Strong Winds Research Articles

Waterbird community response to wetland and climate changes in the Liaohe River Estuary wetlands, China.

The Liaohe River Estuary (LRE) wetland is a critical stopover on the East Asian-Australasian Flyway (EAAF), vital to coastal ecological balance and biodiversity. However, the drivers of changes in waterbird diversity remain unclear. This study utilised random forests to produce reliable time-series wetland mapping from 2010 to 2023. Spatial and temporal changes in wetlands and landscape structures were analysed using landscape pattern indices-the Alpha and Beta diversity analyses were based on monitoring records that assessed waterbird diversity and community structure. The response of waterbirds to wetland, landscape, and climate changes was quantified using a Generalized Linear Mixed Effects Model (GLMM). Between 2010 and 2023, 78 species of waterbirds from 8 orders and 14 families were recorded. In 2023, waterbird counts reached 1,014,908, marking an increase of 868,102 compared to 2010. Over 14 years, waterbird diversity consistently increased, with community structure becoming more stable. Positive responses to climatic factors, such as seasonal precipitation and mean temperatures (both year and seasonal), were observed, while extreme weather inhibited recovery, like heavy precipitation and strong winds. Changes in Suaeda salsa (Sua) and forested areas significantly impacted waterbird diversity compared to other land cover types. These findings highlight the strong influence of climate, wetland, and landscape changes on waterbird diversity and community structure. Managers are encouraged to prioritise monitoring temperature, precipitation, Sua, and forested landscapes while enhancing artificial wetland management to support waterbird diversity and ecological balance in the LRE.

Changes in Equatorial Kelvin Wave Activity in the Upper Troposphere and Lower Stratosphere Associated with Interannual Variability of the Upper-Tropospheric Equatorial Zonal Wind

Abstract We investigate the influence of the background wind regime on interannual variability in equatorial Kelvin waves (including both freely propagating ones and convectively coupled ones) in the upper troposphere and lower stratosphere using the ERA5 reanalysis data. We focus on variability in the number of Kelvin wave cases as a function of the background westerly wind that controls wave propagation, given by the zonal wind index (ZWI) in the equatorial eastern Pacific that is a measure of the strength of the upper branch of the Walker circulation in the Western Hemisphere. The ZWI correlates well with sea surface temperature in the Niño-3.4 region, though one-third of the peaks of negative ZWI (weak westerly) cases occur during seasons other than December–February which is the typical El Niño season. In the positive ZWI (stronger westerly) cases, both the convective activity over the western Pacific and the extratropical Rossby wave activity over the central and eastern Pacific are enhanced. Kelvin waves over the Western Hemisphere appear frequently at 200 hPa but barely reach 100 hPa due to the strong westerly wind below this level that prohibits upward wave propagation. In the negative ZWI period, the number of Kelvin wave cases at 200 hPa decreases due to weaker convection; Kelvin waves reach 100 hPa and propagate even further upward into the stratosphere. The increasing (decreasing) tendency of Kelvin waves already starts 6 months before the weakest (strongest) westerly wind month in the Western Hemisphere (i.e., the ENSO peak seasons). Significance Statement Equatorial Kelvin waves play a critical role in the upward transport of momentum driving the equatorial stratospheric circulation. Our results reveal that background winds in the upper troposphere and the lower stratosphere act as a filter for equatorial Kelvin waves, regulating the number of their packets propagating upward. An investigation of the role of Kelvin wave dissipation in the westerly duct region, i.e., in the upper troposphere over the equatorial eastern Pacific, is needed.

Strong wind is one of the important factors that trigger landslides

Strong wind is one of the important factors that trigger landslides

H-AMR FORGE’d in FIRE. I. Magnetic State Transitions, Jet Launching, and Radiative Emission in Super-Eddington, Highly Magnetized Quasar Disks Formed from Cosmological Initial Conditions

Abstract Quasars are powered by supermassive black hole (SMBH) accretion disks, yet standard thin disk models are inconsistent with many observations. Recently, P. F. Hopkins et al. simulated the formation of a quasar disk feeding an SMBH of mass M = 1.3 × 107 M ⊙ in a galaxy. The disk had surprisingly strong toroidal magnetic fields that supported it vertically from gravity and powered rapid accretion. What feedback can such a system produce? To answer this, we must follow the gas to the event horizon. For this, we interpolated the quasar into the general-relativistic radiation magnetohydrodynamics code H-AMR and performed 3D simulations with BH spins a = 0 and a = 0.9375. This remapping generates magnetic monopoles, which we erase using a novel divergence cleaning approach. Despite the toroidal magnetic field's dominance at large radii, vertical magnetic flux builds up near the event horizon, leading to a magnetic state transition within the inner 200 gravitational radii of the disk. This powers strong winds and, for spinning BHs, relativistic jets that can spin down the BH within 5−10 Myr. Sometimes, vertical magnetic fields of opposite polarity reach the BH, causing a polarity inversion event that briefly destroys the jets and, possibly, the X-ray corona. These strong fields power accretion at rates 5× the Eddington limit, which can double the BH mass in 5–10 Myr. When a = 0.9375 (a = 0), the energy in mechanical outflows and radiation equals about 60% (10%) and 100% (3%) of the accreted rest mass energy, respectively. Much of the light escapes in cool, ≳1300 au photospheres, consistent with quasar microlensing and spectral energy distributions.

A Stronger Kuroshio Intrusion Leads to Higher Chlorophyll a Concentration in the Northern South China Sea

AbstractThe Kuroshio intrusion from the Luzon Strait significantly affects ecosystems in the South China Sea (SCS), especially during the Northeast Monsoon, a time when field observations are notably sparse and where vertical mixing induced by strong winds can obscure the effects of the Kuroshio intrusion. In this study, we address these gaps by reanalyzing data from 20 cruises (5,067 samples) in the SCS between 2004 and 2015. We also carried out two dedicated field cruises during the Northeast and the Southwest Monsoon in 2018. Field observations from both cruises revealed a consistent unimodal relationship between total chlorophyll a (Chla) concentrations in the upper 50 m of the water column and the index of the Kuroshio intrusion. Specifically, a strong Kuroshio intrusion during the Northeast Monsoon significantly enhanced Chla concentrations in the northern SCS. This enhanced Chla concentration during the Northeast Monsoon was primarily driven by increases of Synechococcus and nanophytoplankton that contrasted with the dominance of Prochlorococcus during the Southeast Monsoon. Long‐term remote sensing data corroborated these findings and demonstrated a consistent pattern wherein intrusion by the Kuroshio led to elevated Chla concentrations, particularly during the Northeast Monsoon. There was a significant positive correlation between the intensity of the Kuroshio intrusion and the magnitude of the Chla increase. Furthermore, these findings suggested a concerning possibility: weakening of the Kuroshio intrusion intensity over time might diminish future biogeochemical effects on SCS ecosystems. Continued monitoring and research will be crucial to understanding and responding to these changes.

Study on the Fire Spread Characteristics of High-Rise Building Facades Under Strong Wind Conditions Based on the Combination of WRF and CFD

The spread of fires on the facades of high-rise buildings is highly influenced by atmospheric wind conditions, particularly in strong wind environments. A strong wind environment refers to the situation where the wind speed reaches level 6 or above, or the wind speed is between 10.8 m/s and 13.8 m/s. We conducted an in-depth study of the characteristics of flame spread on the facades of high-rise buildings under strong wind conditions. A nested coupling method based on WRF (Weather Research and Forecasting) and CFD (computational fluid dynamics) software (Ansys Fluent 2021) was used. The mesoscale meteorological simulation software WRF was utilized to obtain regional airflow variation data within a radius of 2 km around the high-rise building. Subsequently, these data were coupled with the CFD software (Ansys Fluent 2021) to simulate and obtain realistic wind field data within a 400 m range around the building. Finally, these realistic wind field data were used for FDS (Fire Dynamics Simulator) fire simulations and model experiments to accurately replicate building fire scenarios under strong wind conditions. The results indicate that using grid nesting for boundary condition division would help to improve the accuracy of fire spread characteristics on the facades of high-rise buildings under strong wind conditions. For a high-rise building, both headwinds and tailwinds promote vertical and horizontal flame spread, with a more significant impact on vertical flame spread speed. Side winds enhance horizontal flame spread but inhibit vertical flame spread. These findings provide a reference for the effective design of fire protection systems for the facades of high-rise buildings.

Self‐Powered System by an Aerodynamic‐Complementary Triboelectric‐Electromagnetic Hybridized Generator with Triple‐Mode Switching Power Management Topology for Wide‐Range Wind Energy Collection and Climate Monitoring

AbstractRotary wind energy harvester has always been the focus of attention in the field of self‐power technology. However, a conflict between start‐up and saturation rotation speed of wind energy harvester hinders the adaptive energy collection from low to strong wind speeds in different wind speed ranges. Herein, a self‐powered system by an aerodynamic‐complementary triboelectric‐electromagnetic hybridized generator (AC‐TEHG) equipped with a triple‐mode switching power management topology (TmSPMT) is proposed to achieve self‐adaptive power supply mode switching in response to different wind speed ranges. Specifically, AC‐TEHG integrates Savonius and wind cup miniaturized turbine to achieve layered energy collection over wide‐range wind speed regions (1.4–16.3 m s−1), where the triboelectric nanogenerator (TENG) and electromagnetic generator (EMG) units have the excellent electrical output with Voc, Isc, and instantaneous peak power reaching 664 V/10.83 V, 35.96 µA/19.84 mA and 8.01 mW/62.45 mW, respectively. AC‐TEHG equipped with TmSPMT can effectively respond to different wind speed ranges of windlessness, low, medium, and high wind speeds for steadily powering commercial electronics. Finally, a wireless self‐powered climate monitoring system is developed to indicate that AC‐TEHG equipped with TmSPMT is a sustainable solution to efficiently power Internet of Things sensors in regions with variable wind speeds.

Yield determinants of Kappaphycus alvarezii seaweed in South Sulawesi, Indonesia

Abstract There has been limited systematic monitoring of seaweed growth with simultaneous measurement of important yield determinants. This study reports on Kappaphycus alvarezii (Cottonii) seaweed experiments in South Sulawesi, Indonesia. The first (June – Oct 2023) experiment showed lower yields for the lowest seedling weight and similar yields for the medium/high seedling weight. During this period, seaweed losses from lines (empty ties), ice-ice disease and fouling by epiphytes increased. The second experiment (March – July 2024) showed high yields for the traditional longline and low yields with tube-nets and cages, due to fouling by epiphytes and mud. Yields did not differ systematically between villages and did not differ systematically between nearshore and further offshore. Yields were highest for planting in March and lowest for planting in August. Stronger winds and rougher sea towards October were shown to be an important yield determinant, but not the only one. During the March-October period losses from lines increased, pressure from the ice-ice disease increased, fouling by mud and epiphytes increased, temperatures decreased and windspeed increased. Yields in the March-October cultivation cycles were determined by a combination of the effects of these five yield determinants. To our best knowledge the current study is the first to study this full range of possible yield determinants and to simultaneously monitor for longer period (five cycles, with bi-weekly sampling) both seaweed biomass and yield determinants.

Impacts of Along-Strait Winds on Sea Level, Sea Surface Temperature, and Current in the Jeju Strait During Summer 2022

The along-strait wind in the Jeju Strait (JS) significantly impacts the sea level, surface temperature, and currents on its northern and southern coasts. During the summer of 2022, the winds in the JS were characterized by strong easterly winds that occurred before the approach of several typhoons. A strong correlation of 0.61 was found between the along-strait wind and the difference in sea level anomaly between the strait’s south and north coasts. A more robust correlation of 0.71 was estimated between the along-strait wind and the difference in sea surface temperature anomaly. When the positive (eastward) wind blows along the strait, it causes upwelling along the north coast, resulting in cold anomalies there and warm anomalies along the south coast, and vice versa. In addition, a positive correlation (r = 0.39–0.50) was estimated between the along-strait wind and along-strait surface current, with a negative correlation (r = −0.68 to −0.44) for the cross-strait current in most observation stations of the JS, except near the coast of Jeju Island. The positive along-strait wind causes the sea level to fall on the north coast and strengthens the eastward surface current. In contrast, the negative (westward) along-strait wind has the opposite effect, causing the sea level to rise near Wando, then slowing down or reversing the eastward surface current. Overall, these findings show that Ekman dynamics can explain wind-induced coastal upwelling and downwelling on both coasts of the JS, providing valuable insights into the environmental conditions surrounding marine organisms in this coastal region.

Reliability Analysis of Residential Buildings under Hurricane Using Embedded FBG Sensors: Remaining Useful Lifetime Analysis

Every year, the lack of reliable and affordable structural health monitoring systems to assess the resilience of residential buildings in coastal areas results in extensive damage from strong winds and hurricanes. This work investigates the use of embedded fiber Bragg grating (FBG) sensors for the structural health monitoring of residential timber buildings for reliability assessment. The remaining useful lifetime (RUL) of buildings after being impacted by hurricane has been estimated using long short-term memory (LSTM) neural network. A proof-of-concept experimental setup has validated the system’s performance and functionality. Multiple one-story and two-story scaled-down (~1:20) prototype timber buildings were constructed and placed in a wind tunnel to assess their structural performance and stability under wind speeds ranging from 0 to 150 mph. FBG sensors attached to the buildings measured strain in real time. The measured strain data are used to estimate the building’s reliability. A mathematical Health Indicator is introduced to determine the level of structural integrity and health under varying load and structural conditions. The FBG sensors demonstrated accurate measurement and real-time monitoring of strain changes in selected structural elements during high wind speeds. Assessment results can inform condition-based maintenance, safety evaluations, and stability reports. Additionally, the system can issue real-time warnings for potential failures and damages, thereby enhancing the overall resilience of residential buildings. Received: 27 September 2024 | Revised: 2 December 2024 | Accepted: 27 December 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Abolghassem Zabihollah: Conceptualization, Validation, Investigation, Resources, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration. Yu Shi: Methodology, Software, Formal analysis, Data curation, Writing – original draft, Writing – review & editing, Visualization.

Klasterisasi Daerah Rawan Bencana Alam Menggunakan Algoritma K-Means

East Java is a province with high vulnerability to disasters, such as floods, landslides, earthquakes, and strong winds, which have an impact on material losses, casualties, and deterioration of socio-economic conditions, especially in rural areas. The lack of mitigation strategies and resource allocation worsens disaster management. This study aims to classify disaster data using the K-Means algorithm to overcome the limitations of descriptive analysis conducted by BPBD East Java. The data used includes 1125 disaster events with variable disaster frequency, total damage, and the number of casualties per sub-district in East Java districts and cities during 2021-2022, obtained from the official website of the East Java BPBD. The K-Means algorithm was chosen because of its efficiency in managing big data and its flexibility in cluster formation. The results of the study show that in 2021, the region in East Java is divided into three clusters based on the level of disaster risk: Cluster 1 (low risk) with 192 sub-districts, Cluster 2 (medium risk) with 35 sub-districts, and Cluster 3 (high risk) with 10 sub-districts. In 2022, significant changes were seen in Cluster 1, which includes 462 sub-districts, Cluster 2 with 20 sub-districts, and Cluster 3 with 11 sub-districts. The results of this study are expected to support the government's decision-making priorities, especially in disaster management and resource allocation based on risk levels

Inverse Energy Cascades in the Boundary Layer During Strong Winds Based on Doppler Lidar

Inverse Energy Cascades in the Boundary Layer During Strong Winds Based on Doppler Lidar

Numerical Simulation of Tornado-Like Vortices Induced by Small-Scale Cyclostrophic Wind Perturbations

This study introduces a tornado perturbation model utilizing the cyclostrophic wind model, implemented through a shallow-water equation framework. Four numerical experiments were conducted: a single cyclonic wind perturbation (EXP1), a single low-geopotential height perturbation (EXP2), a cyclonic wind perturbation with a 0 Coriolis parameter (EXP3), and a single anticyclonic wind perturbation (EXP4). The outputs showed that in a static atmosphere setting, a small-scale cyclonic wind perturbation generated a tornado-like pressure structure. The centrifugal force in the central area exceeded the pressure gradient force, causing air particles to flow outward, leading to a pressure drop and strong pressure gradient. The effect of the Coriolis force is negligible for meso-γ-scale and smaller systems, while for meso-β-scale and larger systems, it begins to have a significant impact. The results indicate that a robust cyclonic and an anticyclonic wind field can potentially generate a pair of cyclonic and anticyclonic tornadoes when the horizontal vortex tubes in an atmosphere with strong vertical wind shear tilt, forming a pair of positive and negative vorticities. These tornadoes are similar but have different rotation directions.

Foliar image-based characterization of airborne particulate matter in an urban area and its implications for remediation

This study addresses the pervasive issue of particulate matter (PM) emission in urban areas, proposing a better approach using scanning electron microscope (SEM) techniques to identify plant species effective in airborne PM removal. Conducted in Bilaspur city, the research strategically selected six plant species across four distinct sites and applied the SEM-Image J method for analysis, yielding significant insights, especially in the respirable PM range. Among the tested plant species, Senna Siamea and Dalbergia Sissoo emerged as consistent and standout performers, displaying the highest PM removal efficiency across all sites. Notably, the smaller leaves of Senna siamea and Dalbergia sissoo prevent PM from being resuspended in the air by strong winds, enhancing their overall performance in combating PM pollution. The SEM-EDS analysis was then employed for morphological and chemical characterizations of the PM, revealing anthropogenic sources as the primary contributors to pollution. Hazardous elements, including arsenic (As), antimony (Sb), iron (Fe), indium (In), terbium (Tb), chlorine (Cl), and iodine (I), were identified, underscoring potential health risks associated with the PM composition. The study underscores the significance of SEM-EDS based plant selection for mitigating airborne PM pollution and improving air quality. Senna Siamea and Dalbergia Sissoo are identified as top choices for effective PM removal, marking a significant step towards sustainable urban environments. The findings contribute valuable insights into the chemical makeup of PM, facilitating a deeper understanding of its sources and potential health implications. Overall, this research serves as a crucial step in developing strategies to combat air pollution and fosters the creation of healthier and more sustainable urban environments.

Climatological Study on Cyclone Genesis and Tracks in Southern Brazil from 1979 to 2019

This study investigates cyclone dynamics and impacts in the Southwestern Atlantic, with a focus on their effects on southern Brazil. As climate change intensifies coastal vulnerability, understanding cyclone behavior has become essential. Using the TRACK and cycloTRACK algorithms, we examined cyclone trajectories and cyclogenesis densities from 1979 to 2019 to analyze seasonal and spatial patterns shaped by large-scale atmospheric circulations, including the Antarctic Oscillation (AAO). The analysis explores trends in cyclone activity across various temporal and spatial scales, identifying key regions of cyclogenesis and trajectory density. Results indicate that the cycloTRACK algorithm is more effective at tracking more intense and consistent cyclones, excluding weaker systems. Seasonal patterns suggest variability in cyclone formation, likely associated with atmospheric instability and ocean–atmosphere interactions. While trends reveal an increase in cyclone passages in southern Brazil, these systems are strongly associated with extreme climatic events in the region, including coastal storms, intense precipitation, strong winds, and high waves. By clarifying cyclone dynamics and seasonal patterns, this study enhances our understanding of cyclone behavior and contributes to improved assessments of regional climate resilience in southern Brazil.

Increased Surface Temperatures of Habitable White Dwarf Worlds Relative to Main-sequence Exoplanets

Abstract Discoveries of giant planet candidates orbiting white dwarf (WD) stars and the demonstrated capabilities of the James Webb Space Telescope bring the possibility of detecting rocky planets in the habitable zones (HZs) of WDs into pertinent focus. We present simulations of an aqua planet with an Earth-like atmospheric composition and incident stellar insolation orbiting in the HZ of two different types of stars—a 5000 K WD and main-sequence K-dwarf star Kepler-62 (K62) with a similar effective temperature—and identify the mechanisms responsible for the two differing planetary climates. The synchronously rotating WD planet's global mean surface temperature is 25 K higher than that of the synchronously rotating planet orbiting K62, due to its much faster (10 hr) rotation and orbital period. This ultrafast rotation generates strong zonal winds and meridional flux of zonal momentum, stretching out and homogenizing the scale of atmospheric circulation, and preventing an equivalent buildup of thick, liquid water clouds on the dayside of the planet compared to the synchronous planet orbiting K62, while also transporting heat equatorward from higher latitudes. White dwarfs may therefore present amenable environments for life on planets formed within or migrated to their HZs, generating warmer surface environments than those of planets with main-sequence hosts to compensate for an ever shrinking incident stellar flux.

Study on Wind Resistance Performance of Transmission Tower Using Fixture-Type Reinforcement Device

Transmission towers are an important component of the electric system, and their tall structural characteristics make them susceptible to failure under strong winds. Therefore, it is crucial to enhance the wind resistance of the transmission tower structures. This paper uses the finite element method to investigate the influence of a fixture-type reinforcement device (FRD) on the load-bearing performance of the transmission tower structure and explores the effects of different numbers of fixture pairs on the reinforcement. Based on this, the paper further analyzes the stress characteristics and failure modes of a typical tower structure under wind loads in two directions and investigates the influence of different reinforcement lengths on the wind resistance performance of the tower structure. The research results indicate that the FRD can effectively improve the deformation mode and failure characteristics of steel components under axial load. At the same time, using the FRD can effectively reduce the deformation of tower structures under strong wind, and only reinforcing three angled steel components can reduce the tower top displacement by about 55% and more.

Weather Associated with Rapid-Growth California Wildfires

Abstract California is vulnerable to large, rapidly growing wildfires that can threaten human lives and damage physical infrastructure. This study analyzes the daily growth of all satellite-observed California fires from 2003 through 2020 and relates that growth to local meteorological and environmental conditions. Fire growth is defined using both absolute and relative metrics (absolute growth—the daily burned area, relative growth—the daily percent increase in burned area). Near-surface environmental conditions are evaluated before, during, and after periods of fire growth for individual fires of varying sizes and spread rates across events with both high and low relative growth. Regional spatial patterns and distributions of near-surface dryness and winds during large absolute-growth events are examined and compared to climatological conditions. Overall, large absolute-growth events generally occur when dead fuels are driest, trailing short periods of increased atmospheric dryness. For large absolute-growth events, high relative growth is usually associated with strong winds while low relative-growth is generally associated with quiescent to climatological winds. Winds during high relative-growth events are greatest in autumn, surpassing the 95th percentile on average. For these autumnal events, atmospheric dryness is less than during other seasons, but atmospheric and fuel dryness are still sufficient for fire. Irrespective of season, winds during particularly rapid-growth events (high absolute and relative growth) are frequently downslope and influenced by local terrain regardless of the climatological conditions.

Paradigm Shift in Typhoon Forecasting for the Korean Peninsula: A Case Study on the Applicability of the Typhoon-Ready System

Climate change has significantly increased multi-hazard disasters caused by typhoons, exposing the limitations of conventional forecasting systems that often neglect regional socio-economic vulnerabilities. This study develops and validates the Typhoon-Ready System (TRS) as an effective disaster-management framework for the Korean Peninsula. The TRS integrates hazard data with socio-economic and environmental vulnerability factors to produce region-specific risk indices. The analysis of four representative typhoons—Lingling (2019), Rusa (2002), Maemi (2003), and Mitak (2019)—demonstrates TRS’s applicability in identifying high-risk zones and supporting disaster preparedness strategies. The TRS framework incorporates indices, such as the Strong Wind Index (SWI), Heavy Rainfall Index (HRI), Storm Surge Index (SSI), and Air Quality Index (AQI), effectively combining meteorological modeling with vulnerability analysis. Results demonstrate that the TRS outperforms traditional systems by accurately identifying high-risk zones and correlating them with observed damage patterns. For example, the TRS successfully pinpointed high wind risks in Seoul and Incheon during Typhoon Lingling and forecasted severe flooding in Gangneung and Samcheok during Typhoon Rusa. By integrating vulnerability factors, including population density, infrastructure aging, and urbanization levels, the TRS provides a more holistic and accurate risk assessment. This research highlights the necessity of a multi-dimensional forecasting approach for enhancing disaster preparedness and resilience against climate change-induced typhoon impacts.

Using Hybrid Deep Learning Models to Predict Dust Storm Pathways with Enhanced Accuracy

As a potential consequence of climate change, the intensity and frequency of dust storms are increasing. A dust storm arises when strong winds blow loose dust from a dry surface, transporting soil particles from one place to another. The environmental and human health impacts of dust storms are substantial. Accordingly, studying the monitoring of this phenomenon and predicting its pathways for early decision making and warning are vital. This study employs deep learning methods to predict dust storm pathways. Specifically, hybrid CNN-LSTM and ConvLSTM models have been proposed for the 24 h-ahead prediction of dust storms in the region under study. The Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) product that includes the dust particles and the meteorological information, such as surface wind speed and direction, relative humidity, surface air temperature, and skin temperature, is used to train the proposed models. These contextual features are selected utilizing the random forest feature importance method. The results indicate an improvement in the performance of both models by considering the contextual information. Moreover, a 0.2 increase in the Kappa coefficient criterion across all forecast hours indicates the CNN-LSTM model outperforms the ConvLSTM model when contextual information is considered.