摘要
利用SuomiNet网GPS大气可降水量数据和地面气象数据研究建立大气可降水量估算模型。利用研究区域GPS PWV和地面气象数据,研究了大气可降水量与地面水汽压间的相关性,并建立二者间的线性、二次多项式和幂函数回归模型,对比分析各模型的估算精度以确定最优估算模型,并检验了不同大气水汽含量下最优模型的估算精度。得出以下结论:1)GPS大气可降水量与地面水汽压变化趋势基本一致,存在着良好的相关关系(相关系数达到0.892)。2)基于地面水汽压的线性、二次多项式和幂函数模型估算大气可降水量均达到一定的估算精度(相对误差均小于19%),其中幂函数估算模型的R2最高,估算误差最小。3)基于地面水汽压的PWV估算模型具有地域性特征,基于研究区域数据所建立的模型更适合于研究区域,其PWV估算精度高于Cole模型、张学文模型和李超模型。4)所建估算模型在不同的大气水汽含量条件下估算精度不同,大汽水汽含量越高,估算结果的相对误差越小。
On the base of the SuomiNet GPS atmospheric precipitable water vapor（PWV） and surface meteorological data,the estimation model of atmospheric precipitable water vapor was established.On the base of the study area GPS PWV and surface meteorological data,the correlation between atmospheric precipitable water vapor and surface vapor pressure（SVP） was studied and the regression models such as linear,quadratic polynomial and power function were established.The estimation precision of the three regression models was compared and analyzed to determine the optimal estimation model,and the estimation precision of the optimal model was tested under different atmospheric water vapour content.The result showed that： there was a good relationship between GPS PWV and SVP（correlation coefficient reached 0.892）;all of the linear,quadratic polynomial and power function regression models reached high estimation precision（relative errors were below 19%）,and the power function regression model had the highest R2 and lowest estimation errors;for estimating PWV from SVP had regional characteristics,the established model based on the study area was more suitable for the study area,the estimation precision of the proposed power function model was higher than models created by Cole,Xuewen Zhang and Chao Li;the estimation precision were different in different atmospheric water vapour content,and the relative estimation precision rose as the PWV rose.
出处
《气象与环境科学》
2013年第2期21-25,共5页
Meteorological and Environmental Sciences
基金
民用航天"十二五"预先研究项目
国土资源部地学空间信息技术重点实验室开放基金(KLGSIT2013-15)资助
关键词
GPS
地面气象数据
地面水汽压
大气可降水量
估算模型
GPS
surface meteorological data
surface vapor pressure
atmospheric precipitable water vapor
estimation model
