[GIS] Interpolation of 17 data points – temperature data

arcgis-10.0arcmapinterpolation

I have 17 location points here in Iceland: weatherstation points.

It's a 10-year, month-to-month, composite of temperature values for each station which means 120 values for each station. 2040 values overall. Since I only have 17 station I only have 17 values visible on my map.

What I want to do is interpolate these points to create a temperature map for the area and I want to be able to compare them, f.i. compare june for every year and make a map of that – compare june-sep for each year – compare december for 5 of those 10 years etc. I basically want to be able to compare what I want.

The distance between each point varies but the area is fairly small so only a few kilometers between stations.

When the interpolation is done I hope to be able to do a time animation from the data.

This is a school project. I am attending The University of Iceland.

So, which method should I use and how?

Thanks in advance.

Einar Ingi

Best Answer

I'm not a quantitative export, but it seems like Spline interpolation might be a good fit.

Polynomial functions are regarded as a good method for interpolation of monthly and yearly climate elements but are less suitable on higher temporal resolutions like days and hours

This paper goes over the various interpolation methods and how they relate to temperature data.

http://www.knmi.nl/bibliotheek/knmipubIR/IR2009-04.pdf

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[GIS] Interpolation of Three Data Points

Would your map be better than using the average temperature for the whole area? Unless you know that there is a physical reason why temperature differences occur I would use the average value everywhere, and not make an interpolation. It may well be that the prediction is more accurate if you do that than if you use an interpolated map. Local differences may be causing the temperature changes rather than a large scale spatial trend.

What do you need the map for?

Where is your study area located? Are you sure you cannot get other data for the area?

Raster Interpolation – Interpolating Temperature Values at Different Depth Intervals Using Krig Function in R

It is very difficult to help if you do not provide example data (as data, not by printing the data). But your approach does not look right to me. Why do you use rasterize? Here is a simplified workflow:

library(raster)
library(fields)

utm.prj = " +proj=utm +zone=21 +south +datum=WGS84 +units=m +no_defs "   
xy <- divetemps[, c("lon.x", "lat.y")]
rast <- raster(ext=extent(xy)+1000, crs=utm.prj, resolution = 500)

m <- fields::Krig(xy, divetemps$depthbin1)
surface <- raster::interpolate(rast, m)

plot(surface)

I think this is conceptually better, but you may still not like the output. I would expect that you would prefer the output of at Thin Plate Spline model:

m <- fields::Tps(xy, divetemps$depthbin1)
surface <- raster::interpolate(rast, m)

And you may consider to not use only x and y but also an additional variable such as elevation to predict temperature. See ?interpolate

Best Answer

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