近年来GRAPES-SDM沙尘模式预报效果检验评估

使用天气学检验方法,对中国气象局兰州干旱气象研究所目前使用的GRAPES-SDM沙尘暴预报业务模式在2008-2011年春季沙尘天气预报情况进行检验评估。结果表明：①自2008年以来,GRAPES-SDM沙尘暴模式对中国北方区域沙尘天气的模拟预报能力较好,TS评分和预报效率保持较高的水平;②模式对内蒙古地区、河套地区及甘肃河西地区的预报效果最好,但常有空报或预报沙尘强度偏强的现象;模式能预报出南疆盆地的沙尘天气,但常有预报范围偏小、强度偏弱的现象;对青海地区的沙尘天气常有漏报现象;③模式对沙尘暴频发地区的预报效果较好,对沙尘天气偶发地区容易漏报,模式对新疆东部、内蒙古中西部地区空报较多;④模式对大范围沙尘天气过程的预报能力较好,对零星沙尘天气预报能力较差。通过检验,我们还提出了改进和完善GRAPES-SDM沙尘暴预报系统的一些建议。     

概率回归方法在宁夏分级沙尘天气预报中的应用检验

利用2010-2013年的2~5月T639模式产品和同时段宁夏25站沙尘实况资料,应用概率回归方法建立了宁夏春季2~5月沙尘和强沙尘两个等级的72 h沙尘概率预报方程,并使用B评分、B_s评分、B_(ias)及T_S评分方法对2014年2~5月预报结果进行了检验。检验结果表明:Bs都为正值,该方法对宁夏春季分级沙尘有预报能力;气候概率越大,预报方程选取的预报因子代表性和物理意义越明确,预报效果也越好;沙尘过程范围越大、强度越强,过程预报效果越明显;且随着预报时效临近,模式调整,预报能力也显著提高;但由于宁夏沙尘正样本数相对较少,尤其是强沙尘气候概率小,因此导致漏报率较高,概率预报偏差率呈现出一致的偏少倾向。     

GRAPES-SDM沙尘模式预报与卫星遥感监测结果对比

GRAPES-SDM沙尘模式和卫星遥感监测是目前沙尘暴监测预报业务中重要的工具.本文使用天气学检验方法,对中国气象局兰州干旱气象研究所目前使用的GRAPES-SDM沙尘模式2012年春季沙尘天气预报情况以及FY-2D卫星遥感产品沙尘指数IDDI的监测效果进行检验评估.结果表明:沙尘模式在西北沙尘暴预报业务中具有很好的预报参考价值,卫星遥感沙尘指数也具有较好的监测效果,但两者均存在一定的问题.沙尘模式对大范围沙尘暴过程有较好的预报能力,但对沙尘强度预报偏强;卫星遥感沙尘指数虽然不能定性地表示沙尘强度,但是在一定程度上能够反映沙尘强度的变化,不过反映沙尘强度的数值及其分布区间还有待于进一步完善.卫星遥感在南疆盆地常会将大片深厚的沙尘气溶胶区域误判为云区,造成对沙尘天气特别是沙尘暴天气未能识别的现象,另外IDDI指数不能用于夜间沙尘监测.     

沙尘模式地表起沙参数不确定性分析

 起沙的参数化方法对沙尘天气的预报质量起着至关重要的作用。首先分析了目前应用广泛的沙尘模式中的参数土壤塑性压力P和cy对地表水平沙通量与垂直沙尘通量计算结果的影响,然后利用WRF-Chem3.0模式对2007年3月27—28日发生在我国西部地区的一次沙尘过程进行了模拟,分析了由于参数值选取的误差而造成沙尘模拟的不确定。结果表明,土壤塑性压力P对模拟结果有很大影响,P值的改变不仅影响PM10浓度大小的预测,而且还影响其浓度中心位置以及分布范围;cy值仅影响沙尘区域内PM10浓度大小的预测,对浓度中心位置和分布范围大小的预测没有影响。     

起沙系数对沙尘数值模拟影响的研究

通过综合考虑地表条件,如土地利用、土壤类型、植被覆盖与土壤湿度等,定义了起沙系数,用于改进沙尘数值预报模式中的起沙通量,为沙尘数值预报模式提供客观准确的地面起沙条件,提高了沙尘天气分布预报的准确率\.起沙风系数的引入能够对沙尘数值模拟产生较大的影响,若起沙系数中应用较高精度的土壤水分资料与土壤类型,将能更准确地反映起沙系数的作用。     

连续强沙尘天气的发展和时空演变机制的数值模拟

2002年4月6-8日由蒙古气旋和地面冷锋引发了一次连续沙尘暴天气,特别是内蒙古中东部、华北和东北大部分地区沙尘持续影响时间较长,强度大。利用与非静力平衡中尺度气象模式完全耦合的区域沙尘数值模式,模拟研究这次强沙尘天气过程中沙尘浓度的空间分布结构和时间演变趋势。模拟结果与地面天气观测、定点沙尘颗粒物浓度观测资料进行对比和检验。结果表明:沙尘数值模式较逼真地刻画出这次连续强沙尘天气的形成、发展、移动、减弱的全过程;客观地揭示了强沙尘天气过程的垂直分布结构和沙尘浓度的时空演变机制;模拟的强沙尘以及输送至下游的浮尘天气范围、强度和出现时间与实况基本一致,特别是对我国华北和东北沙尘的模拟相当成功。高时空分辨率的数值模式对研究沙尘的发生、发展机制和预报预警有重要意义。     

新疆春季沙尘天气与前期月环流特征量的关系

用新疆春季(3～5月)的沙尘日数和北半球500hPa月环流特征量等资料,使应用相关和逐步回归分析方法,研究了新疆春季沙尘天气与前一年的月环流特征量的关系,相关分析和统计结果表明：(1)新疆春季的沙尘天气不仅与同在中纬度地区的西风带上的经向环流、东亚槽等有关,还更多地与中低纬地区的副热带高压以及高纬地区的极涡等系统的月环流特征量有关。(2)新疆春季沙尘天气与前一年夏、秋季的大气环流之间存在明显的隔季相关现象。(3)利用前一年月环流特征量建立了新疆春季沙尘天气日数之间的9个回归模型,9个分析模型都通过了0．001信度检验。其中,南疆的3个模型沙尘暴、扬沙、浮尘和全疆的扬沙、浮尘模型试报效果最好。     

内蒙古沙尘天气少发季节沙尘天气预报技术分析

本文对2010年11月沙尘天气少发季节出现在内蒙古自治区西部地区但漏报的一次沙尘天气过程进行了详细的分析,从实际预报的角度出发,利用实际预报工作中可以用到的高空、地面、数值预报产品等相关资料,从产生沙尘天气的有利条件、大尺度环流形势演变及可能造成的其他灾害性天气、沙尘气候概率等角度较全面剖析了此次沙尘天气过程的漏报。分析得出:(1)沙尘天气不是单一出现的,都会伴随大风天气,但沙尘和大风的落区不尽相同,往往先出现大风再有沙尘天气出现,强沙尘暴区都伴有大风天气;(2)沙尘天气出现前会出现明显升温,无有效降水,沙尘过后有时伴有寒潮、雨雪天气;(3)大气的强斜压不稳定是产生沙尘大风的主要因素;(4)锋前上升运动较强,可达对流层中上层（400 hPa以上）;(5)沙尘出现时段整层对应正涡度区;(6)在沙尘暴过程中,午后产生深厚混合层的区域容易产生高空动量下传并形成地面大风。通过对漏报原因的分析提醒广大预报员在今后的预报工作中要增强多种灾害天气同时出现的预报敏感性,一定要在关注重点灾害的同时,避免对沙尘天气少发期沙尘天气漏报的现象。     

区域气候模式对中国沙尘天气气候特征的模拟研究

现有的沙尘天气数值预报模式多选用中尺度天气模式单向耦合起沙模式的方式,不适合用来模拟沙尘气溶胶的长距离输送过程,也无法研究沙尘气溶胶辐射效应对气象场的反馈及气候变化的影响。利用一个耦合沙尘模式的高分辨率区域气候模式,模拟了2001年中国北方沙尘天气爆发的时空分布特征。模拟结果与站点观测结果对比发现,模式能够较好地模拟出中国北方主要的沙尘源地分布及沙尘天气爆发的季节变率。分析不同粒径沙尘颗粒的垂直分布特征发现,沙源地表土壤粒子特征、地形对起沙颗粒的大小都有影响;直径超过5 μm的大粒子是北方沙尘天气的主要成分,而影响长江以南的沙尘天气主要以1 μm以下的小粒子为主。对沙尘传输路径的模拟结果和实况观测发现,来自于不同沙源的沙尘天气其影响的范围有显著差异,模式能够较好地模拟出中国主要沙尘传输路径。     

蒙古气旋天气过程中的沙尘传输特征

基于沙尘数值预报模式针对不同区域地面起沙的敏感性试验结果,分析讨论了蒙古气旋沙尘暴过程中沙尘传输的特点及形成原因。结果表明：蒙古气旋沙尘暴过程的沙尘传输表现为:沙源区纬度越高,沙尘向东传输越强,纬度越低,向南传输越强;同时,高度越高,沙尘向东传输越强,高度越低,向南传输越强。其形成原因是萨彦岭山地背风坡效应、青藏高原东北侧地形强迫绕流等自然地理因素和蒙古气旋的动力、热力结构共同造成的,具有一定程度的普遍性。     

Characterizing errors in digital elevation models and estimating the financial costs of accuracy

Digital topographic models are the foundation of more advanced modeling applications and ultimately inform planning and decision making in many fields. Despite this, the error associated with these models and derived attributes is commonly overlooked. Little attention has been given in the scientific literature to the benefits gained from having less error in a model or to the corresponding cost associated with reducing model error by choosing one product over another. To address these gaps in knowledge we evaluated the error associated with five digital elevation models (DEMs) and derived attributes of slope and aspect relative to the same attributes derived from LiDAR data. We also estimated the acquisition and processing costs per square kilometer of the five test models and the LiDAR models. We used three measures to characterize model error: (1) root mean square error, (2) mean error (and standard deviation), and (3) area of significant elevation error. We applied these measures to DEM products that are used extensively across a range of applications for planning and managing natural resources. We depicted the relationship between model accuracy (the inverse of error) and cost in two ways. One was accuracy/cost ratio for each model. The other used separate data on accuracy and cost to better guide potential users in choosing between models or deciding on necessary expenditure on models. The main conclusion of our work was that accounting for error in DEMs can inform choice of models and the need for financial outlays.     

沙尘浓度资料与AMSU辐射率资料同化在GRAPES_SDM沙尘暴模式中的应用

利用GRAPES_SDM沙尘暴模式及GRAPES_3DVAR系统,设计了4种试验,分别为没有同化的CTRL控制试验、仅同化探空资料的noPM试验、同时同化探空和PM10的PM试验和同时同化探空和AMSU辐射率资料的NOAA试验,对2011年4月28日-30日发生在中国北方地区的一次大范围沙尘暴过程进行了分析和对比模拟试验。结果发现：仅同化探空资料时,模式能够反映出沙尘天气系统的发展演变情况,但沙尘天气分布范围和强度的模拟效果没有明显改进;初始场中考虑了PM10沙尘浓度的分布后在很大程度上能够改善GRAPES_SDM沙尘暴模式对沙尘分布范围和强度的模拟效果;同化AMSU辐射率资料后,模式对500 hPa环流形势和200 hPa高空急流均有较好的模拟效果,从而模式对沙尘分布范围的模拟能力也有较好的改善,但对沙尘天气强度的模拟略有增强。这不仅说明PM10和ASMU辐射率资料的使用对于提高沙尘暴过程模拟效果是可行的、必要的,而且也为这两种资料用于沙尘暴预报奠定了一定基础。     

风电场风速规律分析及风电功率预报方法研究

利用陕西省某一风电场区域内的观测资料,分析了该风电场的风速规律,并引入最优训练 期方案,研究利用线性回归方法建立风电功率预报模型的可行性。结果表明：该风电场区域,不同 高度的风速及其高度间的风速差异均表现出最大值出现在夜间,最小值出现在白天,从低层到高 层的风速日变化趋势一致的特征。一日中,风速与风电功率在 09:00 ~ 17:00 时段的相关系数明显 小于其它时段。按照风速是否大于 5 m·s^-1 将训练期观测样本分为 2 组,可以明显改善风速与风电 功率的回归关系。以风机轮毂高度处的风速作为预报因子,并引入风电功率与风速之间相关系数 的日变化规律、以及不同风速量级下风速与风电功率之间回归关系的差异性,采用最优训练期方 案和一元线性回归方法建立的风电功率预报方程,具有预报误差小和最优训练期短的特点,满足 实际业务需求。     

基于PCA和BP神经网络的径流预测

径流预测为流域水资源的合理开发利用与统筹配置提供依据。运用多元线性回归、主成分回归、BP神经网络及主成分分析和BP神经网络相结合的方法,对新疆呼图壁河流域石门水文站2009-2011年各月径流量进行预测,并采用相关系数、确定性系数及均方根误差对各模型预测精度进行比较。结果表明：（1）神经网络等智能算法具有高速寻优的能力,对短时间尺度的月径流量的预测结果较好;（2）主成分回归等常规算法能充分反映出某地区径流的年际的稳定性,对全年径流总量的模拟精度较高;（3）主成分分析和BP神经网络相结合的方法,提高了神经网络的收敛速度,同时降低了局部极值的影响,优于简单的BP神经网络,适用于呼图壁河月径流量预测。     

高空急流对一次强沙尘暴过程沙尘传输的影响

使用GRAPES_SDM沙尘暴数值模式,对2011年4月28-30日中国北方强沙尘暴天气进行分析,讨论高空急流在此次过程中对沙尘传输的影响,得出以下结论:（1）GRAPES_SDM沙尘暴模式较好地模拟了此次沙尘暴过程的范围和强沙尘暴中心,整体模拟效果较好;（2）沙尘天气发生时间及移动路径与200 hPa高空急流的加强、移动发展有很好的对应关系;（3）高空强纬向风速的加强能够促使中低层形成垂直环流圈,其下沉支流使高空动量有效下传到近地面,进而在地面形成大风及扬沙和沙尘暴天气,强沙尘暴中心位于此垂直环流圈的下沉支;（4）等熵位涡与高空急流及地面沙尘浓度分布演变有很好的对应关系,等熵位涡位于高空急流北侧,地面沙尘浓度中心位于高空急流出口区、等熵位涡中心西南侧、等值线密集带;高层高值位涡区向下延伸的路径与高空急流北侧纬向风速等值线密集带有非常好的对应关系。本文还通过对高空急流轴线动力、热力结构垂直剖面的分析,探讨了高空急流对大范围沙尘天气影响的可能机制。     

Uncertainty due to DEM error in landslide susceptibility mapping

Terrain attributes such as slope gradient and slope shape, computed from a gridded digital elevation model (DEM), are important input data for landslide susceptibility mapping. Errors in DEM can cause uncertainty in terrain attributes and thus influence landslide susceptibility mapping. Monte Carlo simulations have been used in this article to compare uncertainties due to DEM error in two representative landslide susceptibility mapping approaches: a recently developed expert knowledge and fuzzy logic-based approach to landslide susceptibility mapping (efLandslides), and a logistic regression approach that is representative of multivariate statistical approaches to landslide susceptibility mapping. The study area is located in the middle and upper reaches of the Yangtze River, China, and includes two adjacent areas with similar environmental conditions – one for efLandslides model development (approximately 250 km²) and the other for model extrapolation (approximately 4600 km²). Sequential Gaussian simulation was used to simulate DEM error fields at 25-m resolution with different magnitudes and spatial autocorrelation levels. Nine sets of simulations were generated. Each set included 100 realizations derived from a DEM error field specified by possible combinations of three standard deviation values (1, 7.5, and 15 m) for error magnitude and three range values (0, 60, and 120 m) for spatial autocorrelation. The overall uncertainties of both efLandslides and the logistic regression approach attributable to each model-simulated DEM error were evaluated based on a map of standard deviations of landslide susceptibility realizations. The uncertainty assessment showed that the overall uncertainty in efLandslides was less sensitive to DEM error than that in the logistic regression approach and that the overall uncertainties in both efLandslides and the logistic regression approach for the model-extrapolation area were generally lower than in the model-development area used in this study. Boxplots were produced by associating an independent validation set of 205 observed landslides in the model-extrapolation area with the resulting landslide susceptibility realizations. These boxplots showed that for all simulations, efLandslides produced more reasonable results than logistic regression.     

新亚欧大陆桥新疆段暴雨灾变事件的灰色预测

通过对新亚欧大陆桥新疆段水害严重区段暴雨灾变事件的分析,确定暴雨灾变事件的临界雨强,并运用灰色灾变预测方法,根据暴雨灾变序列确立相应的灾变年份序列,运用ＧＭ（１,１）模型,分别求出灾变年份序列的ＧＭ（１,１）序号响应式和灾变年份预测式。     

Representing and reducing error in natural‐resource classification using model combination

Artificial Intelligence (AI) models such as Artificial Neural Networks (ANNs), Decision Trees and Dempster–Shafer's Theory of Evidence have long claimed to be more error‐tolerant than conventional statistical models, but the way error is propagated through these models is unclear. Two sources of error have been identified in this study: sampling error and attribute error. The results show that these errors propagate differently through the three AI models. The Decision Tree was the most affected by error, the Artificial Neural Network was less affected by error, and the Theory of Evidence model was not affected by the errors at all. The study indicates that AI models have very different modes of handling errors. In this case, the machine‐learning models, including ANNs and Decision Trees, are more sensitive to input errors. Dempster–Shafer's Theory of Evidence has demonstrated better potential in dealing with input errors when multisource data sets are involved. The study suggests a strategy of combining AI models to improve classification accuracy. Several combination approaches have been applied, based on a ‘majority voting system’, a simple average, Dempster–Shafer's Theory of Evidence, and fuzzy‐set theory. These approaches all increased classification accuracy to some extent. Two of them also demonstrated good performance in handling input errors. Second‐stage combination approaches which use statistical evaluation of the initial combinations are able to further improve classification results. One of these second‐stage combination approaches increased the overall classification accuracy on forest types to 54% from the original 46.5% of the Decision Tree model, and its visual appearance is also much closer to the ground data. By combining models, it becomes possible to calculate quantitative confidence measurements for the classification results, which can then serve as a better error representation. Final classification products include not only the predicted hard classes for individual cells, but also estimates of the probability and the confidence measurements of the prediction.