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.     

大尺度水文模型参数不确定性分析的挑战与综合研究框架

水文模型是对自然界复杂水文现象与过程的一种综合近似描述,在水旱灾害防治、水资源管理与开发利用等方面应用广泛。本文分析了大尺度水文模型应用的难点,总结了参数不确定性研究的主要进展,介绍了参数不确定性分析框架“敏感性分析—参数优化—参数区域化”(SOR)的基本概念、重要性与应用情况。论文基于已有认识,建议在水文建模优化过程中引入更全面的参数不确定性分析SOR框架,并加强新一代分布式水文模型与更加成熟的水文气象数据观测系统的开发,以减少来自模型结构与模型驱动数据的不确定性,提高全球变化背景下大尺度水文模型水循环过程模拟和预测的准确性。     

基于多源遥感数据的疏勒河上游山区流域VIC-CAS模型积雪模拟效果评估

积雪积累和消融过程是冰冻圈水文模型的重要组成部分,利用多源遥感数据对水文模型模拟的积雪分布和深度进行评估是进一步增强融雪过程模拟的物理基础,也是提高模拟可靠性的重要手段。基于2002—2013年疏勒河上游山区流域MODIS地表反射率数据集和中国雪深长时间序列数据集,对VIC-CAS模型模拟的逐日积雪覆盖度和雪深进行了综合评估。结果表明：从不同降雪年份来看,VIC-CAS模型可以较好地模拟多雪年（2008年）疏勒河上游山区流域积雪的覆盖度,在平雪年（2004年）和少雪年（2013年）模型模拟精度相对较低。从不同海拔的模拟结果来看,在流域占比最高的4 000~5 000 m高程带精度最高,2 000~3 000 m高程带精度最低;对比模拟雪深与中国雪深产品发现,多雪年的一致性较高,平雪年和少雪年的一致性较低。这表明VIC-CAS模型对疏勒河上游日尺度的积雪覆盖度和雪深的模拟精度相对较低,特别在低海拔处和薄雪情况下,其原因可能是对积雪再分布和风吹雪过程的模拟算法和参数化存在较大的不确定性,需要进一步改进。     

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.     

基于高斯过程回归的地下水模型结构不确定性分析与控制

目前针对模型结构不确定性的研究方法主要为贝叶斯模型平均方法,而该方法受到模型权重计算困难等影响,应用受限。基于数据驱动的模型结构误差统计学习方法最近得到关注。研究采用高斯过程回归方法对地下水模型结构误差进行统计模拟,并将DREAMzs算法与高斯过程回归相结合,对地下水模型和统计模型的参数同时进行识别。基于此方法,分别以理想岩溶裂隙海水入侵过程和溶质运移柱体实验为例,进行地下水数值模拟及预测结果的不确定性分析。相对于不考虑模型结构误差条件的不确定性分析,结果表明,考虑结构误差之后,能够明显减少参数识别过程中的参数补偿影响,且能显著提高模型的预测性能。因此,基于高斯过程回归的模型结构不确定性分析可以一定程度控制地下水数值模拟的不确定性,提高模型预测可靠性。     

黄土骨干曲线模型比较分析

对饱和重塑黄土进行了一系列不同加载条件下的动三轴试验,研究了对每级荷载应用多个滞回圈构造骨干曲线的方法,该方法可以考虑更多的试验信息,减小试验随机性对模拟曲线的影响。并运用该方法对试验数据采用Hardin-Drnevich模型、修正Hardin-Drnevich模型和Martin-Darvidenkov模型3种不同的骨干曲线模型进行数值模拟,分析了不同的骨干曲线模型对试验数据模拟的准确性和参数敏感性。结果表明,在试验条件变化时Hardin-Drnevich模型并不能总是很好地模拟试验骨干曲线,而且在有些情况下偏离较多。修正Hardin-Drnevich模型和Martin-Darvidenkov模型相比于Hardin-Drnevich模型能更好地模拟试验数据。在对不规则动载下黄土动力反应的数值模拟中,可以考虑用修正Hardin-Drnevich模型和Martin-Darvidenkov模型模拟骨干曲线。考虑到Martin-Darvidenkov模型参数对试验数据相对敏感,因此,建议采用修正Hardin-Drnevich模型模拟骨干曲线。     

耦合冻土方案的大气模式对祁连山区春季土壤状况的模拟

对黑河流域上游山区水源涵养林区2003年春季土壤温度、湿度变化进行分析,运用耦合冻土参数化和没有耦合冻土参数化的大气模式MM5对该区春季过程进行了模拟,并与观测值进行对比,对模拟的产流量做了初步分析.结果显示:考虑冻土参数化方案对土壤温度模拟的改进主要体现在表层土壤,对深层土壤温度的模拟没有大的改进;考虑冻土参数化方案改进了对100 cm深度以内的土壤含水量的模拟,很大程度上缩小了模拟值与观测值之间的绝对误差.总体来讲,考虑了冻土参数化的模拟结果在一定程度上改进了模式对黑河流域上游季节冻土区土壤温、湿状况的模拟,在一定程度上逼近祁连山区的春季土壤状况.考虑冻土参数化和没有考虑冻土参数化对产流量的模拟表明,冻土参数化对产流量模拟有很大影响,冻土形成的不透水层可以产生更多的地表径流.尽管考虑冻土参数化过程对模拟结果有一定程度的改进,但模拟结果与实测结果还是有一定差距的.因此,进行寒区冻土过程模拟时,还需要进一步对土壤信息、模式物理过程,大气背景场驱动数据以及局地因素进行详细而精确的考虑,以期进一步提高模式在寒区的模拟性能,特别是为高寒山区无观测地带陆气相互作用研究提供依据.     

考虑空间变异性条件下的边坡稳定可靠度高效敏感性分析

在有限数据条件下,可靠度敏感性分析是研究各种不确定性因素对边坡失稳概率影响规律的重要途径。基于直接蒙特卡洛模拟和概率密度加权分析方法提出了一种高效边坡稳定可靠度敏感性分析方法。所提出的方法通过随机场表征岩土体参数的空间变异性,并采用局部平均理论建立岩土体参数的缩维概率密度函数,用于概率密度加权分析中高效、准确地计算不同敏感性分析方案对应的边坡失稳概率。最后,通过一个工程案例--詹姆斯湾堤坝说明了所提出方法的有效性和准确性。结果表明：在敏感性分析过程中,所提出的方法只需要执行一次直接蒙特卡洛模拟,避免了针对不同敏感性分析方案重新产生随机样本和执行边坡稳定分析,节约了大量的计算时间和计算资源,显著提高了基于蒙特卡洛模拟的敏感性分析计算效率;在概率密度加权分析中采用岩土体参数的缩维概率密度函数能够准确地计算边坡失稳概率,避免了有偏估计,使概率密度加权分析方法适用于考虑空间变异性条件下的边坡稳定可靠度敏感性分析问题。     

滑雪场积雪模拟研究进展

滑雪场的积雪条件是评价其盈利能力的关键指标。通过积雪模型准确地模拟滑雪场积雪条件对评估滑雪旅游目的地的气候风险具有重要意义,积雪模型亦可为滑雪场的造雪量和造雪时间提供科学依据。首先简要概述了滑雪场积雪模拟的发展趋势,介绍了具体的模拟方法。分析发现,应用于滑雪场积雪模拟的模型从早期的经验线性模型发展为简化的物理模型,再到精细的能量平衡模型;从仅模拟自然积雪发展为结合自然积雪和人工造雪,再到细致的考虑雪道维护过程。依据对自然积雪、人工造雪和雪道维护模拟方法的不同,滑雪场的积雪模拟方法可以简单的分为两类：单点或半分布式模拟和分布式模拟。单点或半分布式模拟将滑雪场作为一个点进行研究或等间隔地选取滑雪场多个海拔带进行模拟输出平均结果,通常采用简化的能量平衡模型和基于度日因子的概念模型,以简化的假设来解释人工造雪和雪道维护过程,以相对粗糙的方式表示雪的物理性质。分布式模拟在网格或雪道上划分出来的小单元尺度上对滑雪场积雪进行模拟,采用精细的多层能量平衡模型,详细描述了滑雪场的基础设施、人工造雪和雪道维护过程。由于我国大部分地区自然积雪资源匮乏,积雪模型在我国滑雪场地应用时需重点考虑人工造雪。在雪道人工管理模式方面,一些在欧美滑雪场广泛应用的规则照搬至我国滑雪场可能会出现较大误差。最后,考虑到对滑雪场积雪进行模拟时不需要非常详细地描述雪层内部物理过程,简化的分布式融雪模型更适合用于滑雪场的积雪模拟。     

基于统计理论方法的水文模型参数敏感性分析

参数敏感性分析是模型不确定性量化的重要环节,有助于有效识别关键参数,减少参数的不确定性影响,进而提高参数优化效率。利用Morris筛选方法定性识别相对重要参数,耦合方差分解的Sobol方法和统计理论的响应曲面模型构建一种新的定量敏感性分析方法——RSMSobol方法。以长江支流沿渡河流域的日降雨径流过程模拟为例,系统分析4种不同目标函数响应条件下新安江模型的参数敏感性。结果表明Morris方法和RSMSobol方法的集成应用极大地提高了全局敏感性分析的效率,Morris定性筛选结果为定量评估减少了模型参数维数,采用代理模型技术的RSMSobol方法减少了模型的计算消耗。     

Sensitivity Evaluation of Snow Simulation to Multi-parameterization Schemes in the Noah-MP Model

On account of the latest community Noah land surface model with multi-parameterization (Noah-MP) schemes and its uncertainty breadth in simulation results being difficult to be determined, this study assessed the sensitivity of snow to physics options using meteorological data from the Altay Station in northern Xinjiang. The Noah-MP physics ensemble simulation with the total number of 13 824 was designed without the consideration of the uncertainties of forcing data and parameters. The natural selection approach was used to analyze the sensitivity of physical processes. Based on the results of sensitivity analysis, the uncertainty of ensemble simulation results was further discussed. The results showed that snow was sensitive to the physical processes of surface-layer exchange coefficient, partitioning precipitation into rainfall and snowfall, lower boundary condition of soil temperature, and first-layer snow or soil temperature time scheme; Uncertainties in multi-parameterization ensemble simulation experiments were mainly from sensitive physical processes under the condition of disregarding uncertainties of forcing data and parameters. After removing the parameterization schemes that notably reduced simulation performance in sensitive physical processes, the uncertainty breadth in ensemble simulations decreased significantly. Finally, an optimal combination group of parameterization schemes for this station was configured.     

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.