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Improving Rain/No-Rain Detection Skill by Merging Precipitation Estimates from Different Sources.
- Source :
- Journal of Hydrometeorology; Oct2020, Vol. 21 Issue 10, p2419-2429, 11p
- Publication Year :
- 2020
-
Abstract
- Rain/no-rain detection error is a key source of uncertainty in regional and global precipitation products that propagates into offline hydrological and land surface modeling simulations. Such detection error is difficult to evaluate and/or filter without access to high-quality reference precipitation datasets. For cases where such access is not available, this study proposes a novel approach for improved rain/no-rain detection. Based on categorical triple collocation (CTC) and a probabilistic framework, a weighted merging algorithm (CTC-M) is developed to combine noisy, but independent, precipitation products into an optimal binary rain/no-rain time series. Compared with commonly used approaches that directly apply the best parent product for rain/no-rain detection, the superiority of CTC-M is demonstrated analytically and numerically using spatially dense precipitation measurements over Europe. Our analysis also suggests that CTC-M is tolerant to a range of cross-correlated rain/no-rain detection errors and detection biases of the parent products. As a result, CTC-M will benefit global precipitation estimation by improving the representation of precipitation occurrence in gauge-based and multisource merged precipitation products. [ABSTRACT FROM AUTHOR]
- Subjects :
- PRECIPITATION gauges
TIME series analysis
ALGORITHMS
ABILITY
ESTIMATES
UNCERTAINTY
Subjects
Details
- Language :
- English
- ISSN :
- 1525755X
- Volume :
- 21
- Issue :
- 10
- Database :
- Complementary Index
- Journal :
- Journal of Hydrometeorology
- Publication Type :
- Academic Journal
- Accession number :
- 147009705
- Full Text :
- https://doi.org/10.1175/JHM-D-20-0097.1