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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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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Utah FORGE Project 3-2417: Meteorological Data During 2023 Completion/Circulation

This preliminary data archive includes meteorological data recorded at the Utah FORGE facility over the period of time including the completion/cementing of well 78-16B and the initial 2023 circulation test, largely occurring in June/July/August of 2023. This information may prove useful for understanding seismic and other instrumentation responses during these activities. The meteorological station was installed June 23rd, 2023 during drilling; the circulation test occurred in mid July of 2023 (July 4-19). The included files span this period. All sensors were installed at the 16 pad and hence reflect weather state at the site. This dataset was acquired by the FOGMORE R&D project (Fiber Optic MOnitoring for Reservoir Evolution), Utah FORGE R&D Project 3-2417.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Descriptor: High Temporal Resolution Meteorological Data at Oak Ridge Reservation (ORR-HiResMet)

Access to continuous, quality assessed meteorological data is critical for understanding the climatology and atmospheric dynamics of a region. Research facilities like Oak Ridge National Laboratory (ORNL) rely on such data to assess site-specific climatology, model potential emissions, establish safety baselines, and prepare for emergency scenarios. To meet these needs, on-site towers at ORNL collect meteorological data at 15-minute and hourly intervals. However, data measurements from meteorological towers are affected by sensor sensitivity, degradation, lightning strikes, power fluctuations, glitching, and sensor failures, all of which can affect data quality. To address these challenges, we conducted a comprehensive quality assessment and processing of five years of meteorological data collected from ORNL at 15-minute intervals, including measurements of temperature, pressure, humidity, wind, and solar radiation. The time series of each variable was pre-processed and gap-filled using established meteorological data collection and cleaning techniques, i.e., the time series were subjected to structural standardization, data integrity testing, automated and manual outlier detection, and gap-filling. The data product and highly generalizable processing workflow developed in Python Jupyter notebooks are publicly accessible online. As a key contribution of this study, the evaluated 5-year data will be used to train atmospheric dispersion models that simulate dispersion dynamics across the complex ridge-and-valley topography of the Oak Ridge Reservation in East Tennessee.

Steckler, Morgan R. [Oak Ridge National Laboratory↗
DOE OSTI2024

Utah FORGE Project 3-2417: Meteorological Data During 2024 Stimulation

This preliminary data archive includes meteorological data recorded at the Utah FORGE facility over the period of time including the 16A/16B stimulation activities, largely March/April/May of 2024. This information may prove useful for understanding seismic and other instrumentation response during monitoring. The bulk of relevant stimulation activities occurred between March 25th and April 28th, 2024 and the included files span this period. All sensors were installed at the 78-16 pad and hence reflect weather state at the site. This dataset was acquired by the FOGMORE R&D project (Fiber Optic MOnitoring for Reservoir Evolution), Utah FORGE R&D Project 3-2417.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Surface Meteorological Data from Sequim "Upstairs" PNNL Campus

Surface meteorological data (3m wind speed, wind direction, 2m air temperature, relative humidity, pressure, downwelling irradiance, total rainfall) gathered on the Sequim-PNNL campus ('upstairs'). Equipment is manufactured and calibrated by Campbell Scientific. Placement and height of sensors follows recommended guidelines of the NWS.

meteorological, meteorological data, Meteorologica↗
DOE OSTI2021

Processing Meteorological Data for the CAP-88 PC Model at Los Alamos National Laboratory

The Environmental Protection and Compliance-Compliance Programs (EPC-CP) group at Los Alamos National Laboratory (LANL) uses the Clean Air Act Assessment Package 1988 (CAP-88, Littleton 2020) PC model (Version 4.1) to estimate radiological doses for a set of areal sectors surrounding a release location, in order to satisfy the Environmental Protection Agency (EPA) National Emission Standards for Hazardous Air Pollutants (NESHAP) dose calculation requirement in 40 CFR 61 Subpart H. Among several types of data that must be prepared for CAP-88 input is a text file of meteorological data (“WIND” file), consisting of the joint frequency of wind direction, wind speed, and atmospheric stability categories. EPC-CP produces customized WIND files by running a CAP-88 utility program on a user generated text file of wind data in a different format, known as a STability ARray (STAR) file (Turner, 1964). At LANL, EPC-CP meteorologists prepare customized STAR files with data over desired time periods at selected meteorological towers. A custom program written in Precision Visuals -Workstation Analysis and Visualization Environment (PV-WAVE), a commercial Fortran-like language, is used to read LANL meteorological data and write a STAR file; the executable filename is “Star.out”. However, the outdated PV-WAVE utility program is being phased out by EPC-CP, due to the inefficient process to run it and an inability to modify the code. To preserve the ability to create customized meteorological data for CAP-88 in a way that will be easy to use and maintain, a new replacement utility program, written in the Python programming language, has been developed. The new, improved program reads a data file from any LANL meteorological tower, and at each desired observation time, determines the wind direction, wind speed, and stability categories defined in the CAP-88 documentation. The frequencies of all combinations of the three sets of categories are calculated and written to a file in the STAR format, which can later be converted to a WIND file for input into CAP-88.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Conversion of Site-Specific Meteorological Data for use in CAP-88 PC

CAP-88 PC is a commonly used radiological atmospheric dispersion model. This US EPA-approved model is used to demonstrate compliance with atmospheric emission regulations for radionuclides. While the model includes a large library of meteorological data for use across the United States, there are applications when users may wish to use onsite meteorological data as an input to the CAP-88 PC model. Here, in this work, we present a work-around process for preparing and converting onsite data for use in CAP-88 PC. However, the use of local data should provide a more realistic estimate of doses to members of the public in the immediate vicinity of a facility, although the regulatory agency having jurisdiction may not accept the use of local data for compliance. Additionally, the historical meteorological records from 20+ years ago (at a site many kilometers away) might not be representative of current local weather patterns, highlighting another benefit of using local meteorological data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Use of National Centers for Environmental Prediction (NCEP) Data to Support Severe Accident Consequence Analysis at Locations Without Onsite Meteorological Data

Certain regulatory actions under 10 CFR Parts 50, 52, or the proposed part 53 require the assessment of the potential off-site consequence risks to public safety and the environment from a hypothetical severe accident. As an important part of these analyses, atmospheric transport and dispersion (ATD) modeling relies heavily on the prevailing weather patterns of a site. When considering future deployment of new reactor designs in areas where historical onsite meteorological data is not available,

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Meteorological data from the Billy Bar site at the East River, Colorado

This dataset includes Quality Assessed and Quality Controlled (QA/QC) meteorological data from the Billy Bar field site in the East River Watershed, Colorado in order to inform watershed hydrobiogeochemical processes. The data includes 1-hour aligned time series of Solar Radiation, Wind Speed, Wind Direction, Air Temperature, Relative Humidity, Barometric Pressure, and Precipitation. For each parameter, the QA flags are given: 1 is for extreme (potential abnormal values), and 0 is for values in the expected range. The Rmarkdown document is a stand-alone file/notebook that preserves the text, code, and code results, as well as formatting contained in the original R script. The Data profiling report is a summary and graphical presentation of the data exploration process for data analysis and model building, so that users could focus on understanding data and extracting insights. The report provides a summary of each variable and does data profiling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Quality-Controlled Meteorological Data from the Flood Control District of Maricopa County (FCDMC) Network, Phoenix, Arizona (1987-2024)

This dataset contains 15- or 30-minute interval meteorological data from the Flood Control District of Maricopa County (FCDMC), Arizona, USA, covering eight key variables across multiple sensor stations between 1987 and 2024. Each variable is stored as a separate CSV file, containing time-series data that have undergone rigorous quality control (QC) procedures and, where appropriate, short-gap interpolation for consistency. The quality control (QC) pipeline consisted of four sequential tests: (1) a range test to ensure all values fall within physically realistic limits, (2) a step test to identify abrupt and implausible changes between consecutive records, (3) a proximity test that validates flagged values from step test using data from nearby stations and exceedance probability thresholds, and (4) a persistence test to detect and remove periods of unrealistically constant readings. These thresholds were calibrated to Arizona’s environmental conditions and sensor specifications. After QC, short gaps (≤2 hours) were linearly interpolated to ensure consistent temporal resolution, except for wind variables. Due to a major upgrade in FCDMC’s data transmission system, only ALERT-2 protocol data (2016–2024) for wind variables are included; earlier ALERT-1 data were excluded because of irregular sampling and high missing rates. This dataset supports regional climate and infrastructure resilience studies by providing standardized, high-resolution meteorological data for the greater Phoenix metropolitan area.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Navigation and Meteorological Data from Multiple Sensors on Airborne Platform (NAVMET-AIR) Value-Added Product Report

The Navigation and Meteorological Data from Multiple Sensors on Airborne Platform (NAVMET-AIR) value-added product encompasses the aafnaviwg data set in the ARM Data Center (ADC), known as the IWG file, named for the Inter-Agency Working Group for Airborne Data and Telemetry Systems (IWGADTS). Its purpose is to produce a suite of tools to promote standardization of instrument interface, data format, and data processing. This data set’s contents are navigational and meteorological state variables at 1 Hz. It also contains higher-order data flags created by ARM Aerial Facility (AAF) scientists to aid analysis such as periods when the aircraft is flying level, operating in maneuvers, or flying through cloud. Due to the nature of research flights, the payload of the aircraft consists of many duplicate and overlapping instruments to ensure, in case of instrument failure, there is a backup to record the data. Measurements from multiple instruments are consolidated into a single file. Each variable of this data set is carefully chosen from the onboard instrumentation and quality checked by AAF scientists to create this wholistic and most accurate data set for airborne research. This document is intended to be a hub towards the individual “read me” files produced for each campaign. For reference about what instrument was used for calculations on specific days, the read me files are located in the ARM IOP archive at the addresses listed, or access the IOP (intensive operational period) database at https://www.archive.arm.gov/.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Gap-filled meteorological data (2011-2020) and modeled potential evapotranspiration data from the KCOMTCRE2 WeatherUnderground weather station, from the East River Watershed, Colorado

This dataset is a gap-filled meteorological dataset (years 2011-2020) that includes modeled potential evapotranspiration from a station near the pumphouse on the East River (KCOMTCRE2 WeatherUnderground ) near the top of the ridge at the Pumphouse PLM wells. These datasets are useful as model inputs. The KOMTCRE2 station is located on Mt. Crested Butte in Colorado. More information can be found at: https://www.wunderground.com/dashboard/pws/KCOMTCRE2/table/2020-12-7/2020-12-7/daily. The purpose of this dataset is to use as input to hydrological and flow-reactive transport models as meteorological forcing. One attached file includes metadata files with definitions for all variables and units. Subsequent files include the same time series dataset, but aggregated and gap-filled at different time-stamp intervals. These include 5 minute time-series, 15 minute time-series, 30 minute time-series, 1 hour time-series, daily time-series, and hourly and daily modeled evapotranspiration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

95th percentile doses to public and co-located workers based on five years (2010-2014) of LLNL meteorological data (WSF B625)

This calculation is to be used for accident analysis at the Waste Storage Facilities (WSF). The objective of this analysis is to determine: The 95th percentile dose to the public and co-located workers used for accident analysis consistent with NRC Regulatory Guide 1.145 Position 3 using MACCS2 and five years (2010-2014) of LLNL meteorological data.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Cleaned 5-Minute Resolution Air Quality and Meteorological Data from Nine TCEQ CAMS Sites in Houston, Texas (Nov 2021 – Oct 2022)

These data encompass 5-minute air monitoring and meteorological observations collected in the greater Houston, Texas metropolitan region, at nine (9) Continuous Ambient Monitoring Stations (CAMS) operated by the Texas Commission on Environmental Quality (TCEQ) between November 1, 2021 and October 31, 2022. The CAMS sites (CAMS 1, 8, 35, 45, 148, 403, 405, 410, and 1052) were chosen because their instrumentation includes measurements of PM2.5. These sites also provide continuous multi-parameter air-quality and meteorological measurements. Particulate matter (PM2.5, PM10) was sampled along with several trace gases, including ozone (O3), nitrogen oxides (NO, NO2, NOx), sulfur dioxide (SO2), and carbon monoxide (CO). The data set also contains standard surface meteorological parameters (temperature, humidity, pressure, wind speed, and wind direction). Several sites also include AutoGC-based measurements of volatile organic compounds (VOCs). Air monitoring instruments deployed at the selected sites comprise the following systems: BAM-1020 or TEOM (PM2.5), Thermo Scientific TEI 49i (O3), TEI 42i (NOx), and AutoGCs (VOCs). This data set is similar to the data included within the houairq5mX1.00 datastream, except for a few additional quality control steps. A systematic data cleaning and verification process was performed on the data set to ensure its quality and preparation for analysis. Removal of non-numeric status flags (e.g., [LIM], [QAS], [SPZ], [CAL], [PMA], [AQI], [SPN], [MAL]) was accomplished by employing rule-based string parsing to extract valid numerical values. Missing entries were set to -9999; however, invalid or anomalous values (e.g., 99999) were retained as originally reported by the TCEQ to preserve data provenance. The time sequence was verified for completeness, removal of duplicates, and uniformity at 5-minute intervals. Column labeling was standardized, and corresponding values were assessed for physical plausibility. All timestamps in the data set were reported in Coordinated Universal Time (UTC) as provided by the TCEQ. Further, the latitude and longitude coordinates were added for each CAMS site. A subset of the data (June 1–September 30, 2022) has been used in the following publication: Subba et al. 2025. “Implications of sea breeze circulations on boundary layer aerosols in the southern coastal Texas region.” EGUsphere 2025: 1–49, https://doi.org/10.5194/egusphere-2025-2659.

latitude↗
DOE OSTI2020

Using Meteorological Data to Evaluate Worldwide PV Degradation Rates

Here we automate the process to enable the creation of world maps for specific PV durability metrics. The end goal is to create a tool for the creation of world maps predicting the sensitivity of modules to degradation to inform the development and utilization of module testing standards and to provide information to module designers. TMY (Typical Meteorological Year) Data was aggregated from sites around the world to render insight into factors contributing to the degradation of solar modules. The focus of aggregating data was to create a single dataset to work with utilizing Solar Irradiance values combined with Ambient Temperature, Relative Humidity, Wind Speed and Dew-Point Temperature.

14 SOLAR ENERGY↗
DOE OSTI2024

Deep-Learning-derived Boundary Layer Height from Meteorological Data over the SGP, GOAMAZON, CACTI

The planetary boundary-layer (PBL) height (PBLH) is an important parameter for various meteorological and climate studies. This study presents a multi-structure deep neural network (DNN) model, designed to estimate PBLH by integrating morning temperature profiles with surface meteorological observations. The DNN model is developed by leveraging a rich data set of PBLH derived from long-standing radiosonde records and augmented with high-resolution micropulse lidar and Doppler lidar observations. We access the performance of the DNN with an ensemble of 10 members, each featuring distinct hidden layer structures, which collectively yield a robust 27-year PBLH data set over the Southern Great Plains from 1994 to 2020. The influence of various meteorological factors on PBLH is rigorously analyzed through the importance test. Moreover, the DNN model's accuracy is evaluated against radiosonde observations and juxtaposed with conventional remote-sensing methodologies, including Doppler lidar, ceilometer, Raman lidar, and micropulse lidar. The DNN model exhibits reliable performance across diverse conditions and demonstrates lower biases relative to remote-sensing methods. In addition, the DNN model, originally trained over a plain region, demonstrates remarkable adaptability when applied to the heterogeneous terrains and climates encountered during the GoAmazon (tropical rainforest) and CACTI (middle-latitude mountain) campaigns. These findings demonstrate the effectiveness of deep learning models in estimating PBLH, enhancing our understanding of boundary-layer dynamics with implications for enhancing the representation of PBL in weather forecasting and climate modeling.

54 ENVIRONMENTAL SCIENCES↗