Validation of Satellite-Based PERSIANN Rainfall Estimates Using Surface-Based APHRODITE Data over Iran

Abstract

Abstract:Surface-based precipitation measurements with high accuracy on different spatial-temporal scales have a crucial importance in different land-use planning sectors, especially in arid and semi-arid regions, such as Iran. Because the density of spatial distribution of rain-gauges is not uniform throughout the country, satellite sensor technology is considered useful for precipitation monitoring over the study area. In this study, PERSIANN satellite-based rainfall data were validated through comparison with the APHRODITE surface-based precipitation data. The validation was carried out for annual and seasonal precipitation, as well as an inter-annual comparison. Our analysis was based on a visual comparison and a statistical approach, including linear regression and spatial correlation between APHRODITE and PERSIANN datasets for each 0.25°×0.25° grid cell in the entire country, in the Caspian Sea region, and in the Zagros Mountains, indicating spatial correlation coefficients of 0.62, 0.62, 0.47, respectively. Both APHRODITE data and PERSIANN data showed that spatial distribution of mean annual and seasonal precipitation over Iran has two main patterns: along the Caspian Sea and along the Zagros Mountain chain. In general, PERSIANN underestimates high rainfall rates by 5.5 mm/day in winter but overestimates the low rainfalls in annual and seasonal scales by 0.9 mm/day in summer.

Exponent determination in a poorly gauged basin system in Nigeria based on flow characteristics investigation and regionalization method

“…On the average, Okhunwan, Oyanmi, Owan, and Osse sub-basins indicate spatial correlation coefficients of 0.75, 0.65, 0.70, and 0.78, respectively, showing that PERSIANN-CDR is reliable with good dependability status. These outcomes justify the PERSIANN-CDR performance in recent years due to the growing number of training parameters of the artificial neural network by utilization of polar-orbit satellite data in the PERSIANN algorithm, which supports the adoption of PERSIANN-CDR remotely sensed data for this study as against other satellite rainfall estimates such as the Tropical Rain Measurement Mission (TRMM), The Climate Hazards Group Infrared Precipitation with Station data (CHIRPS) datasets to mention a few [40].…”

The PERSIANN family of global satellite precipitation data: a review and evaluation of products

“…In recent years, several studies focused on the evaluation of the PERSIANN products against ground-based observations over different regions of the globe. Evaluation studies conducted over Iran show that PERSIANN adequately captures the precipitation patterns of mean annual and seasonal precipitation, although it underestimates the amount of rainfall (Jamli, 2015). An evaluation performed at a daily temporal scale (Katiraie-Boroujerdy et al, 2013) shows that PER-SIANN and GPCP-adjusted PERSIANN exhibit good performance over the mountainous regions while underperforming over the coastal region of the Caspian Sea.…”

Simple Short-Term Probabilistic Drought Prediction Using Mediterranean Teleconnection Information

“…Because of the consistency and availability of the record for long-term, needed monthly 226 precipitation data for the basin is acquired from daily 0.25° × 0.25° high resolution PERSIANN-227 CDR (Ashouri et al, 2015) for the period 1983-2010. The accuracy of PERSIANN precipitation 228 data set family for Iran and Urmia Lake basin is demonstrated in some previous publications 229 (Moazami et al, 2013;Bodagh-Jamli, 2015;Ghajarnia et al, 2015;Katiraie-Boroujerdy et al, 230 2013). 231…”