An atmosphere-vegetation interaction model (AVIM) has been coupled with a nine-layer General Cir culation Model (GCM) of Institute of Atmospheic Physics / State Key Laboratory of Numerical Modeling for Atmospheric Sci...An atmosphere-vegetation interaction model (AVIM) has been coupled with a nine-layer General Cir culation Model (GCM) of Institute of Atmospheic Physics / State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (IAP/ LASG), which is rhomboidally truncated at zonal wave number 15, to simulate global climatic mean states. AVIM is a model having inter-feedback between land surface processes and eco-physiological processes on land. As the first step to couple land with atmosphere completely, the physiological processes are fixed and only the physical part (generally named the SVAT (soil-vegetation-atmosphere-transfer scheme) model) of AVIM is nested into IAP/ LASG L9R15 GCM. The ocean part of GCM is prescribed and its monthly sea surface temperature (SST) is the climatic mean value. With respect to the low resolution of GCM, i.e., each grid cell having lon gitude 7.5?and latitude 4.5? the vegetation is given a high resolution of 1.5?by 1.5?to nest and couple the fine grid cells of land with the coarse grid cells of atmosphere. The coupling model has been integrated for 15 years and its last ten-year mean of outputs was chosen for analysis.Compared with observed data and NCEP reanalysis, the coupled model simulates the main characteris tics of global atmospheric circulation and the fields of temperature and moisture. In particular, the simu lated precipitation and surface air temperature have sound results. The work creates a solid base on coupling climate models with the biosphere.展开更多
The simulations and potential forecasting of dust storms are of significant interest to public health and environment sciences.Dust storms have interannual variabilities and are typical disruptive events.The computing...The simulations and potential forecasting of dust storms are of significant interest to public health and environment sciences.Dust storms have interannual variabilities and are typical disruptive events.The computing platform for a dust storm forecasting operational system should support a disruptive fashion by scaling up to enable high-resolution forecasting and massive public access when dust storms come and scaling down when no dust storm events occur to save energy and costs.With the capability of providing a large,elastic,and virtualized pool of computational resources,cloud computing becomes a new and advantageous computing paradigm to resolve scientific problems traditionally requiring a large-scale and high-performance cluster.This paper examines the viability for cloud computing to support dust storm forecasting.Through a holistic study by systematically comparing cloud computing using Amazon EC2 to traditional high performance computing(HPC)cluster,we find that cloud computing is emerging as a credible solution for(1)supporting dust storm forecasting in spinning off a large group of computing resources in a few minutes to satisfy the disruptive computing requirements of dust storm forecasting,(2)performing high-resolution dust storm forecasting when required,(3)supporting concurrent computing requirements,(4)supporting real dust storm event forecasting for a large geographic domain by using recent dust storm event in Phoniex,05 July 2011 as example,and(5)reducing cost by maintaining low computing support when there is no dust storm events while invoking a large amount of computing resource to perform high-resolution forecasting and responding to large amount of concurrent public accesses.展开更多
A brief review of the importance of simulation-based engineering and science(including social sciences)is followed by a historic perspective of model-based simulation.Section 2 is on declarative modeling of component ...A brief review of the importance of simulation-based engineering and science(including social sciences)is followed by a historic perspective of model-based simulation.Section 2 is on declarative modeling of component systems as well as its advantages for self-documentation and for computer-aided checks and coupling.As an example for declarative modeling,General System Theory(GEST)implementor is given.In Sec.3,basic concepts for coupling of component models,and rules for computer-assisted coupling specification are explained.Section 4 is devoted to possible computerized checks in couplings of declarative models such as:(1)automatic unit checking to avoid meaning-less input/output matching at the time of coupling specification,(2)automatic threshold checking to provide warnings and/or to avoid disasters,and(3)automatic unit conversion for convenience of using library models.Section 5 is about several layers of nested couplings for modeling systems of systems.In Sec.6,two types of variable couplings are discussed:(1)couplings with variable connections(to allow input/output relations of models to depend on time or state conditions)and(2)coupling with variable component models(to allow component(or coupled)models to be switched based on time or state conditions).Section 7 is on the use of multimodels as component models in couplings.Section 8 is on types of inputs and their use in couplings as well as on exter-nal inputs to simulation studies.In Sec.9,conclusions and future work for complex systems are outlined.Especially,the values of simulation systems engineering as well as understanding and avoidance of misunderstanding in cognitive and emotive simulations are stressed.Appendix A is a list of almost 50 types of couplings and Appendix B lists over 50 terms related with couplings in modeling and simulation.To show the richness of“input”concept which is important in specification of input/output relations of component models,Appendix C lists almost 150 types of inputs.Information shared in this article may be useful in developing advanced modeling and simulation software,tools and environments.展开更多
基金This paper is financially supported by the NKBRSF(National Key Basic Research Special Funds)project ofChina(G1999043400).the Key Project of Knowledge Innovation Engineering of Chinese Academy of Sciences(ZKCX2-SW-210),and the Lead Project of Innovation
文摘An atmosphere-vegetation interaction model (AVIM) has been coupled with a nine-layer General Cir culation Model (GCM) of Institute of Atmospheic Physics / State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (IAP/ LASG), which is rhomboidally truncated at zonal wave number 15, to simulate global climatic mean states. AVIM is a model having inter-feedback between land surface processes and eco-physiological processes on land. As the first step to couple land with atmosphere completely, the physiological processes are fixed and only the physical part (generally named the SVAT (soil-vegetation-atmosphere-transfer scheme) model) of AVIM is nested into IAP/ LASG L9R15 GCM. The ocean part of GCM is prescribed and its monthly sea surface temperature (SST) is the climatic mean value. With respect to the low resolution of GCM, i.e., each grid cell having lon gitude 7.5?and latitude 4.5? the vegetation is given a high resolution of 1.5?by 1.5?to nest and couple the fine grid cells of land with the coarse grid cells of atmosphere. The coupling model has been integrated for 15 years and its last ten-year mean of outputs was chosen for analysis.Compared with observed data and NCEP reanalysis, the coupled model simulates the main characteris tics of global atmospheric circulation and the fields of temperature and moisture. In particular, the simu lated precipitation and surface air temperature have sound results. The work creates a solid base on coupling climate models with the biosphere.
基金Research reported is supported by NSF(CSR-1117300 and IIP-1160979)NASA(NNX07AD99G)Microsoft Research.
文摘The simulations and potential forecasting of dust storms are of significant interest to public health and environment sciences.Dust storms have interannual variabilities and are typical disruptive events.The computing platform for a dust storm forecasting operational system should support a disruptive fashion by scaling up to enable high-resolution forecasting and massive public access when dust storms come and scaling down when no dust storm events occur to save energy and costs.With the capability of providing a large,elastic,and virtualized pool of computational resources,cloud computing becomes a new and advantageous computing paradigm to resolve scientific problems traditionally requiring a large-scale and high-performance cluster.This paper examines the viability for cloud computing to support dust storm forecasting.Through a holistic study by systematically comparing cloud computing using Amazon EC2 to traditional high performance computing(HPC)cluster,we find that cloud computing is emerging as a credible solution for(1)supporting dust storm forecasting in spinning off a large group of computing resources in a few minutes to satisfy the disruptive computing requirements of dust storm forecasting,(2)performing high-resolution dust storm forecasting when required,(3)supporting concurrent computing requirements,(4)supporting real dust storm event forecasting for a large geographic domain by using recent dust storm event in Phoniex,05 July 2011 as example,and(5)reducing cost by maintaining low computing support when there is no dust storm events while invoking a large amount of computing resource to perform high-resolution forecasting and responding to large amount of concurrent public accesses.
文摘A brief review of the importance of simulation-based engineering and science(including social sciences)is followed by a historic perspective of model-based simulation.Section 2 is on declarative modeling of component systems as well as its advantages for self-documentation and for computer-aided checks and coupling.As an example for declarative modeling,General System Theory(GEST)implementor is given.In Sec.3,basic concepts for coupling of component models,and rules for computer-assisted coupling specification are explained.Section 4 is devoted to possible computerized checks in couplings of declarative models such as:(1)automatic unit checking to avoid meaning-less input/output matching at the time of coupling specification,(2)automatic threshold checking to provide warnings and/or to avoid disasters,and(3)automatic unit conversion for convenience of using library models.Section 5 is about several layers of nested couplings for modeling systems of systems.In Sec.6,two types of variable couplings are discussed:(1)couplings with variable connections(to allow input/output relations of models to depend on time or state conditions)and(2)coupling with variable component models(to allow component(or coupled)models to be switched based on time or state conditions).Section 7 is on the use of multimodels as component models in couplings.Section 8 is on types of inputs and their use in couplings as well as on exter-nal inputs to simulation studies.In Sec.9,conclusions and future work for complex systems are outlined.Especially,the values of simulation systems engineering as well as understanding and avoidance of misunderstanding in cognitive and emotive simulations are stressed.Appendix A is a list of almost 50 types of couplings and Appendix B lists over 50 terms related with couplings in modeling and simulation.To show the richness of“input”concept which is important in specification of input/output relations of component models,Appendix C lists almost 150 types of inputs.Information shared in this article may be useful in developing advanced modeling and simulation software,tools and environments.
