Chin. Phys. B, 2014, Vol. 23(8): 089201    DOI: 10.1088/1674-1056/23/8/089201
 GEOPHYSICS, ASTRONOMY, AND ASTROPHYSICS Prev   Next

# An approach to estimating and extrapolating model error based on inverse problem methods：towards accurate numerical weather prediction

Hu Shu-Juan (胡淑娟)a, Qiu Chun-Yu (邱春雨)a b, Zhang Li-Yun (张利云)b, Huang Qi-Can (黄启灿)a b, Yu Hai-Peng (于海鹏)a, Chou Ji-Fan (丑纪范)a
a College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China;
b School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China
Abstract  Model error is one of the key factors restricting the accuracy of numerical weather prediction (NWP). Considering the continuous evolution of the atmosphere, the observed data (ignoring the measurement error) can be viewed as a series of solutions of an accurate model governing the actual atmosphere. Model error is represented as an unknown term in the accurate model, thus NWP can be considered as an inverse problem to uncover the unknown error term. The inverse problem models can absorb long periods of observed data to generate model error correction procedures. They thus resolve the deficiency and faultiness of the NWP schemes employing only the initial-time data. In this study we construct two inverse problem models to estimate and extrapolate the time-varying and spatial-varying model errors in both the historical and forecast periods by using recent observations and analogue phenomena of the atmosphere. Numerical experiment on Burgers' equation has illustrated the substantial forecast improvement using inverse problem algorithms. The proposed inverse problem methods of suppressing NWP errors will be useful in future high accuracy applications of NWP.
Keywords:  numerical weather prediction      model error      past data      inverse problem
Received:  17 December 2013      Revised:  29 January 2014      Accepted manuscript online:
 PACS: 92.60.Wc (Weather analysis and prediction)
Fund: Project supported by the Special Scientific Research Project for Public Interest (Grant No. GYHY201206009), the Fundamental Research Funds for the Central Universities, China (Grant Nos. lzujbky-2012-13 and lzujbky-2013-11), and the National Basic Research Program of China (Grant Nos. 2012CB955902 and 2013CB430204).
Corresponding Authors:  Hu Shu-Juan     E-mail:  hushuju@lzu.edu.cn