Filling Missing Climate Data Using Arithmetic mean method, Inverse Distance Weighting method MCMC

To know the different kind of methods used download the book using the link:

The arithmetic mean is the simplest and most widely used measure of a mean, or average. It simply involves taking the sum of a group of numbers, then dividing that sum by the count of the numbers used in the series.
Inverse distance weighting (IDW) is a type of deterministic method for multivariate interpolation with a known scattered set of points. The assigned values to unknown points are calculated with a weighted average of the values available at the known points.

Multiple Imputation is a Markov chain Monte Carlo technique developed to work out missing data problems.

Conclusion: The advantages of IDW are that it is simple, easy to understand, and efficient. Disadvantages are that it is sensitive to outliers and there is no indication of error.

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This interesting video tutorial and helpful.

[uy]

hello, for the IDW method, can i use only one neighbour station? or i must at least have two neighbour stations?

[SuperDanisse]

Is it possible to use the multiple imputation technique when there are no index stations? I mean, if you only have your missing data station and there are no index stations, can you use it?

[crispusallen]

Hi, is there a faster way if lets say we have 63 stations and some of them have missing rainfall data?

[mesfinmelaku]

pls help me i have four station data one station has been guaged with full paramters but station in my watershed has no recorded data how can i get the data for my study?

[agriglobe]

very good, how can i connect with you, i have an urgent work to understand

[genetbirhanu]

how to know weather the data above or below 10%