scholarly journals Development of an Unmanned Aerial Vehicle for the Measurement of Turbulence in the Atmospheric Boundary Layer

Atmosphere ◽  
2017 ◽  
Vol 8 (10) ◽  
pp. 195 ◽  
Author(s):  
Brandon Witte ◽  
Robert Singler ◽  
Sean Bailey

This paper describes the components and usage of an unmanned aerial vehicle developed for measuring turbulence in the atmospheric boundary layer. A method of computing the time-dependent wind speed from a moving velocity sensor data is provided. The physical system built to implement this method using a five-hole probe velocity sensor is described along with the approach used to combine data from the different on-board sensors to allow for extraction of the wind speed as a function of time and position. The approach is demonstrated using data from three flights of two unmanned aerial vehicles (UAVs) measuring the lower atmospheric boundary layer during transition from a stable to convective state. Several quantities are presented and show the potential for extracting a range of atmospheric boundary layer statistics.

Atmosphere ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 363 ◽  
Author(s):  
Min-Seong Kim ◽  
Byung Hyuk Kwon

In this work, sensible heat flux estimated using a bulk transfer method was validated with a three-dimensional ultrasonic anemometer or surface layer scintillometer at various sites. Results indicate that it remains challenging to obtain temperature and wind speed at an appropriate reference height. To overcome this, alternative observations using an unmanned aerial vehicle (UAV) were considered. UAV-based wind speed and sensible heat flux were indirectly estimated and atmospheric boundary layer (ABL) height was then derived using the sensible heat flux data. UAV-observed air temperature was measured by attaching a temperature sensor 40 cm above the rotary-wing of the UAV, and UAV-based wind speed was estimated using attitude data (pitch, roll, and yaw angles) recorded using the UAV’s inertial measurement unit. UAV-based wind speed was close to the automatic weather system-observed wind speed, within an error range of approximately 10%. UAV-based sensible heat flux estimated from the bulk transfer method corresponded with sensible heat flux determined using the eddy correlation method, within an error of approximately 20%. A linear relationship was observed between the normalized UAV-based sensible heat flux and radiosonde-based normalized ABL height.


2021 ◽  
Vol 1925 (1) ◽  
pp. 012068
Author(s):  
D G Chechin ◽  
A Yu Artamonov ◽  
N Ye Bodunkov ◽  
M Yu Kalyagin ◽  
A M Shevchenko ◽  
...  

2021 ◽  
Vol 57 (5) ◽  
pp. 526-532
Author(s):  
D. G. Chechin ◽  
A. Yu. Artamonov ◽  
N. E. Bodunkov ◽  
D. N. Zhivoglotov ◽  
D. V. Zaytseva ◽  
...  

2020 ◽  
Vol 20 (4) ◽  
pp. 332-342
Author(s):  
Hyung Jun Park ◽  
Seong Hee Cho ◽  
Kyung-Hwan Jang ◽  
Jin-Woon Seol ◽  
Byung-Gi Kwon ◽  
...  

2021 ◽  
Author(s):  
Shuang Wu ◽  
Lei Deng ◽  
Lijie Guo ◽  
Yanjie Wu

Abstract Background: Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.Methods: To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.Result: The results show that: (1) the soil background reduced the accuracy of the LAI prediction, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data improved LAI prediction accuracy and achieved the best accuracy (R2 = 0.815 and RMSE = 1.023). (3) When compared to other variables, 23 CHM, NRCT, NDRE, and BLUE are crucial for LAI estimation. Even the simple Multiple Linear Regression model could achieve high prediction accuracy (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction.Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.


2013 ◽  
Vol 94 (11) ◽  
pp. 1691-1706 ◽  
Author(s):  
A. A. M. Holtslag ◽  
G. Svensson ◽  
P. Baas ◽  
S. Basu ◽  
B. Beare ◽  
...  

The representation of the atmospheric boundary layer is an important part of weather and climate models and impacts many applications such as air quality and wind energy. Over the years, the performance in modeling 2-m temperature and 10-m wind speed has improved but errors are still significant. This is in particular the case under clear skies and low wind speed conditions at night as well as during winter in stably stratified conditions over land and ice. In this paper, the authors review these issues and provide an overview of the current understanding and model performance. Results from weather forecast and climate models are used to illustrate the state of the art as well as findings and recommendations from three intercomparison studies held within the Global Energy and Water Exchanges (GEWEX) Atmospheric Boundary Layer Study (GABLS). Within GABLS, the focus has been on the examination of the representation of the stable boundary layer and the diurnal cycle over land in clear-sky conditions. For this purpose, single-column versions of weather and climate models have been compared with observations, research models, and large-eddy simulations. The intercomparison cases are based on observations taken in the Arctic, Kansas, and Cabauw in the Netherlands. From these studies, we find that even for the noncloudy boundary layer important parameterization challenges remain.


2021 ◽  
Author(s):  
Marta Wenta ◽  
Agnieszka Herman

<p>The ongoing development of NWP (Numerical Weather Prediction) models and their increasing horizontal resolution have significantly improved forecasting capabilities. However, in the polar regions models struggle with the representation of near-surface atmospheric properties and the vertical structure of the atmospheric boundary layer (ABL) over sea ice. Particularly difficult to resolve are near-surface temperature, wind speed, and humidity, along with diurnal changes of those properties. Many of the complex processes happening at the interface of sea ice and atmosphere, i.e. vertical fluxes, turbulence, atmosphere - surface coupling are poorly parameterized or not represented in the models at all. Limited data coverage and our poor understanding of the complex processes taking place in the polar ABL limit the development of suitable parametrizations. We try to contribute to the ongoing effort to improve the forecast skill in polar regions through the analysis of unmanned aerial vehicles (UAVs) and automatic weather station (AWS) atmospheric measurements from the coastal area of Bothnia Bay (Wenta et. al., 2021), and the application of those datasets for the analysis of regional NWP models' forecasts. </p><p>Data collected during HAOS (Hailuoto Atmospheric Observations over Sea ice) campaign (Wenta et. al., 2021) is used for the evaluation of regional NWP models results from AROME (Applications of Research to Operations at Mesoscale) - Arctic, HIRLAM (High Resolution Limited Area Model) and WRF (Weather Research and Forecasting). The presented analysis focuses on 27 Feb. 2020 - 2 Mar. 2020, the time of the HAOS campaign, shortly after the formation of new, thin sea ice off the westernmost point of Hailuoto island.  Throughout the studied period weather conditions changed from very cold (-14℃), dry and cloud-free to warmer (~ -5℃), more humid and opaquely cloudy. We evaluate models’ ability to correctly resolve near-surface temperature, humidity, and wind speed, along with vertical changes of temperature and humidity over the sea ice. It is found that generally, models struggle with an accurate representation of surface-based temperature inversions, vertical variations of humidity, and temporal wind speed changes. Furthermore, a WRF Single Columng Model (SCM) is launched to study whether specific WRF planetary boundary layer parameterizations (MYJ, YSU, MYNN, QNSE), vertical resolution, and more accurate representation of surface conditions increase the WRF model’s ability to resolve the ABL above sea ice in the Bay of Bothnia. Experiments with WRF SCM are also used to determine the possible reasons behind model’s biases. Preliminary results show that accurate representation of sea ice conditions, including thickness, surface temperature, albedo, and snow coverage is crucial for increasing the quality of NWP models forecasts. We emphasize the importance of further development of parametrizations focusing on the processes at the sea ice-atmosphere interface.</p><p> </p><p>Reference:</p><p>Wenta, M., Brus, D., Doulgeris, K., Vakkari, V., and Herman, A.: Winter atmospheric boundary layer observations over sea ice in the coastal zone of the Bay of Bothnia (Baltic Sea), Earth Syst. Sci. Data, 13, 33–42, https://doi.org/10.5194/essd-13-33-2021, 2021. </p><p><br><br><br><br><br><br></p>


2020 ◽  
Vol 496 (4) ◽  
pp. 5552-5563
Author(s):  
R Sánchez García ◽  
M G Richer ◽  
R Gómez Martínez ◽  
R Avila

ABSTRACT We present computational fluid dynamics simulations of the atmospheric boundary layer (ABL) at the Observatorio Astronómico Nacional in the Sierra San Pedro Mártir (OAN-SPM) whose objective is to model the seeing observed at the site. We constrain the simulations using observations of the seeing, the vertical profile of the wind speed, and the vertical profile of the temperature, the first two resolved as a function of wind direction. We successfully model the seeing observed under typical wind conditions for each direction by adopting input profiles of the wind speed, the turbulent kinetic energy, and the energy dissipation. The resulting vertical profiles of the index of refraction structure constant are qualitatively similar to the mean profile derived from studies at the site.


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