一次江淮气旋暴雪的积雪特征及气象影响因子分析   总被引：4，自引：4，他引：0       下载免费PDF全文

杨成芳  刘畅 《气象》2019,45(2):191-202

利用自动站、人工加密观测及常规观测资料,通过对2017年2月21—22日一次江淮气旋暴雪过程积雪特征的分析,揭示了近地面气象要素对积雪深度的复杂影响。结果表明:(1)江淮气旋系统特有的空间结构导致山东南、北地区的降雪量和积雪深度不均衡分布。(2)积雪深度具有时效性,在降雪结束时达到峰值,因温度的变化导致峰值不一定维持到次日08时。(3)积雪深度是近地面多气象要素共同作用的结果,降水相态、降雪量、降雪强度、气温、地温和风速均有影响。主要表现为:雨夹雪在转为纯雪之前可产生不超过1 cm的积雪,如果不转雪则不会产生有量积雪;各地降雪含水比差异较大,全省平均为0. 5 cm·mm~(-1),低于全国平均值;在降雪不融化的情况下,降雪量、降雪强度越大则积雪越深,降雪强度大是气温和地温都高于0℃时产生积雪的必要条件;地温和气温越低对积雪形成越有利,积雪开始产生时的地温最高阈值多在0℃左右,地温先突降后缓升是积雪产生前后的共性特征,积雪产生后1～2 h内地温略有上升并逐渐趋于稳定;积雪产生时气温一般低于0℃,气温高于0℃时大部分降雪融化;有利于产生积雪的平均风力多不超过2级,极大风则在3～4级以下。  相似文献   

陆面过程模式BCC_AVIM中地表覆盖数据现状     

周文艳  罗勇  史学丽  李伟平  张艳武 《气象》2019,45(10):1476-1482

地表覆盖是陆面和气候模式中的一个重要基础数据。以陆面过程模式BCC_AVIM为例,介绍模式中的地表覆盖数据变量、数据分辨率、不同类型数据的来源,重点比较分类方法差异巨大且类型众多的植被覆盖。综述比较了国际和国内常用的几套全球地表覆盖数据的来源、分类系统和分类方法以及空间分辨率,根据陆面过程模式的地表覆盖数据需求,确定不同全球土地覆盖数据在模式中的应用方法,讨论分析了全球地表覆盖产品在模式应用中存在的差距,提出不同遥感数据产品之间一致性较差的可能解决方案,探讨遥感数据产品在模式中应用的可能方式,以期更好地发挥全球地表覆盖数据产品的作用。  相似文献   

黄河源区多、少雪年土壤冻融特征分析     

姚闯  吕世华  王婷  王俊锋  马翠丽 《高原气象》2019,38(3):474-483

利用2011年10月至2017年12月黄河源区鄂陵湖野外观测数据,对比分析多雪年与少雪年土壤冻结与消融时间、土壤温湿度、地表能量分量的变化特征。结果表明:多雪年地表反照率偏高,净辐射偏低,地表感热输送偏低,土壤由热“源”转为热“汇”的时间晚于少雪年。积雪可减少土壤吸收辐射能量,减少地表感热通量,在土壤完全冻结期与消融期增大地表潜热通量,在完全冻结期,减少土壤向大气的热输送,在消融期,减少大气向土壤的热输送。积雪在冻结期有降温作用,使得多雪年土壤较早发生冻结,且同一时期土壤温度偏低;在完全冻结期有保温作用,使得土壤温度偏高;在消融期有保温(“凉”)作用,使得消融较晚,且同一时期土壤温度偏低。在整个积雪年内,多雪年浅层土壤湿度高于少雪年,积雪对浅层土壤有保湿作用。积雪使土壤开始冻结时间有所提前,开始消融的时间有所滞后,可延长该年土壤完全冻结持续天数。  相似文献   

海南岛不同土地覆盖资料对WRF模拟的影响_*     

冯箫  施萧 《沙漠与绿洲气象(新疆气象)》2019,13(2):123-129

提取GLC2009土地覆盖数据,将其运用到WRF数值模式并与模式自带的MODIS、USGS土地覆盖进行对比,以海南岛做为研究区域,选择一次海风锋天气过程进行敏感性分析,结果表明：①GLC2009对海南岛农耕地、森林的划分最接近实际情况,在海南岛平原以及丘陵地带,MODIS和USGS划分的农耕地比例过高,而在山区森林比例偏低;②GLC2009、MODIS、USGS对海南岛北部的降水模拟出现了空报,但是GLC2009对东海岸的强降水模拟的较好,并避免了MODIS和USGS在海南西南部的降水空报的情况;③GLC2009模拟的10米风场清晰刻画了东海岸海风锋辐合,其相对合理的森林覆盖提高了地表粗糙度并增强了风场辐合、低反照率对地面气温的增加和海陆热力差异的增强有一定的促进作用。④GLC2009对这次海风锋过程中模拟的感热通量和潜热通量都要高于MODIS和USGS,这种陆气间的热通量交换利于强对流天气的触发,发展。  相似文献   

Shallow landslide susceptibility assessment under future climate and land cover changes: A case study from southwest China     

《地学前缘(英文版)》2023,14(4):101542

There is no doubt that land cover and climate changes have consequences on landslide activity, but it is still an open issue to assess and quantify their impacts. Wanzhou County in southwest China was selected as the test area to study rainfall-induced shallow landslide susceptibility under the future changes of land use and land cover (LULC) and climate. We used a high-resolution meteorological precipitation dataset and frequency distribution model to analyse the present extreme and antecedent rainfall conditions related to landslide activity. The future climate change factors were obtained from a 4-member multi-model ensemble that was derived from statistically downscaled regional climate simulations. The future LULC maps were simulated by the land change modeller (LCM) integrated into IDRISI Selva software. A total of six scenarios were defined by considering the rainfall (antecedent conditions and extreme events) and LULC changes towards two time periods (mid and late XXI century). A physically-based model was used to assess landslide susceptibility under these different scenarios. The results showed that the magnitude of both antecedent effective recharge and event rainfall in the region will evidently increase in the future. Under the scenario with a return period of 100 years, the antecedent rainfall in summer will increase by up to 63% whereas the event rainfall will increase by up to 54% for the late 21st century. The most considerable changes of LULC will be the increase of forest cover and the decrease of farming land. The magnitude of this change can reach + 22.1% (forest) and –9.2% (farmland) from 2010 until 2100, respectively. We found that the negative impact of climate change on landslide susceptibility is greater than the stabilizing effect of LULC change, leading to an over decrease in stability over the study area. This is one of the first studies across Asia to assess and quantify changes of regional landslide susceptibility under scenarios driven by LULC and climate change. Our results aim to guide land use planning and climate change mitigation considerations to reduce landslide risk.  相似文献   

Satellite monitoring of urbanization and environmental impacts—A comparison of Stockholm and Shanghai     

《International Journal of Applied Earth Observation and Geoinformation》2015

This study investigates urbanization and its potential environmental consequences in Shanghai and Stockholm metropolitan areas over two decades. Changes in land use/land cover are estimated from support vector machine classifications of Landsat mosaics with grey-level co-occurrence matrix features. Landscape metrics are used to investigate changes in landscape composition and configuration and to draw preliminary conclusions about environmental impacts. Speed and magnitude of urbanization is calculated by urbanization indices and the resulting impacts on the environment are quantified by ecosystem services. Growth of urban areas and urban green spaces occurred at the expense of cropland in both regions. Alongside a decrease in natural land cover, urban areas increased by approximately 120% in Shanghai, nearly ten times as much as in Stockholm, where the most significant land cover change was a 12% urban expansion that mostly replaced agricultural areas. From the landscape metrics results, it appears that fragmentation in both study regions occurred mainly due to the growth of high density built-up areas in previously more natural/agricultural environments, while the expansion of low density built-up areas was for the most part in conjunction with pre-existing patches. Urban growth resulted in ecosystem service value losses of approximately 445 million US dollars in Shanghai, mostly due to the decrease in natural coastal wetlands while in Stockholm the value of ecosystem services changed very little. Total urban growth in Shanghai was 1768 km² and 100 km² in Stockholm. The developed methodology is considered a straight-forward low-cost globally applicable approach to quantitatively and qualitatively evaluate urban growth patterns that could help to address spatial, economic and ecological questions in urban and regional planning.  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   

Evaluating the relationship between biomass,percent groundcover and remote sensing indices across six winter cover crop fields in Maryland,United States     

《International Journal of Applied Earth Observation and Geoinformation》2015

Winter cover crops are an essential part of managing nutrient and sediment losses from agricultural lands. Cover crops lessen sedimentation by reducing erosion, and the accumulation of nitrogen in aboveground biomass results in reduced nutrient runoff. Winter cover crops are planted in the fall and are usually terminated in early spring, making them susceptible to senescence, frost burn, and leaf yellowing due to wintertime conditions. This study sought to determine to what extent remote sensing indices are capable of accurately estimating the percent groundcover and biomass of winter cover crops, and to analyze under what critical ranges these relationships are strong and under which conditions they break down. Cover crop growth on six fields planted to barley, rye, ryegrass, triticale or wheat was measured over the 2012–2013 winter growing season. Data collection included spectral reflectance measurements, aboveground biomass, and percent groundcover. Ten vegetation indices were evaluated using surface reflectance data from a 16-band CROPSCAN sensor. Restricting analysis to sampling dates before the onset of prolonged freezing temperatures and leaf yellowing resulted in increased estimation accuracy. There was a strong relationship between the normalized difference vegetation index (NDVI) and percent groundcover (r² = 0.93) suggesting that date restrictions effectively eliminate yellowing vegetation from analysis. The triangular vegetation index (TVI) was most accurate in estimating high ranges of biomass (r² = 0.86), while NDVI did not experience a clustering of values in the low and medium biomass ranges but saturated in the higher range (>1500 kg/ha). The results of this study show that accounting for index saturation, senescence, and frost burn on leaves can greatly increase the accuracy of estimates of percent groundcover and biomass for winter cover crops.  相似文献   

Reducing background effects in orchards through spectral vegetation index correction     

《International Journal of Applied Earth Observation and Geoinformation》2015

Satellite remote sensing provides an alternative to time-consuming and labor intensive in situ measurements of biophysical variables in agricultural crops required for precision agriculture applications. In orchards, however, the spatial resolution causes mixtures of canopies and background (i.e. soil, grass and shadow), hampering the estimation of these biophysical variables. Furthermore, variable background mixtures obstruct meaningful comparisons between different orchard blocks, rows or within each row. Current correction methodologies use spectral differences between canopies and background, but struggle with a vegetated orchard floor. This background influence and the lack of a generic solution are addressed in this study.Firstly, the problem was demonstrated in a controlled environment for vegetation indices sensitive to chlorophyll content, water content and leaf area index. Afterwards, traditional background correction methods (i.e. soil-adjusted vegetation indices and signal unmixing) were compared to the proposed vegetation index correction. This correction was based on the mixing degree of each pixel (i.e. tree cover fraction) to rescale the vegetation indices accordingly and was applied to synthetic and WorldView-2 satellite imagery. Through the correction, the effect of background admixture for vegetation indices was reduced, and the estimation of biophysical variables was improved (ΔR² = 0.2–0.31).  相似文献   

Modelling the Gross Primary Productivity of West Africa with the Regional Biomass Model RBM+, using optimized 250 m MODIS FPAR and fractional vegetation cover information     

《International Journal of Applied Earth Observation and Geoinformation》2015

Global warming associated with climate change is one of the greatest challenges of today’s world. Increasing emissions of the greenhouse gas CO₂ are considered as a major contributing factor to global warming. One regulating factor of CO₂ exchange between atmosphere and land surface is vegetation. Measurements of land cover changes in combination with modelling the Gross Primary Productivity (GPP) can contribute to determine important sources and sinks of CO₂.The aim of this study is to accurately model the GPP for a region in West Africa with a spatial resolution of 250 m, and the differentiation of GPP based on woody and herbaceous vegetation. For this purpose, the Regional Biomass Model (RBM) was applied, which is based on a Light Use Efficiency (LUE) approach. The focus was on the spatial enhancement of the RBM from the original 1000–250 m spatial resolution (RBM+). The adaptation to the 250 m scale included the modification of two main input parameters: (1) the fraction of absorbed Photosynthetically Active Radiation (FPAR) based on the 1000 m MODIS MOD15A2 FPAR product which was downscaled to 250 m using MODIS NDVI time series; (2) the fractional cover of woody and herbaceous vegetation, which was improved by using a multi-scale approach. For validation and regional adjustments of GPP and the input parameters, in situ data from a climate station and eddy covariance measurements were integrated.The results of this approach show that the input parameters could be improved significantly: downscaling considerably reduces data gaps of the original FPAR product and the improved dataset differed less than 5.0% from the original data for cloud free regions. The RMSE of the fractional vegetation cover varied between 5.1 and 12.7%. Modelled GPP showed a slight overestimation in comparison to eddy covariance measurements. The in situ data was exceeded by 8.8% for 2005 and by 2.0% for 2006. The model results were converted to NPP and also agreed well with previous NPP measurements reported from different studies. Altogether a high accuracy and suitability of the regionally adjusted and downscaled model RBM+ can be concluded. The differentiation between vegetation growth forms allows a separation of long-term and short-term carbon storage based on woody and herbaceous vegetation, respectively.  相似文献