Explore the vast range of titles available.

It is vital to analyze extreme wind speed in marine engineering designs. However, due to the lack of observational data, it is impossible to establish the measured long‐term wind speed series. This study simulates the annual hourly wind field of every tropical cyclone (TC) with a resolution of 5 km in the Northwest Pacific (NWP) from 1981 to 2020. On this basis, combined with the sea surface wind speed data observed by the satellites and the ships, the 40‐year annual maximum wind speed series of NWP are established. The Gumbel, three‐parameter Weibull (Weibull‐3par), two‐parameter Weibull (Weibull‐2par), generalized extreme‐value (GEV) distribution, and the two parameter estimation methods are used to estimate the extreme wind speeds with different return periods (RPs) at four typical locations in the NWP. Meanwhile, the effects of different extreme‐value distributions and different parameter estimation methods on the estimation results are discussed. Subsequently, the best distribution and parameter estimation method for each grid in the NWP are determined by the goodness‐of‐fit test, and then the spatial distributions of extreme wind speeds with different RPs along with uncertainty estimates in the entire NWP are obtained. The results show that extreme wind speeds with RPs of 5, 25, 50, and 100 years in the east of Taiwan and Philippines can reach a maximum of 43.8, 60.8, 70.4, and 81.4 m s−1, respectively. This research establishes 40‐year annual maximum wind speed series in the Northwest Pacific by three kinds of wind speed data. Furthermore, it estimates extreme wind speeds with 5‐, 25‐, 50‐, and 100‐year return periods over the Northwest Pacific by four extreme‐value distributions and two parameter estimation methods.

To investigate an appropriate wind load design for buildings considering dynamic air density changes, classical extreme value and copula theories were utilized. Using wind speed, air temperature, and air pressure data from 123 meteorological stations in Shandong Province from 2004 to 2017, a joint probability distribution model was established for extreme wind speed and air density. The basic wind pressure was calculated for various conditional return periods. The results indicated that the Gumbel and Gaussian mixture model distributions performed well in extreme wind speed and air density fitting, respectively. The joint extreme wind speed and air density distribution exhibited a distinct bimodal pattern. The higher the wind speed was, the greater the air density for the same return conditional period. For the 10-year return period, the air density surpassed the standard air density, exceeding 1.30 kg/m3. The basic wind pressures under the different conditional return periods were more than 10% greater than those calculated from standard codes. Applying the air density based on the conditional return period in engineering design could enhance structural safety regionally.

Large-scale windstorms represent an important atmospheric hazard in the Northeastern US (NE) and are associated with substantial socioeconomic losses. Regional simulations performed with the Weather Research and Forecasting (WRF) model using lateral boundary conditions from three Earth System Models (ESMs: Geophysical Fluid Dynamics Laboratory (GFDL), Hadley Centre Global Environment Model (HadGEM) and Max Planck Institute (MPI)) are used to quantify possible future changes in windstorm characteristics and/or changes in the parent cyclone types responsible for windstorms. WRF nested within MPI ESM best represents important aspects of historical windstorms and the cyclone types responsible for generating windstorms compared with a reference simulation performed with the ERA-Interim reanalysis for the historical climate. The spatial scale and frequency of the largest windstorms in each simulation defined using the greatest extent of exceedance of local 99.9th percentile wind speeds (U > U999) plus 50-year return period wind speeds (U50,RP) do not exhibit secular trends. Projections of extreme wind speeds and windstorm intensity/frequency/geolocation and dominant parent cyclone type associated with windstorms vary markedly across the simulations. Only the MPI nested simulations indicate statistically significant differences in windstorm spatial scale, frequency and intensity over the NE in the future and historical periods. This model chain, which also exhibits the highest fidelity in the historical climate, yields evidence of future increases in 99.9th percentile 10 m height wind speeds, the frequency of simultaneous U > U999 over a substantial fraction (5–25%) of the NE and the frequency of maximum wind speeds above 22.5 ms−1. These geophysical changes, coupled with a projected doubling of population, leads to a projected tripling of a socioeconomic loss index, and hence risk to human systems, from future windstorms.

The average time interval adopted by the historical wind climate observation data of different meteorological stations worldwide is not uniform, leading to a conversion difficulty of the average time interval in the extreme wind speed estimation process. Early observation equipment requires manual intervention, leading to discontinuous data and estimated deviation. This study analyzed wind speed data recorded by 11 meteorological stations in normal wind climate areas to obtain average wind speed sequences with different average time intervals. The extreme wind speeds for some return periods were calculated. Subsequently, the influence of the average time intervals on the prediction results of extreme wind speeds was analyzed. Simultaneously, the data were extracted into a 2 min average wind speed series with hourly, 6-hourly, and no-spacing observation. The estimation results of extreme wind speed for the return periods based on these three data series were calculated and compared in detail. Extreme wind speed for a given return period decreases with an increase in the average time interval in normal wind climate areas and approximates an exponential attenuation. The difference between the extreme wind speed corresponding to the continuous observation series and that corresponding to the observation data series with a spacing of 1 or 6 h meets the Rayleigh distribution. This study proposes a conversion formula for extreme wind speed with different average time intervals in normal wind climate areas. A set of correction methods for wind speed observation data with observation spacings was provided for more accurate estimation results.

In this paper, we use fluctuation analysis to study statistical correlations in wind speed time series. Each time series used here was recorded hourly over 40 years. The fluctuation functions of wind speed time series were found to scale with a universal exponent approximating to 0.7, which means that the wind speed time series are long-term correlated. In the classical method of extreme estimations, data are commonly assumed to be independent (without correlations). This assumption will lead to an overestimation if data are long-term correlated. We thus propose a simple method to improve extreme wind speed estimations based on correlation analysis. In our method, extreme wind speeds are obtained by simply scaling the mean return period in the classical method. The scaling ratio is an analytic function of the scaling exponent in the fluctuation analysis.

Sea surface wind plays an essential role in the simulating and predicting ocean phenomena. However, it is difficult to obtain accurate data with uniform spatiotemporal scale. A high-resolution (10 km) sea surface wind hindcast around the Korean Peninsula (KP) is presented using the weather research and forecasting model focusing on wind speed. The hindcast data for 39 years (1979–2017) are obtained by performing a three-dimensional variational analysis data assimilation, using ERA-Interim as initial and boundary conditions. To evaluate the added value of the hindcasts, the ASCAT-L2 satellite-based gridded data (DASCAT) is employed and regarded as “True” during 2008–2017. Hindcast and DASCAT data are verified using buoy observations from 1997–2017. The added value of the hindcast compared to ERA-Interim is evaluated using a modified Brier skill score method and analyzed for seasonality and wind intensity. Hindcast data primarily adds value to the coastal areas of the KP, particularly over the Yellow Sea in the summer, the East Sea in the winter, and the Korean Strait in all seasons. In case of strong winds (10–25 m·s−1), the hindcast performed better in the East Sea area. The estimation of extreme wind speeds is performed based on the added value and 50-year and 100-year return periods are estimated using a Weibull distribution. The results of this study can provide a reference dataset for climate perspective storm surge and wave simulation studies.

The marine surface wind field (10 m) over the Greek seas is analyzed in this study using The RegCM. The model’s spatial resolution is dynamically downscaled to 10 km × 10 km, in order to simulate more efficiently the complex coastlines and the numerous islands of Greece. Wind data for the 1980–2000 and 2080–2100 periods are produced and evaluated against real observational data from 15 island and coastal meteorological stations in order to assess the model’s ability to reproduce the main characteristics of the surface wind fields. RegCM model shows a higher simulating skill to project seasonal wind speeds and direction during summer and the lowest simulating skill in the cold period of the year. Extreme wind speed thresholds were estimated using percentiles indices and three Peak Over Threshold (POT) techniques. The mean threshold values of the three POT methods are used to examine the inter-annual distribution of extreme winds in the study region. The highest thresholds were observed in three poles; the northeast, the southeast, and the southwest of Aegean Sea. Future changes in extreme speeds show a general increase in the Aegean Sea, while lower thresholds are expected in the Ionian Sea. Return levels for periods of 20, 50, 100, and 200 years are estimated.

The application of extreme-value analysis to long-duration (30 year) global altimeter and radiometer datasets is considered. In contrast to previous extreme-value analyses of satellite data, the dataset is sufficiently long to enable a peaks over threshold analysis to be undertaken. When applied to altimeter data for wind speed and significant wave height, this analysis produces values consistent with buoy validation data and previous numerical model reanalysis datasets. The spatial distributions produced are also consistent with the model reanalysis data. However, the altimeter data shows much greater finescale structure for wind speed, which is consistent with known tropical cyclone activity. The greater data density provided by radiometer measurements offers the potential to address altimeter undersampling. However, issues associated with the radiometer’s inability to measure wind speed in heavy rain events appears to create an unacceptable “fair weather” bias at extreme wind speeds. This renders the radiometer data of wind speed largely unusable for the investigation of wind speed extremes. The study also clearly demonstrates the limitations of the initial distribution method for extreme-value analysis, which is heavily biased by mean conditions.

Recent studies point out the importance of northerly winds and waves in the Arabian Sea, especially those due to shamal and makran events in addition to the northeast monsoon system. We have analyzed climatology and trends of northerly maximum wind speed and significant wave height (Hs) in the Arabian Sea and the connected marginal seas, Arabian/Persian Gulf and the Red Sea, during non-monsoon season derived from 40 years of ERA5 wind and wave data, and estimated monthly, annual and decadal extreme climate and their trends. The study brings out an increasing trend in the northerly maximum wind speed (0.8–1.2 cm/s/year) and Hs (0.42–0.88 cm/year) in the southern and central Arabian Sea, which is consistent with the global trend in extreme wind speed and Hs. The northern Arabian Sea including the Sea of Oman exhibits significant decreasing trend in northerly maximum wind speed (− 1.4 cm/s/year) and Hs (− 0.67 cm/year), while the Gulf and the Red Sea exhibit sectorial contrasting trend, indicating the dominance of localized effects in modifying the regional climate. Distinct features identified in the climate and trends of northerly winds and waves are further discussed.

Extreme wind-waves will impact coastal regions along the east and west coast of India and countries bordering the Indian Ocean rim having implications on coastal flooding and shoreline changes. Detailed investigation was carried out in this study on future extreme wave projections and its relationship with wind speed, sea level pressure (SLP), and sea-surface temperature (SST) for the mid- and end-century under RCP4.5 and RCP8.5 emission scenarios. Projections collectively highlight on maximum extreme wind and wave activity for south Indian Ocean (SIO) during June-July–August (JJA) and September–October–November (SON) seasons. Results indicate that end-century projections show extreme wind activity over central Bay of Bengal (BoB) signifying the likelihood for more extreme events over this region. Mid-century projections show that extreme significant wave height (SWH) intensifies by 1 m for the SIO during JJA season. Also, an increase of 0.4 m in maximum SWH (H max ) is projected for regions in the NIO, northwest AS, northeast BoB, and South China Sea (SCS). Projections for eastern SIO show strengthening of extreme wind speed by 3.0 m/s by the end-century under RCP8.5. The projected changes in H max is maximum for SCS region evident in RCP4.5, whereas the maximum rise is about 23% under RCP8.5 in the end-century. Projected decline in winds and waves over western tropical IO (TIO) is consistent with weak SLP variations and warm ocean temperatures over that region. Significant increment in SST is projected over entire AS during DJF and JJA seasons ranging between 1.5 and 2.0 °C, which is 0.5 °C greater than BoB. Projections for the Gulf of Oman and Persian Gulf show higher warming rates exceeding 2 °C under RCP8.5 by the end-century. Warming in these areas further reduces SLP gradient leading to enhanced weakening of low-level atmospheric circulation and declining H max by the end-century. A westward alignment in these areas with maximum projected changes in SST is noticed in most seasons. Detailed analysis of high wave activity regions shows widening of generalized extreme value fitted probability density function over the BoB, SCS, and SIO indicating more extreme wave activity in the future.