Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/2016WR020330 |
Comparing Approaches to Deal With Non-Gaussianity of Rainfall Data in Kriging-Based Radar-Gauge Rainfall Merging | |
Cecinati, F.1; Wani, O.2,3; Rico-Ramirez, M. A.1 | |
2017-11-01 | |
发表期刊 | WATER RESOURCES RESEARCH |
ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2017 |
卷号 | 53期号:11 |
文章类型 | Article |
语种 | 英语 |
国家 | England; Switzerland |
英文摘要 | Merging radar and rain gauge rainfall data is a technique used to improve the quality of spatial rainfall estimates and in particular the use of Kriging with External Drift (KED) is a very effective radar-rain gauge rainfall merging technique. However, kriging interpolations assume Gaussianity of the process. Rainfall has a strongly skewed, positive, probability distribution, characterized by a discontinuity due to intermittency. In KED rainfall residuals are used, implicitly calculated as the difference between rain gauge data and a linear function of the radar estimates. Rainfall residuals are non-Gaussian as well. The aim of this work is to evaluate the impact of applying KED to non-Gaussian rainfall residuals, and to assess the best techniques to improve Gaussianity. We compare Box-Cox transformations lambda with parameters equal to 0.5, 0.25, and 0.1, Box-Cox with time-variant optimization of lambda normal score transformation, and a singularity analysis technique. The results suggest that Box-Cox with lambda =0.1 and the singularity analysis is not suitable for KED. Normal score transformation and Box-Cox with optimized lambda, or lambda =0.25 produce satisfactory results in terms of Gaussianity of the residuals, probability distribution of the merged rainfall products, and rainfall estimate quality, when validated through cross-validation. However, it is observed that Box-Cox transformations are strongly dependent on the temporal and spatial variability of rainfall and on the units used for the rainfall intensity. Overall, applying transformations results in a quantitative improvement of the rainfall estimates only if the correct transformations for the specific data set are used. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000418736700019 |
WOS关键词 | FLOOD FORECASTING SYSTEM ; WEATHER RADAR ; PRECIPITATION ; PROBABILITY ; TRANSFORMATION ; ATTENUATION ; UNCERTAINTY ; COMBINATION ; EXAMPLES ; FIELDS |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/21139 |
专题 | 资源环境科学 |
作者单位 | 1.Univ Bristol, Dept Civil Engn, Bristol, Avon, England; 2.ETH, Inst Environm Engn, Zurich, Switzerland; 3.Eawag, Swiss Fed Inst Aquat Sci & Technol, Dubendorf, Switzerland |
推荐引用方式 GB/T 7714 | Cecinati, F.,Wani, O.,Rico-Ramirez, M. A.. Comparing Approaches to Deal With Non-Gaussianity of Rainfall Data in Kriging-Based Radar-Gauge Rainfall Merging[J]. WATER RESOURCES RESEARCH,2017,53(11). |
APA | Cecinati, F.,Wani, O.,&Rico-Ramirez, M. A..(2017).Comparing Approaches to Deal With Non-Gaussianity of Rainfall Data in Kriging-Based Radar-Gauge Rainfall Merging.WATER RESOURCES RESEARCH,53(11). |
MLA | Cecinati, F.,et al."Comparing Approaches to Deal With Non-Gaussianity of Rainfall Data in Kriging-Based Radar-Gauge Rainfall Merging".WATER RESOURCES RESEARCH 53.11(2017). |
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