一种气象观测数据求导的新方法  

A new method for calculating the derivation of meteorological observational data

作　　者：王业桂[1] 蔡其发[1] 黄思训[2] 

机构地区：[1]中国科学院大气物理研究所,北京100029 [2]解放军理工大学气象学院,南京211101

出　　处：《物理学报》2010年第6期4359-4368,共10页Acta Physica Sinica

基　　金：国家自然科学基金(批准号:40775023)资助的课题~~

摘　　要：气象观测数据不可避免地带有观测误差,而对带有误差的离散数据求导是一个不适定的反问题.为了解决这一长期困扰气象工作者的问题,利用吉洪诺夫正则化思想,提出了矩形区域内观测数据的二维一阶偏导数重构算法.通过系列模拟观测数据试验检验了该算法的性能,表明该算法有效且计算精度高.另外,应用该算法对气象观测资料进行客观分析,结果表明:该算法作为一种新的客观分析方法是可行的,并且还能提高小尺度天气系统的识别能力.Meteorological observation data have observational errors inevitably. It is an ill-posed inverse problem to perform the derivation of discrete data with observation errors. In order to solve the perplexing problem,this paper puts forward the new algorithm which reconstructs the first-order partial derivatives of the two-dimensional observation data in the rectangular region,which is based on the idea of Tikhonov regularization . We test the performance of the algorithm with a series of simulating observation data,the results show that the algorithm is effective and has higher accuracy. It is feasible to analyze meteorological observation data with the algorithm and can enhance the recognizing ability for the smallscale weather systems.

关 键 词：数值微分 吉洪诺夫正则化 客观分析 

分 类 号：P413[天文地球—大气科学及气象学]