Noah-MP模型中积雪模拟对参数化方案的敏感性评估

针对Noah-MP模型多参数化方案、模拟结果不确定性范围难以确定的特点,选取北疆地区具有代表性的阿勒泰站气象资料作为模型驱动数据,探讨了积雪对多参数化方案的敏感性。在不考虑模型参数和驱动数据不确定性的条件下,设计了集合数为13824的多参数化方案集合模拟试验。选用Natural selection方法对物理过程的敏感性进行分析,并在敏感性分析结果的基础上进一步讨论了模拟结果的不确定性。结果表明:积雪对地表热交换、雨雪分离、土壤温度底层边界条件和第一层积雪或土壤时间方案4个物理过程敏感;在不考虑驱动数据和模型参数不确定性的条件下,多参数化方案集合模拟试验中的不确定性主要来源于敏感物理过程。去除敏感物理过程中能够明显降低模拟性能的参数化方案后,集合模拟结果的不确定性大幅减小。最后,根据分析结果构建了该站雪深和雪水当量模拟的最优参数化方案组合。     

陆面水文过程研究综述

在简单介绍陆面过程模式发展的基础上，从裸土蒸发、植被蒸散、土壤水传输、排水和径流等五个方面详细综述了陆面模式研究中对水文过程的参数化。目前陆面参数化方案中仍存在很大的不确定性，其中陆面水文过程参数化的关键问题包括：土壤分层、土层厚度、根带分布；参数的代表性和移植；观测资料；径流的参数化。分析了径流在陆面模式中的重要性，及目前陆面模式中对径流参数化存在的不足，介绍了部分陆面模式对径流的模拟研究，讨论了未来工作的研究重点。     

积雪水文模拟中的关键问题及其研究进展

针对近年来积雪水文模拟研究的发展趋势, 在简要评述积雪模拟基本方法的基础上, 分别就各类方法的不同着眼点展开讨论, 从模拟方案的简繁、 积雪面积等重要状态变量在模型中如何体现等角度进行了叙述.针对我国青藏高原积雪特征, 提炼出现阶段空间分布式积雪水文模拟中的3个关键问题: 网格尺度积雪空间异质性的模拟、 风吹雪的空间参数化、 季节性冻土下垫面的融雪模拟. 分别就这些问题, 回溯了国内外最新研究进展, 强调了发展积雪衰减曲线在网格尺度积雪模拟中的重要性, 讨论了山区环境中具有操作性的风吹雪空间参数化方案, 分析了冻土下垫面融雪研究存在的一些具体问题.     

陆面模式CLM4.5在青藏高原土壤冻融期的偏差特征及其原因

利用中国区域地面气象要素数据集制作的大气强迫场驱动通用陆面模式CLM4.5（Community Land Model version 4.5）对青藏高原区域进行离线模拟试验,模拟结果与D66、沱沱河（TTH）和玛曲（Maqu）3个站点的观测资料以及GLDAS（Global Land Data Assimilation System）-CLM2模拟结果进行了对比,并分析了陆面模式对冻融过程中土壤温度和湿度模拟的偏差及其可能原因。结果表明：CLM4.5对土壤温度模拟较好（平均RMSE≈3℃）,而GLDAS-CLM2计算的土壤温度偏高,偏差较大（平均RMSE>6℃）,且其偏差大于CLM4.5,尤其在冻融期;CLM4.5能较好地模拟出冻融过程中土壤湿度季节变化,但土壤湿度的模拟值与观测值存在一定偏差（平均RMSE≈0.1 mm³·mm^-3）,GLDAS-CLM2不能反映出土壤湿度在冻融过程中的变化特征。CLM4.5的模拟偏差主要来自大气强迫场,而GLDAS-CLM2的偏差除了大气强迫场的不确定性外,还来自于模式冻融参数化方案的不完善。大气强迫场中的气温和降水对土壤温度和湿度的影响在冻融期和非冻融期表现不同。在非冻融期,土壤温度的模拟主要受气温的影响（r>0.6）,气温偏差对土壤温度偏差的贡献率大于50%;土壤湿度的变化则主要受降水的影响,降水偏差对土壤湿度偏差的贡献率为20%~40%。在冻融期,受土壤水热相互作用的影响,气温和降水对土壤温度和湿度的作用效果减弱;土壤湿度的变化受气温影响显著,其贡献率为10%~20%。陆面模式中冻融参数方案的不完善是冻融过程中土壤温度和湿度偏差的重要来源之一。     

Quantifying initial and wind forcing uncertainties in the Gulf of Mexico

This study aims at analyzing the combined impact of uncertainties in initial conditions and wind forcing fields in ocean general circulation models (OGCM) using polynomial chaos (PC) expansions. Empirical orthogonal functions (EOF) are used to formulate both spatial perturbations to initial conditions and space-time wind forcing perturbations, namely in the form of a superposition of modal components with uniformly distributed random amplitudes. The forward deterministic HYbrid Coordinate Ocean Model (HYCOM) is used to propagate input uncertainties in the Gulf of Mexico (GoM) in spring 2010, during the Deepwater Horizon oil spill, and to generate the ensemble of model realizations based on which PC surrogate models are constructed for both localized and field quantities of interest (QoIs), focusing specifically on sea surface height (SSH) and mixed layer depth (MLD). These PC surrogate models are constructed using basis pursuit denoising methodology, and their performance is assessed through various statistical measures. A global sensitivity analysis is then performed to quantify the impact of individual modes as well as their interactions. It shows that the local SSH at the edge of the GoM main current—the Loop Current—is mostly sensitive to perturbations of the initial conditions affecting the current front, whereas the local MLD in the area of the Deepwater Horizon oil spill is more sensitive to wind forcing perturbations. At the basin scale, the SSH in the deep GoM is mostly sensitive to initial condition perturbations, while over the shelf it is sensitive to wind forcing perturbations. On the other hand, the basin MLD is almost exclusively sensitive to wind perturbations. For both quantities, the two sources of uncertainty have limited interactions. Finally, the computations indicate that whereas local quantities can exhibit complex behavior that necessitates a large number of realizations, the modal analysis of field sensitivities can be suitably achieved with a moderate size ensemble.     

Research Progress on Physical Parameterization Schemes in Numerical Weather Prediction Models

Atmospheric physics in numerical weather prediction model which predominantly determines the evolution of atmospheric processes is mainly described by physical parameterization. As a result, the development of physical parameterization has been a hot research issue in the area of numerical prediction for a long time. In this regard, the theoretical background and history of physical parameterization schemes for convection, microphysics, and planetary boundary layer, were reviewed in this study. It is suggested that the advance of physical parameterization for the model with high-resolution grid spaces should be considered as a principle issue for numerical model development in the future. Although the gird spaces in current operational numerical models generally decrease toward 10 km owing to the rapid development of high-performance computation, yet most of these schemes are designed for coarse grid spaces. Because of this kind of deficiency, the theoretical basis of these schemes inevitably faces controversy. Directions for development of physical parameterization were also suggested according to the trends of research in numerical prediction.     

Progress of the Climate Extension of Weather Research and Forecast(CWRF)Model Application in China

With the development of regional climate simulation, CWRF, the new generation regional climate model, is increasingly used in climate research because of its advanced capability and high skill. The CWRF application in China was introduced from three aspects: its modifications of WRF physics parameterizations, the construction of modeling domain and lateral boundary conditions, the case simulation study and comparison with RegCM, illustrating the accuracy and advantage of CWRF in regional climate simulations. Furthermore, two major CWRF developmental prospects in China were explored: one was to incorporate more accurate physical parameterization schemes and optimized multi-physics ensemble approach; the other was to nest CWRF in GCMs for short-term climate operational forecast and long-term climate change prediction and impact assessment. The status of CWRF applications in China was summarized and the outlook of its further development was pointed out, which provided a meaningful reference for more general research and application.     

可变分辨率展宽网格在区域物理参数化大气模式中的应用

将有限区域展宽网格方法应用于区域物理参数化大气模式中，来检验其模拟湿物理过程的能力。展宽网格模型旨在在一个大的有限空间区域中得到我们所关注的小区域的高分辨率。运用展宽网格模型对南美地区进行模拟的结果表明：当拥有充足的物理参数集时，模型模拟效果良好；并且，如果改进计算机功率，便可得到与始终保持高分辨率模拟具有可比性的输出结果。     

Visualization of uncertainty in natural hazards assessments using an interactive cartographic information system

Natural hazard assessments are always subject to uncertainties due to missing knowledge about the complexity of hazardous processes as well as their natural variability. Decision-makers in the field of natural hazard management need to understand the concept, components, sources, and implications of existing uncertainties in order to reach informed and transparent decisions. Until now, however, only few hazard maps include uncertainty visualizations which would be much needed for an enhanced communication among experts and decision-makers in order to make informed decisions possible. In this paper, an analysis of how uncertainty is currently treated and communicated by Swiss natural hazards experts is presented. The conducted expert survey confirmed that the communication of uncertainty has to be enhanced, possibly with the help of uncertainty visualizations. However, in order to visualize the spatial characteristics of uncertainty, existing uncertainties need to be quantified. This challenge is addressed by the exemplary simulation of a snow avalanche event using a deterministic model and quantified uncertainties with a sensitivity analysis. Suitable visualization methods for the resulting spatial variability of the uncertainties are suggested, and the advantages and disadvantages of their implementation in an interactive cartographic information system are discussed.     

Sensitivity of tropical cyclone intensification to boundary layer and convective processes

This study examines the role of the parameterization of convection, planetary boundary layer (PBL) and explicit moisture processes on tropical cyclone intensification. A high-resolution mesoscale model, National Center for Atmospheric Research (NCAR) model MM5, with two interactive nested domains at resolutions 90 km and 30 km was used to simulate the Orissa Super cyclone, the most intense Indian cyclone of the past century. The initial fields and time-varying boundary variables and sea surface temperatures were taken from the National Centers for Environmental Prediction (NCEP) (FNL) one-degree data set. Three categories of sensitivity experiments were conducted to examine the various schemes of PBL, convection and explicit moisture processes. The results show that the PBL processes play crucial roles in determining the intensity of the cyclone and that the scheme of Mellor-Yamada (MY) produces the strongest cyclone. The combination of the parameterization schemes of MY for planetary boundary layer, Kain-Fritsch2 for convection and Mixed-Phase for explicit moisture produced the best simulation in terms of intensity and track. The simulated cyclone produced a minimum sea level pressure of 930 hPa and a maximum wind of 65 m s⁻¹ as well as all of the characteristics of a mature tropical cyclone with an eye and eye-wall along with a warm core structure. The model-simulated precipitation intensity and distribution were in good agreement with the observations. The ensemble mean of all 12 experiments produced reasonable intensity and the best track.     

气候模式中积雪覆盖率参数化方案的对比研究

利用基于NCEP再分析的近地面气候资料驱动陆面过程模型NCAR CLM3,检验了6种积雪覆盖率参数化方案(CLM3、Douville1995、Roesch2001、Wu2004、Yang1997、Niu2007)模拟的积雪覆盖率的季节变化,并与NOAA AVHRR得到的观测结果进行了对比分析.结果表明,在NCARCLM3的物理过程框架之下,CLM3、Douville1995、Roesch2001三种方案低估了广大地区的积雪覆盖率,模拟的雪线位置偏北,尤其是在秋季积雪初期;Wu2004方案低估了秋季欧亚大陆的积雪覆盖率;Yang1997方案模拟的积雪覆盖率有些偏高,尤其是在积雪覆盖区的南部边缘;考虑积雪密度变化的Niu2007方案一定程度上克服了Yang1997方案的正偏差.春季末期,6种方案模拟的雪线位置都偏北.在地形比较平缓的地区,Niu2007方案的整体效果最好.观测和模拟的积雪覆盖率的出现频数大部分集中在低(小于0.2)和高(大于0.8)覆盖率等级,中等覆盖率所占比例很少.     

次网格积雪参数化在祁连山区斑状积雪带模拟中的应用

运用中尺度大气模式MM5,积雪参数化分别采用简单的积雪参数化方案以及考虑次网格积雪分布和雪密度变化的复杂积雪参数化方案,对黑河流域上游祁连站附近气温和降水进行模拟,与祁连站的观测值对比,检验积雪参数化方案中次网格积雪分布和雪密度变化在该地区气温和降水模拟中的作用.结果表明:简单积雪方案对网格积雪的非0即1描述在斑状积雪带是不合理的,尤其在黑河流域海拔3 300 m以下积雪多为斑状或片状,网格内积雪非均匀性的处理是非常必要的;通过耦合简单和复杂积雪方案的大气模式对气温模拟和观测值比较发现,新方案模拟的气温比旧方案模拟值更接近观测值,在气温低于0℃时改进尤其明显,说明使用复杂积雪/融雪方案可改进斑状积雪带气温的模拟.耦合复杂积雪方案的大气模式模拟的降水与观测值绝对误差低于耦合简单积雪方案模拟结果,复杂积雪方案的模拟结果降水错报率为使用简单积雪方案结果的一半,证明了耦合复杂积雪方案可以提高大气模式对该地区春季降水模拟的准确性.与积雪面积变化相对应,耦合复杂积雪方案模拟出了融雪产流量,而使用简单积雪方案则没有模拟出来.综上所述,耦合考虑次网格积雪分布和雪密度变化的复杂积雪参数化方案比耦合“非0即1”积雪方案可以更准确地模拟祁连山区冬、春季气温和降水.     

湖泊数值模拟研究现状综述

作为陆面过程的重要组成部分,湖泊在天气气候预测中的作用得到了相关研究者的广泛关注,并成为大气科学研究领域中的一个热点.主要综述了当前湖泊模式的基本类型、所考虑的关键物理过程及其参数化方案,并尝试分析了各个方案的优劣以及模拟效果.相关研究表明,现有的湖泊模式对浅湖的模拟比较成熟,而对深湖和冰湖的模拟有待改进.未来的研究亟需构筑适用于大湖、深湖以及冰湖的参数化方案,发展能全面模拟各类型湖泊的数值模式,并且湖泊数值模拟的改进依赖于今后更多高质量的全球湖泊观测结果.     

土壤湿度观测、模拟和估算研究

总结土壤湿度的观测手段和土壤湿度数据集建立的现状,详细阐述与土壤湿度模拟有关的方程离散化求解、物理和生化过程、陆面过程模式比较和陆面模式参数优化等方面的研究进展;综述估算土壤湿度廓线的数据同化方法,仔细比较集合卡曼滤波(EnKF)和四维变分(4-D Var)2类目前流行的同化算法,并对估算土壤湿度廓线的研究工作进行全面评估;最后,对土壤湿度观测、模拟和同化中需继续努力的方向进行了思索和展望。     

青藏铁路沿线地表和路基表面热力学模式(Ⅲ):参数化方案

以物理过程分析为基础,根据野外实测资料设计了青藏铁路沿线地表和路基表面热力学模式中的大气辐射参数化方案,对直接太阳辐射、大气散射辐射、大气向下长波辐射参数进行处理,得到了较好的结果.在无云大气条件下,对直接太阳辐射透过率和大气散射辐射以太阳天顶角进行参数化;对大气向下长波辐射以大气等效辐射率及气温进行参数化;在云天条件下,基于晴阴比的云量参数化和基于气候资料的云天系数参数化都各有较好的效果.对土壤热通量的参数化方法和拖曳系数的取值问题进行了讨论,更完善的方法还有待于与实验测量工作相结合.     

区域中尺度模式云微物理参数化方案特征及其在中国的适用性

云微物理参数化方案在数值模式中起着重要的作用,是影响数值天气预报和气候预测准确性的最大因素。系统回顾了中尺度数值模式中云微物理参数化方案的研究进展,并统计分析了最近十余年云微物理参数化方案在中国范围内的敏感性试验研究成果。Lin方案和Rutledge-Hobbs方案奠定了中尺度模式中云微物理参数化方案的基础,其他方案都是直接或间接在这2个方案的基础上从多方面改进而形成的。这些改进主要体现在:①水凝物粒子分类数目;②冰核活化;③粒子谱分布描述函数;④粒子谱截距的取值;⑤粒子间相互转换阈值大小的设定。中国范围内云微物理参数化方案敏感性试验研究成果统计表明,使用WRF模式中Lin方案的模拟效果较好,MM5模式采用Goddard和Reisner方案效果较好。     

气候模式中云辐射反馈过程机理的评述

云对地气系统的辐射收支具有十分重要的作用,云辐射参数化是目前气候模式中不确定性的主要来源。云可以通过多种途径对辐射产生影响,形成不同符号、不同量值的反馈机制。研究表明,模式气候对不同的云辐射参数化方案十分敏感。预报云水含量方案的引入,改进了对云辐射过程的模拟,但与观测资料相比仍有差距。一般说来,模式中引入云水的相变和相互作用的云粒子大小产生负反馈,而光学厚度和云量产生的是正反馈。云辐射反馈的净作用其大小和符号因模式而异。云辐射与大尺度天气气候背景之间有着紧密的联系,尤其是海温对辐射平衡有显著影响。最后总结了当前云辐射研究中存在的主要问题,并提出了改进的途径。     

Method for Stochastic Inverse Modeling of Fault Geometry and Connectivity Using Flow Data

This paper focuses on fault-related uncertainties in the subsurface, which can significantly affect the numerical simulation of physical processes. Our goal is to use dynamic data and process-based simulation to update structural uncertainty in a Bayesian inverse approach. We propose a stochastic fault model where the number and features of faults are made variable. In particular, this model samples uncertainties about connectivity between the faults. The stochastic three dimensional fault model is integrated within a stochastic inversion scheme in order to reduce uncertainties about fault characteristics and fault zone layout, by minimizing the mismatch between observed and simulated data.     

Soil Moisture Parameterization and Its Influences in Weather and Climate Simulation: A Review

The soil hydrological process in land surface models and its influences on weather and climate simulation have got much attention by scholars both at home and abroad. First, this paper traced the definition and determination of soil moisture, and then reviewed its parameterization in different models, including soil-water characteristic curves for different soil types and numerical methods for solving soil moisture equations. Moreover, methods of soil hydrological parameter determination and uncertainties within land surface models were specified. Also, the importance of methods in parameter sensitivity analysis and optimization was emphasized, and the new datasets of soil hydraulic parameters would play a very important role in the improvement of the land surface model and soil moisture simulation. Finally, the research progress of feedback mechanism between soil moisture and precipitation was summarized. Spatial and temporal distribution of soil moisture and its abnormal would cause a positive feedback, and on the contrary, the meso-scale characteristics of soil moisture distribution had a negative feedback. By summarizing the progresses of the uncertainties and problems in soil moisture simulation, the influences of soil moisture on simulating weather and climate were pointed out.     

Assessing spatial uncertainty in mapping soil erodibility factor using geostatistical stochastic simulation

Soil erosion is one of most widespread process of degradation. The erodibility of a soil is a measure of its susceptibility to erosion and depends on many soil properties. Soil erodibility factor varies greatly over space and is commonly estimated using the revised universal soil loss equation. Neglecting information about estimation uncertainty may lead to improper decision-making. One geostatistical approach to spatial analysis is sequential Gaussian simulation, which draws alternative, equally probable, joint realizations of a regionalised variable. Differences between the realizations provide a measure of spatial uncertainty and allow us to carry out an error analysis. The objective of this paper was to assess the model output error of soil erodibility resulting from the uncertainties in the input attributes (texture and organic matter). The study area covers about 30 km² (Calabria, southern Italy). Topsoil samples were collected at 175 locations within the study area in 2006 and the main chemical and physical soil properties were determined. As soil textural size fractions are compositional data, the additive-logratio (alr) transformation was used to remove the non-negativity and constant-sum constraints on compositional variables. A Monte Carlo analysis was performed, which consisted of drawing a large number (500) of identically distributed input attributes from the multivariable joint probability distribution function. We incorporated spatial cross-correlation information through joint sequential Gaussian simulation, because model inputs were spatially correlated. The erodibility model was then estimated for each set of the 500 joint realisations of the input variables and the ensemble of the model outputs was used to infer the erodibility probability distribution function. This approach has also allowed for delineating the areas characterised by greater uncertainty and then to suggest efficient supplementary sampling strategies for further improving the precision of K value predictions.