1 Long-lead Forecasting of Monthly Rainfall Using Large Scale Climate Signals and Statistical Disaggregation Models Azadeh Ahmadi 1 , Mohammad Karamouz 2 , Sara Nazif 3 , Navideh Noori 4 1 Ph.D. Candidate, School of Civil Engineering, University of Tehran, Tehran, Iran. Email: azadehahmadi@ut.ac.ir 2 Professor, School of Civil Engineering, University of Tehran, Tehran, Iran. Email: karamouz@ut.ac.ir 3 Ph.D. Candidate, School of Civil Engineering, University of Tehran, Tehran, Iran. Email: saranazif@yahoo.com 4 M.Sc., School of Civil Engineering, University of Tehran, Tehran, Iran. Email: navide_noori@yahoo.com ABSTRACT Monsoon, one of the most dynamic climate systems, controls rainfall variation in some countries in Asia such as India, Bangladesh, Pakistan, and Iran. It delivers the main component of annual rainfall in these regions. In this study, the relationship between Iran monsoon rainfalls and some large scale climate signals such as SLP (Sea Level Pressure) and SLP differences over certain effective regions have been examined. The correlation coefficient between various combinations of climate signals with the rainfall at different time lags demonstrates some significant correlations which are used to identify the predictors for the rainfall forecasting. In this paper, a fuzzy rule model has been developed to predict the six-month rainfall in the southeastern part of Iran. Then the long-lead forecasting rainfall is disaggregated to a monthly scale using statistical disaggregation models and by considering the historical share of each month from total 6 month precipitations. In real time, the predicted value of each month during the 6 month time horizon is modified once the observed precipitation in any of the prior months becomes available using the Bayesian Theory. This way the accuracy of prediction will be significantly increased as we approach the last 3 months of the forecast period. The proposed model makes adequate lead time for estimation of the water resources potential for mid-term planning. Since the planning scale of water resources planning is usually monthly, disaggregated rainfall could increase the accuracy of operating schemes in the study area. Keywords: Climate Signals, Fuzzy Rule, Disaggregation, Forecasting, Bayesian Theory INTRODUCTION Different optimization techniques are developed in order to define operating policies for operation of water resources systems and facilities. Forecasts of precipitation and streamflow in the next few months or even seasons are necessary for the real-time operation of water resources systems such as reservoirs. Different methods have been widely used for precipitation prediction. These methods followed different approaches ranging from pure statistical methods to methods that are mostly based 4965 World Environmental and Water Resources Congress 2009: Great Rivers © 2009 ASCE World Environmental and Water Resources Congress 2009 Downloaded from ascelibrary.org by San Francisco State University on 07/30/15. Copyright ASCE. For personal use only; all rights reserved.