# Comparison of GPM IMERG and TRMM 3B43 Products over Cyprus
## 1 Introduction
However, IMERG product performed better than TRMM 3B42 in the detection of precipitation events in terms of specific statistical scores (i.e., probability of detection), over China and across most of the sub-regions. [PERSON] et al. [26] evaluated the GPM-IMERG (v5) final precipitation product against a ground-based gridded data set over Japan, Nepal and the Philippines for two years (2014-2015). Their results showed generally good performance (in terms of statistical scores, like correlation, mean bias, root mean square error) of GPM-IMERG over three regions, although an underestimation was noticed during heavy rainfall events. They also noticed that GPM-IMERG estimates improved its capability in terms of detecting light and heavy precipitation events, although their performance was found to be seasonally dependent. A few studies with evaluation of satellite precipitation products over Cyprus are reported in the literature. [PERSON] et al. [27] performed an analysis of precipitation data from satellite data TRMM 3B43 (versions 7 and 7A) over Cyprus and compared them with the corresponding gauge observations and E-OBS gridded data (i.e., a European daily high-resolution gridded dataset of surface temperature and precipitation to be used for validation of Regional Climate Models and for climate change studies) for a 15-year period (1998-2012). They concluded that correlation between TRMM and E-OBS was higher in summertime (\(\approx\)0.97), but significantly lower in the winter period (\(\approx\)0.60-0.74). It was noticed that the annual correlation tends to decrease considerably with time. They also found that the coefficient of determination between TRMM, E-OBS estimates and gauge data were relatively high (0.929 and 0.932, respectively); however, the variations noticed were attributed to the elevation differences. A study for a 30-year period (1981-2010) for the precipitation database Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) in Cyprus was presented by [PERSON] et al. [28]. The CHIRPS database was evaluated against gauge stations data. Results showed good correlation between monthly CHIRPS values and recorded precipitation with the correlation coefficients found to be around 0.85 and January the month with the highest correlation. The corresponding values for the annual mean ranged between 0.70 and 0.74, with the mountainous stations showing a slightly higher correlation. In a later study, [PERSON] et al. [29] examined the performance of several climatic indices for the CHIRPS precipitation dataset and rain gauges records on high spatial (0.05\({}^{\circ}\)) and temporal (daily) resolution for a period of 30 years (1981-2010). Results indicates quite a promising performance regarding indices related to daily precipitation thresholds, resulting in high correlation scores. However, for indices referring to number of days, results showed medium or no correlation, probably due to the criteria used for the identification of a wet (rainy) day on the CHIRPS dataset. Furthermore, [PERSON] et al. [30], in their study on the accuracy of the GPM IMERG estimates over Cyprus (April 2014 to February 2017), concluded that, overall, a very good agreement (based on the statistical analysis) between monthly IMERG estimates and gauge data was established (coefficient of determination \(r^{2}\) value \(\approx\) 0.93), presenting a tendency of IMERG for underestimation when higher elevation (>1000 m) was considered. They also examined the daily dependency of IMERG estimates and gauge data, considering a series of extreme precipitation events, and they concluded that this is case dependency, while elevation does not have an apparent effect. The objective of this study is to evaluate statistically the performance and improvement of the GPM IMERG product compared to TRMM 3B43V7 estimates, thus exploring, the continuity and uniformity between IMERG and TRMM-era data sets over Cyprus so that they can be used in climate studies as a combined and consistent dataset.