Search NASA⌕ Search

SEARCH · Search NASA

Results for “Meteorologic data”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The Aerosol Model Benchmarking Repository: A toolkit for model intercomparison

The Aerosol Model Benchmarking Repository and Standards (AMBRS) project was initiated to provide tools and to establish community standards for benchmarking aerosol models. This report describes a set of open-source tools for building, running, and analyzing aerosol box model simulations in a standardized framework. The framework consists of three core components: AMBuilder, a CMake-based build system that compiles supported models consistently; AMBRS, a Python module that defines unified numerical experiments and executes them with aligned inputs; and PyParticle, an aerosol analysis package that standardizes output, computes diagnostics, and visualizes simulation results. Together, these tools enable reproducible intercomparison of aerosol schemes and support process-level evaluation of how model simplifications affect predictions of size distributions, cloud condensation nuclei activity, and other relevant properties relevant for the Earth-Energy system. Beyond its role in benchmarking, AMBRS provides a platform for studying aerosol processes across scales and can be used to generate training data for AI/ML applications in support of a broader hierarchical aerosol modeling strategy.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Entropy-Infused Deep Learning Loss Function for Capturing Extreme Values in Wind Power Forecasting

Extreme scenarios in wind power generation occur with higher frequency and larger magnitude in the recent years due to the ever-increasing extreme meteorological factors. Accurate forecasting of the occurrence of extreme values in wind power generation is of great concern to ensure reliable power system operation. Recently, deep learning models have surged in popularity for wind power forecasting, with the mean squared error (MSE) loss function being commonly used. However, the MSE loss function, being sensitive to extreme values, disproportionately penalizes larger errors, cannot adequately capture the extreme values present in wind energy data, and novel loss functions have seldom been tailored for wind power forecasting. To this end, in this paper, we introduce a novel loss function specifically crafted to capture extreme values in wind power forecasting. The experimental results with four fundamental deep learning methods on open source wind power dataset validate that the new loss function is efficient and superior in all cases compared to MSE in capturing extreme values while maintaining forecasting performance.

17 WIND ENERGY↗
DOE OSTI2026

AmeriFlux FLUXNET-1F CA-PB1 Polar Bear 1 - Peat Plateau

This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site CA-PB1 Polar Bear 1 - Peat Plateau. This is the FLUXNET version of the carbon flux data for the site CA-PB1 Polar Bear 1 - Peat Plateau produced by applying the standard ONEFlux (1F) software. Site Description - Ontario Ministry of Environment and Climate Change's Environmental Monitoring and Reporting Branch has established five carbon flux monitoring towers in Ontario’s Far North as part of its Climate Change Modelling and Monitoring program. These long term monitoring stations measure carbon exchange and a suite of soil and meteorological parameters over peatland ecosystems to better understand carbon cycling in Ontario’s Far North. Information produced by these monitoring stations will assist the province in land use planning and the development of climate change adaptation strategies.

Humphreys, Elyn [Carleton University]↗
DOE OSTI2026

AmeriFlux US-NYk NYSM - Staten Island

This is the AmeriFlux version of the carbon flux data for the site US-NYk NYSM - Staten Island. Site Description - Located in an urban setting. It is on a rooftop. Obstructions within 100m include tall buildings. The soil type is N/A - Rooftop. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a NYC Profiler site and a Standard meteorological site.

Miller, Scott [University at Albany]↗
DOE OSTI2026

AmeriFlux US-NYl NYSM - Southold

This is the AmeriFlux version of the carbon flux data for the site US-NYl NYSM - Southold. Site Description - Located in a vineyard setting. It is a flat, open area surrounded by vineyards. Obstructions within 100m include vineyard, trees. The soil type is Haven loam. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a Profiler and Standard meteorological site.

Miller, Scott [University at Albany]↗
DOE OSTI2025

Artificial neural networks estimate evapotranspiration for Miscanthus × giganteus as effectively as empirical model but with fewer inputs

Estimating actual evapotranspiration (ET) is particularly crucial for addressing how vegetation affects the water balance of ecosystems. ET estimation can be complex with empirical models due to their many parameters and reliance on aridity. In contrast, artificial neural networks (ANNs) could potentially estimate ET with fewer and more common meteorological parameters. In this study, we trained two ANNs, one using a feed-forward approach (FFN) and the other a nonlinear auto-regressive network (NARX), to predict ET and compared them to the commonly used empirical model Granger and Gray (GG). We trained our models on a nine-year eddy covariance (EC) dataset for Miscanthu s × giganteus ( M . × giganteus ) from Illinois (UIEF), then tested them using out-of-sample data from both UIEF and a different location in Iowa (SABR) to compare the accuracy of FFN, NARX, and GG models in estimating daily ET. A combination of air temperature (T a ) and solar radiation (R s ) was chosen as inputs due to the highest R 2 for FFN (R 2 = 0.79, 0.81, and 0.79 for training, testing, and validation, respectively) and only T a for NARX (R 2 = 0.70 for out-of-sample validation). The predictive power of the FFN model was superior to the NARX and GG models at the UIEF site (R 2 = 0.84, 0.70, and 0.83 for out-of-sample validation, respectively). Our analysis showed that ANN approaches are as accurate as empirical approaches for estimating ET but use fewer inputs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

ELM model simulations of Plum Island Ecosystems LTER low marsh site 2018-2020

Model simulations using the Department of Energy's Energy Exascale Earth System Model (E3SM) land model (ELM) with improved capabilities to represent vegetation response to salinity and inundation. The simulations were conducted for a tidal salt marsh at Plum Island Ecosystems Long Term Ecological Research (LTER) site near Rowley, Massachusetts, USA; the site is a low marsh dominated by Spartina alterniflora. The model was forced with site-specific meteorology, salinity and tidal cycles from 2018-2020. Four sets of model simulations are included and described below:1. Parameterization of the salinity response function. These simulations tested different combinations of values for optimal salinity and salinity tolerance.2. Model evaluation. This comparison conducted simulations using the default model, the salinity function only, the submergence function only, and both the salinity and submergence functions. 3. Salinity scenarios. These simulations used the 2018 salinity input data varied by -5 to +10 ppt salinity.4. Water level scenarios. These simulations used the tide height varied by -10 to +50 cm. These simulations were used to demonstrate how the salinity and submergence functions better represent carbon uptake by tidal salt marshes.The data package includes netCDF files used as forcing files for tide height and salinity, one for each year 2018-2020 at observed salinity concentrations, and an additional three forcing files in which salinity concentrations were varied 5 ppt lower, 5 ppt higher, and 10 ppt higher than the measured 2018 time series. Also included are python scripts for creating forcing files, plain text parameter and command files for running simulations, model outputs in netCDF format, and python scripts for visualizing outputs. Code for the modified E3SM model is archived in Sulman et al 2023 at doi:10.15485/1991625. More detail about files is provided in the README.md file.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

AmeriFlux US-NYp NYSM - Fredonia

This is the AmeriFlux version of the carbon flux data for the site US-NYp NYSM - Fredonia. Site Description - Located in a vineyard setting. It is in a small open area surrounded by vineyards. Obstructions within 100m include vineyards, trees. The soil type is Niagara silt loam, loamy substratum. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a Standard meteorological site.

Miller, Scott [University at Albany]↗
DOE OSTI2025

Atmospheric and Soil Parameters in Five Urban Sites in Knoxville, Tennessee. 2024

This dataset, which contains ten csv files, reports several meteorological conditions such as temperature, wind speed, solar radiation, and net radiation measured in several urban parks in Knoxville, Tennessee, USA: Cumberland Estates Park (CEP), Socially Equal Energy Efficient Development (SEEED), West View Park (WVP), Victor Ashe Park (VAP), and West Hills Park (WHP). Also, hourly average of soil parameters such soil temperature, moisture, and matric potential measured in the five sites are included in this dataset. Air temperature, wind speed and direction, and solar radiation data were obtained from a METER ATMOS 41 All-in-One Weather Station (Pullman, Washington, USA). Soil moisture and temperature parameters were measured the METER Teros 12 probes embedded to a depth of 5 cm into the soil, while soil matric potential was measured with a METER Teros 21 probe. Incident and emitted radiation (shortwave and longwave) measurements were made using an Apogee (Logan, Utah, USA) net radiometer (Model SN-500-SS). This work is a part of a larger study which investigates the impact of soil moisture and plant evapotranspiration on ambient temperature and relative humidity in several city parks in Knoxville, Tennessee.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

AmeriFlux US-NYg NYSM - Chazy

This is the AmeriFlux version of the carbon flux data for the site US-NYg NYSM - Chazy. Site Description - Located in a crop field setting. It is small open area in an overgrown field. Obstructions within 100m include high vegetation. The soil type is Malone gravelly loam, very stony. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a Profiler and Standard meteorological site.

Miller, Scott [University at Albany]↗
DOE OSTI2024

A framework to enhance disaster debris estimation with AI and aerial photogrammetry

This study addresses the critical need to enhance disaster preparedness and response, focusing on hurricane impact assessment and debris estimation. Accurate assessments in this context are critical for post-event search-and-rescue (SAR) operations and resource distribution. Recent computing advancements are revealing the potential of unmanned aerial vehicles (UAVs) and artificial intelligence (AI) technologies in collecting data and assisting with post-hurricane reconnaissance. However, the use of AI and UAV photogrammetry for accurate disaster impact analysis remains underexplored. To this end, this study proposes a damage and debris analysis framework harnessing reality capture through aerial imagery and photogrammetry. Within this framework, a region-based neural network is leveraged to detect debris locations in aerial imagery with favorable performance. In a testbed within the Beaumont-Port Arthur region, in Southeast Texas, this study performs 3D reality captures of the built environment. Since the accuracy of the 3D reality capture is of importance in research areas associated with time-sensitive disaster response, we further investigate the optimal 2D aerial imagery overlap ratio required to generate a sufficiently accurate 3D model for disaster impact analysis and debris volume estimation. Results indicate that, in the case of aerial imagery for infrastructure systems, a minimum of 60 % overlap is recommended for damage assessment and debris analysis. In contrast, for flat green areas, a minimum of 50 % overlap is adequate. Overall, for disaster response applications, our study reveals that an overlap ratio between 60 % and 70 % is optimal for achieving a balance between time efficiency and data quality in aerial data collection. Furthermore, these quantitative recommendations are crucial for enabling efficient disaster response efforts. Additionally, our study outcomes will improve disaster impact analysis and facilitating timely and effective response strategies.

Artificial intelligence↗
DOE OSTI2026

AmeriFlux US-NYa NYSM - Voorheesville

This is the AmeriFlux version of the carbon flux data for the site US-NYa NYSM - Voorheesville. Site Description - Located in an orchard/vineyard setting. It is an open area surrounded by orchards and some larger trees. Obstructions within 100m include some orchards/trees. The soil type is chenango gravelly silt loam, loamy substratum. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a Standard meteorological site.

Miller, Scott [University at Albany]↗
DOE OSTI2026

AmeriFlux US-NYb NYSM - Schuylerville

This is the AmeriFlux version of the carbon flux data for the site US-NYb NYSM - Schuylerville. Site Description - Located in a grassy field/canal setting. It is in an open field surrounded by trees, right next to the river. Obstructions within 100m include trees, river. The soil type is Teel silt loam. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a Standard meteorological site.

Miller, Scott [University at Albany]↗
DOE OSTI2026

AmeriFlux US-NYc NYSM - Whitehall

This is the AmeriFlux version of the carbon flux data for the site US-NYc NYSM - Whitehall. Site Description - Located in a grassy field/canal setting. It is an open grassy area with trees and water nearby to the west. Obstructions within 100m include trees, canal. The soil type is Kingsbury silty clay. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a Standard meteorological site.

Miller, Scott [University at Albany]↗
DOE OSTI2026

AmeriFlux US-NYe NYSM - Ontario

This is the AmeriFlux version of the carbon flux data for the site US-NYe NYSM - Ontario. Site Description - Located in an orchard setting. It is in an open grassy area near vineyard/orchard, small pond to the south. Obstructions within 100m include orchard/vineyard, small pond. The soil type is loam. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a Profiler and Standard meteorological site.

Miller, Scott [University at Albany]↗
DOE OSTI2026

AmeriFlux US-NYf NYSM - Burt

This is the AmeriFlux version of the carbon flux data for the site US-NYf NYSM - Burt. Site Description - Located in a vineyard/crop field setting. It is in an opening in a vineyard with a crop field to the north. Obstructions within 100m include vineyards. The soil type is Niagara silt loam. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a Standard meteorological site.

Miller, Scott [University at Albany]↗
DOE OSTI2026

AmeriFlux US-NYo NYSM - Penn Yan

This is the AmeriFlux version of the carbon flux data for the site US-NYo NYSM - Penn Yan. Site Description - Located in a crop field setting. It is very flat and open, at the edge of a crop field. Obstructions within 100m include high crops. The soil type is Honeoye silt loam. This site is a part of the New York State Mesonet Flux Network, one of multiple subnetworks (e.g. Profiler, Snow) operated by the New York State Mesonet. It also operates as a Standard meteorological site.

Miller, Scott [University at Albany]↗
DOE OSTI2025

Future Wind Energy Resources and Cost Uncertainties Across the United States

This dataset contains results estimating projections of change of annual capacity factors and levelized cost of energy for several turbine technologies in the 2024 Annual Technology Baseline (ATB). Projections of change are based on downscaled earth system model (ESM) data from Sup3rCC. There has been evidence of reductions in average wind speeds over land in North America since the 1980s, and several models project that average wind speeds will continue to decrease. Concurrently, the cost of wind energy systems in the United States has been decreasing since around 2010, a trend also projected to continue. There is considerable uncertainty in these future projections, with quantitative estimates of future wind resource and system costs varying widely. To study this, we run land-based wind energy models with a range of possible future system costs, turbine designs, and meteorological inputs from multiple downscaled earth system models over the contiguous United States to estimate critical system performance metrics such as annual energy production (AEP) and levelized cost of energy. Where multiple earth system models agree, changes in mean AEP from the time period 2000-2019 to 2040-2059 can be as high as +10% in South Texas or as low as -20% in Iowa. Several additional states in the Midwest that currently have considerable wind generation capacity show the possibility of substantial decreases in AEP by mid-century. Larger turbines and moderate reductions in system costs can offset even the largest projected decreases in wind resource, but much uncertainty remains in the extent to which wind resources will actually change into the future and to what extent wind energy systems can drive down future costs. An analysis of variance shows, in several states in the Midwest, the uncertainty in future wind resource can be almost as important for future changes in the cost of wind energy as the uncertainty in future system costs.

17 WIND ENERGY↗