# Evaluating the Latest IMERG Products in a Subtropical Climate: The Case of Parana State, Brazil
## 1 Introduction  
Precipitation plays a fundamental role in the hydrological cycle. It is considered the main water source input in the soil water balance and runoff and is used as an input in hydrological and climatological modeling. In the management of water resources, knowledge of the volume and intensity of precipitation is essential for the prediction of floods and droughs, the distribution of water for urban and industrial uses, and the planning of irrigation in agriculture and hydraulic infrastructure. Precipitation can be measured by gauges, sensors onboard satellites, and radars [1, 2, 3]. Precipitation gauges are fundamental instruments, and their observations are considered as a reference in many studies [4]. However, to represent spatiotemporal variability of intensity and type of occurrence of precipitation, a dense measuring network is necessary with long-period information, which unfortunately is not the reality in many regions of the world [5]. In South American countries, monitoring by gauges is limited in terms of infrastructure, maintenance, density, and frequency of observations [6, 7]. Regarding indirect methods, weather radars provide precipitation estimates with high spatial and temporal resolution but have limited accuracy in mountainous regions and coldclimates [5; 8]. On the other hand, satellite estimates of precipitation provide vast spatial and temporal coverage and are freely available. Over the last two decades, several satellite precipitation products have been developed, such as Tropical Rainfall Measuring Mission (TRMM) [9]; Climate Prediction Center Morphing Method (CMORPH) [10]; Global Satellite Mapping of Precipitation (GSMaP) [11]; Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) [12]; and Multi-Source Weighted-Ensemble Precipitation (MSWEP) [13]. The TRMM Multi-satellite Precipitation Analysis (TMPA) algorithm combines precipitation estimates from satellite systems with data measured on the Earth's surface, to provide a calibrated final product and results in the \"best\" satellite estimate [14]. A successor to TRMM, the Global Precipitation Measurement (GPM) was launched in 2014, on a joint mission between NASA (National Aeronautics and Space Administration) and JAXA (Japan Aerospace Exploration Agency) and continues to offer products to this day. The GPM constellation consists of the first Dual-frequency phased array Precipitation Radar and a GPM Microwave Imager, which represent the most advanced versions compared to the Precipitation Radar (PR) and the TRMM Microwave Imager (TMI), onboard the TRMM satellite [15]. Relevant improvements in the GPM products include an increase in latitudinal coverage (global coverage of 60\({}^{\circ}\) N/S) and the detection of heavy rain, light rain, and snow [5; 6]. In the era of GPM, the Integrated Multi-satellite Retrievals for GPM (IMERG) algorithm operates, intending to calibrate, unite, and interpolate satellite precipitation estimates with data from gauges [16]. Many studies have analyzed and compared the TRMM data to those obtained by gauge stations [6; 17; 18; 19; 20], which allowed for advances in the application of remote sensing in determining the volume and behavior of precipitation in several countries. Given that precipitation represents the most important parameter of the hydrologic cycle, it can directly affect physical, chemical, and biological processes. The spatial and temporal distribution of the precipitation is key for the understanding of hydrologic responses in watersheds, for example, runoff, streamflow, and flooding. Thus, the accuracy of precipitation data is essential for the good performance of hydrological models and inconsistencies in its estimates can negatively affect the hydrologic investigation. Previous studies have reported some limitations of the IMERG products [21] related to its low performance in estimating the precipitation over North China, where snowfall events can affect the precipitation estimates from satellite products.