فهرست نشریات علمی دسترسی آزاد ایران

This study aimed to evaluate the performance of three satellite precipitation products—CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks), and TRMM (Tropical Rainfall Measuring Mission)—in representing precipitation patterns across northeastern Iran during the 2001–2010 period. For this purpose, data from six selected synoptic meteorological stations obtained from the Iranian Meteorological Organization were compared with satellite-derived datasets accessed through the Google Earth Engine platform. The products were evaluated at daily, monthly, and annual time scales. At the daily scale, statistical indicators including the correlation coefficient (R), root mean square error (RMSE), and bias were calculated. Additionally, independent-samples t-tests were applied to examine differences in monthly mean precipitation, and annual precipitation zoning during dry and wet years was conducted within a Geographic Information System (GIS) framework. All computations related to the Taylor diagram and statistical metrics were carried out using R, while independent-sample t-test analyses were performed in SPSS. The results demonstrated that the performance of satellite products is strongly dependent on temporal scale and climatic conditions. At the daily scale, TRMM showed the highest agreement with ground observations and outperformed CHIRPS and PERSIANN in terms of accuracy. Despite its higher spatial resolution, CHIRPS exhibited weak performance in estimating daily precipitation over the study area. At the annual scale, TRMM performed more reliably, particularly during dry years, whereas all three products tended to underestimate precipitation during wet years. Furthermore, the highest estimation accuracy for all three satellite products was jointly observed at the Torbat-Heydariye station. At the monthly scale, although TRMM occasionally demonstrated relatively better performance, statistically significant differences between satellite-derived and station data were detected in 83% of the stations. Overall, the findings indicate that the selection of satellite precipitation products should be tailored to temporal scale, topographic characteristics, and regional climatic conditions. Regional validation is therefore essential prior to application in water resource management and climatological studies in dry and semi-arid regions.