Meteorological Wind Research Articles

A bioinspired triboelectric wireless anemometer with low cut-in wind speed for meteorological UAVs

Currently, unmanned aerial vehicles (UAVs) play a pivotal role in meteorological wind speed measurements. Nevertheless, existing anemometers exhibit notable issues, including excessive weight, high cost and high cut-in wind speed. This investigation introduces a biomimetic owl wing airfoil wind speed sensor utilizing the principles of the triboelectric nanogenerator (BOW-TENG) designed for UAV applications. Inspired by owl wings, an airfoil configuration with superior aerodynamic characteristics is ascertained, subsequently fabricating it into an impeller, and a wind speed sensor with low weight, low cost and low cut-in wind speed is developed. Experimental results affirm the BOW-TENG’s precision in depicting wind speed across the range of 1.6–10.7 m/s, featuring a sensitivity of 21.893 Hz/(m·s−1), a goodness of fit R2 of 0.9995, and a remarkable resolution of 0.057 m/s. The sensor weighs only 30 g. Furthermore, a cost-effective triboelectric signal processing system is devised for integrating the BOW-TENG into the UAV, as well as the optimal positioning for BOW-TENG on the UAV is identified through simulations. The relative wind direction can be calculated from signals of the BOW-TENGs on the four arms of the UAV. Ultimately, application experiments demonstrate the feasibility of employing BOW-TENG for wireless wind speed transmission from UAVs. This work incorporates bionics and proposes a practical application of triboelectric sensors for UAVs, as well as a solution for developing wind speed sensors in the field of meteorological UAV detection.

Revisiting the quantification of power plant CO2 emissions in the United States and China from satellite: A comparative study using three top-down approaches

Top-down constraints of CO2 emissions from coal-fired power plants are critical to improving the accuracy of CO2 emission inventory and designing carbon reduction strategies. Different top-down models based on satellite observation have been proposed in previous studies, but discrepancies between these models and the underlying drivers are rarely explored, limiting the confidence of their application for monitoring point-source CO2 emissions from satellite. Here, we apply three top-down models to estimate CO2 emissions from individual coal-fired power plants in the United States (US) and China in 2014–2021 from Orbiting Carbon Observatory 2 (OCO-2) satellite observations. The first one applies the Gaussian plume model to optimize emissions by fitting modeled CO2 enhancement to the observation. The second and third methods apply the same inversion framework using the maximum likelihood estimation, but with WRF-Chem and WRF-FLEXPART as forward models, respectively. We evaluate consistency between the three methods in estimating emissions of 10 power plants in the US, using daily reported values from the US Environmental Protection Agency (EPA) as a benchmark, and then apply the three methods to quantify emissions of 13 power plants in China. Results show that the WRF-Chem and WRF-FLEXPART based inversion results are consistent with the EPA reported emission rates, with correlation coefficients (r) of 0.76 and 0.85 and mean biases (MB) of 4.06 and 3.97 ktCO2/d relative to EPA reports at all 10 power plants, respectively. The Gaussian plume model driven by wind fields from WRF-Chem model without the wind rotation shows comparable ability in reproducing the EPA reported emission rates at 7 power plants (r = 0.82, MB = 6.17), but is not applicable for the other three cases. We find that application of the high-resolution three-dimensional wind fields can better capture the shape of observed plumes, especially under complex wind conditions, compared to the Gaussian plume model which relies on wind field at a single point, and thus the Gaussian plume model has difficulty to optimize emissions under inhomogeneous wind fields or when observations are far away from the power plant. In general, using the WRF-FLEXPART model as the forward model in the inverse analysis shows advanced capability to simulate narrow-shape plumes in the absence of numerical diffusion which is inherent in Eulerian model such as WRF-Chem. Emissions estimated by the three top-town methods show a moderate consistency at 13 coal-fired power plant cases in China, with 8 of 13 cases showing differences of <30% between at least two methods. However, large differences emerge when wind fields are inhomogeneous and number of available observations is limited. Using different meteorological wind fields and OCO-2 data versions can also bring substantial differences to the posterior emissions for all three approaches. We find that the posterior CO2 emissions, though only reflecting instantaneous emission rates at satellite overpass time, are not proportional to the reported capacities of these power plants, indicating that attributing CO2 emissions simply based on the capacity of power plants in some bottom-up approaches may have significant discrepancies. Our study exposes the capability and limitation of different top-down approaches in quantifying point-source CO2 emissions, advancing their application for better serving increasing constellations of point-source imagers in the future.

Wind potential in Argentina, situation and prospects

Patagonia Region in Argentina has one of the best wind characteristics of the world. From 1980 there were local motivation, from Governmental Investments to Privates ones. During the 1990s there was an expansion of energy production investments from non renewable energy fuels which stopped in 1999. The energy needs due to demand rise changes from 300MW per year (1999) to 800MW per year (2006). Since 2005 there have been motivation from different private companies including oil ones, to produce electricity from wind energy. This work describes the wind potential in Patagonia in three aspects: meteorological wind potential, technological one based on technical availabilities in Argentina, and business potential for wind energy investments. Also this work intends to show an approach for the present and actual figures on these upper aspects.

A novel wind model downscaling with statistical regression and forecast for the cleaner energy

This study proposes an innovative statistical downscaling approach for meteorological wind models, enhancing cleaner energy applications. This method efficiently and swiftly incorporates topographic and meteorological data into wind park modelling by integrating regression-diagnosis-feature selection with statistical regression and forecasting techniques. The novel method in a de facto reduce predictive errors by approximately an average of 23.21%, lower errors downscaling and forecasting by 5.94% compared to existing methods, and optimise the balance between computational efficiency and forecasting accuracy. In an Arctic wind park case study, we systematically analysed meteorologically simulated wind speeds at various geographic points surrounding the park. The method enabled effective meteorological statistical mapping and accurate wind speed forecasting. Additionally, using the Granger causality test, the model identified a significant influence of surrounding terrain features on wind patterns. This research contributes by 1. introducing a novel downscaling technique surpassing current methods, 2. providing a resource-efficient approach for managing wind energy in complex terrains, and 3. offering a versatile model applicable to broader scenarios. The work marks an advancement in the field, promoting more reliable and efficient wind power and highlighting the importance of statistical downscaling in meteorological contexts for cleaner applications.

Incorporating the impact of roadside barrier effects on dispersion into AERMOD

ABSTRACT This paper focuses on the impact of solid barriers located upwind of a highway in reducing vehicle related concentrations that occur downwind of the roadway, compared to a highway without barriers. Measurements made in the United States Environmental Protection Agency’s meteorological wind tunnel show that the mitigating impact of an upwind barrier is comparable to that of a downwind barrier. Upwind barriers lead to reductions in pollution concentrations by drawing emissions in from the highway toward the barrier. The emissions are then entrained into the flow above the recirculation zone and dispersed vertically as they are advected downwind. This upwind transport of vehicle emissions leads to concentrations at the center of the roadways that are roughly 200–300% higher than those measured on roadways with downwind barriers. This difference between on-road concentrations indicates that although both types of barriers mitigate the impact of vehicle emissions downwind of a roadway, the upwind barrier may create adverse air quality impacts for the people on the road. We have formulated a semiempirical dispersion model that incorporates the physics revealed by the wind tunnel measurements. This model improves upon a model proposed by Ahangar et al. (2017) by adjusting the wind speed to get a more realistic plume dispersion just downwind of the upwind barrier and also by providing vertical profiles of concentrations in addition to ground-level concentrations. The upwind barrier model proposed in this paper and the downwind barrier model described in Francisco et al. (2022) have been incorporated into AERMOD (version 21112) as a nonregulatory option, including the new two-barrier option when modeling both barriers on the same roadway. Implications: Our paper presents an air dispersion model algorithm for modeling the effect of upwind noise barriers on dispersion of traffic-related emissions from roadways, which was incorporated into EPA’s AERMOD and then evaluated using observations from a wind tunnel experiment. The results are compared and contrasted with results from both a no-barrier case and downwind barrier cases. This manuscript expands on previously published work analyzing the effect of barrier height and source-to-barrier distance on downwind dispersion (Atmos. Pollut. Res., 13:101385, 2022, https://doi.org/10.1016/j.apr.2022.101385). The current manuscript uses the same wind tunnel setup as reported there, but focuses on a different subset of cases, namely the upwind barrier cases, when developing dispersion model algorithms to simulate the observed effects. We believe the evaluations of the vertical profiles from the wind tunnel study, development, and incorporation of the upwind barrier algorithms into AERMOD, and model evaluation of these new algorithms are significant contributions to understanding the effects of these commonly used roadside barriers

Talking about the Meteorological wind Direction and Speed of the high Plateau Airport Equipment Anti-icing Precautions

Talking about the Meteorological wind Direction and Speed of the high Plateau Airport Equipment Anti-icing Precautions

Modeling Wind‐Blown Umbrella Clouds in Lagrangian Dispersion Models

AbstractThe ash and gas released by large explosive volcanic eruptions rises to its neutral buoyancy level in the atmosphere, then spreads laterally to form an umbrella cloud. Density stratification of the atmosphere generates buoyancy forces in the cloud, which drive the outward spread. Although umbrella clouds are often modeled as circular axisymmetric structures, in practice they are usually influenced quite strongly by the meteorological wind, with spread in the upwind direction halted by the oncoming wind, and different rates of spreading in the downwind and crosswind directions. In this work, we derive a simple parametrization of non‐axisymmetric umbrella cloud spreading from a much more complex physically based shallow‐layer intrusion model. The new parametrization is quick to evaluate and so is suitable for use in operational Volcanic Ash Transport and Dispersion Models (VATDMs). In contrast to previous parametrizations, in which there is assumed to be no interaction between a circular umbrella cloud and the meteorological wind, here the umbrella cloud is influenced by the wind and adopts a shape determined by the balance of buoyant spreading and downwind drag forces. We apply the new scheme to four historical case studies of eruptions at Puyehue 2011, Pinatubo 1991, Ulawun 2019, and Calbuco 2015. The results are compared with VATDM simulations using a conventional circular umbrella cloud parametrization. Using the new scheme, good descriptions of cloud spread are recovered and the prediction of horizontal ash distribution is improved relative to the axisymmetric parametrization.

Geographical, technical, economic, and environmental potential for wind to hydrogen production in Algeria: GIS-based approach

In this paper, wind resource availability was determined by analyzing the meteorological wind energy potential at the hub height of wind turbines. The wind resource potential was identified in specific geographic locations. Technical factors, such as siting requirements and resource availability, were taken into account in the investigation. The economic potential was calculated for each area of Algeria, considering overall losses. Subsequently, the potential for reducing fossil fuel usage was determined through wind to hydrogen estimation. The main components utilized in this work were a Vestas-90 wind turbine (V90–2.0 MW) and an alkaline electrolyzer. The analysis employed a Geographical Information System (GIS) tool. The results demonstrate Algeria's significant potential for wind to hydrogen production, estimated at 1.093 Gt/year without constraints, and reduced to 1.066 Gt/year when considering constraints. The levelized cost of wind to hydrogen production in Algeria varies between 1.51 and 15.37 US $/kg, depending on the chosen wind turbine. The wind to hydrogen production potential identified has the capacity to replace the entire current fossil fuel consumption in the transportation sector, which amounts to 5.28 Mt H2/year. The utilization of wind to hydrogen in the transport sector results in a significant reduction of CO2 emissions, with a calculated avoidance of 45.69 Mt compared to the use of gasoline and diesel. Hydrogen produced from wind energy resources also could be used to support other sectors, including residential, agricultural, and industry in Algeria.

CFD Prediction for Wind Power Generation by a Small Vertical Axis Wind Turbine: A Case Study for a University Campus

The accuracy of wind power generation predicted by computational fluid dynamics (CFD) simulations combined with meteorological wind data was validated based on comparisons with directly measured data for a small vertical axis wind turbine system installed on a university campus. The CFD simulations were performed in accordance with established guidelines and frameworks for the prediction of urban wind environments. At the rooftop location, where small wind turbines are typically installed, the deviations in wind velocity from the measurements are quite large. However, in the present study, the prediction accuracy for the wind turbine site, which was 4 m above the ground, was acceptable. The total power generation estimated using the assumed power curve based on the rated output of the turbine was 56% larger than that directly measured by the power generator. However, using the power curves obtained from the measurements, the total power generation could be predicted with a high degree of accuracy and with an error of approximately 3%. It is suggested that not only the accuracy of the wind velocity but also that of the power curve is very important because they are directly related to that of the predicted power generation.

Aeolus winds impact on volcanic ash early warning systems for aviation

Forecasting volcanic ash atmospheric pathways is of utmost importance for aviation. Volcanic ash can interfere with aircraft navigational instruments and can damage engine parts. Early warning systems, activated after volcanic eruptions can alleviate the impacts on aviation by providing forecasts of the volcanic ash plume dispersion. The quality of these short-term forecasts is subject to the accuracy of the meteorological wind fields used for the initialization of regional models. Here, we use wind profiling data from the first high spectral resolution lidar in space, Aeolus, to examine the impact of measured wind fields on regional NWP and subsequent volcanic ash dispersion forecasts, focusing on the case of Etna’s eruption on March 2021. The results from this case study demonstrate a significant improvement of the volcanic ash simulation when using Aeolus-assimilated meteorological fields, with differences in wind speed reaching up to 8 m/s when compared to the control run. When comparing the volcanic ash forecast profiles with downwind surface-based aerosol lidar observations, the modeled field is consistent with the measurements only when Aeolus winds are assimilated. This result clearly demonstrates the potential of Aeolus and highlights the necessity of future wind profiling satellite missions for improving volcanic ash forecasting and hence aviation safety.

Neutrally- and stably-stratified boundary layers adjustments to a step change in surface roughness

In this work, we study the development of the internal boundary layer (IBL) induced by a surface roughness discontinuity, where the downstream surface has a roughness length greater than that upstream. The work is carried out in the EnFlo meteorological wind tunnel, at the University of Surrey, in both thermally neutral and stable cases with varying degrees of stability. For the neutrally-stratified boundary layer, the IBL development in the log-law region shows good agreement with the diffusion model proposed by Panofsky and Dutton (Atmospheric turbulence, Wiley, New York, 1984) provided that a modified origin condition is introduced and its growth rate is dictated by a constant diffusion term. However, the model over-predicts the growth of the IBL in the outer layer, where the IBL depth grows slowly with fetch following a power function with exponent n being 0.61 (whereas the original model prescribes napprox 0.8). For the stably-stratified boundary layers, n is found to further reduce as the bulk Richardson number, textrm{Ri}_textrm{b}, increases. The analysis of the top region of the IBL shows that the slow growth rate is due to a combination of the decay of the diffusion term and a significantly negative mean wall-normal velocity, which transports fluid elements towards the wall. Considering these two effects, a modified diffusion model is proposed which well captures the growth of the IBL for both neutrally and stably-stratified boundary layers.Graphical abstract

Analysis of the Suitability of the EOLO Wind-Predictor Model for the Spanish Electricity Markets

Wind energy forecasting is a critical aspect for wind energy producers, given that the chaotic nature and the intermittence of meteorological wind cause difficulties for both the integration and the commercialization of wind-produced electricity. For most European producers, the quality of the forecast also affects their financial outcomes since it is necessary to include the impact of imbalance penalties due to the regularization in balancing markets. To help wind farm owners in the elaboration of offers for electricity markets, the EOLO predictor model can be used. This tool combines different sources of data, such as meteorological forecasts, electric market information, and historic production of the wind farm, to generate an estimation of the energy to be produced, which maximizes its financial performance by minimizing the imbalance penalties. This research study aimed to evaluate the performance of the EOLO predictor model when it is applied to the different Spanish electricity markets, focusing on the statistical analysis of its results. Results show how the wind energy forecast generated by EOLO anticipates real electricity generation with high accuracy and stability, providing a reduced forecast error when it is used to participate in successive sessions of the Spanish electricity market. The obtained error, in terms of RMAE, ranges from 8%, when it is applied to the Day-ahead market, to 6%, when it is applied to the last intraday market. In financial terms, the prediction achieves a financial performance near 99% once imbalance penalties have been discounted.

High-performance piezoelectric nanogenerators based on hierarchical ZnO@CF/PVDF composite film for self-powered meteorological sensor

A high-performance piezoelectric sensor was fabricated for the real-time monitoring of meteorological wind and rainfall.

Applicability of double power law model for the statistical analysis of meteorological wind velocity time series data

Applicability of double power law model for the statistical analysis of meteorological wind velocity time series data

Applicability of double power law model for the statistical analysis of meteorological wind velocity time series data

Applicability of double power law model for the statistical analysis of meteorological wind velocity time series data

Cluster-based ensemble learning for wind power modeling from meteorological wind data

Reliable and efficient power modeling from meteorological wind data is vital for optimal implementation and monitoring of wind energy, and it is important for understanding turbine control, farm operational optimization, and grid load balance. Based on the idea that similar wind conditions lead to similar wind powers; this paper constructs a modeling scheme that orderly integrates three types of ensemble learning algorithms—bagging, boosting, and stacking—and clustering approaches to achieve wind power modeling from multiple wind-based meteorological factors in a wind farm. The paper also investigates the applications of different clustering algorithms and methodologies to determine cluster numbers in the modeling. The results reveal that all ensemble models with clustering exploit the intrinsic information in wind data and thus outperform models without clustering by approximately 15% on average in modeling wind power. The model with the best-performing Farthest First clustering is computationally rapid and with an improvement of around 30% compared with the baselines. Given the diversity introduced by clustering algorithms, the power modeling performance is further boosted by about 5% by introducing stacking that fuses ensembles with varying clusters. The proposed modeling framework thus demonstrates promise by delivering efficient and robust performance on the targeted problem.

The impact of wind upon the Pulse technique measured airtightness of a detached dwelling

The novel Pulse technique measures the building airtightness in a dynamic approach, involving a low-pressure pressurisation process, typically in 1–10Pa. It is known that the wind effect is one of the main sources of uncertainty for airtightness testing. The literature review revealed that the wind impact on measuring building airtightness has been explored in relation to the steady pressurisation method (i.e., the blower door), while there is limited research investigating the validity of the Pulse measurement under various natural wind conditions. In this study, in total, 423 Pulse tests were performed to measure the building airtightness of a five-bedroom dwelling located at the University of Nottingham, UK, under natural wind conditions. The viability of the Pulse technique for delivering airtightness measurements under different wind conditions was assessed, including the impacts of the wind speed and the wind direction on the Pulse measurements. Based on the measured air leakage rates, the threshold of maximum wind speed that led the Pulse measurements to have repeatability greater than ±10% is 5.0 m/s at 2.2 m above ground level (i.e., approximately equivalent to the meteorological wind speed of 7.9 m/s). On the other hand, the directional wind study showed that at lower wind speeds, the wind direction has a lesser influence on the Pulse measurement than the wind speed itself. Practically, multiple Pulse tests are recommended for minimising the wind impact on building airtightness measurement when adverse wind conditions are present.

Correction of Field-Measured Wind Speed Affected by Deterministic Interference Factors

The observations of meteorological wind speed may be biased due to the influence of various distractions. Therefore, the original measured data should be corrected in a targeted manner. Wind tunnel tests (WTT) and computational fluid dynamics (CFD) numerical simulation methods are used to study the local wind environment characteristics of observation sites in Noi Ling Ding Island (NLDI) and Ping An International Financial Center (PAFC) in the Shenzhen area. The interference effects of NLDI and the building components and instruments in the hollow-layer atop PAFC on their corresponding anemometer measurement results were analyzed, and a quantitative description was provided with the wind speed influence coefficient (CVI), which is the wind speed ratio in disturbed and undisturbed cases. Results show that the CVI of WTT at the NLDI site is slightly higher than that of CFD, and the wind speed in the wind-sensitive direction is accelerated by 12% due to the influence of NLDI. The large pillars at the corner of PAFC have a considerable occlusion effect on the wind speed under the wind direction of 45°. An acceleration effect with CVI of 1.163 is found in the dominant wind direction when instruments are absent atop the hollow-layer, whereas a sheltering effect is observed with the CVI of 0.593 when instruments are present. These results are used to correct the recorded wind speed at the NLDI and PAFC sites during Super Typhoon Manghut with No. 201822, and then converted into the 10 min mean wind pressure value at 10 m height under the standard landform. The reference wind pressure values obtained are 0.526 and 0.505 kPa. The analogous conversion values achieve the purpose of mutual verification, and the effectiveness and reliability of the methodologies are presented.

Error Features in Predicting Typhoon Winds: A Case Study Comparing Simulated and Measured Data

Simulating a typhoon’s wind field via mesoscale models is important in terms of providing not only the guidelines for urban planning and onshore/offshore constructions, but also the provision of insight into the dynamics and thermodynamics of tropical cyclone systems. Therefore, the errors that are contained in simulation results were investigated in the present study, in association with large-scale meteorological patterns and localized wind conditions in the typhoon boundary layer. In detail, the full-set three-dimensional simulations of three typhoon cases were carried out in order to provide the typhoon wind fields that were required to compare with the observations that were obtained through land weather stations and offshore buoys. Although the reliability of typhoon simulations has been thoroughly investigated, the previous works mostly concentrated on the configurations and dynamic core of the model. The present study reveals, however, the influences of the characteristics of the specific weather system on the simulation’s results, which provides the foundation for the proposition of empirical corrections to improve the mesoscale simulation results of typhoon wind fields without updating the model’s algorithm.

Sustained Wind Forcing and Water Level Anomalies in Annapolis, Maryland

Abstract Like many coastal communities throughout the mid-Atlantic region, relative sea level rise and accelerating instances of coastal nuisance flooding are having a tangible negative impact on economic activity and infrastructure in Annapolis, Maryland. The drivers of coastal nuisance flooding, in general, are a superposition of global, regional, and local influences that occur across spatial and temporal scales that determine water levels relative to a coastal datum. Most of the research to date related to coastal flooding has been focused on high-impact episodic events, decomposing the global and regional drivers of sea level rise, or assessing seasonal-to-interannual trends. In this study, we focus specifically on the role of short-duration (hours) meteorological wind forcing on water level anomalies in Annapolis. Annapolis is an ideal location to study these processes because of the orientation of the coast relative to the prevailing wind directions and the long record of reliable data observations. Our results suggest that 3-, 6-, 9-, and 12-h sustained wind forcing significantly influences water level anomalies in Annapolis. Sustained wind forcing out of the northeast, east, southeast, and south is associated with positive water level anomalies, and sustained wind forcing out of the northwest and north is associated with negative water level anomalies. While these observational results suggest a relationship between sustained wind forcing and water level anomalies, a more robust approach is needed to account for other meteorological variables and drivers that occur across a variety of spatial and temporal scales. Significance Statement Coastal nuisance flooding, often the result of positive water level anomalies, is having a negative economic impact in Annapolis, Maryland. Coastal flooding research has primarily focused on high-impact episodic events, trends in sea level rise, or seasonal to interannual variability in flooding. In this study we show that short-duration wind forcing (≤12 h) likely has a significant impact on both positive and negative water level anomalies in Annapolis. While this was empirically known by local stakeholders, in this study we attempt to quantify the relationship. These results could help local stakeholders to mitigate against economic and infrastructure losses resulting from coastal nuisance flooding.