The First IBCS Meeting, 1-5 July 2013, Geneva
OPERATIONAL CLIMATE SERVICES:A DIALOGUE ON PRACTICAL ACTION

A Drought Outlook and Early-warning System over I. R. of Iran (DOES)
Iman Babaeian, Maryam karimian, Azadeh Mohamadian, Rahele Modirian, Mansureh Kuhi, Fattemeh Zabol Abbasi,Leili Khazanedari, Ebrhahim Fattahi, Sina Samadi, Majid Habibi, Sharareh Malbusi, Shahzad Ghandhari
ibabaeian@yahoo.com
Climatological Research Institute /I. R. of Iran National Centre for Climatology (http://www.cri.ac.ir)
Mashad, I.R. of Iran, P.O. Box: 91735-676, Tel: 0511-3822203, Fax: 0511-3822311
Abstract
In this research, post processed amounts of precipitation GPVs from MRI-CGCM3/JMATokyo Climate Center model has been used to compute the future three months SPI drought
index to develop Drought Outlook and Early warning System (DOES) over Iran. Recent study
has three main results including: 1-post processing of the model outputs is a substantial need toimprove the skill of numerical models in station scale. 2-Implementation of post processing cansignificantly increase the accuracy of station scale precipitation forecasts. 3- post processing
method used in this research can highly improve the accuracy of drought prediction and earlywarning. Results showed that capability of model to predict the drought indices has been
increased from 66.7% (non-post processed)up to 78.5% when using post processing.

Introduction
In the recent years, because of the increasing of the climatic extreme events,
demands for reliable seasonal precipitation forecasts, drought early warning andpredictions have been increased, especially in water resources and agricultural sectors.
There are many researches on precipitation and other meteorological forecast inseasonal scale, but the number of researches involving drought prediction using
dynamical model outputs is low. As the drought situation in the future months canhelp in agriculture water management and water resources over Iran, therefore, mostof researches use the statistical approaches including regression, time series, fuzzy,fuzzy-logic, neural network and genetic algorithm to predict the drought conditions ofIran for 3 months to one year in ahead. In Mashad Climate Center, located in theNortheast of Iran, we have examined some different methods to predict the drought
situations over Iran. In this regard, the method using statistical post processing of a
dynamical seasonal forecast model has presented in this poster.
Data and Methodology
Two kinds of data are used in this research: 1-observation data of precipitation over 71
weather stations of Iran in the period of 1980-2007 (28 years) and 2-MRI-CGCM3 reforecast
Grid Point Values(GPV) in the same period to the observation years.
Table 1 shows the name and description of GPVs data which are used in this research. Six station
scale MRI-CGCM GPVs are not shown in the figure. They are model outputs of precipitation,
T850, T2m, H500, SST and MSLP.

Figure 1. Using JMP4 to calculate regression between MRI-CGCMs outputs and observed
precipitations over 71 weather stations of Iran.

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 and evaluation periods.
Categorical skill score is determined by computing the skill of post processed and raw model datain 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 tothe SPI software to calculate SPI drought index over 71 weather stations of Iran. Post processed
precipitations and SPI drought indices are compared with observed precipitations and drought
indices for checking the capability of statistical post processing method used on the model
outputs. Then the accuracy of the post processing method is computed over all stations for
precipitation and drought indices.

Results
Skill of DOES in modeling drought outlook using SPI index by post processed precipitation
for two case of dry and wet months (August and February 2001)are shown in figure 2. Leftpanels shows the observed SPI using actual station precipitation. Middle panels shows the SPI
drought index using non post processed precipitation data retrieved from MRI-CGCM3. In theright panels results of SPI index using post processed precipitation data are shown.

Table 1. Name and definition of the MRI-CGCM3 variables used for post processing ofprecipitation over 71 stations of Iran. Six GPVs added to these variables.
Dry
NormalWet

Dry
NormalWet

Figure 2. Drought Outlook and Early-earning System(DOES) results for two dry and wet months of August(up) and February(bellow) using post processing of MRI-CGCM3 outputs. Left panel: observed SPI, Middlepanel: non-post processed SPI and right panel: post processed SPI.

Summary

For improving the accuracy of seasonal precipitation forecasts and drought early warnings,
the outputs 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 outputsof MRI-CGCM3 are available to registered National Meteorological and Hydrological Services(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,NINO3 SST, Z3040, H500, MSLP, SAMOI RAIN, MC RAIN, DL RAIN, THMD, THTR andtotal precipitation (Table 1).
JMP4 software are used to investigate the regression between observed station
precipitation and 26 outputs of MRI-CGCM3 in calibration period of 1980-2000 (calibration
period is changed for small number of stations those had little period of observation). All ofMRI-CGCMs GPVs which had statistically significant regression with observed precipitation
are fed to a regression model to find multiple linear regression model coefficient incalibration period for each months in all selected stations. Figure 1 shows the feature of JMP4in computing regression values.

Post processing of the model outputs is a substantial need for improving skill of numericalseasonal forecast models. Post processing method used in this research can highly improve theaccuracy of drought prediction and early warning. Results showed that capability of model topredict the drought indices has been increased from 66.7% up to 78.5% when using postprocessing. According to the goals of the GFCS, development of a method for drought early
warning system is useful for agriculture and water resource management. This method can beimproved by utilizing other GPVs from different dynamical seasonal forecast models.

Acknowledgement
This research was supported under IRIMO program entitled “Development of drought
outlook and early warning system using post processing of dynamical seasonal forecast models”.
We thank to JMA/Tokyo Climate Center for their close cooperation and providing MRI-CGCM3GPVs.

References--------, 2012, Tokyo Climate Center at http://ds.data.jma.go.jp/tcc/tcc/ available on 3 June2013.
Babaeian, I., Karimain M and Modirian R., 2013, Statistical Post processing MRI-CGCM3
Output for Seasonal Precipitation Forecast over Khorasan-Razavi Province, Iran, Iranian
Journal of Geophysics (accepted).