Statistical uncertainty analysis-based precipitation merging (SUPER) : A new framework for improved global precipitation estimation
(2022) In Remote Sensing of Environment 283.- Abstract
Multi-source merging is an established tool for improving large-scale precipitation estimates. Existing merging frameworks typically use gauge-based precipitation error statistics and neglect the inter-dependence of various precipitation products. However, gauge-observation uncertainties at daily and sub-daily time scales can bias merging weights and yield sub-optimal precipitation estimates, particularly over data-sparse regions. Likewise, frameworks ignoring inter-product error cross-correlation will overfit precipitation observation noise. Here, a Statistical Uncertainty analysis-based Precipitation mERging framework (SUPER) is proposed for addressing these challenges. Specifically, a quadruple collocation analysis is employed to... (More)
Multi-source merging is an established tool for improving large-scale precipitation estimates. Existing merging frameworks typically use gauge-based precipitation error statistics and neglect the inter-dependence of various precipitation products. However, gauge-observation uncertainties at daily and sub-daily time scales can bias merging weights and yield sub-optimal precipitation estimates, particularly over data-sparse regions. Likewise, frameworks ignoring inter-product error cross-correlation will overfit precipitation observation noise. Here, a Statistical Uncertainty analysis-based Precipitation mERging framework (SUPER) is proposed for addressing these challenges. Specifically, a quadruple collocation analysis is employed to estimate precipitation error variances and covariances for commonly used precipitation products. These error estimates are subsequently used for merging all products via a least-squares minimization approach. In addition, false-alarm precipitation events are removed via a reference rain/no-rain time series estimated by a newly developed categorical variable merging method. As such, SUPER does not require any rain gauge observations to reduce daily random and rain/no-rain classification errors. Additionally, by considering precipitation product inter-dependency, SUPER avoids overfitting measurement noise present in multi-source precipitation products. Results show that the overall RMSE of SUPER-based precipitation is 3.35 mm/day and the daily correlation with gauge observations is 0.71 [−] – metrics that are generally superior to recent precipitation reanalyses and remote sensing products. In this way, we seek to propose a new framework for robustly generating global precipitation datasets that can improve land surface and hydrological modeling skill in data-sparse regions.
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- author
- Dong, Jianzhi ; Crow, Wade T. ; Chen, Xi ; Tangdamrongsub, Natthachet ; Gao, Man ; Sun, Shanlei ; Qiu, Jianxiu ; Wei, Lingna ; Gao, Hongkai and Duan, Zheng LU
- organization
- publishing date
- 2022
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Data merging, Error cross correlation, Precipitation, Rain/norain classification, Uncertainty analysis
- in
- Remote Sensing of Environment
- volume
- 283
- article number
- 113299
- publisher
- Elsevier
- external identifiers
-
- scopus:85140295542
- ISSN
- 0034-4257
- DOI
- 10.1016/j.rse.2022.113299
- language
- English
- LU publication?
- yes
- id
- 75a21106-0c7a-4a17-8229-624a53586ade
- date added to LUP
- 2022-12-06 11:37:52
- date last changed
- 2023-05-10 11:04:06
@article{75a21106-0c7a-4a17-8229-624a53586ade,
abstract = {{<p>Multi-source merging is an established tool for improving large-scale precipitation estimates. Existing merging frameworks typically use gauge-based precipitation error statistics and neglect the inter-dependence of various precipitation products. However, gauge-observation uncertainties at daily and sub-daily time scales can bias merging weights and yield sub-optimal precipitation estimates, particularly over data-sparse regions. Likewise, frameworks ignoring inter-product error cross-correlation will overfit precipitation observation noise. Here, a Statistical Uncertainty analysis-based Precipitation mERging framework (SUPER) is proposed for addressing these challenges. Specifically, a quadruple collocation analysis is employed to estimate precipitation error variances and covariances for commonly used precipitation products. These error estimates are subsequently used for merging all products via a least-squares minimization approach. In addition, false-alarm precipitation events are removed via a reference rain/no-rain time series estimated by a newly developed categorical variable merging method. As such, SUPER does not require any rain gauge observations to reduce daily random and rain/no-rain classification errors. Additionally, by considering precipitation product inter-dependency, SUPER avoids overfitting measurement noise present in multi-source precipitation products. Results show that the overall RMSE of SUPER-based precipitation is 3.35 mm/day and the daily correlation with gauge observations is 0.71 [−] – metrics that are generally superior to recent precipitation reanalyses and remote sensing products. In this way, we seek to propose a new framework for robustly generating global precipitation datasets that can improve land surface and hydrological modeling skill in data-sparse regions.</p>}},
author = {{Dong, Jianzhi and Crow, Wade T. and Chen, Xi and Tangdamrongsub, Natthachet and Gao, Man and Sun, Shanlei and Qiu, Jianxiu and Wei, Lingna and Gao, Hongkai and Duan, Zheng}},
issn = {{0034-4257}},
keywords = {{Data merging; Error cross correlation; Precipitation; Rain/norain classification; Uncertainty analysis}},
language = {{eng}},
publisher = {{Elsevier}},
series = {{Remote Sensing of Environment}},
title = {{Statistical uncertainty analysis-based precipitation merging (SUPER) : A new framework for improved global precipitation estimation}},
url = {{http://dx.doi.org/10.1016/j.rse.2022.113299}},
doi = {{10.1016/j.rse.2022.113299}},
volume = {{283}},
year = {{2022}},
}