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Zheng Duan

Biträdande universitetslektor

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Statistical uncertainty analysis-based precipitation merging (SUPER) : A new framework for improved global precipitation estimation

Författare

  • Jianzhi Dong
  • Wade T. Crow
  • Xi Chen
  • Natthachet Tangdamrongsub
  • Man Gao
  • Shanlei Sun
  • Jianxiu Qiu
  • Lingna Wei
  • Hongkai Gao
  • Zheng Duan

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.

Avdelning/ar

  • Institutionen för naturgeografi och ekosystemvetenskap
  • MERGE: ModElling the Regional and Global Earth system

Publiceringsår

2022

Språk

Engelska

Publikation/Tidskrift/Serie

Remote Sensing of Environment

Volym

283

Dokumenttyp

Artikel i tidskrift

Förlag

Elsevier

Ämne

  • Probability Theory and Statistics

Nyckelord

  • Data merging
  • Error cross correlation
  • Precipitation
  • Rain/norain classification
  • Uncertainty analysis

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 0034-4257