Optimization under uncertainty is a challenging topic of practical importance in the Process Systems Engineering.Since the solution of an optimization problem generally exhibits high sensitivity to the parameter varia...Optimization under uncertainty is a challenging topic of practical importance in the Process Systems Engineering.Since the solution of an optimization problem generally exhibits high sensitivity to the parameter variations, the deterministic model which neglects the parametric uncertainties is not suitable for practical applications. This paper provides an overview of the key contributions and recent advances in the field of process optimization under uncertainty over the past ten years and discusses their advantages and limitations thoroughly. The discussion is focused on three specific research areas, namely robust optimization, stochastic programming and chance constrained programming, based on which a systematic analysis of their applications, developments and future directions are presented. It shows that the more recent trend has been to integrate different optimization methods to leverage their respective superiority and compensate for their drawbacks. Moreover, data-driven optimization, which combines mathematical programming methods and machine learning algorithms, has become an emerging and competitive tool to handle optimization problems in the presence of uncertainty based on massive historical data.展开更多
Multi-year experiments are conducted using the most recent version of the Abdus Salam International Centre for Theoretical Physics regional climate model RegCM4(version 4.7) to customize its performance over Southeast...Multi-year experiments are conducted using the most recent version of the Abdus Salam International Centre for Theoretical Physics regional climate model RegCM4(version 4.7) to customize its performance over Southeast Asia - a region with few RCMs applied to date. The model is driven by ERA-Interim reanalysis data at a grid spacing of 25 km using the CORDEX(Coordinated Regional Climate Downscaling Experiment) Southeast Asia domain. The authors focus on comparing the convection schemes of Emanuel and Tiedtke(Tiedtke-1) and Tiedtke with effects of sea surface evaporation introduced(Tiedtke-2). The authors find that, for temperature over land, the model shows reasonable performance in reproducing the present-day climatology in both December–January–February(DJF) and June–July–August(JJA) in all the experiments. Meanwhile, cold biases prevail in both seasons, although portions of warm bias exist in DJF. For precipitation, the spatial pattern and amount, as well as seasonal evolution, are in general reproduced well in the experiments.Better performances of Tiedtke-1 and Tiedtke-2 are evident compared to Emanuel, particularly over ocean. Thereby, the optimal configuration of Reg CM4.7 for future climate change simulations over the region is identified as using the Tiedtke scheme with spray effects considered, along with the default settings for other physical parameterizations.展开更多
To evaluate the downscaling ability with respect to tropical cyclones(TCs)near China and its sensitivity to the model physics representation,the authors performed a multi-physics ensemble simulation with the regional ...To evaluate the downscaling ability with respect to tropical cyclones(TCs)near China and its sensitivity to the model physics representation,the authors performed a multi-physics ensemble simulation with the regional Climate-Weather Research and Forecasting(CWRF)model at a 30 km resolution driven by ERA-Interim reanalysis data.The ensemble consisted of 28 integrations during 1979-2016 with varying CWRF physics configurations.Both CWRF and ERA-Interim can generally capture the seasonal cycle and interannual variation of the TC number near China,but evidently underestimate them.The CWRF downscaling and its multi-physics ensemble can notably reduce the underestimation and significantly improve the simulation of the TC occurrences.The skill enhancement is especially large in terms of the interannual variation,which is most sensitive to the cumulus scheme,followed by the boundary layer,surface and radiation schemes,but weakly sensitive to the cloud and microphysics schemes.Generally,the Noah surface scheme,CAML(CAM radiation scheme as implemented by Liang together with the diagnostic cloud cover scheme of Xu and Randall(1996))radiation scheme,prognostic cloud scheme,and Thompson microphysics scheme stand out for their better performance in simulating the interannual variation of TC number.However,the Emanuel cumulus and MYNN boundary layer schemes produce severe interannual biases.Our study provides a valuable reference for CWRF application to improve the understanding and prediction of TC activity.展开更多
文摘Optimization under uncertainty is a challenging topic of practical importance in the Process Systems Engineering.Since the solution of an optimization problem generally exhibits high sensitivity to the parameter variations, the deterministic model which neglects the parametric uncertainties is not suitable for practical applications. This paper provides an overview of the key contributions and recent advances in the field of process optimization under uncertainty over the past ten years and discusses their advantages and limitations thoroughly. The discussion is focused on three specific research areas, namely robust optimization, stochastic programming and chance constrained programming, based on which a systematic analysis of their applications, developments and future directions are presented. It shows that the more recent trend has been to integrate different optimization methods to leverage their respective superiority and compensate for their drawbacks. Moreover, data-driven optimization, which combines mathematical programming methods and machine learning algorithms, has become an emerging and competitive tool to handle optimization problems in the presence of uncertainty based on massive historical data.
基金This research was jointly supported by the Strategic Priority Research Programme of Chinese Academy of Sciences[grant number Y86101|601]the National Natural Science Foundation of China[grant numbers 41675103 and 41861144015].
文摘Multi-year experiments are conducted using the most recent version of the Abdus Salam International Centre for Theoretical Physics regional climate model RegCM4(version 4.7) to customize its performance over Southeast Asia - a region with few RCMs applied to date. The model is driven by ERA-Interim reanalysis data at a grid spacing of 25 km using the CORDEX(Coordinated Regional Climate Downscaling Experiment) Southeast Asia domain. The authors focus on comparing the convection schemes of Emanuel and Tiedtke(Tiedtke-1) and Tiedtke with effects of sea surface evaporation introduced(Tiedtke-2). The authors find that, for temperature over land, the model shows reasonable performance in reproducing the present-day climatology in both December–January–February(DJF) and June–July–August(JJA) in all the experiments. Meanwhile, cold biases prevail in both seasons, although portions of warm bias exist in DJF. For precipitation, the spatial pattern and amount, as well as seasonal evolution, are in general reproduced well in the experiments.Better performances of Tiedtke-1 and Tiedtke-2 are evident compared to Emanuel, particularly over ocean. Thereby, the optimal configuration of Reg CM4.7 for future climate change simulations over the region is identified as using the Tiedtke scheme with spray effects considered, along with the default settings for other physical parameterizations.
基金supported by the National Climate Center of China under Grants 2211011816501。
文摘To evaluate the downscaling ability with respect to tropical cyclones(TCs)near China and its sensitivity to the model physics representation,the authors performed a multi-physics ensemble simulation with the regional Climate-Weather Research and Forecasting(CWRF)model at a 30 km resolution driven by ERA-Interim reanalysis data.The ensemble consisted of 28 integrations during 1979-2016 with varying CWRF physics configurations.Both CWRF and ERA-Interim can generally capture the seasonal cycle and interannual variation of the TC number near China,but evidently underestimate them.The CWRF downscaling and its multi-physics ensemble can notably reduce the underestimation and significantly improve the simulation of the TC occurrences.The skill enhancement is especially large in terms of the interannual variation,which is most sensitive to the cumulus scheme,followed by the boundary layer,surface and radiation schemes,but weakly sensitive to the cloud and microphysics schemes.Generally,the Noah surface scheme,CAML(CAM radiation scheme as implemented by Liang together with the diagnostic cloud cover scheme of Xu and Randall(1996))radiation scheme,prognostic cloud scheme,and Thompson microphysics scheme stand out for their better performance in simulating the interannual variation of TC number.However,the Emanuel cumulus and MYNN boundary layer schemes produce severe interannual biases.Our study provides a valuable reference for CWRF application to improve the understanding and prediction of TC activity.
