北京一次冬季回流暴雪天气过程的数值分析   总被引：6，自引：0，他引：6  

李青春  程丛兰  高华  丁海燕 《气象》2011,37(11):1380-1388

回流天气是华北地区冬、春、秋季节产生降雨（雪）的主要天气类型,预报员常常因对回流天气系统结构特征认识不足和诊断失误而导致预报的失败,是降雨（雪）预报的难点和重点。利用北京地区高分辨率快速循环同化中尺度数值预报系统（BJ-RUC）对2010年1月2—3日一次典型的回流暴雪天气过程进行模拟,分析数值模式的模拟能力,研究各层主要影响系统结构特征及形成暴雪的关键性条件,探讨典型回流暴雪天气过程的形成机理。主要结论为：数值模式对此次暴雪过程的近地面回流冷空气、中低层低值系统及变化特征、主要降雪时段和降雪量模拟效果较好,对降雪落区的模拟存在一定偏差。低层回流偏东风遇到地形后引起垂直运动主要在低层800 hPa以下,所产生的降雪量不大,而其与上游850～700 hPa低涡系统发展东移其前部的上升运动汇合所形成的大范围、深厚、强烈的上升运动是产生明显降雪的关键性条件。上游低涡系统前部西南暖湿气流相对应的大湿度区移近是产生较强降雪的重要条件。持续的低层回流冷空气湿度较大,对于低层大气起到水汽输送的作用。回流冷空气使低层大气维持长时间的水汽输送并与其上层东移的大湿度区相结合,增加湿层厚度,有利于降雪持续而形成较强降雪。降雪开始时间和降雪强度的变化与对流层中下转偏南风的时间和偏南风风速增大有关。  相似文献   

典型融雪期雪层融雪水出流条件模拟及分析     

张波  刘志辉  高云  房世峰 《干旱区地理》2015,38(3)

融雪期雪层融雪水的运移及流出过程模拟乃是国际冰雪水文学研究的难点之一,准确模拟融雪水的出流过程对于春季融雪型洪水的预报具有重要作用。本研究基于EM50、农业小环境监测仪和一些常规监测手段,获取了典型融雪期雪层的雪粒径、雪深及日气温数据,利用Excel、DPS、Arcgis及SPSS等分析软件对数据进行综合处理,并采用回归分析对融雪水的出流条件进行了建模分析,利用神经网络模型对模拟结果进行检验。结果表明:积温可作为融雪水外流的参考性指标,用于融雪水外流过程的预测分析;雪粒径和雪深都与融雪水外流积温条件存在显著相关性,且相关系数0.96;逐步回归可以很好地模拟融雪水外流的积温条件,模拟的误差仅为124.5℃·min,时间误差为15 min,模拟效果良好。该研究对于进一步探讨融雪期雪层融雪水的出流规律、开展雪层融雪水运移过程的数值模拟等工作具有重要意义。  相似文献   

基于MODIS雪盖数据的北疆雪深多元非线性回归克里金插值     

许剑辉  舒红  李杨 《国土资源遥感》2015,(3)

为了提高北疆地区雪深时空分布监测的准确性,以该区域48个气象站点2006年12月—2007年1月的月平均雪深观测数据为基础,通过分析月均雪深空间自相关性及其与经纬度、高程的相关性,结合MODIS雪盖数据构建了多元非线性回归克里金插值方法,插值获得了北疆地区较高精度的雪深空间分布数据。将插值雪深数据与普通克里金插值法、考虑高程为辅助变量的协同克里金插值法的预测结果进行比较,结果表明:1相对普通克里金和协同克里金方法,多元非线性回归克里金法的12月份雪深预测精度分别提高了15.14%和9.54%,1月份的提高了4.8%和6.7%;2由于充分利用了经纬度和地形信息,多元非线性回归克里金法的雪深预测结果可提供更多细节信息;3预测结果客观地表达了雪深随经纬度和地形变化的趋势,反映了积雪深度的空间变异性;4基于不显著相关的协变量高程的协同克里金插值法预测的雪深数据精度劣于普通克里金插值法的预测结果。  相似文献   

GPS信噪比用于雪深监测研究   总被引：2，自引：0，他引：2  

戴凯阳  张双成  张勤  南阳  赵迎辉 《测绘科学》2015,(12)

针对利用全球导航卫星系统反射信号研究测站地表环境参数已成为一个新兴的研究课题这一现状,该文基于全球定位系统信噪比与信号振幅的变化特征,给出了基于全球定位系统多路径信号的全球定位系统多径反射技术用于雪深探测的基本原理。为了验证算法的有效性,利用美国PBO网络中P360站离散20d的全球定位系统原始观测数据进行雪深探测的反演实验。实验结果表明:全球定位系统多径反射技术反演雪深值与实测雪深值吻合较好,误差均值为0.07m,相关系数大于0.99。因此,利用全球定位系统信噪比可以进行雪深探测,在未来的全球导航卫星系统观测站建立时,可以考虑它在环境监测方面表现出来的潜能。  相似文献   

A GIS-based method for defining snow zones: application to the western United States     

Cara Moore  Brandon Stone  Eric Richer 《国际地球制图》2015,30(1):62-81

This study maps the geographic extent of intermittent and seasonal snow cover in the western United States using thresholds of 2000–2010 average snow persistence derived from moderate resolution imaging spectroradiometer snow cover area data from 1 January to 3 July. Results show seasonal snow covers 13% of the region, and intermittent snow covers 25%. The lower elevation boundaries of intermittent and seasonal snow zones increase from north-west to south-east. Intermittent snow is primarily found where average winter land surface temperatures are above freezing, whereas seasonal snow is primarily where winter temperatures are below freezing. However, temperatures at the boundary between intermittent and seasonal snow exhibit high regional variability, with average winter seasonal snow zone temperatures above freezing in west coast mountain ranges. Snow cover extent at peak accumulation is most variable at the upper elevations of the intermittent snow zone, highlighting the sensitivity of this snow zone boundary to climate conditions.  相似文献   

2011-2013年乌鲁木齐城-郊冬季积雪深度与密度空间分布特征     

李小兰  姜逢清  王少平  王蕾  穆叶赛尔·吐地  张兆永  张登清 《冰川冻土》2015,37(5):1168-1177

利用2011-2013年冬季4次地面实测乌鲁木齐城-郊积雪深度与密度数据,应用普通克里格空间插值方法,分析了乌鲁木齐城-郊冬季积雪深度与密度从2011年12月下旬-2012年2月下旬及2012年1月中旬和2013年同期的时空分布特征.结果表明:乌鲁木齐城-郊冬季积雪深度与密度存在显著地区域分布差异及变化特征.整个冬季位于城东北部的米东石化工业园区积雪均较深,尤其12月和2月,在主城区内部又存在不同下垫面下积雪较多的区域.从12月下旬-次年2月下旬,积雪逐渐累积,且积雪深度比密度具有更大的空间变化幅度.除12月下旬大部分主城区雪密度比郊区大之外,1月中旬、2月下旬主城区雪密度均比城东和城东北方向低.2013年1月中旬积雪与2012年同期相比,平均积雪量明显偏厚,约31 cm,但雪密度变化范围不大且深度与密度的空间分布均发生明显改变.本文结果对于了解乌鲁木齐城区积雪的区域差异,为主城区道路积雪清运、保障道路通畅优化方案及春季融雪洪水防御预案的制定提供基础数据支撑,也可以弥补当前气象站点少且空间分布不匀的不足.此外,本文对卫星遥感数据反演的积雪参数精度验证也具有实际参考价值.  相似文献   

A rain on snow climatology and temporal analysis for the eastern United States     

Lori J. Wachowicz  Thomas L. Mote  Gina R. Henderson 《自然地理学》2020,41(1):54-69

ABSTRACT

Rain-on-snow (ROS) has the potential to produce devastating floods by enhancing runoff from snowmelt. Although a common phenomenon across the eastern United States, little research has focused on ROS in this region. This study used a gridded observational snow dataset from 1960–2009 to establish a comprehensive seasonal climatology of ROS for this region. Additionally, different rain and snow thresholds were compared while considering temporal trends in ROS occurrence at four grid cells representing individual locations. Results show most ROS events occur in MAM (March-April-May). ROS events identified with rainfall >1 cm are more frequent near the east coast and events identified with >1 cm snow loss are more common in higher latitudes and/or elevations. Decreasing trends in DJF (December-January-February) ROS events were identified near the coastal areas, with increasing trends in the northern portion of the domain. Significant decreasing trends in MAM ROS are likewise present on a regional scale. Factors playing a role in snowpack depth and rainfall, such as movement of storm tracks in this region, should be considered with future work to discern mechanisms causing the changes in ROS frequency.  相似文献   

The impacts of mountain pine beetle disturbance on the energy balance of snow during the melt period          下载免费PDF全文

Christopher M. Welch  Paul C. Stoy  F. Aaron Rains  Aiden V. Johnson  Brian L. McGlynn 《水文研究》2016,30(4):588-602

Mountain snowpacks provide most of the annual discharge of western US rivers, but the future of water resources in the western USA is tenuous, as climatic changes have resulted in earlier spring melts that have exacerbated summer droughts. Compounding changes to the physical environment are biotic disturbances including that of the mountain pine beetle (MPB), which has decimated millions of acres of western North American forests. At the watershed scale, MPB disturbance increases the peak hydrograph, and at the stand scale, the ‘grey’ phase of MPB canopy disturbance decreases canopy snow interception, increases snow albedo, increases net shortwave radiation, and decreases net longwave radiation versus the ‘red’ phase. Fewer studies have been conducted on the red phase of MPB disturbance and in the mixed coniferous stands that may follow MPB‐damaged forests. We measured the energy balance of four snowpacks representing different stages of MPB damage, management, and recovery: a lodgepole pine stand, an MPB‐infested stand in the red phase, a mixed coniferous stand (representing one successional trajectory), and a clear‐cut (representing reactive management) in the Tenderfoot Creek Experimental Forest in Montana, USA. Net longwave radiation was lower in the MPB‐infested stand despite higher basal area and plant area index of the other forests, suggesting that the desiccated needles serve as a less effective thermal buffer against longwave radiative losses. Eddy covariance observations of sensible and latent heat flux indicate that they are of similar but opposite magnitude, on the order of 20 MJ m^?2 during the melt period. Further analyses reveal that net turbulent energy fluxes were near zero because of the temperature and atmospheric vapour pressure encountered during the melt period. Future research should place snow science in the context of forest succession and management and address important uncertainties regarding the timing and magnitude of needlefall events. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

Development of a rain‐on‐snow detection algorithm using passive microwave radiometry          下载免费PDF全文

Caroline Dolant  Alexandre Langlois  Benoit Montpetit  Ludovic Brucker  Alexandre Roy  Alain Royer 《水文研究》2016,30(18):3184-3196

Currently observed climate warming in the Arctic has numerous consequences. Of particular relevance, the precipitation regime is modified where mixed and liquid precipitation can occur during the winter season leading to rain‐on‐snow (ROS) events. This phenomenon is responsible for ice crust formation, which has a significant impact on ecosystems (such as biological, hydrological, ecological and physical processes). The spatially and temporally sporadic nature of ROS events makes the phenomenon difficult to monitor using meteorological observations. This paper focuses on the detection of ROS events using passive microwave (PMW) data from a modified brightness temperature (T_B) gradient approach at 19 and 37 GHz. The approach presented here was developed empirically for observed ROS events with coincident ground‐based PMW measurements in Sherbrooke, Quebec, Canada. It was then tested in Nunavik, Quebec, with the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E). We obtained a detection accuracy of 57, 71 and 89% for ROS detection for three AMSR‐E grid cells with a maximum error of 7% when considering all omissions and commissions with regard to the total number of AMSR‐E passes throughout the winter period. Copyright © 2016 John Wiley & Sons, Ltd.  相似文献   

Inference and uncertainty of snow depth spatial distribution at the kilometre scale in the Colorado Rocky Mountains: the effects of sample size,random sampling,predictor quality,and validation procedures          下载免费PDF全文

J. L. McCreight  A. G. Slater  H. P. Marshall  B. Rajagopalan 《水文研究》2014,28(3):933-957

Historically, observing snow depth over large areas has been difficult. When snow depth observations are sparse, regression models can be used to infer the snow depth over a given area. Data sparsity has also left many important questions about such inference unexamined. Improved inference, or estimation, of snow depth and its spatial distribution from a given set of observations can benefit a wide range of applications from water resource management, to ecological studies, to validation of satellite estimates of snow pack. The development of Light Detection and Ranging (LiDAR) technology has provided non‐sparse snow depth measurements, which we use in this study, to address fundamental questions about snow depth inference using both sparse and non‐sparse observations. For example, when are more data needed and when are data redundant? Results apply to both traditional and manual snow depth measurements and to LiDAR observations. Through sampling experiments on high‐resolution LiDAR snow depth observations at six separate 1.17‐km² sites in the Colorado Rocky Mountains, we provide novel perspectives on a variety of issues affecting the regression estimation of snow depth from sparse observations. We measure the effects of observation count, random selection of observations, quality of predictor variables, and cross‐validation procedures using three skill metrics: percent error in total snow volume, root mean squared error (RMSE), and R². Extremes of predictor quality are used to understand the range of its effect; how do predictors downloaded from internet perform against more accurate predictors measured by LiDAR? Whereas cross validation remains the only option for validating inference from sparse observations, in our experiments, the full set of LiDAR‐measured snow depths can be considered the ‘true’ spatial distribution and used to understand cross‐validation bias at the spatial scale of inference. We model at the 30‐m resolution of readily available predictors, which is a popular spatial resolution in the literature. Three regression models are also compared, and we briefly examine how sampling design affects model skill. Results quantify the primary dependence of each skill metric on observation count that ranges over three orders of magnitude, doubling at each step from 25 up to 3200. Whereas uncertainty (resulting from random selection of observations) in percent error of true total snow volume is typically well constrained by 100–200 observations, there is considerable uncertainty in the inferred spatial distribution (R²) even at medium observation counts (200–800). We show that percent error in total snow volume is not sensitive to predictor quality, although RMSE and R² (measures of spatial distribution) often depend critically on it. Inaccuracies of downloaded predictors (most often the vegetation predictors) can easily require a quadrupling of observation count to match RMSE and R² scores obtained by LiDAR‐measured predictors. Under cross validation, the RMSE and R² skill measures are consistently biased towards poorer results than their true validations. This is primarily a result of greater variance at the spatial scales of point observations used for cross validation than at the 30‐m resolution of the model. The magnitude of this bias depends on individual site characteristics, observation count (for our experimental design), and sampling design. Sampling designs that maximize independent information maximize cross‐validation bias but also maximize true R². The bagging tree model is found to generally outperform the other regression models in the study on several criteria. Finally, we discuss and recommend use of LiDAR in conjunction with regression modelling to advance understanding of snow depth spatial distribution at spatial scales of thousands of square kilometres. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献