This paper presents a new method to estimate the height of the atmospheric boundary layer (ABL) by using COSMIC radio occultation bending angle (BA) data. Using the numerical differentiation method… Click to show full abstract
This paper presents a new method to estimate the height of the atmospheric boundary layer (ABL) by using COSMIC radio occultation bending angle (BA) data. Using the numerical differentiation method combined with the regularization technique, the first derivative of BA profiles is retrieved, and the height at which the first derivative of BA has the global minimum is defined to be the ABL height. To reflect the reliability of estimated ABL heights, the sharpness parameter is introduced, according to the relative minimum of the BA derivative. Then, it is applied to four months of COSMIC BA data (January, April, July, and October in 2008), and the ABL heights estimated are compared with two kinds of ABL heights from COSMIC products and with the heights determined by the finite difference method upon the refractivity data. For sharp ABL tops (large sharpness parameters), there is little difference between the ABL heights determined by different methods, i.e., the uncertainties are small; whereas, for non-sharp ABL tops (small sharpness parameters), big differences exist in the ABL heights obtained by different methods, which means large uncertainties for different methods. In addition, the new method can detect thin ABLs and provide a reference ABL height in the cases eliminated by other methods. Thus, the application of the numerical differentiation method combined with the regularization technique to COSMIC BA data is an appropriate choice and has further application value.摘要本文提出一个基于COSMIC弯角数据来确定大气边界层高度的新方法.首先,使用数值微分方法结合正则化技术计算弯角廓线的一阶导数值, 然后把弯角导数廓线的最小值所在的高度定义为边界层高度.为了给出求得的边界层高度的可靠性, 本文根据求得的弯角导数廓线最小值的相对值定义了显著参数. 然后, 本文将该方法应用于2008年1,4,7,10月份的COSMIC弯角数据,求得边界层高度后,本文将其与COSMIC数据本身提供的两种边界层高度,基于COSMIC折射率数据使用有限差分法求得的边界层高度进行了对比.结果表明,对于显著的边界层顶情形(大的显著参数),不同方法求得的结果的偏差较小,即不确定性较小.相反,对于非显著边界层顶的情形(小的显著参数),不同方法之间的偏差较大,即不确定性较大.另外,本文提出的新方法可以识别薄层边界层顶的情形,为容易被其他方法使用质量控制而剔除的个例提供了参考的边界层顶高度.因此,使用数值微分方法结合正则化技术确定边界层高度是一个更优的选择,具有进一步的应用价值.
               
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