
Zheng Duan
Senior lecturer

Statistical uncertainty analysis-based precipitation merging (SUPER) : A new framework for improved global precipitation estimation
Author
Summary, in English
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.
Department/s
- Dept of Physical Geography and Ecosystem Science
- MERGE: ModElling the Regional and Global Earth system
Publishing year
2022
Language
English
Publication/Series
Remote Sensing of Environment
Volume
283
Document type
Journal article
Publisher
Elsevier
Topic
- Probability Theory and Statistics
Keywords
- Data merging
- Error cross correlation
- Precipitation
- Rain/norain classification
- Uncertainty analysis
Status
Published
ISBN/ISSN/Other
- ISSN: 0034-4257