地球信息科学学报
地毬信息科學學報
지구신식과학학보
GEO-INFORMATION SCIENCE
2015年
1期
108-117
,共10页
嵇涛%刘睿%杨华%何太蓉%吴建峰
嵇濤%劉睿%楊華%何太蓉%吳建峰
혜도%류예%양화%하태용%오건봉
空间降尺度%TRMM降水%NDVI%地理因子%川渝地区
空間降呎度%TRMM降水%NDVI%地理因子%川渝地區
공간강척도%TRMM강수%NDVI%지리인자%천투지구
spatial downscaling%TRMM precipitation%NDVI%geographical factors%Sichuan-Chongqing region
大量研究表明，通过传统地面气象站点实测的单点数据，不能有效地反映降水的空间变化特征。目前，以遥感数据获取的降水产品已得到了广泛的应用，但在地形地势复杂区域，遥感降水产品的空间分辨率与数据精度等方面仍然存在着极大的不足。因此，本文以四川重庆（川渝）地区为例，通过建立降水产品降尺度算法，以实现降水产品的降尺度估算，提高降水数据的空间分辨率。依据在不同尺度下（0.25°、0.50°、0.75°和1.00°），TRMM 3B43、地理因子，以及MOD13A3（NDVI）之间存在的相关关系，构建了多元回归模型。通过对比这4种尺度下的回归模型，选择其中精度最高的作为最终的降尺度算法，然后再把这种降尺度算法应用到1 km分辨率下进行降水估算。进一步，以区域差异分析（GDA）和区域比率分析法（GRA）对降尺度估算的降水数据进行校正，并结合部分地面气象站点实测的降水数据进行验证。验证结果表明：降尺度算法是可靠的，能有效提升降水产品的空间分辨率，同时GDA和GRA校正方法能减小误差，进一步提升降水估算的精度，满足区域地表过程应用的需求。
大量研究錶明，通過傳統地麵氣象站點實測的單點數據，不能有效地反映降水的空間變化特徵。目前，以遙感數據穫取的降水產品已得到瞭廣汎的應用，但在地形地勢複雜區域，遙感降水產品的空間分辨率與數據精度等方麵仍然存在著極大的不足。因此，本文以四川重慶（川渝）地區為例，通過建立降水產品降呎度算法，以實現降水產品的降呎度估算，提高降水數據的空間分辨率。依據在不同呎度下（0.25°、0.50°、0.75°和1.00°），TRMM 3B43、地理因子，以及MOD13A3（NDVI）之間存在的相關關繫，構建瞭多元迴歸模型。通過對比這4種呎度下的迴歸模型，選擇其中精度最高的作為最終的降呎度算法，然後再把這種降呎度算法應用到1 km分辨率下進行降水估算。進一步，以區域差異分析（GDA）和區域比率分析法（GRA）對降呎度估算的降水數據進行校正，併結閤部分地麵氣象站點實測的降水數據進行驗證。驗證結果錶明：降呎度算法是可靠的，能有效提升降水產品的空間分辨率，同時GDA和GRA校正方法能減小誤差，進一步提升降水估算的精度，滿足區域地錶過程應用的需求。
대량연구표명，통과전통지면기상참점실측적단점수거，불능유효지반영강수적공간변화특정。목전，이요감수거획취적강수산품이득도료엄범적응용，단재지형지세복잡구역，요감강수산품적공간분변솔여수거정도등방면잉연존재착겁대적불족。인차，본문이사천중경（천투）지구위례，통과건립강수산품강척도산법，이실현강수산품적강척도고산，제고강수수거적공간분변솔。의거재불동척도하（0.25°、0.50°、0.75°화1.00°），TRMM 3B43、지리인자，이급MOD13A3（NDVI）지간존재적상관관계，구건료다원회귀모형。통과대비저4충척도하적회귀모형，선택기중정도최고적작위최종적강척도산법，연후재파저충강척도산법응용도1 km분변솔하진행강수고산。진일보，이구역차이분석（GDA）화구역비솔분석법（GRA）대강척도고산적강수수거진행교정，병결합부분지면기상참점실측적강수수거진행험증。험증결과표명：강척도산법시가고적，능유효제승강수산품적공간분변솔，동시GDA화GRA교정방법능감소오차，진일보제승강수고산적정도，만족구역지표과정응용적수구。
Precipitation data with high spatial resolution is deemed necessary for hydrology, meteorology, ecolo-gy and others. Currently there are mainly two sources of precipitation estimation:meteorological stations and re-mote sensing technology. However, a large number of studies demonstrated that the measurements acquired from conventional meteorological stations are single points of data, and they can not reflect the spatial variation of pre-cipitation effectively, especially in studying the more complex areas. While the technology of remote sensing can not only improve the quality of the actual observations, but also be able to produce reasonably high resolution gridded precipitation fields. These products obtained by satellites have been widely used in previous studies. However, when applied to complex topography region, the spatial resolution of these products is too coarse and data accuracy is not high. Therefore, we present a statistical downscaling algorithm based on the relationships be-tween precipitation and other environmental associated factors such as topography and vegetation in the Sichuan-Chongqing region, which was developed for downscaling the spatial precipitation fields with these remote sens-ing products. This algorithm is demonstrated with the Tropical Rainfall Measuring Mission (TRMM) 3B43 datas-et, the Digital Elevation Model (DEM) from ASTER Global Digital Elevation Model (ASTER GDEM) and Mod-erate resolution Imaging Spectroradiometer (MODIS) 13A3 dataset. The statistical relationship among precipita-tion, geographical factors and Normalized Difference Vegetation Index (NDVI), which is a representation for vegetation, is variable at different scales;therefore, a multiple non-linear regression model was established under four different scales (0.25° , 0.50° , 0.75° and 1.00° , respectively). By applying a downscaling methodology, TRMM 3B43 0.25° × 0.25° precipitation fields were downscaled to 1 km × 1 km pixel resolution for each year from 2000 to 2011. By comparing these four regression models, we first select the regression model with the highest accuracy as the final downscaling algorithm, and then apply this downscaling algorithm (0.25° ) to 1 km resolution for the estimation of high precision in this study. Second, the calibration of downscaling precipitation was conducted based on Geographical Difference Analysis (GDA) and Geographical Ratio Analysis (GRA). The final downscaling estimation results were validated by applying part of meteorological stations measured precipi-tation data for a period of 12 years in Sichuan-Chongqing region. As a whole, these results indicated downscal-ing algorithm is reliable, and can effectively improve spatial resolution of precipitation products. The resultant best value of the 1 km annual precipitation data are achieved through downscaling followed by GDA and GRA calibration for most cases. And the downscaling 1 km annual precipitation has not only been significantly im-proved in the spatial resolution, but also corresponded well with TRMM 3B43 precipitation data and meteorolog-ical stations data achieved for Sichuan-Chongqing region.
