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This paper introduces a novel methodology that estimates the wind profile within the ABL by using a neural network along with predictions from a mesoscale model in conjunction with a single near-surface measurement. A major advantage of this solution compared to other solutions available in the literature is that it requires only near-surface measurements for prediction once the neural network has been trained. An additional advantage is the fact that it can be potentially used to explore the time evolution of the wind profile. Data collected by a LiDAR sensor located at the University of León (Spain) is used in the present research. The information obtained from the wind profile is valuable for multiple applications, such as preliminary calculations of the wind asset or CFD modeling. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this paper is to consolidate the backgrounds of a method to measure the local wind speed profiles by remotely tracking lighter-than-air bubble clusters, in a way that is efficient, safe and easily implementable. The technologies around remote sensing of atmospheric wind profiles are reviewed, together with those associated with particle image velocimetry. In this case, the targets are light liquid bubbles filled with helium, which are monitored from several locations on ground so that the full trajectory can be reproduced and hence, the wind speed derived at each point along the path. The features of the measurement system are detailed, describing its major components. The different applicable data filtering processes, the core of the operation, are reviewed to find the best options for the estimation of the wind profile in real time. The capability to measure the horizontal wind along the ascending path of the targets has been checked by means of simulated scenarios, indoor campaigns and in-field tests. The synthetic scenarios allowed the tuning of photometric parameters as well as the first estimation of the performance and the limitations. The field test campaigns allowed validating the prototype under different configurations and atmospheric conditions. Initial tests were conducted in a Spanish atmosphere research centre (CIBA), where wind data until 100m height is continuously recorded, followed by additional experiments in a more realistic environment, near an airport, where these data could be operationally used in the future. The results from these tests are successful, taking into account the fact that the system is still in an early development phase, while still being able to beat initial performance goals (0.4m/s mean error for wind speed and 15° for wind direction). It is expected that the idea is a reliable and low cost alternative to other remote sensing devices for wind profile measurement in certain applications in the medium-accuracy range. [Copyright &y& Elsevier]

Abstract: This paper establishes the basis for the development of an affordable system with the aim of measuring speed profiles in local wind flows by remotely tracking lighter-than-air bubbles. First, the main components and features of the measurement system are explained, as well as their integrated workflow, with special emphasis on the data processing. The capability of the system to estimate the horizontal wind in real time along the ascending path of the conglomerates of bubbles is modelled and verified in simulated scenarios, assessing the impact of different parameters. Later, field test campaigns are carried out in order to test the measurement system in different atmospheric conditions against calibrated ultrasonic anemometers. The results show the feasibility of the whole system that, in certain conditions and applications like those requiring medium accuracy with a restricted budget, could be a reliable and low cost alternative to other remote sensing devices for wind flow profiling. [Copyright &y& Elsevier]

This paper introduces a new methodology for estimating the wind profile within the ABL (Atmospheric Boundary Layer) using a neural network and a single-point near-ground measurement. An important advantage of this solution when compared with others available in the literature is that it only requires near surface measurements for the prognosis once the neural network is trained. Another advantage is that it can be used to study the wind profile temporal evolution. This work uses data collected by a lidar sensor located at the Universidad de León (Spain). The neural network best configuration was determined using sensibility analyses. The result is a multilayer perceptron with three layers for each altitude: the input layer has six nodes for the last three measurements, the second has 128 nodes and the third consists of two nodes that provide u and v. The proposed method has better performance than traditional methods. The obtained wind profile information obtained is useful for multiple applications, such as preliminary calculations of the wind resource or CFD models. [ABSTRACT FROM AUTHOR]