVAC systems. Specifically, it captures minute-by-minute high-resolution data on the performance of various typical HVAC systems, including a variable-air-volume (VAV) air handling unit (AHU) system with chillers and an ice tank in the Intelligent Building Agents Laboratory (IBAL) at the National Institute of Standards and Technology (NIST), a two-stage air-source heat pump (ASHP) at the NIST, and a water-source heat pump (WSHP) at Texas A&M University (TAMU). The data were generated under a range of controlled conditions reflecting different grid scenarios and climatic influences, as well as various control strategies, building types, occupancy patterns, and occupant behaviors. This dataset provides DE-EE0009153 Final Report 5 detailed insights into the demand flexibility of these systems, including their response to grid signals, occupant behaviors, energy consumption patterns, and operational efficiency under different conditions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Impacts of Year-to-Year Weather Variability and Inter-Panel Spacing on Crop Yields in a Massachusetts Agrivoltaics System

This presentation summarizes recently published work on agrivoltaic field data and irradiance modeling. The body of agrivoltaic field data is still growing, and crop responses to different solar configurations under different local climates are highly varied. We investigate the impact of adding spacing between adjacent solar panels in a fixed-tilt system to improve light diffusion to crops. For four crops (broccoli, peppers, kale, Swiss chard) grown across 3 years in an agrivoltaic system in Massachusetts, we found that only kale had a linearly increasing trend as the inter-panel spacing increased from 0.6 m to 1.5 m (2 ft to 5 ft). However, there were significant year-to-year differences in the yield of agrivoltaic versus control fields. Agrivoltaic and full sun fields produced equivalent yields in a hot, dry year, whereas the full-sun control beds produced more salable yield for all four crops in a warm, wet year. This demonstrates variability of agricultural outcomes and the need for more multi-year studies to ensure agrivoltaic impacts are not under- or overestimated.

14 SOLAR ENERGY↗
DOE OSTI2025

OpenCRUMS USA: An Open Machine Learning Framework for Characterizing Variability in Aerosol Reanalysis Data

Advances in artificial intelligence (AI) have called for exploring how these techniques can be used for exploring patterns in large climate datasets. To that regard, the U.S. Department of Energy AI for Earth System Predictability (AI4ESP) supported a pilot initiative called the Open Classification of Regimes in the Southeast USA (OpenCRUMS USA) project to explore how AI can be used to characterize modes of spatial variability in large climate datasets. For this study, we focus on comparing two methods for characterizing the modes of spatial variability of surface aerosol concentration over the Houston region: empirical orthogonal functions (EOFs) and layerwise relevance propagation (LRP) applied to a convolutional neural network (CNN) classifier. We show that EOF analysis typically attributes spatial variability modes that span all of southeast Texas, prohibiting the attribution of spatial variability to localized regions. However, using LRP on the CNN classifier resolves the explanatory parameters at a finer spatial resolution than EOFs. This allows for the attribution of the spatial variability of surface aerosols to local regions of organic carbon which was not possible using EOFs. In addition, the LRP analysis also suggests that synoptic-scale transport of dust is most prevalent during anticyclonic and pretrough synoptic conditions as categorized by self-organizing maps.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

CROCUS Weather Data at Northeastern Illinois University Rooftop

This dataset is from the Department of Energy Office of Science funded project, Community Research on Urban and Climate Science (CROCUS) (https://crocus-urban.org/). Vaisala WXT sensor is an all-in-one weather instrument that provides 6 of the most important weather parameters: barometric pressure, temperature, relative humidity, rainfall, wind speed and direction. Temperature, pressure, relative humidity, and rainfall are sampled at 1 second frequency, while wind speed/direction is measured at ten per second (10Hz) frequency. These measurements are useful for looking at characterizing local weather, identifying unique weather events, and studying local turbulence, especially given the high temporal resolution of the wind measurements.Datasets are stored in the netCDF data format, and we we encourage users to make use the associated toolkits available from Unidata (https://www.unidata.ucar.edu/software/netcdf/), Project Pythia (https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html), and our “Instrument Cookbooks” (https://crocus-urban.github.io/instrument-cookbooks) for more information on how to process the metadata-rich datasets.

54 ENVIRONMENTAL SCIENCES↗