Wind Field Research Articles

Armature Reaction Analysis and Suppression of Voice Coil Actuator Based on an Improved Magnetic Equivalent Circuit Model

The high thrust density voice coil actuator (VCA) is a key component for the deployers to determine the CubeSats' separation velocity in orbit directly. To improve the VCA's thrust density and power consumption, it is essential to analyze the armature reaction under heavy current work conditions. This paper proposes a dynamic splitting method of the magnetic equivalent circuit (MEC) model for analyzing the armature reaction of the VCA. This MEC model can effectively reveal the interaction mechanism between the dynamic winding field and the permanent magnet (PM) field, which provides an approach to readily and quickly analyze the armature reaction. Based on the understanding of the mechanism, we found an effective way to suppress the armature reaction and propose a new VCA with double PM flux circuits. The electromagnetic performances of the new configuration are evaluated by comparing it with the conventional one. Both the theoretical and experimental results show that the new structure achieves lower magnetic saturation, lower armature reaction, and higher thrust density. A research prototype was constructed and experimentally tested to verify the theoretical results of the armature reaction, magnetic field, thrust, separation velocity, and other performances. The new VCA realizes a wide and accurate modulation of separation velocity for the CubeSats.

Wind direction and speed calculation model with deep learning

Abstract In view of the problems of complex process and weak representation in regular atmospheric wind field detection, this paper adopts deep learning method, uses global high-altitude meteorological detection data, and establishes a deep learning model for calculating wind direction and speed with different altitudes and temperatures by using keras software package. The model is verified by using third-party independent sounding data from a meteorological observatory in Shanghai. The calculation accuracy of the model above 2000 m is 0.9830 and the value of loss function is 0.0482. The accuracy under 2000 m is 0.9164 and the value of loss function is 0.0377. There are significant differences in the performance of the model between under 2000 meters and above 2000 meters due to surface friction. The model shows that wind direction and speed of different height layers can be calculated by using only height and temperature at the same height. This model can also be used to check whether the quality of regular wind detection work is good or not with big old data.

Investigating the Near-Surface Wind Fields of Downbursts Using a Series of High-Resolution Idealized Simulations

Abstract Short-lived and poorly organized convective cells, often called weakly forced thunderstorms (WFTs), are a common phenomenon during the warm season across the eastern and southeastern United States. While typically benign, wet downbursts emanating from such convection can have substantial societal impacts, including tree, power line, and property damage from strong outflow winds. Observational studies have documented the occurrence of severe (25.7 m s−1 or higher) wind speeds from wet downbursts, but the frequency of severe downbursts, including the spatial extent and temporal duration of severe winds, remains unclear. The ability for modern observing networks to reliably observe such events is also unknown; however, answering these questions is important for improving forecast skill and verifying convective warnings accurately. This study attempts to answer these questions by drawing statistical inferences from 97 high-resolution idealized simulations of single-cell downburst events. It was found that while 35% of the simulations featured severe winds, the spatial and temporal extent of such winds is limited—O(10) km2 or less and persisting for around 5 min on average. Furthermore, through a series of simulated network experiments, it is postulated that the probability that a modern mesonet observes a severe wind gust given a severe downburst is around 1%. From these results, a statistical argument is made that most tree impacts associated with pulse convection are likely caused by subsevere winds. Several implications for forecasting, warning, and verifying WFT events fall out from these discussions.

Simulation of multivariate ergodic stochastic processes using adaptive spectral sampling and non-uniform fast Fourier transform

The simulation of multivariate ergodic stochastic processes is critical for structural dynamic analysis and reliability evaluation. Although the traditional spectral representation method (SRM) has a wide application in many areas, it is highly inefficient in simulating stochastic processes with many simulation points or long durations due to the significant computational cost associated with matrix factorizations concerning frequency. To address the encountered challenge, this paper presents an efficient approach for simulating ergodic stochastic processes with limited frequencies. Central to this approach is a fusion of the adaptive spectral sampling and the non-uniform fast Fourier transform (NUFFT) techniques. The adaptive spectral sampling of the envelope spectrum enables the determination of limited non-equispaced frequencies, which are randomly sampled according to a uniform distribution. Thus, the Cholesky decomposition is only required at limited specific frequencies, which dramatically reduces the computational cost of matrix factorizations. Since the randomly sampled frequencies are not equispaced, utilizing FFT to accelerate the summation of trigonometric functions becomes impractical. Then, the NUFFT that adapts the non-equispaced sampling points is employed instead to expedite this process with the non-uniform increment approximated through reduced interpolation. By taking the wind field simulation of a long-span suspension bridge as an example, a parametric analysis is conducted to investigate the effect of random frequencies on the simulation error of the developed approach and the convergence of spectra. Finally, the developed approach is further validated by focusing on the spectra and probabilistic density functions of the simulated wind samples, and the simulation performance is compared with that of the traditional approach. The analytical results demonstrate the efficiency and accuracy of the developed approach in simulating ergodic stochastic processes.

Evolution of downburst-like flows produced by an active-controlled multi-blade facility

The intricate dynamics of vortex structures within the downburst outflow region present significant challenges in studying flow evolutionary features, which are crucial for understanding the effect of such flow on various structures. This study aims to reveal the evolutionary features of downburst-like winds produced by an active-controlled multi-blade (AMBS) facility, using the particle image velocimetry tests and the large-eddy simulation (LES) studies. The numerical simulation indicates that the wind velocity profiles, nonstationary wind velocity time history, and the transient flow patterns of the downward flow impinging on the ground can be well simulated by the LES method. For stationary winds, a series of columnar vortices are produced, and the vortices tend to be more organized as the maximum velocity appears at a lower height. The proper orthogonal decomposition analysis manifests that the primary vortex region is affected by multiple modes of the fluctuating wind field. In addition, the primary vortex structures of the AMBS-generated flow present apparent evolutionary features. During the downward flow impinging on the ground, the nose-shaped wind velocity profile gradually forms at the turntable center and is well developed when the maximum velocity is reached. This observation is in accordance with the findings in available full-scale measurement campaigns.

Effect of tilt angle on wind-induced vibration in pre-stressed flexible cable-supported photovoltaic systems

With the increasing adoption of mountainous photovoltaic installations, pre-stressed flexible cable-supported photovoltaic (PV) systems (FCSPSs) are becoming increasingly popular in large-scale solar power plants due to their evident adaptability to sloping terrain. The wind-induced deformation of FCSPSs significantly influences the wind field. In this study, a two-way fluid–structure interaction (FSI) analysis is conducted to assess the wind-induced vibration response of FCSPSs at various panel tilt angles. Firstly, the analysis approach for wind-induced vibration coefficients of FCSPSs is established, which involves model equivalency, coefficient definitions, model creation, and grid and solution settings. Secondly, the modal analysis is then conducted on FCSPSs at various panel tilt angles. Subsequently, the transient response of the PV structure and the variations in the wind field are evaluated by adjusting the panel tilt angles. Finally, a quantitative analysis is performed to investigate the influence of panel tilt angles on the support reaction and displacement wind-induced vibration coefficients of FCSPSs. The results indicate that the tilt angle has a certain impact on wind-induced vibration coefficients (reaction force, displacement). The effect of the tilt angle on the internal force wind-induced vibration coefficient is more pronounced. However, the variation in displacement due to wind-induced vibrations is significantly greater than the variation in internal force. Meanwhile, the displacement wind-induced vibration coefficient and the support reaction wind-induced vibration coefficient should be considered separately for different tilt angles.

Dynamic Response Analysis of Moving Trains Passing Through the Stationary Thunderstorm Downburst Wind

The safety of the high-speed train traveling through the stationary thunderstorm downburst wind was studied. First, a thunderstorm wind test device was used to simulate the stationary thunderstorm downburst wind. Based on the rigid model pressure measurement tests, the aerodynamic forces of the train traveling along different paths through the stationary thunderstorm downburst wind were measured. The influence of the radial distance of the crossing path on the aerodynamic force coefficients of the train was investigated. On this basis, an unsteady aerodynamic model of the high-speed train crossing through the stationary thunderstorm downburst wind was established, and dynamic response analysis was carried out using the SIMPACK multibody dynamics simulation software to explore further the safety of the high-speed train crossing through the stationary thunderstorm downburst wind. The research showed that the stationary thunderstorm downburst wind field has significant spatial variation characteristics compared with the atmospheric boundary layer wind field. When the train passes through the thunderstorm downburst wind, the radial wind speed and wind yaw angle experienced by the train constantly change, and the change curve shows a symmetrical distribution. The aerodynamic force of the train will undergo sudden loading and unloading processes, and the lateral force coefficient of the train on different paths shows a “pulse-type” variation. Moreover, the lateral force coefficient increases with the increase of wind yaw angle. Under the influence of the thunderstorm downburst wind, the variation trend of the aerodynamic force coefficients of the train is consistent with that under crosswind. However, there are significant differences in the numerical values. Therefore, it is impossible to simply use the formula for calculating the aerodynamic force coefficients of the train under crosswinds to predict the aerodynamic force coefficients of the train under the thunderstorm downburst wind. While passing through the thunderstorm downburst wind, the overturning coefficient index plays a decisive role in the safety of train operations. Train rollover is the main form of train safety accidents, while derailment accidents are not easy. The numerical results obtained in this study are significant for evaluating the operational safety while moving trains traversing the stationary thunderstorm downburst wind.

Numerical investigation of turbulence effect on flight trajectory of spherical windborne debris: A multi-layered approach

Accurate modeling of the turbulent wind field is a crucial component of risk analysis for structures to windborne debris damage. Existing studies typically simplify the complexities of wind turbulence, and the potential influence on the accuracy of debris flight modeling has not been systematically demonstrated. This study takes a multi-layered approach to numerically simulate the flight trajectory of spherical debris in a turbulent wind field. Complexities are incrementally added to the simulated wind field to systematically investigate the influence of spatial correlation and non-Gaussian features of turbulence on debris flight behavior. The sensitivity of debris flight behavior to turbulent wind features will inform the design of debris flight tracking wind tunnel tests and building façade debris vulnerability modeling efforts.

Single-instant spatial wave height forecast using machine learning: An image-to-image translation approach based on generative adversarial networks

An innovative single-instant approach for spatial significant wave height forecast based on a Generative Adversarial Networks (GANs) model is presented. The model's most distinctive feature is its ability to provide spatial wave height forecast with only a single snapshot of wind field input, eliminating the need for continuous wind field sequences while simultaneously maintaining high accuracy and enhanced efficiency. The proposed model architecture comprises an image-to-image translation generator and a patch-style discriminator, working in tandem to forecast high-quality wave height data. The model is trained using a 20-year ECMWF Reanalysis v5 (ERA5) wind and wave dataset. A vast sea area covering 15°-40°N, 105°-130°E in West Pacific is used for this study. The presented model is carefully tested against a 10-year period historical dataset and the results are discussed. The model exhibits outstanding overall performances with Mean Absolute Error (MAE) of 0.16 m, Root Mean Square Error (RMSE) of 0.24 m, and Mean Absolute Percentage Error (MAPE) of 13.4 %. The model proves particularly effective in forecasting extreme wave heights during tropical cyclones, capturing the structure of typhoon-driven waves and providing wave heights with favorable accuracy. It is also proficient at delivering satisfactory statistical wave height forecasts for long-term periods.

Deep Learning Improves GFS Sea Surface Wind Field Forecast Accuracy in the Northwest Pacific Ocean

AbstractSea surface winds influence shipping, fisheries, and coastal projects. However, the current sea surface wind forecast exhibits noticeable biases. This study introduces a deep learning (DL)‐based bias correction model, WindNet, to improve the Global Forecast System (GFS) sea surface wind field forecast in the Northwest Pacific Ocean (NWPO). WindNet reduces the Root Mean Squared Errors (RMSEs) of wind speed at lead times of 24, 48, and 72 hr from 1.41–1.95 to 1.11–1.55 m s−1, achieving percentage reductions of 20.51%–21.28%. Simultaneously, the RMSEs of wind direction are reduced from 29.67–41.45° to 25.38–36.81°, demonstrating percentage reductions of 11.19%–14.46%. During typhoon passages, the RMSEs of wind speed and direction at three forecast lead times after using WindNet are reduced from 1.57–2.42 to 1.24–1.95 m s−1 and from 30.31–42.35° to 25.88–37.64°, demonstrating percentage reductions of 19.42%–21.02% and 11.12%–14.62%. By integrating a Squeeze‐and‐Excitation Network into WindNet, we find that utilizing information from the circulation field, apart from the zonal and meridional wind components at 10 m height, is crucial for the correction of the sea surface wind speed. WindNet can effectively capture the non‐linear relationship between other low‐level‐circulation‐related variables and sea surface wind speed. Therefore, WindNet remarkably enhances sea surface wind field forecast accuracy in NWPO.

Simulating low-frequency wind fluctuations

Abstract. Large-scale flow structures are vital in influencing the dynamic response of floating wind turbines and wake meandering behind large offshore wind turbines. It is imperative that we develop an inflow wind turbulence model capable of replicating the large-scale and low-frequency wind fluctuations occurring in the marine atmosphere since the current turbulence models do not account well for this phenomenon. Here, we present a method to simulate low-frequency wind fluctuations. This method employs the two-dimensional (2D) spectral tensor for low-frequency, anisotropic wind fluctuations presented by Syed and Mann (2024) to generate stochastic wind fields. The simulation method generates large-scale 2D spatial wind fields for the longitudinal u and lateral v wind components, which can be converted into a frequency domain using Taylor's frozen turbulence hypothesis. The low-frequency wind turbulence is assumed to be independent of the high-frequency turbulence; thus, a broad spectral representation can be obtained just by superposing the two turbulent wind fields. The method is tested by comparing the simulated and theoretical spectra and co-coherences of the combined low- and high-frequency fluctuations. Furthermore, the low-frequency wind fluctuations can also be subjected to anisotropy. The resulting wind fields from this method can be used to analyze the impact of low-frequency wind fluctuations on wind turbine loads and dynamic response and to study the wake meandering behind large offshore wind farms.

Numerical simulation of local and upwind temperature stratification on flow and dispersion in a bi-dimensional street canyon.

The thermal effect mainly includes boundary temperature stratification and the local thermal effect. The combined effect of these factors on flow and dispersion in a bi-dimensional canyon was investigated by the RANS and LES methods to evaluate their performance. The results, including the flow field, turbulent kinetic energy, temperature, heat flux, pollutant concentration and fluxes, were compared with the data from wind tunnel experiments. The comparison results showed that the RANS method severely overestimated the impact of windward heating on the flow in the canyon because of the lack of simulated flow separation ability and the limitation of the Boussinesq model, leading to an incorrect flow field and an incorrect temperature and concentration. In contrast, LES performed better mainly because of its ability to simulate flow separation. LES regenerated the right vortexes, flow field and low wind velocity. LES slightly overestimates the overall temperature in the canyon because heat exchange is eliminated in LES but difficult to avoid in the experiment. The difference in the air exchange rate at the roof level between the LES and wind tunnel data was no more than 5%, and the pollutant concentration distribution of the LES was almost the same as that of the experiments. This work emphasizes that the RANS method has limited ability to simulate flow and dispersion when the thermal effect is considered even at a reduced-scale, while LES can simulate the combined effects of incoming flow temperature stratification and local thermal effects. It is therefore suggested that if computing resources are limited and the temperature difference is not large, a steady-state calculation RANS can be used. Otherwise, LES must be performed.

Response of Sediment Dynamics to Tropical Cyclones under Various Scenarios in the Jiangsu Coast

The Jiangsu Coast (JC), China, is an area susceptible to the impact of tropical cyclones (TCs). However, due to the lack of available on-site observation data, nearshore sedimentary dynamic processes under the impact of TCs have not been fully explored. This study developed a 3D wave–current–sediment numerical model for the JC based on the Finite Volume Community Ocean Model (FVCOM) to investigate sediment dynamic responses to TCs under various scenarios, including different tracks, intensities of TCs and tidal conditions. The validation results demonstrated the model’s satisfactory performance. According to the simulation results, typhoons can significantly impact the hydrodynamics and sediment dynamics. During Typhoon Lekima in 2019, strong southeasterly winds substantially increased the current velocity, bottom stress, wave height, and suspended sediment concentration (SSC). Three typical landfall-type typhoons, with prevailing southeasterly winds, brought significant sediment flux from southeast to northwest along the coast, while the typhoon that moved northward in the Yellow Sea induced a relatively small sediment flux from north to south. Typhoons could also induce stripe-like erosion and deposition, which is closely related to seafloor topography, resulting in seabed thickness variations of up to ±0.3 m. Additionally, strengthening typhoon wind fields can lead to increased sediment flux and seabed morphological changes. Typhoon Winnie, particularly at spring tide, had a greater impact on sediment dynamics compared to other landfall typhoons. Numerical simulations showed that the typhoon-induced net sediment flux within the spring tidal cycle could increase by 80% to 100% compared to the neap tidal cycle, indicating the significant influence of tidal conditions on sediment transport during TC events.

Host vegetation connectivity is decisive for the natural spread of pine wilt disease.

Pine wilt disease has caused significant economic, ecological, and social losses in China, but there is a notable lack of research on the dynamic process of its propagation and diffusion over long timescales. This study revealed the spatial and temporal spread of the natural invasion of pine wilt disease through an analysis of long time series at macroscopic scales. We analysed and verified by simulations the driving mechanisms of host and wind fields in the natural spread of pine wilt disease. The research findings indicate that from 1982 to 2019, the number of counties affected by pine wilt disease in the Yangtze River Delta region of China exhibited a pattern of 'steady increase-fluctuation-outbreak'. The host forest played a decisive role in the natural spread of the disease, while the wind field played a supporting role. The study revealed specific contributions from various factors, where host forest landscape connectivity, host forest area share, mean wind speed, and wind frequency accounted for 31.8%, 28.7%, 22.6%, and 8.8%, respectively. The interaction of increased host forest area and increased wind speed can significantly increase the risk of pine wilt disease transmission. To validate these findings, vectorial metacellular automata simulations of pine nematode transmission in the Yangtze River Delta were conducted, yielding results with an accuracy of 0.803. By quantifying the contribution of host forest connectivity to the natural spread of pine wilt disease, this research offers a scientific foundation and innovative insights for preventing and controlling its dissemination. © 2024 Society of Chemical Industry.

Estimation of the Wind Field with a Single High-Frequency Radar

Estimation of the Wind Field with a Single High-Frequency Radar

A framework of data assimilation for wind flow fields by physics-informed neural networks

Various types of measurement techniques, such as Light Detection and Ranging (LiDAR) devices, anemometers, and wind vanes, are extensively utilized in wind energy to characterize the inflow. However, these methods typically gather data at limited points within local wind fields, capturing only a fraction of the wind field’s characteristics at wind turbine sites, thus hindering detailed wind field analysis. This study introduces a framework using Physics-informed Neural Networks (PINNs) to assimilate diverse sensor data types. This includes line-of-sight (LoS) wind speed, velocity magnitude and direction, velocity components, and pressure. Moreover, the parameterized Navier–Stokes (N–S) equations are integrated as physical constraints, ensuring that the neural networks accurately represent atmospheric flow dynamics. The framework accounts for the turbulent nature of atmospheric boundary layer flow by including artificial eddy viscosity in the network outputs, enhancing the model’s ability to learn and accurately depict large-scale flow structures. The reconstructed flow field and the effective wind speed are in good agreement with the actual data. Furthermore, a transfer learning strategy is employed for the online deployment of pre-trained PINN, which requires less time than that of the actual physical flow. This capability allows the framework to reconstruct wind flow fields in real time based on live data. In the demo cases, the maximum error between the effective wind speed reconstructed online and the actual value at the wind turbine site is only 3.7%. The proposed data assimilation framework provides a universal tool for reconstructing spatiotemporal wind flow fields using various measurement data. Additionally, it presents a viable approach for the online assimilation of real-time measurements. To facilitate the utilization of wind energy, our framework’s source code is openly accessible.

Magnetic and mechanical analyses of superconducting coil for the magnetic Czochralsky technique

Solar cells, electronics, and the semiconductor industry all need high-quality silicon crystals. The Czochralsky technique has been widely used to make a high-quality single silicon crystal from the silicon melt. However, this technique needs a strong magnetic field to stabilize the melt and control the temperature. In this paper, two coils with a curved shape have been optimized and simulated to produce a transverse magnetic field for the Czochralsky technique grower. The coils face each other and have an opening angle of 150°, and a NbTi superconducting wire was used to wind the coils with 5110 turns. The current was 106 A and flowing in the same direction. The finding results indicated that the magnetic field at the silicon melt center is 0.4 T, and the peak field in coils is 2.95 T. The optimized curve shape coils were compared with conventional coils (circle shape coils). The comparison indicated that the curve shape coils produced a highly uniform magnetic field, required less current, and significantly reduced the outer diameter of the superconducting magnet. In addition, heat generation by the current leads was calculated. In the case of the curve-shaped coils, less heat leakage has been achieved; as a result, the cooling capacity, magnet size, weight, and cost will be reduced. Furthermore, mechanical analysis was performed, and the results showed that the stress in the coils is within the permissible range for NbTi at 4.2 K.

A potential optical approach for diagnosis of the local magnetic field near the surface of the first wall/divertor tiles by Zeeman effect using polarization-resolved laser-induced breakdown spectroscopy

Magnetic field measurement is the basic diagnosis to obtain the physical engineering parameters of magnetic confinement fusion device and the macro information of plasma discharge. The real-time diagnosis of magnetic field distribution near the plasma-facing components (PFCs) surface provides the important information on the migration and transport model of key elements. In this work, a remote, in-situ approach for the magnetic field measurement near the surface of PFCs by the polarization-resolved laser-induced breakdown spectroscopy (LIBS) based on Zeeman effect is proposed and implemented. The Zeeman characteristics of the emission spectra of laser-induced W, Mo and C plasmas were verified in the laboratory by using different magnetic field configurations. According to the polarization characteristics of the Zeeman sublines of the LIBS spectrum, the intensity and direction of the external local magnetic field were successively identified by using a linear polarizer. Subsequently, a linear array fiber was utilized to determine the polarity of the external magnetic field. And finally, the magnetic field intensity near the lower edge surface of the tungsten baffle of the Experimental Advanced Superconducting Tokamak (EAST) upper divertor was measured when the field coils were demagnetized. This method can supplement the experimental data near the PFCs for the magnetic field configuration of the magnetic confinement fusion device and provide a reference for the wall element analysis model diagnosed by LIBS.

Adjuvant use for the management of pesticide drift, leaching and runoff.

Adjuvants are included in many pesticide spray mixtures to enhance the performance of the applied chemical. Many adjuvants which modify the emulsion or extensional viscosity of the tank-mixture have been found to offer benefits in drift management, primarily by eliminating or reducing the 'Fine' droplets included in the spray with diameters <100-200 μm that can move off-target in unfavorable conditions during ground, airblast and aerial pesticide applications. Among wind tunnel and field studies conducted around the world, there is consensus that while some adjuvants are effective for drift management, the performance varies on a case-by-case basis, requiring verification for each adjuvant which could be achieved through a programme such as certification based on showing a reduction in Fine droplets and/or a reduction in airborne drift. These can be measured in wind tunnel studies according to international standards. This article provides a review of the current science in this subject area, from the approaches to data collection to a review of existing data and regulatory application for encouraging and rewarding the use of appropriate adjuvants that have been demonstrated to reduce airborne spray drift potential and therefore the size of no-spray buffer zones appropriate to protect nontarget sensitive areas from drift exposure. Some adjuvants can offer the same reduction in drift as offered by hooded sprayer retrofits. A drift reduction programme based on adjuvant use could include testing candidate adjuvants for their effect on droplet size and reduction in Fine droplets when sprayed through reference nozzles and compared against sprays without the adjuvant. Testing could also be based alternatively on measurements of drift potential on collectors such as monofilament line in wind tunnel or field studies. Once shown to be effective in reducing 'Fines' or spray drift, adjuvants could be certified and then referenced on pesticide labels and/or regulatory or best management practice schemes to encourage their use and offer reductions in use restrictions or no-spray buffer zone sizes based on drift management. Studies have shown that some adjuvants can reduce pesticide leaching into soils and contamination of groundwater, as well as runoff of active ingredients from plants into the environment. Performance depends on the adjuvant type, the pesticide with which it is used, the soil or plant type, the timing and mass of water input from rainfall and climatic factors. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Short-term trend and temporal variations in atmospheric methane at an Atlantic coastal site in Southwestern Europe

Methane (CH4) was measured at El Arenosillo observatory, in an Atlantic coastal area located in Southwestern Europe, throughout a four-year period (September 2019–December 2023). Short-term trend, temporal variations and patterns were explored. A trend of 12.1 ± 1.1 nmol mol−1 year−1 was obtained based on daily averages. CH4 exhibited its highest mole fraction in cold months and its lowest in summer, and a seasonally varying diurnal cycle was also observed, with peaks in the early morning (7:00–8:00 UTC) and minimum values at 18:00–19:00 UTC. Mitigation strategies should be implemented based on an understanding of CH4 patterns. To achieve this, CH4 variations under an atmosphere governed by two synoptic patterns were studied. The atmospheric dynamic was analysed with the wind fields from ERA5 data reanalysis and the air masses pathways with the back-trajectories computed with the HYSPLIT model. CH4 horizontal distribution was explored with the TROPOMI observations. The CH4 baseline was collected under the influence of Atlantic air, while a rise from 40 to 80 nmol mol−1 was found with continental airflows from the inland Guadalquivir Valley. CH4 under sea-land breezes did not contribute to the increase of CH4. However, XCH4 from TROPOMI showed an accumulation in the marine area of the Gulf of Cadiz. Ongoing CH4 observations will be used to identify potential changes in the trend, understand the contribution of transported CH4 from areas such as the Western Mediterranean Basin, or assess its impact on surface ozone, among other objectives.