新疆阿勒泰克兰河中游地区冬季积雪分布及特性分析

新疆阿勒泰地区是中国季节性积雪水资源最为丰富的地区之一。2016年12月在克兰河中游地区开展了积雪观测,利用直尺和量雪筒测量雪深和雪密度,调查了积雪水资源的分布情况;利用针式温度计测量雪层温度,获取了雪层之间的温度梯度;利用雪特性分析仪和显微镜测量了积雪剖面的雪层密度、液态水含量、介电常数和雪粒径。通过分析研究区积雪水资源的空间分布和积雪特性的垂直分异发现：研究区雪深的分布非常不均匀,北部的雪深总体上大于南部,即使在同一地区,雪深也因风力等原因而分布不均匀;研究区总体上属于"干寒型"积雪,密度较小,且密实化迅速;各雪层属于干雪或者湿度极低的潮雪,绝大多数雪层的液态水含量在0.3%以下;积雪温度总体上从表层到底层逐渐升高,表层温度日变化较大;阴天积雪温度高于晴天,各雪层温度日变化小于晴天,且午后积雪会出现负温度梯度,冷中心出现在积雪次表层;雪粒径较小,雪粒长短轴比的最小值出现在中间层,且符合新雪的粒径特点。     

亚洲高山区积雪物候时空动态及其对气候变化的响应

以亚洲高山区为研究区,在对2000—2020年逐日MODIS积雪产品进行去云处理的基础上提取了每一水文年的积雪日数（snow-covered days, SCD）、积雪开始日期（snow onset date, SOD）、积雪结束日期（snow end date, SED）和积雪持续日数（snow duration days, SDD）等积雪物候参数,并分析了积雪物候时空动态特征及其与气候变化的响应关系。结果表明：亚洲高山区积雪物候空间差异较大,并呈现出主要受海拔影响的垂直地带性分布规律。研究区SED主要集中在3—6月,在低海拔区SED出现在3月份及以前,而高海拔山区则推迟到6月份及以后;SOD主要集中在9—12月,高海拔山脉及高纬度地区的SOD出现较早,而低海拔区的SOD多出现在11月及以后。近20年,研究区SDD主要呈缩短趋势,在13.5%的区域呈显著缩短,而仅7.4%的区域为显著延长;SED主要呈提前趋势,在15.8%的区域显著提前,而仅8.8%的区域为显著推迟;SOD主要呈推迟趋势,在11.4%的区域表现为显著推迟,在8.2%的区域为显著提前。亚洲高山区积雪物候年际变化对气候变化的响应关系明显,积雪累积期气温是影响SOD年际变化的主导因子,而融雪期气温是影响SED年际变化的主导因子;气温的上升,导致了SOD的推迟、SED的提前和SDD的缩短。     

欧亚大陆冬季雪盖同四川夏季旱涝的关系

本文利用1966年～1983年冬季雪盖资料研究了欧亚大陆冬季(12～2月)雪盖面积同后期四川月平均气温和月降雨量之间的关系。研究表明:欧亚大陆冬季雪盖面积对四川省夏季月平均气温为负相关,同月降雨量为正相关。这种关系说明冬季雪盖面积对夏季气温和降雨量有重要的滞后影响,尤其是对川东伏旱地区的作用更为显著。还说明冬季雪盖面积大,夏季月平均气温比多年平均值偏低,月降雨量比多年平均值偏多,高温伏旱轻;相反 冬季雪盖面积小时,夏季月平均气温比多年平均值偏高,月降雨量比多年平均值偏少,高温伏旱严重。     

北半球海冰和陆雪对东亚季风的影响及作用的可能途径

本文应用统计方法分析陆雪和海冰与东亚夏季风的关系。分析结果表明:前期海冰和陆雪,对夏季风强度有影响,而与夏季风同时的海冰和陆雪的异常,却与夏季风相关甚小,这是由于大气状况的变化与下垫面的能量储放有关。本文初步探讨北极海冰对东亚夏季风影响的可能途径,认为海冰通过大西洋海温、大西洋副热带高压及青藏高压,由夏季对流层上层的东西热力环流圈和季风环流圈,对东亚夏季风起一定影响。     

多源信息结合的雪灾交通风险评估研究

本研究将气象观测信息与网络信息相结合,提出了一种多源信息结合的雪灾交通风险评估方法：利用长时间气象观测数据分析雪灾的致灾强度时空特征,计算不同年遇型雪灾致灾强度;对门户网站、高速公路网站中节假日道路拥堵的新闻报道进行信息挖掘,获取高速公路暴露度;采用风险矩阵进行雪灾交通风险评估。将该方法应用于河北省,研究结果如下： ① 近5年来全省降雪有所下降,但长时间尺度来看,各地降雪呈波动变化;积雪深度高值区分布在张家口、承德、石家庄地区,但各年代间会有所变化;降雪次数高值区基本固定,在张家口康保、沽源、崇礼以及承德丰宁西北方向。② 暴露度级别高的路段是连接北京与上海、广州、哈尔滨等城市的高速公路,以及重要省市级联络线。③ 受致灾强度与暴露度的综合影响,河北省雪灾高风险路段集中在京港澳高速（石安G4）、京昆高速G5、京承高速G45、长深高速G25、张承高速G95等。这些路段必须做好雪灾风险防范措施。     

新疆冰雪旅游资源适宜性评价研究

冰雪旅游正在成为冬季旅游出行的新选择。新疆冰雪旅游资源丰富,如何合理开发利用资源成为当地旅游开发亟待解决的问题,因此本文从旅游资源适宜性角度出发,选取自然条件、旅游资源、社会经济和交通可达性等方面的18个影响因子,采用层次分析法（AHP）和熵值法（EWM）确定组合权重,运用GIS空间分析技术和多因子加权评价模型（MCE）对研究区冰雪旅游资源适宜性进行评价。研究结果显示：① 就自然条件来看,伊犁东部、阿勒泰北部沿山脉边缘的丘陵和平原等地区较适宜发展冰雪旅游;② 阿勒泰地区和乌鲁木齐市的旅游资源相关基础设施完备,昌吉和伊犁在旅游资源方面也有较强竞争力;③ 社会经济来看,伊犁、乌鲁木齐市和阿勒泰地区为代表的北疆城市较南疆有更强的游客接待能力;④ 伊犁、乌昌经济带沿线地区交通可达性明显优于南疆各地州市。新疆冰雪旅游综合适宜性指数为1.8622-7.5724之间,其中天山北坡、阿勒泰北部、伊犁、塔城北部、哈密中部等地区为高度适宜;南疆大部分区域处于中低度适宜。建议通过加强新型旅游资源开发和整合,积极承办大型冰雪运动相关赛事,借助丝绸之路经济带发展交通道路建设,以加速推进新疆冰雪旅游发展。本研究可为冰雪旅游区开发和选址提供空间导向和科学依据。     

NORMALIZED DIFFERENCE SNOW INDEX SIMULATION FOR SNOW-COVER MAPPING IN FOREST BY GEOSAIL MODEL

1INTRODUCTION Snow is an important component of the Earth's surface. Up to 50×106km2(34%) ofthe Earth's land surface is sea- sonally snow-covered (VIKHAMAR and SOLBERG, 2002).Comparedtootherlandcovers,snowcoverextent varies dramatically on very short time scales (hours- months). Its presence affects physical, chemical and bio- logical processes at many spatial scales and has impor- tant social impacts. At the global scale, its high albedo strongly influences the Earth's radiation …     

Change in fresh snow density in Tianshan Mountains,China

The fresh snow density was observed with snow analyzer (Snow Fork) at Tianshan Station for Snowcover and Avalanche Research, Chinese Academy of Sciences from February 21 to March 5, 2009. Results show that fresh snow density increases from the 5th h to the 291st h after the snowfall, with an average rate of increase of 4.0×10-4 g/(cm3·h) (R2 = 0.943). Analysis shows that fresh snow density is negatively correlated with the compac-tion rate of fresh snow (R2 = -0.960). Inversely, it is positively correlated ...     

Scaling properties and spatial organization of snow depth fields in sub‐alpine forest and alpine tundra

This paper reports on a study analysing the spatial distribution functions, the correlation structures, and the power spectral densities of high‐resolution LIDAR snow depths (～1 m) in two adjacent 500 m × 500 m areas in the Colorado Rocky Mountains, one a sub‐alpine forest the other an alpine tundra. It is shown how and why differences in the controlling physical processes induced by variations in vegetation cover and wind patterns lead to the observed differences in spatial organization between the snow depth fields of these environments. In the sub‐alpine forest area, the mean of snow depth increases with elevation, while its standard deviation remains uniform. In the tundra subarea, the mean of snow depth decreases with elevation, while its standard deviation varies over a wide range. The two‐dimensional correlations of snow depth in the forested area indicate little spatial memory and isotropic conditions, while in the tundra they indicate a marked directional bias that is consistent with the predominant wind directions and the location of topographic ridges and depressions. The power spectral densities exhibit a power law behaviour in two frequency intervals separated by a break located at a scale of around 12 m in the forested subarea, and 65 m in the tundra subarea. The spectral exponents obtained indicate that the snow depth fields are highly variable over scales larger than the scale break, while highly correlated below. Based on the observations and on synthetic snow depth fields generated with one‐ and two‐dimensional spectral techniques, it is shown that the scale at which the break occurs increases with the separation distance between snow depth maxima. In addition, the breaks in the forested area coincide with those of the corresponding vegetation height field, while in the tundra subarea they are displaced towards larger scales than those observed in the corresponding vegetation height field. Copyright © 2009 John Wiley & Sons, Ltd.     

A neural network method for monitoring snowstorm: A case study in southern China

It has been observed that low temperature, rainfall, snowfall, frost have never occurred over the past 50 years in the southern China, and weather in this area is very complex, so the monitoring equipments are few. Optical and thermal infrared remote sensing is influenced much by clouds, so the passive microwave Advanced Microwave Scanning Radiometer-Earth Observing System （AMSR-E） data are the best choice to monitor and analyze the development of disaster. In order to improve estimation accuracy, the dynamic learn- ing neural network was used to retrieve snow depth. The difference of brightness temperatures of TB18.7v and TB36.sv, TBI8.7H and TB36.sH, TB23,sv and TB89v, TBz3.8H and TB89H are made as four main input nodes and the snow depth is the only one output node of neural network. The mean and the standard deviation of retrieval errors are about 4.8 cm and 6.7 cm relative to the test data of ground measurements. The application analysis indicated that the neural network can be utilized to monitor the change of snow intensity distribution through passive microwave data in the complex weather of the southern China.