Triple Collocation Based Multi-Source Precipitation Merging
Jianzhi Dong, Fangni Lei, Lingna Wei
Abstract
Multi-source precipitation merging has been used for improving global precipitation estimation accuracy. However, current merging techniques heavily rely on gauge-based precipitation and/or streamflow observations, which may contain substantial uncertainties over data-poor regions. This study provides a triple collocation (TC) based framework for merging multi-source precipitation products without the access of high-quality ground observations. In this framework, the error variances of the precipitation products are statistically estimated using TC, which are further employed in parameterizing a least-square-based precipitation merging framework. As validated against high-quality gauge observations collected over Europe, we demonstrate that TC can accurately estimate the relative errors of different precipitation products, which leads to robust multi-source precipitation merging. Results also demonstrate that the TC merged product significantly outperforms the parent products, which is noteworthy - given the strong skills of the reanalyzed (ERA-Interim) precipitation product over Europe. Since TC analysis does not rely on high-quality gauge observations, the proposed TC-based merging framework can be applied globally, and is expected to significantly contribute precipitation data merging over data-poor regions, e.g., Africa and South America.