The Uncertainty of IMERG Over the Western Edge of the Eastern Pacific Fresh Pool: An Error Model Based on SPURS-2 Field Campaign Observations

Satellite precipitation products such as NASA’s Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG) have served as valuable sources for oceanic precipitation information. However, quantifying their uncertainty over oceans remains challenging due to limited reference observations. This study uses a variety of advanced in situ rainfall measurements collected during NASA’s Salinity Processes in the Upper Ocean Regional Study-2 (SPURS-2; August 2016–November 2017) to characterize the uncertainty of IMERG over the western edge of the tropical Eastern Pacific Fresh Pool. A censored, shifted gamma distribution (CSGD) model is implemented for uncertainty quantification of IMERG at its gridded resolution (i.e., 30 min, 0.1°), and rainfall observations from multiple passive aquatic listeners (PALs), mooring gauges, and gauges on the research vessel (R/V) are used to train and validate the model’s performance. The results indicate that the drifting PAL-trained CSGD models perform similarly as the mooring gauge-trained models, showing good fits to the data and successful representations of bias and random error. The CSGD-estimated quantiles align with the empirical quantiles derived from references, demonstrating the model’s ability to reproduce the conditional density distributions of possible “true” precipitation given the IMERG estimates. Verification with independent data from the R/V and GPM Level-2A Dual-frequency Precipitation Radar precipitation product (2ADPR) confirms the model’s performance in generating reliable point-scale rainfall ensembles and in improving heavy rainfall estimation by accounting for IMERG uncertainty. With the newly available oceanic rainfall dataset from 58 PALs globally, this error modeling framework can be readily applied to estimate the uncertainty of IMERG for applications in other ocean regions.