| Application of an Error Statistics Estimation Method to the PSAS Forecast Error Covariance Model |
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| 作者姓名： | Runhua YANG Jing GUO Lars Peter RIISH?JGAARD |
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| 作者单位： | [1]Science Systems and Applications Inc., Lanham, USA [2]Science Applications International Corporation, Beltsville, USA [3]Joint Center for Earth Systems Technology/UMBC, Baltimore, USA [4]Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, USA |
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| 基金项目： | Acknowledgments. We thank Dr. Steve Bloom for his insightful discussion and encouragement throughout the course of this study. We are grateful to our colleagues Drs. Guang-Ping Luo and J. Jusem for providing the computation programs and Dr. Fran Verter for providing the MLE computation. We thank Mr. Thomas 0wens at GMA0 for his help in processing the FVDAS data. A special thanks goes to our anonymous reviewers for their careful review and insightful comments. |
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| 摘 要： | In atmospheric data assimilation systems, the forecast error covariance model is an important component. However, the paralneters required by a forecast error covariance model are difficult to obtain due to the absence of the truth. This study applies an error statistics estimation method to the Pfiysical-space Statistical Analysis System （PSAS） height-wind forecast error covariance model. This method consists of two components： the first component computes the error statistics by using the National Meteorological Center （NMC） method, which is a lagged-forecast difference approach, within the framework of the PSAS height-wind forecast error covariance model; the second obtains a calibration formula to rescale the error standard deviations provided by the NMC method. The calibration is against the error statistics estimated by using a maximum-likelihood estimation （MLE） with rawindsonde height observed-minus-forecast residuals. A complete set of formulas for estimating the error statistics and for the calibration is applied to a one-month-long dataset generated by a general circulation model of the Global Model and Assimilation Office （GMAO）, NASA. There is a clear constant relationship between the error statistics estimates of the NMC-method and MLE. The final product provides a full set of 6-hour error statistics required by the PSAS height-wind forecast error covariance model over the globe. The features of these error statistics are examined and discussed.
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| 关 键 词： | 天气预报 统计误差 协方差模型 PSAS 统计预报方法 |
| 收稿时间： | 2005-02-25 |
| 修稿时间： | 2005-07-25 |
Application of an error statistics estimation method to the PSAS forecast error covariance model |
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| Runhua Yang,Jing Guo,Lars Peter Riish?jgaard.Application of an Error Statistics Estimation Method to the PSAS Forecast Error Covariance Model[J].Advances in Atmospheric Sciences,2006,23(1):33-44. |
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| Authors: | Runhua Yang Jing Guo Lars Peter Riishøjgaard |
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| Institution: | Science Systems and Applications Inc., Lanham, USA, Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, USA,Science Applications International Corporation, Beltsville, USA, Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, USA,Joint Center for Earth Systems Technology/UMBC, Baltimore, USA, Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, USA |
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| Abstract: | In atmospheric data assimilation systems, the forecast error covariance model is an important component. However, the parameters required by a forecast error covariance model are difficult to obtain due to the absence of the truth. This study applies an error statistics estimation method to the Physical-space Statistical Analysis System (PSAS) height-wind forecast error covariance model. This method consists of two components: the first component computes the error statistics by using the National Meteorological Center (NMC) method, which is a lagged-forecast difference approach, within the framework of the PSAS height-wind forecast error covariance model; the second obtains a calibration formula to rescale the error standard deviations provided by the NMC method. The calibration is against the error statistics estimated by using a maximum-likelihood estimation (MLE) with rawindsonde height observed-minus-forecast residuals. A complete set of formulas for estimating the error statistics and for the calibration is applied to a one-month-long dataset generated by a general circulation model of the Global Model and Assimilation Office (GMAO), NASA. There is a clear constant relationship between the error statistics estimates of the NMC-method and MLE. The final product provides a full set of 6-hour error statistics required by the PSAS height-wind forecast error covariance model over the globe. The features of these error statistics are examined and discussed. |
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| Keywords: | forecast error statistics estimation data analysis forecast error covariance model |
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