›› 2014, Vol. 23 ›› Issue (8): 89201-089201.doi: 10.1088/1674-1056/23/8/089201
• GEOPHYSICS, ASTRONOMY, AND ASTROPHYSICS • 上一篇 下一篇
胡淑娟a, 邱春雨a b, 张利云b, 黄启灿a b, 于海鹏a, 丑纪范a
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
摘要: 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.
中图分类号: (Weather analysis and prediction)