数值天气预报中的误差增长及大气的可预报性

ERROR GROWTH IN NUMERICAL PREDICTION AND ATMOSPHERIC PREDICTABILITY

中文摘要:

      本文用数值试验方法,对模式预报中误差增长的物理机制作了初步研究.结果为,初始误差的大小直接影响以后的误差增长,相比而言,初始误差的随机分布形态影响很小.小尺度误差自身增长较快,并通过各尺度之间的非线性相互作用,小尺度误差向大尺度和行星尺度误差转移,促使整个系统的误差增长.地形对误差增长的影响为,当初始误差特征尺度为小尺度(8-21波)时,地形加强误差增长,初始误差为行星尺度(0-3波)时,地形抑制误差增长,可能存在一临界波长,该波长在4-7波之间.故地形对可预报性的影响与初始误差的特征尺度有关.在初始误差相同时,北半球误差增长较南半球块.最后,为提高模式的预报能力,就模式本身及初始化方案等方面进行了讨论.

英文摘要:

      The article is to report some research on the error growth and the atmos-pheric predictability. Some results are obtained by the experiments with a two-level global primitive equations spectral model. The magnitude (RMS) of initial error directly affects the error growth, but its distribution form has little effect on the growth. Small scale error grows rapidly and is transferred to large scale error by interaction between different scale waves, which stimulates the growth of error of whole system. Orographic forcing restrains planetaryscale error (waven-umbers 0-3) and enhances small scale error (wavenumbers 8 or greater) to grow. Hence, orographic effects on the error growth closely depend on the characteristic scale of initial error, and there may be a critical wave number which is between 4 and 7.The error growth is greater in Northen Hemisphere than in Southen Hemisphere when initial errors are the same. In the end we give some discussion about model, initialization scheme, and so on, to improve model prediction.