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Paper Information

Journal:   DESERT (BIABAN)   2006 , Volume 11 , Number 2; Page(s) 49 To 55.
 
Paper: 

A BLENDED MODEL FOR ESTIMATING OF MISSING PRECIPITATION DATA (CASE STUDY OF TEHRAN -MEHRABAD STATION)

 
 
Author(s):  HAJAM S.*, YOUSEFI NOSRAT ELAH, IRANNEZHAD P.
 
* INSTITUTE OF GEOPHYSICS, UNIVERSITY OF TEHRAN
 
Abstract: 

Meteorological stations usually contain some missing data for different reasons. There are several traditional methods for completing data, among them bivariate and multivariate linear and non-linear correlation analysis, double mass curve, ratio and difference methods, moving average and probability density functions are commonly used.
In this paper a blended model comprising the bivariate exponential distribution and the first-order Markov chain is introduced for estimating of missing precipitation data. In this method, the day having the missing precipitation record is marked as either wet or dry using the first-order Markov chain or randomly generated numbers. If the Markov chain model marks the day as wet, then a bivariate exponential distribution is used for estimating the magnitude of the missing precipitation datum. Application of the model to the precipitation data from Tehran Mehrabad station shows a good correlation between the statistics of the predicted precipitation data with observed ones.

 
Keyword(s): TEHRAN, IRAN, BIVARIATE EXPONENTIAL DISTRIBUTION, MARKOV CHAIN, RANDOM NUMBER
 
References: 
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