This paper proposes a simple low cost SIR （sustainability index for roads） that can be easily implemented by any local government that has a flexible pavement road network. The SIR includes the three pillars of sust...This paper proposes a simple low cost SIR （sustainability index for roads） that can be easily implemented by any local government that has a flexible pavement road network. The SIR includes the three pillars of sustainability, economic, social and environmental. The economic pillar is development from a new perspective of pavement deterioration from the Snowy Mountains Engineering Corporation＇s Pavement Management System. The new perspective is easily seen when the deterioration is plotted in three dimensions. This new exponential curve provides an equation for the return on investment in a road network, in terms of a future pavement condition index versus the annual rehabilitation budget. The environmental pillar will be developed by determining which road rehabilitation treatments cause the most environmental damage and recreating the new curve with these treatments being incrementally removed. The resulting curves will provide the annual cost of minimizing environmental damage and the loss of pavement condition index for minimizing environmental damage. The social pillar is, consultation with the community on what pavement condition index they are willing to fund, that is, balancing annual cost, environmental damage and desired pavement condition. This more efficient reporting conforms with the USA Government Accounting Standards Board requirements but not necessarily with the International Financial Reporting Standards. This new SIR reduces the current financial reporting requirement for local govemments in Queensland, Australia and can greatly improve comparability of financial reporting, where local governments calibrate the pavement deterioration factors in their Pavement Management Systems and use the newly developed regional rulebase.展开更多
Most ocean-atmosphere coupled models have difficulty in predicting the E1 Nifio-Southern Oscillation （ENSO） when starting from the boreal spring season. However, the cause of this spring predictability barrier （SPB...Most ocean-atmosphere coupled models have difficulty in predicting the E1 Nifio-Southern Oscillation （ENSO） when starting from the boreal spring season. However, the cause of this spring predictability barrier （SPB） phenomenon remains elusive. We investigated the spatial characteristics of optimal initial errors that cause a significant SPB for E1 Nifio events by using the monthly mean data of the pre-industrial （PI） control runs from several models in CMIP5 experiments. The results indicated that the SPB-related optimal initial errors often present an SST pattern with positive errors in the central-eastern equatorial Pa- cific, and a subsurface temperature pattern with positive errors in the upper layers of the eastern equatorial Pacific, and nega- tive errors in the lower layers of the western equatorial Pacific. The SPB-related optimal initial errors exhibit a typical La Ni- fia-like evolving mode, ultimately causing a large but negative prediction error of the Nifio-3.4 SST anomalies for El Nifio events. The negative prediction errors were found to originate from the lower layers of the western equatorial Pacific and then grow to be large in the eastern equatorial Pacific. It is therefore reasonable to suggest that the E1 Nifio predictions may be most sensitive to the initial errors of temperature in the subsurface layers of the western equatorial Pacific and the Nifio-3.4 region, thus possibly representing sensitive areas for adaptive observation. That is, if additional observations were to be preferentially deployed in these two regions, it might be possible to avoid large prediction errors for E1 Nifio and generate a better forecast than one based on additional observations targeted elsewhere. Moreover, we also confirmed that the SPB-related optimal initial errors bear a strong resemblance to the optimal precursory disturbance for E1 Nifio and La Nifia events. This indicated that im- provement of the observation network by additional observations in the identified sensitive areas would also be helpful in de- tecting the signals provided by the precursory disturbance, which may greatly improve the ENSO prediction skill.展开更多
文摘This paper proposes a simple low cost SIR （sustainability index for roads） that can be easily implemented by any local government that has a flexible pavement road network. The SIR includes the three pillars of sustainability, economic, social and environmental. The economic pillar is development from a new perspective of pavement deterioration from the Snowy Mountains Engineering Corporation＇s Pavement Management System. The new perspective is easily seen when the deterioration is plotted in three dimensions. This new exponential curve provides an equation for the return on investment in a road network, in terms of a future pavement condition index versus the annual rehabilitation budget. The environmental pillar will be developed by determining which road rehabilitation treatments cause the most environmental damage and recreating the new curve with these treatments being incrementally removed. The resulting curves will provide the annual cost of minimizing environmental damage and the loss of pavement condition index for minimizing environmental damage. The social pillar is, consultation with the community on what pavement condition index they are willing to fund, that is, balancing annual cost, environmental damage and desired pavement condition. This more efficient reporting conforms with the USA Government Accounting Standards Board requirements but not necessarily with the International Financial Reporting Standards. This new SIR reduces the current financial reporting requirement for local govemments in Queensland, Australia and can greatly improve comparability of financial reporting, where local governments calibrate the pavement deterioration factors in their Pavement Management Systems and use the newly developed regional rulebase.
基金sponsored by the National Basic Research Program of China(Grant No.2012CB955200)the National Public Benefit(Meteorology)Research Foundation of China(Grant No.GYHY201306018)+2 种基金the National Natural Science Foundation of China(Grant Nos.41230420,41176013)Zhang Jing was supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)the Jiangsu Innovation Cultivation Project for Graduate Student(Grant No.CXZZ13_0502)
文摘Most ocean-atmosphere coupled models have difficulty in predicting the E1 Nifio-Southern Oscillation （ENSO） when starting from the boreal spring season. However, the cause of this spring predictability barrier （SPB） phenomenon remains elusive. We investigated the spatial characteristics of optimal initial errors that cause a significant SPB for E1 Nifio events by using the monthly mean data of the pre-industrial （PI） control runs from several models in CMIP5 experiments. The results indicated that the SPB-related optimal initial errors often present an SST pattern with positive errors in the central-eastern equatorial Pa- cific, and a subsurface temperature pattern with positive errors in the upper layers of the eastern equatorial Pacific, and nega- tive errors in the lower layers of the western equatorial Pacific. The SPB-related optimal initial errors exhibit a typical La Ni- fia-like evolving mode, ultimately causing a large but negative prediction error of the Nifio-3.4 SST anomalies for El Nifio events. The negative prediction errors were found to originate from the lower layers of the western equatorial Pacific and then grow to be large in the eastern equatorial Pacific. It is therefore reasonable to suggest that the E1 Nifio predictions may be most sensitive to the initial errors of temperature in the subsurface layers of the western equatorial Pacific and the Nifio-3.4 region, thus possibly representing sensitive areas for adaptive observation. That is, if additional observations were to be preferentially deployed in these two regions, it might be possible to avoid large prediction errors for E1 Nifio and generate a better forecast than one based on additional observations targeted elsewhere. Moreover, we also confirmed that the SPB-related optimal initial errors bear a strong resemblance to the optimal precursory disturbance for E1 Nifio and La Nifia events. This indicated that im- provement of the observation network by additional observations in the identified sensitive areas would also be helpful in de- tecting the signals provided by the precursory disturbance, which may greatly improve the ENSO prediction skill.
