# Comparison of GPM IMERG and TRMM 3B43 Products over Cyprus
## 1 Introduction  
Satellite observations have been widely used during recent decades for several meteorological, hydrological and climatological applications incorporating precipitation data worldwide [1; 2; 3; 4; 5; 6]. In order to fill in where ground observations are absent or sparse, satellite estimations have been evolving using sophisticated algorithms that can identify rainfall, snow and/or other hydrometeors [7; 8; 9; 10; 11]. However, although Satellite Precipitation (SP) products are able to, overall, capture the variability and magnitude of rainfall, still they cannot accurately estimate the localized rainfall variations. Thus, validation of satellite precipitation products is often needed against ground-based measurements. The Tropical Rainfall Measuring Mission (TRMM) platform placed in orbit during the 1997-2015 period, provided reliable data of high spatial (\(\approx\)25 km) and temporal resolution (3 h), at a geographical coverage between 50\({}^{\circ}\) N and 50\({}^{\circ}\) S [12; 13; 14; 15; 16; 17]. TRMM's successor, namely, the Global Precipitation Mission (GPM) has been in orbit since 2014, giving estimates at even higher resolutions (\(\approx\)10 km; 30 min) and geographical coverage from 60\({}^{\circ}\) S to 60\({}^{\circ}\) N, making it available for a variety of applications, including the assimilation of GPM data in numerical weather prediction models to improve model forecasting skill [15; 16], the monitoring of severe weather events [13; 15; 16; 17], hydrological hazards [18; 19], etc. Several studies attempted to demonstrate the accuracy of TRMM and GPM IMERG (Integrated Multi-satellite Retrievals) estimates in various geographical areas. In their study over mainland China, [PERSON] et al. [20] found that both SP products overestimate light rainfall. This is attributed to the fact that hydrometeors detected by infrared and microwave sensors as well as precipitation radars may partially or even totally evaporate before they are registered by the rain gauges. Furthermore, these authors found an underestimation of moderate and heavy rainfall by both products. A slightly better performance by GPM-IMERG, according to these authors, is attributed to the satellite overpasses and sensor capabilities. In a similar study over Pakistan, [PERSON] et al. [21] found a slight dominance of IMERG; however, both products correlate well with the in situ measurements at a monthly scale, adequately following the temporal pattern. Again, underestimation of moderate and heavy rainfall and overestimation of light events was reported. In their study regarding the area of Singapore, [PERSON] and [PERSON] [22] presented similar results, showing good correlation on a monthly scale, with rain gauges for both products and moderate correlation for daily values. The authors underlined that the better performance of IMERG was not that notable and that the main advantage of the new product was mostly its finer resolution. In a study over the Tibetan plateau (Hexi region), [PERSON] et al. [23] found a better correlation for IMERG, ascribed mostly to the ability to detect better moderate and heavy rainfall; however, they concluded that the improvement was not significant. In their study (China, 2015-2017), [PERSON] et al. [24] evaluated the performance of IMERG (v5) and TRMM 3B42 (v7) and found that, at monthly and annual scale, both datasets were highly correlated with rain gauge observations. Considering daily values, satellite estimates overestimate precipitation for intensities within the range 0 to 25 mm/day and underestimate precipitation for light and heavy intensities. Considering various statistical scores, they found that IMERG, in general, performed better in detecting the observed precipitation. In a similar study (China, March 2014 to February 2017), [PERSON] et al. [25] found severe underestimation with high negative relative biases for both IMERG (v5) and TRMM products.