bias free evaluation
Implement post-processing evaluation that is free of bias. This is in particular interesting when comparing DL with model data, as model data suffers from systematical deviations. A bias-free evaluation can show how much a model is able to predict target's variance.
Therefore, implement two strategies:
(i) Calculate a total mean of a given model for each station and subtract this value from the model's forecasts. (ii) Calculate a running mean of a given model for each station and subtract this series from model's forecast.
Each strategy is then applied to all competing models and evaluation is performed (in addition to the standard evaluation).