Changes In Wind Speed Research Articles

Correction of Sensible Heat Flux from Flux-Gradient Method to Eddy Covariance Method Based on Multi-Layer Perceptron

Abstract. Understanding surface-atmosphere interactions is critical for environmental and meteorological studies. Sensible heat flux, a key component in this interaction, is typically measured using methods such as Eddy Covariance (EC) and Flux-Gradient (FG). The EC method, known for its high temporal resolution and direct measurement capabilities of wind speed, temperature, and humidity changes, requires the use of expensive and complex equipment, making it costly and challenging to implement. On the contrary, the FG method is more accessible and economical, relying on simpler instruments, but often lacks the precision of the EC method. To harness the benefits of both methods, this article uses the Multi-Layer Perceptron (MLP) machine learning method to enhance the accuracy of the FG method's sensible heat flux calculations. Through the MLP model, this paper aims to determine the optimal parameter settings for the specific measurement environment, thereby improving the FG methods accuracy. The data was measured from Guandu Village, Anhui Province, China. This research seeks to demonstrate that the trained MLP model can be applied to similar measurement environments, thus enhancing the FG method's applicability and precision.

Long‐Term Foehn Reconstruction Combining Unsupervised and Supervised Learning

ABSTRACTFoehn winds, characterised by abrupt temperature increases and wind speed changes, significantly impact regions on the leeward side of mountain ranges, e.g., by spreading wildfires. Understanding how foehn occurrences change under climate change is crucial. As foehn is a meteorological phenomenon, its prevalence has to be inferred from meteorological measurements employing suitable classification schemes. Hence, this approach is typically limited to specific periods for which the necessary data are available. We present a novel approach for reconstructing historical foehn occurrences using a combination of unsupervised and supervised probabilistic statistical learning methods. We utilise in situ measurements (available for recent decades) to train an unsupervised learner (finite mixture model) for automatic foehn classification. These labelled data are then linked to reanalysis data (covering longer periods) using a supervised learner (lasso or boosting). This allows us to reconstruct past foehn probabilities based solely on reanalysis data. Applying this method to ERA5 reanalysis data for six stations across Switzerland and Austria achieves accurate hourly reconstructions of north and south foehn occurrence, respectively, dating back to 1940. This paves the way for investigating how seasonal foehn patterns have evolved over the past 83 years, providing valuable insights into climate change impacts on these critical wind events.

Increase in insurance losses caused by North Atlantic hurricanes in a warmer climate

North Atlantic hurricanes are a major driver of property losses in the United States and a critical peril for the reinsurance industry globally. We leverage insurance loss data and stochastic modeling to investigate the impacts of projected changes in hurricane climatology on the insurance industry, for +2 °C and +4 °C warming scenarios. We find that, relative to the historical baseline 1950-2022, expected changes in wind speed and rainfall may increase hurricane losses by 5% −15% (+2 °C) and 10% − 30% (+4 °C), with greater impacts at lower return periods than in the tail. The historical 100-year loss event may therefore be exceeded on average every 80 years (+2 °C) and 70 years (+4 °C). The expected changes in average annual loss are projected to be 10% (+2 °C) and 15% (+4 °C), with the largest relative increase attributable to precipitation-induced losses. Under the extreme SSP5-8.5 scenario, the expected loss inflation due to climate change is thus on the order of 0.5% per annum.

Ultra-Short-Term Wind Power Forecasting in Complex Terrain: A Physics-Based Approach

This paper proposes a method based on Computational Fluid Dynamics (CFD) and the detection of Wind Energy Extraction Latency for a given wind turbine (WT) designed for ultra-short-term (UST) wind energy forecasting over complex terrain. The core of the suggested modeling approach is the Wind Spatial Extrapolation model (WiSpEx). Measured vertical wind profile data are used as the inlet for stationary CFD simulations to reconstruct the wind flow over a wind farm (WF). This wind field reconstruction helps operators obtain the wind speed and available wind energy at the hub height of the installed WTs, enabling the estimation of their energy production. WT power output is calculated by accounting for the average time it takes for the turbine to adjust its power output in response to changes in wind speed. The proposed method is evaluated with data from two WTs (E40-500, NM 750/48). The wind speed dataset used for this study contains ramp events and wind speeds that range in magnitude from 3 m/s to 18 m/s. The results show that the proposed method can achieve a Symmetric Mean Absolute Percentage Error (SMAPE) of 8.44% for E40-500 and 9.26% for NM 750/48, even with significant simplifications, while the SMAPE of the persistence model is above 15.03% for E40-500 and 16.12% for NM 750/48. Each forecast requires less than two minutes of computational time on a low-cost commercial platform. This performance is comparable to state-of-the-art methods and significantly faster than time-dependent simulations. Such simulations necessitate excessive computational resources, making them impractical for online forecasting.

Collocating wind data: A case study on the verification of the CERRA dataset

Abstract Wind resource assessment often relies on reanalysis data or meso-scale models due to the challenges and costs associated with on-site measurement campaigns, especially in offshore locations. While these datasets offer extensive spatial coverage, they often provide the desired parameters on a coarse grid only. The spatial averaging over the grid and the low frequency of the outputs hinders the model’s ability to capture the short-term wind speed variability and events with a time scale of a few minutes to a few dozen of minutes. In this paper we investigate the temporal scales of the wind events resolved by the Copernicus European Regional Re-Analysis (CERRA) dataset, ERA5, and an in-house regional down-scaling of ERA5 using the WRF model by comparing them against measurements. We propose a method to collocate the two sources of wind data (measured and modeled), focusing on the verification of CERRA with respect to first and second-order statistics in the North Sea. The results highlight the superiority of CERRA compared to ERA5 in estimating the wind speed distribution, improving the 95th percentile error from -0.85 to -0.19 ms−1 and the average bias from -0.286 to -0.004 m s−1 across all locations. The wind speed change over one hour is underestimated by both CERRA and ERA5 when collocated with 10-minute measurements at the full hour. However, hourly averaged measurements align well with CERRA’s wind speed variation predictions, while an averaging window of 3.5 hours is needed to align with ERA5’s hourly wind speed changes. The results showed that the synthetic wind speeds exhibit optimal correlation with the measurements when the measurements are averaged over 4.5 to 7 hours, depending on the modeled dataset. This time scale corresponds to a length scale of 160-250 km, assuming a mean wind speed of 10 m s−1, which falls within the meso-scale range. The study contributes to the understanding of model validation in offshore wind energy studies, with the potential to enhance wind resource assessment calculations and improve the results of long-term extrapolations.

Confidence intervals of difference and ratio between two means of delta-Birnbaum-Saunders distributions

During the dry season, the northern region of Thailand consistently grapples with compromised air quality, notably an excessive level of particulate matter with a diameter of less than 2.5 microns (PM2.5), which poses implications for public health. Wind speed emerges as the primary factor that diminishes the concentration of PM2.5, making the evaluation of wind speed changes crucial in understanding and potentially mitigating the impact of air pollution on public health in the region. The wind speed data contain zero and positive values. This can be estimated through the confidence intervals of the difference and ratio between two means of delta-Birnbaum-Saunders distributions using various methods, including the generalized confidence interval (GCI), bootstrap confidence interval (BCI), and generalized fiducial confidence interval (GFCI). These methods rely on variance stabilizing transformations (VST), the Wilson and Hannig methods. According to the simulation, for small sample sizes, the GCI based on the VST and Hannig was the most effective method in terms of coverage probabilities and average lengths. For medium sample sizes, it was found that the performance of GFCI based on the VST method was superior to other methods. Additionally, when confronted with a large sample size, the BCI based on the VST method becomes relevant and will be introduced. To demonstrate the efficacy of our proposed methods, we applied them to wind speed data gathered from Chiang Mai Airport station in Thailand from January to February in both 2022 and 2023.

Imputation based wind speed forecasting technique during abrupt changes in short term scenario

AbstractIt is tough and complex to forecast wind speed due to its intermittent and stochastic nature as well as sudden and abrupt variations in the wind speed. Further, it is required to handle the variety of scenarios e.g. cyber‐attacks, unexpected power device malfunction, communication/sensor outages etc. that can cause the missing data.This paper proposes and employs a de‐noising autoencoder algorithm for wind speed forecasting to ensure the handling of missing data information. At the next step, the data is processed via variational mode decomposition technique to mitigate the noise and improves the model's prediction accuracy. Furthermore, the bi‐directional long‐short term memory deep learning approach is tied with convolution neural network to increase prediction accuracy and anticipating the sudden/abrupt changes in wind speed accurately. Finally, actual wind speed related data is examined to scrutinize meticulousness of projected forecast methodology particularly during sudden/abrupt changes in the wind speed. The parameter indicators of the wind speed forecasting technique exhibit the capability of improved predictions under the diversified conditions.

Comment on “Anticyclonic Suppression of the North Pacific Transient Eddy Activity in Midwinter” by Okajima et al.

AbstractAtmospheric energetics is frequently used to diagnose how different atmospheric processes contribute to the development of transient storm track activity. Okajima et al. (2024), https://doi.org/10.1029/2023gl106932 developed an ad hoc method to separate the contributions of cyclones and anticyclones to the energetics using the value of the curvature of the instantaneous local wind. Here, using simple examples in which the physics is exactly known, it is shown that cyclones embedded within a constant zonal flow exhibit large regions with anticyclonic curvature despite the absence of any real anticyclones. Using the method of Okajima et al., a large fraction of the eddy kinetic energy is erroneously attributed to being associated with anticyclones. Furthermore, the fraction that is misattributed varies substantially with changes in the background wind speed. It is concluded that using the curvature to separate energetics contributions from cyclones and anticyclones is not likely to be physically meaningful.

A neural network model for predicting the temperature of insulated overhead lines

In this work, we develop a neural network model based on experimental heating and cooling curves of insulated wires of overhead lines under changes in wind speed and its direction relative to the wire axis. Insulated SIP-3 wire used in overhead lines was investigated. A multilayer perceptron neural network was employed to model the process of heating and cooling of insulated wire under changes in wind speed and its direction. The average absolute error was taken as a criterion for evaluating the prediction accuracy of wire core and insulation temperatures. The main parameters of the developed neural network model include the number of hidden layers and neurons in each hidden layer, the degree of regulation, and the regulatory rigidity of the model. The modelled heating and cooling curves of insulated wire were compared with those obtained experimentally. The average absolute error was equal to 1.74 and –4.08°C for the predicted core and insulation temperatures, respectively. The difference between the heating curves at low wind speeds was found to range within 9°C. It was shown that an increase in wind speed leads to a decrease in the difference between the curves. Our analysis showed that neural network models used for predicting variations in the temperature of insulated overhead lines should be trained using a larger number of input parameters. This is the main prerequisite for high prediction accuracy of such models, when the difference between the simulated and experimental data does not exceed 5%.

Maximum power point tracking control of wind turbines based on speed hysteresis loop to reduce drive-train loads

By adding compensation torque based on the optimal torque, the torque compensation control method expands the unbalanced torque at varying wind speeds, thereby improving the maximum power point tracking (MPPT) performance of wind turbines (WT). However, while improving the wind energy capture efficiency, such methods will lead to drastic fluctuations of generator torque, resulting in significantly increased drive-train loads. To solve this problem, based on the amplitude-frequency characteristics analysis of the WT system transfer function, it is found rotor speed information can not only reflect the main trend of wind speed changes, but also has the characteristics of not being easily affected by the high-frequency component of wind speed. Therefore, setting compensation torque according to it can effectively alleviate the above phenomenon. On this basis, the MPPT control of WT based on speed hysteresis loop to reduce drive-train loads is proposed. The speed information is used to replace the torque information to set the compensation torque amplitude term, and the speed hysteresis loop is introduced to improve the design of the compensation torque symbol term, which can significantly decreased drive-train loads without compromising on power capture. Finally, the effectiveness of the proposed method is verified by the experiments.

Study on Multi-Parameter Variation of Smoke Flow in Inclined Roadway Fire

ABSTRACT Based on the fluid similarity theory, a “Mine inclined roadway fire simulation experimental system” with similarity ratio of 1:20 is established. The temperature, velocity, density and dynamic pressure of fire smoke under different inlet wind speed (0.5 m/s, 1.0 m/s, 1.5 m/s) were tested and analyzed, and the distribution of temperature, velocity, density and dynamic pressure of inclined roadway during fire was simulated and analyzed by Ansys numerical simulation software. The research shows that: The higher the inlet wind speed, the faster the heat diffusion and the temperature field tends to be flat and long, the wind speed changes in advance and the change is drastic, the gas density decreases, the dynamic pressure first decreases and then increases and then tends to stabilize, but the change trend of temperature, wind speed, gas density and pressure in the overall combustion process remains unchanged. The numerical analysis results are in good agreement with the experimental results. By studying the change law of smoke temperature, velocity, density and dynamic pressure of coal mine inclined roadway fire, the theory of coal mine roadway fire is further perfected, and the level of coal mine fire prevention and rescue is improved.

Nonlinear unsteady aerodynamic forces prediction and aeroelastic analysis of wind-induced bridge response at multiple wind speeds: A deep learning-based reduced-order model

Machine learning-based aerodynamic reduced-order models (ROMs) combine high accuracy with extremely low computational costs, making them highly effective in predicting nonlinear and unsteady bridge aerodynamic forces. Although several machine learning-based nonlinear aerodynamic models have been developed, the majority are built on a single wind speed parameter. However, in nonlinear aerodynamic prediction and aeroelastic analysis of bridges, the variability in incoming wind speed significantly influences the computed results. A ROM relying solely on a single wind speed lacks the ability to accurately forecast the intricate dynamic behaviors arising from changes in wind speed. When the incoming wind speed changes, the model's prediction accuracy significantly decreases. Usually, it is necessary to establish a new database and train a new model, which not only increases time and cost but also greatly reduces the convenience of the ROM. Addressing this challenge, this study proposes a multiple-wind-speed (MWS) nonlinear unsteady bridge aerodynamic model based on the LSTM deep neural network. Taking the Taohuayu Yellow River Bridge in the Henan Province of China as an example, the modeling process of the proposed MWS-ROM is demonstrated, along with non-linear aerodynamic predictions of the deck under various conditions and aerodynamic-elastic analysis of the deck under different wind speeds. The research results show that the trained LSTM network can accurately predict the nonlinear aerodynamic forces of bridges under single and double degrees of freedom vibration conditions. The MWS-ROM performed well in predicting convergent vibrations at low wind speeds and limits cycle oscillations at high wind speeds, aligning closely with results from the CFD full-order model. Compared to CFD, the aerodynamic ROM based on the LSTM network significantly enhances computational efficiency, consequently boosting the convenience and efficiency of bridge flutter analysis. Additionally, the methodology proposed herein can be extended for wind-induced vibration control and response prediction in other types of deck sections.

Comparative examinations of wind speed and energy extrapolation methods using remotely sensed data – A case study from Hungary

Exact knowledge of wind energy potential is a fundamental issue in wind energy utilization. The vertical changes in wind speeds, that is, the wind profile, have a predominant impact on the wind energy available at a location because the kinetic energy of moving air is proportional to the square of the wind speed. Roughness describes the resistance of a 3D surface to moving air. The exponent α of the power law of Hellmann and the roughness length (z0) are two parameters that describe the effects of the roughness of the surface on the wind profile. They can be used for the vertical extrapolation of wind speeds. The exponent α can be determined using multiple height level wind speed measurement data, whereas a reliable technique for the calculation of the roughness length requires detailed knowledge of the 3D geometry of the measurement site. In the present study, the exponent α was calculated based on SODAR wind speed measurements, while (z0) was determined using a combination of GIS and UAS-based aerial survey methods. Wind speeds measured at 50 m were extrapolated for height levels of 80, 90, 100, 110, and 120 m using dynamic power law exponent values. Wind power was determined using the power law (method V1), roughness length (method V2), frequency distribution (method W-RF), and gamma distribution (method W-G), and Windographer software was compared to the values calculated from the empirical (measured) wind speeds. A comparative statistical analysis of the datasets of the power law and roughness length methods on monthly/diurnal, annual/diurnal, and month/direction contexts showed no significant differences at all height levels. Differences can be detected in the distribution of the signs of the differences at heights of 80 and 120 m for the entire dataset. Underestimation was dominant with a significant frequency (over 70 %) in the case of both methods and heights. There were no significant differences between the wind power estimations provided by the different methods, and all the methods involved in the study underestimated the wind speeds and wind energy potential for each height level. Methods V1 and V2 can be used alternatively, depending on the input data available for analysis. The major advantage of method V2 is that it provides the same accuracy as V1, which requires a UAS-based aerial survey at the beginning, but continuous wind measurements must be performed at a lower height only. This means that there is no need for a high measurement tower, which makes the measurements simpler, more cost-effective, and causes much less disturbance to the environment. Another important advantage of the methods presented here is that they use a dynamic approach of power law exponent values that provide a more realistic estimation of wind speed and energy on a diurnal scale.

Spatiotemporal distribution of global wind erosion over the past four decades

Abstract Wind erosion is a critical environmental issue that degrades land and air quality, affecting global ecosystems, agriculture, and human health. Yet, on the global scale, the long-term spatial variability and controlling factors of wind erosion remain highly uncertain. Here, we develop a high-resolution spatiotemporal dataset of global wind erosion from 1980 to 2020 using the Revised Wind Erosion Equation model, integrated with comprehensive meteorological, terrestrial ecology, and soil datasets. Our analysis indicates that wind erosion annually impacted 359 ± 25 petagrams of soils worldwide during this period. Approximately 70% of this erosion occurred in just ten countries, predominantly in Africa and the Middle East. Due to higher erosion intensities, pasturelands, accounting for 28% of all non-barren land use types, disproportionately contributed to 70% of the erosion in these areas. Furthermore, our analysis highlights an upward trend in global wind erosion over the past four decades, with affected areas expanding worldwide. Although our study reinforces that changing wind speeds and a drier climate are central factors impacting global wind erosion, we find that increasing erosion intensities in pasturelands may also exacerbate erosion in North Africa, South America, and East Asia. This has broad implications for soil erosion issues that impact food productivity, human health, and ecosystem stability. This research provides insights for developing wind erosion warnings and targeted mitigation strategies, supporting global efforts to combat environmental degradation and promote sustainable development.

TRACER Perspectives on Gulf-Breeze and Bay-Breeze Circulations and Coastal Convection

Abstract This study explores gulf-breeze circulations (GBCs) and bay-breeze circulations (BBCs) in Houston–Galveston, investigating their characteristics, large-scale weather influences, and impacts on surface properties, boundary layer updrafts, and convective clouds. The results are derived from a combination of datasets, including satellite observations, ground-based measurements, and reanalysis datasets, using machine learning, changepoint detection method, and Lagrangian cell tracking. We find that anticyclonic synoptic patterns during the summer months (June–September) favor GBC/BBC formation and the associated convective cloud development, representing 74% of cases. The main Tracking Aerosol Convection Interactions Experiment (TRACER) site located close to the Galveston Bay is influenced by both GBC and BBC, with nearly half of the cases showing evident BBC features. The site experiences early frontal passages ranging from 1040 to 1630 local time (LT), with 1300 LT being the most frequent. These fronts are stronger than those observed at the ancillary site which is located further inland from the Galveston Bay, including larger changes in surface temperature, moisture, and wind speed. Furthermore, these fronts trigger boundary layer updrafts, likely promoting isolated convective precipitating cores that are short lived (average convective lifetime of 63 min) and slow moving (average propagation speed of 5 m s−1), primarily within 20–40 km from the coast.

Changes in turbulent processes caused by atmospheric gravity waves from troposphere

We have determined that changes in temperature and wind speed recorded in the Earth‘s upper atmosphere above tropospheric sources (hurricanes) can be explained by the propagation of atmospheric gravity waves (AGW). We carried out modeling of the propagation of AGW with a period of 65 min and kx=10−5 m−1 using multi-layer methods in a non-homogeneous, non-isothermal atmosphere, taking into account viscosity and thermal conductivity. We obtained that disturbances in the horizontal component of the velocity are five times greater than the increase in the vertical component of the velocity, and temperature changes can reach 30 K. We should note that the disturbances of temperature and pressure as a result of AGW spreading are superimposed onto the usual view of changes of pressure and temperature with the altitude and reach the maximum amplitude in the range from 90 to 100 km. The obtained changes in the temperature of the upper atmosphere and the velocity with height as a result of the presence of AGW made it possible to estimate the values of the coefficients of turbulent viscosity and thermal conductivity in the upper atmosphere of the Earth above tropospheric energy sources. Intensification of turbulent processes was recorded in the range of altitudes from 75 to 100 km.

Detached eddy simulation of traffic-induced air pollution in an urban highway with complex surrounding morphology

Air pollution caused by traffic is a major contributor to unhealthy ambient air quality in the vicinity of urban highways and puts the health of residents and pedestrians at risk. Therefore, it is imperative to examine local pollution reduction methods. The present paper is concerned with Detached Eddy Simulation (DES) of an urban area with high concentration of air pollutants. As an example of a complex surrounding morphology, the studied domain encompasses a 2.5 km stretch of high-traffic highway, a bus terminal, and the nearby residential buildings. The numerical procedure is validated with some benchmark wind tunnel and numerical data. The locations of pollution accumulation have been identified and the effect of the ambient wind speed on the change of the maximum pollution concentration points has been investigated. Furthermore, potential pollution reduction strategies for such complex morphologies have been proposed based on the geometry change to prevent the formation of critical zones of pollution accumulation. The results show that critical zones are generally formed behind the walls of upstream buildings, adjacent to the upstream wall of the highway, and in front of the walls of downstream buildings. It was also found that changes in the ambient wind speed do not significantly alter the location of these critical zones. Furthermore, increasing the distance between adjacent buildings and the highway from 21 m to 30 m, can result in an average reduction of 76% in maximum carbon monoxide concentration values. An investigation into the impact of upstream buildings height indicates that reducing the height of certain buildings can effectively diminish pollution concentration to zero in residential areas and surrounding sidewalks. Additionally, increasing the depth of the highway and erecting 2 m solid barriers on either side of the highway are identified as two other effective techniques for reducing pollution concentration on both sides of the highway. The findings can be utilised to develop novel strategies aimed at enhancing air quality for residents and pedestrians.

Defining algal bloom phenology in Lake Erie

Elucidating the impact of global climate change on aquatic ecosystems, particularly through phenological shifts in primary producers, is critical for understanding ecological resilience. Here, we focus on the phenological shifts in chlorophyll as a proxy for algae biomass and primary production in aquatic ecosystems, specifically in Lake Erie as well as concentrations of the toxin microcystin. By tracking temporal changes in each, we identified key phenological phases important to estimate duration, magnitude, and intensity of harmful algal blooms (HABs). Determining which influential biotic and abiotic factors such as temperature, wind speed, nutrient availability, and climate change is most important, is a long-term management need for Lake Erie, which can be explored using our methodology. Our novel statistical framework employing Bayesian generalized additive mixed models described seasonal chlorophyll and particulate microcystin concentration from Lake Erie and our simple geometric method identified the start, peak, and end of algal blooms. This research enhances our understanding of the ecological effects of nutrient pollution on aquatic ecosystems and provides a repeatable method for determining phenological events without the need for user defined cutoffs which aids in the management and mitigation of HABs, safeguarding water quality in regions dependent on lakes for drinking water.

Optimization Design of Bionic Leading-edge Protuberances on Horizontal Axis Wind Turbine Blades

Abstract The complex operating environments of horizontal axis wind turbines has made the phenomenon of blade flow separation more obvious. As a passive flow control method, the bionic humpback whale leading-edge protuberances have been shown to effectively enhance the performance of the wind turbine. This study employs a multi-objective genetic algorithm and Computational Fluid Dynamics method to analyse the impact of leading-edge protuberances on the aerodynamic performance and flow characteristics of the blades. The results indicate a significant improvement in blade performance after optimization in the wind turbine’s output power. The streamwise vortices generated by the bionic protuberances not only reduce the suction surface pressure, but also suppress the spanwise flow over the blade surface. The length of the flow separation Region is reduced from 0.410 to 0.368 radius. In the process of wind speed change, the tangential forces on the bionic blade exhibit a wave-like variation. The bionic protuberance alters the pressure coefficient of the position. Compared with the original blade, the pressure coefficient of the trough sections on both sides of the bionic blade protuberance is improved. In this study, the bionic protuberance applied to the leading-edge of horizontal-axis wind turbine blades is proposed for the first time, and holds practical value for guiding practical applications.

Response of Water Temperature to Wind Speed and Air Temperature in Lake Laut Tawar, Aceh Province, Indonesia

Changes in water temperature impact the dynamics of lake ecosystems. Changing climate factors, including wind speed and air temperature, influence the water temperature of lakes. This research aims to analyze the response of water temperature to wind speed and air temperature in Lake Laut Tawar. Observations were conducted from August to September 2023, with a sampling frequency of every two weeks. The results revealed that water temperature, wind speed, and air temperature in Lake Laut Tawar fluctuated according to the presence of light, namely day and night factors. Variations in sunlight intensity lead to hourly fluctuations in air temperatures, while wind speeds vary hourly due to changes in air pressure, consequently resulting in hourly variations in water temperature as well. During daylight hours, air temperature surpasses water temperature, whereas during nighttime hours, water temperature exceeds air temperature. Heat transfer from the air to the water contributes to an increase in water temperature, while the release of heat energy from the surface water into the air leads to a decrease in water temperature. Changes in the water temperature of Lake Laut Tawar are primarily influenced by changes in wind speed and air temperature by 80 percent simultaneously. However, while air temperature showed a partial response, wind speed did not exhibit a significant response. The relationship between these variables can be expressed through a mathematical model Tw = 0.356 Ta + 0.025 W + 15.674, where Tw is water temperature (°C), Ta is air temperature (°C), and W is wind speed (km/minute). Another factor that influences the water temperature of Lake Laut Tawar is the inlet water temperature, which was not observed in this research.