An investigation of the difference in seasonal precipitation forecast skills between the multiple linear regression （MLR） ensemble and the simple multimodel ensemble mean （EM） was based on the forecast quality of ...An investigation of the difference in seasonal precipitation forecast skills between the multiple linear regression （MLR） ensemble and the simple multimodel ensemble mean （EM） was based on the forecast quality of individual models. The possible causes of difference in previous studies were analyzed. In order to make the simulation capability of studied regions relatively uniform, three regions with different temporal correlation coefficients were chosen for this study. Results show the causes resulting in the incapability of the MLR approach vary among different regions. In the Nifio3.4 region, strong co-linearity within individual models is generally the main reason. However, in the high latitude region, no significant co-linearity can be found in individual models, but the abilities of single models are so poor that it makes the MLR approach inappropriate for superensemble forecasts in this region. In addition, it is important to note that the use of various score measurements could result in some discrepancies when we compare the results derived from different multimodel ensemble approaches.展开更多
The shapes of block within the rock mass have an important effect on the rock properties, so it＇s very important to evaluate the shapes of rock fragmentation and to determine the geometric characteristics distributio...The shapes of block within the rock mass have an important effect on the rock properties, so it＇s very important to evaluate the shapes of rock fragmentation and to determine the geometric characteristics distribution of the block. The previous methods to classify rock block shape are based on the assumption that a block shape is approximately orthogonal, which is acceptable in only a few rock masses. This paper proposes a new method for block shape classification using triangular diagram together with parameters of co-linearity e and volume coefficient K, which combines the shape categorization with block volume for statistical analysis. Rock block equivalent size calculation methods based on block shape is proposed and the block cumulative percentage of total volume statistical analysis is given. In order to verify this block shape classification method, three ideal rock masses with approximately orthogonal joint sets have been generated and simulated.展开更多
基金supported by the National Key Technology Research and Development Program(Grant No.2006BAC02B04)the Major State Basic Research Development Program of China(Grant No.2006CB400503)
文摘An investigation of the difference in seasonal precipitation forecast skills between the multiple linear regression （MLR） ensemble and the simple multimodel ensemble mean （EM） was based on the forecast quality of individual models. The possible causes of difference in previous studies were analyzed. In order to make the simulation capability of studied regions relatively uniform, three regions with different temporal correlation coefficients were chosen for this study. Results show the causes resulting in the incapability of the MLR approach vary among different regions. In the Nifio3.4 region, strong co-linearity within individual models is generally the main reason. However, in the high latitude region, no significant co-linearity can be found in individual models, but the abilities of single models are so poor that it makes the MLR approach inappropriate for superensemble forecasts in this region. In addition, it is important to note that the use of various score measurements could result in some discrepancies when we compare the results derived from different multimodel ensemble approaches.
基金supported by the Yunnan Provincial College and Institute Science and Technology Cooperation Projects (Grant No.2006YX26)the Technological Innovation and Technical Development Special Projects in Yunnan Province (Grant No.2007GA007)
文摘The shapes of block within the rock mass have an important effect on the rock properties, so it＇s very important to evaluate the shapes of rock fragmentation and to determine the geometric characteristics distribution of the block. The previous methods to classify rock block shape are based on the assumption that a block shape is approximately orthogonal, which is acceptable in only a few rock masses. This paper proposes a new method for block shape classification using triangular diagram together with parameters of co-linearity e and volume coefficient K, which combines the shape categorization with block volume for statistical analysis. Rock block equivalent size calculation methods based on block shape is proposed and the block cumulative percentage of total volume statistical analysis is given. In order to verify this block shape classification method, three ideal rock masses with approximately orthogonal joint sets have been generated and simulated.
