Anemometer Research Articles

MoWe: Motion Observation for Wind Estimation of Sailing Robots

ABSTRACTToward sustained mobility in complex marine environment, there is an urgent need for sailing robots to operate robustly when wind measurements are unavailable (e.g., wind sensors are damaged). This study proposes an effective motion observation‐based wind estimation (MoWe) scheme, which enables the robotic sailboat to consistently acquire wind information from its own maneuvers. MoWe incorporates motion analysis (MA) and data‐driven (DD) methods. In the MA method, dead zone constraints of the robotic sailboat are identified as crucial references in deriving wind direction. For the DD approach, the sailing robot as shown in Figure 1 is employed to collect a data set, which serves as the basis for regressing an estimator. We conducted extensive validation tests in both simulation and experiments. Results indicate favorable performance for both methods in simulated scenarios. Notably, the DD method exhibited higher estimation accuracy in all experiments. The mean absolute error (MAE) of estimated wind direction was 4.25°, with the range of confidence interval spanning from 25.13° to 33.56°, demonstrating the robustness of the DD method. Furthermore, the estimation of wind direction has been successfully applied in straight‐line sailing tests, and the wind magnitude has been estimated. The MAE of wind magnitude estimation was 1.13m/s.

Investigation of a Calibration Change in the Ocean Surface Wind Measurements from the TAO Buoy Array

Abstract During routine analyses of the calibration stability of ocean surface wind retrievals from satellites, we identified a significant bias between satellite measurements and wind observations from the Tropical Atmosphere Ocean (TAO) buoy array, starting in mid-2020 until the present. After extensive investigation, we determined that the bias did not arise from anomalies in the satellites’ calibration or coding errors, as it appeared regardless of which satellite these buoys were compared to. A sudden increase of about 10% (0.5–0.8 m s−1) in wind speed observations was first identified in over 40 TAO buoys that were serviced between March and September 2020. Our concerns were shared with scientists at the National Data Buoy Center (NDBC), who confirmed our estimates. The exact source of this sudden change is still under investigation, but it appears to be related to changes in the calibration of buoy anemometers installed during recent service trips. By 2024, all currently operating TAO buoys under NDBC management have been serviced since 2020, and they all manifest a sudden wind speed increase postservice in the public-facing buoy data. This change is a source of concern because the stability of the integrated satellite–buoy system is vital for international ocean observation programs. The aim of this paper is to inform the research community about this spurious wind signal in the TAO array, discuss the impact it could have on the research community, and prevent it from being misinterpreted as climate variability, impacting the calibration of other observing systems, or affecting derived data products such as ocean surface fluxes.

Experimental Modeling of Wind Resources for Electricity Generation in the Ningo‐Prampram Region of Ghana

ABSTRACTWind energy, as a renewable energy source, plays a critical role in achieving the world's energy transition goals. This study assesses the wind resource characteristics and potential for wind power production in the Ningo‐Prampram District of Ghana. On‐site wind measurements were conducted using an air flow anemometer at various reference points within the study area. The collected site data were statistically validated against existing satellite data using T‐test analysis, which revealed no significant difference between the ground and satellite measurements (p > 0.05), despite observed changes in weather patterns. The results showed that the mean wind speed in the area ranged between 4 and 5.6 m/s at a height of 10 m, with a predominant wind direction from the East. The district exhibited a moderate turbulence intensity of 0.226, indicating fairly good suitability for wind power projects. A feasibility analysis determined that the study area, covering 142.03 km², has the potential to accommodate a 40 MW wind farm using 20 Enercon‐82‐E2 wind turbines (2 MW each) with an 80 m hub height. This installation could reduce Ghana's Wind Utility Scale total target for 2030 by 25.256%. These findings suggest that the Ningo‐Prampram District possesses favorable conditions for sustainable wind power development.

Tilts of Atmospheric Radar-Scattering Structures Measured by Long-Term Windprofiler Radar Studies

Month-long and seasonally persistent apparent tilts in atmospheric radar scatterers have been measured with a network of six windprofiler radars over periods of two or more years. The method used employs cross-correlations between vertical winds and horizontal winds measured using the radars. It is shown that large-scale apparent tilts that persisted for many weeks and months were not uncommon at many sites, with typical tilts varying from horizontal to ~3–4° from horizontal. The azimuthal and zenithal alignment of the tilts depend on local orography as well as local seasonal atmospheric conditions. It is demonstrated that these apparent tilts are not, in general, true large-scale phenomena, but rather are a manifestation of coordinated motions within turbulent and quasi-specular radar-scattering structures at scales between a few metres and tens of metres, with these structures themselves being defined by larger-scale and longer-term physical processes. Windshear combined with breaking gravity waves seems to be a particularly effective mechanism for producing these tilts, although other possibilities are also discussed. Implications for the interpretation of the nature of turbulent eddies, the accuracy of vertical wind measurements, and the nature of layering and scattering in the real atmosphere, are discussed. A method which allows for accurate measurements of the mean off-horizontal alignment of anisotropic scatterers and turbulent eddies is introduced.

Towards Mobile Wind Measurements Using Joust Configured Ultrasonic Anemometer for Applications in Gas Flux Quantification

Small uncrewed aerial systems (sUASs) can be used to quantify emissions of greenhouse and other gases, providing flexibility in quantifying these emissions from a multitude of sources, including oil and gas infrastructure, volcano plumes, wildfire emissions, and natural sources. However, sUAS-based emission estimates are sensitive to the accuracy of wind speed and direction measurements. In this study, we examined how filtering and correcting sUAS-based wind measurements affects data accuracy by comparing data from a miniature ultrasonic anemometer mounted on a sUAS in a joust configuration to highly accurate wind data taken from a nearby eddy covariance flux tower (aka the Tower). These corrections had a small effect on wind speed error, but reduced wind direction errors from 50° to >120° to 20–30°. A concurrent experiment examining the amount of error due to the sUAS and the Tower not being co-located showed that the impact of this separation was 0.16–0.21 ms−1, a small influence on wind speed errors. Lower wind speed errors were correlated with lower turbulence intensity and higher relative wind speeds. There were also some loose trends in diminished wind direction errors at higher relative wind speeds. Therefore, to improve the quality of sUAS-based wind measurements, our study suggested that flight planning consider optimizing conditions that can lower turbulence intensity and maximize relative wind speeds as well as include post-flight corrections.

Accurate Tracking of Agile Trajectories for a Tail-Sitter UAV Under Wind Disturbances Environments

To achieve more robust and accurate tracking control of high maneuvering trajectories for a tail-sitter fixed-wing unmanned aerial vehicle (UAV) operating within its full envelope in outdoor environments, a novel control approach is proposed. Firstly, the study rigorously demonstrates the differential flatness property of tail-sitter fixed-wing UAV dynamics using a comprehensive aerodynamics model, which incorporates wind effects without simplification. Then, utilizing the derived flatness functions and the treatments for singularity, the study presents a complete process of the differential flatness transform. This transformation maps the desired maneuver trajectory to a state-input trajectory, facilitating control design. Leveraging an existing controller from the reference literature, trajectory tracking is implemented. Subsequently, a low-cost wind estimation method operating during all flight phases is proposed to estimate the wind effects involved in the model. The wind estimation method involves generating a virtual wind measurement utilizing a low-fidelity tail-sitter model. The virtual wind measurement is integrated with real wind data obtained from the pitot tube and processed through fusion using an extended Kalman filter. Finally, the effectiveness of our methods is confirmed through comprehensive real-world experiments conducted in outdoor settings. The results demonstrate superior robustness and accuracy in controlling challenging agile maneuvering trajectories compared to the existing method. Additionally, the test results highlight the effectiveness of our method in wind estimation.

Design of a Low-cost Radiation Weather Station.

Combining a traditional weather station with radiation monitors draws the public's attention to the magnitude of background radiation and its typical variation while providing early indications of unplanned radiological releases, such as nuclear power plant accidents or terrorist acts. Several networks of combined weather and radiation monitoring sensors exist, but these fail to be affordable for broad distribution. This work involves creating an affordable system to accumulate data from multiple locations into a single open-source database. The data collected should thus serve as a friendly database for high school students. The system is designed around an inexpensive sensor package featuring a cup anemometer, wind direction vane, and tip bucket rain gauge. A Raspberry Pi 4 microcomputer interfaces through RJ11 and RJ45 connectors to these and other sensors. Custom-designed circuits were implemented on printed circuit boards supporting sensor chips for temperature, pressure, humidity, and air electrical resistance. The outdoor board communicates with ultraviolet light, soil moisture, and temperature sensors, relaying data using wired connections indoors where a Raspberry Pi 4 and indoor circuit board are located. The indoor board employs wireless internet protocol to communicate with a homemade Geiger-Mueller counter and a consumer-grade temporal radon monitor. The system employs an internet connection to transfer data to a cloud-based storage system. This enables a website with continuously updated pages dedicated to each established system to display collected data. Weatherproofed fused filament fabricated indoor and outdoor cases were designed. Sensor functions were tested for functionality and accuracy.

The Quasi‐Periodic Nighttime Traveling Ionospheric Disturbances on 13 May 2024 During the Recovery Phase of a SuperStorm

AbstractOn 10–14 May 2024, one of the most powerful geomagnetic storms occurred, with the Dst less than −400 nT. Quasi‐periodic nighttime traveling ionospheric disturbances (TIDs) observed during the recovery phase on 13 May are explored using the detrended total electron content (dTEC) and the neutral wind measurements from Fabry‐Perot interferometers (FPI) at Millstone station. At middle latitudes, the strong westward plasma drifts drove an obvious nighttime ionospheric trough at 52° magnetic latitude. Equatorward of this middle‐latitude trough, quasi‐periodic TIDs in dTEC were found, with a period of 1 hr and phase speeds ranging from 305.6 to 649.3 m/s. Based on the AE index, the periodic energy deposition during substorms might be responsible for the quasi‐periodic oscillations of TIDs. Fortunately, FPI‐measured horizontal winds at Millstone showed a quasi‐periodic oscillation as well, indicating their potential key role.

Nonlinear Interactions of Planetary‐Scale Waves in Mesospheric Winds Observed at 52°N Latitude and Two Longitudes

AbstractNine‐years of mesospheric wind measurements, from two meteor radars at 52°N latitude, were analyzed to study planetary waves (PWs) and tides through estimating their zonal wavenumbers. The analysis reveals that multi‐day oscillations are predominantly driven by PW normal modes (NMs), which exhibit distinct seasonal variations and statistical association with Sudden Stratospheric Warming events. Specifically, a prominent 6‐day NM emerges in April, followed by dominant 4‐ and 2‐day NMs persisting until June, with subsequent peaks of 2‐, 4‐, and 6‐day NMs extending from July to October. Furthermore, this study presents the first observational verification of the frequencies and zonal wavenumbers of over 10 secondary waves, arising from nonlinear interactions among planetary‐scale waves. A notable finding is the prevalence of non‐migrating components in the winter 24‐hr and summer 8‐hr tides, phenomena attributed to the nonlinear interactions. Our findings highlight the complexity of atmospheric nonlinear dynamics in generating diverse planetary‐scale periodic oscillations.

Comparing and analyzing the accuracy and stability of calculating SF6 tracer gas mixing results in mine tunnels using RNG model and DES model through experiments

The tracer gas wind measurement method must measure the average concentration based on the known mixing distance of the tracer gas in order to accurately calculate the air volume. we established a T-shaped cross-ventilation duct with a square cross-sectional side of 0.3 m to simulate mine tunnels experimentally, and created the above duct model in Fluent software. Using the renormalization group (RNG k-ε) and detached eddy simulation (DES) models in Fluent to calculate the concentration and mixing distance of sulfur hexafluoride (SF6) tracer gas in air collection duct, and conducted SF6 blending experiments. In the first 7m stage of air collection duct, the SF6 mixing rate in the experiment was the fastest, while the RNG k-ε model was the slowest. However, after 7 meters, the mixing rate in the experiment rapidly slowed down, while there was no significant change in the RNG k-ε model. The rate of change of the DES model was between the above two results, but the concentration change was the most unstable. And the mixed distance results of the three were highly consistent. The results of this study provide a reference for selecting appropriate numerical simulation models for future calibration work of mine ventilation systems.

Feature selection and response prediction on a suspension bridge due to wind effect by machine learning

This research introduces an efficient machine learning approach for predicting the structural health of long-span suspension bridges, focusing on their responses to dynamic environmental load like earthquakes and wind. Unlike traditional analytical methods that rely on simplifications, this study utilizes real-world data from anemometers and accelerometers to enhance prediction accuracy and efficiency. Also instead of using whole signals, the study uses fewer time frame signal. The main contribution of this paper is the application of support vector regression (SVR), optimized through Observer-Teacher-Learner-Based-Optimization (OTLBO) for parameter tuning. The optimization process is further refined using Multi-Objective Observer-Teacher-Learner-Based-Optimization (MOOTLBO), targeting feature selection and dimension reduction to improve model performance significantly. Hardanger Bridge serves as a case study, demonstrating the model's capability to accurately predict acceleration responses to wind. By inputting wind sensor data and analyzing acceleration outputs, the enhanced SVR model, through OTLBO and MOOTLBO, shows remarkable predictive accuracy, validated against six performance indices. This methodological advancement suggests that the machine learning-based model could potentially supplant traditional finite element models, offering a more adaptable, efficient, and accurate tool for structural health monitoring (SHM). This advancement addresses critical discrepancies in SHM systems, such as data gaps or sensor malfunctions, by providing a reliable prediction method that ensures the ongoing safety and integrity of bridge structures.

A Comprehensive IoT Cloud-based Wind Station Ready for Real-time Measurements and Artificial Intelligence Integration

Accurate and efficient wind measurement and monitoring are crucial for various applications, including renewable energy, meteorology, public safety, and environmental research. This paper presents an integrated approach for a cloud-computing Internet of Things-based Automated Weather Station, with edge devices, designed to provide real-time wind measurements in diverse environments. The presented system comprises a dedicated station frame, a self-sufficient solar power setup, an ultrasonic wind sensor, a data transmission node, cloud computing capabilities, and an end-user visualization application. Calibration procedures reduced the maximum mean error of the ultrasonic wind sensors from 0.7 m/s to 0.2 m/s, achieving a 71% improvement in measurement accuracy. Using this method, all station data are processed every 3 seconds and stored ready for Artificial Intelligence usage with an approximate latency of 150–300 milliseconds, representing up to an 85% reduction in processing delay compared to similar solutions with over 1-second latencies. The lightweight and resistant frame design facilitates easy deployment, while the power setup with sub-watt efficiency ensures a reliable energy source. The ultrasonic wind sensor was subjected to calibration procedures and design improvements to increase its performance under specific rain conditions. Internet of Things communication via Hypertext Transfer Protocol enables efficient data transmission between the Automated Weather Station and the cloud server, which processes and stores the data for future analysis. The responsive and auto-adaptive web application allows user-friendly data visualization, making the wind data easily accessible and interpretable. The proposed cloud-computing Internet of Things-based Automated Weather Station framework demonstrates significant potential for accurate and efficient wind measurement and monitoring, paving the way for future advancements in high temporal resolution wind monitoring systems capable of producing big data prepared for subsequent machine learning model approaches.

Hemispheric Asymmetry of Annual/Semi-Annual Oscillations in Mesospheric Neutral Winds and Pressure Between Northern and Southern High Latitude Regions

We present oscillating features from long-term neutral wind (meteor radar) and pressure (microwave limb sounder) measurements at Esrange (67°N, 20°E, 2007–2018) in the northern hemisphere and King Sejong Station (KSS; 62°S, 58°W, 2007–2017) in the Southern Hemisphere using the Lomb-Scargle periodogram and wavelet analysis. In zonal winds and pressure, we estimated the height profiles for the amplitude ratio between annual oscillation (AO) and semi-annual oscillation (SAO) at both sites. Over KSS, the ratio indicates that SAO increases with altitude, whereas AO decreases, with SAO becoming the dominant oscillating component at around 90 km. However, the ratio mostly remains constant with an altitude due to the steady formation of a westward wind field throughout the height in summer. More intensive gravity wave activity over KSS drives meridional residual circulation in summer mesopause, creating a more powerful SAO signature above 90 km.

Investigation of MEMS Thermal Wind Sensors in Martian Environment

Investigation of MEMS Thermal Wind Sensors in Martian Environment

Global Simulation of the Solar Wind: A Comparison with Parker Solar Probe Observations during 2018–2022

Global magnetohydrodynamic (MHD) models play an important role in the infrastructure of space weather forecasting. Validating such models commonly utilizes in situ solar wind measurements made near the Earth’s orbit. The purpose of this study is to test the performance of G3DMHD (a data driven, time-dependent, 3D MHD model of the solar wind) with Parker Solar Probe (PSP) measurements. Since its launch in 2018 August, PSP has traversed the inner heliosphere at different radial distances sunward of the Earth (the closest approach ∼13.3 R ⊙), thus providing a good opportunity to study evolution of the solar wind and to validate heliospheric models of the solar wind. The G3DMHD model simulation is driven by a sequence of maps of the photospheric field extrapolated to the assumed source surface (2.5 R ⊙) using the potential field model from 2018 to 2022, which covers the first 15 PSP orbits. The Pearson correlation coefficient (cc) and the mean absolute scaled error (MASE) are used as the metrics to evaluate the model performance. It is found that the model performs better for both magnetic intensity (cc = 0.75; MASE = 0.60) and the solar wind density (cc = 0.73; MASE = 0.50) than for the solar wind speed (cc = 0.15; MASE = 1.29) and temperature (cc = 0.28; MASE = 1.14). This is due primarily to lack of accurate boundary conditions. The well-known underestimate of the magnetic field in solar minimum years is also present. Assuming that the radial magnetic field becomes uniformly distributed with latitude at or below 18 R ⊙ (the inner boundary of the computation domain), the agreement in the magnetic intensity significantly improves (cc = 0.83; MASE = 0.49).

Test Flight of a Stratospheric Sonic Anemometer Prototype

Abstract Stratospheric sonic anemometers are an emerging technology for making wind velocity measurements in low pressure environments. This paper explores the challenges, design, and high-altitude testing of a digital sonic anemometer for applications including high-altitude balloon navigation, atmospheric gravity wave research, solar activity research, and planetary science on Mars. The system was flown on NASA’s Sensor Package for Attitude, Rotation, and Relative Observable Winds (SPARROW-3) balloon test flight out of Fort Sumner, New Mexico, in August 2022. The mission recorded relative wind data throughout the flight at temperatures as low as −40°C and at the balloon’s float altitude of 38 km (3.8-mb pressure; 1 mb = 1 hPa). The device surpassed all previous sonic anemometers regarding maximum operational altitude. However, only two of the three axes operated well above 32 km, leading to some validation challenges. The system achieved a velocity resolution of 0.1 m s−1 (standard deviation) and recorded three-dimensional wind measurement every 2.25 s. Future plans anticipate addressing the faulty axis and increasing performance to a resolution of 0.01 m s−1 and a wind sampling rate of 10 Hz.

An Asymmetric Model of the Tropical Cyclone Surface Wind Field and Probabilistic Climatology of Structural Parameters

Abstract A new parametric model of a tropical cyclone’s (TC) surface wind field is proposed, in which parameters are calibrated on over 20 years of direct surface wind measurements from aircraft reconnaissance. The model extends previous formulations by representing structural asymmetries in the inner core and far field. The surface wind representation eliminates the need for uncertain aircraft flight (gradient) level-to-surface extrapolation or pressure gradient formulation conversions to wind speed. Parameters are expanded to wavenumber-1 asymmetric Fourier components around symmetric mean values; symmetric values are found to depend primarily on maximum wind speed and geographic location, while some asymmetric parameters show statistically significant relationships with motion and environmental vertical shear, consistent with recent studies. Finally, a probabilistic representation of the climatology of basin-wide TC wind field structure is constructed by fitting a multivariate statistical distribution to the optimized model parameters. The low-dimensional formulation is suitable for computationally efficient reconstruction of historical TC wind fields, from depression to Saffir–Simpson category-5 intensities, as well as for stochastic simulation in the context of catastrophe risk modeling.

Understanding the impact of data gaps on long-term offshore wind resource estimates

Abstract. In the context of a wind farm project, the wind resource is assessed to predict the power output and the optimal positioning of wind turbines. This requires taking wind measurements on the site of interest and extrapolating these to the long term using so-called “measure, correlate, and predict” (MCP) methods. Sensor, power supply, and software failures are common phenomena. These disruptions cause gaps in the measured data, which can especially be long in offshore measurement campaigns due to harsh weather conditions causing system failures and preventing servicing and redeployment. The present study investigates the effect of measurement data gaps on long-term offshore wind estimates by analyzing the bias they introduce in the parameters commonly used for wind resource assessment. Furthermore, it aims to show how filling the gaps can mitigate their effect. To achieve this, we perform investigations for three offshore sites in Europe with 2 years of concurrent measurements. We use reanalysis data and various MCP methods to fill gaps in the measured data and extrapolate these data to the long term. Current standards demand high data availability (80 % or 90 %) for wind measurement campaigns, so we expect that the effect of missing data on the uncertainty in long-term extrapolations is of the same order of magnitude as other uncertainty components such as the measurement uncertainty or the inter-annual variability. Nevertheless, our results show that the effects of gaps are considerably smaller than the other uncertainty components. For instance, gaps of 180 d cause an average deviation of the long-term mean wind speed of less than 0.04 m s−1 and a 95th percentile deviation of less than 0.075 m s−1 for all tested sites. Due to the low impact of gaps, gap filling does not have the potential to significantly reduce the uncertainty in the long-term extrapolation.

Typhoon damage assessment of power transportation networks using bias-corrected typhoon wind field with dense wind measurements

Effective preparedness and prompt restoration efforts are crucial to minimize losses in typhoon-prone areas. Achieving this necessitates reliable estimates of structural damage before typhoons make landfall. This paper develops a damage assessment framework for estimating structural damage in power transportation networks. Within this framework, a typhoon wind field model, a reliability-based fragility model, and a procedure to estimate the damaged number of towers or poles are integrated. A key feature of the framework is a proposed scale factor to correct the inherent bias in the wind field model, with its stochastic nature characterized by probabilistic models based on dense typhoon wind observations. The proposed scale factor is then incorporated into the fragility model to address the variability of the fragility model. The developed framework is applied to assess the damage to concrete poles in the 10 kV distribution networks of Zhanjiang, Guangdong Province, China during three typhoon events. For these events, the predicted number of failed poles has a relative mean error of less than 20% compared to actual values, highlighting the effectiveness of the scale factor in improving wind field model accuracy. The variability in the predicted number of failures is also quantified.

A probabilistic coral rubble mechanical instability model applied with field observations from the Great Barrier reef

Unstable coral rubble hinders coral recruitment and recovery of coral reefs after damage from cyclones and bleaching events. If coral rubble remains unstable under typical everyday environmental conditions, areas of coral rubble will not be able to recover. Evaluating the probability of rubble instability over regional scale reef systems can assist the optimization of coral reef restoration efforts. Currently, robust and verified models for such applications do not exist. This paper presents a comprehensive assessment method to predict the probability of coral rubble instability, which combines a fluid-structural interaction approach with a statistical regional wave climate model. The hydrodynamic model employs non-linear wave theory to determine near-bed velocity, pressure gradients, and the corresponding drag and inertia forces acting on the coral rubble. The instability model assesses when overturning or sliding forces exceed resisting forces, considering thousands of combinations of different coral sizes and densities to calculate the proportion of instability under a given wave forcing. The model was calibrated and validated using prior laboratory experiments as reported by Kenyon et al. (2023b). The hydrodynamic and instability models use an extensive dataset of non-cyclonic wave climates (hindcast from over 30 years of wind measurements) specific to the region around Heron Reef, Great Barrier Reef, Australia, enabling a comprehensive evaluation of the probability of rubble instability in this area. Results indicate that the overall probability of rubble instability (Pr3) reaches 0.74 in water depths less than 2 m (typical of reef crests or reef flats), while it declines to below 0.21 at a depth of 12 m (typical deeper parts of the fore reef). Coral rubble on reef crests near Heron Reef, which are sheltered by surrounding formations, demonstrates low probability of instability. Thus, coral rubble instability is influenced by both its specific location within the reef and the position of the reef relative to other nearby reefs. By integrating the rubble instability model with non-cyclonic wave climate data, a map of the probability of rubble instability was generated for eight reefs in the Capricorn and Bunker Group (CBG). This map provides valuable guidance for coral reef restoration efforts, significantly reducing the need for extensive field-based data.