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

作者：Dong, Jianzhi^*; Crow, Wade T.; Chen, Xi; Tangdamrongsub, Natthachet; Gao, Man; Sun, Shanlei; Qiu, Jianxiu; Wei, Lingna; Gao, Hongkai; Duan, Zheng

来源：REMOTE SENSING OF ENVIRONMENT, 2022, 283: 113299.

DOI：10.1016/j.rse.2022.113299

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

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.

• 单位
  中山大学; 天津大学