Volna-3 sodar measurements of moments of wind velocity field and estimates of measurement errors

2000 ◽  
Author(s):  
V. A. Fedorov
Keyword(s):  
Velocity Field ◽  
Wind Velocity ◽  
Measurement Errors ◽  
Sodar Measurements

Statistical Characteristics of Thermal Convection Structures based on Acoustic Sounding Data

2020 ◽  
Author(s):  
Natalia Vazaeva ◽  
Otto Chkhetiani ◽  
Michael Kurgansky ◽  
Margarita Kallistratova ◽  
Vasily Lyulyukin ◽  
...  
Keyword(s):  
Velocity Field ◽  
Wind Velocity ◽  
Vertical Velocity ◽  
Thermal Convection ◽  
Rayleigh Distribution ◽  
Arid Zones ◽  
Grey Zone ◽  
Time Step ◽  
Limit Values ◽  
Acoustic Sounding

<p>The thermal convection structures (TCS) and their characteristics manifestations in the atmospheric boundary layer were investigated using the data from acoustic Doppler sodar LATAN-3M. A longwave LATAN-3M sodar with a vertical resolution of 20 m in 2007 and 10 m in 2016, 2018, 2019, a pulse emission interval of 5 s in 2007 and 3 s in 2016, 2018, 2019, an altitude range of 400–600 m in 2007 and 350 m in 2016, 2018, 2019, and a basic carrier frequency of 2 kHz in 2007 and 3 kHz in 2016, 2018, 2019 had measured the profiles of the wind velocity components which were used for calculating the scale of TCS. Experimental data were being obtained during the field campaigns organized by the A.M. Obukhov Institute of Atmospheric Physics RAS in Rostov region and over semi-arid zones of the Caspian lowland in the eastern part of Kalmykia Republic, Russia.</p><p>The wind was weak and the convection was well-developed in the case studies over July of years 2007, 2016, 2018, 2019. A moving rectangular filter was used for averaging the original data of the horizontal and vertical wind-velocity components. The averaging interval had been empirically chosen and, in this case, amounted to 10 min. At such values, the spatiotemporal velocity-field structure was adequately reproduced.</p><p>The original method of acoustic sounding data treatment for extracting TCS has been developed and put to an evaluation test. The episodes of the vertical velocities above limit values at which TCS aroused hypothetically were considered. As the threshold, a few alternatives were used: 0.3 m/s, 0.6 m/s and 1.2 m/s. The duration of vertical velocity excess over the threshold, the maximum velocity within this interval and the horizontal scale were calculated. It is assumed that TCS move forward with some averaged velocity during any relatively small time step. In this case, the spatial distribution of velocity field and its time variations have been reproduced suitably.</p><p>The statistical distribution was close to Rayleigh distribution:</p><p><em>p</em>(<em>U</em>) = (2<em>U</em>/<em>U<sub>0</sub></em><sup>2</sup>)*<em>exp </em>((<em>U<sub>m</sub></em><sup>2</sup>-<em>U </em><sup>2</sup>)/<em>U<sub>0</sub></em><sup>2</sup>),</p><p>where <em>U<sub>0</sub></em><sup>2 </sup>= (<<em>U </em><sup>2</sup>>-<em>U<sub>m</sub></em><sup>2</sup>), <<em>U </em><sup>2</sup>> is the root-mean-square vertical velocity of TCS, and <em>U<sub>m</sub></em> – the threshold for vertical velocity. This closeness can facilitate the understanding of the processes in the so-called “grey-zone” of numerical simulation and be implemented in the parameterization, forecast of TCS. Note that Rayleigh distribution is applied to the statistics of the intense moist convective vortices and also of the height of the ocean waves.</p><p>This work was supported by Russian Foundation for Basic Research (projects No.19-05-50110, No.19-05-01008, No.17-05-41121), and by fundamental research program of Russian Academy of Science (program No.1).</p>

Simulation of Stochastic Wind Velocity Field on Long-Span Bridges

2001 ◽  
Vol 127 (4) ◽  
pp. 408-409 ◽  
Author(s):  
Chunhua Liu ◽  
Luyu Wang ◽  
Yinghong Cao
Keyword(s):  
Velocity Field ◽  
Wind Velocity ◽  
Long Span Bridges ◽  
Long Span

Determination of Wind Velocity Projections Taking into Account Measurements of Airspeed, Angles of Attack and Sideslip

2021 ◽  
Vol 22 (10) ◽  
pp. 553-560
Author(s):  
O. N. Korsun ◽  
M. H. Om ◽  
K. Z. Latt
Keyword(s):  
Wind Speed ◽  
Coordinate System ◽  
Wind Velocity ◽  
Measurement Errors ◽  
Vertical Component ◽  
Main Idea ◽  
Measurement Tool ◽  
Unknown Parameters ◽  
Satellite Navigation System ◽  
Using Data

The paper deals with the problem of estimating the projections of the wind velocity in flight. The proposed method allows to obtain estimates for three projections of wind speed in the normal Earth coordinate system using data from the satellite navigation system, as well as on-board aerometric measurements of airspeed, angles of attack and glide. The main idea underlying the method is that satellite measurements of three aircraft velocity projections relative to the Earth’s coordinate system are very accurate (errors usually do not exceed 0.2 m/s). This makes it possible to use satellite velocity measurements as a kind of reference, just as in practical metrology, in order to assess the errors of measurement tools, they are compared with a standard, that is, a significantly more accurate measurement tool. In order to implement this approach not in a metrological laboratory, but on board an aircraft, it is proposed to use the relationships known from the flight dynamics between the velocity projections in the Earth’s and associated coordinate systems, the angles of attack and glide, and the wind speed. Then, the three wind speed projections are assigned unknown parameters, which are found using parameter identification. It is assumed that the wind has a constant speed and direction in the processed section of the flight. The accuracy characteristics of the proposed algorithm were evaluated based on the data obtained on the flight simulator of a modern training aircraft. In the course of simulation, random measurement errors were generated at the levels corresponding to the flight experiment. The influence of the type of maneuvers on the accuracy the three wind speed projections estimates was also studied. It is shown that for all considered maneuvers, that is "barrel", "snake", stepwise inputs, the errors in estimating the horizontal components of wind speed generally do not exceed 5 %, the vertical component 10 %, with the duration of the sliding processing interval of 0.5 and 1.0 s, which allows not only to estimate the constant wind speed, but also to track its change.

Development of a simulation model for a three-dimensional wind velocity field using Ténès Algeria as a case study

2012 ◽  
Vol 16 (1) ◽  
pp. 29-36 ◽  
Author(s):  
Fouad Boukli Hacène ◽  
Miloud Tahar Abbés ◽  
Nachida Kasbadji Merzouk ◽  
Larbi Loukarfi ◽  
Hacene Mahmoudi ◽  
...  
Keyword(s):  
Velocity Field ◽  
Simulation Model ◽  
Wind Velocity ◽  
Three Dimensional

Evaluation of wind-induced errors for the Hotplate precipitation gauge using computational fluid dynamic simulations.

2020 ◽  
Author(s):  
Enrico Chinchella ◽  
Arianna Cauteruccio ◽  
Mattia Stagnaro ◽  
Andrea Freda ◽  
Luca Giovanni Lanza
Keyword(s):  
Particle Size ◽  
Wind Speed ◽  
Velocity Field ◽  
Wind Velocity ◽  
Complex Geometry ◽  
Collection Efficiency ◽  
Fluid Dynamic ◽  
Strong Wind ◽  
Precipitation Gauge ◽  
Number Of Particles

<p>Wind is recognised as the major environmental source of error in precipitation measurements. For traditional catching type gauges, which are composed by a funnel to collect the precipitation and a container with a bluff body shape, the exposure effect produces the updraft and acceleration of the velocity field in front and above of the collector. These divert the trajectories of approaching hydrometeors producing  a relevant under-catch, which increases with increasing the wind velocity. This problem has been recently addressed in the literature using Computational Fluid Dynamics (CFD) simulations and a Lagrangian Particle Tracking (LPT) model to provide correction curves for various instruments, which closely match the under-catch observed in field measurements.</p><p>The present work concentrates on the Hotplate precipitation gauge developed at the Research Applications Laboratory, National Center for Atmospheric Research in Boulder, Colorado. The Hotplate differs from the traditional catching type gauges because it operates by means of an indirect thermodynamic principle. Therefore, it is not equipped with any funnel to collect the precipitation and is composed by a small disk with a diameter of 13 cm with two thin aluminium heated plates on the upper and lower faces. On the plates three concentric rings are installed to prevent the hydrometeors from sliding off during strong wind conditions.</p><p>In order to quantify the wind-induced error, the Unsteady Reynolds Averaged Navier Stokes (URANS) equations were numerically solved, with a k-ω SST turbulence closure model, to calculate the airflow velocity field around the instrument. Numerical results were validated by comparison with wind tunnel flow velocity measurements from pressure probes and a Particle Image Velocimetry (PIV) technique.</p><p>Then, with the objective to calculate the Collection Efficiency (CE) the hydrometeor trajectories were modelled using a literature LPT model (Colli et al. 2015) that solves the particle motion equation under the effects of gravity and wind. The path of each particle was analysed, considering the complex geometry of the gauge body, to establish whether it is captured by the instrument or not.</p><p>For various particle size/wind velocity combinations, the ratio between the number of particles captured by the instrument and the number of particles that would be captured if the instrument was transparent to the wind was calculated. Finally, the CE curve was derived assuming a suitable particle size distribution for solid precipitation.</p><p>The results show that the Hotplate gauge presents a very unique response to the wind if compared with more traditional instruments. The CE indeed decreases with increasing the wind speed up to 7.5 m/s, where the effect of geometry starts to overcome the aerodynamic effect, and slowly reverses the trend beyond that value. This effect is so prominent at high wind speed that slightly beyond 15 m/s the under-catch fully disappears and the instrument starts to exhibit a rapidly increasing over-catching bias.</p><p><strong>References:</strong></p><p>Colli, M., Lanza, L.G., Rasmussen, R., Thériault, J.M., Baker, B.C. & Kochendorfer, J. An improved trajectory model to evaluate the collection performance of snow gauges.  Journal of Applied Meteorology and Climatology, 2015, 54, 1826–1836.</p>

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