Merge global forecasting capabilities from `prob_model_global`

[joeloskarsson]

Global forecasting can be viewed as a special case of LAM, with no boundary conditions to consider. Therefore it is not a hard task to allow for global forecasting within the Neural-LAM framework. This has the benefit of being able to use any models in here also for global forecasting.

Global forecasting, as done in Oskarsson et al. (2024), is currently implemented on the branch prob_model_global. This issue is to keep track of merging of the prob_model_global branch into main.

The main changes in this branch include:

• [ ] Add script for creating of global forcing https://github.com/mllam/neural-lam/blob/f68a5132a1d6aafcd5695705762d08ce29cbb777/create_global_forcing.py#L26 This will likely be taken care of through #34, rather than merging create_global_forcing.py directly.
• [ ] Add script for creating global static grid features https://github.com/mllam/neural-lam/blob/f68a5132a1d6aafcd5695705762d08ce29cbb777/create_global_grid_features.py#L23 Also likely better handled in #34.
• [ ] Add script for creating global mesh graphs (multiscale and hierarchical) https://github.com/mllam/neural-lam/blob/f68a5132a1d6aafcd5695705762d08ce29cbb777/create_global_mesh.py#L128 Will see if this should sit in this repo or in weather-model-graphs, and to what extent we keep the graphcast dependency for graph generation.
• [ ] Allow for 3d graph plotting of global graphs https://github.com/mllam/neural-lam/blob/f68a5132a1d6aafcd5695705762d08ce29cbb777/plot_global_graph.py#L99 It is perhaps better to first move the 3d plotting functionality to weather-model-graphs and do this there. See point above.
• [ ] Enable generating variable weights based on pressure levels https://github.com/mllam/neural-lam/blob/f68a5132a1d6aafcd5695705762d08ce29cbb777/create_parameter_weights.py#L53 This is better handled by defining more general variable weighting setup where one can either set these weights manually per-variable in config files or use some pre-defined strategies.
• [ ] Add ERA5 dataset class https://github.com/mllam/neural-lam/blob/f68a5132a1d6aafcd5695705762d08ce29cbb777/neural_lam/era5_dataset.py#L13 This should not be added as a separate new class, as all functionality in there should be possible through the generic dataset created in #54. But it needs to be checked that the result of #54 will work with global ERA5 data.
• [ ] Add functionality to save forecasts to zarr+xarray for later plotting or evaluation https://github.com/mllam/neural-lam/blob/f68a5132a1d6aafcd5695705762d08ce29cbb777/neural_lam/forecast_to_xarr.py#L199
• [ ] Add grid weighting (spatial points weighted differently) to loss and metrics https://github.com/mllam/neural-lam/blob/f68a5132a1d6aafcd5695705762d08ce29cbb777/neural_lam/metrics.py#L41
• [ ] Make boundary forcing optional when rolling out forecasts https://github.com/mllam/neural-lam/blob/f68a5132a1d6aafcd5695705762d08ce29cbb777/neural_lam/models/ar_model.py#L150
• [ ] Plot example predictions for specific variables and lead times also during validation steps https://github.com/mllam/neural-lam/blob/f68a5132a1d6aafcd5695705762d08ce29cbb777/neural_lam/models/base_graph_model.py#L183
• [ ] Allow for plotting of global data https://github.com/mllam/neural-lam/blob/f68a5132a1d6aafcd5695705762d08ce29cbb777/neural_lam/vis.py#L79 Plotting functionality should be based on arbitrary projections rather than hard-coded to MEPS case. This should be done already by #54, but it needs to be checked that this works also for global forecasts.