scholarly journals Observing crosswind over urban terrain using scintillometer and Doppler lidar

2014 ◽  
Vol 7 (7) ◽  
pp. 6431-6456
Author(s):  
D. van Dinther ◽  
C. R. Wood ◽  
O. K. Hartogensis ◽  
A. Nordbo ◽  
E. J. O'Connor

Abstract. In this study, the crosswind (wind component perpendicular to a path, U⊥) is measured by a scintillometer and Doppler lidar above the urban environment of Helsinki, Finland, for 3 weeks. The scintillometer allows acquisition of a path-averaged value of U⊥ (U⊥), while the Doppler lidar allows acquisition of path-resolved U⊥ (U⊥ (x), where x is the position along the path). The goal of this study is to evaluate the applicability of scintillometer U⊥-measurements for conditions where U⊥ (x) is variable. If the scintillometer is applicable in such variable-wind conditions, it can also be used in the urban environment. Two methods were applied to obtain U⊥ from the scintillometer signal; the cumulative spectrum method (relies on scintillation spectra), and the lookup table method (relies on time-lagged correlation functions). Both methods compared reasonably well with the Doppler lidar measurements, especially considering the challenging urban environment in which they were measuring; with RMSE of 0.71 and 0.73 m s−1. This indicates that both measurement technologies are able to obtain U⊥ in the complex urban environment. The in detail investigation of four cases indicate that the cumulative spectrum method is less susceptible to a variable U⊥ (x) than the lookup table method. However, the lookup table method can be adjusted to improve its capabilities to obtain U⊥ for conditions where U⊥ (x) is variable.

2015 ◽  
Vol 8 (4) ◽  
pp. 1901-1911 ◽  
Author(s):  
D. van Dinther ◽  
C. R. Wood ◽  
O. K. Hartogensis ◽  
A. Nordbo ◽  
E. J. O'Connor

Abstract. In this study, the crosswind (wind component perpendicular to a path, U⊥) is measured by a scintillometer and estimated with Doppler lidar above the urban environment of Helsinki, Finland, for 15 days. The scintillometer allows acquisition of a path-averaged value of U⊥ (U⊥), while the lidar allows acquisition of path-resolved U⊥ (U⊥ (x), where x is the position along the path). The goal of this study is to evaluate the performance of scintillometer U⊥ estimates for conditions under which U⊥ (x) is variable. Two methods are applied to estimate U⊥ from the scintillometer signal: the cumulative-spectrum method (relies on scintillation spectra) and the look-up-table method (relies on time-lagged correlation functions). The values of U⊥ of both methods compare well with the lidar estimates, with root-mean-square deviations of 0.71 and 0.73 m s−1. This indicates that, given the data treatment applied in this study, both measurement technologies are able to obtain estimates of U⊥ in the complex urban environment. The detailed investigation of four cases indicates that the cumulative-spectrum method is less susceptible to a variable U⊥ (x) than the look-up-table method. However, the look-up-table method can be adjusted to improve its capabilities for estimating U⊥ under conditions under for which U⊥ (x) is variable.


2014 ◽  
Vol 31 (1) ◽  
pp. 62-78 ◽  
Author(s):  
Daniëlle van Dinther ◽  
Oscar K. Hartogensis

Abstract In this study the crosswind (U⊥) is determined from the time-lag correlation function [r12(τ)] measured by a dual large-aperture scintillometer; U⊥ is defined as the wind component perpendicular to a path—in this case, the scintillometer path. A scintillometer obtains a path-averaged U⊥, which for some applications is an advantage compared to other wind measurement devices. Four methods were used to obtain U⊥: the peak method, the Briggs method, the zero-slope method, and the lookup table method. This last method is a new method introduced in this paper, which obtains U⊥ by comparing r12(τ) of a measurement to r12(τ) of a theoretical model. The U⊥ values obtained from the scintillometer were validated with sonic anemometer measurements. The best results were obtained by the zero-slope method for U⊥ < 2 m s−1 and by the lookup table method for U⊥ > 2 m s−1. The Briggs method also showed promising results, but it is not always able to obtain U⊥. The results showed that a high parallel wind component (>2.5 m s−1) on the scintillometer path can cause an overestimation of U⊥ mainly for low U⊥ values (<2 m s−1).


2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.


2005 ◽  
Vol 86 (6) ◽  
pp. 825-838 ◽  
Author(s):  
Chris G. Collier ◽  
Fay Davies ◽  
Karen E. Bozier ◽  
Anthony R. Holt ◽  
Doug R. Middleton ◽  
...  

Author(s):  
Masood Taheri Andani ◽  
Andrew Peterson ◽  
Josh Munoz ◽  
Mehdi Ahmadian

The application of Doppler-based LIght Detection and Ranging (LIDAR) technology for determining track curvature and lateral irregularities, including alignment and gage variation, are investigated. The proposed method uses track measurements by two low-elevation, slightly tilted LIDAR sensors nominally pointed at the rail gage face on each track. The Doppler LIDAR lenses are installed with a slight forward angle to measure track speed in both longitudinal and lateral directions. The lateral speed measurements are processed for assessing the track gage and alignment variations, using a method that is based on the frequency bandwidth dissimilarities between the vehicle speed and track geometry irregularity. Using the results from an extensive series of tests with a body-mounted Doppler LIDAR system on-board a track geometry measurement railcar, the study indicates a close match between the LIDAR measurements and those made with existing sensors on-board the railcar. The field testing conducted during this study indicates that LIDAR sensors could provide a reliable, non-contact track monitoring instrument for field use in various weather and track conditions, potentially in a semi-autonomous or autonomous manner.


Author(s):  
Masood Taheri Andani ◽  
Abdullah Mohammed ◽  
Ashish Jain ◽  
Mehdi Ahmadian

This paper investigates the application of Doppler Light Detection and Ranging (LIDAR) sensors for the assessment of the top of rail lubricity condition and layer material. Different top of rail conditions are distinguished by the system using a new pair of rail surface indices defined based on LIDAR measurements. These indices provide quantitative representations of the top of rail condition due to the fact that Doppler frequency range and spectral magnitude of a backscattered LIDAR beam are functions of the rail surface figure as well as the light absorption properties of the surface material. Laboratory tests are conducted to demonstrate the feasibility of the proposed top of rail indexing operation. The results indicate that LIDAR sensors are capable of detecting and distinguishing between different top of rail surface conditions. Instrumenting rail inspection vehicles with Doppler LIDAR systems reduces reliance on empirical top of rail lubricity and surface assessments (such as observing the sheen of the rail or tactilely sensing various residues on the rail), in favor of reliable and repeatable measurements.


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