seasonal rainfall prediction based on synoptically patterns by using adaptive neuro-fuzzy inference system(anfis)

weather process prediction is the tool for managers to planning the future political for maximum operation. the aim of this research is relation investigation of large scale synoptically patterns with seasonal rainfall of khorasan province. in this research, we have analyzed 37 years of rainfall data in khorasan province that is located the northeastern part of iran .we attempted to train adaptive neuro-fuzzy inference system(anfis) based on synoptically patterns with 37 years of rainfall data. for performance evaluation, anfis predicted outputs were compared with the actual rainfall data. in this study, at the first step, the relationships between synoptically pattern variations including sea level pressure(slp), sea surface temperature(sst), sea surface pressure difference(?slp), sea surface temperature differenc(?sst), air temperature at 850 hpa, geopotential high at 500 hpa level, relative humidity at 300 hpa are investigated .in the second step, model was calibrated from 1970 to 1992. finally, rainfall prediction is performed from 1993 to 2002. simulation results reveal that adaptive neuro-fuzzy inference system(anfis) is promising and efficient