伴随模式同化系统在修正模式地形中的应用

任何模式都只是实际大气的一种近似模拟，存在误差。鉴于传统的四维资料伴随模式同化系统都是假设模式完全正确仅对初始场进行修正。将伴随模式同化系统应用于修正模式地形误差，通过对不同初始地形的修正试验表明，MM5伴随模式同化系统能很好地对地形误差进行修正，能够反演出与初始气象要素场分辨率相匹配、与模式更协调的地形场，为伴随模式同化系统的广泛应用提供了一种新的思路。     

三维真实感地形建模与可视化

研究了三维真实地形建模与可视化的整个流程,利用OpenGL通用图形程序接口实现了基于DEM数据与遥感影像的三维地形的快速生成与仿真;并将三维地形生成技术应用到了天山公路三维地质灾害场景可视化中。详细阐述了以遥感影像作为地形纹理的处理方式与流程以及构建真实地形场景技术的方法和优化方案,提出了在实际应用中建立个性化三维地形系统的重要性。     

Simulation on slope uncertainty derived from DEMs at different resolution levels： a case study in the Loess Plateau

Slope is one of the crucial terrain variables in spatial analysis and land use planning, especially in the Loess Plateau area of China which is suffering from serious soil erosion. DEM based slope extracting method has been widely accepted and applied in practice. However slope accuracy derived from this method usually does not match with its popularity. A quantitative simulation to slope data uncertainty is important not only theoretically but also necessarily to applications. This paper focuses on how resolution and terrain complexity impact on the accuracy of mean slope extracted from DEMs of different resolutions in the Loess Plateau of China. Six typical geomorphologic areas are selected as test areas, representing different terrain types from smooth to rough. Their DEMs are produced from digitizing contours of 1:10,000 scale topographic maps. Field survey results show that 5 m should be the most suitable grid size for representing slope in the Loess Plateau area. Comparative and math-simulation methodology was employed for data processing and analysis. A linear correlativity between mean slope and DEM resolution was found at all test areas, but their regression coefficients related closely with the terrain complexity of the test areas. If taking stream channel density to represent terrain complexity, mean slope error could be regressed against DEM resolution (X) and stream channel density (S) at 8 resolution levels and expressed as(0.0015S2 0.031S-0.0325)X-0.0045S2-0.155S 0.1625, with a R2 value of over 0.98. Practical tests also show an effective result of this model in applications. The new development methodology applied in this study should be helpful to similar researches in spatial data uncertainty investigation.     

青岛海陆风三维结构的数值模拟

本文采用 1个陡地形影响修正的三维中尺度流体静力的气象学模式 ,对青岛地区海陆风的日变化规律和三维结构进行了较细致的分析。结果显示 ,青岛有多支海陆风存在 ,且每支海陆风出现的时间、强度和向内陆伸展的距离有很大的不同。这其中沿岸山地的机械和热力作用扮演着重要的角色。在观测站 ,模拟结果和实测资料等方面有较好的一致性。     

基于色层图与纹理融合的地形晕渲

针对三维地形晕渲的具体需求和存在问题,提出了一种基于色层图与纹理融合的晕渲方法。根据自定义或色层表主色调插值生成色彩连续的色层图,对地形高程进行着色;在地表材质分块的基础上,通过计算相邻材质边界上的多重纹理坐标,使用多重纹理映射进行融合过渡;最后给出了实验结果。     

采用Creator生成三维地形

介绍了Creator地形转换的四种算法，讨论了在Creator中生成地形的过程，并以福建省闽清地区水口店为研究区域，建立直观性的、真实性的三维地形。     

地形性中尺度环流的数值模拟及验证

建立了一个高分辨三维边界层诊断模式以模拟下垫面热力和动力的 非均匀性对边界层结构的影响。模式模拟了广东大亚湾核电站厂址区域（12 km×12 km） 47个典型天气系统下的地形性中尺度环流,其大坑的模拟风向、风速和温度与观测值 相比,风向差小于17.5#+[o]的概率为87.5%,风速比值在0.7～1.3之间的概率为72.9%, 温度差小于0.4#+[o]C的概率为72.9%。     

提孜那甫河流域地表太阳辐射估算及其影响因素分析

地表太阳辐射是地球表层主要能量来源,对地表能量平衡、能量交换以及生态水文过程等具有决定性意义。山区地形复杂,其地表太阳辐射时空差异较大且较难估算。采用适用于山区的地表太阳辐射模型对西北昆仑山提孜那甫河流域地表太阳辐射时空分布进行了估算,分析了该流域季节太阳辐射空间分布规律并探讨了地形和云2个重要因素对太阳辐射空间分布的影响。结果表明:（1） 地形因子中周围地形阻挡即地形开阔度（Sky view factor,SVF）与年总太阳辐射的关系最为显著,太阳辐射随SVF增加而增加。（2） 年总太阳辐射随着高程增加首先减少,再而随之增加。探究SVF随高程的变化,发现其与太阳辐射随高程的变化趋势较为一致,因此在山区复杂地形下地表太阳辐射估算中仅利用高程对其校正存在明显不足,需综合考虑地形效应。（3） 研究计算了季节云出现频率空间分布与太阳辐射空间分布的相关系数,结果表明夏季太阳辐射受云影响较其他季节显著。定量分析了地形因子以及云对地表太阳辐射空间分布影响的贡献率,周围地形阻挡SVF对地表太阳辐射空间分布的影响最大,高程和云次之。因此综合考虑地形和云对太阳辐射的影响在山区太阳辐射模拟中是非常必要的,研究可为山区地表太阳辐射模拟提供理论依据,并为山区生态水文过程研究提供方法支撑。     

顾及多分析尺度的地形部位面向对象分类方法

地形部位是地表形态的基本单元,其分类和提取在地貌发育、数字土壤制图、景观生态制图等领域有着重要的应用。康鑫等提出的多尺度Geomorphons地形部位分类法（简称多尺度Geomorphons法）利用高程相对差异信息和地形部位多尺度特征,可避免受地形属性计算及单一分析尺度约束而误分类,然而其存在分类破碎及分析尺度域难以确定的问题。基于此,本文以多尺度Geomorphons法为基础,提出了其适宜分析尺度域确定方法,建立了以初始地形部位数据层组合的对象多尺度分割和分类方法,进而构建了顾及多分析尺度的地形部位面向对象分类方法。以陕北黄土高原区域5 m分辨率DEM为实验数据,对面向对象分类方法进行了验证与评价。实验结果表明：①均值变点法可有效解决分析尺度域难以确定的问题,实验样区适宜分析尺度域为[5×5, 33×33]栅格单元;②以0,255为二值化的地形部位数据层组合适用于多尺度分割,尺度、形状及紧致度参数组合影响分割结果,且对于实验样区存在最优分割参数;③与多尺度Geomorphons法相比,本文方法得到的地形部位分类结果完整性较好,在地表形态对应和地理认知等方面更具合理性。     

Exploring the sensitivity of coastal inundation modelling to DEM vertical error

As sea level is projected to rise throughout the twenty-first century due to climate change, there is a need to ensure that sea level rise (SLR) models accurately and defensibly represent future flood inundation levels to allow for effective coastal zone management. Digital elevation models (DEMs) are integral to SLR modelling, but are subject to error, including in their vertical resolution. Error in DEMs leads to uncertainty in the output of SLR inundation models, which if not considered, may result in poor coastal management decisions. However, DEM error is not usually described in detail by DEM suppliers; commonly only the RMSE is reported. This research explores the impact of stated vertical error in delineating zones of inundation in two locations along the Devon, United Kingdom, coastline (Exe and Otter Estuaries). We explore the consequences of needing to make assumptions about the distribution of error in the absence of detailed error data using a 1 m, publically available composite DEM with a maximum RMSE of 0.15 m, typical of recent LiDAR-derived DEMs. We compare uncertainty using two methods (i) the NOAA inundation uncertainty mapping method which assumes a normal distribution of error and (ii) a hydrologically correct bathtub method where the DEM is uniformly perturbed between the upper and lower bounds of a 95% linear error in 500 Monte Carlo Simulations (HBM+MCS). The NOAA method produced a broader zone of uncertainty (an increase of 134.9% on the HBM+MCS method), which is particularly evident in the flatter topography of the upper estuaries. The HBM+MCS method generates a narrower band of uncertainty for these flatter areas, but very similar extents where shorelines are steeper. The differences in inundation extents produced by the methods relate to a number of underpinning assumptions, and particularly, how the stated RMSE is interpreted and used to represent error in a practical sense. Unlike the NOAA method, the HBM+MCS model is computationally intensive, depending on the areas under consideration and the number of iterations. We therefore used the HBM+ MCS method to derive a regression relationship between elevation and inundation probability for the Exe Estuary. We then apply this to the adjacent Otter Estuary and show that it can defensibly reproduce zones of inundation uncertainty, avoiding the computationally intensive step of the HBM+MCS. The equation-derived zone of uncertainty was 112.1% larger than the HBM+MCS method, compared to the NOAA method which produced an uncertain area 423.9% larger. Each approach has advantages and disadvantages and requires value judgements to be made. Their use underscores the need for transparency in assumptions and communications of outputs. We urge DEM publishers to move beyond provision of a generalised RMSE and provide more detailed estimates of spatial error and complete metadata, including locations of ground control points and associated land cover.