Wind Research Articles

Impact of Meteorological Conditions on Overhead Transmission Line Outages in Lithuania

This study investigates the impact of meteorological conditions on unplanned outages of overhead transmission lines (OHTL) in Lithuania’s 0.4–35 kV power grid from January 2013 to March 2023. Data from the Lithuanian electricity distribution network operator and the Lithuanian Hydrometeorological Service were integrated to attribute outage events with weather conditions. A Bayesian change point analysis identified thresholds for these meteorological factors, indicating points at which the probability of outages increases sharply. The analysis reveals that wind gust speeds, particularly those exceeding 21 m/s, are significant predictors of increased outage rates. Precipitation also plays a critical role, with a 15-fold increase in the relative number of outages observed when 3 h accumulated rainfall exceeds 32 mm, and a more than 50-fold increase for 12 h snowfall exceeding 22 mm. This study underscores the substantial contribution of lightning discharges to the number of outages. In forested areas, the influence of meteorological conditions is more significant. Furthermore, the research emphasizes that combined meteorological factors, such as strong winds accompanied by rain or snow, significantly increase the risk of outages, particularly in these forested regions. These findings emphasize the need for enhanced infrastructure resilience and targeted preventive measures to mitigate the impact of extreme weather events on Lithuania’s power grid.

Lack of Synonymity Between ‘Cubital Gust of Wind’ and Rheumatoid Arthritis

Lack of Synonymity Between ‘Cubital Gust of Wind’ and Rheumatoid Arthritis

Attitude Control System for Quadrotor Using Robust Monte Carlo Model Predictive Control

Monte Carlo Model Predictive Control (MCMPC) is a kind of non-linear Model Predictive Control (MPC) that determines control inputs using the Monte Carlo method. The Monte Carlo method used in MCMPC can handle discontinuous phenomena, and there have been reports of its application in control systems for quadrotors, where the objective is to maintain the stability of the attitude during collisions. However, as with conventional MPC, concerns remain about control performance degradation due to modeling errors and external disturbances such as unexpected wind gusts. In this study, we propose a novel Robust Monte Carlo Model Predictive Control (RMCMPC), which improves the robustness of MCMPC by considering the hypersurface known from the Sliding Mode Control (SMC) theory. The proposed RMCMPC is applied to a quadrotor attitude control system, and its effectiveness is validated through numerical simulations.

Vapor kinetic energy for the detection and understanding of atmospheric rivers

Poleward water vapor transport in the midlatitudes mainly occurs in meandering filaments of intense water vapor transport, spanning thousands of kilometers long and hundreds of kilometers wide and moving eastward. The water vapor filaments are known as atmospheric rivers (ARs). They can cause extreme wind gusts, intense precipitation, and flooding along densely populated coastal regions. Many recent studies about ARs focused on the statistical analyses of ARs, but a process-level understanding of ARs remains elusive. Here we show that ARs are streams of air with enhanced vapor kinetic energy (VKE) and derive a governing equation for Integrated VKE to understand what contributes to the evolution of ARs. We find that ARs grow mainly because of potential energy conversion to kinetic energy, decay largely owing to condensation and turbulence, and the eastward movement is primarily due to horizontal advection of VKE. Our VKE framework complements the integrated vapor transport framework, which is popular for identifying ARs but lacks a prognostic equation for understanding the physical processes.

Simulation and experimental study of aerodynamic characteristics of a rotor adsorption wall-climbing robot

Abstract High-rise buildings are prone to gusts of wind, especially crosswinds. In order to investigate the effect of crosswinds on the aerodynamic characteristics of rotor blades at different near-wall distances, a CFD simulation model is designed for four near-wall distances and five wind speeds, and the change curves of rotor pulling force, pressure cloud diagrams and velocity cloud diagrams are obtained by using Fluent for the various conditions. The mechanism and pattern of its influence on the aerodynamic characteristics of rotor blades are studied by observing the pulling force change curve and cloud diagram. Finally, an experimental platform is built to verify the correctness of the simulation results. The simulation and experimental results have a similar law, i.e., crosswinds do not only have a negative effect on reducing the pulling force generated by the rotor blades, but can also have a gain effect on the pulling force in some cases. This law has some application value, which can be utilized to compensate the rotor speed accordingly. When the pulling force is reduced, the speed is increased appropriately to improve the reliability of the adsorption, and when the pull force is increased, the speed is reduced appropriately, which can reduce the power consumption and increase the range.

Short and Medium-Range Predictability of Warm-Season Derechos. Part II: Convection-Allowing Ensemble Forecasts

Abstract This study explores convection-allowing ensemble (CAE) forecasts of a subset of 24 warm-season (May–August) progressive derechos from 2012 to 2022. The derecho cases were stratified into low predictability and moderate predictability based on the performance of Storm Prediction Center’s Convective Outlooks. The Model for Prediction Across Scales (MPAS) is used with a global mesh of varying horizontal resolution, with 60 km spacing over most of the globe decreasing to 3 km spacing where the derechos occurred. A 10-member ensemble was initialized at 0000 UTC from 5 days before the derecho (D5) to the day of the event (D1). The CAE is evaluated using objective metrics as well as subjective assessments of convective mode, coverage, initiation timing, and spatial displacement of simulated convection. The objective evaluation indicates that the MPAS CAE has skill in predicting severe wind gusts, even in the medium range (3–5 days before). The skill diminishes faster with lead time in low-predictability cases compared to moderate-predictability cases. Overall, more than half of the ensemble members generated a sustained, progressive bowing mesoscale convective system (MCS) on D1 in both low- and moderate-predictability cases, but the percentages decrease substantially for forecasts with progressively greater lead time. Furthermore, the environmental differences among ensemble members that produce bowing MCSs, multicell clusters, and supercells are minimal. These results indicate that correctly predicting sustained bowing MCSs prior to observed derechos is challenging for the MPAS CAE for lead times longer than 1–2 days, reflecting convective-scale complexity and predictability limits.

Convectively Induced Secondary Circulations and Wind‐Driven Heat Fluxes in the Surface Energy Balance Over Land

AbstractIncreased resolution has enabled kilometer‐scale weather and climate models to partially resolve secondary circulations, including horizontal convective rolls (HCRs) and cold pool gust fronts. Although these circulations are ubiquitous in convective boundary layers over land, their impacts on the surface energy balance are largely unknown. Doppler lidar and surface observations were combined with DOE E3SM land model experiments, revealing increased surface winds (5 m/s) and heat fluxes (50 W/m2) in convergent branches of HCRs. Larger wind‐driven flux responses (up to 150 W/m2) were found along gust fronts. Surface energy balance shifts to accommodate wind‐driven fluxes, reducing ground heat conduction and longwave cooling. Our findings from the US Southern Great Plains are broadly relevant to modeling convective boundary layers. In particular, widely used subgrid wind gust parameterizations were found to be physically inconsistent with resolved secondary circulations and could worsen climate prediction biases at kilometer‐scales.

Future Derecho Potential in the United States

Abstract This study uses high-resolution, convection-permitting, dynamically downscaled regional climate simulation output to assess how long-lived, convectively induced, extratropical windstorms known as derechos may change across the CONUS during the 21st century. Three 15-year epochs including a historical period (1990 – 2005), and two, separate late-21st century periods (2085 – 2100) employing intermediate (RCP 4.5) and pessimistic (RCP 8.5) greenhouse gas concentration scenarios are evaluated. A mesoscale convective system (MCS) identification and tracking tool catalogs derecho candidates across epochs using simulated radar reflectivity and maximum 10-meter wind speed as a proxy for near-surface severe wind gusts. Results indicate that MCS-based windstorms, including derechos, are more frequent, widespread, and intense in both future climate scenarios examined for most regions of the central and eastern CONUS. Increases are suggested across all parts of the year, with significant changes in populations concentrated during the early spring and summer months, suggesting the potential for a longer, more extreme MCS windstorm season. This research provides insights for forecasters, emergency managers, and wind-vulnerable stakeholders on how these events may change across the 21st century so that they may mitigate, adapt to, and become resilient against severe convective storm perils.

Insurance loss model vs. meteorological loss index – how comparable are their loss estimates for European windstorms?

Abstract. Windstorms affecting Europe are among the natural hazards with the largest socio-economic impacts. Therefore, many sectors like society, the economy, or the insurance industry are highly interested in reliable information on associated impacts and losses. In this study, we compare – for the first time – estimated windstorm losses using a simplified meteorological loss index (LI) with losses obtained from a complex insurance loss (catastrophe) model, namely the European Windstorm Model of Aon Impact Forecasting. To test the sensitivity of LI to different meteorological input data, we furthermore contrast LI based on the reanalysis dataset ERA5 and its predecessor ERA-Interim. We focus on similarities and differences between the datasets in terms of loss values and storm rank for specific historical storm events in the common reanalysis period across 11 European countries. Our results reveal higher LI values for ERA5 than for ERA-Interim for all of Europe (by roughly a factor of 10), coming mostly from the higher spatial resolution in ERA5. The storm ranking is comparable for western and central European countries for both reanalyses, confirmed by high correlation values between 0.6 and 0.89. Compared to the Aon Impact Forecasting model, LI ERA5 shows comparable storm ranks, with correlation values ranging between 0.45 and 0.8. In terms of normalized loss, LI exhibits overall lower values and smaller regional differences. Compared to the market perspective represented by the insurance loss model, LI seems to have particular difficulty in distinguishing between high-impact events at the tail of the wind gust distribution and moderate-impact events. Thus, the loss distribution in LI is likely not steep enough, and the tail is probably underestimated. Nevertheless, it is an effective index that is suitable for estimating the impacts of storm events and ranking storm events, precisely because of its simplicity.

Dynamics Modeling and Motion Evaluation of a Near-Ground Tethered Balloon Cable System under Severe Wind Environments

Weather survival poses a significant challenge for the utilization of tethered balloons. The dynamic modeling of tethered balloon systems presents challenges due to the flexible nature of the cables and the intricate nature of gust forces. The present study introduces a new approach for modeling near-ground tethered balloon systems, which enables the analysis of their dynamic responses and performance evaluation under complex boundary conditions. First, finite cylindrical rigid bodies that are joined together by bushing forces to describe the dynamics of the tethered cable. The properties of the flexible cables under severe bending and translation can be illustrated by the dynamics model. Second, a three-dimensional dynamics model based on the multibody dynamics theory is created to deal with the interaction of the tethered balloon system and flexible cables. The dynamic responses of the tethered balloon system under challenging operating conditions are investigated, focusing on the number of cable segments and the place and direction of gust wind impacts. This model allows for precise assessment and optimization of the system’s overall performance to improve weather resistance. The results show that compared to computational fluid dynamics (CFDs) methods, the multibody system dynamics-based balloon model improved the solution time by 80%, with a pitch angle deviation of only 0.0016°. Moreover, the bushing model effectively reduced cable force and enabled accurate reflection of the system’s motion characteristics.

Aeroacoustic analysis of fixed- and variable-pitch controlled multirotors and noise reduction via rotor phase control

This research conducts a comparative analysis of the aerodynamic and aeroacoustic characteristics of fixed-pitch and variable-pitch controlled multirotors (i.e., revolution per minute and collective pitch control). The study encompasses hovering and forward flight conditions, considering wind gust effects. Specifically, high-resolution time-frequency analysis is conducted to investigate the interference and modulation effects of multirotor noise. The analysis reveals significant variations in spectral characteristics depending on the control method. Notably, the frequencies of tonal noise from variable-pitch controlled multirotor do not exhibit short-periodic modulation, in contrast to those of fixed-pitch controlled multirotor. Considering the distinctive spectral characteristics of variable-pitch controlled multirotor, this study explores the effectiveness of noise reduction in variable-pitch controlled multirotor by implementing rotor phase control methods. To validate the noise reduction in time series simulations, a deep learning model is employed to determine the dynamically optimized rotor phases. A multilayer perceptron neural network was trained to derive the optimal rotor phase angles over time, considering the target observer points and flight conditions. The results demonstrate a substantial reduction in noise at the target observer points.

Innovation in clean energy from man-made wind and small-wind generation

The need to reduce global emissions leads us to look for various sources of clean energy. In recent decades, wind technology has advanced significantly, enabling large-scale power generation in both marine and terrestrial environments, as well as the development of mini-wind solutions. However, we often underestimate the capacity of certain human activities and production processes to generate clean energy, wasting their true potential. This work focuses on using artificially generated wind gusts to transform them into clean electricity through small wind turbines. The proposal is developed in four phases: (1) identify activities that generate wind, (2) collect data on wind speed and direction, (3) perform a descriptive statistical analysis of the wind resource, and (4) select the appropriate technology to calculate the electricity generation. The proposal is evaluated using the air flow produced by the air conditioning systems of a data center in Colombia. The results are analyzed from technical, economic, environmental, and political perspectives. Through small wind power, an annual production of approximately 468 MWh is estimated, avoiding the emission of 300 metric tons of CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{CO}}_2$$\\end{document}.

Fluid–structure interaction simulations of wind gusts impacting a hyperbolic paraboloid tensile structure

The paper focuses on fluid–structure interactions (FSI) between a turbulent, gusty fluid flow, and a membrane structure. Lightweight structures are particularly vulnerable to wind gusts and can be completely destroyed by them, making it essential to develop and evaluate numerical simulation methods suited for these types of problems. In this study, a thin-walled membrane in the shape of a hyperbolic paraboloid (hypar) is analyzed as a real-scale example. The membrane structure is subjected to discrete wind gusts of varying strength from two different directions. A partitioned FSI approach is employed, utilizing a finite-volume flow solver based on the large-eddy simulation technique and a finite-element solver developed for shell and membrane structures. A recently proposed source-term formulation enables the injection of discrete wind gusts within the fluid domain in front of the structure. In a step-by-step analysis, first the fluid flow around the structure, initially assumed to be rigid, is investigated, including a grid sensitivity analysis. This is followed by examining the two-way coupled FSI system, taking the flexibility of the membrane into account. Finally, the study aims to assess the impact of wind gusts on the resulting deformations and the induced stresses in the tensile material, with a particular focus on the influence of different wind directions.

Evaluating Machine Learning–Based Probabilistic Convective Hazard Forecasts Using The HRRR: Quantifying Hazard Predictability and Sensitivity to Training Choices

Abstract The High-Resolution Rapid Refresh (HRRR) model provides hourly updating forecasts of convective-scale phenomena, which can be used to infer the potential for convective hazards (e.g., tornadoes, hail, and wind gusts), across the United States. We used deterministic 2019–20 HRRR, version 4 (HRRRv4), forecasts to train neural networks (NNs) to generate 4-hourly probabilistic convective hazard forecasts [neural network probability forecasts (NNPFs)] for HRRRv4 initializations in 2021, using storm reports as ground truth. The NNPFs were compared to the skill of a smoothed updraft helicity (UH) baseline to quantify the benefit of the NNs. NNPF skill varied by initialization time and time of day but was all superior to the UH forecast. NNPFs valid at hours between 1800 and 0000 UTC were most skillful in aggregate, significantly exceeding the baseline forecast skill. Overnight NNPFs (i.e., valid 0600–1200 UTC) were least skillful, indicating a diurnal cycle in hazard predictability that was present across all HRRRv4 initializations. We explored the sensitivity of HRRRv4 NNPF skill to NN training choices. Including an additional year of 2021 HRRRv4 forecasts for training slightly improved skill for 2022 HRRRv4 NNPFs, while reducing the training dataset size by 40% using only forecasts with storm reports was not detrimental to forecast skill. Finally, NNs trained with 2018–20 HRRRv3 forecasts led to a reduction in NNPF skill when applied to 2021 HRRRv4 forecasts. In addition to documenting practical predictability challenges with convective hazard prediction, these findings reinforce the need for a consistent model configuration for optimal results when training NNs and provide best practices when constructing a training dataset with operational convection-allowing model forecasts. Significance Statement Convective hazards, such as hail and tornadoes, are often challenging to predict. To improve hazard predictions, we used machine learning (ML) to generate forecasts of convective hazards across the United States leveraging forecasts of prior events. The ML hazard forecasts were consistently better than a non-ML approach and varied in skill based on the time of day, with nighttime forecasts being particularly challenging. ML forecasts of wind gusts and hail were more skillful than tornadoes. Different strategies of constructing the dataset of prior events led to differences in forecast performance; thus, this work provides recommendations for how to assemble these datasets and train ML models to generate improved forecasts of severe weather events.

Curvature in plants

Curvatures are ubiquitous in the animal kingdom - the spiral shell of the nautilus and the corkscrew horns of the blackbuck being iconic examples. Dynamic changes in curvature (i.e., curving) are most striking in the locomotion of some animal species - swimming in fishes and mollusks, looping in leeches, undulatory locomotion in snakes and lampreys, and also sperm motility through flagellum beating. When it comes to plants, which are sessile organisms with a rigid body, the terms 'curvature' and 'curving' evoke very different images - leaves of grass swaying in the breeze, a trunk dangerously bent by a powerful gust of wind, a branch sagging under the weight of its own fruits, as well as the frail arabesques of twining plants like the morning glory and the ivy, which were so influential in the Art Nouveau movement and many other artistic traditions. These various vegetal curves not only prompt creative inspiration in the mind of the beholder, they also initiate signaling cascades leading to developmental responses of the plant. Conversely, curvature can result from a biologically active process in response to an internal or external stimulus. Active curving or decurving are indeed important aspects of the plastic interplay of the developmental program of plants and their environment. Although easily accessible to observation, curvature and curving have only recently become the focus of active research in plant development. Lying at the nexus of biology, physics and mathematics, they require an interdisciplinary approach. The aim of this primer is to give readers an intuitive but accurate understanding of what curvature and curving are, as observed in the plant kingdom, then a more formal definition. We will discuss their role in plant development, both as a signal and as a response, and finally the practical issues and solutions involved in measuring plant curvatures.

Assessing Downburst Kinematics Using Video Footage Analysis

Measurements of downburst outflows using standard meteorological instruments (e.g., anemometers) are rare due to their transient and localized nature. However, video recordings of such events are becoming more frequent. This short communication (Technical Note) study presents a new approach to estimating the kinematics of a downburst event using video footage recordings of the event. The main geometric dimensions of the event, such as downdraft diameter, cloud base height, outflow depth, and the radius of the outflow at a given moment in time, are estimated by sizing them against reference structures of known dimensions that are present in the video footage. From this analysis, and knowing the frame rate of the video recording, one can estimate the characteristic velocities in the downburst event, such as the mean downdraft velocity and the mean velocity of the radial outflow propagation. The proposed method is tested on an August 2015 downburst event that hit Tucson, Arizona, United States. The diameter of the downburst outflow increased with the time from approximately 1.10 km to 3.35 km. This range of values indicates that the event was a microburst. The mean descending velocity of downburst downdraft was 8.9 m s−1 and the horizontal velocity of outflow propagation was 17.7 m s−1. The latter velocity is similar to the measured wind gust at the nearby weather station and Doppler radar. The outflow depth is estimated at 160 m, and the cloud base height was approximately 1.24 km. Estimating the kinematics of downbursts using video footage, while subject to certain limitations, does yield a useful estimation of the main downburst kinematics that contribute to a better quantification of these localized windstorms.

Convection-permitting climate model representation of severe convective wind gusts and future changes in southeastern Australia

Abstract. Previous research has suggested that the frequency and intensity of surface hazards associated with thunderstorms and convection, such as severe convective winds (SCWs), could potentially change in a future climate due to global warming. However, because of the small spatial scales associated with SCWs, they are unresolved in global climate models, and future climate projections are uncertain. Here, we evaluate the representation of SCW events in a convection-permitting climate model (Bureau of Meteorology Atmospheric Regional Projections for Australia, BARPAC-M) run over southeastern Australia for the months of December–February. We also assess changes in SCW event frequency in a projected future climate for the year 2050 and compare this with an approach based on identifying large-scale environments favourable for SCWs from a regional parent model (BARPA-R). This is done for three different types of SCW events that have been identified in this region, based on clustering of the large-scale environment. Results show that BARPAC-M representation of the extreme daily maximum wind gust distribution is improved relative to the gust distribution simulated by the regional parent model. This is due to the high spatial resolution of BARPAC-M output, as well as partly resolving strong and short-lived gusts associated with convection. However, BARPAC-M significantly overestimates the frequency of simulated SCW events, particularly in environments having steep low-level temperature lapse rates. A future decrease in SCW frequency under conditions with steep lapse rates is projected by BARPAC-M, along with less frequent favourable large-scale environments. In contrast, an increase in SCW frequency is projected under conditions of high surface moisture, with more frequent favourable large-scale environments. Therefore, overall changes in SCWs for this region remain uncertain, due to different responses between event types, combined with historical model biases.

Detailed analysis of local climate at the CTAO-North site on La Palma from 20 yr of MAGIC weather station data

ABSTRACT The Observatorio del Roque de los Muchachos will host the northern site of the Cherenkov Telescope Array Observatory (CTAO), in an area about 200 m below the mountain rim, where the optical telescopes are located. The site currently hosts the MAGIC (Major Atmospheric Gamma-ray Imaging Cherenkov) telescopes, which have gathered a unique series of 20 yr of weather data. We use advanced profile-likelihood methods to determine seasonal cycles, the occurrence of weather extremes, weather downtime, and long-term trends correctly taking into account data gaps. The fractality of the weather data is investigated by means of multifractal detrended fluctuation analysis. The data are published according to the Findable, Accessible, Interoperable, and Reusable (FAIR) principles. We find that the behaviour of wind and relative humidity show significant differences compared to the mountain rim. We observe an increase in temperature of $0.55\pm 0.07\mathrm{(stat.)}\pm 0.07\mathrm{(syst.)}$$^{\circ }$C decade−1, the diurnal temperature range of $0.13\pm 0.04\mathrm{(stat.)}\pm 0.02\mathrm{(syst.)}$$^{\circ }$C decade−1 (accompanied by an increase of seasonal oscillation amplitude of $\Delta C_m=0.29\pm 0.10\mathrm{(stat.)}\pm 0.04\mathrm{(syst.)}$$^{\circ }$C decade−1), and relative humidity of $4.0\pm 0.4\mathrm{(stat.)}\pm 1.1\mathrm{(syst.)}$ per cent decade−1, and a decrease in trade wind speeds of $0.85\pm 0.12\mathrm{(stat.)}\pm 0.07\mathrm{(syst.)}$ (km h−1) decade−1. The occurrence of extreme weather, such as tropical storms and long rains, remains constant over time. We find a significant correlation of temperature with the North Atlantic Oscillation Index and multifractal behaviour of the data. The site shows a weather-related downtime of 18.5 per cent–20.5 per cent, depending on the wind gust limits employed. No hints are found of a degradation of weather downtime under the assumption of a linear evolution of environmental parameters over time.

A Case Study of the Possible Meteorological Causes of Unexpected Fire Behavior in the Pantanal Wetland, Brazil

This study provides insights into large fires in the Pantanal by analyzing the atmospheric conditions that influenced the rapid fire evolution between 13 and 14 November 2023, when fire fronts spread rapidly, leading to critical situations for firefighters. The observation-based analysis helped us to identify some characteristics of the fire’s evolution and the meteorological conditions in the region. Furthermore, two simulations were run with the Meso-NH model, which was configured with horizontal resolutions of 2.5 km and 5 km. The fire behavior, characterized by satellite observations, revealed periods with a sudden increase in active fire numbers. High temperatures and low relative humidity in the region characterized the fire weather conditions. The simulations confirmed the critical fire condition, showing the benefits of increasing the resolution of numerical models for the Pantanal region. The convection-resolving simulation at 2.5 km showed the repeated development of gust fronts in the late afternoon and early evening. This study highlights this dynamic that, coupled with intense surface wind gusts, was crucial for the intensification of the fire spread and unexpected behavior. This study is a first step toward better understanding fire dynamics in the Pantanal through atmospheric modeling, and it can support strategies for firefighting in the region.

Increased rupture of cypress pollen type due to atmospheric water in central and southeastern Spain

This study aims to investigate the meteorological variables determining Cupressaceae pollen grain disruption in the environment. A parallel sampling of pollen grains and disrupted Cupressaceae pollen grains was performed in six cities using two Spanish aerobiological networks. The pollen concentrations, disrupted pollen concentrations, percentage of disrupted pollen and number of days when the percentage of disrupted pollen was above or equal to 50 % were quantified during two pollen seasons. The concentrations were determined following the standardised method EN 16868. Results show that the concentrations of pollen grains and disrupted pollen grains were not determined by geographical features and rarely by bioclimatic variables or indexes but by the ornamental use of the specimens in the vicinity of the pollen sampler, highlighting the possibility of using management practices to reduce exposure to allergens in the cities. African dust outbreaks coincided with higher concentrations of pollen grains and disrupted pollen grains, but the reduced percentage of disrupted pollen grains pointed to a non-causal relationship with long-distance transport. The effect of wind and maximum gusts remained negligible. The triggering factor for pollen disruption was the amount of water in the atmosphere, mainly reported as relative humidity. Rainfall increased the effect of disruption due to pollen grain swelling caused by its wash-out effect. The higher the relative humidity, the higher the disrupted pollen concentrations. This aligns with the mechanism of Cupressaceae reproduction since the family needs a water medium in the form of pollination droplets for the pollination tube to develop and the pollen grain to perform its biological function. Therefore, people that develop allergic symptoms to Cupresaceae pollen should avoid exposure during days with high relative humidity in the main pollen season.