Seasonal prediction of Asian-Australian monsoon （A-AM） precipitation is one of the most important and challenging tasks in climate prediction. In this paper, we evaluate the performance of Grid Atmospheric Model of ...Seasonal prediction of Asian-Australian monsoon （A-AM） precipitation is one of the most important and challenging tasks in climate prediction. In this paper, we evaluate the performance of Grid Atmospheric Model of IAP LASG （GAMIL） on retrospective prediction of the A-AM interannual variation （IAV）, and determine to what extent GAMIL can capture the two major observed modes of A-AM rainfall IAV for the period 1979-2003. The first mode is associated with the turnabout of warming （cooling） in the Nifio 3.4 region, whereas the second mode leads the warming/cooling by about one year, signaling precursory conditions for ENSO. We show that the GAMIL one-month lead prediction of the seasonal precipitation anomalies is primarily able to capture major features of the two observed leading modes of the IAV, with the first mode better predicted than the second. It also depicts the relationship between the first mode and ENSO rather well. On the other hand, the GAMIL has deficiencies in capturing the relationship between the second mode and ENSO. We conclude： （1） successful reproduction of the E1 Nifio-excited monsoon-ocean interaction and E1 Nifio forcing may be critical for the seasonal prediction of the A-AM rainfall IAV with the GAMIL; （2） more efforts are needed to improve the simulation not only in the Nifio 3.4 region but also in the joining area of Asia and the Indian-Pacific Ocean; （3） the selection of a one-tier system may improve the ultimate prediction of the A-AM rainfall IAV. These results offer some references for improvement of the GAMIL and associated seasonal prediction skill.展开更多
Researchers in P.R.China commonly create triangulate irregular networks(TINs) from contours and then convert TINs into digital elevation models(DEMs).However,the DEM produced by this method can not precisely describe ...Researchers in P.R.China commonly create triangulate irregular networks(TINs) from contours and then convert TINs into digital elevation models(DEMs).However,the DEM produced by this method can not precisely describe and simulate key hydrological features such as rivers and drainage borders.Taking a hilly region in southwestern China as a research area and using ArcGISTM software,we analyzed the errors of different interpolations to obtain distributions of the errors and precisions of different algorithms and to provide references for DEM productions.The results show that different interpolation errors satisfy normal distributions,and large error exists near the structure line of the terrain.Furthermore,the results also show that the precision of a DEM interpolated with the Australian National University digital elevation model(ANUDEM) is higher than that interpolated with TIN.The DEM interpolated with TIN is acceptable for generating DEMs in the hilly region of southwestern China.展开更多
基金the support of the National Natural Science Foundation of China(Grant Nos.40523001 and 40605022)the Chinese Acadiemy of the International Partnership Creative Group entitled"Climate System Model Development and Application Studies".
文摘Seasonal prediction of Asian-Australian monsoon （A-AM） precipitation is one of the most important and challenging tasks in climate prediction. In this paper, we evaluate the performance of Grid Atmospheric Model of IAP LASG （GAMIL） on retrospective prediction of the A-AM interannual variation （IAV）, and determine to what extent GAMIL can capture the two major observed modes of A-AM rainfall IAV for the period 1979-2003. The first mode is associated with the turnabout of warming （cooling） in the Nifio 3.4 region, whereas the second mode leads the warming/cooling by about one year, signaling precursory conditions for ENSO. We show that the GAMIL one-month lead prediction of the seasonal precipitation anomalies is primarily able to capture major features of the two observed leading modes of the IAV, with the first mode better predicted than the second. It also depicts the relationship between the first mode and ENSO rather well. On the other hand, the GAMIL has deficiencies in capturing the relationship between the second mode and ENSO. We conclude： （1） successful reproduction of the E1 Nifio-excited monsoon-ocean interaction and E1 Nifio forcing may be critical for the seasonal prediction of the A-AM rainfall IAV with the GAMIL; （2） more efforts are needed to improve the simulation not only in the Nifio 3.4 region but also in the joining area of Asia and the Indian-Pacific Ocean; （3） the selection of a one-tier system may improve the ultimate prediction of the A-AM rainfall IAV. These results offer some references for improvement of the GAMIL and associated seasonal prediction skill.
基金Funded by the Natural Science Foundation of Chongqing under Grant No. CSTC2006AB1015.
文摘Researchers in P.R.China commonly create triangulate irregular networks(TINs) from contours and then convert TINs into digital elevation models(DEMs).However,the DEM produced by this method can not precisely describe and simulate key hydrological features such as rivers and drainage borders.Taking a hilly region in southwestern China as a research area and using ArcGISTM software,we analyzed the errors of different interpolations to obtain distributions of the errors and precisions of different algorithms and to provide references for DEM productions.The results show that different interpolation errors satisfy normal distributions,and large error exists near the structure line of the terrain.Furthermore,the results also show that the precision of a DEM interpolated with the Australian National University digital elevation model(ANUDEM) is higher than that interpolated with TIN.The DEM interpolated with TIN is acceptable for generating DEMs in the hilly region of southwestern China.
