I downloaded yearly Temp/Precip observation by the globe from here: Free open source climate data archive, and I download those for my research case: Terrestrial Air Temperature: 1900-2014 Gridded Monthly Time Series (V 4.01) and Terrestrial Precipitation: 1900-2014 Gridded Monthly Time Series (V 4.01). I need to clip out all data observation record of all German weather stations. However, I intend to extract average annual temperature and annual precipitation for each of these station coordinates of Germany.

But, when I tried to load those data and read them in R, I ran into problems. Respective data archive's website can be found look here. I think data format could be NetCDF4 format based on the description from above web link 4. I installed raster and ncdf4 package to read the data in R, but such operations were failed.

Here is how data look like:

list.files("stella/data/air_temp_1980_2014/", recursive = TRUE)

 [1] "air_temp.1980" "air_temp.1981" "air_temp.1982" "air_temp.1983"
 [5] "air_temp.1984" "air_temp.1985" "air_temp.1986" "air_temp.1987"
 [9] "air_temp.1988" "air_temp.1989" "air_temp.1990" "air_temp.1991"
[13] "air_temp.1992" "air_temp.1993" "air_temp.1994" "air_temp.1995"
[17] "air_temp.1996" "air_temp.1997" "air_temp.1998" "air_temp.1999"
[21] "air_temp.2000" "air_temp.2001" "air_temp.2002" "air_temp.2003"
[25] "air_temp.2004" "air_temp.2005" "air_temp.2006" "air_temp.2007"
[29] "air_temp.2008" "air_temp.2009" "air_temp.2010" "air_temp.2011"
[33] "air_temp.2012" "air_temp.2013" "air_temp.2014"

Here is what I did in R:

list.files("stella/data/air_temp_1980_2014/", recursive = TRUE)


Here is error that raised by Rstudio:

> nc_open("stella/data/air_temp_1980_2014/air_temp.1980")
Error in R_nc4_open: NetCDF: Unknown file format
Error in nc_open("stella/data/air_temp_1980_2014/air_temp.1980") : 
  Error in nc_open trying to open file stella/data/air_temp_1980_2014/air_temp.1980

> raster("stella/data/air_temp_1980_2014/air_temp.1980")
Error in .local(.Object, ...) : Couldn't determine X spacing

Error in .rasterObjectFromFile(x, band = band, objecttype = "RasterLayer",  : 
  Cannot create a RasterLayer object from this file.

FYI, here I put the data on the fly for the test on your machine, files can be found on the fly, Here and Here.

How can I read those data in R?

Plus, I only need to pull out all station coordinates of germany from yearly Temp observation for the globe?

How can I make this happen in R?

Any workaround to do this?

Any more thoughts?

How can I read those data correctly in R (as data.frame object in R) ?

How to deal with this data?

Any thoughts?

  • You have no file extensions on the listed data so, raster has no idea on how to parse them. Try adding ".nc" the the end of one of the files and try reading it just using stack(), you do not need nc.open() and raster() is for reading a single raster so you would need to specify which layer. Also, I do not see the links that your provided on the website, there is however a v4.01 of the data that you may want to look at in the download section. Your question about subsetting to Germany has already been answered elsewhere and please do not ask multiple questions. Mar 29, 2018 at 17:07
  • These aren't netCDF files; they're ASCII text files.
    – dbaston
    Mar 29, 2018 at 17:08
  • You really need to look at your data! These are tab separated files and can be read using scan() or many other functions in R. The problem is we have no information on the data (eg., resolution). Why not just use the actual cdf files available on the site you linked? Mar 29, 2018 at 17:26
  • 1
    I am done here, the data does not even have headers. A hint is that you can use scan() to read a space separated file, assign column names to the resulting data.frame and then use sp::coordinates() to create an SpatialPointsDataFrame object that can then be intersected with a boundary of Germany. This last step is detailed in your previous question. We can only go so far in helping you without you actually learning the software. There are plenty of online resources for learning R, which can be found via a search engine. Mar 29, 2018 at 17:54
  • fwiw, you can tell raster to use NetCDF despite no ".nc" extension with raster(file, ncdf = TRUE), otherwise read them with rgdal::readGDAL and convert to raster from there
    – mdsumner
    Mar 30, 2018 at 5:33

1 Answer 1


These data are not in a netcdf format but, rather a space delimited ASCII format. This data is a bit difficult to deal with because of the lack of headers and any type of unique station identifier.

Returns a vector of file names on disk and pulls associate years from file name.

f <- list.files(getwd())
y <- as.numeric(unlist(lapply(strsplit(f, "[.]"), function(x) {x[2]})))

Reads files, adds associated year and combines into single data.frame.

stations <- data.frame()
  for(i in 1:length(f)) {
    d <- read.table(f[1], sep = "" , header = FALSE, na.strings = "")
      d[,"year"] <- y[i]
        names(d) <- c("long","lat", format(ISOdate(2000, 1:12, 1), "%b"), "year")
    stations <- rbind(stations, d)  
stations$ID <- paste(stations[,1], stations[,2], sep="_")  

Since there is no unique identifier for a given station, it is difficult to aggregate the duplicate coordinates to each given unique station location. I had to get creative and use a unique concatenated vector of the coordinate pairs. Once you have the data subset to the desired region, you can use merge() or which(x %in% y) to create a subset index, with the ID column, containing the unique coordinate pairs to relate back to the data. However, since the data is by year, in a wide format, you cannot store it as a spatial class object without duplicating the feature geometry. Please give some though on how you are going to analyze this data before just jumping in.

xy <- unique( paste(stations[,1], stations[,2], sep="_")  )
stations.xy <- data.frame(long = as.numeric(unlist(lapply(strsplit(xy, "_"), function(x) {x[1]}))), 
                          lat =  as.numeric(unlist(lapply(strsplit(xy, "_"), function(x) {x[2]}))))
    stations.xy$ID <- xy                      
    coordinates(stations.xy) <- ~long+lat


I am skipping any overlay/subset analysis because it is covered in your previous question. However, this can easily be accomplished using raster::intersect, spatialEco:point.in.poly, sp::over, rgeos::gIntersects, etc...

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