Download ECMWF netCDF files

All current unbundled releases of the netCDF Fortran library source code, including pre-release code. Unless you are interested in working with the development version of netCDF-Fortran, you will want to use one of the releases linked above. These releases include pre-release code. This is because the data has different spatial grids, which can cause issues for the current grib to NetCDF converter and produce incorrect results.

However, all information is available online at the slightly reduced 0. Access to this data is in general much slower. Please note that the ERA5 back extension preliminary version 'raw' format from MARS is not yet available expected around the end of Please be aware that there is an additional queueing system for downloading data from the ECMWF's MARS archive - expect several hours to several days for submitted requests to complete at this time.

To retrieve MARS data efficiently and get your data quicker! Please read: Technical issue fixed on ERA5.

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A t tachments 1 Page History. Jira links. A t tachments 0 Page History. Jira links. If your software supports both, use GRIB as first choice.

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Final note, although -t ecmwf means CDO uses the ECMWF parameter table , it will actually use the as the parameterCategory which is completely wrong at time of writing. This grid is difficult to view and transform using common tools such as NCL, Panoply, ncview, etc. One could get the idea of using a CDO compiled with cgribex to transform this new netcdf but this does not work.

The transformation only works on grib files. It would help if eccodes also provides a transformation routine or this. I had a talk to the eccodes guys who stated that they will only concentrate on providing a netcdf file defined on a reduced gaussian grid.

Would it be such a difficult task to put a function in eccodes to define the data on a regular lat lon grid instead? I'm going to move this discussion to the OpenIFS Forums as your post there raises similar issues, otherwise this page will become very long.

When converting grib to netcdf files with 10 members and 4 time steps, CDO creates 40 time steps by concatenating all the members. Any way to avoid that? I think this is really a question for the CDO forums rather than here. I don't know CDO that well to know if it has operators for working with ensemble data. My suggestion would be to split the GRIB file first into separate files, 1 per ensemble. The ensemble member is encoded in the GRIB file.

I've not tried with ERA5 but it's generally better to directly request netcdf output rather than grib on the download request. Add the keyword:. I normally use the CDO commands for regridding. Hello, Thank you for your reply. I have tried but the CDS Copernicus server also tries to use the grib to netcdf ecCodes command and then fails. Hi Shane. I'm not very familiar with wave model data.

I think best you contact Copernicus support directly and ask them to help. Thank you, I have already asked for help but no solution has been found yet. I'd suggest using the eccodes software library, with your favourite programming language python,fortran,c and write your own program to read the GRIB file, manipulate the data however you need, then write it out as netCDF however you like?

There are examples codes available on the eccodes website: API examples. Note: use the following to get the latitudinal grid spacing of the input that can be used in specifying gridx above.

Metview can be downloaded from here: Metview Releases or installed as a package on most linux distributions. Thanks for your guide. I have an ERA5 file "forecast" type which is stored in grib format and data is distributed on a reduced Gaussian grid. So far, it seems the command does the expected job when the main variable is an "analysis" one, such as t2m, u10, etc. Everytime I use cdo -setgridtype,regular Let me be more specific:.

Contains dim "time" with 62 values, one every 12 hours e. Contains dim "step" with 12 values, representing 12 hours of forecast i. Contains dim "time" with a single value, the first of the dataset i. Contains dim "step" with values, I assume they are different forecast time, so it kinds of reshapes i.