Assessment of the IMERG Early-Run Precipitation Estimates over South American Country of Chile

Abstract

Accurate rainfall measurement is a challenge, especially in regions with diverse climates and complex topography. Thus, knowledge of precipitation patterns requires observational networks with a very high spatial and temporal resolution, which is very difficult to construct in remote areas with complex geological features such as desert areas and mountains, particularly in countries with high topographical variability such as Chile. This study evaluated the performance of the near-real-time Integrated Multi-satellite Retrievals for GPM (IMERG) Early product throughout Chile, a country located in South America between 16° S–66° S latitude. The accuracy of the IMERG Early was assessed at different special and temporal scales from 2015 to 2020. Relative Bias (PBIAS), Mean Absolute Error (MAE), and Root-Mean-Squared Error (RMSE) were used to quantify the errors in the satellite estimates, while the Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI) were used to evaluate product detection accuracy. In addition, the consistency between the satellite estimates and the ground observations was assessed using the Correlation Coefficient (CC). The spatial results show that the IMERG Early had the best performance over the central zone, while the best temporal performance was detected for the yearly precipitation dataset. In addition, as latitude increases, so do errors. Also, the satellite product tends to slightly overestimate the precipitation throughout the country. The results of this study could contribute towards the improvement of the IMERG algorithms and open research opportunities in areas with high latitudes, such as Chile.

Capability of satellite data to estimate observed precipitation in southeastern South America

“…Thus, IMERG can improve precipitation estimations in zones of high elevations. Consistently, da Silva et al (2023) reported that IMERG achieves a lower incidence of error than other satellite products for regions situated at elevations beneath 2000 m.…”

“…In this regard, several studies have compared and evaluated the accuracy of different satellite products using data from ground‐based measurement stations as reference in different regions of South America. The satellite products previously studied include the Tropical Rainfall Measuring Mission (TRMM; Silva Lelis et al, 2018; Vila et al, 2009), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS; Cerón et al, 2020), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN; de Goncalves et al, 2006; Derin et al, 2016; Hsu et al, 1997), different products of the Integrated Multi‐satellite Retrievals for Global Precipitation Measurements (IMERG; da Silva et al, 2023; Gadelha et al, 2019; Getirana et al, 2020; Pradhan et al, 2022; Rodrigues et al, 2021; Rozante et al, 2018), and a combination of several satellite products (Baez‐Villanueva et al, 2018; Hobouchian et al, 2012; Palharini et al, 2020). In general, satellite products reproduce precipitation with different biases over South America.…”

Evaluation of GPM IMERG Product Over the Yellow River Basin Using an Improved Error-Component Procedure

“…These three processing stages cater to the diverse needs of different data users. However, the accuracy of the inversion process in IMERG has been a subject of interest for numerous researchers [3], [7], [8], [9].…”