This study investigates the seasonal evolution of the dominant modes of the Eurasian snowpack and atmospheric circulation from autumn to the subsequent spring using snow water equivalent （SWE）, snow cover frequency ...This study investigates the seasonal evolution of the dominant modes of the Eurasian snowpack and atmospheric circulation from autumn to the subsequent spring using snow water equivalent （SWE）, snow cover frequency （SCF）, and 500 hPa geopotential height data. It is found that the Eurasian SWE/SCF and circulation dominant modes are stably coupled from autumn to the subsequent spring.The temporal coherence of the seasonal evolution of the dominant modes is examined.The seasonal evolution of the Eurasian circulation and SWE dominant modes exhibit good coherence, whereas the evolution of the Eurasian SCF dominant mode is incoherent during the autumn-winter transition season. This incoherence is associated with a sign-change in the SCF anomalies in Europe during the autumn-winter transition season, which is related to the wind anomalies over Europe. In addition, the surface heat budget associated with the Eurasian SWE/SCF and circulation dominant modes is analyzed. The sensible heat flux （SHF） related to the wind-induced thermal advection dominates the surface heat budget from autumn to the subsequent spring, with the largest effect during winter. The surface net shortwave radiation is mainly modulated by snow cover rather than cloud cover, which is estimated to be as important as, or likely superior to, the SHF for the surface heat budget during spring.The NCEP-NCAR surface heat flux reanalysis data demonstrate a consistency with the SWE/SCF and air temperature observational data, indicating a good capability for snow-atmosphere interaction analysis.展开更多
This paper presents modelling of a post-combustion CO2 capture process using bootstrap aggregated extreme learning machine （ELM）. ELM randomly assigns the weights between input and hidden layers and obtains the weig...This paper presents modelling of a post-combustion CO2 capture process using bootstrap aggregated extreme learning machine （ELM）. ELM randomly assigns the weights between input and hidden layers and obtains the weights between the hidden layer and output layer using regression type approach in one step. This feature allows an ELM model being developed very quickly. This paper proposes using principal component regression to obtain the weights between the hidden and output layers to address the collinearity issue among hidden neuron outputs. Due to the weights between input and hidden layers are randomly assigned, ELM models could have variations in performance. This paper proposes combining multiple ELM models to enhance model prediction accuracy and reliability. To predict the CO2 production rate and CO2 capture level, eight parameters in the process were utilized as model input variables： inlet gas flow rate, CO2 concentration in inlet flow gas, inlet gas temperature, inlet gas pressure, lean solvent flow rate, Jean solvent temperature, lean loading and reboiler duty. The bootstrap re-sampling of training data was applied for building each single ELM and then the individual ELMs are stacked, thereby enhancing the model accuracy and reliability. The bootstrap aggregated extreme learning machine can provide fast learning speed and good generalization performance, which will be used to optimize the CO2 capture process.展开更多
基金supported by the National Natural Science Foundation of China[grant numbers 4142100441210007]+1 种基金the Chinese Academy of Sciences(CAS)-Peking University(PKU)Partnership Programthe Atmosphere-Ocean Research Center(AORC)and International Pacific Research Center(IPRC)at University of Hawaii
文摘This study investigates the seasonal evolution of the dominant modes of the Eurasian snowpack and atmospheric circulation from autumn to the subsequent spring using snow water equivalent （SWE）, snow cover frequency （SCF）, and 500 hPa geopotential height data. It is found that the Eurasian SWE/SCF and circulation dominant modes are stably coupled from autumn to the subsequent spring.The temporal coherence of the seasonal evolution of the dominant modes is examined.The seasonal evolution of the Eurasian circulation and SWE dominant modes exhibit good coherence, whereas the evolution of the Eurasian SCF dominant mode is incoherent during the autumn-winter transition season. This incoherence is associated with a sign-change in the SCF anomalies in Europe during the autumn-winter transition season, which is related to the wind anomalies over Europe. In addition, the surface heat budget associated with the Eurasian SWE/SCF and circulation dominant modes is analyzed. The sensible heat flux （SHF） related to the wind-induced thermal advection dominates the surface heat budget from autumn to the subsequent spring, with the largest effect during winter. The surface net shortwave radiation is mainly modulated by snow cover rather than cloud cover, which is estimated to be as important as, or likely superior to, the SHF for the surface heat budget during spring.The NCEP-NCAR surface heat flux reanalysis data demonstrate a consistency with the SWE/SCF and air temperature observational data, indicating a good capability for snow-atmosphere interaction analysis.
基金The work was supported by the EU through the project ＂Research and Development in Coal-fired Supercritical Power Plant with Post-combustion Carbon Capture using Process Systems Engineering techniques＂ （Project No. PIRSES-GA-2013-612230） and National Natural Science Foundation of China （61673236）.
文摘This paper presents modelling of a post-combustion CO2 capture process using bootstrap aggregated extreme learning machine （ELM）. ELM randomly assigns the weights between input and hidden layers and obtains the weights between the hidden layer and output layer using regression type approach in one step. This feature allows an ELM model being developed very quickly. This paper proposes using principal component regression to obtain the weights between the hidden and output layers to address the collinearity issue among hidden neuron outputs. Due to the weights between input and hidden layers are randomly assigned, ELM models could have variations in performance. This paper proposes combining multiple ELM models to enhance model prediction accuracy and reliability. To predict the CO2 production rate and CO2 capture level, eight parameters in the process were utilized as model input variables： inlet gas flow rate, CO2 concentration in inlet flow gas, inlet gas temperature, inlet gas pressure, lean solvent flow rate, Jean solvent temperature, lean loading and reboiler duty. The bootstrap re-sampling of training data was applied for building each single ELM and then the individual ELMs are stacked, thereby enhancing the model accuracy and reliability. The bootstrap aggregated extreme learning machine can provide fast learning speed and good generalization performance, which will be used to optimize the CO2 capture process.
