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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Smart Methane Emission Detection System Development (Final Report)

Working with the Department of Energy’s National Energy Technology Laboratory, Southwest Research Institute® (SwRI®) developed a system to identify methane leaks reliably, accurately, and autonomously at critical midstream sections of the natural gas distribution network in real- time for the purpose of mitigating methane emissions using Optical Gas Imaging (OGI) cameras. SwRI’s Smart Leak Detection – Methane (SLED/M) adds a high degree of automation to the process of methane leak detection to minimize sources of human error, minimize response time to a leak event, and maximize midstream visibility. Furthermore, SwRI has been working towards integrating Quantitative OGI (QOGI) capabilities into this existing technology. By leveraging Deep Learning, SwRI now has the capability to estimate fugitive emission leak rates quickly and reliably, which allows operators to detect emissions, quantify leak rate, prioritize repairs, and validate the repairs in a single instrument. The next generation QOGI technology leverages the same cameras used in Leak Detection and Repair (LDAR) programs, with improvements in safety and speed for traditional quantification-based repairs, ultimately leading to less overhead cost for the operators. The goals for this research were to develop two types of models with the following goals: 1. Run in real-time on the edge (≥ 12 Hz) 2. Classification: Achieve less than 5% false positive detection 3. Classification: Achieve ≥ 95% methane plume detection rate 4. Regression: achieve ≤ 10 standard cubic feet per hour (scfh) prediction > 70% of the time In order to achieve these results, multiple infrared (IR) and other sensors were investigated in tandem with the midwave IR (MWIR) OGI to provide additional information to train the underlying models. Information on atmospheric conditions including humidity, temperature, pressure, and solar radiation was provided by a weather station. Several machine learning and deep learning architectures and methods, including looking at quantized classification networks and regressions networks, were explored. As further data was collected, curated, and labeled, it allowed for more refined regressive networks to be adequately trained, leading to better insight into the true flow rates being observed. An important valuable deliverable of this research effort was the development of an advanced network which underwent multiple iterations capable of giving a continuous output. The current network has a predicted mean average percentage error (MAPE) of 12.3% just outside our target goal of 10.00%, but an accuracy of 97.78% at ±50 scfh, well within the overall goal for the Department of Energy (DOE) program. Upon closer inspection, it was observed that more than 10% of datapoints contributing to the MAPE predictions were the result of low flow rate predictions and are beyond the sensitivity of instrument measurement as a result of normal operational variation and noise.

03 NATURAL GAS↗
DOE OSTI2026

Evaluation of daily gridded climate products using in situ FLUXNET data and tree growth modeling

Gridded climate data products have facilitated research in climate and ecology by providing meteorological data continuously across large spatial scales. However, the sensitivity of scientific outcomes to dataset choice remains poorly understood, and evaluation using station-based records can favor datasets built heavily on weather stations. Here, we evaluate seven high-resolution daily gridded datasets covering the contiguous United States using independent meteorology from the FLUXNET2015 dataset, with a focus on the implications of dataset choice for process-based tree growth modeling. We find that gridded products tend to capture temperature accurately while consistently overestimating the magnitude and frequency of precipitation and its extremes. Moreover, datasets vary in how they define a ‘day,’ which significantly affects temporal alignment with FLUXNET2015 observations. Despite differences among the datasets, the interannual variability in tree ring simulations is insensitive to dataset choice, likely because daily-scale biases are averaged out through accumulated growth across several months. However, inaccuracies in temperature and precipitation can significantly bias modeled xylem cell production, with systematically higher annual precipitation in the gridded datasets leading to greater xylem production compared to simulations using in situ data. Our results suggest that model applications, especially those that integrate to time scales longer than one day, are likely insensitive to climate dataset choice, but applications that are sensitive to daily climate variations or to absolute climate values need to carefully consider biases in gridded climate products.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Monthly Mean In Situ Surface Flux Observations Paired with Satellite-Derived and Reanalysis-Based Flux Data for the Great Lakes Region, 2001–2020

Surface radiative and turbulent heat fluxes over the Great Lakes strongly influence regional hydrological and meteorological processes, and their accurate representation is critical for numerical weather prediction and coupled atmosphere–lake modeling. However, direct flux observations are spatially sparse across the region, so gridded reanalysis and satellite-derived products are often used for climatological analyses and model evaluation despite differences in their flux representations. This dataset provides processed, quality-controlled, monthly mean surface flux observations from the Great Lakes Evaporation Network (GLEN), AmeriFlux, and the National Data Buoy Center, paired with spatiotemporally matched flux estimates from two reanalysis products, the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset (ERA5) and the Modern Era Reanalysis for Research and Applications, version 2 (MERRA-2), and two satellite-derived products, the Clouds and Earth's Radiant Energy Systems Energy Balanced and Filled (CERES-EBAF) and the Cloud, Albedo and Surface Radiation dataset from AVHRR data - Edition 3 (CLARA-A3). The dataset includes sixteen observational stations with variable temporal coverage within 2001–2020. For each station, a CSV file contains monthly time series of available flux variables, including surface downwelling shortwave radiation (SW), surface downwelling longwave radiation (LW), sensible heat (SH) flux, and latent heat flux (LH), alongside matched gridded product values where available. Columns in the CSV file correspond to different variables sourced from each dataset, with column titles structured as "{dataset}_{variable}". Columns with relevant metadata are also provided in each CSV file, including station latitude and longitude, monthly timestamps, and the name of the sourced observational data. These files are structured for direct use in common analysis tools, including Microsoft Excel, Python pandas, and Python matplotlib. This dataset supports climatological analysis of the Great Lakes regional surface energy budget, evaluation of satellite-derived and reanalysis-based flux products, and development or validation of flux representations in numerical weather prediction and coupled atmosphere–lake models.

Great Lakes↗
DOE OSTI2020

Simulation of Continental Shallow Cumulus Populations Using an Observation-Constrained Cloud-System Resolving Model

Continental shallow cumulus (ShCu) clouds observed on 30 August 2016 during the Holistic Interactions of Shallow Clouds, Aerosols, and Land-Ecosystems (HI-SCALE) field campaign are simulated by using an observation-constrained cloud-system resolving model. On this day, ShCu forms over Oklahoma and southern Kansas and some of these clouds transition to deeper, precipitating convection during the afternoon. We apply a four-dimensional ensemble-variational (4DEnVar) hybrid technique in the Community Gridpoint Statistical Interpolation (GSI) system to assimilate operational data sets and unique boundary layer measurements including a Raman lidar, radar wind profilers, radiosondes, and surface stations collected by the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) atmospheric observatory into the Weather Research and Forecasting (WRF) model to ascertain how improved environmental conditions can influence forecasts of ShCu populations and the transition to deeper convection. Independent observations from aircraft, satellite, as well as ARM's remote sensors are used to evaluate model performance in different aspects. Several model experiments are conducted to identify the impact of data assimilation (DA) on the prediction of clouds evolution. The analyses indicate that ShCu populations are more accurately reproduced after DA in terms of cloud initiation time and cloud base height, which can be attributed to an improved representation of the ambient meteorological conditions and the convective boundary layer. Extending the assimilation to 18 UTC (local noon) also improved the simulation of shallow-to-deep transitions of convective clouds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Optimizing Non-Terrestrial Hybrid RF/FSO Links With Reinforcement Learning: Navigating Through Clouds

In the pursuit of ubiquitous broadband connectivity, there has been a significant shift towards the vertical expansion of communication networks into space, particularly through the exploitation of low Earth orbit (LEO) satellite constellations, which are favored for their relatively low latency. However, this approach faces many challenges that need to be addressed, including atmospheric turbulence, high path loss, and dynamic cloud formations. High-altitude pseudo-satellites (HAPS) have emerged as promising relaying layers between LEO satellites and ground stations, enhancing coverage, latency, and direct terrestrial user connectivity. While radio frequency (RF) bands suffer from congestion and limited bandwidth, free space optical (FSO) communications offer higher data rates, but are susceptible to misalignment and weather-induced signal degradation. To address these challenges, a hybrid RF/FSO approach has been proposed to take advantage of both technologies by dynamic switching between RF and FSO based on propagation channel conditions. This paper introduces a reinforcement learning-based algorithm designed to optimize the trajectory of HAPS, maneuver around cloudy areas, and seamlessly switch between the RF and FSO communication modes to maximize the achievable capacity. The proposed approach aims to maximize system performance by intelligently adapting to environmental conditions and offering a promising solution for next-generation space communication networks.

actor-critic algorithm↗
DOE OSTI2021

Accessibility and Availability of Gas Stations for Liquid Fuel Supply During Severe Weather Events

Energy infrastructure is a pivotal lifeline with wider inter-dependencies. Electric power is essential to the continued functioning of critical assets; liquid fuels and other physical primary energy commodities that are used to supply the local energy needs of these assets during extreme events when electricity infrastructure is compromised. During Superstorm Sandy, the disruption of fuel supplies in the Tri-state area necessitated increasing situational awareness about oil and natural gas (ONG) systems to redirect fuel supplies to first responders and for restoration efforts. During winter storm events over the past several years, the demand for ONG for heating and power generation has skyrocketed in the north-east, which subsequently had caused ONG price to hit their highest level in years. ONG is a major energy source for restoration and recovery activities. However, very little, if any, situational awareness information about ONG supply and demand is available to those coordinating emergency efforts. Often this lack of information is due to the decentralized nature of the ONG supply chain and proprietary data maintained by private entities. To understand the impact of fuel availability on response and recovery efforts, this research focuses on (i) determining the accessibility to gas stations for liquid fuel supply across space and time, and (ii) examining the spatio-temporal variation of fuel availability and demand during extreme weather events. The broader scope of the project focuses on providing spatio-temporal situational awareness information about ONG availability and demand across the fuel supply chain for positioning of resources for response activities.

Kar, Bandana↗
DOE OSTI2023

Coupling Shared E-scooters and Public Transit: A Spatial and Temporal Analysis

The integration of shared e-scooters with public transit is a promising solution for urban mobility's first/last-mile challenge. This study explores spatiotemporal factors influencing this integration, using 35-day e-scooter trip data from Chicago. Employing a random-effect negative binomial approach, we modeled the frequency of e-scooter trips to access/egress to/from bus stops and train stations. Results indicate that weather conditions, design features like intersection density, and multimodal network density significantly influence usage. The transit system characteristics such as service frequency have a positive effect on the integration of e-scooters and trains while a similar effect for bus and e-scooter integration was not significant. Furthermore, safety-related variables such as accident and crime rates as well as demographic characteristics were also revealed to be significant factors in our study. These findings offer vital insights to urban planners and policymakers for infrastructure, safety enhancements, and interventions to encourage efficient e-scooter-public transit integration.

Chicago↗
DOE OSTI2022

Evaluation of WRF simulation of deep convection in the US Southern Great Plains

The Southern Great Plains (SGP) exhibits a relatively high frequency of periods with extremely high rainfall rates (RR) and hail. Here, seven months of 2017 are simulated using the Weather Research and Forecasting (WRF) model applied at convection permitting resolution with the Mibrandt-Yau microphysics scheme. Simulation fidelity is evaluated, particularly during intense convective events, using data from ASOS stations, dual-polarization RADAR, gridded data sets and observations at the DoE Atmospheric Radiation Measurement site. The spatial gradients and temporal variability of precipitation and the cumulative density functions for both RR and wind speeds exhibit fidelity. Odds ratios >1 indicate WRF is also skillful in simulating high composite reflectivity (cREF, used as a measure of widespread convection) and RR > 5 mmhr –1 over the domain. Detailed analyses of the ten days with highest spatial coverage of cREF >30 dBZ show spatially similar reflectivity fields and high RR in both RADAR data and WRF simulations. However, during periods of high reflectivity, WRF exhibits a positive bias in terms of very high RR (> 25 mmhr –1 ) and hail occurrence, and during the summer and transition months, maximum hail size is underestimated. For some renewable energy applications fidelity is required with respect to the joint probabilities of wind speed and RR and/or hail. While partial fidelity is achieved for the marginal probabilities, performance during events of critical importance to these energy applications is currently not sufficient. Further research into optimal WRF configurations in support of potential damage quantification for these applications is warranted.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Soil Temperature Sensor Data, May - December 2024, Five sites in Knoxville, Tennessee

This dataset contains surface soil temperature measurements from five urban parks in Knoxville: Cumberland Estates Park (CE), Socially Equal Energy Efficient Development (SD), West View Park (WV), Victor Ashe Park (VA), and West Hills Park (WH). The dataset comprises 29 Excel (.xlsx) and Comma-Separated Values (.csv) files documenting soil temperature recorded by six HOBO Pendant MX Water Temperature Data Loggers at each site, except for CE, which has five sensors. Each logger was installed at a depth of 10 inches and positioned approximately 3 to 6 feet from the weather station at each site. To open both csv and xlsx files, users can utilize spreadsheet applications such as Microsoft Excel, Google Sheets, LibreOffice Calc, and Apple Numbers. This dataset is part of a broader study examining the effects of soil moisture and plant evapotranspiration on ambient temperature and relative humidity across multiple urban parks in Knoxville, Tennessee.

EARTH SCIENCE > LAND SURFACE > SOILS↗
DOE OSTI2026

Coastal Urban Boundary-layer Interactions with Convection (CUBIC)

The CUBIC project added three extra weather‑profiling stations across Houston during the 2021 TRACER campaign to better understand how sea breezes, urban heat, and local circulations affect air quality and help trigger storms. These observations, combined with drone data and model simulations, give a clearer picture of how the boundary layer behaves in a coastal city.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Hydrogen and Electric Charging Infrastructure for Heavy-Duty Trucks: A Nationally Scalable Megaregion Assessment

Decarbonizing regional and long-haul freight is challenging due to the limitations of battery-electric commercial vehicles and infrastructure constraints. Hydrogen fuel cell medium- and heavy-duty vehicles (MHDVs) offer a viable alternative, aligning with the decarbonization goals of the Department of Energy and commercial entities. Historically, alternative fuels like compressed natural gas and liquefied propane gas have faced slow adoption due to barriers like infrastructure availability. To avoid similar issues, effective planning and deploying zero-emission hydrogen fueling infrastructure is crucial. This research develops deployment plans for affordable, accessible, and sustainable hydrogen refueling stations, supporting stakeholders in the decarbonized commercial vehicle freight system. It aims to benefit underserved and rural energy-stressed communities by improving air quality, reducing noise pollution, and enhancing energy resiliency. This research also provides a blueprint for replacing diesel in over-the-road Class 8 freight truck applications with hydrogen fueling solutions. The study focuses on the Texas Triangle Megaregion (I-45, I-35, and I-10), the I-10 corridor between San Antonio, TX, and Los Angeles, CA, and the I-5/CA-99 corridors between Los Angeles, CA, and San Francisco, CA. This area represents a significant portion of U.S. heavy-duty freight movement, carrying ~8.5% of the national freight volume. Using the OR-AGENT (Optimal Regional Architecture Generation for Efficient National Transport) modeling framework, the study conducts an advanced assessment of commercial vehicles, road and freight networks, and energy systems. The framework integrates data on freight mobility, traffic, weather, and energy pathways to deliver a region-specific, optimized vehicles powertrain architectures, infrastructure deployment solutions, operational logistics, and energy pathways. By considering all vehicle origin-destination pairs utilizing these corridors and all feasible fueling station location options, the framework's genetic algorithm identifies the minimum number and optimal locations of hydrogen refueling stations, ensuring no vehicle is stranded. It also determines fuel schedules and quantities at each station. A roadmap for station deployment based on multiple adoption trajectories ensures a strategic rollout of hydrogen refueling infrastructure.

Sujan, Vivek [ORNL] (ORCID:0000000269882342)↗
DOE OSTI2026

Tendencies of Soil Microbial NO Emissions During HI‐SCALE as Predicted by a Nitrification/Denitrification Scheme

Many atmospheric chemical processes, including the formation of secondary organic aerosol (SOA), are strongly modulated by the reactions of NO and NO 2 (NO x ). Though NO x is controlled by anthropogenic emissions near urban areas, in rural areas soil microbes can be a significant contribution to NO emission globally. The relative rates of emissions of different nitrogen-containing species (e.g., NO, N 2 O, HONO, and N 2 ) are strong functions of soil properties such as temperature, moisture content, pH, and soil carbon and nitrogen pools. However, typical large-scale biogeochemical models either express these emissions simplistically, or not at all. Here we investigate the potential impact of soil NO emissions on atmospheric chemistry and SOA formation over regional and monthly time scales, specifically the 2016 spring and summer Intensive Observational Periods (IOPs) of the Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystems (HI-SCALE) field campaign. We implement the soil NO nitrification/denitrification parameterization of Rasool et al. (2019) into the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), supplemented by a 1-km soil moisture analysis. We then simulate both IOPs over the U.S. Great Plains, evaluating against ground stations and flight data. We show that soil NO emissions can account for a large fraction of total NO x and locally increase O 3 concentrations by up to 25%, while alleviating negative biases of gases and aerosols toward observations. Soil moisture and temperature changes between IOP1 and IOP2 lead to overall differences in emissions, but with large regional variability due to heterogeneous surface characteristics.

Atmoshpheric Chemistry↗
DOE OSTI2026

GridPIQ Reference Data

GridPIQ uses dozens of publicly available datasets to provide context for a user's grid project, as well as defaults for users to choose from. Users can choose to import their own data to better customize their analysis or use GridPIQ-supplied defaults. This allows users to get up and running with an analysis very quickly without having to spend significant time pulling together input data. To run an electric vehicle (EV) smart charging project, a user will need to provide or select from prepopulated values for the regional load profile shape and peak load, region of interest and closest weather station, EV charging profile, number of EVs to add for the analysis, maximum EV charging power, location of chargers relative to grid infrastructure, and allowable charging times (for coordinated charging mode). The outputs of the analysis are changes in air quality, EV energy consumption, EV peak demand, and EV hourly consumption profile—before and after project implementation." For a detailed description of the tool methodology, including all the publicly available datasets used by the tool, see the [GridPIQ documentation](https://gridpiq.pnnl.gov/v2-beta/doc/).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

The Influence of Soil Properties on Sea-Breeze Circulations in the Southeast U.S.

Sea-breeze circulations (SBCs) are common weather phenomena at and near coastal regions. They form because of a thermal gradient between the land surface at the coast and the sea surface. In a mid-day regime, a “thermal low” generated at the warm coast will lead to rising air motion, creating a wind shift coming from the sea near the surface displacing the coastal air. A “return flow” moving back towards the sea is generated by upper-level divergence because of the rising motion from the thermal low. SBCs propagate and serve as a method of urban pollutant dispersion in the Los Angeles region of California and are constrained to the coast due to the topography of surrounding mountains serving as a boundary for further inland propagation. Within the northeast U.S. SBCs are seen in the warm season but tend to remain coastally bound due to Coriolis distortion over long distances. Within the southeast U.S. (SEUS), the paradigmatic example of SBCs occurs over the Florida peninsula, where thunderstorms form on a nearly daily occurrence due to the convergence of SBCs from the east and west sides of the peninsula. However, there are further examples of sea-breezes in the SEUS that warrant study. Within the region bordering the SEUS and the Mid-Atlantic, just east of the southern Appalachian Mountains, warm-season SBCs form at the coast of Georgia and the Carolinas. Relatively flat topography ~150-200km inland allows for mostly unimpeded inland SBC propagation. Through visual analysis, Viner et al. catalogued several SBCs that propagated as far inland as the Central Savannah River Area surrounding Augusta, Georgia. Wermter et al. found that while the land-sea thermal gradient at the coast can influence coastal SBC genesis, the inland-coastal thermal gradient over the land is the primary influencer on the speed and depth of inland propagation of SBCs in this region. Additionally, soil moisture itself is a known correlative factor to sea-breeze formation, as it influences the soil temperature and the thermal gradient needed for SBC genesis and inland penetration. Physick determined that higher latent heat fluxes associated with wetter soil dampen the land-sea thermal gradient and suppress the formation of a SBC. Physick determined that higher latent heat fluxes associated with wetter soil dampen the land-sea thermal gradient and suppress the formation of a SBC. Conversely, drier soil enhances the thermal gradient and promotes SBC formation. However, while there is an inverse relationship between soil moisture and SBCs, higher soil moisture can actually promote more convective rainfall following a SBC if it does not significantly impact the thermal gradient. While the relationship between soil moisture and SBC formation has been conceptually explored and modeled numerically, there is a research gap in observed connections. The Soil Moisture Active Passive (SMAP) satellite mission has been operational since 2015 and has been used to create high-resolution re-analytical Level 4 (L4) datasets of soil moisture and soil temperature at different soil depths: the surface (0-5cm) and rootzone (0-1m). The surface soil temperature effectively acts as the “skin temperature” of the surface at these levels, and a spatial map of the land-sea as well as the coastal-inland thermal gradients can be represented. SMAP data are also assimilated in some atmospheric models such at the High Resolution Rapid Refresh (HRRR) mesoscale model. We propose leveraging the use of SMAP products to fill in spatial gaps left by weather and mesonet stations within the SEUS region, as well as assessing the effectiveness of utilizing SMAP products towards SBC forecasting in both deterministic and machine learning (ML) models.

58 GEOSCIENCES↗
DOE OSTI2022

A meteorology and snow dataset from adjacent forested and meadow sites at Crested Butte, CO, USA

This dataset contains meteorology and snow observation data collected at sites in the southwestern Colorado Rocky Mountains during water years 2019-2021. Data collection had an emphasis on paired open-forest sites and included three forested elevations. In total, we present 270 snow pit observations, 4,019 snow depth measurements, and three years of meteorological forcing from two weather stations (one in a meadow, the other in an adjacent forest). The dataset is described in a forthcoming publication of the same name: A meteorology and snow dataset from adjacent forested and meadow sites at Crested Butte, CO, USA (Bonner et al., 2022).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

AmeriFlux US-GL1 Stannard Rock

This is the AmeriFlux version of the carbon flux data for the site US-GL1 Stannard Rock. Site Description - Stannard Rock is located 39 km from the nearest shore (Keweenaw Peninsula) in Lake Superior, 44 miles NNE of Marquette, Michigan, and 24 miles ESE of Manitou Island. The site is located on the historic Stannard Rock Lighthouse, which was completed in 1882. Eddy covariance instrumentation was installed in 2008 by a network of scientists from both US and Canada, eventually to be called the Great Lakes Evaporation Network (GLEN). The intent of GLEN has been to provide observations of over-lake meteorology and evaporation, improve forecasting of Great Lakes water levels, and support a wide variety of stakeholders, including the National Weather Service (NWS), Environment and Climate Change Canada, National Oceanic and Atmospheric Administration, U.S. Coast Guard, recreational boaters and commercial shipping, emergency management officials, and the Great Lakes research community. The eddy covariance station along with other ancillary meteorological instrumentation is located at an approximate elevation of 39.2 meters above mean lake water level. Meteorological data from the lighthouse are sent to the National Data Buoy Center, where they can be viewed in real-time at http://www.ndbc.noaa.gov/station_page.php?station=stdm4 Uncorrected half-hour fluxes were computed directly on the logger using a 30-minute block averaging period as high-frequency data were not available. The half hour flux measurements downloaded from the datalogger were post-processed with the following filters and corrections. Latent and sensible heat and carbon dioxide fluxes were corrected with 2-D coordinate rotation. Although the primary objective of data collection at Stannard Rock was to quantify the evaporative flux, additional preliminary measurements of the carbon dioxide concentration and flux from the LI-7500 are also included in this dataset but it is advised to use the carbon data with caution. Carbon data reported in this dataset includes turbulent fluxes of CO2 with no storage correction (FC, µmol m-2 s-1) and CO2 density in mole fraction of wet air (CO2), which was originally output on the datalogger as average CO2 density (mg m-2 s-1) and converted into µmol mol-1 using air temperature and pressure in post-processing. Webb, Pearman, and Leuning terms were applied to account for density fluctuations for water vapor and CO2. Sonic path length, high-frequency attenuation and sensor separation were accounted for according to Horst and Massman, and the oxygen absorption correction for the KH2O hygrometer was also applied. Latent and sensible heat fluxes were assumed to be unrealistic above an absolute value of 1000 W m-2 and were removed. Both carbon flux (FC) and carbon dioxide mole fraction in wet air (CO2) and were assumed to be unrealistic above 1000 µmol m-2 s-1 and 1000 µmol mol-1 respectively. Spikes in latent and sensible heat and carbon fluxes and densities (often due to periods of precipitation) were identified by computing the mean and standard deviation over a moving, overlapping window of 336 half-hours (7 days), similar to Shao et al., and were removed when the flux was more than 1.5 standard deviations from the moving window’s mean. While Vickers and Mahrt use a threshold of 3.5 standard deviations from the mean, a conservative value of 1.5 was chosen due to the noisy nature of over-lake data at this particular site. This process was repeated twice for latent and sensible heat, and carbon dioxide fluxes and densities and therefore it is possible that some real, realistic data was filtered out in this process. No detrending was performed. As per AmeriFlux standards, no friction velocity (USTAR, m s-1) filtering was performed.

Spence, Chris↗
DOE OSTI2025

AmeriFlux FLUXNET-1F US-GL1 Stannard Rock

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-GL1 Stannard Rock. This is the FLUXNET version of the carbon flux data for the site US-GL1 Stannard Rock produced by applying the standard ONEFlux (1F) software. Site Description - Stannard Rock is located 39 km from the nearest shore (Keweenaw Peninsula) in Lake Superior, 44 miles NNE of Marquette, Michigan, and 24 miles ESE of Manitou Island. The site is located on the historic Stannard Rock Lighthouse, which was completed in 1882. Eddy covariance instrumentation was installed in 2008 by a network of scientists from both US and Canada, eventually to be called the Great Lakes Evaporation Network (GLEN). The intent of GLEN has been to provide observations of over-lake meteorology and evaporation, improve forecasting of Great Lakes water levels, and support a wide variety of stakeholders, including the National Weather Service (NWS), Environment and Climate Change Canada, National Oceanic and Atmospheric Administration, U.S. Coast Guard, recreational boaters and commercial shipping, emergency management officials, and the Great Lakes research community. The eddy covariance station along with other ancillary meteorological instrumentation is located at an approximate elevation of 39.2 meters above mean lake water level. Meteorological data from the lighthouse are sent to the National Data Buoy Center, where they can be viewed in real-time at http://www.ndbc.noaa.gov/station_page.php?station=stdm4 Uncorrected half-hour fluxes were computed directly on the logger using a 30-minute block averaging period as high-frequency data were not available. The half hour flux measurements downloaded from the datalogger were post-processed with the following filters and corrections. Latent and sensible heat and carbon dioxide fluxes were corrected with 2-D coordinate rotation. Although the primary objective of data collection at Stannard Rock was to quantify the evaporative flux, additional preliminary measurements of the carbon dioxide concentration and flux from the LI-7500 are also included in this dataset but it is advised to use the carbon data with caution. Carbon data reported in this dataset includes turbulent fluxes of CO2 with no storage correction (FC, µmol m-2 s-1) and CO2 density in mole fraction of wet air (CO2), which was originally output on the datalogger as average CO2 density (mg m-2 s-1) and converted into µmol mol-1 using air temperature and pressure in post-processing. Webb, Pearman, and Leuning terms were applied to account for density fluctuations for water vapor and CO2. Sonic path length, high-frequency attenuation and sensor separation were accounted for according to Horst and Massman, and the oxygen absorption correction for the KH2O hygrometer was also applied. Latent and sensible heat fluxes were assumed to be unrealistic above an absolute value of 1000 W m-2 and were removed. Both carbon flux (FC) and carbon dioxide mole fraction in wet air (CO2) and were assumed to be unrealistic above 1000 µmol m-2 s-1 and 1000 µmol mol-1 respectively. Spikes in latent and sensible heat and carbon fluxes and densities (often due to periods of precipitation) were identified by computing the mean and standard deviation over a moving, overlapping window of 336 half-hours (7 days), similar to Shao et al., and were removed when the flux was more than 1.5 standard deviations from the moving window’s mean. While Vickers and Mahrt use a threshold of 3.5 standard deviations from the mean, a conservative value of 1.5 was chosen due to the noisy nature of over-lake data at this particular site. This process was repeated twice for latent and sensible heat, and carbon dioxide fluxes and densities and therefore it is possible that some real, realistic data was filtered out in this process. No detrending was performed. As per AmeriFlux standards, no friction velocity (USTAR, m s-1) filtering was performed.

Spence, Chris [Environment and Climate Change Cana↗
DOE OSTI2023

Toward Improved Regional Hydrological Model Performance Using State-Of-The-Science Data-Informed Soil Parameters

Accurate soil moisture and streamflow data are an aspirational need of many hydrologically relevant fields. Model simulated soil moisture and streamflow hold promise but models require validation prior to application. Calibration methods are commonly used to improve model fidelity but misrepresentation of the true dynamics remains a challenge. In this study, we leverage soil parameter estimates from the Soil Survey Geographic (SSURGO) database and the probability mapping of SSURGO (POLARIS) to improve the representation of hydrologic processes in the Weather Research and Forecasting Hydrological modeling system (WRF-Hydro) over a central California domain. Our results show WRF-Hydro soil moisture exhibits increased correlation coefficients ( r ), reduced biases, and increased Kling-Gupta Efficiencies (KGEs) across seven in situ soil moisture observing stations after updating the model's soil parameters according to POLARIS. Compared to four well-established soil moisture data sets including Soil Moisture Active Passive data and three Phase 2 North American Land Data Assimilation System land surface models, our POLARIS-adjusted WRF-Hydro simulations produce the highest mean KGE (0.69) across the seven stations. More importantly, WRF-Hydro streamflow fidelity also increases, especially in the case where the model domain is set up with SSURGO-informed total soil thickness. The magnitude and timing of peak flow events are better captured, r increases across nine United States Geological Survey stream gages, and the mean KGE across seven of the nine gages increases from 0.12 to 0.66. Our pre-calibration parameter estimate approach, which is transferable to other spatially distributed hydrological models, can substantially improve a model's performance, helping reduce calibration efforts and computational costs.

54 ENVIRONMENTAL SCIENCES↗