Changelog

In development: Xarray support which will enable the use of any input data at any time frequency.
28.03.2021: Updated sea-ice and major refactoring
- M2R has been heavily refactored to make the code more readable, which is part of a larger rewriting of the code.
- Fixed a bug in how sea-ice thickness and concentration is interpolated. Current code correctly interpolates and saves the sea-ice variables to file.
22.07.2020: atmospheric forcing toolbox for generating ERA5 forcing for ROMS to be used together with models2roms). This makes it easy to generate high temporal (hourly) and spatial (0.25x0.25) atmospheric forcing for any grid. The toolbox downloads and converts all ERA5 atmospheric variables into the proper units and names as required by ROMS. The new atmospheric forcing requires the time variables for all atmospheric variables to be changed to ocean_time in the varinfo.dat file Using one time unit across ROMS makes it less prone to errors.

03.09.2019:Added option to use SODA3 5 day averages as forcing files (SODA3_5DAY). This provides a great opportunity to generate forcing that contains much higher temporal resolution compared to the usual monthly forcing fields. To use this option, define
- self.oceanindatatype = 'SODA3_5DAY'
- self.timefrequencyofinputdata = "5days"(used in method: createlistofdays)

15.04.2019:Added `LDFLAG` option in `compile.py` for compiling Fortran files using f2py on Python3.7

13.03.2019: Model2roms has been refactored and the code is now easier to read. Added support for interpolating and writing BGC (biogeochemistry) data to the BRY, CLIM, and INIT files. The BGC currently only supports NorESM BGC data as input and the output format (variable names, units) is intended to be used as input to the ERSEM model.

05.09.2018: Model2roms has been refactored and improved in the following way: we now use an object to store all of the configurations (configM2R.py), the run script has been improved (runM2R.py), supports SODA3 and GLORYS2V4 as forcing inputfiles, and severeal minor bugs has been fixed. Support for using Earth System Modeling Framework as the default interpolation method. This allows the input data to be on any kind of grid structure (e.g. irregular) as long as geographical information such as longitude and latitude of grid cells are available. The implementation uses the Python interface to ESMF which can be found here: www.earthsystemcog.org/projects/esmpy/. Using ESMF significantly increases the speed of the interpolation. As an example, interpolating one variable (e.g. temperature distribution) from a global irregular grid to a local non-rectangular grid of size 1250x789, at 70 different depth levels, takes 3 seconds on a Mac Laptop Pro. Additional information as to how to install ESMF and ESMPy from source on Mac OSX is available www.trondkristiansen.com/. For most people, installing using Anaconda would be the best option:
```
```bash
conda create -n model2roms`
conda config --add channels conda-forge
conda activate model2roms
conda install esmpy netcdf4 progressbar
```
```

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