ORCID Profile
0000-0002-8634-6920
Current Organisations
Sun Yat-Sen University
,
University of Delhi
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Publisher: Springer Science and Business Media LLC
Date: 2015
Publisher: Springer Science and Business Media LLC
Date: 21-06-2017
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-4247
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 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 [& #8722 ] & #8211 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.
Publisher: University of Zagreb, Faculty of Science, Department of Mathematics
Date: 19-06-2018
DOI: 10.3336/GM.53.1.11
Publisher: IEEE
Date: 07-2017
Publisher: ISTE Group
Date: 2014
Publisher: Springer Science and Business Media LLC
Date: 03-06-2019
Location: United States of America
Location: China
No related grants have been discovered for Lalit Vashisht.