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

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

HAJAM S.; YOUSEFI NOSRAT ELAH; IRANNEZHAD P.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Desert Year:2006 | Volume:11 | Issue:2 Page(s): 49-55

Download Full-Text

View:

304

Download:

Cites:

Information Journal Paper

Title

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

Author(s)

HAJAM S. | YOUSEFI NOSRAT ELAH | IRANNEZHAD P. | Issue Writer Certificate

Keywords

TEHRAN

IRAN

BIVARIATE EXPONENTIAL DISTRIBUTION

MARKOV CHAIN

RANDOM NUMBER

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.

Cites

No record.

References

No record.

Cite

APA: Copy

HAJAM, S., YOUSEFI, NOSRAT ELAH, & IRANNEZHAD, P.. (2006). A BLENDED MODEL FOR ESTIMATING OF MISSING PRECIPITATION DATA (CASE STUDY OF TEHRAN -MEHRABAD STATION). DESERT (BIABAN), 11(2), 49-55. SID. https://sid.ir/paper/536776/en

Vancouver: Copy

HAJAM S., YOUSEFI NOSRAT ELAH, IRANNEZHAD P.. A BLENDED MODEL FOR ESTIMATING OF MISSING PRECIPITATION DATA (CASE STUDY OF TEHRAN -MEHRABAD STATION). DESERT (BIABAN)[Internet]. 2006;11(2):49-55. Available from: https://sid.ir/paper/536776/en

IEEE: Copy

S. HAJAM, NOSRAT ELAH YOUSEFI, and P. IRANNEZHAD, “A BLENDED MODEL FOR ESTIMATING OF MISSING PRECIPITATION DATA (CASE STUDY OF TEHRAN -MEHRABAD STATION),” DESERT (BIABAN), vol. 11, no. 2, pp. 49–55, 2006, [Online]. Available: https://sid.ir/paper/536776/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops