We compared data of sea surface wind from the European Centre for Medium-Range Weather Forecasts Interim Reanalysis(ERA-Interim) with that collected from eight buoys deployed in the Yellow and East China seas.The buoy...We compared data of sea surface wind from the European Centre for Medium-Range Weather Forecasts Interim Reanalysis(ERA-Interim) with that collected from eight buoys deployed in the Yellow and East China seas.The buoy data covered a period from 2010 to 2011,during which the longest time series without missing data extended for 329 days.Results show that the ERA-Interim wind data agree well with the buoy data.The regression coefficients between the ERA-Interim and observed wind speed and direction are greater than 0.7 and 0.79,respectively.However,the ERA-Interim wind data overestimate wind speed at most of the buoy stations,for which the largest bias is 1.8 m/s.Moreover,it is found from scatter plots of wind direction that about 13%of the ERA-Interim wind data can be classified as bad for wind speeds below6 m/s.Overall,the ERA-Interim data forecast both the wind speed and direction well,although they are not very representative of our observations,especially those where the wind speed is below 6 m/s.展开更多
Using the International Comprehensive Ocean-Atmosphere Data Set(ICOADS) and ERA-Interim data, spatial distributions of air-sea temperature difference(ASTD) in the South China Sea(SCS) for the past 35 years are compare...Using the International Comprehensive Ocean-Atmosphere Data Set(ICOADS) and ERA-Interim data, spatial distributions of air-sea temperature difference(ASTD) in the South China Sea(SCS) for the past 35 years are compared,and variations of spatial and temporal distributions of ASTD in this region are addressed using empirical orthogonal function decomposition and wavelet analysis methods. The results indicate that both ICOADS and ERA-Interim data can reflect actual distribution characteristics of ASTD in the SCS, but values of ASTD from the ERA-Interim data are smaller than those of the ICOADS data in the same region. In addition, the ASTD characteristics from the ERA-Interim data are not obvious inshore. A seesaw-type, north-south distribution of ASTD is dominant in the SCS; i.e., a positive peak in the south is associated with a negative peak in the north in November, and a negative peak in the south is accompanied by a positive peak in the north during April and May. Interannual ASTD variations in summer or autumn are decreasing. There is a seesaw-type distribution of ASTD between Beibu Bay and most of the SCS in summer, and the center of large values is in the Nansha Islands area in autumn. The ASTD in the SCS has a strong quasi-3a oscillation period in all seasons, and a quasi-11 a period in winter and spring. The ASTD is positively correlated with the Nio3.4 index in summer and autumn but negatively correlated in spring and winter.展开更多
The extraction of spectral parameters is very difficult because of the limited energy resolution for NaI( TI) gamma-ray detectors. For statistical fluctuation of radioactivity under complex environment,some smoothing ...The extraction of spectral parameters is very difficult because of the limited energy resolution for NaI( TI) gamma-ray detectors. For statistical fluctuation of radioactivity under complex environment,some smoothing filtering methods are proposed to solve the problem. These methods include adopting method of arithmetic moving average,center of gravity,least squares of polynomial,slide converter of discrete funcion convolution etc. The process of spectrum data is realized,and the results are assessed in H / FWHM( Peak High / Full Width at Half Maximum) and peak area based on the Matlab programming. The results indicate that different methods smoothed spectrum have respective superiority in different ergoregion,but the Gaussian function theory in discrete function convolution slide method is used to filter the complex γ-spectrum on Embedded system platform,and the statistical fluctuation of γ-spectrum is filtered well.展开更多
A comparison study is performed to contrast the improvements in the tropical Pacific oceanic state of a low-resolution model respectively via data assimilation and by an increase in horizontal resolution. A low resolu...A comparison study is performed to contrast the improvements in the tropical Pacific oceanic state of a low-resolution model respectively via data assimilation and by an increase in horizontal resolution. A low resolution model (LR) (1°lat by 2°lon) and a high-resolution model (HR) (0.5°lat by 0.5°lon) are employed for the comparison. The authors perform 20-yr numerical experiments and analyze the annual mean fields of temperature and salinity. The results indicate that the low-resolution model with data assimilation behaves better than the high-resolution model in the estimation of ocean large-scale features. From 1990 to 2000, the average of HR's RMSE (root-mean-square error) relative to independent Tropical Atmosphere Ocean project (TAO) mooring data at randomly selected points is 0.97℃ compared to a RMSE of 0.56℃ for LR with temperature assimilation. Moreover, the LR with data assimilation is more frugal in computation. Although there is room to improve the high-resolution model, the low-resolution model with data assimilation may be an advisable choice in achieving a more realistic large-scale state of the ocean at the limited level of information provided by the current observational system.展开更多
基金Supported by the National Natural Science Foundation of China(No.41276026)the Ocean Special Project(No.XDA11020301)the National Basic Research Program of China(973 Program)(No.2009CB421205)
文摘We compared data of sea surface wind from the European Centre for Medium-Range Weather Forecasts Interim Reanalysis(ERA-Interim) with that collected from eight buoys deployed in the Yellow and East China seas.The buoy data covered a period from 2010 to 2011,during which the longest time series without missing data extended for 329 days.Results show that the ERA-Interim wind data agree well with the buoy data.The regression coefficients between the ERA-Interim and observed wind speed and direction are greater than 0.7 and 0.79,respectively.However,the ERA-Interim wind data overestimate wind speed at most of the buoy stations,for which the largest bias is 1.8 m/s.Moreover,it is found from scatter plots of wind direction that about 13%of the ERA-Interim wind data can be classified as bad for wind speeds below6 m/s.Overall,the ERA-Interim data forecast both the wind speed and direction well,although they are not very representative of our observations,especially those where the wind speed is below 6 m/s.
基金National Natural Science Foundation of China(41475120)
文摘Using the International Comprehensive Ocean-Atmosphere Data Set(ICOADS) and ERA-Interim data, spatial distributions of air-sea temperature difference(ASTD) in the South China Sea(SCS) for the past 35 years are compared,and variations of spatial and temporal distributions of ASTD in this region are addressed using empirical orthogonal function decomposition and wavelet analysis methods. The results indicate that both ICOADS and ERA-Interim data can reflect actual distribution characteristics of ASTD in the SCS, but values of ASTD from the ERA-Interim data are smaller than those of the ICOADS data in the same region. In addition, the ASTD characteristics from the ERA-Interim data are not obvious inshore. A seesaw-type, north-south distribution of ASTD is dominant in the SCS; i.e., a positive peak in the south is associated with a negative peak in the north in November, and a negative peak in the south is accompanied by a positive peak in the north during April and May. Interannual ASTD variations in summer or autumn are decreasing. There is a seesaw-type distribution of ASTD between Beibu Bay and most of the SCS in summer, and the center of large values is in the Nansha Islands area in autumn. The ASTD in the SCS has a strong quasi-3a oscillation period in all seasons, and a quasi-11 a period in winter and spring. The ASTD is positively correlated with the Nio3.4 index in summer and autumn but negatively correlated in spring and winter.
基金Sponsored by the Natural Science Fundation of Jiangxi Province(Grant No.20114BAB211026 and 20122BAB201028)the Open Science Fund from Key Laboratory of Radioactive Geology and Exploration Technology Fundamental Science for National Defense,East China Institute of Technology(Grant No.2010RGET11)
文摘The extraction of spectral parameters is very difficult because of the limited energy resolution for NaI( TI) gamma-ray detectors. For statistical fluctuation of radioactivity under complex environment,some smoothing filtering methods are proposed to solve the problem. These methods include adopting method of arithmetic moving average,center of gravity,least squares of polynomial,slide converter of discrete funcion convolution etc. The process of spectrum data is realized,and the results are assessed in H / FWHM( Peak High / Full Width at Half Maximum) and peak area based on the Matlab programming. The results indicate that different methods smoothed spectrum have respective superiority in different ergoregion,but the Gaussian function theory in discrete function convolution slide method is used to filter the complex γ-spectrum on Embedded system platform,and the statistical fluctuation of γ-spectrum is filtered well.
基金This study is supported by the Key Program of Chinese Academy of Sciences KZCX3 SW-221the National Natural Science Foundation of China(Grant No.40233033 and 40221503).
文摘A comparison study is performed to contrast the improvements in the tropical Pacific oceanic state of a low-resolution model respectively via data assimilation and by an increase in horizontal resolution. A low resolution model (LR) (1°lat by 2°lon) and a high-resolution model (HR) (0.5°lat by 0.5°lon) are employed for the comparison. The authors perform 20-yr numerical experiments and analyze the annual mean fields of temperature and salinity. The results indicate that the low-resolution model with data assimilation behaves better than the high-resolution model in the estimation of ocean large-scale features. From 1990 to 2000, the average of HR's RMSE (root-mean-square error) relative to independent Tropical Atmosphere Ocean project (TAO) mooring data at randomly selected points is 0.97℃ compared to a RMSE of 0.56℃ for LR with temperature assimilation. Moreover, the LR with data assimilation is more frugal in computation. Although there is room to improve the high-resolution model, the low-resolution model with data assimilation may be an advisable choice in achieving a more realistic large-scale state of the ocean at the limited level of information provided by the current observational system.
