Sonic Detection And Ranging Research Articles

Investigating turbulence distribution in the lower atmosphere using time-lapse imagery from a camera bank

The atmosphere’s surface layer (first 50–100 m above the ground) is extremely dynamic and is influenced by surface radiative properties, roughness, and atmospheric stability. Understanding the distribution of turbulence in the surface layer is critical to many applications, such as directed energy and free space optical communications. Several measurement campaigns in the past have relied on weather balloons or sonic detection and ranging (SODAR) to measure turbulence up to the atmospheric boundary layer. However, these campaigns had limited measurements near the surface. We have developed a time-lapse imaging technique to profile atmospheric turbulence from turbulence-induced differential motion or tilts between features on a distant target, sensed between pairs of cameras in a camera bank. This is a low-cost and portable approach to remotely sense turbulence from a single site without the deployment of sensors at the target location. It is thus an excellent approach to study the distribution of turbulence in low altitudes with sufficiently high resolution. In the present work, the potential of this technique was demonstrated. We tested the method over a path with constant turbulence. We explored the turbulence distribution with height in the first 20 m above the ground by imaging a 30 m water tower over a flat terrain on three clear days in summer. In addition, we analyzed time-lapse data from a second water tower over a sloped terrain. In most of the turbulence profiles extracted from these images, the drop in turbulence with altitude in the first 15 m or so above the ground showed a h m dependence, where the exponent m varied from −0.3 to −1.0, quite contrary to the widely used value of −4/3.

Assessment of wind resource considering local turbulence based on data acquisition with SODAR

This work presents a new methodology to evaluate the influence of wind speed data corrections in the fit of the Weibull distribution. Corrections are made for data measured by Sonic Detection and Ranging (SODAR) and MERRA-2 base data. SODAR data are corrected through Turbulence Intensity (TI). The MERRA-2 data correction uses National Institute of Meteorology (INMET) weather station data to find a local scale factor. The results showed that the corrected data present a better fit in the Weibull distribution and evidence that corrections are necessary when wind speed averages are used to evaluate the wind resource. Wind speed data were also applied to simulate the energy production by a commercial turbine to demonstrate the contrast in the total energy generated. The new methodology shows that IT must be considered in the evaluation of wind resources.

A novel signal conditioning system for SODAR

The sonic detection and ranging (SODAR) instrument is widely used for measuring the atmospheric boundary layer (ABL). SODAR operates on an acoustic principle and creates an ABL structure using air-backscattered signals. These signals have extremely low amplitude and are highly susceptible to environmental noise. In this paper, a novel signal conditioning circuit with high gain, low noise, and high quality factor (Q) has been designed for the preamplifier of SODAR. This signal conditioning circuit is comprised of an ultra-low noise amplifier and a dual-amplifier band-pass filter. The designed preamplifier circuit is implemented with SODAR at the CSIR-National Physical Laboratory in New Delhi. The electrical characteristics of the proposed system are compared to the existing design of a single low-noise amplifier and state variable filter. The gain and signal-to-noise ratio performance of the designed preamplifier circuit are significantly improved in comparison to the existing one.

Analyzing motorcycle low-frequency noise

This paper presents analyses of the 3D acoustic fields generated by motorcycles at very low frequencies ([Formula: see text] Hz) at the idle speed and during sudden acceleration. Diagnosis and analyses of sound sources at low frequencies have always been a significant challenge because the directivity of low-frequency sound is very poor. To date, there are no research papers and/or reports that have demonstrated low-frequency sources localization and radiation patterns of any kind at high spatial resolution in 3D space. This study shows that by using sound viewer technologies, which include the passive sonic detection and ranging (SODAR), the Helmholtz equation least squares (HELS) method, advanced signal processing, denoising, etc., the locations of sound sources and visualization of the sound fields can be determined with high spatial resolution, even at frequencies below 150 Hz. In particular, the HELS method allows for reconstructing all the acoustic quantities, including the acoustic pressure, time-averaged acoustic intensity, time-averaged acoustic power on the source surfaces and in 3D space. The hardware needed consists of a 3D array with six free-field precision microphones with pre-amplifiers, a miniature wide-angle camera, an eight-channel digital signal processor and a laptop computer.

Evaluation of Profiles of Standard Deviation of Vertical Wind in the Urban Area of Rome: Performances of Monin–Obukhov Similarity Theory Using Different Scaling Variables

The parametrizations of meteorological variables provided by the Monin–Obukhov similarity theory (MOST) is of major importance for pollutant dispersion assessment. However, the complex flow pattern that characterizes the urban areas limits the applicability of the MOST. In this work, the performance of different existing parametrizations of the standard deviation of vertical wind velocity were tested in the city of Rome. Results were compared with experimental data acquired by a sonic detection and ranging (SODAR) and a sonic anemometer. Different scaling variables estimated from the anemometer data by considering two coordinate systems—one aligned with the geodetic reference frame and the other following the flow streamlines—were used to evaluate the effects of flow distortion due to the presence of buildings. Results suggest that the MOST parametrizations perform better if the scaling variables obtained using the coordinate system following the flow streamlines are used. This estimation of the scaling variables would make it possible to overcome the difficulties in conducting measurements of turbulent fluxes, either at different altitudes or even in the constant flux layer.

Prediction of temporal atmospheric boundary layer height using long short-term memory network

Nowadays, the city’s rapid growth of industrialisation, population, human activities, vehicular traffic density, unplanned urbanisation with poor ventilation contributes to increasing large amount of pollutants concentration. Atmospheric Boundary Layer (ABL) height is a basic parameter to define the pollution carrying capacity of any area in a big city. In the time series analysis and prediction of ABL height, the existing models use linear (AR, ARMA, ARIMA etc.) and non-linear (ANN, ANFIS etc) algorithms, but these models less capable of identifying the hidden pattern and underlying dynamics of ABL patterns. This paper presents a Long Short-Term Memory (LSTM) model using deep learning-based algorithms for temporal/seasonal and annual ABL height prediction and identified the latent dynamics of the ABL height pattern. The results of the model have been compared with the measurements made by SOnic Detection And Ranging (SODAR) system. LSTM model is used for prediction and to analyse their performance affected by the model. The observed ABL height data and model data are used to predict the ABL height by applying the neural network of LSTM. It is observed from the analysis that the optimal results can be achieved when the number of neurons is equal to 32, an epoch is equal to 500. To obtain the acceptable accuracy of prediction, various error-based performance indices have been calculated. Mean Absolute Percentage Error (MAPE) and relative Root Mean Square Error (rRMSE) have been calculated for the updated network with predicted values 17.3% and 7.33%, and, for the updated network with observed values 10.62% and 5.95%, respectively. Also, the performance of the proposed model has been estimated for the annual and seasonal prediction of ABL height. The results depict rRMSE values (7.49% and 5.59%) as lowest during post-monsoon for seasonal prediction and (10.29% and 5.86%) highest for annual prediction.

A comprehensive study of different feature selection methods and machine-learning techniques for SODAR structure classification

Sonic detection and ranging (SODAR) echogram provides atmospheric boundary layer (ABL) structure and its height. In the present research, highly accurate and reliable machine-learning methods have been derived and successfully applied on the SODAR echogram for the ABL structure. Five feature selection methods and eight classification methods have been studied in terms of their performance. 133 statistical features have been calculated from 1698 SODAR echogram images. To ensure the unbiased estimation of different structures, machine-learning methods have been used. Furthermore, ten cross-validations have been used to find accuracy. It is found that the boosted tree classifier (overall prediction performance 52.02%) has the highest prognostic presentation with 133 features. After application of the Laplacian method for feature selection, the classifier (overall prediction performance 62.19%) has the highest prognostic presentation with 20 features. The large variability analysis indicates the choice of a classification method for performance variation. Identification of optimal machine-learning methods for SODAR echogram applications is a crucial step towards the ABL structure application, providing an automatic structure classification method for atmospheric and pollutants studies.

Operational concept and validation of a new airport low-level wind information system

AbstractPilots are sometimes not provided with sufficient information to avoid go-arounds or other operational disruptions that result from low-level wind disturbances. We identified issues with existing windshear alerting systems and developed three types of airport low-level wind information systems to enhance pilot situational awareness of wind conditions by providing landing aircraft with quantitative and visualised wind information for ultimately mitigating air service disruptions due to low-level wind disturbances. The three systems, Airport Low-level Wind Information (ALWIN) and Low-level Turbulence Advisory System (LOTAS), both of which use Doppler radar/lidar, and Sodar-based Low-level Wind Information (SOLWIN), which uses Doppler SOnic Detection And Ranging (SODAR), have different costs and capabilities that allow the most cost-effective system to be selected for an airport according to its scale and local weather characteristics. This paper presents the operational concepts of our newly developed airport low-level wind information systems and describes their validation.

Configuration of Statistical Postprocessing Techniques for Improved Low-Level Wind Speed Forecasts in West Texas

Abstract The wind energy industry needs accurate forecasts of wind speeds at turbine hub height and in the rotor layer to accurately predict power output from a wind farm. Current numerical weather prediction (NWP) models struggle to accurately predict low-level winds, partially due to systematic errors within the models due to deficiencies in physics parameterization schemes. These types of errors are addressed in this study with two statistical postprocessing techniques—model output statistics (MOS) and the analog ensemble (AnEn)—to understand the value of each technique in improving rotor-layer wind forecasts. This study is unique in that it compares the techniques using a sonic detection and ranging (SODAR) wind speed dataset that spans the entire turbine rotor layer. This study uses reforecasts from the Weather Research and Forecasting (WRF) Model and observations in west Texas over periods of up to two years to examine the skill added to forecasts when applying both MOS and the AnEn. Different aspects of the techniques are tested, including model horizontal and vertical resolution, number of predictors, and training set length. Both MOS and the AnEn are applied to several levels representing heights in the turbine rotor layer (40, 60, 80, 100, and 120 m). This study demonstrates the degree of improvement that different configurations of each technique provides to raw WRF forecasts, to help guide their use for low-level wind speed forecasts. It was found that both AnEn and MOS show significant improvement over the raw WRF forecasts, but the two methods do not differ significantly from each other.

Preliminary analysis of operating conditions of acoustic sounder (SODAR) before the installation

The SOnic Detection And Ranging (SODAR) is a surface-based remote sensing instrument, based on Doppler effect and sound signal. This paper discusses the operating conditions of Monostatic SODAR system, error in the measured Atmospheric Boundary Layer (ABL) height by taking account of the antenna characteristics which are pulse transmission, receiving durations, spectral augmentation of the received echo and atmospheric refraction effects. The Monostatic SODAR is able to receive even very weak echo by providing a good directional response with high conversion efficiency. The location and orientation of the antenna are finalized after a detailed analysis of the site with relative to several parameters like comparative measurement of acoustic background noise and frequency response of noise. Also, before installation of the system, the transducer has to be individually characterized for it’s transmitted and received conversion efficiency, and variations in the product of these efficiencies (conversion gain). The antenna is systematically characterized, in an acoustic anechoic and reverberation chamber, with respect to its conversion efficiencies and directional response.

Long-term trend of ventilation coefficient over Delhi and its potential impacts on air quality

A ventilation coefficient (VC) is an essential parameter and plays an important role in the dispersion of air pollutants and is one of the factors that determine the pollution potential over a region. A comprehensive SODAR (Sonic Detection And Ranging) data set of about 8- years is analysed to study the long term trends, variations and time series model simulation of VC. The Box–Jenkins popular ARIMA (AutoRegressive Integrated Moving Average) model was applied to simulate the VC over Delhi region. The ARIMA models have been developed for different months from January to December as most suitable for simulating and forecasting the VC over the observation site. The Stationary R-squared, R-squared, Root Mean Square Error and Normalized BIC etc. are used to test the validity and applicability of the developed ARIMA models revealing significant accuracy in the model performance. It is evident from the modeled consequences that some features of the past continue to impact on the future values of VC, and these models can be reliably used for future predictions. The long term average value of VC has been found to be 1249 ± 236 m2/s and on yearly basis VC trend increase is observed to be 70 m2/s/year.

Evaluation of an atmospheric model with surface and ABL meteorological data for energy applications in structured areas

This paper provides the performance evaluation of the meteorological component of The Air Pollution Model (TAPM), a nestable prognostic model, in predicting meteorological variables in urban areas, for both its surface layer and atmospheric boundary layer (ABL) turbulence parameterizations. The model was modified by incorporating four urban land surface types, replacing the existing single urban surface. Control runs were carried out over the wider area of Kozani, an urban area in NW Greece. The model was evaluated for both surface and ABL meteorological variables by using measurements of near-surface and vertical profiles of wind and temperature. The data were collected by using monitoring surface stations in selected sites as well as an acoustic sounder (SOnic Detection And Ranging (SODAR), up to 300 m above ground) and a radiometer profiler (up to 600 m above ground). The results showed the model demonstrated good performance in predicting the near-surface meteorology in the Kozani region for both a winter and a summer month. In the ABL, the comparison showed that the model’s forecasts generally performed well with respect to the thermal structure (temperature profiles and ABL height) but overestimated wind speed at the heights of comparison (mostly below 200 m) up to 3–4 ms−1.

Sonic booms in atmospheric turbulence ground measurements in a hot desert climate

The Sonic Booms in Atmospheric Turbulence (SonicBAT) Project flew a series of 20 F-18 flights with 69 supersonic passes at Edwards Air Force Base in July 2016 to quantify the effect of atmospheric turbulence on sonic booms. Most of the passes were at a pressure altitude of 32,000 feet and a Mach number of 1.4, yielding a nominal sonic boom overpressure of 1.6 pounds per square foot. Atmospheric sensors such as GPS sondeballoons, Sonic Detection and Ranging (SODAR) acoustic sounders, and ultrasonic anemometers were used to characterize the turbulence state of the atmosphere for each flight. Spiked signatures in excess of 7 pounds per square foot were measured at some locations, as well as rounded sonic-boom signatures with levels much lower than the nominal. This presentation will quantify the range of overpressure and Perceived Level of the sonic boom as a function of turbulence parameters, and also present the spatial variation of these quantities over the array. Comparison with historical data will also be shown.

Study of the Thermal Internal Boundary Layer in Sea Breeze Conditions Using Different Parameterizations: Application of the WRF Model in the Greater Vitória Region

Abstract In the present study, the physical parameterizations of the Weather Research and Forecasting (WRF) model are verified for making accurate inferences about the dynamics of the Thermal Internal Boundary Layer (TIBL) generated by sea breeze in an urban center with an island in a bay along a coastal region with rugged topography. The simulations were performed using parameterizations from Yonsei University (YSU), Mellor-Yamada-Janjic (MYJ) and Asymmetric Convective Model version 2 (ACM2) for the atmospheric boundary layer (ABL) and Noah and Rapid Update Cycle (RUC) for the Land Surface Model (LSM). The data inferred by the WRF model were compared with those obtained by a Surface Meteorological Station (SMS) and by measurements generated using Light Detection and Ranging (LIDAR), Sonic Detection and Ranging (SODAR) and radiosonde. The simulations showed that although the object of this research was a region with high geographical complexity, the YSU parameterization set (non-local closure) for the ABL and the Noah parameterization for the LSM presented satisfactory results in determining ABL height generated by the sea breeze on the day in question.

Conceptual model for runway change procedure in Guarulhos International Airport based on SODAR data

ABSTRACTIn this work, we qualify and quantify the advantages of using SODAR (sonic detection and ranging) from current scenarios of Aeronautical Meteorology, with the goal of establishing a conceptual model for runway change procedures at Guarulhos International Airport (São Paulo, Brazil). The methods consist of analysing data from the Department of Airspace Control (DECEA) and Brazilian Airport Infrastructure (Infraero) about the reports of runway changes in Guarulhos in addition to SODAR data from September 2011 and December 2013. It is noted that in 234 analysed cases of runway change, there were significant periods of weak-intensity wind on the surface as well as the anticipated modification wind direction at altitude detected by sonic detection and ranging (SODAR), indicating future changes in levels closer to the surface. By examining the intersection of both scenarios, it is possible to observe that there is enough time for the air traffic controller to anticipate the needed runway change while minimising the impact on the aircraft flow, and this period has an average duration of 1 hour and 24 minutes. This confirms that the preliminary analysis of the information provided by SODAR can help predict alterations in wind direction, requiring redirection and bringing advantages in economic and security terms.

Preliminary Assessment of Remote Wind Sites

Wind energy is becoming a reliable and affordable source of clean energy and is rapidly expanding to remote places around the world. A crucial input for wind farming prospect is the assessment of potential wind sites. Sites, especially remotely located, often do not have a wind resource map and thus lack credible historical records of wind resources. Measurement campaigns to map these sites are costly and time consuming. In this paper, a method for preliminary wind resource assessment for remote sites is proposed. The method is a combination of interpolation and extrapolation of data from the surrounding sites to the potential wind farm site. Two interpolation techniques, viz., Inverse Distance Weighting (IDW) and Triangulated Irregular Network (TIN), are applied to the data set recorded by Sonic Detection and Ranging (SODAR) in West Texas, USA with the surrounding sites within 300km radius of the potential site. Extrapolation is done by using a power law with the exponent equal to 1/7. The resulting values of the wind speeds are validated with the available 200 m meteorological tower measurements at the potential site in Reese, Lubbock West-Texas, USA. Root mean square error (RMSE) of daily averages of wind speed ranged from 1.5 to 3 meters per seconds.

Automating source localization and separation using sonic detection and ranging

The sonic detection and ranging (SODAR) technology developed previously (Wu and Zhu, JASA, 2013) is automated for performing blind sources localization and separation. In particular, source localization results are displayed in terms of space-frequency or space-time correlations. In other words, one can either view distributions of sound sources in three-dimensional (3-D) space versus any user-defined frequency bands, or distributions of sound sources in 3-D space versus time history. The sound pressure level (SPL) values associated with the identified sources are also calculated and displayed in these space-frequency and space-time correlation graphs. To acquire a better understanding of the distribution of sound sources together with their SPL values in 3-D space, a 3-D viewer using Google SketchUp software is employed to view these results in both space-frequency and space-time correlations. With this 3-D viewer, one is able to rotate and look at any source from any perspective, and zoom in and out to examine the details of relative positions of individual sources. The information on source locations together with windowing and filtering technologies enable us to separate the individual source signals. Examples of using this blind sources localization and separations in a non-ideal environment that involves random background noise and unspecified interfering signals are demonstrated.

Passive sonic detection and ranging for locating sound sources

A passive sonic detection and ranging (SODAR) technology is developed to locate sound sources that emit arbitrarily time-dependent signals in a typical environment encountered in practice in real time. This passive SODAR is built on a comprehensive approach including the pre-processing of input data to enhance the signal-to-noise ratio, acoustic modeling of sound radiation from a point source, iterative triangulations, and post-processing of output data to ensure the accuracy in source localization. Moreover, it employs an optimization process to extend the source detection range and improve the source localization accuracy in a highly non-ideal environment that involves a large number of unspecified reflected and diffracted sound waves. This is accomplished through computations based on the source locations predicted by the individual units of four microphones that are not lying on the same plane. Experimental results confirm that passive SODAR works for arbitrarily time-dependent signals that include continuous, transient, impulsive, random, narrow-, and broadband sounds with frequencies above 20 Hz. The minimum number of microphones that are required in passive SODAR is six. These microphones can be mounted anywhere as long as they are not on the same plane and the lines of sight from sound sources remain unblocked.

On the way to harness high-altitude wind power: Defining the operational asset for an airship wind generator

The utilization of high-altitude wind energy potential is a very attractive challenge. This is mainly due to the perspective of producing a large amount of electricity from a renewable source without using land, and having a very low visibility impact. The wind flow on the upper levels of the surface boundary layer is characterized by good power with low variability of wind conditions.Many innovative wind energy conversion flying devices are currently under development for this purpose. The technological solutions can be grouped in:•land installed generators (such as kite systems);•airship flying generators that can produce electricity far from the ground and are tethered by control wires, also used for the power transmission.Flying generators could prove more problematic in maintaining flying conditions due to their heavier weight, but they have the advantage that they can be moved to different locations with less intervention on the ground.Another important characteristic that the airship wind generator has, is the possibility of changing its height of operation very quickly and simply by using control cables.In this study, the operational scenario of the possible application of an innovative airship wind generator is studied and discussed. The possibility of adjusting the height of operation was analysed, and a methodology for choosing the optimal operating level was developed and applied to real test cases. Numerical and experimental techniques used to assess the characteristics of high-altitude winds, were also considered.Remotely sensed wind data obtained from a monostatic phased-array Doppler Sonic Detection and Ranging (SODAR) system were used to validate and tune the CFD model in one of the real-test cases investigated.

Wind profile observations in tropical cyclone events using wind-profilers and doppler SODARs

Since the mean wind profile in the tropical cyclone boundary layer is important in both the fields of meteorology and wind engineering, the wind measurements taken during the passages of several tropical cyclones or typhoons in Hong Kong by the wind-profilers and the Doppler Sonic Detection And Ranging (SODAR) have been utilized to calculate both the hourly and 10-min mean wind profiles. These observed profiles were then fitted to the log-law, the power-law and an empirical profile model taking into consideration the low-level jet. The results showed that the low-level jet, which is commonly observed for the mean wind profile in the tropical cyclone boundary layer over the sea, was not found. In addition, we found that the surface roughness derived by fitting the observed profile to the log-law was unusually high (z0>1.5m). The possible explanations include (1) the influence of large scale topography and (2) the high waves introduced by strong typhoon winds.