DOI: 10.1002/jgrd.50545
论文题名: Statistically combining rainfall characteristics estimated from remote-sensed and rain gauge data sets in the Brazilian Amazon-Tocantins Basins
作者: Clarke R.T. ; Buarque D.C.
刊名: Journal of Geophysical Research Atmospheres
ISSN: 21698996
出版年: 2013
卷: 118, 期: 14 起始页码: 7467
结束页码: 7480
语种: 英语
英文关键词: Amazon-Tocantins basins
; geostatistical model
; remote-sensed data
Scopus关键词: Gages
; Mean square error
; Rain gages
; Amazon-Tocantins basins
; Geostatistical modeling
; Predictor variables
; Rainfall characteristics
; remote-sensed data
; Root mean square errors
; Satellite data sets
; Spatial heterogeneity
; Rain
; data set
; estimation method
; numerical model
; rainfall
; raingauge
; remote sensing
; satellite data
; statistical analysis
; Amazonas [Brazil]
; Brazil
英文摘要: This paper explores the use of a parametric geostatistical model for combining rainfall characteristics derived from rain gauge data with the same characteristics derived from remote-sensed data sets. Hypotheses can then be tested about which predictors significantly increase precision of an estimated characteristic. Although applicable wherever ground-level data and remote-sensed data are to be combined, the statistical procedure set out in the paper is developed for two examples of rainfall characteristics: (i) G, the mean annual rainfall at an ungauged site, conditional on knowledge of two predictor variables T (the mean annual rainfall calculated from the TRMM 3B42 data set for 1998-2009), and C (mean annual rainfall derived from the CMORPH data set for 2003-2009); (ii) the mean annual maximum 1 day rainfall H, interpolated using the same modeling procedure with predictor variables T and C derived from annual maximum 1 day rainfalls in the same remote-sensed data sets. Prediction errors showed no bias, skewness of distribution, or spatial heterogeneity. The model's generality means that it could be used with any predictors other than T and C, possibly derived from other satellite data sets or radar. Provided that predictor variables are correlated with the variable to be predicted, it is not necessary for the model relating them to be fitted using data from identical periods nor for the grid spacing of T and C to be identical. Model performance was evaluated by using a "leave-one-site-out" procedure, which showed that the root mean square error (RMSE) of model predictions at omitted sites was smaller than RMSEs obtained from five other well-known spatial predictors. Key Points We combine rainfall information from ground-level and remote-sensed data-sets We use a parametric geostatistical model for this purpose To estimate mean annual rainfall, and mean of annual maximum one-day rains ©2013. American Geophysical Union. All Rights Reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/63542
Appears in Collections: 影响、适应和脆弱性 气候减缓与适应
There are no files associated with this item.
作者单位: Instituto de Pesquisas Hidraulicas, Avenida Bento Goncalves 9500, Porto Alegre - RS, CEP 91501-970, Brazil
Recommended Citation:
Clarke R.T.,Buarque D.C.. Statistically combining rainfall characteristics estimated from remote-sensed and rain gauge data sets in the Brazilian Amazon-Tocantins Basins[J]. Journal of Geophysical Research Atmospheres,2013-01-01,118(14)