Suggestions for a “simple” model
A “simple” model is one where DART can advance the model through a function call. As we saw above, Lorenz 63 falls into this category and can be used as a reference. Here we provide some further advice on how to add this kind of model to DART.
The primary consideration with a simple model is how you will store the state. If you have only a single type of variable in your state vector (for example, the Lorenz 63 model), here are some hints on how to implement your initialization and meta data routines:
Routine # / name 
Suggested implementation 


Your model_size will likely be set by namelist, so read it, allocate an array of that size, and precompute all the locations for each state vector item. Call add_domain() with the model size so DART knows how long the state vector is. 

Return 
If you have more than a single type of variable in the state vector (for example, “concentration”, “wind”, etc. as in
the DART/models/simple_advection
model):
Routine # / name 
Suggested implementation 


Read from the namelist the number of fields to be used in the state vector. Use add_domain() to indicate which netCDF vars should be read. Read in any auxiliary data needed by interpolation code (for example, the grid topology). Cache the grid locations of the state variables as appropriate, and use get_domain_size() to compute the model_size. 

Call get_model_variable_indices() and get_state_kind() to figure out the (i,j,k) indices and which variable this offset is. Use the (i,j,k) index to compute the grid location and return it along with the quantity. 
Now, for either type of simple model, the following applies:
Routine # / name 
Suggested implementation 


Deallocate any arrays allocated in static_init_model() 

If possible, embed the code that computes x(t+1) = F(x(t)) or call a separate subroutine to advance the model state from one time to another. 

Return a namelist or a fixed value for the minimum model advance time. 

Find the (i,j,k) indices which enclose that location, or search for the cell number. For some models you can compute (i,j) directly from a regular lat/lon grid, and in others you may have to search over a deformed grid. Any model code or utilities available for this purpose may prove very helpful as a starting point. In the end, you will use get_state() to retrieve an ensemblesized array of values for each offset into the state vector, and then do interpolation to get an array of expected values. 

Optionally add any desired attributes to the output diagnostic files. 
The remaining routines can mostly use the defaults, except possibly for 11. read_model_time() and 12. write_model_time(), which may need to be customized if using a model restart file that already stores time in a particular format.
Note that there is often no need to convert vertical obs or states in a simple model without vertical coordinate choices.