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| 1 | Extended Application of the Conditional Nonlinear Optimal Parameter Perturbation Method in the Common Land Model显示文摘An extension of the conditional nonlinear optimal parameter perturbation (CNOP-P) method is applied to the parameter optimization of the Common Land Model (CoLM) for the North China Plain with the differential evolution (DE) method. Using National Meteorological Center (NMC) Reanalysis 6-hourly surface flux data and National Center for Environmental Prediction/Department of Energy (NCEP/DOE) Atmospheric Model Intercomparison Project Ⅱ (AMIP-Ⅱ) 6-hourly Reanalysis Gaussian Grid data, two experiments (Ⅰ and Ⅱ) were designed to investigate the impact of the percentages of sand and clay in the shallow soil in CoLM on its ability to simulate shallow soil moisture. A third experiment (Ⅲ) was designed to study the shallow soil moisture and latent heat flux simultaneously. In all the three experiments, after the optimization stage, the percentages of sand and clay of the shallow soil were used to predict the shallow soil moisture in the following month. The results show that the optimal parameters can enable CoLM to better simulate shallow soil moisture, with the simulation results of CoLM after the double-parameter optimal experiment being better than the single-parameter optimal experiment in the optimization slot. Furthermore, the optimal parameters were able to significantly improve the prediction results of CoLM at the prediction stage. In addition, whether or not the atmospheric forcing and observational data are accurate can seriously affect the results of optimization, and the more accurate the data are, the more significant the results of optimization may be. | 王波 霍振华 | 2013 | Advances in Atmospheric Sciences2013，30，4： | 3 |
| 2 | A useful approach to sensitivity and predictability studies in geophysical fluid dynamics: conditional non-linear optimal perturbation显示文摘In atmospheric and oceanic studies,it is important to investigate the uncertainty of model solutions.The conditional non-linear optimal perturbation(CNOP)method is useful for addressing the uncertainty.This paper reviews the development of the CNOP method and its computational aspects in recent years.Specifically,the CNOP method was first proposed to investigate the effects of the optimal initial perturbation on atmosphere and ocean model results.Then,it was extended to explore the influences of the optimal parameter perturbation,model tendency perturbation and boundary condition perturbation.To obtain solutions to these optimal perturbations,four kinds of optimization approaches were developed:the adjoint-based method,the adjoint-free method,the intelligent optimization method and the unconstrained optimization method.We illustrate the calculation process of each method and its advantages and disadvantages.Then,taking the Zebiak–Cane model as an example,we compare the CNOPs related to initial conditions(CNOP-Is)calculated by the above four methods.It was found that the dominant structures of the CNOP-Is for different methods are similar,although some differences in details exist.Finally,we discuss the necessity and possible direction for designing a more effective optimization approach related to the CNOP in the future. | Qiang Wang Mu Mu Guodong Sun | 2020 | National Science Review2020，7，1： | 3 |
| 3 | 垂向湍流扩散系数的不确定性对深层叶绿素最大值现象模拟的影响显示文摘深层叶绿素最大值(Deep Chlorophyll Maximum, DCM)现象的数值模拟是研究海洋表层生态系统和全球碳循环的重要组成部分之一。但是由于自身的复杂性和观测的局限性,数值模式中物理参数的不确定性给模拟结果带来了一定程度的误差。其中,垂向湍流扩散系数(vertical turbulence diffusion)是模式所包含的物理参数中很难直接通过观测来确定的参数,它在模式中的来源和取值往往具有很大的不确定性。本文通过条件非线性最优(参数)扰动(Conditional nonlinear optimal perturbation related toparameter, CNOP-P)方法,研究了垂向湍流扩散系数的不确定性对模式模拟结果的影响。我们发现,垂向湍流扩散系数对 DCM 模拟产生最大影响的 CNOP 型扰动位于生产力层的上半部分。并且,去掉生产力层内湍流扩散系数的误差,模式模拟的改进程度最高达到了 80%。可见,垂向湍流扩散对生态系统的发展和保持起着极其重要的作用,改进垂向湍流扩散系数的不确定性,对 DCM 的数值模拟有着重要意义。 | 高永丽 | 2019 | 海洋科学2019，43，2： | 2 |
| 4 | 非线性最优化方法在大气-海洋科学研究中的若干应用显示文摘本文主要介绍了作者研究组近几年来将非线性最优化方法应用于大气-海洋科学研究中的有关工作,重点是基于非线性最优化所提出的条件非线性最优扰动(CNOP)方法的理论框架及近几年的发展,以及在大气-海洋科学研究中的最新应用成果,主要包括集合预报、一些高影响海-气环境事件的可预报性、模式参数敏感性的识别以及模式倾向误差和边界条件误差的评估等.此外,本文也讨论了应用CNOP方法面临的困难与挑战,并展望了未来的发展. | 穆穆 王强 | 2017 | 中国科学:数学2017，47，10： | 1 |
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