A New Event-Based Error Decomposition Scheme for Satellite Precipitation Products

Understanding the nature and origin of errors in satellite precipitation products is important for applications and product improvement. Here we propose a new error decomposition scheme incorporating precipitation event (continuous rainy periods) information to characterize satellite errors. Under this framework, the errors are attributed to the inaccuracies in event occurrence, timing (event start/end time), and intensity. The Integrated MultisatellitE Retrieval for Global Precipitation Measurement (IMERG) is used as our test product to apply the method over CONUS. The above-listed factors contribute approximately 30%, 20%, and 50% to the total bias, respectively. Significant asymmetry exists in the temporal distribution of biases throughout events: early event endings cause threefold more precipitation amount bias than late event beginnings, while early event beginnings cause fourfold more bias than late event endings. Dominant contributors vary across seasons and regions. The proposed error decomposition provides insight into sources of error for improved retrievals. Satellite remote sensing offers unique capabilities to map global precipitation at daily to sub-daily scales, important for hydrologic applications and decision-making. However, inherent uncertainties and errors in satellite precipitation products necessitate a comprehensive understanding of their characteristics and sources for effective utilization and enhancement. Here, we propose a new event-based error decomposition scheme to characterize satellite errors. This approach is based on the understanding that precipitation occurs as individual events (continuous rainy periods); hence, any quantitative inaccuracy in a satellite product can be attributed to the imperfect delineation of diverse event facets: (a) occurrence (completely missed/falsely detected events), (b) timing (wrong start/end times of the detected events), and (c) intensity (inaccurate precipitation rates during the events). We apply the method to a popular high-resolution satellite product, the Integrated MultisatellitE Retrieval for Global Precipitation Measurement (IMERG) over CONUS. Results show that, nationwide, the above three error types contribute on average about 30%, 20%, and 50% to the total bias, respectively. A large fraction of errors is associated with events starting/ending too early in the satellite product. Dominant error types are season- and region-dependent. The event-based error breakdown offers potential to diagnose error sources and guide algorithm improvement for satellite precipitation products. A new event-based error decomposition scheme for satellite precipitation products is proposed, dividing the total bias into 10 componentsInaccuracies in event occurrence, timing, and intensity contribute on average to about 30%, 20%, and 50% of the total amount biasA large fraction of errors is associated with events starting/ending too early in the satellite product