scholarly journals ON THE CORRECTION OF LAND-BASED WIND MEASUREMENTS FOR OCEANOGRAPHIC APPLICATIONS

1980 ◽  
Vol 1 (17) ◽  
pp. 43 ◽  
Author(s):  
S.A. Hsu
Keyword(s):  
Wind Speed ◽  
Gulf Of Alaska ◽  
Internal Boundary Layer ◽  
Internal Boundary ◽  
Data Sets ◽  
Onshore Wind ◽  
Mean Wind Speed ◽  
Wind Measurements ◽  
Nocturnal Inversion

Simultaneous offshore and onshore wind measurements were made at stations ranging from Somalia, near the equator, to the Gulf of Alaska. Offshore data obtained from standard U.S. NOAA buoys, research platforms, and merchant ships were compared with data from coastal stations. The results indicated that, under the commonly observed speed of 5-6 m/s, land measurements of mean wind speed are only 63% of the offshore mean speed. Furthermore, it was found that only those stations located in the beach area that measure wind speed above both the internal boundary layer and the nocturnal inversion height represent offshore conditions. In order to correct land-measured wind data, a formula is developed and verified by all 2/3 existing data sets. A simplified equation, i.e., U = 3 U1 s is proposed for offshore applications. Criteria for in situ wind measurements near the coast are outlined. Data reduction procedures for inland stations are also provided.

scholarly journals Seasonal effects in the long-term correction of short-term wind measurements using reanalysis data

2021 ◽  
Vol 6 (6) ◽  
pp. 1473-1490
Author(s):  
Alexander Basse ◽  
Doron Callies ◽  
Anselm Grötzner ◽  
Lukas Pauscher
Keyword(s):  
Wind Speed ◽  
Reference Data ◽  
Measurement Data ◽  
Theoretical Framework ◽  
Reanalysis Data ◽  
Seasonal Effects ◽  
Data Sets ◽  
Mean Wind Speed ◽  
Wind Measurements

Abstract. Measure–correlate–predict (MCP) approaches are often used to correct wind measurements to the long-term wind conditions on-site. This paper investigates systematic errors in MCP-based long-term corrections which occur if the measurement on-site covers only a few months (seasonal biases). In this context, two common linear MCP methods are tested and compared with regard to accuracy in mean, variance, and turbine energy production – namely, variance ratio (VR) and linear regression with residuals (LR). Wind measurement data from 18 sites with different terrain complexity in Germany are used (measurement heights between 100 and 140 m). Six different reanalysis data sets serve as the reference (long-term) wind data in the MCP calculations. All these reanalysis data sets showed an overpronounced annual course of wind speed (i.e., wind speeds too high in winter and too low in summer). However, despite the mathematical similarity of the two MCP methods, these errors in the data resulted in very different seasonal biases when either the VR or LR methods were used for the MCP calculations. In general, the VR method produced overestimations of the mean wind speed when measuring in summer and underestimations in the case of winter measurements. The LR method, in contrast, predominantly led to opposite results. An analysis of the bias in variance did not show such a clear seasonal variation. Overall, the variance error plays only a minor role for the accuracy in energy compared to the error in mean wind speed. Besides the experimental analysis, a theoretical framework is presented which explains these phenomena. This framework enables us to trace the seasonal biases to the mechanics of the methods and the properties of the reanalysis data sets. In summary, three aspects are identified as the main influential factors for the seasonal biases in mean wind speed: (1) the (dis-)similarity of the real wind conditions on-site in correlation and correction period (representativeness of the measurement period), (2) the capability of the reference data to reproduce the seasonal course of wind speed, and (3) the regression parameter β1 (slope) of the linear MCP method. This theoretical framework can also be considered valid for different measurement durations, other reference data sets, and other regions of the world.

A study of wind speed modification and internal boundary-layer heights in a coastal region

1988 ◽  
Vol 42 (4) ◽  
pp. 313-335 ◽  
Author(s):  
Hans Bergström ◽  
Per-Erik Johansson ◽  
Ann-Sofi Smedman
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Coastal Region ◽  
Internal Boundary Layer ◽  
Internal Boundary

scholarly journals Development of an in situ Acoustic Anemometer to Measure Wind in the Stratosphere for SENSOR

2021 ◽  
Author(s):  
Song Liang ◽  
Hu Xiong ◽  
Wei Feng ◽  
Yan Zhaoai ◽  
Xu Qingchen ◽  
...  
Keyword(s):  
Wind Speed ◽  
Meridional Wind ◽  
Floating Platform ◽  
Level Flight ◽  
Subsequent Publication ◽  
Wind Measurements ◽  
Commercial Off The Shelf ◽  
Solar Storms ◽  
Acoustic Anemometer

Abstract. The Stratospheric Environmental respoNses to Solar stORms (SENSOR) campaign investigates the influence of solar storms on the stratosphere. This campaign employs a long-duration zero-pressure balloon as a platform to carry multiple types of payloads during a series of flight experiments in the mid-latitude stratosphere from 2019 to 2022. This article describes the development and testing of an acoustic anemometer for obtaining in situ wind measurements along the balloon trajectory. Developing this anemometer was necessary, as there is no existing commercial off-the-shelf product, to the authors' knowledge, capable of obtaining in situ wind measurements on a high-altitude balloon or other similar floating platform in the stratosphere. The anemometer is also equipped with temperature, pressure, and humidity sensors from a Temperature-Pressure-Humidity measurement module, inherited from a radiosonde developed for sounding balloons. The acoustic anemometer and other sensors were used in a flight experiment of the SENSOR campaign that took place in the Da chaidan District (95.37° E, 37.74° N) on 4 September 2019. The zonal and meridional wind speed observations, which were obtained during level flight at an altitude exceeding 20 km, are presented. This is the first time that in situ wind measurements were obtained during level flight at this altitude. In addition to wind speed measurements, temperature, pressure, and relative humidity measurements during ascent are compared to observations from a nearby radiosonde launched four hours earlier. Further analysis of the wind data will presented in a subsequent publication. The problems experienced by the acoustic anemometer during the 2019 experiment show that the acoustic anemometer must be improved for future experiments in the SENSOR campaign.

scholarly journals Validation of Dual-Doppler Wind Profiles with in situ Anemometry

2015 ◽  
Vol 32 (5) ◽  
pp. 943-960 ◽  
Author(s):  
W. Scott Gunter ◽  
John L. Schroeder ◽  
Brian D. Hirth
Keyword(s):  
High Resolution ◽  
Wind Speed ◽  
Doppler Radar ◽  
Heavy Precipitation ◽  
Atmospheric Conditions ◽  
Wind Speeds ◽  
Time Histories ◽  
Mean Wind Speed ◽  
Wind Profiles

AbstractTypical methods used to acquire wind profiles from Doppler radar measurements rely on plan position indicator (PPI) scans being performed at multiple elevation angles to utilize the velocity–azimuth display technique or to construct dual-Doppler synthesis. These techniques, as well as those employed by wind profilers, often produce wind profiles that lack the spatial or temporal resolution to resolve finescale features. If two radars perform range–height indicator (RHI) scans (constant azimuth, multiple elevations) along azimuths separated by approximately 90°, then the intersection of the coordinated RHI planes represents a vertical set of points where dual-Doppler wind syntheses are possible and wind speed and direction profiles can be retrieved. This method also allows for the generation of high-resolution wind time histories that can be compared to anemometer time histories. This study focuses on the use of the coordinated RHI scanning strategy by two high-resolution mobile Doppler radars in close proximity to a 200-m instrumented tower. In one of the first high-resolution, long-duration comparisons of dual-Doppler wind synthesis with in situ anemometry, the mean and turbulence states of the wind measured by each platform were compared in varying atmospheric conditions. Examination of mean wind speed and direction profiles in both clear-air (nonprecipitating) and precipitating environments revealed excellent agreement above approximately 50 m. Below this level, dual-Doppler wind speeds were still good but slightly overestimated as compared to the anemometer-measured wind speeds in heavy precipitation. Bulk turbulence parameters were also slightly underestimated by the dual-Doppler syntheses.

Ten years of the Wind Atlas of South Africa: Final results from WASA 3

2021 ◽  
Author(s):  
Andrea Hahmann ◽  
Chris Lennard ◽  
Rogier Floors ◽  
Dalibor Cavar ◽  
Niels G. Mortensen ◽  
...  
Keyword(s):  
South Africa ◽  
Wind Speed ◽  
Power Density ◽  
Horizontal Resolution ◽  
Data Sets ◽  
Error Statistics ◽  
Mean Power ◽  
Mean Wind Speed ◽  
Wind Climate

<p>We present the evolution of the methods used to create and validate the various numerical wind atlases during the past ten years of the Wind Atlas for South Africa (WASA) project. In WASA 3, we improved on the previous numerical wind atlases by:</p><ul><li>Creating an ensemble of 2-year simulations to find the optimal set of parameterisations and surface conditions for the wind climate of South Africa.</li> <li>Using a new method of generalisation and downscaling of the WRF-derived wind climate using the PyWAsP engine.</li> <li>Producing the most extensive to date wind climatology for South Africa, 30 years (1990–2019) simulation covering all South Africa at 3.33 km × 3.33 km spatial resolution and 30 minutes time output.</li> </ul><p>We will discuss these three areas and their improvements to the wind atlas' quality. The WASA 3 wind atlas' final error statistics show that the new WRF + PyWAsP method has a MAPE of 11.8% and 3.5% for the long-term mean power density and mean wind speed, respectively. These statistics are improved from those in WASA 1 and WASA 2.</p><p>When disregarding the two masts (WM09 and WM11) located in highly complex terrain, where the methodology was never designed, the use of the WRF and WRF + PyWAsP downscaling narrows the error distributions for both long-term wind speed and power density compared to the global reanalysis, ERA5.</p><p>The validated numerical wind atlas has further been used to model the wind resources of the entire land area of South Africa using the microscale WAsP model. Raster data exist with a horizontal resolution of 250 meters and three levels of 50, 100 and 150 meters a.g.l. of mean wind speed, power density, air density, Weibull <em>A </em>and<em> k </em>parameters, and ruggedness index.  These data sets and the WRF dataset will be made available in the public domain at the end of the project. Data sets for other heights above the ground and offshore can easily be added later.</p>

scholarly journals Wind Measurements from Arc Scans with Doppler Wind Lidar

2015 ◽  
Vol 32 (11) ◽  
pp. 2024-2040 ◽  
Author(s):  
H. Wang ◽  
R. J. Barthelmie ◽  
A. Clifton ◽  
S. C. Pryor
Keyword(s):  
Wind Speed ◽  
Sonic Anemometer ◽  
Elevation Angle ◽  
Horizontal Wind ◽  
Horizontal Wind Speed ◽  
Energy Applications ◽  
Mean Wind Speed ◽  
Wind Measurements ◽  
Wind Lidar ◽  
Doppler Wind Lidar

AbstractDefining optimal scanning geometries for scanning lidars for wind energy applications remains an active field of research. This paper evaluates uncertainties associated with arc scan geometries and presents recommendations regarding optimal configurations in the atmospheric boundary layer. The analysis is based on arc scan data from a Doppler wind lidar with one elevation angle and seven azimuth angles spanning 30° and focuses on an estimation of 10-min mean wind speed and direction. When flow is horizontally uniform, this approach can provide accurate wind measurements required for wind resource assessments in part because of its high resampling rate. Retrieved wind velocities at a single range gate exhibit good correlation to data from a sonic anemometer on a nearby meteorological tower, and vertical profiles of horizontal wind speed, though derived from range gates located on a conical surface, match those measured by mast-mounted cup anemometers. Uncertainties in the retrieved wind velocity are related to high turbulent wind fluctuation and an inhomogeneous horizontal wind field. The radial velocity variance is found to be a robust measure of the uncertainty of the retrieved wind speed because of its relationship to turbulence properties. It is further shown that the standard error of wind speed estimates can be minimized by increasing the azimuthal range beyond 30° and using five to seven azimuth angles.

Effect of Mean Wind Speed Variations on Power Generation of Wind Farm

2012 ◽  
Vol 215-216 ◽  
pp. 1298-1307
Author(s):  
Chen Guo ◽  
Yan He
Keyword(s):  
Power Generation ◽  
Wind Speed ◽  
Wind Farm ◽  
High Reliability ◽  
Wind Farms ◽  
Measured Data ◽  
Data Sets ◽  
Mean Wind Speed ◽  
Relationship Of ◽  
The Relationship

Through two methods, wind speed data sets sequence, the elements of which increased in Mean Wind Speed (MWS) orderly, are introduced first, and a numerical integration method depending on Weibull fitting result and power curve data to calculate Power Generation (PG) is proposed in this paper. Then, with measured data of 3 wind farms, PG with different heights are calculated and contrastive studies are made, employing the proposed data sets processing and PG calculating methods. Research results indicate that the PG calculating method has high reliability, and Equivalent Available Duration (EAD) increases about 50-60h when MWS increased by 0.1m/s. The results provide important basis for studies on the relationship of PG variation and measured data correction methods.

scholarly journals Winter Subseasonal Wind Speed Forecasts for Finland from ECMWF

2021 ◽  
Vol 18 ◽  
pp. 127-134
Author(s):  
Otto Hyvärinen ◽  
Terhi K. Laurila ◽  
Olle Räty ◽  
Natalia Korhonen ◽  
Andrea Vajda ◽  
...  
Keyword(s):  
Wind Speed ◽  
Lead Time ◽  
Reference Data ◽  
Data Sets ◽  
Weather Forecasts ◽  
The Third ◽  
Probabilistic Forecasts ◽  
Medium Range ◽  
Mean Wind Speed ◽  
Gaussian Regression

Abstract. The subseasonal forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts) were used to construct weekly mean wind speed forecasts for the spatially aggregated area in Finland. Reforecasts for the winters (November, December and January) of 2016–2017 and 2017–2018 were analysed. The ERA-Interim reanalysis was used as observations and climatological forecasts. We evaluated two types of forecasts, the deterministic forecasts and the probabilistic forecasts. Non-homogeneous Gaussian regression was used to bias-adjust both types of forecasts. The forecasts proved to be skilful until the third week, but the longest skilful lead time depends on the reference data sets and the verification scores used.

scholarly journals Intercalibration of HRDI and WINDII wind measurements

1997 ◽  
Vol 15 (9) ◽  
pp. 1089-1098 ◽  
Author(s):  
M. D. Burrage ◽  
W. R. Skinner ◽  
P. B. Hays
Keyword(s):  
Wind Speed ◽  
Upper Atmosphere ◽  
The Other ◽  
Data Sets ◽  
Atmospheric Emission ◽  
Validation Test ◽  
Doppler Imager ◽  
Imaging Interferometer ◽  
Wind Measurements

Abstract. The High Resolution Doppler Imager (HRDI) and the Wind Imaging Interferometer (WINDII) in- struments, which are both on the Upper Atmosphere Research Satellite, measure winds by sensing the Doppler shift in atmospheric emission features. Because the two observation sets are frequently nearly coincident in space and time, each provides a very e.ective validation test of the other. Discrepancies due to geophysical di.erences should be much smaller than for comparisons with other techniques (radars, rockets, etc.), and the very large sizes of the coincident data sets provide excellent statistics for the study. Issues that have been examined include relative systematic o.sets and the wind magnitudes obtained with the two systems. A significant zero wind position di.erence of ~6 m s–1 is identified for the zonal component, and it appears that this arises from an absolute perturbation in WINDII winds of –4 m s–1 and in HRDI of +2 m s–1. Altitude o.sets appear to be relatively small, and do not exceed 1 km. In addition, no evidence is found for the existence of a systematic wind speed bias between HRDI and WINDII. However, considerable day-to-day variability is found in the quality of the agreement, and RMS di.erences are surprisingly large, typically in the range of 20±30 m s–1.

Sign in / Sign up

Export Citation Format

Share Document