comparison the persiann model with the interpolation method to estimate daily precipitation (a case study: north khorasan)

abstract precipitation as the most important factor plays the main role in many application researches which are based on climatic parameters. many researches in the field of hydrology, hydrometeorology and agriculture employs rain-gauges (such as synoptic and climatologic stations) data. precipitation characteristics, such as rainfall intensity and duration, usually exhibit significant spatial variation, even within small watersheds; while rain gauge network density could not provide desirable cover. nearly all related researches use interpolation methods for places without rain gauge data. many studies showed that the estimated error was usually high by usual interpolation methods. employing satellite data with high spatial and temporal resolution could provide accurate precipitation estimation. persiann (precipitation estimation from remotely sensed information using artificial neural network) model works based on the ann (artificial neural network) system which uses multivariate nonlinear input-output relationship functions to fit local cloud top temperature (tb) to pixel rain rates (r). in this study, persiann model and two interpolation methods (kriging & idw) were employed to estimate precipitation for north-khorasan between the years 2006 until 2008. results show better correlation between persiann outputs and station data than other two interpolation methods. while correlation coefficient for kendal`s test is 0.805 between model and bojnord station data, this coefficient is 0.488 for idw and 0.565 for kriging methods.