期刊文献+

基于MODIS数据的中国地面水汽压模拟与分析 被引量:12

Estimation and Analysis of Near Surface Vapor Pressure in China Based on MODIS Data
原文传递
导出
摘要 采用2006年MODIS逐日红外与近红外大气可降水量(MOD05)数据,结合常规气象观测资料,建立了中国逐月地面水汽压模拟统计模型,得到了2006年1km×1km中国逐月地面水汽压数据集。通过对全国33个台站和河南省100个加密站的验证,地面水汽压的模拟值与实测值的相关性均达0.96以上,并且均有90%以上的样本相对误差平均值小于20%。研究结果表明:2006年中国地面水汽压月平均值在3.47~17.13hPa之间,全国年平均值为8.87hPa,地面水汽压呈现出显著的地带性分布;分析了地面水汽压随海拔、坡度和坡向等地形因子的变化规律,较好地反映出地面水汽压的宏观分布趋势和局地分布特征;基于MODIS数据的地面水汽压模型为复杂地形条件下能量收支平衡和大气水循环的研究提供了一种切实可行的方法。 Combined daily infrared data with near-infrared data(MOD05)from the Moderate Resolution Imaging Spectroradiometer(MODIS)in year 2006,monthly precipitable water vapor was calculated using a weighted arithmetic.The monthly average precipitable water vapor ranged between 0.10 cm and 3.50 cm.With routinely observed meteorological data at 619 stations across China's Mainland,an empirical model was build to estimate monthly near surface vapor pressure with the spatial resolution of 1 km×1 km.Due to an extensive territory and different types of climates of China,an optimized method was employed to estimate the parameters of the empirical model.The main idea of the optimized method was to identify similar geographic coordinates and altitudes.The optimized parameters exhibit significant heterogeneity.Results are given as follows.1)The empirical model appears to be capable of generating monthly near surface vapor pressure.Comparison between the simulations and observations of near surface vapor pressure at 33 stations across China indicates a correlation coefficient up to 0.97 and more than 90%samples with relative errors smaller than 20%.In particular,validation performed at 100 weather stations in Henan Province shows a correlation coefficient of 0.96 and the mean relative errors smaller than 20%,in which the mean relative errors in eight months were within 10%;2)Across the entire country,the monthly average vapor pressure in 2006 ranged from 3.47 hPa to 17.13 hPa,with an annual mean of 8.87 hPa.The spatial variation varied between 3.20 and 8.66,with an annual mean of 5.36.The value and spatial variation of the monthly vapor pressure were higher in summer(Jun,Jul,and Aug)but lower in winter(Dec,Jan,and Feb).The maximum value occurred in Aug whereas the minimum value in Jan.A particular investigation into Yunnan Province,Southwest China,was performed.The spatial distribution of vapor pressure corresponded to DEM and river networks;3) Distribution of the monthly vapor pressure varied markedly with the topography.Changes in vapor pressure with the altitude,slope,and aspect were analyzed to look at distributions of vapor pressure at local and regional scales.The monthly vapor pressure varied regularly with the topography in low altitudes.There were generally two peaks and two troughs in these vapor pressure values in different months,the same as in different altitudes.However,there existed a more complex characterization of the monthly vapor pressure at high altitudes such as over the Tibet Plateau.The proposed model for generating near surface vapor pressure based on MODIS data seems to be a promising tool in study of the energy balance and atmospheric water cycle under rugged terrains.
出处 《资源科学》 CSSCI CSCD 北大核心 2012年第1期74-80,共7页 Resources Science
基金 国家自然科学基金(编号:40971023) 国家重点基础研究发展规划项目(编号:2010CB428406)
关键词 MODIS 大气可降水量 地面水汽压 地形影响 MODIS; Precipitable water vapor; Near surface vapor pressure; Terrain influence;
  • 相关文献

参考文献23

  • 1DeCosmo J., Katsaros K. B., Smith S. D., et al. Air-sea exchange of water vapor and sensible heat: The Humidity Exchange over the Sea (HEXOS) results [J]. Journetl of Geophysical research, 1996,101(C5): 12001 - 12016.
  • 2Held Isaac M., Soden Brian J. Annual review of energy and the environment [J]. Water vapor feedbaek and global warming, 2000, 25: 441-475.
  • 3Rasmusson Eugene M. Atmospheric water vapor transport and the water balance of North America [J]. Monthly Weather Review, 1968, 96( 10): 720 - 734.
  • 4Starr P., Peixoto, J. P. On the global balance of water vapor and the hydrology of deserts [J]. Tellus, 1958, 10(2):188 - 194.
  • 5Solomon Susan, Rosenlof Karen.H., Portmann Robert.W, et al. Contributions of stratospheric water vapor to decadal changes in the rate of global warming [J]. Science, 2010, 327: 1219-1223.
  • 6Martins L. Lages, Ribeiro. A. Silva, Sousa .J. Alves. Measurement uncertainty of dew-point temperature in a two-pressure humidity generator [J]. International JoumM of Tbermophysics, DOI: 10.1007/s10765-011-1005-z.
  • 7于贵瑞,何洪林,刘新安,牛栋.中国陆地生态信息空间化技术研究(Ⅰ)——气象/气候信息的空间化技术途径[J].自然资源学报,2004,19(4):537-544. 被引量:69
  • 8翁笃鸣 方先金 李占青.我国自由大气与山地水汽压垂直递减规律的初步研究[J].南京气象学院学报,1985,8(2):152-160.
  • 9杨阳,缪启龙,邱新法,高阳华.GIS支持的起伏地形下重庆市水汽压的空间分布[J].中国农业气象,2006,27(1):11-15. 被引量:5
  • 10Jarvis C H, Stuart N A. Comparison among strategies for interpolating maximum and minimum daily air temperatures.Part II: The interaction between number of guiding variables and the type of interpolation method[J]. Journal of Applied Meteorology, 2001, 40(6):1075-1084.

二级参考文献114

共引文献388

同被引文献185

引证文献12

二级引证文献59

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部