You think the difficult part is merging observations with the last forecast? I guess it's a very underdetermined problem, but isn't the loss function (compare the forecast grid with later observations) the same whether you're doing grid_t0 -> grid_t1 or (observations, grid_t0) -> grid'_t0 -> grid_t1? I don't know enough about ML to know how much complexity the extra step adds, but doesn't seem like a massive difference.
Observation assimilation is a huge field in and of itself. Observables have biases that have to be included in assimilation, they also have finite resolution and so observation operators need to be taken into account.
