Anemometer Research Articles

Research on optimizing the optical local-oscillator power of Coherent Doppler LiDAR to enhance wind velocity measurements

Due to weak echo signals that become progressively overwhelmed by noise, measurement accuracy and effective detection range of the Coherent Doppler wind LiDAR (CDL) are often compromised. While increasing the optical local-oscillator power (OLP) can amplify the echo signal, it is constrained by the nonlinear effects of the detector. This paper introduces a method for optimizing the OLP in CDL systems. Theoretical analysis has been proposed to explore the amplification effect of OLP on echo signals, and the nonlinear effects of detectors have been studied. Simulations are performed to explore the influence of varying OLP on the signal-to-noise ratio (SNR) across different detector α (quadratic nonlinear coefficient) values. The spectral analysis method is used to directly compute the SNR of actual atmospheric wind field signals under various OLP settings. Results demonstrate consistency between calculated and simulated values, enabling determination of optimal OLP and the detector α values from fitted curves. Comparative experiments confirm significant enhancement in effective detection range (> 1.5 km) with ± 0.5 m/s accuracy. The innovation of this study lies in combining the OLP optimization method with real atmospheric wind field echo signal experiments. It addresses the challenges of directly measuring the nonlinear parameters of the detector and determining the optimal OLP. This study offers valuable theoretical and experimental insights for enhancing the wind measurement performance of CDL.

Operational SYM‐H Forecasting With Confidence Intervals Using Deep Neural Networks

AbstractIn this study, we develop a robust real‐time forecast system for the SYM‐H index using Deep Neural Networks and real‐time Solar Wind measurements along with Interplanetary Magnetic Field parameters. This system provides not only one‐off forecasts but also quantile‐based confidence intervals, offering a range within which the observed SYM‐H values are expected to fall, enhancing forecast reliability and usability. The model is tested both on historical level 2 science‐ready data and on preliminary observations, which are closer to the operational environment that the model is expected to work on, demonstrating the model's robustness and practical utility in real‐time scenarios. The integration of quantile forecasts into SYM‐H prediction models represents a significant advancement, providing decision‐makers with more accurate and trustworthy information to manage the hazard of geomagnetic storms.

Comparative examinations of wind speed and energy extrapolation methods using remotely sensed data – A case study from Hungary

Exact knowledge of wind energy potential is a fundamental issue in wind energy utilization. The vertical changes in wind speeds, that is, the wind profile, have a predominant impact on the wind energy available at a location because the kinetic energy of moving air is proportional to the square of the wind speed. Roughness describes the resistance of a 3D surface to moving air. The exponent α of the power law of Hellmann and the roughness length (z0) are two parameters that describe the effects of the roughness of the surface on the wind profile. They can be used for the vertical extrapolation of wind speeds. The exponent α can be determined using multiple height level wind speed measurement data, whereas a reliable technique for the calculation of the roughness length requires detailed knowledge of the 3D geometry of the measurement site. In the present study, the exponent α was calculated based on SODAR wind speed measurements, while (z0) was determined using a combination of GIS and UAS-based aerial survey methods. Wind speeds measured at 50 m were extrapolated for height levels of 80, 90, 100, 110, and 120 m using dynamic power law exponent values. Wind power was determined using the power law (method V1), roughness length (method V2), frequency distribution (method W-RF), and gamma distribution (method W-G), and Windographer software was compared to the values calculated from the empirical (measured) wind speeds. A comparative statistical analysis of the datasets of the power law and roughness length methods on monthly/diurnal, annual/diurnal, and month/direction contexts showed no significant differences at all height levels. Differences can be detected in the distribution of the signs of the differences at heights of 80 and 120 m for the entire dataset. Underestimation was dominant with a significant frequency (over 70 %) in the case of both methods and heights. There were no significant differences between the wind power estimations provided by the different methods, and all the methods involved in the study underestimated the wind speeds and wind energy potential for each height level. Methods V1 and V2 can be used alternatively, depending on the input data available for analysis. The major advantage of method V2 is that it provides the same accuracy as V1, which requires a UAS-based aerial survey at the beginning, but continuous wind measurements must be performed at a lower height only. This means that there is no need for a high measurement tower, which makes the measurements simpler, more cost-effective, and causes much less disturbance to the environment. Another important advantage of the methods presented here is that they use a dynamic approach of power law exponent values that provide a more realistic estimation of wind speed and energy on a diurnal scale.

Smelling-based hunting optimization for battery–Super/Ultracapacitor hybrid energy storage station in wind/solar generation system using Siamese neural network

To ensure the continuous supply of power in remote areas utilizing renewable energy resources is significant. Hence, in this research, an effective energy management method for a small-scale hybrid wind-solar-battery-Ultra-capacitor-based microgrid is proposed. Hybrid Energy Storage System (HESS) has been presented in conjunction with wind and solar energy conversion technologies characterized by coupling of power electronic converters, neural networks, optimization, battery, controllers, and ultra-capacitor storage systems to attain the intended performance. The power balance is regulated via an energy management system by considering the fluctuation in load demand and renewable energy power generation. Further, the voltage controller utilizes the deep Siamese neural network (deep SNN) that effectively carries out energy management among the hybrid renewable energy sources. The smelling-based hunting optimization assists in optimal parameter tuning and training of the deep SNN for enhancing the HESS’s efficiency. In addition, the microgrid operates independently and offers a testing area for different energy management systems and testing scenarios. The proposed small-scale microgrid, which is based on renewable energy, can serve as a significant testing area for methods utilized in smart grid applications. The proposed SBHO model’s efficiency is determined by varying the voltage, current, and power of the wind, solar, battery, and ultra-capacitor measurements. The current capacity of the battery reaches −11.06A and the battery voltage reaches 259.831 V in 0.82 s. The DC load measurement utilizing the SBHO approach obtained a DC bus voltage of 357.11V, a load current of 3.348 A within 0.82 s, and in DC power load attained the 1195.58 W within 0.82 s. The battery’s SOC by applying the smelling-based hunting optimization is gradually increased to 50.008% in 0.82s.In terms of the PV measurement, the PV current, PV voltage, and PV power are obtained as 4.238A,241.08V, and 1021.64W for the SBHO approach which surpasses other competent techniques. The ultra-capacitor current ranges from 35A to 40A with reduced heavy discharge of SOC from 98% obtained on evaluating the performance. The output power of wind using a boost converter remains 3000 W with few harmonics.

МГД-волны в области предфронта межпланетных ударных волн 6 и 7 сентября 2017 г.

We analyze strong space weather disturbances during first ten days of September 2017, using the geomagnetic Dst index, parameters of normals to interplanetary shock fronts, direct measurements of interplanetary magnetic field, solar wind, and cosmic ray parameters. By applying spectral analysis methods to interplanetary medium data, we analyze MHD waves at the pre-front of two interplanetary shocks responsible for geomagnetic disturbances on September 6 and 7, 2017. The main results are as follows: the contribution of three branches of MHD waves (Alfvén, fast and slow magnetosonic) to the observed spectrum of the interplanetary magnetic field modulus has been established. We have confirmed the conclusion that the generation of Alfvén waves and fast magnetosonic waves is due to the presence of low-energy proton fluxes (Ep~1 MeV) at the pre-front of interplanetary shocks. We have also discovered a predominant contribution of slow magnetosonic waves to the observed spectrum of the interplanetary magnetic field modulus, but its reason is yet unknown. It is noted that different orientations of the normals to the interplanetary shock fronts and to the direction of the interplanetary magnetic field average vector on spacecraft located fairly close to each other may indicate waviness of the shock front structure.

MHD waves at the pre-front of interplanetary shocks on September 6 and 7, 2017

We analyze strong space weather disturbances during first ten days of September 2017, using the geomagnetic Dst index, parameters of normals to interplanetary shock fronts, direct measurements of interplanetary magnetic field, solar wind, and cosmic ray parameters. By applying spectral analysis methods to interplanetary medium data, we analyze MHD waves at the pre-front of two interplanetary shocks responsible for geomagnetic disturbances on September 6 and 7, 2017. The main results are as follows: the contribution of three branches of MHD waves (Alfvén, fast and slow magnetosonic) to the observed spectrum of the interplanetary magnetic field modulus has been established. We have confirmed the conclusion that the generation of Alfvén waves and fast magnetosonic waves is due to the presence of low-energy proton fluxes (Ep~1 MeV) at the pre-front of interplanetary shocks. We have also discovered a predominant contribution of slow magnetosonic waves to the observed spectrum of the interplanetary magnetic field modulus, but its reason is yet unknown. It is noted that different orientations of the normals to the interplanetary shock fronts and to the direction of the interplanetary magnetic field average vector on spacecraft located fairly close to each other may indicate waviness of the shock front structure.

CUSTR- A VHF phased array radar for observation of atmospheric dynamics and ionospheric plasma irregularities

A VHF phased array radar for atmospheric dynamics observation is installed at the University of Calcutta, Kolkata. The Calcutta University Stratosphere-Troposphere Radar (CUSTR) operates at 53 MHz with 475 three sub-element Yagi-Uda antenna array. The CUSTR system is a high-power fully active phased array system with a dedicated 2 kW solid-state Transmit-Receiver Module (TRM) attached to each antenna, providing a total peak power of 950 kW with 47.5 kW average power at 5% maximum duty ratio. The system provides electronic beam steering capability to steer the beam 360° in azimuth direction with a maximum off-zenith angle of 40°. The array is configured with 25 sub-arrays and operates either in a combined mode in Doppler Beam Swinging or in Spaced Antenna mode using a multi-channel digital receiver system. The TRM has been designed to provide a very short pulse of 0.5 µs and a long pulse of 128 µs. The system description along with the initial scientific observations such as atmospheric boundary layer, troposphere-lower stratosphere wind measurements along with preliminary wind validation, mesosphere echoes and ionosphere irregularities are presented.

Short-term prediction of horizontal winds in the mesosphere and lower thermosphere over coastal Peru using a hybrid model

The mesosphere and lower thermosphere (MLT) are transitional regions between the lower and upper atmosphere. The MLT dynamics can be investigated using wind measurements conducted with meteor radars. Predicting MLT winds could help forecast ionospheric parameters, which has many implications for global communications and geo-location applications. Several literature sources have developed and compared predictive models for wind speed estimation. However, in recent years, hybrid models have been developed that significantly improve the accuracy of the estimates. These integrate time series decomposition and machine learning techniques to achieve more accurate short-term predictions. This research evaluates a hybrid model that is capable of making a short-term prediction of the horizontal winds between 80 and 95 km altitudes on the coast of Peru at two locations: Lima (12°S, 77°W) and Piura (5°S, 80°W). The model takes a window of 56 data points as input (corresponding to 7 days) and predicts 16 data points as output (corresponding to 2 days). First, the missing data problem was analyzed using the Expectation Maximization algorithm (EM). Then, variational mode decomposition (VMD) separates the components that dominate the winds. Each resulting component is processed separately in a Long short-term memory (LSTM) neural network whose hyperparameters were optimized using the Optuna tool. Then, the final prediction is the sum of the predicted components. The efficiency of the hybrid model is evaluated at different altitudes using the root mean square error (RMSE) and Spearman’s correlation (r). The hybrid model performed better compared to two other models: the persistence model and the dominant harmonics model. The RMSE ranged from 10.79 to 27.04 m s−1, and the correlation ranged from 0.55 to 0.94. In addition, it is observed that the prediction quality decreases as the prediction time increases. The RMSE at the first step reached 6.04 m s−1 with a correlation of 0.99, while at the sixteenth step, the RMSE increased up to 30.84 m s−1 with a correlation of 0.5.

Exploration of Deep-Learning-Based Error-Correction Methods for Meteorological Remote-Sensing Data: A Case Study of Atmospheric Motion Vectors

Meteorological satellite remote sensing is important for numerical weather forecasts, but its accuracy is affected by many things during observation and retrieval, showing that it can be improved. As a standard way to measure wind from space, atmospheric motion vectors (AMVs) are used. They are separate pieces of information spread out in the troposphere, which gives them more depth than regular surface or sea surface wind measurements. This makes rectifying problems more difficult. For error correction, this research builds a deep-learning model that is specific to AMVs. The outcomes show that AMV observational errors are greatly reduced after correction. The root mean square error (RMSE) drops by almost 40% compared to ERA5 true values. Among these, the optimization of solar observation errors exceeds 40%; the discrepancies at varying atmospheric pressure altitudes are notably improved; the degree of optimization for data with low QI coefficients is substantial; and there remains potential for enhancement in data with high QI coefficients. Furthermore, there has been a significant enhancement in the consistency coefficient of the wind’s physical properties. In the assimilation forecasting experiments, the corrected AMV data demonstrated superior forecasting performance. With more training, the model can fix things better, and the changes it makes last for a long time. The results show that it is possible and useful to use deep learning to fix errors in meteorological remote-sensing data.

Arrayed ultrasonic wind speed and direction measurement based on the BNF-FLOC-MUSIC algorithm

An arrayed ultrasonic wind measurement method based on the BNF-FLOC-MUSIC algorithm is proposed to address the issue of low measurement accuracy and poor noise suppression capabilities of current array wind measurement methods in impulse noise backgrounds. The proposed method utilizes an array structure consisting of one transmitting ultrasonic sensor and five receiving sensors. Continuous sampling is performed leveraging this structure, and the received array signals are processed using a bounded nonlinear function (BNF). Subsequently, the fractional lower-order covariance (FLOC) operations are applied to suppress impulse noise’s influence further. Finally, combining these steps with the Multiple Signal Classification (MUSIC) algorithm enables high-precision wind speed and direction measurement. The effectiveness and superiority of the method are examined through simulation experiments and actual measurement systems, and the errors of wind speed and wind direction angle in actual measurement are 1.2% and 2°, respectively, which satisfy the design requirements of the ultrasonic anemometer.

Evaluating solar ramp rate correlations by simple radiation and wind measurements

The stability of photovoltaic (PV) energy output is critical to power supply and is a crucial factor for the reliability of power supply systems but is significantly influenced by the unpredictable shading caused by clouds. Assessing the variability of solar radiation across a spatially dispersed PV fleet can be challenging, as it is usually lower than that taken by routine measurements at a singular, localized point. This study refines the previously developed spatial radiation variability model by depicting the attenuation mode of solar irradiance correlation across spatial distances and considering the differences in the along-wind and cross-wind directions under various weather and solar radiation conditions. These modes are further connected to the variables that properly depict the weather conditions and are straightforward to measure. The data being adopted in developing and validating the model are collected from 17 pyranometers over a 0.56 km2 area for about half a year with a 1-s resolution. The model's performance, with a percentage root-mean-square error that surpasses isotropic models by 2.42 %–10.6 % for solar ramp intervals ranging from 5 to 240 s, underscores its accuracy and potential for optimizing the integration of PV fleets into power grids.

Study on Wind Farm Flow Field Characteristics Based on Boundary Condition Optimization of Complex Mountain Numerical Simulation

The accurate prediction of the flow field characteristics of complex mountains is of great practical significance for the development and construction of wind farms, but it is not yet fully understood. The main purpose of this study is to propose a method for the study of flow field characteristics under complex mountain conditions, which can optimize the boundary conditions required for numerical simulation through the wind acceleration ratio and, at the same time, couple the numerical simulation and wind measurement data to reflect the real mountain flow field distribution. The results show that the proposed method has good applicability in complex mountain wind farms, can reproduce the real flow field distribution, and has a certain practical value. Wind speed distribution and turbulence intensity are greatly affected by boundary conditions such as wind speed and wind direction and are also affected by the shielding effect brought by terrain changes. The contrast between 120° and 150° wind direction is more obvious. When the incoming wind moves to the top of a mountain or the ridgeline, it will form a low-speed wake area behind it, resulting in reduced wind speed, increased turbulence intensity, and an unstable flow field.

Uncertainty analysis method of high-speed wind tunnel standard model test results based on GUM and MCM

Abstract The standard model is a standard calibration model that has a well-known geometric shape and can represent the aerodynamic layout characteristics of a certain type of aircraft. It has many functions, including evaluating the accuracy of wind tunnel test data, verifying the integrity of the wind tunnel flow field and measurement system, and developing and verifying wind tunnel test technology. Conducting wind tunnel tests using standard models is a key step in realizing the functions of standard models. However, all existing literature on standard model tests has not effectively evaluated the uncertainty of the test results, which makes it impossible to evaluate the reliability of the test results. Based on the GUM and MCM uncertainty analysis methods, this paper establishes a method for quantitatively evaluating the uncertainty of the test results of high-speed wind tunnel standard model tests through rigorous mathematical derivation, providing technical support for precisely quantifying the uncertainty of standard model test results.

Case studies of the winds in the urban area of Hong Kong – Microclimate station observations and high resolution numerical simulations

Hong Kong, renowned for its densely packed urban areas, poses unique challenges for understanding the effects of buildings on local meteorological conditions. To address this, the Hong Kong Observatory has started building a network of urban meteorological monitoring stations since 2017 for monitoring, analysing and studying urban microclimate. This paper presents an observational and numerical study focusing on wind measurements obtained from wind sensors installed on two smart lampposts in Tsim Sha Tsui, a major urban area in Hong Kong. Two representative high wind conditions in Hong Kong, Super Typhoon Saola in 2023 and a strong monsoon case characterized by prevailing easterly winds, are considered. With the use of high resolution computational fluid dynamic simulations, major features of actual observations can be reproduced. This suggests that district scale or even street scale weather services could be possible in the future with sufficient computational power.

High-resolution wind speed measurements with quadcopter uncrewed aerial systems: calibration and verification in a wind tunnel with an active grid

Abstract. As a contribution to closing observational gaps in the atmospheric boundary layer (ABL), the Simultaneous Wind measurement with Uncrewed Flight Systems in 3D (SWUF-3D) fleet of uncrewed aerial systems (UASs) is utilized for in situ measurements of turbulence. To date, the coefficients for the transformation terms used in our algorithm for deriving wind speeds from avionic data have only been determined via calibration flights in the free field. Therefore, we present in this work calibration and verification under laboratory conditions. The UAS measurements are performed in a wind tunnel equipped with an active grid and constant temperature anemometers (CTAs) as a reference. Calibration is performed in x- and y-coordinate directions of the UAS body frame at wind speeds of 2 … 18 m s−1. For systematic verification of the measurement capabilities and identification of limitations, different measurement scenarios like gusts, velocity steps, and turbulence are generated with the active grid. Furthermore, the measurement accuracy under different angles of sideslip (AoSs) and wind speeds is investigated, and we examined whether the calibration coefficients can be ported to other UASs in the fleet. Our analyses show that the uncertainty in measuring the wind speed depends on the wind speed magnitude and increases with extreme velocity changes and with higher wind speeds, resulting in a root-mean-square error (RMSE) of less than 0.2 m s−1 for steady wind speeds. Applying the calibration coefficients from one UAS to others within the fleet results in comparable accuracies. Flights in gusts of different strengths yield an RMSE of up to 0.6 m s−1. The maximal RMSE occurs in the most extreme velocity steps (i.e., a lower speed of 5 m s−1 and an amplitude of 10 m s−1) and exceeds 1.3 m s−1. For variances below approx. 0.5 and 0.3 m2 s−2, the maximal resolvable frequencies of the turbulence are about 2 and 1 Hz, respectively. The results indicate successful calibration but with susceptibility to high AoSs in high wind speeds, no necessity for wind tunnel calibration for individual UASs, and the need for further research regarding turbulence analysis.

Hybrid fiber/bulk laser source designed for CO2 and wind measurements at 2.05 µm: publisher's note.

This publisher's note contains a correction to Opt. Lett.49, 969 (2024)10.1364/OL.510598.

Evaluation of MCP Correlation Algorithms Applied to Wind Data Series

This work aimed to develop methodologies for analysing statistical correlations among wind data series using various Measure-Correlate-Predict (MCP) methods, with the goal of selecting the most suitable method for extrapolating long-term data with minimal associated uncertainty. It was analysed the minimum time required for a wind measurement campaign when applying this methodology. Fifteen local wind measurement stations were selected. The long-term wind data reanalysis series that exhibited the strongest correlation with the measured wind data at each station was then chosen. Multiple tests were conducted with different simultaneous periods between the measured data series and the long-term series. Fifteen correlation algorithms were tested for each concurrent period. The performance of each model was evaluated using the RMSE (Root Mean Square Error) and MBE (Mean Bias Error) associated with each MCP. Analysis of the errors identified measurement periods with the lowest associated error ranging from 1 to 5 years and a single-factor ANOVA analysis was conducted. Finally, t-significance tests were performed. The study concluded that the Neural Network was the most effective MCP method. Additionally, it was determined that the minimum number of years required for a local measurement campaign should be between 2 and 3 years.

Dynamics of constant temperature anemometers for the Martian Atmosphere

The objective of this paper is to understand the limits on the response time generated by the thermal dynamics of the Martian wind sensor of the MEDA instrument, working under Constant Temperature Operation (CTO). This technology has been flown aboard the Mars Science Laboratory, InSight, and Mars 2020. It will be shown that active control thermal anemometry enables a time response to wind fluctuations faster than the time constants defined by the thermal inertia of the sensor components. It will also be shown that the temperature redistributions on the temperature-uncontrolled parts of the supporting structure impose a limit on the bandwidth of the sensor. We present a model of the MEDA hot film anemometers describing what parameters control their dynamical response, why wind tunnel testing confirmed them to be faster than 1 s, and criteria to select materials that reduce the response time to approach 0.2 s.

Seasonal Dependency of the Solar Cycle, QBO, and ENSO Effects on the Interannual Variability of the Wind DW1 in the MLT Region

AbstractThe migrating diurnal tide (DW1) is derived by fitting Hough Mode Extensions to the TIMED/TIDI near‐global wind measurements within the mesosphere and lower thermosphere between 85 and 100 km from 2004 to 2014. The tidal amplitude peaks around the equinoxes with a large interannual variability of up to 50%. The correlation coefficients between the tidal amplitude variability and the solar cycle as represented by F10.7, stratospheric Quasi‐Biennial Oscillation (QBO), and El Niño‐Southern Oscillation (ENSO) are calculated every 10 days revealing seasonal dependencies. The interannual variability is positively correlated with QBO from spring to fall, maximizing around the equinoxes; anti‐correlated to the solar cycle in early winter; and anti‐correlated to ENSO in early winter and slightly in March. Multivariate linear regressions are performed to quantitatively analyze the linear relationships between the DW1 amplitude and those factors. The fittings perform best with the QBO at 30 and 50 hPa both being considered. The contribution of QBO peaks around January and October may be related to the polar vortex modulated by QBO in the northern and southern hemispheres, respectively. The correlation between the DW1 amplitude and ENSO is negative with time lags <∼5 months during early winter and spring.

Standardization of Dual-Doppler Radar–Derived Wind Fields during a Hurricane Landfall

Abstract A new methodology for standardizing radar-derived elevated dual-Doppler (DD)-synthesized wind maps to the near surface is presented, leveraging the spatial variability found within the horizontal wind speed fields. The methodology is applied to a dataset collected by Texas Tech University (TTU) using two TTUKa-band mobile radar systems during the landfall of Hurricane Delta (2020) in coastal Louisiana. Relevant portions of the DD wind fields are extracted from multiple heights between 100 and 400 m above ground level, combined into 10-min segments and standardized to a reference height of 10 m and an open exposure roughness length of 0.03 m. Extractions from these standardized wind fields are compared and validated against the standardized wind measurements from a micronet of seven TTU StickNet platforms providing “ground truth” within the DD analysis domain. The validation efforts confirm the developed DD wind field standardization methodology yields robust results with correlation coefficients greater than 0.88 and mean biases less than 1%. The results of this study provide a new means for incorporating elevated DD radar data into new and existing surface wind field analysis systems geared toward generating a wind field of record during a hurricane landfall. Significance Statement This work presents a new methodology for using radar-generated wind fields during a hurricane landfall to support the construction of a surface wind field analysis. Because radar-based wind measurements are inherently elevated above the surface and because the wind conditions well above the ground do not directly translate to the wind conditions experienced at the ground, a method for standardizing the elevated radar wind fields to the surface provides tremendous value to generating a spatially continuous wind field of record during a hurricane landfall event to better inform event response and recovery efforts. In addition, the need for detailed wind fields of record from landfalling hurricanes that approach structural design limits is critical as a single design-level storm can alter building code return period analysis. Detailed wind fields for these high-impact events can directly inform associated updates in building codes ultimately contributing to a more resilient built environment.