In this article we explain how to prepare and configure OpenIFS 43r3 for a nudged simulation. Therein the model needs to read meteorological parameters at the grid scale from pre-computed external forcing files. These forcing files have to be created prior to the nudged OpenIFS model experiment and this process is also described here. 

Newtonian Relaxation

OpenIFS uses initial and boundary conditions to calculate its own model dynamics, i.e. meteorological variables that are resolved on the grid scale. It is however possible to constrain the model dynamics with external data. Newtonian relaxation, sometimes referred to as "nudging", is a simple form of data assimilation which allows the user to constrain or "force" the model's meteorological fields with reanalysis data. This is sometimes referred to as running the model in "offline" mode. In nudged configuration the model's dynamics is continually nudged towards the meteorological reanalysis independent of the run length of the experiment. 

This method relaxes the model state towards gridded re-analysis data (or towards output from another atmospheric model, or gridded observational data) by adding a non-physical relaxation term to the model equations (Jeuken et al., 1996). In the equation below X represents a prognostic model variable and F_model the model forcing which determines the evolution of X. The relaxation term G (X_obs - X) includes the relaxation coefficient G (in s^-1) which determines the "tightness" of the nudging.

\frac{\partial X}{\partial t} = F_{model}(X) + G(X_{obs} - X)

This method can be useful, for instance, in sensitivity studies which aim to isolate the model physics or chemistry while preventing feedbacks to the model dynamics. Another example for its use is to align a climate model simulation closer to historic meteorology for comparison with measurements.