Basu Sukanta
Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, The Netherlands.
Boundary Layer Meteorol. 2019;170(1):29-44. doi: 10.1007/s10546-018-0391-1. Epub 2018 Sep 24.
The Monin-Obukhov similarity theory-based wind speed and potential temperature profiles are inherently coupled to each other. We have developed hybrid approaches to disentangle them, and as a direct consequence, the estimation of Obukhov length (and associated turbulent fluxes) from either wind-speed or temperature measurements becomes an effortless task. Additionally, our approaches give rise to two easily measurable indices of atmospheric stability. We compare these approaches with the traditional gradient and profile methods that require both wind-speed and temperature profile data. Using Monte-Carlo-type numerical experiments we demonstrate that, if the input profiles are free of any random errors, the performance of the proposed hybrid approaches is almost equivalent to the profile method and better than the gradient method. However, the proposed hybrid approaches are less competitive in comparison to their traditional counterparts in the presence of random errors.
基于莫宁-奥布霍夫相似性理论的风速和位温廓线本质上是相互耦合的。我们已经开发出混合方法来解开它们之间的耦合，结果是，从风速或温度测量值估算奥布霍夫长度（以及相关的湍流通量）变得轻而易举。此外，我们的方法还产生了两个易于测量的大气稳定度指标。我们将这些方法与需要风速和温度廓线数据的传统梯度法和廓线法进行了比较。通过蒙特卡洛类型的数值实验我们证明，如果输入廓线没有任何随机误差，所提出的混合方法的性能几乎与廓线法相当，且优于梯度法。然而，在存在随机误差的情况下，与传统方法相比，所提出的混合方法竞争力较弱。
