基于多源数据的青藏高原雪深重建

青藏高原地形复杂,积雪时空分布异质性较强且大部分地区积雪较薄,而被动微波遥感因其空间分辨率低以及雪深反演中的不确定性,极大地限制了其反演青藏高原雪深的精度。本文尝试将多源遥感数据以及与积雪模型（SnowModel）相结合,来重建更高质量的青藏高原雪深数据。首先,利用MODIS积雪面积比例产品,根据构建的积雪衰减曲线以及经验的融合规则对低分辨率被动微波雪深进行了降尺度;然后,结合MODIS/被动微波融合雪深数据和SnowModel对研究区进行雪深数据同化实验;最后,利用地面站实测雪深数据对MODIS/被动微波融合雪深以及同化输出雪深的精度进行了分析和对比。结果表明,基于数据同化方法得到的雪深数据更接近地面观测雪深值,通过均方根误差以及相关系数的对比,同化雪深结果优于MODIS/被动微波融合雪深结果。     

东北典型林区雪深反演算法的验证与分析

积雪对自然环境和人类活动都有极其重要的影响。积雪参数（雪面积、雪深和雪水当量）反演对水文模型和气候变化研究有着实际的意义。然而,目前森林区的雪深遥感反演精度一直有待于进一步提高。东北地区是我国最大的天然林区和重要的季节性积雪区之一,本文利用FY3B卫星微波成像仪（MWRI）L1级亮温数据和L2级雪水当量数据,以及东北典型林区野外实测雪深数据,对Chang算法、NASA 96算法和FY3B雪深业务化反演算法进行了验证与分析。结果表明：在东北典型林区的雪深反演中,Chang算法和NASA 96算法反演的雪深波动都比较大,当森林覆盖度f≤0.6时,NASA 96算法表现比较好,均方根误差值在3种算法中较小,但当f >0.6时,NASA 96算法失真严重。当考虑纯森林像元（f=1）时,Chang算法低估了雪深47%。当f≤0.3时,FY3B业务化算法始终优于Chang算法。整体上,FY3B业务化算法相对稳定,具有较高的精度。     

利用CEEMDAN进行GNSS-MR雪深反演

将自适应噪声的完全集合经验模态分解方法替换常用的二次多项式方法,对原始信噪比（signal-to-noise, SNR）进行信号分解,直接提取相应的本征模态函数,再应用于GNSS-MR技术反演雪深。以美国科罗拉多州NWOT测站GPS数据进行实验的结果表明,该方法与传统方法相比,均方根误差降低30.7%,与实测数据相关系数为0.965,验证了该方法的有效性。     

1979-2010年青藏高原积雪深度时空变化遥感分析

积雪深度是表征积雪特征的重要参数,也是区域气候变化最敏感的响应因子之一。利用1979-2010年逐日中国雪深长时间序列数据集,采用GIS空间分析和地统计方法,分析了青藏高原积雪深度的时空变化规律及异常空间分布特征。结果表明：近32年来,青藏高原雪深呈显著增加趋势,增加速率为0.26 cm/10a,其中,昆仑高寒荒漠地带雪深增加最为明显,增加速率达0.73 cm/10a;20世纪80年代至90年代青藏高原雪深呈逐步增加趋势,21世纪初变化平稳;青藏高原4个季节雪深变化均呈现为上升趋势,尤以冬季增加最为明显,增加速率达0.57 cm/10a。青藏高原东南、西部和南部为雪深分布高值区;逐像元回归分析表明,高原雪深呈增加趋势的像元数占全区像元总数的67.1%,其中有91.3%为轻度和中度增加,主要分布在高原北部和西部;最大雪深变化基本维持在-0.1~0.1 cm/a（45.47%）之间,在昆仑北翼山地、柴达木山地、羌塘高寒地带南部等局部地区最大雪深有增加趋势,主要是轻度增加,面积比例为36.66%。果洛那曲高寒地带、青南高寒地带和羌塘高寒地带为青藏高原积雪深度异常变化敏感区。     

陆态网络基准站的重力水平梯度分析

以中国大陆构造环境监测网络2010~2011年实测重力水平梯度数据为基础,通过正演计算3种典型地形的水平梯度,与实测结果进行对比分析。结果表明,半数以上测点存在较大的水平梯度,由此产生的干扰会极大地降低观测结果的精度与可靠性。因此,有条件的测点应进行水平梯度观测并进行校正。     

基于傅里叶级数的中国沿海地区Tm模型精化研究

本文采用中国沿海地区13个探空站2010~2014年实测地表温度T_s与平均温度T_m数据,利用傅里叶级数分析法精化中国沿海地区T_m模型,并将2015年探空站实测T_m数据与精化模型进行对比检验。结果表明,精化模型在T_m探测方面具有更高的计算精度,其计算大气可降水量的误差概率分布趋近于正态分布,具有较强的稳定性。     

基于滑动奇异谱分析方法的重力固体潮提取

提出采用滑动奇异谱分析（singular spectrum analysis,SSA）方法进行长时间重力固体潮改正提取。采用61 d静态相对重力测量数据,利用滑动SSA方法将数据按连续时间段10 d、15 d、20 d、25 d、30 d、40 d和50 d分别分为52组、47组、42组、37组、32组、22组和12组进行重力固体潮改正提取实验。参考理论值分别选用CG-5重力仪自带软件计算结果（CG5_Data）和实测重力数据采用Eterna调和分析获得潮波参数后用TSoft软件计算结果（ET_Data）。结果表明,参考理论值选用ET_Data时,残差的RMS和STD比选用CG-5重力仪计算结果小;采用不同方法提取的重力固体潮改正残差的标准差均小于11 μGal;与SSA方法相比,当选择合适的连续时间段时,滑动SSA方法提取固体潮能降低结果残差的离散程度;选择连续时间段30 d、40 d时,滑动SSA提取结果比SSA提取结果残差的RMS分别小0.219 μGal和0.602 μGal（CG5_Data）以及0.430 μGal和0.665 μGal（ET_Data）。     

有限元建模与实测应变分析——以九江地震台为例北大核心CSCD

为研究区域地质构造的地壳应变状态,利用数字高程模型(DEM)数据构建数字化三维地形模型,再添加区域地质岩石属性建立有限元三维地质模型,并施加重力和热荷载,进行有限元与实测应变分析。以九江地震台为例,利用有限元模型及台站实测温度和应变数据,展开区域应变分析。结果表明,有限元建模应变解算结果与实测应变结果较符,均值量级皆为10^(-5),都呈明显的季节性周期变化趋势;模型热应变和定向应变分别能较好地反映温度造成的应变差异和不同方向的应变变化;模型应变能在一定程度上反映区域应变的变化范围和量级大小,有益于缺乏实测应变条件的区域开展应变分析。     

基于星间链路体制的北斗卫星时间同步可行性分析

BD-3试验星上搭载了Ka频段星间链路（ISL）设备,可进行Ka波段对地观测。推导了星间链路时分观测体制双向时间同步的数学模型;以ISL星地观测数据为样本分析ISL体制时间同步的可行性,并与L波段观测结果比对。结果表明,ISL体制钟速拟合参数与L波段结果一致性较好,7 d和3 d弧段拟合精度在1 ns以内,1 d和1 h弧段拟合精度均在0.2 ns以内,较L波段拟合精度有所提高,其中1 d为最优拟合时长;利用7 d的实测星地钟差进行向后1 d的钟差预报,结果显示,ISL星地钟差1 d预报误差为2 ns,较L波段预报精度略有提高;最后采用星地星间联合钟差观测,2 d内观测残差为0.52 ns,验证了ISL钟差观测的可行性。     

插补GNSS坐标时间序列的改进DINEOF算法

针对GNSS坐标时间序列插补中数据插值经验正交函数（data interpolating empirical orthogonal functions,DINEOF）算法受低相关度站点影响、连续长空缺插值效果欠佳的问题,提出相关数据插值经验正交函数（coefficient data interpolating empirical orthogonal functions,CDINEOF）算法。首先计算目标站点与其周围站点数据的相关度,然后筛选出相关度较高的数据构建观测矩阵,最后采用DINEOF算法对观测矩阵中的缺失数据进行插补。通过模拟数据和实测数据验证新方法的可行性,并与DINEOF算法和多项式插值法的结果进行对比分析。模拟数据实验结果表明,当观测数据存在连续长空缺时,CDINEOF算法的插值效果优于DINEOF算法和多项式插值法。实测数据实验结果表明,CDINEOF算法在保留方差最大化方面效果最好,与DINEOF算法和多项式插值法相比分别提升了11.8%和6.7%。     

多卫星组合的GPS-MR土壤湿度反演

单天中卫星低高度角状态持续时间较短,导致基于单颗GPS卫星多路径信噪比SNR的土壤湿度反演时间分辨率较低。为保证土壤湿度反演结果的可靠性和准确性,同时改善土壤湿度反演的时间分辨率,顾及信噪比有效高度角区间,提出一种基于多GPS卫星组合的GPS-MR高时间分辨率土壤湿度反演方法。实验结果表明,多卫星延迟相位组合能较好地表征土壤湿度变化趋势,二者相关系数优于0.92;土壤湿度反演时间分辨率由1 d提升为2 h。     

Spatial distribution of snow depth based on geographically weighted regression kriging in the Bayanbulak Basin of the Tianshan Mountains,China

Snow depth is a general input variable in many models of agriculture,hydrology,climate and ecology.This study makes use of observational data of snow depth and explanatory variables to compare the accuracy and effect of geographically weighted regression kriging(GWRK)and regression kriging(RK)in a spatial interpolation of regional snow depth.The auxiliary variables are analyzed using correlation coefficients and the variance inflation factor(VIF).Three variables,Height,topographic ruggedness index(TRI),and land surface temperature(LST),are used as explanatory variables to establish a regression model for snow depth.The estimated spatial distribution of snow depth in the Bayanbulak Basin of the Tianshan Mountains in China with a spatial resolution of 1 km is obtained.The results indicate that 1)the result of GWRK's accuracy is slightly higher than that of RK(R~2=0.55 vs.R~2=0.50,RMSE(root mean square error)=0.102 m vs.RMSE=0.077 m);2)for the subareas,GWRK and RK exhibit similar estimation results of snow depth.Areas in the Bayanbulak Basin with a snow depth greater than 0.15m are mainly distributed in an elevation range of 2632.00–3269.00 m and the snow in this area comprises 45.00–46.00% of the total amount of snow in this basin.However,the GWRK resulted in more detailed information on snow depth distribution than the RK.The final conclusion is that GWRK is better suited for estimating regional snow depth distribution.     

Effect of snow-cover duration on plant species diversity of alpine meadows on the eastern Qinghai-Tibetan Plateau

Although snow cover plays an important role in structuring plant diversity in the alpine zone, there are few studies on the relationship between snow cover and species diversity of alpine meadows on the eastern Qinghai-Tibetan Plateau. To assess the effect of snow cover on plant species diversity of alpine meadows, we used ten parallel transects of 60 m × 1 m for this study and described the changes in species diversity and composition associated with snow depth. With the division of snow depth into six classes, the highest species richness （S） and species diversity （H′） occurred with an intermediate snow depth, i.e., class Ⅲ and class Ⅳ, showing a unimodal curve with the increase in snow depth. The relationship between snow depth and plant diversity （both richness and Shannon index） could be depicted by quadratic equations. There was no evident relationship between diversity （both S and H′） and soil water content, which implied that other more important factors influenced species diversity. The patterns of diversity found in our study were largely attributed to freeze-thaw alteration, length of growing season and disturbances of livestock grazing. Furthermore, snow depth affected species composition, as evaluated by the Sorensen＇s index of similarity. In addition, almost all species limited to one snow depth class were found only in class Ⅲand class Ⅳ, indicating that intermediate snow depth was suitable for the survival and growth of many alpine species.     

Snow cover influences the thermal regime of active layer in Urumqi River Source,Tianshan Mountains,China

Snow cover is characterized by the high albedo, low thermal conductivity, and notable heat transition during phase changes. Thus, snow cover significantly affects the ground thermal regime. A comparison of the snow cover in high latitudes or high-altitude snowy mountain regions indicates that the eastern Tianshan Mountains (China) show a characteristically thin snow cover (snow depth below 15 cm) with remarkable temporal variability. Based on snow depth, heat flux, and ground temperature from 2014 to 2015 in the Urumqi River source, the spatialtemporal characteristics of snow cover and snow cover influences on the thermal conditions of active layer in the permafrost area were analyzed. During the autumn (Sept. - Oct.), thin and discontinuous snow cover can noticeably accelerate the exothermic process of the ground, producing a cooling effect on the shallow soil. During the winter (Nov. - Mar.), it is inferred that the effective thermal insulation starts with snow depth exceeding 10 cm during early winter. However, the snow depth in this area is generally below 15 cm, and the resulting snow-induced thermal insulation during the winter is very limited. Due to common heavy snowfalls in the spring (Apr. to May), the monthly mean snow thickness in April reached to 15 cm and remained until mid-May. Snow cover during the spring significantly retarded the ground warming. Broadly, snow cover in the study area exerts a cooling effect on the active layer and plays a positive role in the development and preservation of permafrost.     

THE FEATURES OF SEA-ICE COVER, SNOW DISTRIBUTION AND ITS DENSIFICATION IN THE CENTRAL ARCTIC OCEAN

I.INTKODUCTIONTheArcticOcean,withanareaofapproximately9.5X106krnZ,ispredominantlysea--icecoveredthroughouttheyearinitscentralarea,whilethesouthedgeofmarginalicezone(MIZ)variesseasonally.ThemaxinltlmofIcecoverextentoccursbetweenFebruaryandMarch,whilethemininlunlisbetweenAugustandseptember.Placingtheiceedgeto8%iceconcentration(percentarealcoveragesofseaice)isopleths,variationofextentofsea--icecoveroftheArcticOceanisI)etween9X106--16X106kmZIbytheobservationofasatellite--bornescanningm…     

Retrieval of Snow Depth on Sea Ice in the Arctic Using the FengYun-3B Microwave Radiation Imager

Snow on sea ice is a sensitive indicator of climate change because it plays an important role regulating surface and near surface air temperatures. Given its high albedo and low thermal conductivity, snow cover is considered a key reason for amplified warming in polar regions. This study focuses on retrieving snow depth on sea ice from brightness temperatures recorded by the Microwave Radiation Imager(MWRI) on board the FengYun(FY)-3 B satellite. After cross calibration with the Advanced Microwave Scanning Radiometer-EOS(AMSR-E) Level 2 A data from January 1 to May 31, 2011, MWRI brightness temperatures were used to calculate sea ice concentrations based on the Arctic Radiation and Turbulence Interaction Study Sea Ice(ASI) algorithm. Snow depths were derived according to the proportional relationship between snow depth and surface scattering at 18.7 and 36.5 GHz. To eliminate the influence of uncertainties in snow grain sizes and sporadic weather effects, seven-day averaged snow depths were calculated. These results were compared with snow depths from two external data sets, the IceBridge ICDIS4 and AMSR-E Level 3 Sea Ice products. The bias and standard deviation of the differences between the MWRI snow depth and IceBridge data were respectively 1.6 and 3.2 cm for a total of 52 comparisons. Differences between MWRI snow depths and AMSR-E Level 3 products showed biases ranging between-1.01 and-0.58 cm, standard deviations from 3.63 to 4.23 cm, and correlation coefficients from 0.61 to 0.79 for the different months.     

Effects of snow cover on ground thermal regime: A case study in Heilongjiang Province of China

The important effects of snow cover to ground thermal regime has received much attention of scholars during the past few decades. In the most of previous research, the effects were usually evaluated through the numerical models and many important results are found. However, less examples and insufficient data based on field measurements are available to show natural cases. In the present work, a typical case study in Mohe and Beijicun meteorological stations, which both are located in the most northern tip of China, is given to show the effects of snow cover on the ground thermal regime. The spatial(the ground profile) and time series analysis in the extremely snowy winter of 2012–2013 in Heilongjiang Province are also performed by contrast with those in the winter of 2011–2012 based on the measured data collected by 63 meteorological stations. Our results illustrate the positive(warmer) effect of snow cover on the ground temperature(GT) on the daily basis, the highest difference between GT and daily mean air temperature(DGAT) is as high as 32.35℃. Moreover, by the lag time analysis method it is found that the response time of GT from 0 cm to 20 cm ground depth to the alternate change of snow depth has 10 days lag, while at 40 cm depth the response of DGAT is not significant. This result is different from the previous research by modeling, in which the response depth of ground to the alteration of snow depth is far more than 40 cm.