Is there any particular rule that told us in what condition we allowed to rebuilding missing data in a synoptic station? For example, if a station has 15% missing data over 30 years, can it be filling? In other words, is there a specific rule that says up to what percent of missed data we can reconstruct it and used in drought studies? Can this data be filled in by taking a regression from the station itself, or should it be filled in by nearby stations? The time steps are in monthly. Do you have any referenced article for that?
Thank you in advanced Best Regards
Some beautiful links that may be useful for the incitement of your query: Article Trend analysis of climate time series: A review of methods
https://www.e-education.psu.edu/meteo469/?q=book/export/html/118
and
https://www.longdom.org/open-access/modeling-of-data-from-climate-science-using-introductory-statistics-2332-2594-1000129.pdf
You can look at this example:
Interpolation of Missing Temperature Data at Meteorological Stations Using P-BSHADE
Article Interpolation of Missing Temperature Data at Meteorological ...
yes you can with the double-mass method it is the most commonly used.
Or the average score is used to fill in, although this is the fastest for sloths 🤣.
The double-mass method is based on an example station, that is a station that has the best record of all and with it and the method are filled in the others.
The double-mass method is to fill in data, the most commonly used.
I don't think there's such a limiting rule. Of course, if there's a station that has only 50 percent of data better don’t fills it hahahaha, that's going to make a good Soup of data 🤣🤣.
Dear Behzad Navidi Nassaj
you can read the attached paper and watch this video
https://www.youtube.com/watch?v=uCZtypQPFYg
Good luck.
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