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 estmating 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 and randomly generated numbers. If the Markov chain model marks the day as wet, then a bivariate exponential distribution is used for estimating the magnitute 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.
Hajjam, S. , & Yusefi, N. (2006). A blended model for estimating of missing precipitation data (Case study of Tehran - Mehrabad station). Desert, 11(2), 49-55. doi: 10.22059/jdesert.2006.31874
MLA
S. Hajjam; N. Yusefi. "A blended model for estimating of missing precipitation data (Case study of Tehran - Mehrabad station)", Desert, 11, 2, 2006, 49-55. doi: 10.22059/jdesert.2006.31874
HARVARD
Hajjam, S., Yusefi, N. (2006). 'A blended model for estimating of missing precipitation data (Case study of Tehran - Mehrabad station)', Desert, 11(2), pp. 49-55. doi: 10.22059/jdesert.2006.31874
CHICAGO
S. Hajjam and N. Yusefi, "A blended model for estimating of missing precipitation data (Case study of Tehran - Mehrabad station)," Desert, 11 2 (2006): 49-55, doi: 10.22059/jdesert.2006.31874
VANCOUVER
Hajjam, S., Yusefi, N. A blended model for estimating of missing precipitation data (Case study of Tehran - Mehrabad station). Desert, 2006; 11(2): 49-55. doi: 10.22059/jdesert.2006.31874
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