| A Sensitivity Study of Single Column Model |
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| 作者姓名： | Min Dong Qin Xu |
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| 作者单位： | Cooperative Institute for Mesoscale Meteorological Studies University of Oklahoma / NOAA，100 E.Boyd，Norman，OK.73019，Cooperative Institute for Mesoscale Meteorological Studies University of Oklahoma / NOAA，100 E.Boyd，Norman，OK.73019 |
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| 摘 要： | A single column model (SCM) is constructed by extracting the physical subroutines from the NCAR Community Climate Model version 1 (CCM1).Simulated data are generated by CCM1 and used to validate the SCM and to study the sensitivity of the SCM to errors in its input data.It is found that the SCM temperature predictions are moderately sensitive to errors in the input horizontal temperature flux convergence and moisture flux convergence.Two types of error are concerned in this study,random errors due to insufficient data resolution,and errors due to insufficient data area coverage.While the first type of error can be reduced by filtering and/or increasing the data resolution,it is shown that the second type of error can be reduced by enlarging the data area coverage and using a suitable method to compute the input flux convergence terms.
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| 收稿时间： | 4 January 1996 |
A sensitivity study of single column model |
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| Min Dong,Qin Xu.A Sensitivity Study of Single Column Model[J].Advances in Atmospheric Sciences,1996,13(3):313-324. |
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| Authors: | Min Dong Qin Xu |
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| Institution: | Cooperative Institute for Mesoscale Meteorological Studies University of Oklahoma/ NOAA, 100 E. Boyd, Norman, OK 73019,Cooperative Institute for Mesoscale Meteorological Studies University of Oklahoma/ NOAA, 100 E. Boyd, Norman, OK 73019 |
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| Abstract: | A single column model (SCM) is constructed by extracting the phytical subroutine from the NCAR Community Climate Model version 1 (CCM1). Simulated data are generated by CCM1 and used to validate the SCM and to study the sensitivity of the SCM to errors in its input data. It is found that the SCM temperature predictions ire mod-erately sensitive to errors in the input horizontal temperature flux convergence and moisture flux convergence. Two types of error are concerned in this study: random errors due to insufficient data resolution, and errors due to insufficient data area coverage. While the first type of error can be reduced by filtering and / or increasing the data resolution, it is shown that the second type of error can be reduced by enlarging the data area coverage and using a suitable method to compute the input flux convergence terms. |
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| Keywords: | Single column model Input data errors Sensitivity study |
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