利用STL(Seasonal-Trend decomposition using Loess,时间序列分解算法)模型对工业园区运行阶段的建筑碳排放进行预测分析,介绍电碳模型构建技术路线、模型构建的STL和线性回归,给出某高新园区2021年到2022年碳排放量的预测结果。可为...利用STL(Seasonal-Trend decomposition using Loess,时间序列分解算法)模型对工业园区运行阶段的建筑碳排放进行预测分析,介绍电碳模型构建技术路线、模型构建的STL和线性回归,给出某高新园区2021年到2022年碳排放量的预测结果。可为园区规划双碳路径、制定减排计划、开展节能减排手段提供数据支撑,辅助园区实现绿色低碳发展目标。展开更多
The chemistry version of the Weather Re- search and Forecasting model （WRF/Chem） was coupled with the anthropogenic emission inventory of David Streets to investigate the impacts of secondary aerosols on a persisten...The chemistry version of the Weather Re- search and Forecasting model （WRF/Chem） was coupled with the anthropogenic emission inventory of David Streets to investigate the impacts of secondary aerosols on a persistent fog event from 25 to 26 October 2007, in Northem China. The spatial distribution of the simulated fog is consistent with satellite observations, and the time-height distributions of the simulated boundary layer where the fog formed are also in good agreement with these observations. The sensitivity studies show that the secondary aerosols of SO4, NO3, and NH4 formed from gaseous precursors of SO2, NOx, and NH3 had substantial impacts on the formation processes and microphysical structure of the fog event. The decrease of the secondary aerosols obviously reduced the liquid water path and column droplet number concentration of the fog below the 1-km layer, and the corresponding area-averaged liquid water path and droplet number concentration of the fog decreased by 43% and 79%, respectively. The concentra- tions of NOx and NO3 were found to be extremely high in this case. The concentration of interstitial aerosol NO3 was much higher than the SO4 and NH4, but the concentration of SO4 was highest in the cloud-borne aerosols. The average activation ratios for SO4, NO3, and NH4 were 34%, 31%, and 30%, respectively, and the maximum ra- tios reached 62%, 86%, and 55% during the fog episode.展开更多
In order to realize the prediction of a chaotic time series of mine water discharge,an approach incorporating phase space reconstruction theory and statistical learning theory was studied.A differential entropy ratio ...In order to realize the prediction of a chaotic time series of mine water discharge,an approach incorporating phase space reconstruction theory and statistical learning theory was studied.A differential entropy ratio method was used to determine embedding parameters to reconstruct the phase space.We used a multi-layer adaptive best-fitting parameter search algorithm to estimate the LS-SVM optimal parameters which were adopted to construct a LS-SVM prediction model for the mine water chaotic time series.The results show that the simulation performance of a single-step prediction based on this LS-SVM model is markedly superior to that based on a RBF model.The multi-step prediction results based on LS-SVM model can reflect the development of mine water discharge and can be used for short-term forecasting of mine water discharge.展开更多
The accurate simulation of the equatorial sea surlhce temperature （SST） variability is crucial for a proper representation or prediction of the El Nino-Southern Os- cillation （ENSO）. This paper describes the trop...The accurate simulation of the equatorial sea surlhce temperature （SST） variability is crucial for a proper representation or prediction of the El Nino-Southern Os- cillation （ENSO）. This paper describes the tropical variability simulated by the Max Planck Institute （MPI） forr meteorology coupled atmosphere-ocean general circulation model （CGCM）. A control simulation with pre-industrial greenhouse gases is analyzed, and the simulation of key oceanic features, such as SST, is compared with observa- tions. Results from the 400-yr control simulation show that the model＇s ENSO variability is quite realistic in terms of structure, strength, and period. Also, two related features （the annual cycle of SST and the-phase locking of ENSO events）, which are significant in determining the model＇s performance of realistic ENSO prediction, are further validated to be well reproduced by the MPI cli mate model, which is an atmospheric model ECHAM5 （which fuses the EC tbr European Center and HAM for Hamburg） coupled to an MPI ocean model （MPI-OM）, ECHAMS/MPI-OM.展开更多
文摘利用STL(Seasonal-Trend decomposition using Loess,时间序列分解算法)模型对工业园区运行阶段的建筑碳排放进行预测分析,介绍电碳模型构建技术路线、模型构建的STL和线性回归,给出某高新园区2021年到2022年碳排放量的预测结果。可为园区规划双碳路径、制定减排计划、开展节能减排手段提供数据支撑,辅助园区实现绿色低碳发展目标。
基金supported by the National Meteorology Public Welfare Industry Research Project(GYHY200806001)the National Science and Technology Support Program (2006BAC12B03)
文摘The chemistry version of the Weather Re- search and Forecasting model （WRF/Chem） was coupled with the anthropogenic emission inventory of David Streets to investigate the impacts of secondary aerosols on a persistent fog event from 25 to 26 October 2007, in Northem China. The spatial distribution of the simulated fog is consistent with satellite observations, and the time-height distributions of the simulated boundary layer where the fog formed are also in good agreement with these observations. The sensitivity studies show that the secondary aerosols of SO4, NO3, and NH4 formed from gaseous precursors of SO2, NOx, and NH3 had substantial impacts on the formation processes and microphysical structure of the fog event. The decrease of the secondary aerosols obviously reduced the liquid water path and column droplet number concentration of the fog below the 1-km layer, and the corresponding area-averaged liquid water path and droplet number concentration of the fog decreased by 43% and 79%, respectively. The concentra- tions of NOx and NO3 were found to be extremely high in this case. The concentration of interstitial aerosol NO3 was much higher than the SO4 and NH4, but the concentration of SO4 was highest in the cloud-borne aerosols. The average activation ratios for SO4, NO3, and NH4 were 34%, 31%, and 30%, respectively, and the maximum ra- tios reached 62%, 86%, and 55% during the fog episode.
基金supported by the Science and Research projects for Ph.D. candidates in the faculty of Xuzhou Normal University (No.08XLR12)Natural Science Foundation of Xuzhou Normal University (No.09XLA10)
文摘In order to realize the prediction of a chaotic time series of mine water discharge,an approach incorporating phase space reconstruction theory and statistical learning theory was studied.A differential entropy ratio method was used to determine embedding parameters to reconstruct the phase space.We used a multi-layer adaptive best-fitting parameter search algorithm to estimate the LS-SVM optimal parameters which were adopted to construct a LS-SVM prediction model for the mine water chaotic time series.The results show that the simulation performance of a single-step prediction based on this LS-SVM model is markedly superior to that based on a RBF model.The multi-step prediction results based on LS-SVM model can reflect the development of mine water discharge and can be used for short-term forecasting of mine water discharge.
基金supported by the National Program for Support of Top-notch Young Professionals, the National Basic Research Program of China (Grant Nos. 2012CB955202 and 2012CB417404)"Western Pacific Ocean System: Structure, Dynamics, and Consequences" of the Chinese Academy Sciences (WPOS+1 种基金 Grant No. XDA10010405)the National Natural Science Foundation of China (Grant No. 41176014)
文摘The accurate simulation of the equatorial sea surlhce temperature （SST） variability is crucial for a proper representation or prediction of the El Nino-Southern Os- cillation （ENSO）. This paper describes the tropical variability simulated by the Max Planck Institute （MPI） forr meteorology coupled atmosphere-ocean general circulation model （CGCM）. A control simulation with pre-industrial greenhouse gases is analyzed, and the simulation of key oceanic features, such as SST, is compared with observa- tions. Results from the 400-yr control simulation show that the model＇s ENSO variability is quite realistic in terms of structure, strength, and period. Also, two related features （the annual cycle of SST and the-phase locking of ENSO events）, which are significant in determining the model＇s performance of realistic ENSO prediction, are further validated to be well reproduced by the MPI cli mate model, which is an atmospheric model ECHAM5 （which fuses the EC tbr European Center and HAM for Hamburg） coupled to an MPI ocean model （MPI-OM）, ECHAMS/MPI-OM.
