GRAPES非静力数值预报模式的三维变分资料同化系统的发展

Development of 3-D variational data assimilation system forthe nonhydrostatic numerical weather prediction model-GRAPES

中文关键词:  GRAPES，非静力模式，资料同化，数值预报

英文关键词:GRAPES, Nonhydrostatic model, Data assimilation, Numerical prediction

中文摘要:

      为了减少分析变量与模式状态变量之间的插值误差，改善业务预报模式的初值质量，在GRAPES等压面三维变分资料同化系统的基础上，研究发展了针对GRAPES区域模式的非静力模式变量三维变分资料同化系统（GRAPES m3DVAR）。该资料同化系统的垂直坐标及其分析变量的水平分布格式、垂直跳点方案与GRAPES预报模式保持完全一致。由于垂直坐标的变化和非静力关系，m3DVAR分析系统中设计了求解动力学约束方程的新方案。通过有效的高精度数学方案，避免了地形追随坐标下平衡方程的非线性项造成的复杂计算，有效解决了非静力平衡条件下求解平衡方程中非线性项的切线性方程和伴随方程引起的困难。重新构造各种观测算子，并考虑了质量场和风场之间的平衡约束关系、背景误差协方差结构，实现对探空、地面资料、船舶报等常规观测的同化。理想单点试验和实际资料的多变量资料同化分析结果表明，非静力模式变量三维变分资料同化系统能够正确地描写多变量之间的相互作用以及物理约束关系，分析结果合理，能够有效减少原等压面三维变分资料同化系统的分析与模式变量之间需要相互插值、变换产生的误差，在一定程度上提高了分析场质量，对预报模式的初值具有一定改善。

英文摘要:

      Based on the GRAPES 3-D variational data assimilation system (p3DVAR), which is defined on isobaric surfaces, a new system (m3DVAR) is reconstructed to match with the nonhydrostatic GRAPES model in order to reduce the errors caused by spatial interpolation and variables transformations, thus improving the quality of initial value for the operational weather prediction model. The analysis variables of the m3DVAR system are completely consistent with the predictands of the GRAPES model in terms of spatial staggering and physical definition. Because of vertical hybrid ordinate and nonhydrostatic conditions, a new scheme for solving dynamical constraint equations is designed for the m3DVAR data assimilation system. To deal with the difficulties in solving the nonlinear balance equation at sigma levels, dynamical balance constraints between mass and wind fields are reformulated, and an effective mathematical scheme is implemented to avoid complicated problems caused by the nonlinear equations under the terrain following coordinates. Meanwhile, new observation operators are designed for routine observational data, and background error covariance is also calculated. Currently, the m3DVAR system is able to assimilate all the conventional observations data, such as sounding, surface and ship data, etc. The multivariate ideal experiments with single point observation are performed to test validity of the system. The results show that the m3DVAR data assimilation system is able to correctly describe the multivariate analysis and the relationship of physical constraints. The differences of innovation and analysis residual for π also show that the analysis error of the m3DVAR system is less than that of the p3DVAR system. The Ts scores of the 24-hour precipitation prediction in August 2006 indicate that the m3DVAR system has smaller errors of model initial values relative to the p3DVAR system. So, the m3DVAR system can improve the analysis quality and initial values for the GRAPES model.