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This study investigates the novel approach of synergizing desert vegetation with shelter forests to enhance windbreak efficiency in a transitional zone between the Korla oasis and the Taklimakan Desert, northwest China. Through an extensive field survey and experimental setup, we evaluated the impact of different shelterbelt configurations on wind speed reduction. Three types of shelter forests were examined: multi-row Poplar (Populus alba), single-row Jujube (Ziziphus jujube), and a mixed-species layout combining one row of Jujube and two rows of Poplar trees. Wind speed measurements were recorded at multiple heights across three zones—open field, between desert vegetation and shelterbelt, and leeward of the shelterbelt—over a three-month period (April to June, 2023). The findings reveal a significant reduction in wind speed, particularly on the leeward side, with multi-row and mixed-species configurations proving the most effective. The highest synergistic efficiency, observed in the mixed-species shelter forest, showed a windbreak efficiency improvement of over 20% compared to desert vegetation alone. This study provides new insights into the combined effectiveness of desert vegetation and shelter forests, offering a strategic framework for designing shelterbelts in arid environments. These results underscore the critical role of diverse, structured vegetation arrangements in combating wind erosion and contribute to the development of sustainable ecological management practices for desert regions worldwide. [ABSTRACT FROM AUTHOR]

Accurate wind speed measurement in low-pressure conditions is crucial for the thermal performance validation and attitude control of stratospheric aircraft. As air density decreases, traditional wind speed measurement systems based on principles such as dynamic pressure, heat transfer, ultrasound, and particle velocimetry face significant challenges when applied in low-pressure environments, often failing to achieve the required measurement accuracy. This paper presents the development of a wind speed simulation system based on a rotation method designed to operate in low-pressure conditions, utilizing a space environment simulation chamber in conjunction with a high-precision turntable. The system was employed to conduct response tests on a constant heat flow thermal sphere anemometer within a stratospheric pressure range of 1 kPa to 30 kPa. The experimental results revealed that at extremely low Reynolds numbers, the probe signal exhibited increasing nonlinearity, significantly affecting the response curve at pressures below 15 kPa. While the sensitivity of the hot-sphere probe remained relatively stable at wind speeds above 5 m/s, it decreased nonlinearly as the pressure dropped when wind speeds fell below 5 m/s. Furthermore, this paper analyzes the impact of various interpolation methods on wind speed conversion errors, providing valuable data to support the future development and validation of stratospheric aircraft. [ABSTRACT FROM AUTHOR]

A dual-wavelength spaceborne Martian polarized wind imaging Michelson interferometer based on liquid crystal tunable filters (LCTF-MPWIMI) has been proposed for the remote sensing detection of dynamic parameters such as wind speed and temperature in the middle and upper atmosphere of Mars. Using the detected Martian oxygen atom emission lines at 557.7 nm and 630.0 nm as observation spectral lines, this technology extends the detection altitude range for Martian atmospheric wind speed and temperature to 60–180 km. By leveraging the different spectral line visibility of the interferograms at the two wavelengths, a novel method for measuring Martian atmospheric temperature is proposed: the dual-wavelength spectral line visibility product method. This new approach reduces the uncertainty of temperature detection compared to traditional single spectral line visibility methods, while maintaining the precision of wind speed measurements. The feasibility of the LCTF-MPWIMI for measuring wind and temperature fields in the Martian middle and upper atmosphere has been validated through theoretical modeling and computer simulations. The interferometer, as a key component of the system, has been designed and analyzed. The proposed LCTF-MPWIMI instrument is free of mechanical moving parts, offering flexible wavelength selection and facilitating miniaturization. The dual-wavelength temperature measurement method introduced in this work provides superior temperature measurement precision compared to any single spectral line when the signal-to-noise ratio (SNR) of the interferograms is comparable. Moreover, this method does not impose specific requirements on the atomic state of the spectral lines, making it broadly applicable to similar interferometric wind measurement instruments. These innovations offer advanced tools and methodologies for measuring wind speeds and temperatures in the atmospheres of Mars and other planets. [ABSTRACT FROM AUTHOR]

The current article elucidates a study centered on the development of an anemometer leveraging an inertial sensor for wind speed measurement in the northeast region of Brazil, focusing on renewable energy generation. The study encompassed a series of experiments aimed at calibrating the anemometer, analyzing the noise generated by the inertial sensor, and scrutinizing the data acquired during wind speed measurement. The calibration process unfolded in three stages: initial noise analysis, subsequent inertial data analysis, and the derivation of calibration curves. The first two stages involved experiments conducted at an average sampling rate of 10 Hz. Simultaneously, the third stage incorporated data collected over a 1 h duration while maintaining the same sampling rate. The outcomes underscore the suitability of the anemometer based on an inertial sensor for wind energy systems and diverse applications. While the wind readings from the prototype exhibit considerable fluctuations, a three-length moving average filter is applied to the prototype's output to mitigate these fluctuations. The calibration surface was established using observational data, and the resultant surface is detailed. Data analysis assumes paramount significance in wind speed measurement, and the K-NN algorithm demonstrated superior efficacy in estimating the correspondence between measured and control data. [ABSTRACT FROM AUTHOR]

Oceans alleviate the accumulation of atmospheric CO 2 by absorbing approximately a quarter of all anthropogenic emissions. In the deep oceans, carbon uptake is dominated by aquatic phase chemistry, whereas in biologically active coastal seas the marine ecosystem and biogeochemistry play an important role in the carbon uptake. Coastal seas are hotspots of organic and inorganic matter transport between the land and the oceans, and thus they are important for the marine carbon cycling. In this study, we investigate the net air–sea CO 2 exchange at the Utö Atmospheric and Marine Research Station, located at the southern edge of the Archipelago Sea within the Baltic Sea, using the data collected during 2017–2021. The air–sea fluxes of CO 2 were measured using the eddy covariance technique, supported by the flux parameterization based on the p CO 2 and wind speed measurements. During the spring–summer months (April–August), the sea was gaining carbon dioxide from the atmosphere, with the highest monthly sink fluxes typically occurring in May, being -0.26 µ mol m -2 s -1 on average. The sea was releasing the CO 2 to the atmosphere in September–March, and the highest source fluxes were typically observed in September, being 0.42 µ mol m -2 s -1 on average. On an annual basis, the study region was found to be a net source of atmospheric CO 2 , and on average, the annual net exchange was 27.1 gC m -1 yr -1 , which is comparable to the exchange observed in the Gulf of Bothnia, the Baltic Sea. The annual net air–sea CO 2 exchanges varied between 18.2 (2018) and 39.1 gC m -1 yr -1 (2017). During the coldest year, 2017, the spring–summer sink fluxes remained low compared to the other years, as a result of relatively high seawater p CO 2 in summer, which never fell below 220 µ atm during that year. The spring–summer phytoplankton blooms of 2017 were weak, possibly due to the cloudy summer and deeply mixed surface layer, which restrained the photosynthetic fixation of dissolved inorganic carbon in the surface waters. The algal blooms in spring–summer 2018 and the consequent p CO 2 drawdown were strong, fueled by high pre-spring nutrient concentrations. The systematic positive annual CO 2 balances suggest that our coastal study site is affected by carbon flows originating from elsewhere, possibly as organic carbon, which is remineralized and released to the atmosphere as CO 2. This coastal source of CO 2 fueled by the organic matter originating probably from land ecosystems stresses the importance of understanding the carbon cycling in the land–sea continuum. [ABSTRACT FROM AUTHOR]

This paper proposes a novel L1 neural network (NN) adaptive control strategy to address the challenges posed by the non-affine nonlinear characteristics of wind turbines operating at high wind speed. These challenges complicate the design of control inputs and the issues of ensuring system transient performance due to unmodeled dynamics and external disturbances in variable-pitch control. This strategy comprises an estimator, control laws, and adaptive laws. The L1 adaptive controller ensures rapid adaptation and robustness of the system, and the NN is utilized to approximate the ideal implicit controller. This approach guarantees the uniform boundedness of system transient responses without the need for wind speed measurements. Finally, the new strategy proposed in this work is compared with traditional NN adaptive and PID control methods through simulation experiments, demonstrating enhanced robustness and superior transient performance. [ABSTRACT FROM AUTHOR]

Improved urban greenhouse gas (GHG) flux estimates are crucial for informing policy and mitigation efforts. Atmospheric inversion modeling (AIM) is a widely used technique combining atmospheric measurements of trace gas, meteorological modeling, and a prior emission map to infer fluxes. Traditionally, AIM relies on mid‐afternoon observations due to the well‐represented atmospheric boundary layer in meteorological models. However, confining flux assessment to daytime observations is problematic for the urban scale, where air masses typically move over a city in a few hours and AIM therefore cannot provide improved constraints on emissions over the full diurnal cycle. We hypothesized that there are atmospheric conditions beyond the mid‐afternoon under which meteorological models also perform well. We tested this hypothesis using tower‐based measurements of CO2 and CH4, wind speed observations, weather model outputs from INFLUX (Indianapolis Flux Experiment), and a prior emissions map. By categorizing trace gas vertical gradients according to wind speed classes and identifying when the meteorological model satisfactorily simulates boundary layer depth (BLD), we found that non‐afternoon observations can be assimilated when wind speed is >5 m/s. This condition resulted in small modeled BLD biases (<40%) when compared to calmer conditions (>100%). For Indianapolis, 37% of the GHG measurements meet this wind speed criterion, almost tripling the observations retained for AIM. Similar results are expected for windy cities like Auckland, Melbourne, and Boston, potentially allowing AIM to assimilate up to 60% of the total (24‐hr) observations. Incorporating these observations in AIMs should yield a more diurnally comprehensive evaluation of urban GHG emissions. Plain Language Summary: It is crucial to improve greenhouse gas (GHG) emission estimates to inform policy and mitigation strategies. However, the current model techniques used to estimate such emissions rely on incorporating only mid‐afternoon observations of atmospheric concentrations of GHGs. For cities, this limits a detailed understanding of emissions during hours of the day when emissions are the highest, such as the morning rush hours. This constraint is due to the limitations on how well meteorological models can describe the atmosphere during stable conditions, such as when calm winds prevail. To understand if there are any atmospheric conditions when meteorological models have good performance, for non‐afternoon hours, we used atmospheric measurements of carbon dioxide and methane, alongside meteorological model outputs. We found that observations during non‐afternoon hours are suitable for use in models when wind speed is greater than 5 m/s. This means that it is possible to double the amount of data that goes into the modeled GHG emission estimates. With this finding, emission estimates will potentially be improved, leading to a better evaluation of the diurnal cycle of GHG emissions. Key Points: Assimilating non‐afternoon greenhouse gas (GHG) observations in atmospheric inversions is reliable when wind speeds are greater than 5 m/sInclusion of non‐afternoon atmospheric observations during windy conditions doubles the current data assimilation in atmospheric inversionsAdditional observations in atmospheric inversions have the potential to improve GHG emissions estimates [ABSTRACT FROM AUTHOR]

Background and Objectives Evapotranspiration is considered the water requirement of plants. Therefore, its measurement is necessary for all agricultural and irrigation projects. Reference evapotranspiration (ET0) is one of the main components of the hydrological cycle associated with agricultural systems. Accurate estimation and prediction of ET0 is essential in managing water resources, planning irrigation, and determining the water requirement of plants. Prediction of the ET0 by providing information about the future state in different time scales can help to make appropriate decisions, plan, and apply water resources management methods. Also, assessing agricultural drought conditions by well-known indices such as the Standardized Precipitation-Evaporation Index (SPEI) and Palmer Drought Severity Index (PDSI) directly requires ET0 of the region. The sharp decrease in the level of Lake Urmia and the threat to the region's ecosystem have also made the need for accurate calculation of ET0 more significant than in the past. One of the solutions to calculate ET0 is to use the FAO-56 Penman- Mantis equation (FAO-56 PM), an acceptable alternative for the scarce lysimeter data. However, the Penman- Mantis equation is highly dependent on the wind speed parameter, so a small error in the wind speed measurement causes a significant error. Therefore, this study aims to provide an innovative and reliable model for estimating ET0 without the need for wind speed parameters in Tabriz and Urmia stations. Methodology In this study, to predict daily ET0 from intelligent models including multi-layer perceptron neural network (ANN-MLP), support vector regression (SVR), and support vector regression combined with firefly algorithm (SVR-FFA) in Urmia and Tabriz stations during the period 2002-2022 was used. The input parameters of the models include minimum relative humidity (RHmin), maximum relative humidity (RHmax), average relative humidity (RHavg), sunshine hours (SSH), minimum temperature (Tmin), maximum temperature (Tmax), average temperature (Tavg), and average soil temperature (Tsoil) was obtained from Iran Meteorological Organization (IRIMO). Also, four different scenarios were used to run the models. The selection of different input combinations was based on the correlation coefficient, so the first combination had the lowest correlation and the last combination had the highest correlation concerning ET0. Also, data from 2002-2015 for 14 years were considered for model training and from 2022-2016 for 6 years for model testing. Correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), and normalized root mean square error (NRMSE) indices were used to evaluate the used models. Findings The comparison and evaluation of the models used in Tabriz station showed that the SVR-FFA-4 model was the best model in this station with the RMSE of 1.23 mm day-1. Among the SVR models, the SVR-4 model showed a good performance with the RMSE of 1.95 mm day-1 after the combined model. Finally, the ANN-4 model also obtained an acceptable accuracy compared to other ANN combinations by having the RMSE of 1.99 mm day-1. Finally, the evaluation of the results used for the Urmia station shows that the SVR-FFA-3 model has made the best predictions compared to other models with a RMSE of 1.16mm day-1. The SVR-3 and SVR-4 models had a higher accuracy than other SVR combinations with a RMSE of 1.78 mm day-1, but the third scenario was chosen as the appropriate model in the SVR model due to having less input. Among the ANN combinations, the ANN-3 model has a good performance compared to the other combinations of this model with the RMSE of 1.81 mm day-1. Conclusion The results of this study revealed that in both stations, the hybrid model showed higher accuracy than the individual models. Therefore, in Tabriz station, the SVR-FFA-4 model had the best performance with an error rate of 1.23 mm day-1. In the Urmia station, the SVR-FFA-3 model showed good accuracy with an error rate of 1.16 mm day-1. Finally, it is suggested to use the hybrid model to predict the daily reference evapotranspiration in the northwest of the country. One of the limitations of this research is the lack of access to the parameters of dew point temperature and solar radiation. Therefore, it is suggested to use these parameters in the subsequent studies. [ABSTRACT FROM AUTHOR]

We consider the estimation of the conditional tail moment at extreme levels for the class of Weibull‐type distributions. A two‐step procedure is introduced where in the first stage one estimates the conditional tail moment at an intermediate level, followed by an extrapolation in the second stage. The asymptotic properties of the estimators introduced in the two stages are derived under suitable assumptions. The finite sample properties of the proposed estimator are examined with a simulation experiment. We conclude with two applications on real life data: wind speed measurements collected at an offshore wind farm and PM2.5 air pollution data. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method using a machine learning-based Adaptive Neuro-Fuzzy Inference System (ANFIS) to optimize Maximum Power Point Tracking (MPPT) in variable-speed Wind Turbines (WT). The ANFIS algorithm, blending artificial neural networks and fuzzy logic, addresses issues with traditional wind speed sensors, such as cost, imprecision, and susceptibility to adverse weather conditions. An initial offline-trained ANFIS is suggested to understand turbine power characteristics, and subsequently estimate varying wind speed, addressing strong nonlinearity due to WT aerodynamics and wind speed fluctuations. A second ANFIS efficiently tracks the maximum power point, overcoming limitations of linear controllers. Implemented in Matlab/Simulink for a 3.5 kW WT, the approach demonstrates effectiveness, precision, and faster response time in wind speed estimation and accurate MPPT compared to alternatives. A notable advantage is its independence from instantaneous wind speed measurement, providing a cost-effective solution for wind energy systems. [ABSTRACT FROM AUTHOR]

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 m 2 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. [ABSTRACT FROM AUTHOR]

We estimated hourly air–water gas transfer velocities (k600) for carbon dioxide in the Rhode River, a mesohaline sub-estuary of the Chesapeake Bay. Gas transfer velocities were calculated from estuary-specific parameterizations developed explicitly for shallow microtidal estuaries in the mid-Atlantic region of the United States, using standardized wind speed measurements. Combining the gas transfer velocity with continuous measurements of pCO2 in the water and in the overlying atmosphere, we determined the direction and magnitude of CO2 flux at hourly intervals across a 3-year record (1 July 2018 to 1 July 2021). Continuous year-round measurements enabled us to document strong seasonal cycling, whereby the Rhode River is primarily autotrophic during cold-water months (December–May) and largely net heterotrophic in warm-water months (June–November). Although there is inter-annual variability in CO2 flux in the Rhode River, the annual mean condition is near carbon neutral. Measurement at high temporal resolution across multiple years revealed that CO2 flux and apparent trophic status can reverse during a single 24 h period. pCO2 and CO2 flux are mediated by temperature effects on biological activity and are inverse to temperature-dependent physical solubility of CO2 in water. Biological/biogeochemical carbon fixation and mineralization are rapid and extensive, so sufficient sampling frequency is crucial to capture unbiased extremes and central tendencies of these estuarine ecosystems. [ABSTRACT FROM AUTHOR]

A sonic anemometer targeted at wind speed measurements on the surface of Mars is described. This environment requires transducer operation in 4–10 mbar CO2 at temperatures between 143 and 293 K (–130 °C and 20 °C, respectively). Over these ranges, transducer pressure and temperature sensitivity could be a source of measurement error. To investigate this, four candidate transducers were tested using transmission mode ultrasonic testing and impedance measurements: two narrowband piezoelectric transducers, a broadband capacitive transducer, and a micromachined capacitive ultrasound transducer. A system model was used for comparison and interpretation, and implications for a sonic anemometer were examined. Variation of transducer characteristics, including diffraction effects, across 2–10 mbar in CO2 and 190–293 K (–83 °C–20 °C) result in ±2.3% error in wind speed measurement and ±1.1% error in speed of sound measurement for the worst case but only ±0.14% error in wind and ±0.07% error in speed of sound for the best transducer operated off resonance. The acoustic conditions on Mars are similar to those in Earth's stratosphere at 30–42 km of altitude. Hence, testing was also conducted in dry air over the same range of pressures and temperatures with relevance to a secondary application of the instrument as a stratospheric anemometer for high altitude balloon missions on Earth. [ABSTRACT FROM AUTHOR]

The diurnal cycle of dust aerosols on Mars is studied by analyzing lidar observations at the Phoenix landing site under cloud‐ and fog‐free conditions and in the absence of elevated, long‐range transported dust layers. There is a pronounced diurnal cycle in the dust‐layer height with minimum heights of 4–6 km occurring between 11:00 and 17:00 local time. The ratio of the aerosol optical depth (AOD) within the lowermost 2 km to the total AOD reaches peak values at the same time. This can be explained by local dust emissions driven by the diurnal cycle of heating and cooling in the boundary layer. Analysis of wind and pressure measurements show that the gustiness of surface winds and the frequency of convective vortices undergo diurnal variations resembling those of AOD, indicating that these processes are the main drivers for local dust emissions. Plain Language Summary: Dust particles suspended in air are important for the radiative energy budget of Mars. By interacting with solar radiation, these aerosols impact the temperature, dynamics, and composition of the atmosphere, as well as the surface temperature of Mars. Dust aerosols are produced by wind‐lifting processes ranging from small‐scale eddies to planetary‐scale dust storms, resulting in spatially and temporally varying concentrations. Here the focus is on the diurnal cycle of dust emissions as observed by the Phoenix lander. By jointly analyzing light‐scattering observations by a lidar instrument and measurements of wind speed and air pressure, one can correlate the concentration of aerosols in air in proximity to the ground to meteorological processes. The results reveal distinct maxima in aerosol loads, the intensity of wind gusts, and the frequency of dust devils around local noon and early afternoon, and corresponding minima around midnight. This indicates that gustiness and dust devils, both fueled by solar heating during the day, are among the main causes for local dust emissions. During daytime the aerosols are concentrated at altitudes of 4–6 km. This can provide us with an indication for the degree of vertical mixing in the planetary boundary layer of Mars. Key Points: Diurnal variation of local dust emissions, wind gustiness, and convective vortices are analyzed for the entire Phoenix missionPeak values are observed in early afternoon, indicating that gustiness and convective vortices are main drivers for local dust emissionsTypical dust‐layer heights in early afternoon are 4–6 km, consistent with typical boundary‐layer heights [ABSTRACT FROM AUTHOR]

This article underscores the importance of accurate wind speed measurements and highlights the necessity of standardizing anemometers using robust mathematical models to ensure reliable data across a range of fields, including meteorology, wind energy, aviation, and civil engineering. Two classical mathematical modeling methods--the Least Squares Method and Newton's Divided Differences Method--are presented and compared. The paper evaluates the performance of these methods in anemometer standardization, taking into account their practical applications, limitations, and accuracy under varying experimental conditions. Previous studies demonstrating the efficacy of both methods across different domains are referenced. A methodology is proposed for comparing the two approaches through a controlled wind tunnel experiment using a calibrated digital anemometer under diverse flow conditions. The results will enable researchers and practitioners to select the most appropriate method for their specific needs, thereby enhancing the precision and reliability of wind speed measurements in a wide array of applications. In conclusion, the paper aims to provide a comprehensive analysis of the advantages and limitations of the Least Squares Method and Newton's Divided Differences Method in anemometer standardization, ultimately contributing to improved measurement quality in various sectors. [ABSTRACT FROM AUTHOR]

Dual-Doppler lidar measurements were made during the American WAKE ExperimeNt to provide height-resolved measurements of wind speed and direction at multiple locations immediately south of the leading row turbines in the King Plains wind farm in Oklahoma. These so-called virtual tower measurements were performed to characterize the inflow into the wind farm and to assess possible upwind blockage effects due to the collective action of the wind farm. The campaign was conducted from 12 November 2022 to 17 October 2023, during which time 14 unique virtual tower locations were sampled with heights ranging from 240 to 490 m AGL. The wind retrieval algorithm provided estimates of the horizontal winds and their uncertainties with a vertical resolution of about 10 m, while also accounting for the tilt of the lidar platform. The virtual tower results are compared to collocated lidar wind profiling data at the A1 site, which was located roughly 2.4 rotor diameters south of the nearest turbine. The wind speed difference between the wind profiler and the virtual tower was found to be quite sensitive to atmospheric stability and wind direction below 250 m AGL. The largest differences were observed for inflow under stable conditions, where the profiler wind speeds were observed to be about 22% lower than the virtual tower near hub height. These results suggest that there are persistent horizontal gradients in the flow upwind of the wind farm which result in biased estimates using standard ground-based lidar wind profiling methods. [ABSTRACT FROM AUTHOR]

In the last few years, urban trees have emerged as an effective nature‐based solution to mitigate increasing air pollutant levels due to urbanization and industrialization. This study aims to assess the synergistic effect of urban trees on improving air quality by combining real‐time PM2.5 monitoring with the i‐Tree Eco model. The monitoring was conducted during rush hours with high traffic volume and during non‐rush hours, in both the tree alley and a non‐tree road section within the industrial areas of the north‐west region of the National Capital Territory of Delhi, India. The i‐Tree Eco model was run using the diameter at breast height values of tree species present in the study area, and the PM2.5 reduction ability of the trees was quantified. The results from both approaches indicated that urban trees can significantly reduce the traffic‐fed PM2.5 concentrations. Therefore, it is suggested that tree plantations be integrated into air pollution management strategies in urbanized regions with high traffic volumes. Although this study explores the initial link between trees and air quality in Delhi, further research incorporating local wind speed and direction measurements would provide a more comprehensive understanding of how trees influence air quality in any highly polluted urban setting. [ABSTRACT FROM AUTHOR]

Ocean surface wind speed (i.e., wind speed 10 m above sea level) is a critical parameter used by atmospheric models to estimate the state of the marine atmospheric boundary layer (MABL). Accurate surface wind speed measurements in diverse locations are required to improve characterization of MABL dynamics and assess how models simulate large-scale phenomena related to climate change and global weather patterns. To provide these measurements, this study introduces and evaluates a new surface wind speed data product from the NASA Langley Research Center nadir-viewing High Spectral Resolution Lidar – generation 2 (HSRL-2) using data collected as part of the NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) mission. The HSRL-2 can directly measure vertically resolved aerosol backscatter and extinction profiles without additional constraints or assumptions, enabling the instrument to accurately derive atmospheric attenuation and directly determine surface reflectance (i.e., surface backscatter). Also, the high horizontal spatial resolution of the HSRL-2 retrievals (0.5 s or ∼ 75 m along track) allows the instrument to probe the fine-scale spatial variability in surface wind speeds over time along the flight track and over breaks in broken cloud fields. A rigorous evaluation of these retrievals is performed by comparing coincident HSRL-2 and National Center for Atmospheric Research (NCAR) Airborne Vertical Atmosphere Profiling System (AVAPS) dropsonde data, owing to the joint deployment of these two instruments on the ACTIVATE King Air aircraft. These comparisons show correlations of 0.89, slopes of 1.04 and 1.17, and y intercepts of - 0.13 and - 1.05 ms-1 for linear and bisector regressions, respectively, and the overall accuracy is calculated to be 0.15 ± 1.80 ms-1. It is also shown that the dropsonde surface wind speed data most closely follow the HSRL-2 distribution of wave slope variance using the distribution proposed by Hu et al. (2008) rather than the ones proposed by Cox and Munk (1954) and Wu (1990) for surface wind speeds below 7 ms-1 , with this category comprising most of the ACTIVATE data set. The retrievals are then evaluated separately for surface wind speeds below 7 ms-1 and between 7 and 13.3 ms-1 and show that the HSRL-2 retrieves surface wind speeds with a bias of ∼ 0.5 ms-1 and an error of ∼ 1.5 ms-1 , a finding not apparent in the cumulative comparisons. Also, it is shown that the HSRL-2 retrievals are more accurate in the summer (- 0.18 ± 1.52 ms-1) than in the winter (0.63 ± 2.07 ms-1), but the HSRL-2 is still able to make numerous (N=236) accurate retrievals in the winter. Overall, this study highlights the abilities and assesses the performance of the HSRL-2 surface wind speed retrievals, and it is hoped that further evaluation of these retrievals will be performed using other airborne and satellite data sets. [ABSTRACT FROM AUTHOR]

In order to study the influence of the wind field error on the measurement results of the mine ultrasonic time difference wind speed sensor, the computational fluid dynamics method was used to analyze the flow field and acoustic channel velocity distribution of sensors with different structure types under different wind directions and wind speeds. The research results indicate that the measured wind speed of the opposite-type structure is significantly affected by the wind direction angle than the reflection-type structure. The corrected relative error of the opposite-type structure at a 45 ° wind direction angle is the smallest and better than the reflection-type structure. Among three reflection-type structures, the concave transducers has the smallest impact on wind speed measurement, followed by raised transducers, and semi-raised transducers has the greatest impact. The measurement of concave transducers is least affected by the wind direction angle, while the raised and semi-raised transducers have similar effects. Among the three installation methods, the semi-raised installation has better flow field consistency at different flow velocity, and the corrected measurement error is the smallest. Choosing an appropriate structural layout for ultrasonic wind speed sensors can improve measurement accuracy.

Land surface models (LSMs) should have reliable forcing, validation, and surface attribute data as the foundation for effective model development and improvement. Eddy covariance flux tower data are considered the benchmarking data for LSMs. However, currently available flux tower datasets often require multiple aspects of processing to ensure data quality before application to LSMs. More importantly, these datasets lack site-observed attribute data, limiting their use as benchmarking data. Here, we conducted a comprehensive quality screening of the existing reprocessed flux tower dataset, including the proportion of gap-filled data, external disturbances, and energy balance closure (EBC), leading to 90 high-quality sites. For these sites, we collected vegetation, soil, topography information, and wind speed measurement height from literature, regional networks, and Biological, Ancillary, Disturbance, and Metadata (BADM) files. Then we obtained the final flux tower attribute dataset by global data product complement and plant functional types (PFTs) classification. This dataset is provided in NetCDF format complete with necessary descriptions and reference sources. Model simulations revealed substantial disparities in output between the attribute data observed at the site and the defaults of the model, underscoring the critical role of site-observed attribute data and increasing the emphasis on flux tower attribute data in the LSM community. The dataset addresses the lack of site attribute data to some extent, reduces uncertainty in LSMs data source, and aids in diagnosing parameter as well as process deficiencies. The dataset is available at https://doi.org/10.5281/zenodo.10939725 (Shi et al., 2024). [ABSTRACT FROM AUTHOR]

Wind speed is an important variable in the assessment of thermal comfort. Different types of meteorological devices provide different accuracy of air velocity (va) measurements, which under limited air flow conditions, may result in a discrepancy in actual thermal stress level. Simultaneous measurements on warm summer days, performed with a cup anemometer and hot-wire probe, prove that too high starting threshold of the first of these sensors can lead to a discrepancy of actual wind speed, and as a consequence can distort MRT (estimated with globe thermometers) and PET values on average up to 10 °C and 1 °C, respectively. [ABSTRACT FROM AUTHOR]

Wind farm parameterizations (WFPs) are used in mesoscale models for predicting wind farm power production and its impact on wind resources while considering the variability of the regional wind climate. However, the performance of WFPs is influenced by various factors including atmospheric stability. In this study, we compared two widely used WFPs in the Weather Research and Forecasting (WRF) model to large-eddy simulations (LES) of turbine wakes performed with the same model. The Fitch WFP and the explicit wake parameterization were evaluated for their ability to represent wind speed and turbulent kinetic energy (TKE) in a two-turbine wind farm layout under neutral, unstable, and stable atmospheric stability conditions. To ensure a fair comparison, the inflow conditions were kept as close as possible between the LES and mesoscale simulations for each type of stability condition, and the LES results were spatially aggregated to align with the mesoscale grid spacing. Our findings indicate that the performance of WFPs varies depending on the specific variable (wind speed or TKE) and the area of interest downwind of the turbine when compared to the LES reference. The WFPs can accurately depict the vertical profiles of the wind speed deficit for either the grid cell containing the wind turbines or the grid cells in the far wake, but not both simultaneously. The WFPs with an explicit source of TKE overestimate TKE values at the first grid cell containing the wind turbine; however, for downwind grid cells, agreement improves. On the other hand, WFPs without a TKE source underestimate TKE in all downwind grid cells. These agreement patterns between the WFPs and the LES reference are consistent under the three atmospheric stability conditions. However, the WFPs resemble less the wind speed and TKE from the LES reference under stable conditions than that under neutral or unstable conditions. [ABSTRACT FROM AUTHOR]

Observations of the wind speed at heights relevant for wind power are sparse, especially offshore, but with emerging aid from advanced statistical methods, it may be possible to derive information regarding wind profiles using surface observations. In this study, two machine learning (ML) methods are developed for predictions of (1) coastal wind speed profiles and (2) low-level jets (LLJs) at three locations of high relevance to offshore wind energy deployment: the US Northeastern Atlantic Coastal Zone, the North Sea, and the Baltic Sea. The ML models are trained on multiple years of lidar profiles and utilize single-level ERA5 variables as input. The models output spatial predictions of coastal wind speed profiles and LLJ occurrence. A suite of nine ERA5 variables are considered for use in the study due to their physics-based relevance in coastal wind speed profile genesis and the possibility to observe these variables in real-time via measurements. The wind speed at 10 ma.s.l. and the surface sensible heat flux are shown to have the highest importance for both wind speed profile and LLJ predictions. Wind speed profile predictions output by the ML models exhibit similar root mean squared error (RMSE) with respect to observations as is found for ERA5 output. At typical hub heights, the ML models show lower RMSE than ERA5 indicating approximately 5 % RMSE reduction. LLJ identification scores are evaluated using the symmetric extremal dependence index (SEDI). LLJ predictions from the ML models outperform predictions from ERA5, demonstrating markedly higher SEDIs. However, optimization utilizing the SEDI results in a higher number of false alarms when compared to ERA5. [ABSTRACT FROM AUTHOR]

Oceans alleviate the accumulation of atmospheric CO2 by absorbing approximately a quarter of all anthropogenic emissions. In the deep oceans, carbon uptake is dominated by aquatic phase chemistry, whereas in biologically active coastal seas the marine ecosystem and biogeochemistry play an important role in the carbon uptake. Coastal seas are hotspots of organic and inorganic matter transport between the land and the oceans, and thus important for the marine carbon cycling. In this study, we investigate the net air-sea CO2 exchange at the Utö Atmospheric and Marine Research Station, located at the southern edge of the Archipelago Sea within the Baltic Sea, using the data collected during 2017–2021. The air-sea fluxes of CO2 were measured using the eddy covariance technique, supported by the flux parametrization based on the p CO2 and wind speed measurements. During the spring-summer months (April–August), the sea was gaining carbon dioxide from the atmosphere, with the highest monthly sink fluxes typically occurring in May, being -0.26 μmol m-2 s-1 on average. The sea was releasing the CO2 to the atmosphere in September–March, and the highest source fluxes were typically observed in September, being 0.42 μmol m-2 s-1 on average. On the annual basis, the study region was found to be a net source of atmospheric CO2, and on average, the annual net exchange was 27.1 gC m-2 y-1, which is comparable to the exchange observed in the Gulf of Bothnia, the Baltic Sea. The annual net air-sea CO2 exchanges varied between 18.2 gC m-2 y-1 (2018) and 39.1 gC m-2 y-1 (2017). During the coldest year, 2017, the spring-summer sink fluxes remained low compared to the other years, as a result of relatively high seawater p CO2 in summer, which never fell below 220 μatm during that year. The spring-summer phytoplankton blooms of 2017 were weak, possibly due to the cloudy summer and deeply mixed surface layer, which restrained the photosynthetic fixation of dissolved inorganic carbon in the surface waters. The algal blooms in spring-summer 2018 and the consequent p CO2 drawdown were strong, fueled by high pre-spring nutrient concentrations. The systematic positive annual CO2 balances suggest that our coastal study site is affected by carbon flows originating from elsewhere, possibly as organic carbon which is remineralized and released to the atmosphere as CO2. This coastal source of CO2 fueled by the organic matter originating probably from land ecosystems stresses the importance of understanding the carbon cycling in the land-sea continuum. [ABSTRACT FROM AUTHOR]

An architecture for a UV wind lidar dedicated to measuring vertical and lateral wind in front of an aircraft for gust load alleviation is presented. To optimize performance and robustness, it includes a fiber laser architecture and a Quadri Mach-Zehnder (QMZ) interferometer with a robust design to spectrally analyze the backscattered light. Different lidar parameters have been selected to minimize the standard deviation of wind speed measurement projected onto the laser axis, calculated through end-to-end simulations of the instrument. The optimization involves selecting an emission/reception telescope to maximize the amount of collected photons backscattered between 100 m and 300 m, a background filter to reduce noise from the scene, and photo-multiplier tubes (PMT) to minimize detection noise. Simulations were performed to evaluate lidar performance as a function of laser parameters. This study led to the selection of three laser architectures: a commercial solid-state laser, a design of a fiber laser, and a hybrid fiber laser resulting in standard deviations of 0.18 m/s, 0.17 m/s, and 0.09 m/s, respectively, at 10 km of altitude. To reconstruct the vertical and lateral wind on the flight path, the lidar is addressed to four different directions to measure four different projections of the wind. We calculate analytically (and validate through simulations) the addressing angle with respect to the flight direction that minimizes the root mean squared error (RMSE) between the reconstructed vertical and lateral wind components and the actual ones, assuming turbulence that follows the Von Karman turbulence model. We found that the optimum angle for an estimation at 100 m is about 50°, resulting in an improvement of about 50 % compared to an angle of 15°–20° typically used in current studies. [ABSTRACT FROM AUTHOR]

As a contribution to closing observational gaps in the atmospheric boundary layer (ABL), the SWUF-3D fleet of unmanned aerial systems (UAS) is utilized for in situ measurements of turbulence. To date, the algorithm for wind measurement has only been calibrated in the free field. We therefore present in this work the calibration and verification under laboratory conditions. The UAS measurements are performed in a wind tunnel with active grid and constant temperature anemometers (CTAs) as a reference. Calibration is performed in x - and y -coordinate directions of the UAS body frame in 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 (AoS) and wind speeds is investigated and it is examined whether the calibration coefficients can be ported to other UAS of the fleet. Our analyses show that the uncertainty depends on the wind speed magnitude and increases with higher wind speeds, resulting in an overall root-mean-square error (RMSE) of less than 0.2 m s-1. Applying the calibration coefficients from one UAS to others within the fleet results in comparable accuracies. Flights in different gusts 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 m2 s-2 and 0.3 m2 s-2, the maximal resolvable frequencies of the turbulence are about 2 Hz and 1 Hz respectively. We report successful calibration but with susceptibility to high AoS in high wind speeds, no necessity of wind tunnel calibration for individual UAS, and the need for further research regarding turbulence analysis. [ABSTRACT FROM AUTHOR]

The growing integration of renewable energy sources, especially offshore wind (OSW), is introducing frequency stability challenges to electric power grids. This paper presents a novel hybrid deloading control strategy that enables modular multilevel converter (MMC)-based wind energy conversion systems (WECSs) to actively contribute to grid frequency regulation. This research investigates a permanent-magnet synchronous generator (PMSG)-based direct-drive configuration, sourced from the International Energy Agency's (IEA's) 15 MW reference turbine model. Specifically, phase-locked loop (PLL)-free grid-forming (GFM) control is employed via the grid-side converter (GSC), and DC-link voltage control is realized through the machine-side converter (MSC), both of which boost the energy support for the integrated AC grid's frequency stability. This control strategy combines the benefits of over-speeding and pitch control modes, facilitating smooth transitions between different modes based on real-time wind speed measurements. In addition, the practical challenges of MMCs, such as circulating currents and capacitor voltage imbalances, are addressed. Numerical simulations under varying wind speeds and loading conditions validate the enhanced frequency regulation capability of the proposed approach. [ABSTRACT FROM AUTHOR]

Cup anemometers measure average wind speed in the atmosphere and have been used for one and a half centuries by meteorologists. Within the last half century, cup anemometers have been used extensively in wind energy to measure wind resources and performance of wind turbines. Meteorologists researched cup anemometer behaviour and found dynamic overspeeding to be an inherent and significant systematic error. The wind energy community has strong accuracy requirements for power performance measurements on wind turbines, and this led in the last 2 decades to new research on cup anemometer characteristics, which was taken to a new level with the development of improved calibration procedures, cup anemometer calculation models and classification methods. Research projects in wind energy demonstrated, by field and wind tunnel measurements, that angular response was a significant contributor to uncertainty and that dynamic overspeeding was a significant but less important contributor. Earlier research was mainly made on cup anemometers with hemispherical cups on long arms, ​​​​​​​and dynamic overspeeding was considered an inherent and high uncertainty error for cup anemometers. Research on conical cups on short arms has now shown that zero or low overspeeding is present on a well-designed cup anemometer, providing a much lower overspeeding uncertainty error. Different cup anemometer calculation models were investigated in order to find derived overspeeding characteristics. The general and often used parabolic torque coefficient model showed that zero overspeeding is present when the speed ratio roots of the torque coefficient curve go through the equilibrium speed ratio and zero. The two-cup drag model is a special case of the parabolic torque coefficient model but with the second root being reciprocal to the equilibrium speed ratio. The drag model always results in a positive maximum overspeeding of the order of 1.1 times the turbulence intensity squared. A linear torque coefficient results in maximum overspeeding levels equal to the turbulence intensity squared. Torque characteristics of a cup anemometer with hemispherical cups fit slightly well to the drag model, but a cup anemometer with conical cups does not fit to the drag model nor the parabolic model; it fits better to a partial linear model and even better to an optimized torque model. The most accurate modelling of cup anemometer characteristics is at present made with the ACCUWIND model (Dahlberg et al., 2006). This model uses tabulated torque coefficient and angular response data measured in a wind tunnel. The ACCUWIND model is found in International Electrotechnical Commission (IEC) wind turbine power performance standards, where it is used in a classification system for estimation of operational uncertainties. For an actual comparison of two cup anemometers, with respectively hemispherical and conical cups, the influence of dynamic overspeeding was found to be relatively low compared to angular response, but for conical cups it was specifically low. [ABSTRACT FROM AUTHOR]

In the present paper, the effect of air density variations on cup anemometer performance is analyzed. The effect on the sensor's performance is mainly due to the difference between the altitude at which the cup anemometer is working and the altitude at which this instrument was calibrated. Data from the available literature are thoroughly analyzed, focusing on explaining the coupled effect of the air temperature on both the rotor's friction torque and the air density (that is, related to the aerodynamic torque on the rotor). As a result, the effect of air density variation at constant temperature (that is, leaving aside any variation of friction forces at the anemometer rotor shaft) on the sensor transfer function (i.e., on the calibration constants) is evaluated. The analysis carried out revealed a trend change in the variation with air density of the transfer function of the cup anemometer. For densities greater than 0.65, the calibration constants of the instrument have a variation with density that must necessarily change suddenly as the start-up speed, represented by the calibration constant B, becomes zero around this value of air density. To highlight the relevance of the present research, some estimations of the effect of wind speed measurement errors associated with air density changes on the Annual Energy Production (AEP) of wind turbines are included. A 1.5% decrease in the AEP forecast at air density corresponding to 2917 m above sea level is estimated for 3000–4500 kW wind turbines. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of controlling and optimizing a hybrid renewable energy system, specifically consisting of a photovoltaic (PV) generator and a wind energy conversion system comprising a wind turbine and a permanent magnet synchronous machine. The primary control objectives involve maximizing power extraction from both the PV and wind turbine while ensuring efficient injection of the extracted energy into the electrical network and maintaining a suitable power factor. This study presents two key innovations that distinguish it from existing research. Firstly, it introduces a new hybrid PV/aerogenerator energy source that eliminates the need for a DC/DC converter in the PV structure. Secondly, unlike previous works that rely on various measurements such as wind speed, temperature, and irradiance, our approach only requires measurements of electrical variables. This advancement significantly reduces the number of sensors required and enhances the overall reliability of the system. [ABSTRACT FROM AUTHOR]

Fabry Perot interferometer (FPI) is an essential ground‐based passive optical observation device to detect middle and upper atmospheric information. Three FPIs are located at Kunming (103.8°E, 25.6°N), Xinglong (117.4°E, 40.2°N), and Mohe (122.3°E, 53.5°N), China. The diurnal and annual variation of night wind at 87, 97, and 250 km are investigated from 2019 to 2021, compared to Horizontal Wind Model 14 (HWM14) to check the prediction accuracy for the local wind field. Our results are as follows: (a) At 87 km, the zonal winds are similar in Kunming and Xinglong, but the meridional winds are generally stronger in Kunming. The zonal and meridional winds in both locations are dominated by semidiurnal variations. (b) At 97 km, the duration of semidiurnal variation of zonal winds and diurnal variation of meridional winds in Kunming is long, which is opposite to that of Xinglong. (c) At 250 km, the wind speed increases with latitude for both zonal and meridional winds, which are both dominated by diurnal variations for all the three sites. Unlike 87 and 97 km, the entire wind field at 250 km is dominated by annual variation, except for a significant semiannual variation at midnight in Mohe. (d) Overall, the HWM14 predictions are stronger than the FPI measurements at peak wind speeds and similar at 87 and 97 km, which surpasses the performance at 250 km. Especially at 250 km, the model results are worse for the zonal winds of Kunming and the meridional winds of Mohe. These results will help to understand the wind field in the middle and upper atmosphere and improve the accuracy of the model. Key Points: Characteristics of mesospheric and thermospheric winds over the three stations are studied using three FPIs and compared with HWM14The winds over the three observatories showed latitudinal variation characteristicsOverall, HWM14 predicts stronger winds than FPI measurements [ABSTRACT FROM AUTHOR]

Accurate wind speed measurement in low-pressure conditions is crucial for the thermal performance validation and attitude control of stratospheric aircraft. As air density decreases, traditional wind speed measurement systems based on principles such as dynamic pressure, heat transfer, ultrasound, and particle velocimetry face significant challenges when applied in low-pressure environments, often failing to achieve the required measurement accuracy. This paper presents the development of a wind speed simulation system based on a rotation method designed to operate in low-pressure conditions, utilizing a space environment simulation chamber in conjunction with a high-precision turntable. The system was employed to conduct response tests on a constant heat flow thermal sphere anemometer within a stratospheric pressure range of 1 kPa to 30 kPa. The experimental results revealed that at extremely low Reynolds numbers, the probe signal exhibited increasing nonlinearity, significantly affecting the response curve at pressures below 15 kPa. While the sensitivity of the hot-sphere probe remained relatively stable at wind speeds above 5 m/s, it decreased nonlinearly as the pressure dropped when wind speeds fell below 5 m/s. Furthermore, this paper analyzes the impact of various interpolation methods on wind speed conversion errors, providing valuable data to support the future development and validation of stratospheric aircraft.

The current article elucidates a study centered on the development of an anemometer leveraging an inertial sensor for wind speed measurement in the northeast region of Brazil, focusing on renewable energy generation. The study encompassed a series of experiments aimed at calibrating the anemometer, analyzing the noise generated by the inertial sensor, and scrutinizing the data acquired during wind speed measurement. The calibration process unfolded in three stages: initial noise analysis, subsequent inertial data analysis, and the derivation of calibration curves. The first two stages involved experiments conducted at an average sampling rate of 10 Hz. Simultaneously, the third stage incorporated data collected over a 1 h duration while maintaining the same sampling rate. The outcomes underscore the suitability of the anemometer based on an inertial sensor for wind energy systems and diverse applications. While the wind readings from the prototype exhibit considerable fluctuations, a three-length moving average filter is applied to the prototype’s output to mitigate these fluctuations. The calibration surface was established using observational data, and the resultant surface is detailed. Data analysis assumes paramount significance in wind speed measurement, and the K-NN algorithm demonstrated superior efficacy in estimating the correspondence between measured and control data.

The Low-Level Jet (LLJ) and the Urban Heat Island (UHI) are common nocturnal phenomena in the atmospheric boundary layer. While the canopy layer UHI has been studied extensively, interactions of the LLJ and the urban atmosphere in general (and the UHI in particular) have received less attention. In the framework of the PANAME initiative in the Paris region, continuous measurements of wind speed and vertical velocity profiles were recorded with two Doppler Wind Lidars (DWL) – for the first time allowing for a detailed investigation of the summertime LLJ characteristics in the region. Jets are detected for 70 % of the examined nights, often simultaneously at an urban and a suburban site highlighting the LLJ regional spatial extent. Emerging at around sunset, the mean LLJ duration is ∼10 h, the mean wind speed is ∼9 ms−1, and the average core height is 400 m above the city. For many jets, results show signatures in the temporal evolution that indicate the inertial oscillation mechanism plays a role in the jet development: a clockwise veering of the wind direction and a rapid acceleration followed by a slower deceleration. The LLJ core induces variance in the vertical velocity (σ2w) above the urban canopy layer. It is shown that σ2w is a powerful indicator for the air temperature spatial contrasts as UHI intensity decreases exponentially with increasing σ2w and strong values only occur when σ2w is very weak. This study demonstrates how DWL observations in cities provide valuable insights into near-surface processes relevant to human and environmental health. [ABSTRACT FROM AUTHOR]

A hexacopter unmanned aerial vehicle (UAV) was fitted with a three-dimensional sonic anemometer to measure three-dimensional wind speed, air temperature, relative humidity, and pressure. To obtain accurate results for three-dimensional wind speeds, we developed an algorithm to correct biases caused by the propeller-induced airflow disturbance, UVA movement, and changes in flight attitude in the three-dimensional wind measurements. The wind measurement platform was built based on a custom-designed integration kit that couples seamlessly to the UAV, equipped with a payload and the sonic anemometer. Based on an accurate digital model of the integrated UAV-payload-anemometer platform, computational fluid dynamics (CFD) simulations were performed to quantify the wind speed disturbances caused by the rotation of the UAV's rotor on the anemometer during the UAV's steady flight under headwind, tailwind, and crosswind conditions. Through analysis of the simulated data, regression equations were developed to predict the wind speed disturbance, and the correction algorithm for rotor disturbances, motions, and attitude changes was developed. To validate the correction algorithm, we conducted a comparison study in which the integrated UAV system flew around a meteorological tower on which three-dimensional wind measurements were made at multiple altitudes. The comparison between the corrected UAV wind data and those from the meteorological tower demonstrated an excellent agreement. The corrections result in significant reductions in wind speed bias caused mostly by the propellers, along with notable changes in the dominant wind direction and wind speed in the original data. The algorithm enables reliable and accurate wind speed measurements in the atmospheric boundary layer made from rotorcraft UAVs. [ABSTRACT FROM AUTHOR]

We estimated hourly air-water gas transfer velocities (k 600) for carbon dioxide in the Rhode River, a mesohaline subestuary of the Chesapeake Bay. Gas transfer velocities were calculated from estuary-specific parameterizations developed explicitly for shallow, microtidal estuaries in the Mid-Atlantic region of the United States, using standardized wind speed measurements. Combining the gas transfer velocity with continuous measurements of p CO2 in the water and in the overlying atmosphere, we determined the direction and magnitude of CO2 flux at hourly intervals across a 3-year record (01 July 2018 to 01 July 2021). Continuous year-round measurements enabled us to document strong seasonal cycling whereby the Rhode River is net autotrophic during cold-water months (Dec–May), and largely net heterotrophic in warm-water months (Jun–Nov). Although there is inter-annual variability in CO2 flux in the Rhode River, the annual mean condition is near carbon neutral. Measurement at high temporal resolution across multiple years revealed that CO2 flux can reverse during a single 24-hour period. p CO2 and CO2 flux are mediated by temperature effects on biological activity and are inverse to temperature-dependent physical solubility of CO2 in water. Biological/biogeochemical carbon fixation and mineralization are rapid and extensive, so sufficient sampling frequency is crucial to capture unbiased extremes and central tendencies of these estuarine ecosystems. [ABSTRACT FROM AUTHOR]

SARAL/AltiKa, the first microwave altimeter operating at Ka-band frequency, recently completed nine years of operations in orbit. During these years, it has catered to many applications related to operational oceanography, climate sciences, hydrology and cryosphere. More specifically, in oceanography, SARAL has contributed immensely to operational wave and circulation modelling, eddy detection/tracking, ocean current generation and many more. However, since Feb 2019, SARAL has moved from the drifting phase (DP) to the mispointing phase (MP) due to the malfunctioning of the star sensor of the spacecraft. In this study, we analyse the instrument's performance and its waveforms during its ongoing MP. We find out that during the MP, significant wave height (SWH) measurements are anomalously high between 18 and 24 m, and wind speed measurements are between 16 and 19 m/s. In sea surface height anomaly (SSHA), there is a steady rise in negative values during the MP. In the return waveform, ∼15% degradation in Brown-type waveforms in the open ocean region is noticed. These changes significantly impact the SARAL applications. Two important applications of wave forecast and eddy detection are discussed here as examples. Following this, we also recommend using provided quality flags so that the data can be further explored for various ocean applications. [ABSTRACT FROM AUTHOR]

Coherent Doppler wind lidar has become a primary remote sensing technique for measuring atmospheric wind fields in recent years. However, the high bandwidth of the time domain echo signal has limited real-time data acquisition and processing. In this work, we propose a real-time data acquisition and preprocessing integrated solution. This approach is implemented using on-chip system field programmable gate array (FPGA) hardware while utilizing a 7-channel base-4 polyphase fast Fourier transform calculation module and a pipelined structure operation. Within a 1 s data collection time, the real-time rapid acquisition and spectral preprocessing of lidar echo signals at a range of 9.9 km can be achieved. We compare the observation data with the measurement data from Windcube100s and radiosondes, and the results indicate that the coherent wind lidar developed in this paper can detect heights above 4.0 km, with effective data acquisition rates of 82% and 61% in the height ranges of 1500–2000 m and 2000–2500 m, respectively. At the height range of 3.5 km, the correlations for horizontal wind speed and direction measured by the lidar were all above 0.99, with the wind speed and direction measurement deviations being better than 0.56 m/s and 8.40 ∘ , respectively. [ABSTRACT FROM AUTHOR]

The correlation coefficients between the electron fluence, the solar wind speed and the geomagnetic activity Ap-index with different delay times are calculated. Calculations are based on measurements of magnetospheric electron fluxes with energy >2 MeV in geostationary orbits for 35 years (1987–2021).We use >2 MeV relativistic electron flux measurements from the different Energetic Particle Sensors (EPS) on the geosynchronous spacecrafts GOES. The correlation coefficients between electron fluences on neighboring days are calculated as well. There is significant inertia in the behavior of the fluence of high-energy magnetospheric electrons. It is found that the fluence of high-energy magnetospheric electrons is weakly related to the level of geomagnetic activity on the same day, but correlates with the geomagnetic activity Ap-index observed 2–3 days earlier. The fluence of high-energy magnetospheric electrons is quite closely related to the speed of the solar wind, especially with the speed measured 2 days earlier. In general, in cycles 22–24 of solar activity, higher correlation coefficients of the electron fluence with the solar wind speed and the Ap-index are observed in the phases of decline and minimum of solar activity. A three-parameter model has also been obtained based on the prehistory of the fluence behavior, data on the Ap-index of geomagnetic activity, and measurements of the solar wind speed. The created model allows to make the prediction of high-energy magnetospheric electrons fluence for the next day.The model shows good agreement with the experimental data with a high correlation coefficient (0.82) for the entire period 1987–2021. [ABSTRACT FROM AUTHOR]

Liquefied Petroleum Gas (LPG) is a hydrocarbon gas that exists in liquefied form as an energy-saving, clean, convenient fuel, it has been widely used in civil and industrial applications such as oil fields, gas and oil industry. This present work considers the flammable gas release and dispersion safety evaluation caused by the LPG (50 % propane, 50 % n-butane) leakage and diffusion from an accidentally punctured pipe (with the diameters varies from 1-3 mm) during the operation of a 33 m3 LPG tank. Process equipment during a puncture can swiftly release hazardous compounds in sufficient quantities to distribute throughout a working and local area in clouds. LPG leakage diffusion model is created using the computational fluid dynamics (CFD) method, and the characteristics of LPG dispersion and the various of LPG release rate - dispersed by natural wind in an open area, with wind speeds range of 1.0 - 10.0 m/s corresponding to different atmospheric stability classes from A to F are computed and simulated. The result shows that wind speeds have a considerable impact on the spread of LPG from the release site to different distances in the domain. The maximum LPG concentration distribution is 101.71 ppm at 1 m/s wind velocity, while the minimum is 3.12 ppm at 10 m/s wind velocity. This suggests that the dispersion of LPG in the environment is minimal, accounting for roughly 0.01% of its total volume, which implies that LPG stations are not at significant risk in the event of a small puncture ranging from 1 to 3 mm in diameter. In the simulation work, the realizable k-ε model is utilised for the turbulence model. This study can provide guidance to authorities to comply with fire protection regulations, firefighting and prevention, and emergency response measures, which can ensure the safety of the factory operating in particular and neighbouring factories in the industrial area in general. [ABSTRACT FROM AUTHOR]

The ventilation system significantly affects the energy saving, thermal comfort, and pollutant dispersion characteristics of high-speed trains. Using Computational Fluid Dynamics (CFD), the effects of combining side-wall air supply with side roof or ceiling air supply, on the performance of a highspeed train ventilation system is studied. The results show that the addition of side-wall air supply aids energy saving and emission reduction, resulting in an increase in energy utilisation coefficient of between 5% and 30%. The number of measurement points with wind speed less than 0.3 m/s increases by 3%~9%, which can improve passenger comfort. Also, the concentration of pollutants can be reduced by 6%~29%, and the diffusion distance along the length of the train can be reduced by 18%~45%, which means more efficient ventilation and a shorter diffusion range of pollutants. However, it reduces temperature uniformity. The results of this study can be used for ventilation optimisation of high-speed trains, to reduce ventilation energy consumption, improve thermal comfort, and increase ventilation efficiency. [ABSTRACT FROM AUTHOR]

Experiments aimed at studying the mechanisms of cross-polarized microwave radiation scattering by the surface layer of water under conditions of extremely high wind speeds have been conducted in the wind–wave flume of Large Thermo-Stratified Wind-Wave Tank of the Institute of Applied Physics, Russian Academy of Sciences (IAP RAS). It is shown that the normalized radar cross section (NRCS) can be represented as a result of incoherent addition of contributions from breaking wave crests and from nonbreaking wind waves. The effect of smoothing of the water surface after passing the breaking crest made it possible to measure the NRCS of the breaking area in cross polarization, while no dependence of the NRCS on wind speed or the sounding angle was revealed. The NRCS of nonbreaking wind waves was calculated within the framework of the small slope model using the experimentally measured spectra of wind waves. It was shown that the NRCS in cross polarization increases monotonically with increasing wind speed, including hurricane conditions. In this case, the contribution of nonbreaking wind waves to NRCS experiences saturation at wind speeds exceeding 25 m/s. The monotonic increase in NRCS at higher wind speeds is associated with an increase in the surface area occupied by breaking. A composite model of microwave radiation scattering on the surface of rough water has been constructed and verified based on comparison with measurement data. The possibility of constructing a geophysical model function for ocean conditions based on the proposed composite model is shown, which can be used for remote measurements of wind speed under sea storm and hurricane conditions, including space-based instruments. [ABSTRACT FROM AUTHOR]

Inflow wind field measurements from nacelle-based lidar systems offer great potential for different applications including turbine control, load validation, and power performance measurements. On floating wind turbines nacelle-based lidar measurements are affected by the dynamic behavior of the floating foundations. Therefore, the effects on lidar wind speed measurements induced by floater dynamics must be understood. In this work, we investigate the influence of floater motions on wind speed measurements from forward-looking nacelle-based lidar systems mounted on floating offshore wind turbines (FOWTs) and suggest approaches for correcting motion-induced effects. We use an analytical model, employing the guide for the expression of uncertainty in measurements (GUM) methodology and a numerical lidar simulation for the quantification of uncertainties. It is found that the uncertainty of lidar wind speed estimates is mainly caused by the fore--aft motion of the lidar, resulting from the pitch displacement of the floater. Therefore, the uncertainty is heavily dependent on the amplitude and the frequency of the pitch motion. The bias of 10 min mean wind speed estimates is mainly influenced by the mean pitch angle of the floater and the pitch amplitude. We correct motion-induced biases in time-averaged lidar wind speed measurements with a model-based approach, employing the developed analytical model for uncertainty and bias quantification. Testing of the approach with simulated dynamics from two different FOWT concepts shows good results with remaining mean errors below 0.1m s-1 For the correction of motion-induced fluctuation in instantaneous measurements, we use a frequency filter to correct fluctuations caused by floater pitch motions for instantaneous measurements. The correction approach's performance depends on the pitch period and amplitude of the FOWT design. [ABSTRACT FROM AUTHOR]

In order to quantitatively analyze the data measurement accuracy of ocean buoys under normal and extreme sea conditions, in this study, we simulated the six-degree-of-freedom motion response of self-designed ocean buoys under different sea conditions based on a separated vortex simulation and the fluid volume method and analyzed the impact of the unsteady motion of buoys on data measurement. The results indicate that under normal sea conditions, the deviation between the numerical method used in this paper and the experimental results is less than 10%. The heaving motion of a buoy is most sensitive to changes in wave conditions. The fluctuation intensity of buoy motion is modulated by the height and wavelength of waves. When the wave height and wavelength are similar to the overall geometric size of a buoy, the wave characteristics of the buoy's heave, yaw, and pitch motion are significant. In addition, under extreme sea conditions, the movement of the buoy can also cause a deviation in the measured velocity in the transverse flow direction, but the overall deviation is less than 10%. In extreme sea conditions, the wind speed measurement results should be corrected to improve the measurement accuracy of a buoy. [ABSTRACT FROM AUTHOR]

This paper reports on the location of sources contributing to a point flux measurement in the southern taiga, Russia. The measurement tower is surrounded by a coniferous forest with a mean aerodynamically active height of 27 m (h). Aerodynamical parameters of the forest, such as displacement height d and aerodynamic roughness z 0 , derived from wind speed profile measurements for 2017–2019, were used to estimate the seasonal and daily behavior of the flux footprint. Two analytical footprint models driven by d and z0 were used to estimate the footprint for canopy sources. The Lagrangian simulation (LS) approach driven by flow statistics from measurements and modeling was used to estimate the footprint for ground-located sources. The Flux Footprint Prediction (FFP) tool for assessing canopy flux footprint applied as the option in the EddyPro v.7 software was inspected against analytical and LS methods. For model comparisons, two parameters from estimated footprint functions were used: the upwind distance (fetch) of the peak contribution in the measured flux (Xmax) and the fetch that contributed to 80% of the total flux (CF80). The study shows that Xmax varies slightly with season but relies on wind direction and time of day. All methods yield different Xmax values but fall in the same range (60–130 m, around 2–5 h); thus, they can estimate the maximum influence distance with similar confidence. The CF80 values provided by the FFP tool are significantly lower than the CF80 values from other methods. For instance, the FFP tool estimates a CF80 of about 200 m (7 h), whereas other methods estimate a range of 600–1100 m (25–40 h). The study emphasizes that estimating the ground source footprint requires either the LS method or more complex approaches based on Computational Fluid Dynamics (CFD) techniques. These findings have essential implications in interpreting eddy-flux measurements over the quasi-homogeneous forest. [ABSTRACT FROM AUTHOR]

Today, increasing concerns about greenhouse gas emissions, climate change, and resource depletion from fossil fuels have drawn attention to wind energy. In this context, wind turbine technologies are constantly evolving to eliminate such concerns by using wind energy. The wind speed from the measurement mast at a height of 80 m was used in wind turbines of different capacities and was investigated. To assess the potential of the system that produces electricity from wind energy, it has been analyzed in terms of energy, exergy, and economic. The energy and exergy efficiencies of each wind turbine were analyzed with the wind speed and meteorological data. When the average monthly power calculated for each turbine is proportioned to the turbine capacity, the energy efficiency varies between 10 and 70%. Enercon_1500 and Enercon_3050 values are high, while Enercon_3500 and Enercon_2350 have low efficiency compared to other turbines. The annual total energy production is 12.19 GWh for the highest Enercon_4200 and 4.48 GWh for the lowest Enercon_1500. The exergy efficiencies range from 20 to 79% for selected wind turbines. In the last part of the study, monthly average electricity production costs were determined by using the turbines selected for the determined region. When compared in terms of unit electricity cost, the Enercon_1500 turbine is higher, while the Enercon_4200 is lower. [ABSTRACT FROM AUTHOR]

This study introduces and evaluates ocean surface wind speed retrieval capabilities of the High Spectral Resolution Lidar – generation 2 (HSRL-2) instrument through comparison with wind speed data collected by National Center for Atmospheric Research (NCAR) Airborne Vertical Atmospheric Profiling System (AVAPS) dropsondes. Wind speed is derived from HSRL-2 measurements of the transmitted laser's specular reflection off the ocean surface. The magnitude of the surface reflectivity is determined by the surface's wave-slope variance, which is driven by surface winds. The assessment relies on the multi-year airborne data set collected as part of NASA's Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) campaign, where HSRL-2 retrievals and AVAPS dropsonde measurements of surface wind speeds were horizontally synchronized owing to their joint deployment on one of two aircraft used during the mission. A total of 577 collocated HSRL-2 - dropsonde surface wind speed data points over the northwest Atlantic Ocean are used for this study. Treating the dropsonde wind speeds as truth, it is found that, through two established wind speed – wave-slope parameterizations, the HSRL-2 wind speed retrievals have small errors (0.15 m s−1 ± 1.80 m s−1 and 0.62 m s−1 ± 1.70 m s−1) and high correlation coefficients (0.89 and 0.88) with dropsonde wind speed measurements. Also, HSRL-2 wind speed error is higher in winter than in summer due at least partly to the higher frequency of low wind speeds and reduced cloud fraction in summer. Two research flights from 28 August 2020 and 1 March 2020 serve as detailed case studies to show the success of the collocation method based on ACTIVATE's spatial-coordination strategy and how HSRL-2 wind speed retrievals can enhance science-oriented studies such as those related to cloud evolution and general air-sea interaction. Another case flight examined from 11 January 2022 demonstrates the challenge of conducting HSRL-2 wind speed retrievals in high cloud fraction conditions. Overall, this study highlights the airborne HSRL-2's ability to retrieve surface wind speeds with accuracy as well as the potential of using dropsondes to validate aircraft instrument data sets within a field campaign. [ABSTRACT FROM AUTHOR]

This paper addresses the design of a robust nonlinear dynamic controller for a wind turbine. The turbine is equipped with a permanent magnet synchronous generator. The control problem involves tracking a suitable reference value for the turbine's angular velocity, which corresponds to the wind speed. This issue is tackled by compensating for variations in the electrical and mechanical parameters present in the mathematical model. Additionally, the problem is approached under the assumption that wind speed cannot be directly measured, a fact verified in practical scenarios. This situation is particularly relevant for real-world applications, where only nominal parameter values are accessible and accurate wind speed measurement is challenging due to disturbances caused by the turbine or other factors, despite the use of appropriate sensors. To achieve precise tracking of the angular velocity reference, effective compensation of perturbation terms arising from parameter uncertainties and errors in wind estimation becomes crucial. To address this problem, a wind velocity estimator is employed in conjunction with high-order sliding mode parameter estimators, ensuring the turbine's operation attains a high level of performance. [ABSTRACT FROM AUTHOR]