Poisson-Gumbel joint distribution model uses maximum wind speed corresponding to multiple typhoons to construct sample sequence.Thresholds are usually used to filter sample sequences to make them more consistent with ...Poisson-Gumbel joint distribution model uses maximum wind speed corresponding to multiple typhoons to construct sample sequence.Thresholds are usually used to filter sample sequences to make them more consistent with Poisson distribution.However,few studies have discussed the threshold setting and its impact on Poisson-Gumbel joint distribution model.In this study,a sample sequence based on the data of Qinzhou meteorological station from 2005 to 2018 were constructed.We set 0%,5%,10%,20%and 30%gradient thresholds.Then,we analyzed the influence of threshold change on the calculation results of maximum wind speed in different return periods.The results showed that:(1)When the threshold increases,the maximum wind speed of each return period will decrease gradually.This indicates that the length of the sample series may have a positive effect on the return period wind speed calculation in Gumbel and Poisson-Gumbel methods.Although the augment of the threshold increases the average value of the maximum wind speed of the sample sequence,it shortens the length of the sample sequence,resulting in a lower calculated value of the maximum wind speed.However,this deviation is not large.Taking the common 10%threshold as an example,the maximum wind speed calculation deviation in the 50 a return period is about 1.9%;(2)Theoretically,the threshold is set to make the sample sequence more consistent with Poisson distribution,but this example showed that the effect is worth further discussion.Although the overall trend showed that the increase of the threshold can makeχ2 decrease,the correlation coefficient of linear fitting was only 0.182.Taking Qinzhou meteorological station data as an example,theχ2 of 20%threshold was as high as 6.35,meaning that the selected sample sequence was not ideal.展开更多
Using the data on the maximum wind speed within ten minutes every month in the period 1971-2009 in Zhucheng City of Shandong Province, we conduct statistical analysis of the maximum wind speed in Zhucheng City. The re...Using the data on the maximum wind speed within ten minutes every month in the period 1971-2009 in Zhucheng City of Shandong Province, we conduct statistical analysis of the maximum wind speed in Zhucheng City. The results show that over thirty-nine years, the annual maximum wind speed in four seasons in Zhucheng City tends to decline. The annual maximum wind speed declines at the rate of 1.45 m/s every 10 years. It falls fastest in winter, with decline rate of 1.73 m/s every 10 years; it is close to the average annual maximum wind speed in spring and autumn, with decline rate of 1.44 m/s and 14.8 m/s every 10 years, respectively; it falls slowest in summer, and the extreme value of the maximum wind speed occurs mainly in spring. The curve of changes in the monthly maximum wind speed in Zhucheng City assumes diminishing shape of "two peaks and one trough". We conduct preliminary analysis of the windy weather situation, and put forth specific defensive measures against the hazards of strong winds in the different periods.展开更多
In this study, the maximum wind speed （WSmax） changes across China from 1956 to 2004 were analyzed based on observed station data, and the changes of WSmax for 2046-2065 and 2080-2099 are projected using three globa...In this study, the maximum wind speed （WSmax） changes across China from 1956 to 2004 were analyzed based on observed station data, and the changes of WSmax for 2046-2065 and 2080-2099 are projected using three global climate models （GFDL_CM2_0, CCCMA_CGCM3, and MRI_CGCM2） that have participated in the IPCC Fourth Assessment Report （AR4）. The observed annual and seasonal WSmax and the frequency of gale days showed obvious declining trends. The annual WSmax decreased by approximately 1.46 m s^-i per decade, and the number of gale days decreased by 3.0 days per decade from 1956 to 2004. The amplitudes of the annual and seasonal WSmax decreases are larger than those of the annual and seasonal average wind speeds （WSavg）. The weakening of the East Asian winter and summer monsoons is the cause for the distinct decreases of both WSmax and WSavg over the whole China. The decrease of WSmax in the southeast coastal areas of China is related to the reduced intensity of cold waves in China and the decreasing number （and decreasing intensity） of land-falling typhoons originated in the Northwest Pacific Ocean. The global climate models GFDL_CM2_0, MRI_CGCM2, and EBGCM （the ensemble of above men-tioned three global climate models） consistently suggest that the annual and seasonal WSmax values will decrease during 2046-2065 and 2080 2099 relative to 1981 2000. The models also suggest that decreases in WSmax for whole China during 2046-2065 and 2080 2099 are related to both the reduced intensity of cold waves and the reduced intensity of the winter monsoon, and the decrease in WSmax in the southeast coastal areas of China is corresponding to the decreasing number of tropical cyclones over the Northwest Pacific Ocean in the summer during the same periods.展开更多
Based on gradient wind equations, including frictional force, and considering the effect of the movement of a tropical cyclone on wind speed, the Fujita Formula is improved and further simplified, and the numerical sc...Based on gradient wind equations, including frictional force, and considering the effect of the movement of a tropical cyclone on wind speed, the Fujita Formula is improved and further simplified, and the numerical scheme for calculating the maximum wind speed radius and wind velocity distribution of a moving tropical cyclone is derived. In addition, the effect of frictional force on the internal structure of the tropical cyclone is discussed. By comparison with observational data, this numerical scheme demonstrates great advantages, i.e. it can not only describe the asymmetrical wind speed distribution of a tropical cyclone reasonably, but can also calculate the maximum wind speed in each direction within the typhoon domain much more accurately. Furthermore, the combination of calculated and analyzed wind speed distributions by the scheme is perfectly consistent with observations.展开更多
基金This work was supported by the Second Tibet Plateau Scientific Expedition and Research Program(STEP)under grant number 2019QZKK0804the National Natural Science Foundation of China“Study on the dynamic mechanism of grassland ecosystem response to climate change in Qinghai Plateau”under grant number U20A2098.
文摘Poisson-Gumbel joint distribution model uses maximum wind speed corresponding to multiple typhoons to construct sample sequence.Thresholds are usually used to filter sample sequences to make them more consistent with Poisson distribution.However,few studies have discussed the threshold setting and its impact on Poisson-Gumbel joint distribution model.In this study,a sample sequence based on the data of Qinzhou meteorological station from 2005 to 2018 were constructed.We set 0%,5%,10%,20%and 30%gradient thresholds.Then,we analyzed the influence of threshold change on the calculation results of maximum wind speed in different return periods.The results showed that:(1)When the threshold increases,the maximum wind speed of each return period will decrease gradually.This indicates that the length of the sample series may have a positive effect on the return period wind speed calculation in Gumbel and Poisson-Gumbel methods.Although the augment of the threshold increases the average value of the maximum wind speed of the sample sequence,it shortens the length of the sample sequence,resulting in a lower calculated value of the maximum wind speed.However,this deviation is not large.Taking the common 10%threshold as an example,the maximum wind speed calculation deviation in the 50 a return period is about 1.9%;(2)Theoretically,the threshold is set to make the sample sequence more consistent with Poisson distribution,but this example showed that the effect is worth further discussion.Although the overall trend showed that the increase of the threshold can makeχ2 decrease,the correlation coefficient of linear fitting was only 0.182.Taking Qinzhou meteorological station data as an example,theχ2 of 20%threshold was as high as 6.35,meaning that the selected sample sequence was not ideal.
文摘Using the data on the maximum wind speed within ten minutes every month in the period 1971-2009 in Zhucheng City of Shandong Province, we conduct statistical analysis of the maximum wind speed in Zhucheng City. The results show that over thirty-nine years, the annual maximum wind speed in four seasons in Zhucheng City tends to decline. The annual maximum wind speed declines at the rate of 1.45 m/s every 10 years. It falls fastest in winter, with decline rate of 1.73 m/s every 10 years; it is close to the average annual maximum wind speed in spring and autumn, with decline rate of 1.44 m/s and 14.8 m/s every 10 years, respectively; it falls slowest in summer, and the extreme value of the maximum wind speed occurs mainly in spring. The curve of changes in the monthly maximum wind speed in Zhucheng City assumes diminishing shape of "two peaks and one trough". We conduct preliminary analysis of the windy weather situation, and put forth specific defensive measures against the hazards of strong winds in the different periods.
基金Supported by the Open Laboratory Fund of the Institute of Plateau Meteorology of China Meteorological Administration(CMA)(LPM2012005)National Natural Science Foundation of China (41205114)CMA Special Public Welfare Research Fund(GYHY201106018 and GYHY200806009)
文摘In this study, the maximum wind speed （WSmax） changes across China from 1956 to 2004 were analyzed based on observed station data, and the changes of WSmax for 2046-2065 and 2080-2099 are projected using three global climate models （GFDL_CM2_0, CCCMA_CGCM3, and MRI_CGCM2） that have participated in the IPCC Fourth Assessment Report （AR4）. The observed annual and seasonal WSmax and the frequency of gale days showed obvious declining trends. The annual WSmax decreased by approximately 1.46 m s^-i per decade, and the number of gale days decreased by 3.0 days per decade from 1956 to 2004. The amplitudes of the annual and seasonal WSmax decreases are larger than those of the annual and seasonal average wind speeds （WSavg）. The weakening of the East Asian winter and summer monsoons is the cause for the distinct decreases of both WSmax and WSavg over the whole China. The decrease of WSmax in the southeast coastal areas of China is related to the reduced intensity of cold waves in China and the decreasing number （and decreasing intensity） of land-falling typhoons originated in the Northwest Pacific Ocean. The global climate models GFDL_CM2_0, MRI_CGCM2, and EBGCM （the ensemble of above men-tioned three global climate models） consistently suggest that the annual and seasonal WSmax values will decrease during 2046-2065 and 2080 2099 relative to 1981 2000. The models also suggest that decreases in WSmax for whole China during 2046-2065 and 2080 2099 are related to both the reduced intensity of cold waves and the reduced intensity of the winter monsoon, and the decrease in WSmax in the southeast coastal areas of China is corresponding to the decreasing number of tropical cyclones over the Northwest Pacific Ocean in the summer during the same periods.
基金supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 40425009 and 40730953
文摘Based on gradient wind equations, including frictional force, and considering the effect of the movement of a tropical cyclone on wind speed, the Fujita Formula is improved and further simplified, and the numerical scheme for calculating the maximum wind speed radius and wind velocity distribution of a moving tropical cyclone is derived. In addition, the effect of frictional force on the internal structure of the tropical cyclone is discussed. By comparison with observational data, this numerical scheme demonstrates great advantages, i.e. it can not only describe the asymmetrical wind speed distribution of a tropical cyclone reasonably, but can also calculate the maximum wind speed in each direction within the typhoon domain much more accurately. Furthermore, the combination of calculated and analyzed wind speed distributions by the scheme is perfectly consistent with observations.
