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53 results for “WIND DIRECTION”
Wind speed and direction data from benchmark stations at the HJ Andrews Experimental Forest, 1973 to present
A three-level hydro-climatological network for data monitoring was established in 1994. The networks at each level are nested to form a coordinated program of data acquisition and measurement. A future vision of linking the benchmark meteorological stations with regional weather stations to expand the future scope of studies was also considered in designing this network. The first-level in this top-down approach consists of Benchmark Meteorological Stations (BMS) and Benchmark Stream Stations. The BMS are designed to represent the environment across the Andrews. These stations are intended to provide complete, long-term, high temporal resolution, meso-scale hydroclimatological data. The location of the BMS network is based on factors such as elevation, aspect, vegetation gradients, and accessibility. Collected meteorological parameters are generally standardized across the BMS as well as methods and instrumentation. Secondary Meteorological Stations also follow standardized methods and serve similar purposes but are somewhat limited in meteorological parameters collected. The Primary Meteorological Station (PRIMET), Central Meteorological Station (CENMET), Upper Lookout Meteorological Station (UPLMET), and Vanilla Leaf Meteorological Station (VANMET) are the four Benchmark Stations, Climatic Station at Watershed 2 (CS2MET) and the Hi-15 Meteorological Station (H15MET) are Secondary Stations. These wind parameters were previously part of database code MS001, but were separated out into their own database in 2024.
Hubbard Brook Experimental Forest: Wind Speed and Wind Direction Measurements, 1965 - present
Wind data have been measured by an anemometer mounted 3 m above the ground at Hubbard Brook Experimental Forest Headquarters since 1965. Prior to 1981, every mile of wind movement caused a tick mark on a strip-chart recorder, and wind direction as N, S, E, W, or a combination, was recorded continuously. From 1981-2003, wind speed and direction were measured with a MetOne wind speed sensor. In June 2003 the MetOne was replaced with a R.M. Young company wind speed direction sensor (model 05103). Since that time, wind direction (azimuth) is based on a 0 to 360 degree scale. These data were gathered at the Hubbard Brook Experimental Forest in Woodstock, NH, which is operated and maintained by the USDA Forest Service, Northern Research Station.
HURRECON Model for Estimating Hurricane Wind Speed, Direction and Damage
HURRECON is a simple meteorological model that estimates hurricane surface wind speed and direction based on the track, size, and intensity of a hurricane and the surface type (land or water). The model also estimates Fujita-scale wind damage as a function of peak 1/4 mile wind speed and wind gust factor. Estimates can be generated for a single site or a rectangular region. The model is based on published empirical studies of many hurricanes. HURRECON can be used to study the impacts of individual hurricanes or to reconstruct the hurricane disturbance regime for a particular region. For more information on the most recent version of the model please see the published paper (Boose, E. R., K. E. Chamberlin and D. R. Foster. 2001. Landscape and regional impacts of hurricanes in New England. Ecological Monographs 71: 27-48). Additional information is contained in the documentation that accompanies the program. For an updated version of the HURRECON model in R and Python, please see HF446.
Bonanza Creek LTER: Hourly Wind Speed and Direction at Various Heights from 1988 to Present in the Caribou-Poker Creeks Research Watershed near Fairbanks, Alaska
Hourly wind data from sefveral sites with the the Caribou Poker Creeks Research Watershed. Wind data should be used cautiously during winter months due to ice and snow build up on sensors.
Bonanza Creek LTER: Hourly Wind Speed and Direction at 3m and 10 m from 1988 to Present in the Bonanza Creek Experimental Forest near Fairbanks, Alaska
This dataset contains the hourly output from Wind sensors for the Bonanza Creek Experimental Forest (BCEF). This includes Level 3 weather stations as well as smaller scale and temporal studies. This data can be sorted and viewed by site, year, hour, height of measurement, mean, min, and max value. Updates of each site are different since some are still on going while other had only a 2-3 year life cycle. Winter measurements may be inaccurate due to snow cover.
Hubbard Brook Experimental Forest: 15 Minute Wind Speed and Direction Measurements, 2012 – present
Wind speed and direction have been recorded at 15-minute resolution by an R.M. Young company wind sensor at three locations within Hubbard Brook Experimental Forest since 2012. These data were gathered at the Hubbard Brook Experimental Forest in Woodstock, NH, which is operated and maintained by the USDA Forest Service, Northern Research Station.
PIE LTER 15-minute Wind speed and direction in the lower Plum Island Sound at the Ipswich Bay Yacht Club pier, Ipswich, MA, year 2023.
Wind speed and direction measurements for 2023 at Ipswich Bay Yacht Club, Ipswich, MA. Wind speed is measured every 5 seconds and reported as an average in 15 minute intervals. Maximum wind speed is also reported for each 15 minute interval with a timestamp. Wind direction is measured every 15 minutes.
Wind tunnel distributed temperature sensing with actively heated fibers and microstructures for detecting wind direction
<p>Wind tunnel tests were performed using distributed temperature sensing with actively heated fibers that had microstructures attached in opposing directions on neighboring fibers. These microstructures created a temperature difference between fibers that depended on wind speed, providing a prototype for distributed sensing of wind direction. These data are connected to a publication detailing this work and method, <a href="https://www.atmos-meas-tech-discuss.net/amt-2019-188/">"Distributed observations of wind direction using microstructures attached to actively heated fiber-optic cables"</a>.</p> <p>Data are stored in a netcdf format and includes the instrument reported temperature ('instr_temp') and calibrated temperature ('cal_temp') with the various parameters tested in the linked paper available as coordinates, labeled along an 'expname' dimension.</p> <p>The included ipython notebooks provide examples and explanations for using these laboratory data.</p>
Vertical profiles of urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland
<p><strong>Vertical Profiles of Urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland</strong></p> <p>=================================</p> <p>README version 1.3, 21/07/2022</p> <p>==================================</p> <p>Contact info:</p> <p>Paul Leahy, University College Cork</p> <p>paul.leahy@ucc.ie | +353 21 4902017</p> <p>================================</p> <p> </p> <p><strong>Contents</strong></p> <p><strong>1. Measurement location and time period</strong></p> <p><strong>2. What is measured (brief description)</strong></p> <p><strong>3. Instrumentation</strong></p> <p><strong>4. CSV file detailed descriptions</strong></p> <p>================================</p> <p> </p> <p><strong>1. Measurement location and time period: </strong></p> <p>North roof of Kane Building, University College Cork (UCC), Ireland.</p> <p>Lat 51 d 53 m 34 s N.</p> <p>Long 8 d 29 m 39 s W.</p> <p>Roof is c. 39 m above sea level, and c. 26 m above ground level (ground level reference point is the car park West of the UCC Kane Building).</p> <p>The measurements were taken over a time period of several months in the years 2013 / 2014.</p> <p>=================================</p> <p><strong>2. What is measured (brief description):</strong></p> <p>* LiDAR Wind speed (horizontal and vertical), wind direction, turbulence intensity at 5 altitudes; reference point (0 m) for these altitudes is the top of the LiDAR instrument c. 1.2 m above roof level.</p> <p>* Air temperature, atmospheric pressure, relative humidity.</p> <p>* Wind speed and direction from an ultrasonic anemometer mounted on top of the instrument (c. 1.2 m above roof level).</p> <p>* 10-minute average values (2 files) and high-resolution (c. 23 sec) data (1 file) are provided.</p> <p>See 'CSV file detailed description' below for detailed information.</p> <p>* Diagnostic information.</p> <p>=================================</p> <p><strong>2.1 Surrounding terrain:</strong></p> <p>Surrounding area is urban/suburban. The aspect is northerly.</p> <p>To the West: 2-5 storey buildings, open spaces, suburban.</p> <p>To the South: 2-3 storey buildings, open spaces, trees, river.</p> <p>To the East: 2-3 storey buildings, open spaces.</p> <p>To the North: A higher section of the Kane Building roof (47 m asl), 1-3 storey buildings, suburban.</p> <p>=================================</p> <p><strong>3. Instrumentation:</strong></p> <p>ZephIR 175 continuous wave wind profiling LiDAR with integrated sonic anemometer, temperature, humidity, air temperature pressure sensors and GPS.</p> <p>=================================</p> <p><strong>4. CSV files detailed description:</strong></p> <p><strong>4.1 Data on 10-minute averages:</strong></p> <p>Filename 05092013-03122013_10min_res.csv contains:</p> <p>10 minute averaged data from 05/09/2013 to 03/12/2013.</p> <p>Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m above instrument level.</p> <p> </p> <p>Filename 03122013-07082014_10min_res.csv contains:</p> <p>10 minute averaged data from: 03/12/2013 to 07/08/2014.</p> <p>Measurement altitudes: 148 m, 90 m, 50 m, 35 m, 15 m above instrument level.</p> <p>Note: from 19/06/2014 onwards, LiDAR data missing (MET data continues).</p> <p> </p> <p>The first two rows contain header information.</p> <p>Row 1 contains location information (GPS record)) and the measurement altitudes for wind speeds.</p> <p>Sample GPS record: N51535775W8296590 = 51 d 53.5775 m North; 8 d 29.6590 m West.</p> <p>Row 2 contains the data column headers including units.</p> <p> </p> <p>Wind speeds at each altitude are recorded:</p> <p>No of Packets (= number of scan units averaged over) []</p> <p>Wind direction (mean) [deg]</p> <p>Horizontal wind speed (mean) & standard deviation [m/s]</p> <p>Vertical wind speed (mean) & standard deviation [m/s]</p> <p>Horizontal variance [m^2/s^2] </p> <p>Horizontal min [m/s] </p> <p>Horizontal max [m/s] </p> <p>TI (turbulence intensity) []</p> <p> </p> <p>Other meteorological data:</p> <p>Air temperature [<sup>o</sup>C]</p> <p>Pressure [mbar]</p> <p>Rel. Humidity [%]</p> <p>Rain indicator [unitless] Higher values indicate more rain during the averaging interval.</p> <p>Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer)</p> <p>Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer).</p> <p> </p> <p>Other housekeeping and diagnostic data:</p> <p>Instrument tilt [deg]</p> <p>Instrument bearing [deg]</p> <p>GPS data [degrees N, degrees W]</p> <p>Battery voltage [V] </p> <p>Optics, electronics and battery temperature [<sup>o</sup>C]</p> <p> </p> <p>=====================================================</p> <p> </p> <p><strong>4.2 Data with high time resolution (~23 s):</strong></p> <p> </p> <p>Filename 05092013-11112013_23s_res.csv contains:</p> <p>High resolution data from 05/09/2013 to 11/11/2013</p> <p>Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m.</p> <p> </p> <p>Note on time resolution:</p> <p>The time resolution of processed wind measurements is c. 3 seconds per wind level, and around 8 seconds to reset to the first level. A full wind profile measurement at 5 altitudes therefore takes around (5 x 3) + 8 = 23 s to complete.</p> <p>The raw scanning resolution of the instrument is higher than this, as each wind measurement is an average of several values.</p> <p> </p> <p>Row 1 contains location information (lat, long) and the vertical measurement levels for wind speeds.</p> <p>Row 2 contains the data column headers including units.</p> <p> </p> <p>Wind speeds at each altitude are recorded:</p> <p>No of Packets (= scan units averaged over) []</p> <p>Wind direction (mean) [deg]</p> <p>Horizontal wind speed (mean) & standard deviation [m/s]</p> <p>Vertical wind speed (mean) & standard deviation [m/s]</p> <p>Horizontal variance [m^2/s^2] not defined as measurement interval is too short.</p> <p>Horizontal min [m/s] not defined as measurement interval is too short. </p> <p>Horizontal max [m/s] not defined as measurement interval is too short. </p> <p>TI (turbulence intensity) [] not defined as measurement interval is too short.</p> <p> </p> <p>Other meteorological data:</p> <p>Air temperature [<sup>o</sup>C]</p> <p>Pressure [mbar]</p> <p>Rel. Humidity [%]</p> <p>Rain indicator [unitless] Higher values indicate more rain during the scanning interval.</p> <p>Wind Speed [m/s] (column 'MET Wind Speed' measured at the top of the instrument by the ultrasonic anemometer)</p> <p>Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer.</p> <p> </p> <p>Other housekeeping and diagnostic data:</p> <p>Instrument tilt [deg]</p> <p>Instrument bearing [deg]</p> <p>GPS data [degrees N, degrees W]</p> <p>Battery voltage [V] </p> <p>Optics, electronics and battery temperature [<sup>o</sup>C]</p> <p> </p> <p>=====================================================</p> <p><strong>4.3 Quality control indicators:</strong></p> <p> </p> <p>9998 atmospheric conditions which adversely affect LiDAR wind speed measurements e.g. fog</p> <p>9999 high quality wind speed measurement not possible e.g. very low wind speed or obscuration of optical path</p> <p>Status Flag 'Green' => good</p> <p>=======================================================</p> <p> </p>
HURRECON Model for Estimating Hurricane Wind Speed, Direction, and Damage (R and Python)
The HURRECON model estimates wind speed, wind direction, enhanced Fujita scale wind damage, and duration of EF0 to EF5 winds as a function of hurricane location and maximum sustained wind speed. Results may be generated for a single site or an entire region. Hurricane track and intensity data may be imported directly from the US National Hurricane Center's HURDAT2 database. HURRECON is available in R and Python. The R version is available on CRAN as HurreconR. The model is an updated version of the original HURRECON model written in Borland Pascal for use with Idrisi (see HF025). New features include support for: (1) estimating wind damage on the enhanced Fujita scale, (2) importing hurricane track and intensity data directly from HURDAT2, (3) creating a land-water file with user-selected geographic coordinates and spatial resolution, and (4) creating plots of site and regional results. The model equations for estimating wind speed and direction, including parameter values for inflow angle, friction factor, and wind gust factor (over land and water), are unchanged from the original HURRECON model. For more details and sample datasets, see the project website on GitHub (https://github.com/hurrecon-model).
PIE LTER 15-minute Wind speed and direction in the lower Plum Island Sound at the Ipswich Bay Yacht Club pier, Ipswich, MA, year 2024.
Wind speed and direction measurements for 2024 at Ipswich Bay Yacht Club, Ipswich, MA. Wind speed is measured every 5 seconds and reported as an average in 15 minute intervals. Maximum wind speed is also reported for each 15 minute interval with a timestamp. Wind direction is measured every 15 minutes.
PIE LTER 15-minute Wind speed and direction in the lower Plum Island Sound at the Ipswich Bay Yacht Club pier, Ipswich, MA, year 2025.
Wind speed and direction measurements for 2025 at Ipswich Bay Yacht Club, Ipswich, MA. Wind speed is measured every 5 seconds and reported as an average in 15 minute intervals. Maximum wind speed is also reported for each 15 minute interval with a timestamp. Wind direction is measured every 15 minutes.
2005_2018_Wind_Speed_Direction
<p><strong>Abstract:</strong></p> <p>European Wind characteristics at 10m in height derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses (ERA) data. The data defines characteristics such as windspeed direction from and direction. Monthly mean values for the years 2005-2018 at 0.125 of a degree Clipped to the E4warning extent. </p> <table> <tbody> <tr> <td><strong>PROJECTION:</strong></td> <td>Geographic</td> </tr> <tr> <td><strong>DATUM:</strong></td> <td>WGS84</td> </tr> </tbody> </table> <p><strong>File Names: </strong></p> <p>The last 4 digits of the file name present Month and Year of file. </p> <p>dir in file names refer to direction.</p> <p>speed in file names refer to windspeed</p> <p> </p> <p> </p>
Air pollution in a tropical city: the relationship between wind direction and lichen bio-indicators in San José, Costa Rica
<p>Lichens are good bio-indicators of air pollution, but in most tropical countries there are few studies on the subject; however, in the city of San José, Costa Rica, the relationship between air pollution and lichens has been studied for decades. In this article we evaluate the hypothesis that air pollution is lower where the wind enters the urban area (Northeast) and higher where it exits San José (Southwest). We identified the urban parks with a minimum area of approximately 5 000m² and randomly selected a sample of 40 parks located along the passage of wind through the city. To measure lichen coverage, we applied a previously validated 10 x 20cm template with 50 random points to five trees per park (1.5m above ground, to the side with most lichens). Our results (years 2008 and 2009) fully agree with the generally accepted view that lichens reflect air pollution carried by circulating air masses. The practical implication is that the air enters the city relatively clean by the semi-rural and economically middle class area of Coronado, and leaves through the developed neighborhoods of Escazú and Santa Ana with a significant amount of pollutants. In the dry season, the live lichen coverage of this tropical city was lower than in the May to December rainy season, a pattern that contrasts with temperate habitats; but regardless of the season, pollution follows the pattern of wind movement through the city</p>
Air pollution in a tropical city: the relationship between wind direction and lichen bioindicators in San Jose, Costa Rica
<p>Lichens are good bio-indicators of air pollution, but in most tropical countries there are few studies on the subject; however, in the city of San José, Costa Rica, the relationship between air pollution and lichens has been studied for decades. In this article we evaluate the hypothesis that air pollution is lower where the wind enters the urban area (Northeast) and higher where it exits San José (Southwest). We identified the urban parks with a minimum area of approximately 5 000m² and randomly selected a sample of 40 parks located along the passage of wind through the city. To measure lichen coverage, we applied a previously validated 10 x 20cm template with 50 random points to five trees per park (1.5m above ground, to the side with most lichens). Our results (years 2008 and 2009) fully agree with the generally accepted view that lichens reflect air pollution carried by circulating air masses. The practical implication is that the air enters the city relatively clean by the semi-rural and economically middle class area of Coronado, and leaves through the developed neighborhoods of Escazú and Santa Ana with a significant amount of pollutants. In the dry season, the live lichen coverage of this tropical city was lower than in the May to December rainy season, a pattern that contrasts with temperate habitats; but regardless of the season, pollution follows the pattern of wind movement through the city</p>
Air pollution in a tropical city: the relationship between wind direction and lichen bio-indicators in San José, Costa Rica
<p>Lichens are good bio-indicators of air pollution, but in most tropical countries there are few studies on the subject; however, in the city of San José, Costa Rica, the relationship between air pollution and lichens has been studied for decades. In this article we evaluate the hypothesis that air pollution is lower where the wind enters the urban area (Northeast) and higher where it exits San José (Southwest). We identified the urban parks with a minimum area of approximately 5 000m² and randomly selected a sample of 40 parks located along the passage of wind through the city. To measure lichen coverage, we applied a previously validated 10 x 20cm template with 50 random points to five trees per park (1.5m above ground, to the side with most lichens). Our results (years 2008 and 2009) fully agree with the generally accepted view that lichens reflect air pollution carried by circulating air masses. The practical implication is that the air enters the city relatively clean by the semi-rural and economically middle class area of Coronado, and leaves through the developed neighborhoods of Escazú and Santa Ana with a significant amount of pollutants. In the dry season, the live lichen coverage of this tropical city was lower than in the May to December rainy season, a pattern that contrasts with temperate habitats; but regardless of the season, pollution follows the pattern of wind movement through the city</p>
Vertical profiles of air temperature, relative humidity, wind speed and direction observed using UAV over the Mukhrino peatland in June 2022
<p>Vertical profiles of air temperature and relative humidity were measured using the iMetXQ2 sensor onboard DJI Phantom 4 quad-copter; vertical profiles of wind speed and direction were obtained from the Phantom 4 flight logs as produced by the DJI proprietary algorithm. </p>
Aerodynamic Roughness Controlled by Wind Direction, with Implications for Glacial Surface Energy Balance and Melt Rate
<p>This repository includes raw datasets, Python scripts, and output data products associated with the MRes project '<span>Aerodynamic Roughness Controlled by Wind Direction, with Implications for Glacial Surface Energy Balance and Melt Rate</span>', by Josh Abrahams, University of Leeds. </p>
Fig. 3 in Relationship between host searching and wind direction in Ophraella communa (Coleoptera: Chrysomelidae)
Fig. 3. Mean numbers (± SE) of Ophraella communa eggs and adults at various cardinal directions from the center of each plot on common ragweed plants in the concentric bands. Data represented by columns bearing the same letters were not significantly different (LSD, a = 0.05). The dominant direction of the wind during the period of the entire experiment was from the south.
Fig. 1 in Relationship between host searching and wind direction in Ophraella communa (Coleoptera: Chrysomelidae)
Fig. 1. Plot design to study the location of host plants by Ophraella communa adults in relation to wind direction. This diagram displays the planting pattern for 1 m wide concentric plots of the host, common ragweed, separated by 1 m wide concentric barriers of a non-host, sunflower.
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