A 2-D MICROSCOPIC SIMULATION OF HEAT AND MASS TRANSPORT IN DRY SNOW

The problem of acid deposition and its effects on the environment is receiving increasing attention in North America and Europe. The interaction between

seasonal snowcovers and deposited pollutants is of particular importance because a snowpack accumulates and stores pollutants which can ultimately be released in a rapid pulse with the first melt water in the springtime. As a direct result of an impurity pulse, water quality degrades with deleterious effects on the local environment and aquatic biological species. The timing and severity of an inpurity pulse is dependent upon the redistribution of pollutants in a snowpack which is attributed to a process known as temperature gradient metamorphism.

This work investigates the influence of geometry, density and temperature on the coupled heat and mass transport in idealized, two dimensional ice lattice cells. Mass flux, concentration and temperature distributions, as well as effective diffusion coefficients and thermal conductivities are presented as functions of temperature, geometry, and density. A finite element model of the coupled, heat and mass transport is used to analyze the problem on a microscopic scale in two dimensions. Deforming meshes are used to simulate the growth/decay process which occurs over time in an ice lattice pore.     

露天矿汽车运输功与台阶高度的关系

以１８ｍ台阶和１２ｍ台阶的露天为例，分析了台阶高度与矿岩势能、矿岩运距及汽车运行速度的关系，认为单一汽车运输的露天矿，随着台阶高度的增加，汽车运输功有减小的趋势。     

Assessment of FY-3 Snow Cover Product

Due to the unique function that snow played in modulating energy and water exchanges in climate and hydrology system,it is important to estimate snow distribution and produce high quality products for short-term climate prediction and water resources management.National Satellite Meteorological Center publics FY-3 snow cover fraction product since 2009.It is necessary to evaluate the snow cover fraction product in order to verify the precision of retrieval algorithms and provide an objective evidences for climate studies.based on MODIS MOD10C1(MYD10C1) Global Daily Snow Cover Dataset,we carries out an evaluate of FY-3 snow cover fraction product from 2010 to 2014 based on five examine indexes,and analyses the bias distribution of snow cover fraction product in different time scales further.It is concluded that FY-3 snow product is a better time space consistency with MODIS MOD10C1(MYD10C1).For example,the consistency of two products is better in snow accumulation period,while it is reducing influenced by cloud detectionin snow melting time.At the same time,bias of snow cover fraction products have obviously changes in inter-annual time,seasonal and monthly.compares to MODIS products,FY-3 snow product is higher in North China,but it coverts to lower in whole China since 2012.Bias of two products decreases from snow accumulation period to snow melt period.In monthly time scale,North eastern China and north of Sinkiang area is sensitive area of snow variation.Bias is more stable because of Tibet Plateau is influenced by topography and covered with snow all the year.     

The Study on Snow Depth Retrieval in Xinjiang Region based on FY3B-MWRI Data

The snow depth is one of the necessary parameters of weather and hydrological model,which is not only used to study the balance of surface radiation,but also to study the hydrological effects of snow.Meanwhile,the snow depth monitoring plays an important role in snow-melt runoff forecasting,water resources management and flood control.the sites to measure snow depth data to constructe existing snow depth retrieval models based on FY3B-MWRI data mainly distribute in middle,east and south of China,and the sites in Xinjiang region are relative less.Thus,it causes the poor precision of the snow depth retrieval algorithm in the Xinjiang region.In this paper,we select Xinjiang region as the study area and select FY3B-MWRI as the data source.According to the topography characteristics and surface land cover characteristics of the region,we use regression analysis method to study the different snow depth retrieval algorithms of the three land covertypes (forestland,farmland and grassland).Then we verify the accuracy of the algorithms comparing to the field measured data.The results show that the R² and RMSE of the three land covertypes are 0.758,2.58,0.729,3.21,0.854,5.70 respectively,so the algorithms of this paper have a high accuracy of the snow depth retrieval in Xinjiang region.     

基于FY-MWRI的中国西部被动微波积雪判识算法北大核心CSCD

积雪是冰冻圈中分布最广泛的要素,在气候变化以及水文循环中扮演着重要角色。微波遥感因其全天时全天候工作、具有一定穿透性等优势,成为积雪监测的重要手段。利用FY-3C卫星同步观测获取的微波成像仪(MWRI)被动微波亮度温度数据、融合可见光红外扫描仪(VIRR)与中等分辨率成像光谱仪(MERSI)数据得到的积雪产品,结合MODIS地表分类数据、地表温度数据,发展了基于国产卫星数据的被动微波积雪判识算法。首先提取无云覆盖的不同地表类型被动微波数据像元样本,然后对各地表类型的微波特征进行分析,利用空间聚类的方法,得到TB19V-TB19H、TB19V-TB37V、TB22V、TB22V-TB89V、(TB22V-TB89V)—(TB19V-TB37V)这五类可以较好地区分积雪和其他类似积雪地表的指标。最后应用MODIS积雪产品为参考对该积雪判识算法进行精度评价,该算法在中国西部积雪判识总体精度为87.1%,漏判率为4.6%,误判率为23.3%;Grody算法判识总体精度为78.6%,漏判率为9.8%,误判率为30.7%,该算法判识精度高于Grody算法;通过Kappa系数分析比较,该算法积雪判识结果的Kappa系数值为47.3%,高于Grody算法判识结果的Kappa系数值39.9%,表明该算法积雪判识结果与MODIS积雪产品判识结果一致性更好。     

利用实测资料评估被动微波遥感雪深算法

利用SSM/I微波亮温数据,结合地面站点实测资料,比较Chang算法和Che算法在前苏联、中国及蒙古境内6种不同积雪类型的反演精度,结果表明:被广泛应用于全球雪深反演的Chang算法低估了前苏联境内雪深7.6cm,相对误差为-24.3%,而分别高估中国及蒙古境内雪深9.2cm与11.4cm,相对误差分别为108.8%和180.9%,区域反演效果很差;针对中国境内积雪的Che算法严重低估前苏联境内雪深,整体低估21.3cm,相对误差为-68.6%,RMSE为31.4cm;在中国及蒙古境内反演效果有所改善。6个积雪类型中,植被较单一,地形较平坦的苔原型积雪和草原型积雪雪深的反演效果较好。随着纬度和积雪深度的增加被动微波雪深反演有由高估变为低估的趋势。Che算法反演的雪深大体以40°N为界,以北表现为低估,以南表现为高估,另一方面,整体上该算法在雪深低于6.7cm时表现为低高估,高于6.7cm表现为低估;因此,全球算法应用到局部地区需要进行修正,不同下垫面性质以和气候条件下形成的积雪的被动微波反演应区别对待。     

被动微波雪深反演算法在东北地区的时空分析与验证

Chang算法及改进算法是被动微波遥感雪深反演算法中较简单的经验算法。为了评价改进的Chang算法在东北地区的适用性,对改进的Chang算法进行分析与验证。从空间上,选取了84个野外数据采样点和48个气象站点对改进的Chang算法进行分析与验证。结果表明:森林下垫面改进的Chang算法会低估雪深3.6cm,而农田下垫面改进的Chang算法会高估雪深1.5cm。从时间序列上,选取五营、呼中、庆安和巴彦4个气象站点2012年11月15日~2013年2月28日的时间序列雪深数据,对改进的Chang算法进行分析与验证。结果表明:森林下垫面改进的Chang算法会低估雪深,五营站点低估雪深13.7cm,呼中站点低估雪深8.3cm,农田下垫面改进的Chang算法会高估雪深,庆安站点高估雪深3.4cm,巴彦站点高估雪深0.8cm。无论从空间上还是时间序列上,验证结果都表明,农田下垫面时改进的Chang算法的精度比森林下垫面时要高。此外,站点雪深不变而改进的Chang算法反演的雪深却在增大,这可能是由于期间雪粒径不断增大的缘故。     

融合FY-3C号和FY-4A号卫星数据的积雪面积变化研究—以祁连山区为例

祁连山区积雪类型丰富、判识复杂,是中国积雪研究的典型区域。因此,精确地监测祁连山区积雪面积变化及其时空演变,对祁连山区生态环境和社会经济发展等具有重要意义。FY-3C MULSS利用多阈值积雪指数模型提供全球日积雪覆盖产品,FY-4A AGRI传感器每15~60 min提供一景覆盖全球的多光谱影像。基于FY-4A AGRI高时间分辨率的特征,构建适合于FY-4A号数据的动态多阈值多时相云隙间积雪识别方法,很大程度上减小了云对光学数据识别积雪造成的影响,并结合FY-3C MULSS积雪覆盖日产品较高空间分辨率的优势,融合得到去除云后的FY3C4积雪覆盖数据。利用Landsat 8 OLI卫星数据对融合后的积雪数据进行对比验证,结果表明融合FY-3C和FY-4A后的数据能更好地判识祁连山区的积雪覆盖情况。以MODIS MOD10A2积雪产品为真实值,随机检验了2018年3月~2019年3月融合后数据的积雪判识精度,发现无云情况下方法的总体精度可达到85.25%。进一步研究发现祁连山区积雪面积在海拔、气候和坡向等因素的影响下时空分布极不均匀,总体呈现出冬春季节大于夏秋季节,以及东部积雪面积大于西部积雪面积的特征。     

《登幽州台歌》与《江雪》意境比较

陈子昂的《登幽州台歌》与柳宗元的《江雪》是千古传诵的名篇。对这两首诗歌的写作背景以及意境进行比较分析——两首诗歌的写作背景有其相似性,都是由于怀才不遇促就而成。同时,两首诗歌从各自独特的角度写出了同样的孤独和超越的意境。     

一种新型的雨雪视频图像复原方法

针对雨、雪等恶劣天气极易影响视频图像质量的问题,提出了一种新型雨雪视频图像复原方法。该方法在深入研究视频帧序列时域特性的基础上,将R、G、B 3维彩色空间属性数据进行投影降维,通过研究低维数据的散布形状来揭示高维数据的聚类特征,进而依据统计特性进行雨雪去除,实现视频图像复原的目的。该方法依据雨雪区和背景区的数据特性进行分类,能提供更准确的类别中心,尤为关键的是,该方法所需视频序列帧数少,不需要进行数据的彩色空间转换,简化了运算。6个经典视频场景的实验结果表明,复原后的图像清晰度高,具有较强的鲁棒性。