WIP

experiments/calibration/minimal_working_example/minimal_working_example.jl

@@ -48,7 +48,7 @@ rng = Random.MersenneTwister(rng_seed) # Ollie: pseudo-random number?
 
 initial_ensemble = EKP.construct_initial_ensemble(rng, prior_g1, N_ensemble) # Ollie: what does this do? 5 random values of g1?
 
-ensemble_kalman_process = EKP.EnsembleKalmanProcess(initial_ensemble, y, Γ, Inversion(); rng = rng) # Ollie: do we need y and Γ?
+ensemble_kalman_process = EKP.EnsembleKalmanProcess(initial_ensemble, lhf_target, EKP.Inversion(); rng = rng) # Ollie: observation_series (lhf_target) needs to be ::OS? how?, do we need Γ? Do we need rng?
 
 # We are now ready to carry out the inversion. At each iteration, we get the ensemble from the last iteration, apply
 # ClimaLand_lhf(g1) to each ensemble member, and apply the Kalman update to the ensemble.