My final objective is to perform a regression (latitude predicted by precipitation).

To achieve that, I have download this dataset from Copernicus: https://surfobs.climate.copernicus.eu/dataaccess/access_eobs.php#datafiles

Here, I have faced my first challenge as I have never used NetCDF data source before. Following some good advice found on StackExchange, I have followed this author guidelines: https://semba-blog.netlify.app/11/03/2018/converting-netcdf-files-into-data-frame/ where he shows how to: Read and Convert NetCDF files into data frame in R.

I have managed to reproduce pretty well everything explained, I think, but I am missing something: I have a 3-dimensions variable, as precipitation (rr) is function of latitude, longitude and time:

> print(rr)
File C:\Users\Dell\Desktop\~\climate_data\rr_ens_mean_0.1deg_reg_v25.0e.nc (NC_FORMAT_NETCDF4):

     1 variables (excluding dimension variables):
        short rr[longitude,latitude,time]   (Chunking: [705,465,1])  (Compression: level 1)
            units: mm
            _FillValue: -9999
            long_name: rainfall
            scale_factor: 0.100000001490116
            add_offset: 0
            standard_name: thickness_of_rainfall_amount
            cell_methods: time: mean

     3 dimensions:
        longitude  Size:705 
            units: degrees_east
            long_name: Longitude values
            axis: X
            standard_name: longitude
        latitude  Size:465 
            units: degrees_north
            long_name: Latitude values
            axis: Y
            standard_name: latitude
        time  Size:26298   *** is unlimited *** 
            units: days since 1950-01-01 00:00
            long_name: Time in days
            calendar: standard
            standard_name: time
            cell_methods: time: mean 

However, in the process followed I am "losing" the time information. Basically, here is how my data frame looks:

Random sample of observations showing the rainfall
Longitude   Latitude    rainfall
-8.9501396  50.94986    NA
21.5498603  49.54986    0.0
16.0498603  70.74986    NA
-18.7501395 45.94986    NA
13.7498603  61.14986    3.9
13.5498603  31.94986    0.0
19.0498603  29.94986    NA
32.3498603  30.94986    0.0
-24.1501395 31.64986    NA
33.7498603  40.74986    5.0
42.0498602  57.54986    0.0
-6.0501396  64.34986    NA
0.2498604   34.04986    1.6
20.2498603  49.04986    0.0

My code is the following:


### https://semba-blog.netlify.app/11/03/2018/converting-netcdf-files-into-data-frame/
#read the nc file
rr = raster("C:/Users/Dell/Desktop/~/climate_data/rr_ens_mean_0.1deg_reg_v25.0e.nc",
            level = 1, 
             varname = "rr")

#define projection

#convert to data frame
rr.df = raster::as.data.frame(rr, xy = TRUE)

#random sample of 12 observations 
rr.df %>% sample_n(50) %>% kableExtra::kable("html", row.names = FALSE, col.names = c("Longitude", "Latitude", "rainfall"), align = "c", caption = "Random sample of 50 observations showing the Sea level pressure") %>%
  kableExtra::column_spec(column = 1:3, width = "5cm", color = 1)


  geom_raster(data = rr.df,  aes(x = x, y = y, fill = rainfall), interpolate = FALSE)+
  #geom_sf(data = spData::world, fill = rainfall, col = "black")+
  # geom_path(data = data, aes(x = lon, y = lat, col = id), size = .75)+
  coord_sf(xlim = c(10, 25), ylim = c(55, 70))+
  # scale_color_jco(name = "Argo float")+
  theme(legend.position = c(.85,.2), 
        legend.background = element_rect(colour = 1),
        legend.key.width = unit(.75, "lines"), 
        legend.text = element_text(size = 11, colour = 1),
        axis.text = element_text(colour = 1, size = 12))+  
  geom_label(aes(x = 60, y = 0, label = "a"))+
  labs(x = NULL, y = NULL)+
  scale_fill_gradientn(name = "rr", colours = oce::oceColors9A(120))+
  scale_x_continuous(breaks = seq(10, 25, 5))+
  scale_y_continuous(breaks = seq(55, 70, 5))

What can I do to translate my NetCDF file into a data frame while keeping, for the precipitation, the latitude, longitude and time information that is crucial to perform my regression?

  • try tidync : tidync(file) |> hyper_tibble() still have to convert the raw numbers to datetime with the metadata
    – mdsumner
    Jul 9, 2022 at 14:28
  • thank you for helping! However, while doing this I have a RAM size issue (even with 80GO), by any chance would you know a way to subset my data to what is of interest before doing your line of code?
    – Recology
    Jul 10, 2022 at 10:53
  • Seems that I have find out, using the hyper_filter function! just need to find out how to convert raw numbers to datetime
    – Recology
    Jul 10, 2022 at 11:03
  • see hyper_filter for lazy subset, hyper_array and hyper_tibble materialize the data in memory after subset
    – mdsumner
    Jul 11, 2022 at 3:18

1 Answer 1


new user and I don't have the rep points to comment so sorry that this is only a partial answer. I've found this article: https://rpubs.com/boyerag/297592 and this should get you started:


rr_lon <- ncvar_get(nc_data, "longitude")
rr_lat <- ncvar_get(nc_data, "latitude")
rr_time <- ncvar_get(nc_data, "time")
rr_rain <- ncvar_get(nc_data, "rr")

However, these could not be put in to a dataframe due to unequal numbers and the "NDVI" mentioned in the linked article does not seem relevant to the rainfall files but I hope this is a step in the right direction

  • The problem of this is that my file is too big to use a "ncvar_get" (and I have 80GO RAM). I have done some subset to try again, but then I have some error messages saying that : first argument (nc) is not of class ncdf4!
    – Recology
    Jul 11, 2022 at 8:45
  • Can I ask how you are subsetting. Is it using subset() or along the lines of this advice?: gis.stackexchange.com/questions/360225/…
    – Mark O
    Jul 11, 2022 at 23:06
  • I used different methods, including the one on your link, without success
    – Recology
    Jul 12, 2022 at 8:37
  • have you experimented with using the smaller chunks: surfobs.climate.copernicus.eu/dataaccess/access_eobs_chunks.php ? Other than that I can only suggest getting access to a more powerful computer, perhaps an R Server instance
    – Mark O
    Jul 12, 2022 at 23:23

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