Prediction of Extreme Air Temperature and Wind Speed Along the Northern Sea Route (NSR) with Application for the Safety of Polar Vessels

Comparing annual extreme winds in Iran predicted by numerical weather forecasting and Gram-Charlier statistical model with meteorological observation data

Wind turbines need to convert the kinetic energy of normal wind speed into electric power but the structure needs to withstand the wind loads exerted by the extreme wind speed on the mast and blades. Also high rise buildings around the world are designed for a wind speed whose probability of exceedence is 2% (Gomes and Vickery (1977), Milne (1992), Kristensen et al., (2000), Sacre (2002) and Miller (2003)). Recently, the State of Kuwait has approved construction of multistory buildings up to about 70 floors. For safe and optimal design of these high rise buildings, extreme wind speeds for different return periods and from different directions are essential. Wind data, measured at 10 m above the ground level at different locations can be used for the prediction of extreme wind speeds at that elevation. These unexpected high wind speed from different directions dictates the design of many structures like towers, high rise buildings, power transmission lines, devises for controlling the sand movements in desert areas, ship anchoring systems in ports and harbors, wind power plants on land and sea, chimneys etc. Also normal and extreme wind data is required for ground control and operation of aircrafts, planning for mitigating measures of life and properties during extreme winds, movements of dust etc. One of the factors for fixing the insurance premium for buildings, aircrafts, ships and tall towers by insurance companies is based on the safety and stability of these structures for extreme winds. The extreme wind speed, whose probability of occurrence is very rare, is also responsible for generating high waves in the seas, which dictates the design, operation and maintenance of all types of marine structures. How does one know the maximum wind speed which is expected at a specified location on the earth for a return period of 50 years or 100 years? This is a billion dollar question. The down to earth answer is Install anemometers and measure the wind speed for 50 years or 100 years. One cannot wait for 50 to 100 years to obtain the maximum wind speed for that such a large period. The procedure is to use the available and reliable past data and apply the extreme value statistical models to predict the expected wind speeds for certain return periods (Gumbel (1958), Miller (2003)). Most of the countries around the world have the code for design wind speed and wind zoning systems. As on today, Kuwait does not have a code for design wind speed. Wind speed and its directions have been measured in many places in Kuwait for certain projects (for example, Abdal et al., (1986), Ayyash and Al-Tukhaim (1986)). It is also reported that the hinterland areas of Kuwait has wind power potential of about 250 W/m2 which is appreciable (Ayyash and Al-

Wind speed forecasting represents a significant challenge in the global transition to sustainable energy systems. Wind energy, characterised by zero greenhouse gas emissions and relatively low cost, is a renewable resource that depends heavily on meteorological conditions, which are inherently variable and unpredictable. This variability and intermittency present substantial obstacles to ensuring a consistent power supply, underscoring the importance of accurate wind speed prediction as a critical area of research. Among the various approaches explored to address this challenge, machine learning (ML) has emerged as a prominent solution. ML includes methodologies such as regression (predicting continuous values of wind speed) and nominal classification (predicting discrete categories of wind speed). In nominal classification, wind speeds are discretised into classes to provide essential information for wind farm operations. In this study, wind speeds are categorised into four classes: 1) very low speeds, 2) moderate speeds, 3) high speeds, and 4) extreme wind speeds. While both very low and extreme speeds result in no power generation, this work focuses on the extreme wind speed class, as these events often necessitate turbine shutdowns to prevent structural damage.To address the challenges of wind speed forecasting with a focus on extreme wind events, we propose the use of ordinal classification, a ML paradigm specifically designed for tasks where output categories exhibit a natural order, as is the case in this work. This study evaluates hourly wind speed predictions for a wind farm in Spain, using data collected over more than 15 years. Additionally, input features include meteorological variables such as temperature, wind components (u and v), and sea level pressure, among others. Forecasts are generated for three time horizons (1h, 4h, and 8h) to provide sufficient lead time for mitigating risks associated with extreme wind conditions. Two ordinal classification models based on artificial neural networks (ANNs) are analysed: 1) an ANN coupled with the cumulative link model (CLM), and 2) an ANN using a soft labelling optimisation technique. Additionally, other competitive ordinal and nominal classification methods are included for comparative analysis.The results demonstrate that the proposed models outperform a number of nominal and ordinal classification methods. The ANN coupled with CLM delivers superior overall performance across all four classes, while the ANN employing the soft labelling approach achieves higher accuracy in predicting extreme wind speed events. These findings underscore the potential of ordinal classification to enhance wind speed forecasting, contributing to more effective wind farm management and the broader integration of renewable energy sources.

Kuwait is planning to install wind mills in its territorial waters in the Arabian Gulf. For optimal design of offshore wind mills and marine infrastructures, extreme wind and gust speed for different return periods, say 25 yr, 50 yr and 100 yr is an important input. Without such information, the design will be either very conservative and expensive or unsafe. A scientific estimate of the extreme wind and gust speed is hence obligatory. Recently Kuwait has approved a multi-billion dollar plan to develop Boubyan Island into a commercial seaport to serve as the main gateway for Kuwait, Iraq and the surrounding region. Kuwaiti government is also planning to develop Failaka Island as a tourist hub in this region. Knowledge of extreme wind and gust will be required for appropriate design of all marine infrastructures in these islands. The present work is carried out for nine marine locations in the territorial waters of Kuwait. Extreme 10 minute average wind and Gust speed from different directions and for different return periods were predicted for these nine locations. Measured wind speed by the climatological office of Directorate General of Civil Aviation is used for this analysis. The extreme wind speeds are predicted based on Gumbel distribution. Within these 9 locations, the 10 min. average 100 year wind speed in the marine area of Kuwait from different directions is in the range of 25.5 to 33.0 m/s. Without considering the effect of direction, it is in the range of 24.5 to 33.5 m/s. Similarly, the extreme gust speed for 100 year return period from different directions is in the range of 38.5 to 77.5 m/s. Without considering the effect of direction, it is in the range of 38.0 to 92.5 m/s.

The movement of chemicals away from the intended target under the effect of weather factors is a serious environmental problem. In order to minimize drift hazard, operators should be provided with a tool that would help them to decide whether or not to spray depending upon prevailing weather conditions. For this purpose, a simple model for the prediction of air temperature, wind speed, and vapor pressure at different heights above the crop canopy was developed. This model is mainly based on the incoming short-wave radiation, soil and crop types, crop height, relative humidity, wind speed and air temperature measured in the field at heights of 4 and 0.8 m, and 1.5 and 0.8 m, respectively, the cloud coefficient, and the fraction of sky covered with cloud. Although many approximations were made throughout the development of the model and no stability corrections were taken into consideration for simplification purposes, results show a good agreement between measured and predicted values. This prediction model can then be a valuable engineering tool that could be used, not only for spraying purposes, but also for many other weather-dependent agricultural operations.

To assess the influence of climate change on the estimates of extreme wind speeds induced by typhoons, the present study employs a Monte Carlo simulation approach to forecast the extreme wind speeds in the proximity of Hong Kong when the sea surface temperatures rise as projected by various climate change models according to the Representative Concentration Pathway (RCP) 8.5. In addition, the present study shows the first attempt to quantitatively assess the uncertainty buried in the prediction of the extreme wind speed in association with typhoons taking the rise in sea surface temperatures, and therefore climate change, into consideration. It is found that climate change leads, with high confidence, to the increase in extreme wind speeds brought about by typhoons. From the numerical simulation, it is found that the mean wind speeds associated with typhoons impacting Hong Kong rise from 10.8 m/s (1961–1990) to 12.4 m/s (2051–2080), and the extreme wind speed is 47.5 m/s during 2051–2080 under the RCP 8.5 climate scenario, which is 21.2% higher than that corresponding to the period of 1961–1990. As for the quantification of uncertainties in the extreme wind estimates, the inter-quartile ranges for the sea surface temperatures projected by various climate models in July and October are 9.5% and 8.2% in 2050, respectively, and go up to 9.6% and 9.9% in 2080. The extreme wind speeds with 50 years return period show inter-quartile ranges of 14.2% in 2050, and the value decreases to 12.8% in 2080.

• The hourly wind speed prediction is an important problem. • A feed-forward (FF) multi-layer perceptron (MLP) artificial neural network (ANN) is used for prediction. • The FF MLP ANN is optimized by 9 state-of-art metaheuristic algorithms. • In the experiments, 38 years of hourly wind data belonging to 5 cities were used. • Grey Wolf Optimizer produced best results in terms of mean square error (MSE). The growing population has tremendously increased the daily energy demand all around the world. India is the second-most crowded nation in the world with approximately 1.4 billion people. New and renewable energy is on the agenda of India and in 2021 India possesses the fourth-largest installed capacity of wind power. Accurate prediction of wind speed is vital in wind farm design and operation. In this work, an hourly wind speed prediction with an artificial neural network optimized by a metaheuristics approach is conducted. A feed-forward (FF) multi-layer perceptron (MLP) artificial neural network (ANN) is used for the prediction of the hourly wind speed. In this study, 38 years of hourly wind data belonging to 5 cities (Ambur, Hosur, Kumbakonam, Nagapattinam, and Pudukottai) were used. These cities have different specific properties such as latitude, longitude, and altitude. The FF MLP ANN is optimized by 9 state-of-art metaheuristic algorithms. In this work, Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), Biogeography Based Optimization (BBO), Evolutionary Strategy (ES), Genetic Algorithm (GA), Grey-Wolf-Optimizer (GWO), Population-Based Incremental Learning (PBIL), Particle Swarm Optimization (PSO), Tree-Seed Algorithm (TSA) have been used to optimize the weights of the ANN. GWO outperforms other metaheuristic algorithms in the prediction of wind speed with a FF MLP ANN model, with a success percentage rate of approximately 3% to 10,000%.

The effect of anthropogenic climate change on extreme near-surface wind speeds is uncertain. Observed trends are weak and difficult to disentangle from internal variability, and model projections disagree on the sign and magnitude of trends. Standard coarse-resolution climate models represent the fine structures of relevant physical phenomena such as extratropical cyclones (ETCs), upper-level jet streaks, surface energy fluxes, and land surface variability less skillfully than their high-resolution counterparts. Here, we use simulations with the NCAR Community Earth System Model with both uniform (110 km) resolution and the variable-resolution configuration (VR-CESM-SONT, from 110 to 7 km) to study the effect of refined spatial resolution on projections of extreme strong and weak wind speeds in the Great Lakes region under end-of-century RCP8.5 forcing. The variable-resolution configuration projects strengthening of strong-wind events in the refined region with the opposite occurring in the uniform-resolution simulation. The two configurations provide consistent changes to synoptic-scale circulations associated with high-wind events. However, only the variable-resolution configuration projects weaker static stability, enhanced turbulent vertical mixing, and consequentially enhanced surface wind speeds because boundary layer dynamics are better captured in the refined region. Both models project increased frequency of extreme weak winds, though only VR-CESM-SONT resolves the cold-season inversions and summertime high temperatures associated with stagnant wind events. The identifiable mechanism of the changes to strong winds in VR-CESM-SONT provides confidence in its projections and demonstrates the value of enhanced spatial resolution for the study of extreme winds under climate change. Significance Statement In this study, we compare climate change projections of high and low extreme wind speeds in the Great Lakes region between a standard coarse-resolution simulation and a high-resolution simulation performed using the same climate model. The fine-resolution simulation projects strengthening high wind speeds, opposite to the coarse-resolution simulation. Both project increasing frequency of extreme weak winds, but the human-health-related impacts of stagnant winds are only captured at fine resolution. The changes in the coarse-resolution simulation are explained by changes to large-scale circulation, while the fine-resolution changes are linked to local processes the coarse model does not resolve. This helps explain the diverging projections of strong winds and gives greater credibility to the fine-resolution simulation.

Extreme wind speeds, gusts, and wind wave heights associated with midlatitude cyclones pose a hazard to shipping lanes and offshore infrastructure operating in the North Atlantic Ocean seas surrounding the British Isles. Several studies have assessed the variability of wind and waves in this region using reanalyses, but few have used surface observations of extreme wind speeds and wave heights. Here, we use a network of marine surface stations to derive the 2012–2020 climatology of daily maximum wind speed events. An algorithm is used to attribute the extreme wind events, characterized as exceeding the 20 and 25 m·s−1 thresholds, to the cyclone warm conveyor belt (WCB), and early (CCBa) and returning (CCBb) cold conveyor belt jets; cyclones are matched with up to 90% of extreme wind events. The CCBb is most frequently associated with the strong wind speeds, accounting for 46 and 59% of the events exceeding the two thresholds, respectively. The CCBb also leads to the largest number of compound wind and wave hazard events (37 out of 87). Although the WCB is associated with the second largest number of extreme wind events, the CCBa accounts for the second largest number of compound extreme wind and wave events (24). The ERA5 reanalysis underestimates the observed extreme wind speeds, and associated gusts and wind‐wave heights, during extreme wind events for all the conveyor belt jets. The wind speeds and associated gusts are most underestimated, by median values of 4.5 and 5.5 m·s−1, respectively, and similar percentage error (), when associated with the CCBb; however, the wind‐wave heights are most underestimated, by a median of 3.4 m, when associated with the CCBa. Hence, while the marine CCBb jet, found in mature cyclones, is both most hazardous and underestimated in the ERA5 near the British Isles, the CCBa jet can be nearly as hazardous when considering compound wind‐wave events.

<strong class="journal-contentHeaderColor">Abstract.</strong> The main goals of a wind resource assessment (WRA) at a given site are to estimate the wind speed and annual energy production (AEP) of the planned wind turbines. Several steps are involved in going from initial wind speed estimations of specific locations to a comprehensive full-scale AEP assessment. These steps differ significantly between the chosen tool and the individuals performing the examination. The goal of this work is to compare different WRA simulation tools at the Perdig&atilde;o site in Portugal, for which a large amount of wind measurement data is available, in terms of both accuracy and costs. Results from nine different simulations from five different modellers were obtained via the "IEA Wind Task 31 Comparison metrics simulation challenge for wind resource assessment in complex terrain", consisting of a range of linear models, Reynolds-Averaged Navier-Stokes (RANS) computational fluid dynamics models and Large Eddy Simulations (LES). The wind speed and AEP prediction errors for three different met mast positions across the site were investigated and further translated into relative &ldquo;skill&rdquo; and &ldquo;cost&rdquo; scores, using a method previously developed by the authors. This allowed the most optimal simulation tool in terms of accuracy and cost to be chosen for this site. It was found that the RANS simulations achieved very high prediction accuracy at relatively low costs for both wind speed and AEP estimations. The LES simulations achieved great wind speed prediction for certain conditions, but at a much higher cost, which in turn also reduced the number of possible simulations, leading to a decrease in AEP prediction accuracy. For some of the simulations, the forest canopy was explicitly modelled, which was proven to be beneficial for wind speed predictions at lower heights above the ground, but lead to under-estimations of wind speeds at upper heights, decreasing the AEP prediction accuracy. Lastly, low correlation qualities between wind speed and AEP prediction error were found for each position, showing that accurate wind modelling is not necessarily the only important variable in the WRA process, and that all the steps must be considered.

<strong class="journal-contentHeaderColor">Abstract.</strong> The main goals of a wind resource assessment (WRA) at a given site are to estimate the wind speed and annual energy production (AEP) of the planned wind turbines. Several steps are involved in going from initial wind speed estimations of specific locations to a comprehensive full-scale AEP assessment. These steps differ significantly between the chosen tool and the individuals performing the examination. The goal of this work is to compare different WRA simulation tools at the Perdig&atilde;o site in Portugal, for which a large amount of wind measurement data is available, in terms of both accuracy and costs. Results from nine different simulations from five different modellers were obtained via the "IEA Wind Task 31 Comparison metrics simulation challenge for wind resource assessment in complex terrain", consisting of a range of linear models, Reynolds-Averaged Navier-Stokes (RANS) computational fluid dynamics models and Large Eddy Simulations (LES). The wind speed and AEP prediction errors for three different met mast positions across the site were investigated and further translated into relative &ldquo;skill&rdquo; and &ldquo;cost&rdquo; scores, using a method previously developed by the authors. This allowed the most optimal simulation tool in terms of accuracy and cost to be chosen for this site. It was found that the RANS simulations achieved very high prediction accuracy at relatively low costs for both wind speed and AEP estimations. The LES simulations achieved great wind speed prediction for certain conditions, but at a much higher cost, which in turn also reduced the number of possible simulations, leading to a decrease in AEP prediction accuracy. For some of the simulations, the forest canopy was explicitly modelled, which was proven to be beneficial for wind speed predictions at lower heights above the ground, but lead to under-estimations of wind speeds at upper heights, decreasing the AEP prediction accuracy. Lastly, low correlation qualities between wind speed and AEP prediction error were found for each position, showing that accurate wind modelling is not necessarily the only important variable in the WRA process, and that all the steps must be considered.

Interactions between extreme climate and urban morphology: Investigating the evolution of extreme wind speeds from mesoscale to microscale

Subseasonal forecasts of 100-m wind speed and surface temperature, if skillful, can be beneficial to the energy sector as they can be used to plan asset availability and maintenance, assess risks of extreme events, and optimally trade power on the markets. In this study, we evaluate the skill of the European Centre for Medium-Range Weather Forecasts’ subseasonal predictions of 100-m wind speed and 2-m temperature. To the authors’ knowledge, this assessment is the first for the 100-m wind speed, which is an essential variable of practical importance to the energy sector. The assessment is carried out on both forecasts and reforecasts over European domain gridpoint wise and also by considering several spatially averaged domains, using several metrics to assess different attributes of forecast quality. We propose a novel way of synthesizing the continuous ranked probability skill score. The results show that the skill of the forecasts and reforecasts depends on the choice of the climate variable, the period of the year, and the geographical domain. Indeed, the predictions of temperature are better than those of wind speed, with enhanced skill found for both variables in the winter relative to other seasons. The results also indicate significant differences between the skill of forecasts and reforecasts, arising mainly due to the differing ensemble sizes. Overall, depending on the choice of the geographical domain and the forecast attribute, the results show skillful predictions beyond 2 weeks, and in certain cases, up to 6 weeks for both variables, thereby encouraging their implementation in operational decision-making.

ABSTRACTThe extreme wind speed at an offshore location was predicted using Monte Carlo simulation (MCS) and measure‐correlate‐predict (MCP) method. The Gumbel distribution could successfully express the annual maximum wind speed of extratropical cyclone. On the other hand, the estimated extreme wind speed of tropical cyclones by analytical probability distribution shows larger uncertainty. In the mixed climate like Japan, the extreme wind speed estimated from the combined probability distribution obtained by MCP and MCS methods agrees well with the observed data as compared with the combined probability distribution obtained by the MCP method only. The uncertainty of extreme wind speed due to limited observation period of wind speed and pressure was also evaluated by the Gumbel theory and Monte Carlo simulation. As a result, it was found that the uncertainty of 50 year recurrence wind speed obtained by MCS method is considerably smaller than that obtained by MCP method in the mixed climate. Copyright © 2014 John Wiley &amp; Sons, Ltd.

Long-term measured wind data are absolutely necessary to estimate extreme offshore wind speed. However, it is almost impossible to collect offshore wind measured data. Therefore, typhoon simulation is widely used to analyze offshore wind conditions. In this paper, 74 typhoons which affected the western sea of Korea during 1978-2012(35 years) were simulated using Holland(1980) model. The results showed that 49.02 m/s maximum wind speed affected by BOLAVEN(1215) at 100 m heights of HeMOSU-1 (Herald of Meteorological and Oceanographic Special Unit 1) was the biggest wind speed for 35 years. Meanwhile, estimated wind speeds were compared with observed data for MUIFA, BOLAVEN, SANBA at HeMOSU-1. And to estimate extreme wind speed having return periods, extreme analysis was conducted by assuming 35 annual maximum wind speed at four site(HeMOSU-1, Gunsan, Mokpo and Jeju) in western sea of the Korean Peninsular to be Gumbel distribution. As a results, extreme wind speed having 50-year return period was 50 m/s, that of 100-year was 54.92 m/s at 100 m heights, respectively. The maximum wind speed by BOLAVEN could be considered as a extreme winds having 50-year return period.