An online model correction method based on an inverse problem: Part I—Model error estimation by iteration

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• 作者：Haile Xue ; Xueshun Shen ; Jifan Chou
• 关键词：model error ; past data ; inverse problem ; error estimation ; model correction ; GRAPES ; GFS
• 刊名：Advances in Atmospheric Sciences
• 出版年：2015
• 出版时间：October 2015
• 年：2015
• 卷：32
• 期：10
• 页码：1329-1340
• 全文大小：3,241 KB
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• 作者单位：Haile Xue (1) (3)
  Xueshun Shen (1) (2)
  Jifan Chou (3)
  
  1. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, 100081, China
  3. School of Atmospheric Sciences, Lanzhou University, Lanzhou, 730000, China
  2. Center for Numerical Prediction, China Meteorological Administration, Beijing, 100081, China
  
• 刊物主题：Atmospheric Sciences; Meteorology; Geophysics/Geodesy;
• 出版者：Springer Berlin Heidelberg
• ISSN：1861-9533

文摘

Errors inevitably exist in numerical weather prediction (NWP) due to imperfect numeric and physical parameterizations. To eliminate these errors, by considering NWP as an inverse problem, an unknown term in the prediction equations can be estimated inversely by using the past data, which are presumed to represent the imperfection of the NWP model (model error, denoted as ME). In this first paper of a two-part series, an iteration method for obtaining the MEs in past intervals is presented, and the results from testing its convergence in idealized experiments are reported. Moreover, two batches of iteration tests were applied in the global forecast system of the Global and Regional Assimilation and Prediction System (GRAPES-GFS) for July–August 2009 and January–February 2010. The datasets associated with the initial conditions and sea surface temperature (SST) were both based on NCEP (National Centers for Environmental Prediction) FNL (final) data. The results showed that 6th h forecast errors were reduced to 10% of their original value after a 20-step iteration. Then, off-line forecast error corrections were estimated linearly based on the 2-month mean MEs and compared with forecast errors. The estimated error corrections agreed well with the forecast errors, but the linear growth rate of the estimation was steeper than the forecast error. The advantage of this iteration method is that the MEs can provide the foundation for online correction. A larger proportion of the forecast errors can be expected to be canceled out by properly introducing the model error correction into GRAPES-GFS.