In offshore engineering design, it is considerably significant to have an adequately accurate estimation of marine environmental parameters, in particular, the extreme wind speed of tropical cyclone （TC） with differ...In offshore engineering design, it is considerably significant to have an adequately accurate estimation of marine environmental parameters, in particular, the extreme wind speed of tropical cyclone （TC） with different return periods to guarantee the safety in projected operating life period. Based on the 71-year （1945-2015） TC data in the Northwest Pacific （NWP） by the Joint Typhoon Warning Center （JTWC） of US, a notable growth of the TC intensity is observed in the context of climate change. The fact implies that the traditional stationary model might be incapable of predicting parameters in the extreme events. Therefore, a non-stationary model is proposed in this study to estimate extreme wind speed in the South China Sea （SCS） and NWP. We find that the extreme wind speeds of different return periods exhibit an evident enhancement trend, for instance, the extreme wind speeds with different return periods by non- stationary model are 4.1%-4.4% higher than stationary ones in SCS. Also, the spatial distribution of extreme wind speed in NWP has been examined with the same methodology by dividing the west sea areas of the NWP 0°-45°N, 105°E-130°E into 45 subareas of 5° × 5°, where oil and gas resources are abundant. Similarly, remarkable spacial in-homogeneity in the extreme wind speed is seen in this area： the extreme wind speed with 50-year return period in the subarea （15°N-20°N, 115°E-120°E） of Zhongsha and Dongsha Islands is 73.8 m/s, while that in the subarea of Yellow Sea （30°N-35°N, 120°E-125°E） is only 47.1 m/s. As a result, the present study demonstrates that non-stationary and in-homogeneous effects should be taken into consideration in the estimation of extreme wind speed.展开更多
Multiyear observed time series of wind speed for selected points of the Arctic region (data of station network from the Kola Peninsula to the Chukotka Peninsula) are used to highlight the important peculiarities of wi...Multiyear observed time series of wind speed for selected points of the Arctic region (data of station network from the Kola Peninsula to the Chukotka Peninsula) are used to highlight the important peculiarities of wind speed extreme statistics. How largest extremes could be simulated by climate model (the INM-CM4 model data from the Historical experiment of the CMIP5) is also discussed. Extreme value analysis yielded that a volume of observed samples of wind speeds are strictly divided into two sets of variables. Statistical properties of one population are sharply different from another. Because the common statistical conditions are the sign of identity of extreme events we therefore hypothesize that two groups of extreme wind events adhere to different circulation processes. A very important message is that the procedure of selection can be realized easily based on analysis of the cumulative distribution function. The authors estimate the properties of the modelled extremes and conclude that they consist of only the samples, adhering to one group. This evidence provides a clue that atmospheric model with a coarse spatial resolution does not simulate special mechanism responsible for appearance of largest wind speed extremes. Therefore, the tasks where extreme wind is needed cannot be explicitly solved using the output of climate model. The finding that global models are unable to capture the wind extremes is already well known, but information that they are members of group with the specific statistical conditions provides new knowledge. Generally, the implemented analytical approach allows us to detect that the extreme wind speed events adhere to different statistical models. Events located above the threshold value are much more pronounced than representatives of another group (located below the threshold value) predicted by the extrapolation of law distributions in their tail. The same situation is found in different areas of science where the data referring to the same nomenclature are adhering to different statistical models. This result motivates our interest on our ability to detect, analyze, and understand such different extremes.展开更多
Typhoons are one of the most serious natural disasters that occur annually on China’s southeast coast.A technique for analyzing the typhoon wind hazard was developed based on the empirical track model,and used to gen...Typhoons are one of the most serious natural disasters that occur annually on China’s southeast coast.A technique for analyzing the typhoon wind hazard was developed based on the empirical track model,and used to generate 1000-year virtual typhoons for Northwest Pacific basin.The influences of typhoon decay model,track model,and the extreme value distribution on the predicted extreme wind speed were investigated.We found that different typhoon decay models have least influence on the predicted extreme wind speed.Over most of the southeast coast of China,the predicted wind speed by the non-simplified empirical track model is larger than that from the simplified tracking model.The extreme wind speed predicted by different extreme value distribution is quite different.Four super typhoons Meranti(2016),Hato(2017),Mangkhut(2018)and Lekima(2019)were selected and the return periods of typhoon wind speeds along the China southeast coast were estimated in order to assess the typhoon wind hazard.展开更多
The purpose of the present study is to investigate the extreme values of the ice drift speed,which are also considered in the light of the magnitude of the simultaneous wind speed.The relationship between wind speed a...The purpose of the present study is to investigate the extreme values of the ice drift speed,which are also considered in the light of the magnitude of the simultaneous wind speed.The relationship between wind speed and ice drift speed is studied.The long-term ice drift data is collected by using local subsurface measurements based on acoustic Doppler current profilers(ADCP)in the Beaufort Sea during the period of 2006-2017.Upward-looking sonars(ULS)are deployed in order to observe the ice thickness as well as to identify events that correspond to open water conditions.The relationship between the ice drift speed and the wind speed is also investigated.It is found that the magnitude of the average ice drift speed is approximately 2.5%of the wind speed during the winter season.Estimation of the extreme values of the ice drift speed is studied by application of the average conditional exceedance rate(ACER)method.It is found that the extreme ice drift speed during the ice melt season(i.e.the summer season)is approximately20%-30%higher than that during the ice growth season(i.e.the winter season).The extreme ice drift speed can be effectively estimated based on the 2.5%wind speed.Moreover,the extreme ice drift speed can be obtained based on the extreme values of 2.5%of the wind speed based on multiplying with an amplification factor which varies in the range from 1.7 to 2.0 during the growth season,corresponding to increasing return periods of 10,25,50 and 100years.展开更多
The marine accidents are among the main components of the Zanzibar Disaster Management Policy (2011) and the Zanzibar Blue Economy Policy (2020). These policies aimed to institute legal frame works and procedures for ...The marine accidents are among the main components of the Zanzibar Disaster Management Policy (2011) and the Zanzibar Blue Economy Policy (2020). These policies aimed to institute legal frame works and procedures for reducing both the frequency of marine accidents and their associated fatalities. These fatalities include deaths, permanent disabilities and loss of properties which may result into increased poverty levels as per the sustainable development goal one (SDG1) which stipulates on ending the poverty in all its forms everywhere. Thus, in the way to support these Government efforts, the influence of climate and weather on marine accidents along Zanzibar and Pemba Channels was investigated. The study used the 10 years (2013-2022) records of daily rainfall and hourly wind speed acquired from Tanzania Meteorological Authority (TMA) (for the observation stations of Zanzibar, Pemba, Dares Salaam and Tanga), and the significant wave heights data, which was freely downloaded from Globally Forecasting System (GFS-World model of 13 km resolution). The marine accident records were collected from TASAC and Zanzibar Maritime Authority (ZMA), and the anecdotal information was collected from heads of quay and boat captains in different areas of Zanzibar. The Mann Kendal test, was used to determine the slopes and trends direction of used weather parameters, while the Pearson correlations analysis and t-tests were used to understand the significance of the underlying relationship between the weather and marine accidents. The paired t-test was used to evaluate the extent to which weather parameters affect the marine accidents. Results revealed that the variability of extreme weather events (rainfall, ocean waves and wind speed) was seen to be among the key factors for most of the recorded marine accidents. For instance, in Pemba high rainfall showed an increasing trend of extreme rainfall events, while Zanzibar has shown a decreasing trend of these events. As for extreme wind events, results show that Dar es Salaam and Tanga had an increasing trend, while Zanzibar and Pemba had shown a decreasing trend. As for the monthly variability of frequencies of extreme rainfall events, March to May (MAM) season was shown to have the highest frequencies over all stations with the peaks at Zanzibar and Pemba. On the other hand, high frequency of extreme wind speed was observed from May to September with peaks in June to July, and the highest strength was observed during 09:00 to 15:00 GMT. Moreover, results revealed an increasing trend of marine accidents caused by bad weather except during November. Also, results showed that bad weather conditions contributed to 48 (32%) of all 150 recorded accidents. Further results revealed significant correlation between the extreme wind and marine accidents, with the highest strong correlation of r = 0.71 (at p ≤ 0.007) and r = 0.75 (at p ≤ 0.009) at Tanga and Pemba, indicating the occurrence of more marine accidents at the Pemba channel. Indeed, strong correlation of r = 0.6 between extreme rainfall events and marine accidents was shown in Pemba, while the correlations between extremely significant wave heights and marine accidents were r = 0.41 (at p ≤ 0.006) and r = 0.34 (p ≤ 0.0006) for Pemba and Zanzibar Channel, respectively. In conclusion, the study has shown high influence between marine accidents and bad weather events with more impacts in Pemba and Zanzibar. Thus, the study calls for more work to be undertaken to raise the awareness on marine accidents as a way to alleviate the poverty and enhance the sustainable blue economy.展开更多
An increasing number of marine structures have been built for coastal protection and marine development in recent years,and wind,which is crucial to marine structures,should be analyzed.Therefore,typhoon frequency,win...An increasing number of marine structures have been built for coastal protection and marine development in recent years,and wind,which is crucial to marine structures,should be analyzed.Therefore,typhoon frequency,wind climate,wind energy assess-ment,and extreme wind speed in the South China Sea(SCS)are investigated in detail in this study.The data are obtained from the China Meteorological Administration,the European Centre for Medium-range Weather Forecasts,and the National Centers for Envi-ronmental Prediction.The offshore wind energy potential is analyzed at five sites near the coast.The spatial and monthly frequencies of tropical cyclones for different intensity categories are analyzed.The extreme wind speed is fitted by five distribution models,and the generalized extreme value(GEV)distribution is selected as the most suitable function according to the goodness of fit.The spa-tial distributions of extreme wind speeds in the SCS are plotted on the basis of the GEV distribution and ERA5 data sets.The influ-ences of the distribution models and data sets on the calculated results are discussed.Moreover,the monthly extreme wind speed and comparison with the results of previous studies are analyzed.This study provides a reference for the design of wind turbines.展开更多
Statistical properties of winds near the Taichung Harbour are investigated. The 26 years'incomplete data of wind speeds, measured on an hourly basis, are used as reference. The possibility of imputation using simu...Statistical properties of winds near the Taichung Harbour are investigated. The 26 years'incomplete data of wind speeds, measured on an hourly basis, are used as reference. The possibility of imputation using simulated results of the Auto-Regressive (AR), Moving-Average (MA), and/ or Auto-Regressive and Moving-Average (ARMA) models is studied. Predictions of the 25-year extreme wind speeds based upon the augmented data are compared with the original series. Based upon the results, predictions of the 50- and 100-year extreme wind speeds are then made.展开更多
The escalation of compound extreme events has resulted in noteworthy economic and property losses.Recognizing the intricate interconnections among these events has become imperative.To tackle this challenge,we have fo...The escalation of compound extreme events has resulted in noteworthy economic and property losses.Recognizing the intricate interconnections among these events has become imperative.To tackle this challenge,we have formulated a comprehensive framework for the systematic analysis of their dependencies.This framework consists of three steps.(1)Define extreme events using Mahalanobis distance thresholds.(2)Represent dependencies among multiple extreme events through a point process-based method.(3)Verify dependencies with residual tail coefficients,determining thefinal dependency structure.Applying this framework to assess the extreme dependence of precipitation on wind speed and temperature in China,revealed four distinct dependency structures.In northern,Jianghuai,and southern China,precipitation heavily relies on wind speed,while tempera-tures maintain relative independence.In northeastern and northwestern China,precipitation exhibits relative independence,yet a notable dependence exists between temperatures and wind speed.In southwestern China,precipitation strongly depends on temperature,while wind speed remains relatively indepen-dent.The Qinghai–Tibet Plateau region displays a significant dependence relationship among precipitation,wind speed,and temperature,with weaker dependence between extreme wind speed and temperature.This framework is instrumental for analyzing dependencies among extreme values in compound events.展开更多
基金financially supported by the Ministry of Science and Technology(863 program)(2006AA09A103-4)the National Natural Science Foundation of China(11232012)the Chinese Academy of Sciences(CAS)knowledge innovation program(KJCXYW-L02)
文摘In offshore engineering design, it is considerably significant to have an adequately accurate estimation of marine environmental parameters, in particular, the extreme wind speed of tropical cyclone （TC） with different return periods to guarantee the safety in projected operating life period. Based on the 71-year （1945-2015） TC data in the Northwest Pacific （NWP） by the Joint Typhoon Warning Center （JTWC） of US, a notable growth of the TC intensity is observed in the context of climate change. The fact implies that the traditional stationary model might be incapable of predicting parameters in the extreme events. Therefore, a non-stationary model is proposed in this study to estimate extreme wind speed in the South China Sea （SCS） and NWP. We find that the extreme wind speeds of different return periods exhibit an evident enhancement trend, for instance, the extreme wind speeds with different return periods by non- stationary model are 4.1%-4.4% higher than stationary ones in SCS. Also, the spatial distribution of extreme wind speed in NWP has been examined with the same methodology by dividing the west sea areas of the NWP 0°-45°N, 105°E-130°E into 45 subareas of 5° × 5°, where oil and gas resources are abundant. Similarly, remarkable spacial in-homogeneity in the extreme wind speed is seen in this area： the extreme wind speed with 50-year return period in the subarea （15°N-20°N, 115°E-120°E） of Zhongsha and Dongsha Islands is 73.8 m/s, while that in the subarea of Yellow Sea （30°N-35°N, 120°E-125°E） is only 47.1 m/s. As a result, the present study demonstrates that non-stationary and in-homogeneous effects should be taken into consideration in the estimation of extreme wind speed.
文摘Multiyear observed time series of wind speed for selected points of the Arctic region (data of station network from the Kola Peninsula to the Chukotka Peninsula) are used to highlight the important peculiarities of wind speed extreme statistics. How largest extremes could be simulated by climate model (the INM-CM4 model data from the Historical experiment of the CMIP5) is also discussed. Extreme value analysis yielded that a volume of observed samples of wind speeds are strictly divided into two sets of variables. Statistical properties of one population are sharply different from another. Because the common statistical conditions are the sign of identity of extreme events we therefore hypothesize that two groups of extreme wind events adhere to different circulation processes. A very important message is that the procedure of selection can be realized easily based on analysis of the cumulative distribution function. The authors estimate the properties of the modelled extremes and conclude that they consist of only the samples, adhering to one group. This evidence provides a clue that atmospheric model with a coarse spatial resolution does not simulate special mechanism responsible for appearance of largest wind speed extremes. Therefore, the tasks where extreme wind is needed cannot be explicitly solved using the output of climate model. The finding that global models are unable to capture the wind extremes is already well known, but information that they are members of group with the specific statistical conditions provides new knowledge. Generally, the implemented analytical approach allows us to detect that the extreme wind speed events adhere to different statistical models. Events located above the threshold value are much more pronounced than representatives of another group (located below the threshold value) predicted by the extrapolation of law distributions in their tail. The same situation is found in different areas of science where the data referring to the same nomenclature are adhering to different statistical models. This result motivates our interest on our ability to detect, analyze, and understand such different extremes.
基金Supported by the National Key Research and Development Program of China(Nos.2016YFC1402000,2018YFC1407003,2016YFC1402004)the National Natural Science Foundation of China(Nos.U1606402,41421005)the Strategic Priority Research Program of the Chinese Academy of Sciences(Nos.XDA19060202,XDA19060502)。
文摘Typhoons are one of the most serious natural disasters that occur annually on China’s southeast coast.A technique for analyzing the typhoon wind hazard was developed based on the empirical track model,and used to generate 1000-year virtual typhoons for Northwest Pacific basin.The influences of typhoon decay model,track model,and the extreme value distribution on the predicted extreme wind speed were investigated.We found that different typhoon decay models have least influence on the predicted extreme wind speed.Over most of the southeast coast of China,the predicted wind speed by the non-simplified empirical track model is larger than that from the simplified tracking model.The extreme wind speed predicted by different extreme value distribution is quite different.Four super typhoons Meranti(2016),Hato(2017),Mangkhut(2018)and Lekima(2019)were selected and the return periods of typhoon wind speeds along the China southeast coast were estimated in order to assess the typhoon wind hazard.
基金Open Access funding provided by NTNU Norwegian University of Science and Technology(incl St.Olavs Hospital-Trondheim University Hospital)。
文摘The purpose of the present study is to investigate the extreme values of the ice drift speed,which are also considered in the light of the magnitude of the simultaneous wind speed.The relationship between wind speed and ice drift speed is studied.The long-term ice drift data is collected by using local subsurface measurements based on acoustic Doppler current profilers(ADCP)in the Beaufort Sea during the period of 2006-2017.Upward-looking sonars(ULS)are deployed in order to observe the ice thickness as well as to identify events that correspond to open water conditions.The relationship between the ice drift speed and the wind speed is also investigated.It is found that the magnitude of the average ice drift speed is approximately 2.5%of the wind speed during the winter season.Estimation of the extreme values of the ice drift speed is studied by application of the average conditional exceedance rate(ACER)method.It is found that the extreme ice drift speed during the ice melt season(i.e.the summer season)is approximately20%-30%higher than that during the ice growth season(i.e.the winter season).The extreme ice drift speed can be effectively estimated based on the 2.5%wind speed.Moreover,the extreme ice drift speed can be obtained based on the extreme values of 2.5%of the wind speed based on multiplying with an amplification factor which varies in the range from 1.7 to 2.0 during the growth season,corresponding to increasing return periods of 10,25,50 and 100years.
文摘The marine accidents are among the main components of the Zanzibar Disaster Management Policy (2011) and the Zanzibar Blue Economy Policy (2020). These policies aimed to institute legal frame works and procedures for reducing both the frequency of marine accidents and their associated fatalities. These fatalities include deaths, permanent disabilities and loss of properties which may result into increased poverty levels as per the sustainable development goal one (SDG1) which stipulates on ending the poverty in all its forms everywhere. Thus, in the way to support these Government efforts, the influence of climate and weather on marine accidents along Zanzibar and Pemba Channels was investigated. The study used the 10 years (2013-2022) records of daily rainfall and hourly wind speed acquired from Tanzania Meteorological Authority (TMA) (for the observation stations of Zanzibar, Pemba, Dares Salaam and Tanga), and the significant wave heights data, which was freely downloaded from Globally Forecasting System (GFS-World model of 13 km resolution). The marine accident records were collected from TASAC and Zanzibar Maritime Authority (ZMA), and the anecdotal information was collected from heads of quay and boat captains in different areas of Zanzibar. The Mann Kendal test, was used to determine the slopes and trends direction of used weather parameters, while the Pearson correlations analysis and t-tests were used to understand the significance of the underlying relationship between the weather and marine accidents. The paired t-test was used to evaluate the extent to which weather parameters affect the marine accidents. Results revealed that the variability of extreme weather events (rainfall, ocean waves and wind speed) was seen to be among the key factors for most of the recorded marine accidents. For instance, in Pemba high rainfall showed an increasing trend of extreme rainfall events, while Zanzibar has shown a decreasing trend of these events. As for extreme wind events, results show that Dar es Salaam and Tanga had an increasing trend, while Zanzibar and Pemba had shown a decreasing trend. As for the monthly variability of frequencies of extreme rainfall events, March to May (MAM) season was shown to have the highest frequencies over all stations with the peaks at Zanzibar and Pemba. On the other hand, high frequency of extreme wind speed was observed from May to September with peaks in June to July, and the highest strength was observed during 09:00 to 15:00 GMT. Moreover, results revealed an increasing trend of marine accidents caused by bad weather except during November. Also, results showed that bad weather conditions contributed to 48 (32%) of all 150 recorded accidents. Further results revealed significant correlation between the extreme wind and marine accidents, with the highest strong correlation of r = 0.71 (at p ≤ 0.007) and r = 0.75 (at p ≤ 0.009) at Tanga and Pemba, indicating the occurrence of more marine accidents at the Pemba channel. Indeed, strong correlation of r = 0.6 between extreme rainfall events and marine accidents was shown in Pemba, while the correlations between extremely significant wave heights and marine accidents were r = 0.41 (at p ≤ 0.006) and r = 0.34 (p ≤ 0.0006) for Pemba and Zanzibar Channel, respectively. In conclusion, the study has shown high influence between marine accidents and bad weather events with more impacts in Pemba and Zanzibar. Thus, the study calls for more work to be undertaken to raise the awareness on marine accidents as a way to alleviate the poverty and enhance the sustainable blue economy.
基金by the NSFC-Shandong Joint Fund(No.U1706226)the Fundamental Research Funds for the Central Universities.
文摘An increasing number of marine structures have been built for coastal protection and marine development in recent years,and wind,which is crucial to marine structures,should be analyzed.Therefore,typhoon frequency,wind climate,wind energy assess-ment,and extreme wind speed in the South China Sea(SCS)are investigated in detail in this study.The data are obtained from the China Meteorological Administration,the European Centre for Medium-range Weather Forecasts,and the National Centers for Envi-ronmental Prediction.The offshore wind energy potential is analyzed at five sites near the coast.The spatial and monthly frequencies of tropical cyclones for different intensity categories are analyzed.The extreme wind speed is fitted by five distribution models,and the generalized extreme value(GEV)distribution is selected as the most suitable function according to the goodness of fit.The spa-tial distributions of extreme wind speeds in the SCS are plotted on the basis of the GEV distribution and ERA5 data sets.The influ-ences of the distribution models and data sets on the calculated results are discussed.Moreover,the monthly extreme wind speed and comparison with the results of previous studies are analyzed.This study provides a reference for the design of wind turbines.
基金The project is partly supported by the National Science Council, Contract Nos. NSC-89-261 l-E-019-024 (JZY), and NSC-89-2611-E-019-027 (CRC).
文摘Statistical properties of winds near the Taichung Harbour are investigated. The 26 years'incomplete data of wind speeds, measured on an hourly basis, are used as reference. The possibility of imputation using simulated results of the Auto-Regressive (AR), Moving-Average (MA), and/ or Auto-Regressive and Moving-Average (ARMA) models is studied. Predictions of the 25-year extreme wind speeds based upon the augmented data are compared with the original series. Based upon the results, predictions of the 50- and 100-year extreme wind speeds are then made.
基金National Key R&D Program of China,Grant/Award Number:2022YFC3002705National Natural Science Foundation of China,Grant/Award Number:5220904China Institute of Water Resources and Hydropower Research,Grant/Award Number:SKL2022TS11。
文摘The escalation of compound extreme events has resulted in noteworthy economic and property losses.Recognizing the intricate interconnections among these events has become imperative.To tackle this challenge,we have formulated a comprehensive framework for the systematic analysis of their dependencies.This framework consists of three steps.(1)Define extreme events using Mahalanobis distance thresholds.(2)Represent dependencies among multiple extreme events through a point process-based method.(3)Verify dependencies with residual tail coefficients,determining thefinal dependency structure.Applying this framework to assess the extreme dependence of precipitation on wind speed and temperature in China,revealed four distinct dependency structures.In northern,Jianghuai,and southern China,precipitation heavily relies on wind speed,while tempera-tures maintain relative independence.In northeastern and northwestern China,precipitation exhibits relative independence,yet a notable dependence exists between temperatures and wind speed.In southwestern China,precipitation strongly depends on temperature,while wind speed remains relatively indepen-dent.The Qinghai–Tibet Plateau region displays a significant dependence relationship among precipitation,wind speed,and temperature,with weaker dependence between extreme wind speed and temperature.This framework is instrumental for analyzing dependencies among extreme values in compound events.
