Journal article
Multi-scale investigation of conditional errors in radar-rainfall estimates
Advances in water resources, Vol.156, p.104041
10/2021
DOI: 10.1016/j.advwatres.2021.104041
Abstract
•The study examines multi-scale aspects of radar-rainfall (R-R) conditional error.•The behavior of R-R standardized error varies with the magnitude of rainfall and shows conditional tendency.•The distribution of R-R standardized error approaches the Gaussian distribution as temporal scale increases.•The error feature regarding spatial scale appears to be almost scale-invariant.
This study examines multi-scale aspects of radar-rainfall (R-R) conditional error, defined as the difference between a given R-R value and the conditional average of corresponding reference observations. To decompose the systematic and random error components, the authors adopted the second-order separation method while previous studies relied on addressing the first-order moment (i.e., conditional bias) only. The authors applied a non-parametric kernel regression approach to characterize the conditional mean and standard deviation and thus to derive a distribution of random component, standardized error. This empirical study is based on data from two rain gauge networks, consisting of 66 and 115 gauges, and two R-R estimates (the Iowa Flood Center and Multi-Radar Multi-Sensor products) over the Iowa domain in the United States. The authors explored the effect of multiple temporal (1–24 h) and spatial (0.5–32 km) scales on the conditional error structure. They found that the error distribution gradually approaches the Gaussian distribution with longer temporal scale while the error feature regarding spatial scale appears to be almost scale-invariant.
Details
- Title: Subtitle
- Multi-scale investigation of conditional errors in radar-rainfall estimates
- Creators
- Bong-Chul SeoWitold F Krajewski - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Advances in water resources, Vol.156, p.104041
- Publisher
- Elsevier Ltd
- DOI
- 10.1016/j.advwatres.2021.104041
- ISSN
- 0309-1708
- eISSN
- 1872-9657
- Language
- English
- Date published
- 10/2021
- Academic Unit
- Civil and Environmental Engineering; IIHR--Hydroscience and Engineering
- Record Identifier
- 9984197178702771
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