I am looking for a good storage format for large, gridded datasets. The application is meteorology, and we would prefer a format that is common within this field (to help exchange data with others). I don't need to deal with special data structures, and there should be a Fortran API. I am currently considering HDF5, GRIB2 and NetCDF4.
How do these formats compare in terms of data compression? What are their main limitations? How steep is the learning curve? Are there any other storage formats worth investigating?
I have not found a great deal of material outlining the differences and pros/cons of these formats (there is one relevant SO thread, and a presentation comparing GRIB and NetCDF).
Sorry I'm not in meteorology, but it looks to me that the scientific community is moving towards HDF5, see for example the NERSC page:
http://www.nersc.gov/users/training/online-tutorials/introduction-to-scientific-i-o/
I had to take the same choice for astrophysics data, as we historically use FITS, and I found quite easy to start using HDF5, as there are APIs not only fortran and C but also C++, and also a python package (h5py).