عنوان مقاله [English]
Changes in the incidental behaviors are among the most important aspects of global climate change with significant consequences on human society and the environment. Monitoring and measuring heavy rainfall events are important for understanding the nature of severe weather fundamentals and future assessment. In this study, Global Precipitation Measurement (GPM) experiments with ground station data were performed at 20 synoptic stations for intense daily detection (25 mm and above) of precipitation over an 8-year period (2021-2014). Statistics such as coefficient of determination (R2), correlation coefficient (R) and root mean square error (RMSE) were used to compare and evaluate the observational and satellite data. Comparison of the maps obtained from GPM satellites and ground stations showed that the spatial distribution of precipitation from two similar bases is the same and the low and high rainfall areas correspond to the region. GPM satellite detected precipitation zones well so that the spatial correlation coefficient between GPM satellite and observed was 0.81. The results of the ANOVA test between the observational data and the GPM satellites showed that due to the low significance level of p-value of 0.000, the assumption that the average precipitation is the same between the two databases is rejected. There is a significant relationship between the average precipitation at ground and satellite stations. Also, the results of Kolmogorov-Smirnov test showed that since the obtained p-value (0.819) is a number higher than the error value of the test (0.05), so the null hypothesis based on the equality of precipitation values recorded at ground stations and modeled are the same and the null hypothesis is confirmed.