Bayesian Calibration of a Large-Eddy Resolving Model towards Campaign Measurements with an Ensemble Kalman Smoother

Bayesian model calibration with data assimilation methods receives continued interest for climate models, where turbulence-resolving large eddy simulations (LES) often serve as the ground truth for cloud processes. This study extends the calibration hierarchy towards the LES themselves, which are in turn validated against measurement data. Ensemble data assimilation for turbulent simulations is approached from a smoother perspective by calibrating a semi-idealistic LES simulation against averaged measurements with an ensemble Kalman smoother (EnKS).

This work re-visits the well-known test case simulating marine stratocumulus clouds (DYCOMS-II), which has been used extensively for forward model validation. Sub-grid scale (SGS) turbulence parameters are calibrated alongside the parameterized initial condition and forcing, aiming at a wholistic uncertainty representation. For the PyCLES model (dx=35 m), the calibrated setup achieves an improvement over the default model configuration used in previous studies. Experiments with different advection schemes reveal how the calibration result varies for implicit and explicit SGS modeling. For some of the tested schemes, consistent model errors on some observations require manual specification of larger data uncertainties in order to stabilize the calibration.

Through the analysis of partial increments, the EnKS methodology provides insights to parametric model sensitivities, and a means to explore a large parameter space. However, the performance is hindered by weak nonlinearities in the parameter-to-data map, which includes a nonlinear normalizing parameter transform. The practical application of EnKS-based calibration for LES models is facilitated by relying directly on a perturbed physics ensemble, which is commonly used for sensitivity studies. The results also invite to re-interpret biases found in previous model intercomparison studies on the DYCOMS-II case, as well as to consider the influence of uncertainties in initial condition and forcing when assessing parametric sensitivities in LES simulations.