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At least 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
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 OSTI2023

TAMU TRACER Upper Air Radiosonde Data June-September 2022 Southeast Texas

The following dataset relates to the TAMU TRACER radiosonde IOP, which involved launching iMet-4 and iMet-4C radiosonde models from various locations in southeast Texas. The radiosondes were launched between June and September 2022 during the TRACER IOP. Telemetry data for all IOPs were copied using the iMet-OS II software provided by radiosonde manufacturer, Intermet Systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

TRACER CLAMPS1 Doppler Lidar VAD Data

These files contain 24 hour periods of data collected from the CLAMPS1 Halo Streamline XR Doppler lidar. The Doppler lidar conducts regular conical scans at a set elevation angle. These data are then passed through a typical VAD algorithm to retrieve horizontal wind speed and direction profiles. These data were collected during the TRACER project. For the TRACER campaign, the CLAMPS1 Doppler lidar collected PPI scans at 70 deg elevation every 20 minutes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Soil Moisture Data for TRACER project (Houston, TX)

The purpose of this study was to collect and distribute ground-truth soil water content and meteorological data in the Houston, TX, area, supporting the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility, and the 2022 field campaign for the Tracking Aerosol Convection Interactions ExpeRiment (TRACER). The files herein contain soil water content and meteorological data for three stations that the Bureau of Economic Geology at UT Austin installed in the Houston, TX, area during the period of performance. The data includes soil moisture, volumetric water content, electrical conductivity of soil, soil temperature, rain precipitation, air temperature and other parameters. Data files within this data set contain measurements at both sub-hourly and 1-hour resolution measurements. The sub-hourly meteorological data file names end with “TRACER_SubHourly_met.dat”, and the sub-hourly soil data files end with “TRACER_SubHourly_soil.dat”. For the 1-hour resolution data, the files ending with "_Soil_flagged.dat” contain mean hourly volumetric soil water content and temperature measured at 5, 10, 20 and 50 cm depths. The files ending with “_Meteoro_flagged.dat” contain mean hourly measured precipitation, air temperature and humidity, wind speed and direction, and solar radiation. All data have undergone QA/QC procedures that are described by Caldwell et al. (2019) and Dorigo et al. (2013) for the soil-specific data, and EPA (2008) for the meteorological data.

Air temperature↗
DOE OSTI2024

Automated Generation of Weather and Climate Analysis Products

Wind roses are an important part to site operations as they depict the wind speed and direction percentage over a time period. In this project, I automated the production of wind roses for SRS meteorological towers. I developed a script to sequence through the dataset by period and create a wind rose for each of those period sets. Graphics were generated for every 4-hour period of the day, for each of the 4 heights of the instruments, for every half of each month. A 10-year climatological period consisted of wind speed and directions measurements at 15-minute intervals. We used the years 2014-2024 as the wind instruments on the tower were upgraded to sonic anemometers in early 2014. I then compared these wind roses to those from a previous study done in 2003 to analyze the differences and similarities in the wind patterns. The wind roses are also used to understand environmental transport conditions at SRS. When comparing the different heights of the anemometers to the 2003 report, there is a similarity in the fact that the direction is relatively the same across the levels, with the wind speeds increasing as you get higher in the air. When comparing the season of our graphs against the 2003 report, we noticed that the winter and summer months appear to have very similar wind directions. However, in the spring we noticed that there were more southerly winds compared to the 2003 report which had more westerly winds. Finally, in the fall months, we noticed that there was a higher percentage of northeasterly winds, while the 2003 report showed more southeasterly winds. We can use these summaries to estimate the directional dependence of dose that the surrounding areas receive throughout the year according to time of day.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Generation of realistic input parameters for simulating atmospheric point-spread functions at astronomical observatories

High-fidelity simulated astronomical images are an important tool in developing and measuring the performance of image-processing algorithms, particularly for high precision measurements of cosmic shear – correlated distortions of images of distant galaxies due to weak gravitational lensing caused by the large-scale mass distribution in the Universe. For unbiased measurements of cosmic shear, all other sources of correlated image distortions must be modeled or removed. One such source is the correlated blurring of images due to optical turbulence in the atmosphere, which dominates the point-spread function (PSF) for ground-based instruments. In this work, we leverage data from weather forecasting models to produce wind speeds and directions, and turbulence parameters, that are realistically correlated with altitude. To study the resulting correlations in the size and shape of the PSF, we generate simulated images of the PSF across a ~10 square-degree field of view – the size of the camera focal plane for the Vera C. Rubin Observatory in Chile – using weather data and historical seeing for a geographic location near the Observatory. We make quantitative predictions for two-point correlation functions (2PCF) that are used in analyses of cosmic shear. We observe a strong anisotropy in the two-dimensional 2PCF, which is expected based on observations in real images, and study the dependence of the orientation of the anisotropy on dominant wind directions near the ground and at higher altitudes.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2004

Wind Profiles Obtained with a Molecular Direct Detection Doppler Lidar During IHOP-2002

The Goddard Lidar Observatory for Winds (GLOW) is a mobile direct detection Doppler lidar system which uses the double edge technique to measure the Doppler shift of the molecular backscattered laser signal at a wavelength of 355 nm. In the spring of 2002 GLOW was deployed to the western Oklahoma profiling site (36 deg 33.500 min. N, 100 deg. 36.371 min. W) to participate in the International H2O Project (IHOP). During the IHOP campaign over 240 hours of wind profiles were obtained with the GLOW lidar in support of a variety of scientific investigations.

Gentry, Bruce M.↗
NASA NTRS2006

The Tropospheric Wind Lidar Technology Experiment (TWiLiTE): An Airborne Direct Detection Doppler Lidar Instrument Development Program

Global measurement of tropospheric winds is a key measurement for understanding atmospheric dynamics and improving numerical weather prediction. Global wind profiles remain a high priority for the operational weather community and also for a variety of research applications including studies of the global hydrologic cycle and transport studies of aerosols and trace species. In addition to space based winds, a high altitude airborne system flown on UAV or other advanced platforms would be of great interest for studying mesoscale dynamics and hurricanes. The Tropospheric Wind Lidar Technology Experiment (TWiLiTE) project was selected in 2005 by the NASA Earth Sun Technology Office as part of the Instrument Incubator Program. TWiLiTE will leverage significant research and development investments in key technologies made in the past several years. The primary focus will be on integrating these sub-systems into a complete molecular direct detection Doppler wind lidar system designed for autonomous operation on a high altitude aircraft, such as the NASA WB57, so that the nadir viewing lidar will be able to profile winds through the full troposphere. TWiLiTE is a collaboration involving scientists and technologists from NASA Goddard, NOAA ESRL, Utah State University Space Dynamics Lab and industry partners Michigan Aerospace Corporation and Sigma Space Corporation. NASA Goddard and it's partners have been at the forefront in the development of key lidar technologies (lasers, telescopes, scanning systems, detectors and receivers) required to enable spaceborne global wind lidar measurement. The TWiLiTE integrated airborne Doppler lidar instrument will be the first demonstration of a airborne scanning direct detection Doppler lidar and will serve as a critical milestone on the path to a fixture spaceborne tropospheric wind system. The completed system will have the capability to profile winds in clear air from the aircraft altitude of 18 h to the surface with 250 m vertical resolution and less than 2 meters per second velocity accuracy. The instrument design, technologies and predicted performance will be presented.

Gentry, Bruce↗
DOE OSTI2025

A Large-Eddy Simulation Study in WRF on Wind over Broadband Waves of Different Directions and Spreading Widths

We use the Weather Research and Forecasting (WRF) Model coupled with moving waves to conduct large-eddy simulations (LESs) of wind over broadband waves with different propagating directions and spreading widths. Our results show that wind-opposing waves can double the form drag, and the wave propagating direction affects the mean wind velocity, velocity variances, and pressure stress. On the other hand, waves with wider spread tend to reduce the form drag in the streamwise direction. Results further indicate that the wave direction can impact the bulk drag coefficient by as much as 25%, while the wave directional spreading width can change it by 5%. Based on our wave-phase-resolved simulations, we demonstrate that the parameterization of sea surface roughness is significantly influenced by the direction of wave propagation relative to the wind, a factor that the commonly used Charnock relation does not account for.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Reduced-Order Modeling of Hydrogen Releases from Vent Stacks and with Wind

This report describes computational modeling in the HyRAM+ software for study of hydrogen behavior in two common scenarios. First, models of unignited plumes exiting a vent stack were considered. It was shown that entrainment and vent backpressure were major factors in plume physics. Second, HyRAM+ was extended to include wind effects on plumes by modifying the plume momentum balance and entrainment modeling. Use of the model showed that plume shape and length changed with wind speed and direction; in all cases, wind causes a shortening of the plume along the streamline. While the no-wind case in HyRAM+ has been validated and the newly developed wind model was fitted to very limited experimental data, more controlled experimental configurations would help validate the models and ensure accurate simulation of hydrogen plume behavior for vent stack releases or in wind.

08 HYDROGEN↗
DOE OSTI2022

Behavior and mechanisms of Doppler wind lidar error in varying stability regimes

Abstract. Wind lidars are widespread and important tools in atmospheric observations. An intrinsic part of lidar measurement error is due to atmospheric variability in the remote-sensing scan volume. This study describes and quantifies the distribution of measurement error due to turbulence in varying atmospheric stability. While the lidar error model is general, we demonstrate the approach using large ensembles of virtual WindCube V2 lidar performing a profiling Doppler-beam-swinging scan in quasi-stationary large-eddy simulations (LESs) of convective and stable boundary layers. Error trends vary with the stability regime, time averaging of results, and observation height. A systematic analysis of the observation error explains dominant mechanisms and supports the findings of the empirical results. Treating the error under a random variable framework allows for informed predictions about the effect of different configurations or conditions on lidar performance. Convective conditions are most prone to large errors (up to 1.5 m s−1 in 1 Hz wind speed in strong convection), driven by the large vertical velocity variances in convective conditions and the high elevation angle of the scanning beams (62∘). Range-gate weighting induces a negative bias into the horizontal wind speeds near the surface shear layer (−0.2 m s−1 in the stable test case). Errors in the horizontal wind speed and direction computed from the wind components are sensitive to the background wind speed but have negligible dependence on the relative orientation of the instrument. Especially during low winds and in the presence of large errors in the horizontal velocity estimates, the reported wind speed is subject to a systematic positive bias (up to 0.4 m s−1 in 1 Hz measurements in strong convection). Vector time-averaged measurements can improve the behavior of the error distributions (reducing the 10 min wind speed error standard deviation to <0.3 m s−1 and the bias to <0.1 m s−1 in strong convection) with a predictable effectiveness related to the number of decorrelated samples in the time window. Hybrid schemes weighting the 10 min scalar- and vector-averaged lidar measurements are shown to be effective at reducing the wind speed biases compared to cup measurements in most of the simulated conditions, with time averages longer than 10 min recommended for best use in some unstable conditions. The approach in decomposing the error mechanisms with the help of the LES flow field could be extended to more complex measurement scenarios and scans.

17 WIND ENERGY↗
NASA NTRS1972

Project Fog Drops. Part 1: Investigations of warm fog properties

A detailed study was made of the micrometeorological and microphysical characteristics of eleven valley fogs occurring near Elmira, New York. Observations were made of temperature, dew point, wind speed and direction, dew deposition, vertical wind velocity, and net radiative flux. In fog, visibility was continuously recorded and periodic measurements were made of liquid water content and drop-size distribution. The observations were initiated in late evening and continued until the time of fog dissipation. The vertical distribution of temperature in the lowest 300 meters and cloud nucleus concentration at several heights were measured from an aircraft before fog nucleus concentrations at several heights were measured from an aircraft before fog formation. A numerical model was developed to investigate the life cycle of radiation fogs. The model predicts the temporal evolution of the vertical distributions of temperature, water vapor, and liquid water as determined by the turbulent transfer of heat and moisture. The model includes the nocturnal cooling of the earth's surface, dew formation, fog drop sedimentation, and the absorption of infrared radiation by fog.

Pilie, R. J.↗
NASA NTRS1978

Seasat-A opens new phase in earth observations

The data output of the Seasat-A, the first satellite designed specifically for oceanographic observation, is described. Wave height and direction, ice distribution, surface wind speed and direction, ocean-surface temperature, and atmospheric water content will be monitored by the satellite. Imaging radar, microwave scatterometer, microwave radiometer and visual and infrared radiometer experiments are included in the Seasat-A observing program. Two types of orbits have been developed: a near-repeat cycle of three days, and a near-repeat cycle of 25 days. Both orbit types offer fine coverage (18.5 km crossing separations) of the earth. Synthetic aperture radar data on computer-compatible tapes and geophysical data records available through the Seasat-A mission are described.

Cutting, E.↗
NASA NTRS1978

Estimation of Venus wind velocities from high-resolution infrared spectra

Zonal velocity profiles in the Venus atmosphere above the clouds were estimated from measured asymmetries of HCl and HF infrared absorption lines in high-resolution Fourier interferometer spectra of the planet. These asymmetries are caused by both pressure-induced shifts in the positions of the hydrogen-halide lines perturbed by CO2 and Doppler shifts due to atmospheric motions. Particularly in the case of the HCl 2-0 band, the effects of the two types of line shifts can be easily isolated, making it possible to estimate a profile of average Venus equatorial zonal velocity as a function of pressure in the region roughly 60 to 70 km above the surface of the planet. The mean profiles obtained show strong vertical shear in the Venus zonal winds near the cloud-top level, and both the magnitude and direction of winds at all levels in this region appear to vary greatly with longitude relative to the sub-solar point.

Smith, M. A. H.↗
NASA NTRS1989

Combined VHF Dopplar radar and airborne (CV-990) measurements of atmospheric winds on the mesoscale

Hourly measurements of wind speed and direction obtained using two wind profiling Doppler radars during two prolonged jet stream occurrences over western Pennsylvania were analyzed. In particular, the time-variant characteristics of derived shear profiles were examined. To prevent a potential loss of structural detail and retain statistical significance, data from both radars were stratified into categories based on the location data from the Penn State radar were also compared to data from Pittsburgh radiosondes. Profiler data dropouts were studied in an attempt to determine possible reasons for the apparently reduced performance of profiling radars operating beneath a jet stream. Temperature profiles for the radar site were obtained using an interpolated temperature and dewpoint temperature sounding procedure developed at Penn State. The combination of measured wind and interpolated temperature profiles allowed Richardson number profiles to be generated for the profiler sounding volume. Both Richardson number and wind shear statistics were then examined along with pilot reports of turbulence in the vicinity of the profiler.

Fairall, Christopher W.↗
NASA NTRS1991

Interpretation of combined wind profiler and aircraft-measured tropospheric winds and clear air turbulence

In the first experiment, it was found that wind profilers are far better suited for the detailed examination of jet stream structure than are weather balloons. The combination of good vertical resolution with not previously obtained temporal resolution reveals structural details not seen before. Development of probability-derived shear values appears possible. A good correlation between pilot reports of turbulence and wind shear was found. In the second experiment, hourly measurements of wind speed and direction obtained using two wind profiling Doppler radars during two prolonged jet stream occurrences over western Pennsylvania were analyzed. In particular, the time-variant characteristics of derived shear profiles were examined. Profiler data dropouts were studied in an attempt to determine possible reasons for the apparently reduced performance of profiling radar operating beneath a jet stream. Richardson number and wind shear statistics were examined along with pilot reports of turbulence in the vicinity of the profiler.

Thomson, D. W.↗
NASA NTRS2011

30 WS North Base Wind Study

The 30 Weather Squadron (30 WS) is concerned about strong winds observed at their northern towers without advance warning. They state that terrain influences along the extreme northern fringes of Vandenberg Air Force Base (VAFB) make it difficult for forecasters to issue timely and accurate high wind warnings for northeasterly wind events. These events tend to occur during the winter or early spring when they are under the influence of the Great Basin high pressure weather regime. The Launch Weather Officers (LWOs) have seen these rapid wind increases in the current northern Towers 60, 70 and 71 in excess of their 35 kt operational warning threshold. For this task, the 30 WS requested the Applied Meteorology Unit (AMU) analyze data from days when these towers reported winds in excess of 35 kt and determine if there were any precursors in the observations that would allow the LWOs to better forecast and warn their operational customers for these wind events. The 30 WS provided wind tower data for the cool season (October - March) from the period January 2004-March 20 IO. The AMU decoded and evaluated the wind tower data for 66 days identified by the 30 WS as having high-wind events. Out of the 66 event days, only 30 had wind speed observations of > or =35 kt from at least one of the three northern towers. The AMU analyzed surface and upper air charts to determine the synoptic conditions for each event day along with tower peak wind speed and direction time series and wind rose charts for all 30 event days. The analysis revealed a trend on all event days in which the tower winds shifted to the northeast for a period of time before the first recorded > or =35 kt wind speed. The time periods for the 30 event days ranged from 20 minutes to several hours, with a median value of 110 minutes. This trend, if monitored, could give the 30 WS forecasters a precursor to assist in issuing an operational warning before a high wind event occurs. The AMU recommends developing a high-wind alert capability for VAFB using a local mesoscale model to forecast these wind events. The model should incorporate all of the VAFB local data sets and have a forecast capability of between 2 to 24 hours. Such a model would allow the meteorologists at VAFB to alert the operational customers of high wind events in a timely manner so protective action could be taken.

Wheeler, Mark↗
DOE OSTI2020

How Should Machine Learning Be Successfully Used for Wind Speed Vertical Extrapolation?

An accurate characterization of the wind resource available at hub-height is required for an efficient and bankable wind farm project. However, direct measurement of wind speed at the constantly increasing height of the hub of commercial wind turbines is oftentimes challenging and expensive, so that it is common practice to vertically extrapolate the wind resource from lower and more easily accessible levels. Conventional techniques for wind speed vertical extrapolation include the use of a power law and a logarithmic profile. While simple, the limits in accuracy of these methods have been shown in various studies. Recently, machine learning has been proposed as a new method to vertically extrapolate winds. All the published studies on the topic assess the performance of machine learning techniques in vertically extrapolating the wind resource at the same location where the algorithm has been trained. However, in real-world applications, the wind resource is measured at the instrument location, but it then needs to be extrapolated at hub height at the location of the wind turbines within the find farm. To be able to fully recommend the use of machine learning techniques over the simple power law and logarithmic law, the spatial variability of the performance improvements of the machine learning approaches needs to be assessed. Here, we propose a round-robin validation of a machine learning-based method for wind speed extrapolation. We use 20 months of observations at four locations spanning a 100 km wide region at the Southern Great Plains (SGP) atmospheric observatory, in north-central Oklahoma. At each location, we train a random forest to predict 30-min average wind speed at 143 m AGL. We use as input features lidar wind speed at 65 m AGL, time of day, sonic anemometer wind speed at 4 m AGL, turbulent kinetic energy, and Obukhov length. First, we perform a same-site comparison of the performance of the proposed random forest against the conventional techniques for wind speed extrapolation (namely power law and logarithmic profile, with widely accepted stability corrections). We find that the random forest outperforms the power law in vertically extrapolating wind speed in all the considered stability regimes, with a 33% reduction in MAE for stable conditions, and a 31% reduction in unstable conditions. Similar results are found when comparing predictions of extrapolated winds from the logarithmic profile and the random forest with the observed values. Next, we propose a round-robin validation, to use the random forest trained at each site to extrapolate wind speed at the remaining three sites. We find that the performance of the random forest approach degrades when the algorithm is tested at a site different than the training one. However, even under those circumstances, the machine learning-based approach still outperforms the conventional techniques for wind speed extrapolation, with, on average, a reduction in mean absolute error between 15 and 20% over the conventional methods, with the largest benefits obtained under stable conditions.

Monte Carlo↗