An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications.In this work,a hybrid statistical–dynamical downscaling method and a high-resolution dyna...An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications.In this work,a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean,minimum and maximum air temperatures to investigate the quality of localscale estimates produced by downscaling.These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute over a near-coastal region of western Finland.The dynamical downscaling is performed with the Weather Research and Forecasting(WRF)model,and the statistical downscaling method implemented is the Cumulative Distribution Function-transform(CDF-t).The CDF-t is trained using 20 years of WRF-downscaled Climate Forecast System Reanalysis data over the region at a 3-km spatial resolution for the central month of each season.The performance of the two methods is assessed qualitatively,by inspection of quantile-quantile plots,and quantitatively,through the Cramer-von Mises,mean absolute error,and root-mean-square error diagnostics.The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling(for all seasons).The hybrid method proves to be less computationally expensive,and also to give more skillful temperature forecasts(at least for the Finnish near-coastal region).展开更多
Because the static evaluation is unable to reflect the dynamic features of economic benefits and the contribution of sustainable development of low-carbon new energy to economic benefits is huge,this article puts forw...Because the static evaluation is unable to reflect the dynamic features of economic benefits and the contribution of sustainable development of low-carbon new energy to economic benefits is huge,this article puts forward the dynamic evaluation model of economic benefits under the development of low-carbon new energy.Total energy,energy consumption structure,industrial structure,GDP,total population and energy supply structure were taken as independent variables,and the carbon intensity was taken as the dependent variable.Through t-test and decision coefficient,total energy,energy consumption structure,GDP and total population were determined as the main factors of influencing low-carbon economy.Based on these four main factors,the dynamic evaluation index system of economic benefits was constructed.Experimental results show that the proposed model can comprehensively reflect the economic benefit level and the contribution of low-carbon new energy.Therefore,this method has high evaluation accuracy,which can provide scientific reference for the economic benefit management of relevant management departments.展开更多
基金Botnia-Atlantica, an EU-programme financing cross border cooperation projects in Sweden, Finland and Norway, for their support of this work through the WindCoE project
文摘An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications.In this work,a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean,minimum and maximum air temperatures to investigate the quality of localscale estimates produced by downscaling.These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute over a near-coastal region of western Finland.The dynamical downscaling is performed with the Weather Research and Forecasting(WRF)model,and the statistical downscaling method implemented is the Cumulative Distribution Function-transform(CDF-t).The CDF-t is trained using 20 years of WRF-downscaled Climate Forecast System Reanalysis data over the region at a 3-km spatial resolution for the central month of each season.The performance of the two methods is assessed qualitatively,by inspection of quantile-quantile plots,and quantitatively,through the Cramer-von Mises,mean absolute error,and root-mean-square error diagnostics.The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling(for all seasons).The hybrid method proves to be less computationally expensive,and also to give more skillful temperature forecasts(at least for the Finnish near-coastal region).
文摘Because the static evaluation is unable to reflect the dynamic features of economic benefits and the contribution of sustainable development of low-carbon new energy to economic benefits is huge,this article puts forward the dynamic evaluation model of economic benefits under the development of low-carbon new energy.Total energy,energy consumption structure,industrial structure,GDP,total population and energy supply structure were taken as independent variables,and the carbon intensity was taken as the dependent variable.Through t-test and decision coefficient,total energy,energy consumption structure,GDP and total population were determined as the main factors of influencing low-carbon economy.Based on these four main factors,the dynamic evaluation index system of economic benefits was constructed.Experimental results show that the proposed model can comprehensively reflect the economic benefit level and the contribution of low-carbon new energy.Therefore,this method has high evaluation accuracy,which can provide scientific reference for the economic benefit management of relevant management departments.
