Abstract
This study evaluated the performance of the early, late and final runs of IMERG version 06 precipitation products at various spatial and temporal scales in China from 2008 to 2017, against observations from 696 rain gauges. The results suggest that the three IMERG products can well reproduce the spatial patterns of precipitation, but exhibit a gradual decrease in the accuracy from the southeast to the northwest of China. Overall, the three runs show better performances in the eastern humid basins than the western arid basins. Compared to the early and late runs, the final run shows an improvement in the performance of precipitation estimation in terms of correlation coefficient, Kling–Gupta Efficiency and root mean square error at both daily and monthly scales. The three runs show similar daily precipitation detection capability over China. The biases of the three runs show a significantly positive (p < 0.01) correlation with elevation, with higher accuracy observed with an increase in elevation. However, the categorical metrics exhibit low levels of dependency on elevation, except for the probability of detection. Over China and major river basins, the three products underestimate the frequency of no/tiny rain events (P < 0.1 mm/day) but overestimate the frequency of light rain events (0.1 ≤ P < 10 mm/day). The three products converge with ground-based observation with regard to the frequency of rainstorm (P ≥ 50 mm/day) in the southern part of China. The revealed uncertainties associated with the IMERG products suggests that sustaining efforts are needed to improve their retrieval algorithms in the future.
Highlights
The spatial distribution of the continuous and categorical evaluation metrics for the three runs of Integrated Multi-satellite Retrievals for GPM (IMERG) products over China are shown in Figures 2 and 3, respectively
This study provides a comprehensive evaluation of daily precipitation from different runs of the latest version (V06) of IMERG against 696 key synoptic stations from 1 January 2008 to 31 December 2017 across China
We analyzed the accuracy of the IMERG products at various spatial and temporal scales through various performance metrics
Summary
Precipitation plays a critical role in water cycle and energy balance [1,2,3,4]. Understanding the spatial and temporal variability of precipitation is essential for many applications including hydrological modeling, climatic prediction and water resource management as well as environmental and ecological risk analysis [2,5,6,7]. Precipitation estimates can be obtained from three sources: ground-based observations, model simulations and remote sensing observations [8,9]. Ground-based observation is the most accurate method of retrieving precipitation records. It is largely limited by the sparse ground networks of rain gauges and the discontinuity of the recording sequences [10,11]
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