Abstract
In this study, a comprehensive assessment on precipitation estimation from the latest Version 06 release of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) algorithm is conducted by using 24 rain gauge observations at daily scale from 2001 to 2016. The IMERG V06 dataset fuses Tropical Rainfall Measuring Mission (TRMM) satellite data (2000–2015) and Global Precipitation Measurement (GPM) satellite data (2014–present), enabling the use of IMERG data for long-term study. Correlation coefficient (CC), root mean square error (RMSE), relative bias (RB), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were used to assess the accuracy of satellite-derived precipitation estimation and measure the correspondence between satellite-derived and observed occurrence of precipitation events. The probability density distributions of precipitation intensity and influence of elevation on precipitation estimation were also examined. Results showed that, with high CC and low RMSE and RB, the IMERG Final Run product (IMERG-F) performs better than two other IMERG products at daily, monthly, and yearly scales. At daily scale, the ability of satellite products to detect general precipitation is clearly superior to the ability to detect heavy and extreme precipitation. In addition, CC and RMSE of IMERG products are high in Southeastern Jinan City, while RMSE is relatively low in Southwestern Jinan City. Considering the fact that the IMERG estimation of extreme precipitation indices showed an acceptable level of accuracy, IMERG products can be used to derive extreme precipitation indices in areas without gauged data. At all elevations, IMERG-F exhibits a better performance than the other two IMERG products. However, POD and FAR decrease and CSI increase with the increase of elevation, indicating the need for improvement. This study will provide valuable information for the application of IMERG products at the scale of a large city.
Highlights
In recent years, the hydrological cycle has been under the influences of global climate change and intensified human activity [1]
Continuous verification statistics quantifying the accuracy of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) products are shown in the figure
While this study has shown that IMERG
Summary
The hydrological cycle has been under the influences of global climate change and intensified human activity [1]. Spatial and temporal distribution of the components of the cycle have changed, directly affecting regional water balance and inducing natural disasters, such as high intensity rain events, flood, a heat wave, and drought [2,3,4]. Variability in precipitation can lead to regional droughts and floods, which is crucial to water resources management and to meeting the needs of human societies [5]. To forecast floods, monitor droughts, and manage emergencies associated with natural disasters, it is critical to have high-resolution precipitation data [6,7]. Precipitation data are used as basic drivers in various hydrological models. Accuracy of these input data is important. Precipitation data are usually collected using ground-level rainfall gauges, radar [8], or satellite sensors
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