Many studies that discuss observed trends in wind speed focus primarily on regions of the Northern Hemisphere, so there is little research directed to the Southern Hemisphere. This paper pre- sents a preliminary inves...Many studies that discuss observed trends in wind speed focus primarily on regions of the Northern Hemisphere, so there is little research directed to the Southern Hemisphere. This paper pre- sents a preliminary investigation of possible statistically significant trends in wind speed over the Southern Hemisphere, with a detailing on the South American continent, between 1961 and 2008. Thus, data from the 20th Century Reanalysis V2 were examined with statistical tests of Mann- Kendall and Sen’s Bend in order to establish the significance and the magnitude of detected trends. The previous results indicate statistically significant trends of increase in average wind speedover the equatorial region of the planet, as well as in the eastern sector of the South Pacific and South Atlantic Oceans. In South America, the most significant trends of decrease in wind speed were noted in some areas of the southern sector of the continent, even as in the adjacent Atlantic Ocean to Argentina. Further studies should be performed to physically support the occurrence of these trends in wind speed. In addition, other observed and reanalysis data sets should be explored to update and corroborate these primary analyzes.展开更多
Methods of the comprehensive evaluation have been studied for many years. However, the change speed of evaluated objects was rarely considered by the existing evaluation methods. An evaluation matrix is proposed to re...Methods of the comprehensive evaluation have been studied for many years. However, the change speed of evaluated objects was rarely considered by the existing evaluation methods. An evaluation matrix is proposed to remedy this deficiency. Firstly, the change speed state （CSS） of the evaluated objects is analyzed based on double inspiriting control lines （DICLs）, and a matrix of the CSS is constructed. Then, 72 elements in the matrix are analyzed, and formulas describing each CSS are given. The efficiency of the proposed evaluation matrix is proved when the CSS merges with the change speed trend （CST） in the dynamic comprehensive evaluation. Finally, a computing example shows that the proposed evaluation matrix is feasible in the dynamic comprehensive evaluation with the speed feature.展开更多
This research quantitatively recognized the wind speed change using wind speed trend and trend of wind speed variability from 1961 to 2012 and regionalized the wind speed change on a county-level basis.The mean wind s...This research quantitatively recognized the wind speed change using wind speed trend and trend of wind speed variability from 1961 to 2012 and regionalized the wind speed change on a county-level basis.The mean wind speed observation data and linear fitting method were used.The findings suggested that level-I regionalization includes six zones according to wind speed trend value in different regions,viz.Northeast ChinaeNorth China substantial declining zone,EasteCentral China declining zone,Southeast China slightly declining zone,Southwest China very slightly declining zone,Northwest China declining zone,and QinghaieTibetan Plateau slightly declining zone.Level-II regionalization divides China into twelve regions based on trend of wind speed variability and the level-I regionalization results.展开更多
It is now well known that coastal urban local climate has been showing changing pattern due to global climate change. This communication attempts to explore fluctuating pattern of urban average monthly wind speed duri...It is now well known that coastal urban local climate has been showing changing pattern due to global climate change. This communication attempts to explore fluctuating pattern of urban average monthly wind speed during past 50 years (1961-2010). It shows peculiar results taking Karachi (24?53'N, 67?00'E), a coastal mega-city of Pakistan, as a case study. Mann-Kendall trend test shows that March, April and October and both summer and winter seasons show positive trends for the average monthly wind speed during the whole study period (1961-2010). For the earlier 25 years data, it has been found that January, March, May, August, November and December and annual wind speed data have shown the negative trends. Only summer season has shown the positive trend for the wind speed. Similarly, for the most recent 25 years data it has been found that January, February, March, April, May, June, October, November and December and annual and both summer and winter wind speed data have shown the positive trends showing some degree of change in wind speed pattern. Probabilistic analysis reveals that average monthly wind speed data sets follow lognormal, logistic, largest extreme value, and Weibull (two-and three-parameters) probability distributions. Change point analysis has also confirmed the change in the pattern of observed average monthly wind speed data near 1992. The analysis performed reveals the effect of global warming on the local urban wind speed which appears to be temporal non-stationary.展开更多
This study investigates the long-term changes of monthly sea surface wind speeds over the China seas from 1988 to 2015. The 10-meter wind speeds products from four major global reanalysis datasets with high resolution...This study investigates the long-term changes of monthly sea surface wind speeds over the China seas from 1988 to 2015. The 10-meter wind speeds products from four major global reanalysis datasets with high resolution are used: Cross-Calibrated Multi-Platform data set(CCMP), NCEP climate forecast system reanalysis data set(CFSR),ERA-interim reanalysis data set(ERA-int) and Japanese 55-year reanalysis data set(JRA55). The monthly sea surface wind speeds of four major reanalysis data sets have been investigated through comparisons with the longterm and homogeneous observation wind speeds data recorded at ten stations. The results reveal that(1) the wind speeds bias of CCMP, CFSR, ERA-int and JRA55 are 0.91 m/s, 1.22 m/s, 0.62 m/s and 0.22 m/s, respectively.The wind speeds RMSE of CCMP, CFSR, ERA-int and JRA55 are 1.38 m/s, 1.59 m/s, 1.01 m/s and 0.96 m/s,respectively;(2) JRA55 and ERA-int provides a realistic representation of monthly wind speeds, while CCMP and CFSR tend to overestimate observed wind speeds. And all the four data sets tend to underestimate observed wind speeds in Bohai Sea and Yellow Sea;(3) Comparing the annual wind speeds trends between observation and the four data sets at ten stations for 1988-1997, 1988–2007 and 1988–2015, the result show that ERA-int is superior to represent homogeneity monthly wind speeds over the China seaes.展开更多
文摘Many studies that discuss observed trends in wind speed focus primarily on regions of the Northern Hemisphere, so there is little research directed to the Southern Hemisphere. This paper pre- sents a preliminary investigation of possible statistically significant trends in wind speed over the Southern Hemisphere, with a detailing on the South American continent, between 1961 and 2008. Thus, data from the 20th Century Reanalysis V2 were examined with statistical tests of Mann- Kendall and Sen’s Bend in order to establish the significance and the magnitude of detected trends. The previous results indicate statistically significant trends of increase in average wind speedover the equatorial region of the planet, as well as in the eastern sector of the South Pacific and South Atlantic Oceans. In South America, the most significant trends of decrease in wind speed were noted in some areas of the southern sector of the continent, even as in the adjacent Atlantic Ocean to Argentina. Further studies should be performed to physically support the occurrence of these trends in wind speed. In addition, other observed and reanalysis data sets should be explored to update and corroborate these primary analyzes.
基金supported by the National Natural Science Foundation of China (7127217671302028)+1 种基金the Fundamental Scientific Research Funds for the Central Universities (HEUCF110914)the Heilongjiang Postdoctoral Fund (3236310094)
文摘Methods of the comprehensive evaluation have been studied for many years. However, the change speed of evaluated objects was rarely considered by the existing evaluation methods. An evaluation matrix is proposed to remedy this deficiency. Firstly, the change speed state （CSS） of the evaluated objects is analyzed based on double inspiriting control lines （DICLs）, and a matrix of the CSS is constructed. Then, 72 elements in the matrix are analyzed, and formulas describing each CSS are given. The efficiency of the proposed evaluation matrix is proved when the CSS merges with the change speed trend （CST） in the dynamic comprehensive evaluation. Finally, a computing example shows that the proposed evaluation matrix is feasible in the dynamic comprehensive evaluation with the speed feature.
基金supported by the National Basic Research Program of China (2012CB955404,2012CB955402)the National Natural Science Foundation of China (41321001)
文摘This research quantitatively recognized the wind speed change using wind speed trend and trend of wind speed variability from 1961 to 2012 and regionalized the wind speed change on a county-level basis.The mean wind speed observation data and linear fitting method were used.The findings suggested that level-I regionalization includes six zones according to wind speed trend value in different regions,viz.Northeast ChinaeNorth China substantial declining zone,EasteCentral China declining zone,Southeast China slightly declining zone,Southwest China very slightly declining zone,Northwest China declining zone,and QinghaieTibetan Plateau slightly declining zone.Level-II regionalization divides China into twelve regions based on trend of wind speed variability and the level-I regionalization results.
文摘It is now well known that coastal urban local climate has been showing changing pattern due to global climate change. This communication attempts to explore fluctuating pattern of urban average monthly wind speed during past 50 years (1961-2010). It shows peculiar results taking Karachi (24?53'N, 67?00'E), a coastal mega-city of Pakistan, as a case study. Mann-Kendall trend test shows that March, April and October and both summer and winter seasons show positive trends for the average monthly wind speed during the whole study period (1961-2010). For the earlier 25 years data, it has been found that January, March, May, August, November and December and annual wind speed data have shown the negative trends. Only summer season has shown the positive trend for the wind speed. Similarly, for the most recent 25 years data it has been found that January, February, March, April, May, June, October, November and December and annual and both summer and winter wind speed data have shown the positive trends showing some degree of change in wind speed pattern. Probabilistic analysis reveals that average monthly wind speed data sets follow lognormal, logistic, largest extreme value, and Weibull (two-and three-parameters) probability distributions. Change point analysis has also confirmed the change in the pattern of observed average monthly wind speed data near 1992. The analysis performed reveals the effect of global warming on the local urban wind speed which appears to be temporal non-stationary.
基金The National Key R&D Program of China under contract No.2016YFC1401905the National Natural Science Foundation of China under contract No.41776004the Fundamental Research Funds for the Central Universities under contract No.2016B12514
文摘This study investigates the long-term changes of monthly sea surface wind speeds over the China seas from 1988 to 2015. The 10-meter wind speeds products from four major global reanalysis datasets with high resolution are used: Cross-Calibrated Multi-Platform data set(CCMP), NCEP climate forecast system reanalysis data set(CFSR),ERA-interim reanalysis data set(ERA-int) and Japanese 55-year reanalysis data set(JRA55). The monthly sea surface wind speeds of four major reanalysis data sets have been investigated through comparisons with the longterm and homogeneous observation wind speeds data recorded at ten stations. The results reveal that(1) the wind speeds bias of CCMP, CFSR, ERA-int and JRA55 are 0.91 m/s, 1.22 m/s, 0.62 m/s and 0.22 m/s, respectively.The wind speeds RMSE of CCMP, CFSR, ERA-int and JRA55 are 1.38 m/s, 1.59 m/s, 1.01 m/s and 0.96 m/s,respectively;(2) JRA55 and ERA-int provides a realistic representation of monthly wind speeds, while CCMP and CFSR tend to overestimate observed wind speeds. And all the four data sets tend to underestimate observed wind speeds in Bohai Sea and Yellow Sea;(3) Comparing the annual wind speeds trends between observation and the four data sets at ten stations for 1988-1997, 1988–2007 and 1988–2015, the result show that ERA-int is superior to represent homogeneity monthly wind speeds over the China seaes.
