OPERATIONAL CLIMATE SERVICES:A DIALOGUE ON PRACTICAL ACTION
A Drought Early warning and Outlook system over I. R. of Iran (DOES)
Iman Babaeian, Maryam karimian, Rahele Modirian, Azadeh Mohamadian, Mansureh Kuhi, FattemehZabol Abbasim, Leili Khazanedari, Ebrhahim Fattahi, Sina Samadi, Sharareh Malbusi
ibabaeian@yahoo.com
Climatological Research Institute (National Centre for Climatology),
Mashad, I.R. of Iran, P.O. Box: 91735-676, Tel: 0511-3822306-8, Fax: 0511-3822311

Abstract:
In the recent years, because of the increasing of the climatic extreme events, demands
for reliable seasonal precipitation forecasts and drought early warning and predictions have beenincreased, especially in water resources and agricultural sectors. In this regards and for
improving the accuracy of seasonal precipitation forecasts and drought early warnings, theoutputs of MRI-CGCM3 seasonal forecast model including gridded precipitation and 20parameter and climate indices has been used to station scale post processing of the model
precipitation over 71 synoptic weather stations of Iran over the period of 2001-2007. The
outputs of MRI-CGCM3 are available to registered National Meteorological and HydrologicalServices (NMHSs) on the website of the Tokyo Climate Center of the Japan Meteorological
Agency (JMA/TCC). Model variables and indices which were used in the post-processing were
WIO rain, Z2030, Z5060, WIO SST, T850, T2m, SST, NINOWEST SST, WNP RAIN, NINO3SST, Z3040, H500, SLP, SAMOI RAIN, MC RAIN, DL RAIN, THMD, THTR and total
precipitation. The skill of multivariate post-processing was evaluated using Mean Square Skill
Score, Mean Bias Error, relative error and categorical skill score over the training andevaluation periods. Categorical skill score is determined by computing the skill of postprocessed and raw model data in forecasting five precipitation category of above normal, above
normal to normal, normal, normal to below normal and below normal. Post-processed
precipitation outputs were entered to the SPI software to calculate SPI drought index over 71
weather stations of Iran. Post processed precipitations and SPI drought indices are comparedwith observed precipitations and drought indices for checking the capability of statistical postprocessing method used on the model outputs. Then the accuracy of the post processing method
is computed over all stations for precipitation and drought indices.
Recent study has three main results including: firstly, post processing of the model
outputs is a substantial need to improving the numerical models. The second point confirms that
implementation of post processing can significantly increase the accuracy of station scaleprecipitation forecasts. Finally, post processing method used in this research can highly improve
the accuracy of drought prediction and early warning. Results showed that capability of model
to predict the drought indices has been increased from 66.7% up to 78.5% after post processing.
This project is a basic research toward using numerical seasonal forecasting model in Iran.
Development of a method for drought early warning system is very useful for
agriculture and water resource management. According to the goals of the GFCS, this project
can be improved by utilizing other GPVs from different dynamical seasonal forecast models.
Keywords: Seasonal forecasting, MRI-CGCM3, SPI index, Early warning, Postprocessing.