Severe Weather Research Articles

Spatial Resolution Requirements for FDTD Modeling of Geoelectric Fields

AbstractTo ensure the robustness of both civilian and military infrastructure, it is important to protect electric power grids, smart grids, and other electrotechnologies from known and possibly as‐of‐yet unknown space weather hazards. Space weather can generate intense geoelectric fields at the surface of the Earth, as well as large voltage gradients across long distances of the Earth. These voltage gradients can lead to geomagnetically induced currents (GICs), which are known to produce hazards to electric power grids. The finite‐difference time‐domain (FDTD) method is a powerful and versatile method that has already been applied to the study of geoelectric fields. The advantages of FDTD over other methods are that it can account for more geometrical complexities and realistic time waveforms and that it directly solves for geoelectric fields. Snell's Law predicts that any electromagnetic waves incident on the ground should essentially propagate straight downwards into the low resistivity ground. For this reason, vertical FDTD grid resolutions of 1/3 of a skin depth were usually chosen, while the horizontal grid resolution was relaxed. We find, however, that there is another important consideration for choosing an FDTD grid resolution applied to real‐world scenarios: localized field variations due to currents generated by ground features. It turns out the grid resolution requirements are much stricter when taking this physics into account.

A Statistical Analysis of the Morphology of Storm‐Enhanced Density Plumes Over the North American Sector

AbstractThe storm‐enhanced density (SED) is a large‐scale midlatitude ionospheric electron density enhancement in the local afternoon sector, which exhibits substantial spatial gradients and thus can impose detrimental effects on modern navigation and communication systems, causing potential space weather hazards. This study has identified a comprehensive list of 49 SED events over the continental US and adjacent regions, by examining strong geomagnetic storms occurring between 2000 and 2023. The ground‐based Global Navigation Satellite System (GNSS) total electron content and data from a new TEC‐based ionospheric data assimilation system were used to analyze the characteristics of SED. For each derived SED events, we have quantified its morphology by employing a Gaussian function to parameterize key characteristics of the SED, such as the plume intensity, central longitude, and half‐width. A statistical analysis of SEDs was conducted for the first time to characterize their climatological features. We found that the SED distribution exhibits a higher peak intensity and a narrower width as geomagnetic activity strengthens. The peak intensity of SED has maximum values around the equinoxes in their seasonal distribution. Additionally, we observed a solar cycle dependence in the SED distribution, with more events occurring during the solar maximum and declining phases compared to the solar minimum. SED plumes exhibit a sub‐corotation feature with respect to the Earth, characterized by a westward drift speed between 50 and 400 m/s and a duration of 3–10 hr. These information advanced the current understanding of the spatial‐temporal variation of SED characteristics.

Dual Impacts of Space Heating Electrification and Climate Change Increase Uncertainties in Peak Load Behavior and Grid Capacity Requirements in Texas

AbstractAround 60% of households in Texas currently rely on electricity for space heating. As decarbonization efforts increase, non‐electrified households could adopt electric heat pumps, significantly increasing peak (highest) electricity demand in winter. Simultaneously, anthropogenic climate change is expected to increase temperatures, the potential for summer heat waves, and associated electricity demand for cooling. Uncertainty regarding the timing and magnitude of these concurrent changes raises questions about how they will jointly affect the seasonality of peak demand, firm capacity requirements, and grid reliability. This study investigates the net effects of residential space heating electrification and climate change on long‐term demand patterns and load shedding potential, using climate change projections, a predictive load model, and a direct current optimal power flow (DCOPF) model of the Texas grid. Results show that full electrification of residential space heating by replacing existing fossil fuel use with higher efficiency heat pumps could significantly improve reliability under hotter futures. Less efficient heat pumps may result in more severe winter peaking events and increased reliability risks. As heating electrification intensifies, system planners will need to balance the potential for greater resource adequacy risk caused by shifts in seasonal peaking behavior alongside the benefits (improved efficiency and reductions in emissions).

Bibliometric Analysis of Weather Radar Research from 1945 to 2024: Formations, Developments, and Trends.

In the development of meteorological detection technology and services, weather radar undoubtedly plays a pivotal role, especially in the monitoring and early warning of severe convective weather events, where it serves an irreplaceable function. This research delves into the landscape of weather radar research from 1945 to 2024, employing scientometric methods to investigate 13,981 publications from the Web of Science (WoS) core collection database. This study aims to unravel, for the first time, the foundational structures shaping the knowledge domain of weather radar over an 80-year period, exploring general features, collaboration, co-citation, and keyword co-occurrence. Key findings reveal a significant surge in both publications and citations post-1990, peaking in 2022 with 1083 publications and 13832 citations, signaling sustained growth and interest in the field after a period of stagnation. The United States, China, and European countries emerge as key drivers of weather radar research, with robust international collaboration playing a pivotal role in the field's rapid evolution. Analysis uncovers 30 distinct co-citation clusters, showcasing the progression of weather radar knowledge structures. Notably, deep learning emerges as a dynamic cluster, garnering attention and yielding substantial outcomes in contemporary research efforts. Over eight decades, the focus of weather radar investigations has transitioned from hardware and software enhancements to Artificial Intelligence (AI) technology integration and multifunctional applications across diverse scenarios. This study identifies four key areas for future research: leveraging AI technology, advancing all-weather observation techniques, enhancing system refinement, and fostering networked collaborative observation technologies. This research endeavors to support academics by offering an in-depth comprehension of the progression of weather radar research. The findings can be a valuable resource for scholars in efficiently locating pertinent publications and journals. Furthermore, policymakers can rely on the insights gleaned from this study as a well-organized reference point.

Internet of things weather monitoring system

The IoT Weather Monitoring System aims to develop a comprehensive and efficient solution for real-time weather data collection, analysis, and dissemination using the Internet of Things (IoT) technology. With the increasing need for accurate and up-to-date weather information, this project leverages interconnected sensors, data processing techniques, and cloud computing to create a reliable and accessible weather monitoring system. The system comprises a network of weather sensors strategically deployed in various locations to capture meteorological parameters such as temperature, humidity, air pressure, and rainfall. These sensors are integrated with microcontrollers and wireless communication modules to transmit data to a central data hub. The data hub employs advanced data processing algorithms to clean, aggregate, and analyze the incoming data streams, ensuring the accuracy and quality of the information. To enhance user accessibility, the processed weather data is made available through a user- friendly web application and mobile interface. Users can view current weather conditions, historical trends, and receive alerts for severe weather events based on the analyzed data. Additionally, the system's cloud-based architecture allows for scalability and remote management, ensuring seamless operation and easy expansion to accommodate growing demands.

Unveiling the nexus between atmospheric visibility, remotely sensed pollutants, and climatic variables across diverse topographies: A data-driven exploration empowered by artificial intelligence

Deteriorating visual range (VR) can cause challenges for the transportation sector, resulting in economic and life losses. Air pollutants, smoke, fog, and many meteorological parameters such as air temperature (T), relative humidity (RH), wind speed (WS), and wind direction (WD) can contribute to light extinction and degrade VR. Advancements in geospatial technologies have triggered artificial intelligence to analyze and model the relationships among environmental and climatological parameters. This paper aims to assess the potential of supervised machine learning models for the parameterization of VR over Pakistan's diverse topography by utilizing meteorological parameters and some pollutants. The daily data from 2005 to 2020 of VR, T, RH, WS, WD, Aerosol Optical Depth (AOD), Nitrogen dioxide (NOx), Sulfate, Sulfur dioxide (SOx), and Dust were acquired. Ten machine learning models, including Random Forest (RF), Extreme Gradient Boosting (XGB), Artificial Neural Networks (ANN), Support Vector Machine (SVM), Decision Trees (DT), Gradient Boosting Machine (GBM), Causal, Unbiased, Binned, and Intermittent, Search, and Tree (CUBIST), Multi-Layer Perceptron (MLP), Multivariate Adaptive Regression Splines (MARS), and K-Nearest Neighbor (KNN) were gauged for VR estimation. We also coupled the Bagged Extreme Gradient Boosting (BG-XG) model by combining XGB and bagging technique. BG-XG performed better than the rest of the models, with coefficients of determination of 0.90 for the training and 0.70 to 0.90 for the validation set. Degradation in the VR was highly dependent on the changes in RH followed by SOx and dust associated with anthropogenic emissions. RH, SO4, and SO2 emerged as the most important parameters for the VR decline. Proposed model parameters can be helpful in accurate VR projections and improving severe weather alerts, including analyzing and managing air pollution. This work will also be helpful to improve aviation and transportation safety for pilots, drivers, and automated vehicles to minimize low-visibility accidents.

High‐modulus glass fiber for wind renewable energy generation: Selective review on the recent research and development

AbstractTo effectively manage turbine blade weight and blade deflection under severe weather conditions, longer and stiffer blades are required, fiber glass producers have devoted significant efforts to developing and commercializing high‐modulus (HM) glass fiber products of the first generation. The current focuses aim at the commercialization of the second generation and the development of the third‐generation products. This article briefly reviews four key areas: (a) the benefit of longer blades on wind energy generation, (b) characteristics of HM glass fibers of various generations, (c) fundamental science and understanding behind HM glass fiber development, and (d) finally statistically based composition (C)–property (P) and structure (S)–property (P) modeling approaches in new glass design.

Leveraging Sentinel-2 and Geographical Information Systems in Mapping Flooded Regions around the Sesia River, Piedmont, Italy

Sentinel-2 data are crucial in mapping flooded areas as they provide high spatial and spectral resolution but under cloud-free weather conditions. In the present study, we aimed to devise a method for mapping a flooded area using multispectral Sentinel-2 data from optical sensors and Geographical Information Systems (GISs). As a case study, we selected a site located in Northern Italy that was heavily affected by flooding events on 3 October 2020, when the Sesia River in the Piedmont region was hit by severe weather disturbance, heavy rainfall, and strong winds. The method developed for mapping the flooded area was a thresholding technique through spectral water indices. More specifically, the Normalized Difference Water Index (NDWI) and the Modified Normalized Difference Water Index (MNDWI) were chosen as they are among the most widely used methods with applications across various environments, including urban, agricultural, and natural landscapes. The corresponding flooded area product from the Copernicus Emergency Management Service (EMS) was used to evaluate the flooded area predicted by our method. The results showed that both indices captured the flooded area with a satisfactory level of detail. The NDWI demonstrated a slightly higher accuracy, where it also appeared to be more sensitive to the separation of water from soil and areas with vegetation cover. The study findings may be useful in disaster management linked to flooded-area mapping and area rehabilitation mapping following a flood event, and they can also valuably assist decision and policy making towards a more sustainable environment.

Precipitation Characteristics at Different Developmental Stages of the Tibetan Plateau Vortex in July 2021 Based on GPM-DPR Data

The Tibetan Plateau vortex (TPV), as an α-scale mesoscale weather system, often brings severe weather conditions like torrential rain and severe convective storms. Based on the detections from the Global Precipitation Measurement (GPM) Core Observatory’s Dual-frequency Precipitation Radar (DPR) and the FY-4A satellite’s Advanced Geostationary Radiation Imager (AGRI), combined with ERA5 reanalysis data, the precipitation characteristics of a TPV moving eastward during 8–13 July 2021 at different developmental stages are explored in this study. It was clear that the near-surface precipitation rate of the TPV during the initial stage at the eastern Tibetan Plateau (TP) was below 1 mm·h−1, implying overall weak precipitation dominated by stratiform clouds. After moving out of the TP, the radar reflectivity factor (Ze), precipitation rate, and normalized intercept parameter (dBNw) significantly increased, while the proportion of convective clouds gradually rose. Following the TPV movement, the distribution range and vertical thickness of Ze, mass-weighted mean diameter (Dm), and dBNw tended to increase. The high-frequency region of Ze appeared at 15–20 dBZ, while Dm and dBNw occurred at around 1 mm and 33 mm−1·m−3, respectively. Near the melting layer, Ze was characterized by a significant increase due to the aggregation and melting of ice crystals. The precipitation rate of convective clouds was generally greater than that of stratiform clouds, whilst both of them increased during the movement of the TPV. Particularly, at 01:00 on 12 July, there was a significant increase in the precipitation rate and Dm of convective clouds, while dBNw noticeably decreased. These findings could provide valuable insights into the three-dimensional structure and microphysical characteristics of the precipitation during the movement of the TPV, contributing to a better understanding of cloud precipitation mechanisms.

The Impact of Installation Angle on the Wind Load of Solar Photovoltaic Panels

In order to explore the wind load characteristics acting on solar photovoltaic panels under extreme severe weather conditions, based on the Shear Stress Transport (SST) κ-ω turbulence model, numerical calculations of three-dimensional incompressible viscous steady flow were performed for four installation angles and two extreme wind directions of the solar photovoltaic panels. The wind load characteristics on both sides of the photovoltaic panels were obtained, and the vortex structure characteristics were analyzed using the Q criterion. The results indicate that, under different installation angles, the windward side pressure of the solar photovoltaic panel is generally higher than the leeward side. The leeward side is prone to forming larger vortices, increasing the fatigue and damage risk of the material, which significantly impacts the solar photovoltaic panel. As the installation angle increases, the windward side pressure of the solar photovoltaic panel also gradually increases. Therefore, optimal installation methods include installing the panel facing the wind at angles of 30° and 45°, or installing it facing away from the wind at a 60° angle, to minimize the impact of wind load on the solar photovoltaic panel.

Modeling the dynamics of anti-microbial resistant cholera infection with reinfection

Cholera continues to pose a significant global public health challenge. The occurrence of severe weather events, including floods, droughts, and cyclones, has led to a surge in new cholera outbreaks and exacerbated both epidemic and endemic conditions in numerous countries. We propose a ten-state ordinary differential system of equations to model antimicrobial resistant cholera infections, considering both direct and indirect transmission pathways. Antimicrobial resistant cholera infection is considered to occur in some infected individuals receiving antibiotic treatment. The model integrates key interventions such as vaccination, screening, and treatment to curb cholera spread. Loss of infection-acquired immunity for the recovered individuals is also considered in the model. A threshold quantity referred to as the model reproduction number Rc is computed and stability analysis of equilibrium points for the full model and submodels is performed. Sensitivity analysis is performed and specific measures to reduce parameters associated with cholera transmission and enhance parameters related to control measures are suggested. Numerical simulations are carried out to support theoretical findings. The results obtained places a significant emphasis on antimicrobial resistance and its impact on cholera control. The importance of minimizing antimicrobial resistance through targeted interventions is highlighted. Therefore, there is an urgent need to implement policies aimed at mitigating the spread of antimicrobial resistance.

Weather Forecasting

My Project is on implementation of a weather forecasting using python and machine learning. real-time weather data from locations across the world. Through Python scripting, the project generates accurate forecasts for diverse regions, providing essential meteorological parameters such as temperature, wind speed, humidity, and weather conditions. Weather forecasting plays a crucial role in various aspects of human life, from planning outdoor activities to making strategic decisions in sectors like agriculture, transportation, and emergency management. In this project, we explore the application of Python programming language in weather forecasting, employing both open-source libraries and APIs to access and analyze weather data. Through this project, we aim to demonstrate the versatility of Python in harnessing weather data for forecasting purposes, showcasing its capabilities in data retrieval, analysis, visualization, and predictive modelling. By empowering users with accessible tools and methodologies, we envision a future where weather forecasting becomes more accurate, reliable, and actionable, contributing to informed decision-making and enhanced societal resilience in the face of changing weather patterns. Weather forecasting is the scientific process of predicting atmospheric conditions at a specific location and time. It involves the collection and analysis of data from various sources, such as satellites, weather stations, and radar systems. This data is then input into numerical weather prediction models, which use mathematical equations to simulate the behavior of the atmosphere. Advances in technology, such as high-performance computing and machine learning, have significantly improved the accuracy and reliability of weather forecasts. These predictions are crucial for a wide range of applications, including agriculture, disaster management, transportation, and daily planning, helping to mitigate the impacts of severe weather events and enhance societal preparedness and safety. Despite significant progress, challenges remain due to the inherently chaotic nature of the atmosphere, which limits the precision of long-term forecasts

Compound mortality impacts from extreme temperatures and the COVID-19 pandemic

Extreme weather and coronavirus-type pandemics are both leading global health concerns. Until now, no study has quantified the compound health consequences of the co-occurrence of them. We estimate the mortality attributable to extreme heat and cold events, which dominate the UK health burden from weather hazards, in England and Wales in the period 2020-2022, during which the COVID-19 pandemic peaked in terms of mortality. We show that temperature-related mortality exceeded COVID-19 mortality by 8% in South West England. Combined, extreme temperatures and COVID-19 led to 19 (95% confidence interval: 16–22 in North West England) to 24 (95% confidence interval: 20–29 in Wales) excess deaths per 100,000 population during heatwaves, and 80 (95% confidence interval: 75–86 in Yorkshire and the Humber) to 127 (95% confidence interval: 123–132 in East of England) excess deaths per 100,000 population during cold snaps. These numbers are at least ~2 times higher than the previous decade. Society must increase preparedness for compound health crises such as extreme weather coinciding with pandemics.

Research on online monitoring method for time-varying tension in transmission lines based on operational modal response

The cumulative overload of conductor tension under severe weather conditions is an important cause of accelerated fatigue fracture of transmission lines. Traditional tension measurement methods require the replacement of ball head hanging rings, which poses safety risks. In this paper, a method for monitoring conductor tension based on acceleration data under operating conditions is proposed. Firstly, a modal order extraction based method for identifying the modal frequencies of conductor operation is proposed, and then the time-varying tension of the conductor is estimated based on the instantaneous modal frequencies. Since this method directly installs sensors on the conductor, there is a certain error in the obtained intrinsic characteristic data of the conductor. Therefore, a modal correction method is used to remove the influence of the sensors. The accuracy of modal identification, modal correction, and tension identification methods is verified through finite element models. Based on the above methods, a monitoring system for conductor tension status is designed, and the feasibility of this system is verified through experiments. Finally, the vibration data obtained from the field engineering pilot test is successfully used for conductor tension analysis. The results show that the proposed method can effectively identify time-varying tension and provide a new approach for monitoring the status of transmission line conductors.

Study on Nowcasting Method of Severe Convective Weather Based on SA-PredRNN++

Severe convective weather, characterized by short-term intense precipitation, thunderstorms, and strong winds, poses significant threats to human life and property. Therefore, accurate and efficient prediction of severe convective weather is crucial for disaster prevention. Currently, utilizing deep learning for radar echo extrapolation stands as the primary method for forecasting severe convective weather. We propose a predictive recurrent neural network model that integrates a self-attention mechanism, specifically designed for radar echo extrapolation in severe convective weather forecasting. The self-attention mechanism offers the advantage of being lightweight, as it does not substantially increase the model parameters. Additionally, it facilitates global attention extraction, thereby enhancing the model's accuracy to some extent. By utilizing radar echo images from the previous hour as input, the model undergoes self-learning to achieve the best forecast for radar echo extrapolation in the subsequent two hours. Research findings demonstrate that our model outperforms other models in accurately predicting severe convective weather within this two-hour timeframe.

Analysis of the Multi-Dimensional Characteristics of City Weather Forecast Page Views and the Spatiotemporal Characteristics of Meteorological Disaster Warnings in China

In order to provide insights into how various page views are influenced by public engagement with weather information and to shed light on the patterns of warning issuance across different seasons and regions, this study analyzes the multi-dimensional characteristics of city weather forecast page views and the spatiotemporal characteristics of early warning information in China, from 1 March 2020 to 31 August 2023. This is achieved by utilizing the daily page views of city weather forecasts and meteorological warning data, comparing the public’s attention to weather during holidays versus regular days, assessing the public’s attention to weather under different meteorological warning levels, and performing statistical analysis of the spatiotemporal scale of meteorological disasters. Our analysis shows that compared to weekends and holidays, the public pays more attention to the weather on weekdays, and the difference between weekdays and national statutory holidays is more significant. Due to the widespread impact of heat waves, typhoons, severe convective weather, and geological disasters caused by heavy rainfall, public awareness and participation in flood season weather forecasting have significantly increased. Under red alerts, flash floods, typhoons, and geological risks are the primary concerns. Orange alerts predominantly feature flash floods, rainstorms, typhoons, snowstorms, and cold waves, while sandstorms attract the most attention during yellow alerts. Droughts, however, receive relatively less attention regardless of the warning level. Seasonal patterns in the issuance of meteorological warnings reveal a peak in summer, particularly with typhoons and rainstorms being the main concerns in July, followed by high temperatures and additional typhoon warnings in August. Heavy sea surface wind warnings exhibit a strong seasonal trend, with the majority issued during the winter months. Regionally, southern China experiences the highest frequency of severe convection weather warnings, with provinces such as Jiangxi, Guangxi, and Hunan being the most affected.

Seasonal trends and population status of the highly threatened Pteropus livingstonii in the Comoros archipelago

Flying foxes of the genus Pteropus, especially those inhabiting islands, face increasing pressure from anthropogenic threats. A first step to implementing effective conservation actions is to establish monitoring projects to understand a species’ population status and trend. Pteropus species are highly affected by seasonality which further requires regular, repeated, and long-term data to understand population trends, and reactions to severe weather events. In the present case study, a regular, bi-annual population census was implemented on Comoros between 2016 and 2023 for the highly threatened Livingstone’s fruit bat, Pteropus livingstonii, and compared the results of standardized monitoring to historical population data. Seasonality had a large impact on the number of bats found at roost sites, with more bats present in the wet season, but the data over the past eight years revealed no significant in- or decrease in the number of bats counted on the island Anjouan. We estimated around 1,200–1,500 bats on Anjouan and 300–400 bats on Mohéli, and found that landcover type has no measurable effect on population distribution at roost sites. Our study highlights the need for long-term surveys to understand past population trends and that single counts are not sufficient to draw final conclusions of a species’ status.

Bivariate extreme value analysis of extreme temperature and mortality in Canada, 2000-2020.

Climate change increases the risk of illness through rising temperature, severe precipitation and worst air pollution. This paper investigates how monthly excess mortality rate is associated with the increasing frequency and severity of extreme temperature in Canada during 2000-2020. The extreme associations were compared among four age groups across five sub-blocks of Canada based on the datasets of monthly T90 and T10, the two most representative indices of severe weather monitoring measures developed by the actuarial associations in Canada and US. We utilize a combined seasonal Auto-regressive Integrated Moving Average (ARIMA) and bivariate Peaks-Over-Threshold (POT) method to investigate the extreme association via the extreme tail index and Pickands dependence function plots. It turns out that it is likely (more than 10%) to occur with excess mortality if there are unusual low temperature with extreme intensity (all except Northeast Atlantic (NEA), Northern Plains (NPL) and Northwest Pacific (NWP) for age group 0-44), while extreme frequent high temperature seems not to affect health significantly (all except NWP). Particular attention should be paid to NWP and Central Arctic (CAR) since population health therein is highly associated with both extreme frequent high and low temperatures (both for all age groups). The revealed extreme dependence is expected to help stakeholders avoid significant ramifications with targeted health protection strategies from unexpected consequences of extreme weather events. The novel extremal dependence methodology is promisingly applied in further studies of the interplay between extreme meteorological exposures, social-economic factors and health outcomes.

Multi-agent system architecture for winter road maintenance: a real Spanish case study

Road safety remains a critical issue in contemporary society, where the sudden deterioration of road conditions due to weather-related natural phenomena poses significant risks. These abrupt changes can lead to severe safety hazards on the roads, making real-time monitoring and control essential for maintaining road safety. In this context, technological advancements, especially in sensor networks and intelligent systems, play a fundamental role in efficiently managing these challenges. This study introduces an innovative approach that leverages a sophisticated sensor platform coupled with a multi-agent system. This integration facilitates the collection, processing, and analysis of data to preemptively determine the appropriate chemical treatments for roads during severe winter conditions. By employing advanced data analysis and machine learning techniques within a multi-agent framework, the system can predict and respond to adverse weather effects swiftly and with a high degree of accuracy. The proposed system has undergone rigorous testing in a real-world environment, which has verified its operational effectiveness. The results from the deployment of the multi-agent architecture and its predictive capabilities are encouraging, suggesting that this approach could significantly enhance road safety in extreme weather conditions. Furthermore, the proposed architecture allows the system to evolve and scale over time. This paper details the design and implementation of the system, discusses the results of its field tests, and explores potential improvements.

Exploring Climate-Induced Agricultural Risk in Saudi Arabia: Evidence from Farming Communities of Medina Region

Agriculture is vital to the Saudi Arabian economy since it provides food and other necessities to people living in rural areas, as well as those living in adjacent cities. Notwithstanding its considerable economic importance, the agricultural sector is confronted with formidable obstacles due to climate change, such as elevated temperatures, floods, extreme droughts, and decreased agricultural yields. Building a farming system capable of being climate resilient requires the comprehension of the vulnerabilities of farm households and related systems. This paper deals with the potential agricultural risks resulting from climate change in Saudi Arabia. In addition to concentrating on precipitation and temperature, the present research incorporates the three main components of the Climate Change Vulnerability Index (CCVI): sensitivity, exposure, and adaptive capability. The results of this research reveal a notable challenge that farmers in Saudi Arabia encounter, as they are very susceptible to the impacts of climate change. The Climate Change Vulnerability Index (CCVI) has a score of 0.730, signifying a substantial degree of vulnerability. The farmers in this region are very susceptible to uncertainties caused by climate change, as indicated by the significant exposure score of 0.725. This exposure comprises a wide range of concerns resulting from fluctuations in temperature, patterns of rainfall, and occurrences of severe weather, all of which have an immediate and negative effect on agriculture. Farmers in that region are more susceptible to the effects of climate change, which could jeopardize their livelihoods and agricultural operations, as shown by the susceptibility component (SVI) of 0.559. Moreover, the adaptive capacity (AVI) score of 0.567 highlights the fact that farmers have limited access to resources, skills, and experience, hindering their ability to tackle the challenges that have been caused by climate change in this region successfully. The results emphasize the immediate necessity for specific policies and assistance to enhance the resilience of agricultural communities in the area, as well as to mitigate the potential adverse effects of climate change on their livelihoods.