A framework for merging precipitation retrievals and gauge-based observations based on a novel concept namely virtual gauges

Multi‐source data merging via weighted average (WA) is widely applied to enhance large‐scale precipitation estimates. However, these data sets usually contain substantial conditional biases with respect to extreme precipitation (EP) events—undermining their utility for extreme event analysis. Nevertheless, the main source of such EP biases remains unknown. Here, we demonstrate that WA algorithms are responsible for less than 1% of total EP biases. Instead, EP biases originate from the multi‐source precipitation inputs, which are not adequately adjusted prior to WA. Specifically, current data‐merging frameworks only correct the monthly means or statistical distributions of the remote sensing/reanalysis precipitation inputs prior to WA. Such procedures are insufficient for adjusting EP timing uncertainties, which eventually propagate into the WA‐based merged data set as an EP bias. Therefore, developing algorithms that iteratively adjust EP timing and intensity errors should be prioritized in future precipitation merging frameworks.

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A framework for merging precipitation retrievals and gauge-based observations based on a novel concept namely virtual gauges

Cited by 3 publications

References 41 publications

Integrating satellite and reanalysis precipitation products for SWAT hydrological simulation in the Jing River Basin, China

Integrating satellite and reanalysis precipitation products for SWAT hydrological simulation in the Jing River Basin, China

Improving daily precipitation estimation using a double triple collocation-based (DTC) merging framework

The Conditional Bias of Extreme Precipitation in Multi‐Source Merged Data Sets

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