Application of the Thermal Image Velocimetry to Measure and Visualize Spatial Distribution of Near Surface Wind

Abstract. Thermal Image Velocimetry (TIV) is a near-target remote sensing technique for estimating two- dimensional near-surface wind velocity based on spatiotemporal displacement of fluctuations in surface brightness temperature captured by an infrared camera. The addition of an automated parameterization and the combination of ensemble TIV results into one output made the method more suitable to changing meteorological conditions and less sensitive to noise stemming from the airborne sensor platform. Three field campaigns were carried out to evaluate the algorithm over turf, dry grass and wheat stubble. The derived velocities were validated with independently acquired observations from fine wire thermocouples and sonic anemometers. It was found that the TIV technique correctly derives atmospheric flow patterns close to the ground. Moreover, the modified method resolves wind speed statistics close to the surface at a higher resolution than the traditional measurement methods. Adaptive Thermal Image Velocimetry (A-TIV) is capable of providing contact-less spatial information about near-surface atmospheric motion and can help to be a useful tool in researching turbulent transport processes close to the ground.

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ESTIMATING NEAR-SURFACE WIND FIELD BY AERIAL THERMAL IMAGE VELOCIMETRY

Journal of Japan Society of Civil Engineers Ser B1 (Hydraulic Engineering) ◽

10.2208/jscejhe.73.i_457 ◽

2017 ◽

Vol 73 (4) ◽

pp. I_457-I_462

Author(s):

Motoyuki HIJIKATA ◽

Atsushi INAGAKI ◽

Manabu KANDA ◽

Yukihiko YAMASHITA

Keyword(s):

Wind Field ◽

Surface Wind ◽

Thermal Image ◽

Near Surface ◽

Image Velocimetry ◽

Surface Wind Field

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Recurrence Characteristics Analysis of Near-Surface Wind Speed Signal with Different Sampling Frequencies

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.599-601.1605 ◽

2014 ◽

Vol 599-601 ◽

pp. 1605-1609 ◽

Cited By ~ 1

Author(s):

Ming Zeng ◽

Zhan Xie Wu ◽

Qing Hao Meng ◽

Jing Hai Li ◽

Shu Gen Ma

Keyword(s):

Wind Speed ◽

Surface Wind ◽

Surface Wind Speed ◽

Recurrence Plot ◽

Sampling Frequency ◽

Near Surface ◽

Gas Leakage ◽

Time Period ◽

Characteristics Analysis ◽

Quantification Analysis

The wind is the main factor to influence the propagation of gas in the atmosphere. Therefore, the wind signal obtained by anemometer will provide us valuable clues for searching gas leakage sources. In this paper, the Recurrence Plot (RP) and Recurrence Quantification Analysis (RQA) are applied to analyze the influence of recurrence characteristics of the wind speed time series under the condition of the same place, the same time period and with the sampling frequency of 1hz, 2hz, 4.2hz, 5hz, 8.3hz, 12.5hz and 16.7hz respectively. Research results show that when the sampling frequency is higher than 5hz, the trends of recurrence nature of different groups are basically unchanged. However, when the sampling frequency is set below 5hz, the original trend of recurrence nature is destroyed, because the recurrence characteristic curves obtained using different sampling frequencies appear cross or overlapping phenomena. The above results indicate that the anemometer will not be able to fully capture the detailed information in wind field when its sampling frequency is lower than 5hz. The recurrence characteristics analysis of the wind speed signals provides an important basis for the optimal selection of anemometer.

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Evaluation of HRCLDAS and ERA5 Datasets for Near-Surface Wind over Hainan Island and South China Sea

Atmosphere ◽

10.3390/atmos12060766 ◽

2021 ◽

Vol 12 (6) ◽

pp. 766

Author(s):

Yi Jiang ◽

Shuai Han ◽

Chunxiang Shi ◽

Tao Gao ◽

Honghui Zhen ◽

...

Keyword(s):

South China Sea ◽

South China ◽

Surface Wind ◽

Hainan Island ◽

The South China Sea ◽

Wind Data ◽

The South ◽

Near Surface ◽

China Sea

Near-surface wind data are particularly important for Hainan Island and the South China Sea, and there is a wide range of wind data sources. A detailed understanding of the reliability of these datasets can help us to carry out related research. In this study, the hourly near-surface wind data from the High-Resolution China Meteorological Administration (CMA) Land Data Assimilation System (HRCLDAS) and the fifth-generation ECMWF atmospheric reanalysis data (ERA5) were evaluated by comparison with the ground automatic meteorological observation data for Hainan Island and the South China Sea. The results are as follows: (1) the HRCLDAS and ERA5 near-surface wind data trend was basically the same as the observation data trend, but there was a smaller bias, smaller root-mean-square errors, and higher correlation coefficients between the near-surface wind data from HRCLDAS and the observations; (2) the quality of HRCLDAS and ERA5 near-surface wind data was better over the islands of the South China Sea than over Hainan Island land. However, over the coastal areas of Hainan Island and island stations near Sansha, the quality of the HRCLDAS near-surface wind data was better than that of ERA5; (3) the quality of HRCLDAS near-surface wind data was better than that of ERA5 over different types of landforms. The deviation of ERA5 and HRCLDAS wind speed was the largest along the coast, and the quality of the ERA5 wind direction data was poorest over the mountains, whereas that of HRCLDAS was poorest over hilly areas; (4) the accuracy of HRCLDAS at all wind levels was higher than that of ERA5. ERA5 significantly overestimated low-grade winds and underestimated high-grade winds. The accuracy of HRCLDAS wind ratings over the islands of the South China Sea was significantly higher than that over Hainan Island land, especially for the higher wind ratings; and (5) in the typhoon process, the simulation of wind by HRCLDAS was closer to the observations, and its simulation of higher wind speeds was more accurate than the ERA5 simulations.

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Observation and simulation of near-surface wind and its variation with topography in Urumqi, West China

Journal of Meteorological Research ◽

10.1007/s13351-016-6012-3 ◽

2016 ◽

Vol 30 (6) ◽

pp. 961-982 ◽

Cited By ~ 7

Author(s):

Lili Jin ◽

Zhenjie Li ◽

Qing He ◽

Qilong Miao ◽

Huqiang Zhang ◽

...

Keyword(s):

Surface Wind ◽

Near Surface ◽

West China

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Characteristics of near-surface wind regimes in the Taklimakan Desert, China

Geomorphology ◽

10.1016/j.geomorph.2007.07.008 ◽

2008 ◽

Vol 96 (1-2) ◽

pp. 39-47 ◽

Cited By ~ 29

Author(s):

Ruiping Zu ◽

Xian Xue ◽

Mingrui Qiang ◽

Bao Yang ◽

Jianjun Qu ◽

...

Keyword(s):

Surface Wind ◽

Taklimakan Desert ◽

Near Surface ◽

The Taklimakan Desert ◽

Wind Regimes

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Snow density along the route traversed by the Japanese-Swedish Antarctic Expedition 2007/08

Journal of Glaciology ◽

10.3189/2012jog11j201 ◽

2012 ◽

Vol 58 (209) ◽

pp. 529-539 ◽

Cited By ~ 14

Author(s):

Shin Sugiyama ◽

Hiroyuki Enomoto ◽

Shuji Fujita ◽

Kotaro Fukui ◽

Fumio Nakazawa ◽

...

Keyword(s):

Wind Speed ◽

Surface Wind ◽

Coastal Region ◽

Snow Density ◽

Syowa Station ◽

Near Surface ◽

A Value ◽

Surface Snow ◽

The Mean ◽

The Antarctic

AbstractDuring the Japanese-Swedish Antarctic traverse expedition of 2007/08, we measured the surface snow density at 46 locations along the 2800 km long route from Syowa station to Wasa station in East Antarctica. The mean snow density for the upper 1 (or 0.5) m layer varied from 333 to 439 kg m-3 over a region spanning an elevation range of 365-3800 ma.s.l. The density variations were associated with the elevation of the sampling sites; the density decreased as the elevation increased, moving from the coastal region inland. However, the density was relatively insensitive to the change in elevation along the ridge on the Antarctic plateau between Dome F and Kohnen stations. Because surface wind is weak in this region, irrespective of elevation, the wind speed was suggested to play a key role in the near-surface densification. The results of multiple regression performed on the density using meteorological variables were significantly improved by the inclusion of wind speed as a predictor. The regression analysis yielded a linear dependence between the density and the wind speed, with a coefficient of 13.5 kg m-3 (m s-1)-1. This relationship is nearly three times stronger than a value previously computed from a dataset available in Antarctica. Our data indicate that the wind speed is more important to estimates of the surface snow density in Antarctica than has been previously assumed.

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Turbulence-Induced Anti-Stokes Flow and the Resulting Limitations of Large-Eddy Simulation

Journal of Physical Oceanography ◽

10.1175/jpo-d-17-0208.1 ◽

2018 ◽

Vol 48 (1) ◽

pp. 117-122 ◽

Cited By ~ 6

Author(s):

Brodie Pearson

Keyword(s):

Stokes Flow ◽

Energy Production ◽

Surface Wind ◽

Stokes Drift ◽

Near Surface ◽

Eddy Simulation ◽

Surface Wind Stress ◽

Planetary Rotation ◽

Large Eddy ◽

Anti Stokes

AbstractThis study shows that the presence of Stokes drift us in the turbulent upper ocean induces a near-surface Eulerian current that opposes the Stokes drift. This current is distinct from previously studied anti-Stokes currents because it does not rely on the presence of planetary rotation or mean lateral gradients. Instead, the anti-Stokes flow arises from an interaction between the Stokes drift and turbulence. The new anti-Stokes flow is antiparallel to us near the ocean surface, is parallel to us at depth, and integrates to zero over the depth of the boundary layer. The presence of Stokes drift in large-eddy simulations (LES) is shown to induce artificial energy production caused by a combination of the new anti-Stokes flow and LES numerics. As a result, care must be taken when designing and interpreting simulations of realistic wave forcing, particularly as rotation becomes weak and/or us becomes perpendicular to the surface wind stress. The mechanism of the artificial energy production is demonstrated for a generalized LES subgrid scheme.

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Physical properties and spatial distribution of the sea ice surface layer (SSL/snow) during the autumn phase of the MOSAiC expedition

10.5194/egusphere-egu21-1467 ◽

2021 ◽

Author(s):

Ruzica Dadic ◽

Martin Schneebeli ◽

Henna-Reeta Hannula ◽

Amy Macfarlane ◽

Roberta Pirazzini

Keyword(s):

Surface Layer ◽

Spatial Distribution ◽

Sea Ice ◽

Physical Properties ◽

Surface Scattering ◽

Climate Feedback ◽

Energy Fluxes ◽

Scattering Layer ◽

Near Surface ◽

Ice Surface

Snow cover dominates the thermal and optical properties of sea ice and the energy fluxes between the ocean and the atmosphere, yet data on the physical properties of snow and its effects on sea ice are limited. This lack of data leads to two significant problems: 1) significant biases in model representations of the sea ice cover and the processes that drive it, and 2) large uncertainties in how sea ice influences the global energy budget and the coupling of climate feedback. The &#160;MOSAiC research initiative enabled the most extensive data collection of snow and surface scattering layer (SSL) properties over sea ice to date. During leg 5 of the MOSAiC expedition, we collected multi-scale (microscale to 100-m scale) measurements of the surface layer (snow/SSL) over first year ice (FYI) and MYI on a daily basis. The ultimate goal of our measurements is to determine the spatial distribution of physical properties of the surface layer. During leg 5 of the MOSAiC expedition, that surface layer changed from the &#160;surface scattering layer (SSL), &#160; characteristic for the melt season, to an early autumn snow pack. Here, &#160;we will present data showing both a) the physical properties and the spatial distribution of the SSL during the late melt season and b) the transition of the sea ice surface from the SSL to the fresh autumn snowpack. The structural properties of this transition period are poorly documented, and this season is critical &#160;for the initialization of sea ice and snow models. Furthermore, these data are crucial to interpret simultaneous observations of surface energy fluxes, surface optical and remote sensing data (microwave signals in particular), near-surface biochemical activity, and to understand the sea ice &#160;processes that occur as the sea ice transitions from melting to freezing.

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Seeding metrics for image velocimetry performances in rivers

10.5194/egusphere-egu21-9229 ◽

2021 ◽

Author(s):

Silvano Fortunato Dal Sasso ◽

Alonso Pizarro ◽

Sophie Pearce ◽

Ian Maddock ◽

Matthew T. Perks ◽

...

Keyword(s):

Spatial Distribution ◽

Optical Sensors ◽

Large Scale ◽

Surface Flow ◽

Surface Velocity ◽

Seeding Density ◽

Earth System ◽

Science Data ◽

Image Velocimetry ◽

Hydraulic Conditions

Optical sensors coupled with image velocimetry techniques are becoming popular for river monitoring applications. In this context, new opportunities and challenges are growing for the research community aimed to: i) define standardized practices and methodologies; and ii) overcome some recognized uncertainty at the field scale. At this regard, the accuracy of image velocimetry techniques strongly depends on the occurrence and distribution of visible features on the water surface in consecutive frames. In a natural environment, the amount, spatial distribution and visibility of natural features on river surface are continuously challenging because of environmental factors and hydraulic conditions. The dimensionless seeding distribution index (SDI), recently introduced by Pizarro et al., 2020a,b and Dal Sasso et al., 2020, represents a metric based on seeding density and spatial distribution of tracers for identifying the best frame window (FW) during video footage. In this work, a methodology based on the SDI index was applied to different study cases with the Large Scale Particle Image Velocimetry (LSPIV) technique. Videos adopted are taken from the repository recently created by the COST Action Harmonious, which includes 13 case study across Europe and beyond for image velocimetry applications (Perks et al., 2020). The optimal frame window selection is based on two criteria: i) the maximization of the number of frames and ii) the minimization of SDI index. This methodology allowed an error reduction between 20 and 39% respect to the entire video configuration. This novel idea appears suitable for performing image velocimetry in natural settings where environmental and hydraulic conditions are extremely challenging and particularly useful for real-time observations from fixed river-gauged stations where an extended number of frames are usually recorded and analyzed.&#160;References Dal Sasso S.F., Pizarro A., Manfreda S.,&#160;Metrics for the Quantification of Seeding Characteristics to Enhance Image Velocimetry Performance in Rivers.&#160;Remote Sensing,&#160;12, 1789 (doi: 10.3390/rs12111789), 2020.Perks M. T., Dal Sasso S. F., Hauet A., Jamieson E., Le Coz J., Pearce S., &#8230;Manfreda S, Towards harmonisation of image velocimetry techniques for river surface velocity observations. Earth System Science Data, https://doi.org/10.5194/essd-12-1545-2020, 12(3), 1545 &#8211; 1559, 2020.Pizarro A., Dal Sasso S.F., Manfreda S.,&#160;Refining image-velocimetry performances for streamflow monitoring: Seeding metrics to errors minimisation,&#160;Hydrological Processes, (doi: 10.1002/hyp.13919), 1-9, 2020.Pizarro A., Dal Sasso S.F., Perks M. and Manfreda S., Identifying the optimal spatial distribution of tracers for optical sensing of stream surface flow, Hydrology and Earth System Sciences, 24, 5173&#8211;5185, (10.5194/hess-24-5173-2020), 2020.

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