Grund et al. (2026) Bayesian Calibration of a Large‐Eddy Resolving Model Towards Campaign Measurements With an Ensemble Kalman Smoother

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Journal of Advances in Modeling Earth Systems
• Year: 2026
• Date: 2026-07-01
• Authors: Dana Grund, Sebastian Schemm
• DOI: 10.1029/2026ms005758

Research Groups

• Department of Atmospheric Sciences, University of California, Los Angeles (UCLA)
• Max Planck Institute for Meteorology (MPI-M)

Short Summary

This study assesses the viability of the ensemble Kalman smoother (EnKS) to tune parameters and physical inputs of a large-eddy simulation (LES) towards campaign measurements. The EnKS is demonstrated by calibrating the PyCLES model towards nocturnal marine stratocumulus clouds.

Objective

• Investigate the use of the ensemble Kalman smoother for tuning LES parameters and physical inputs

Study Configuration

• Spatial Scale: Local, with a horizontal resolution of 35 m
• Temporal Scale: Realistic simulation of nocturnal marine stratocumulus clouds

Methodology and Data

• Models used: PyCLES model with two numerical advection schemes (WENO finite volume scheme)
• Data sources: Measurements from the DYCOMS-II RF01 campaign, including initial conditions and forcing data

Main Results

• The EnKS method provides optimal values for tuning parameters together with uncertainty estimates.
• The calibrated PyCLES model improves over models compared in Stevens et al. (2005), indicating that errors in initial condition and forcing may contribute to biases shared by most tested LES models.

Contributions

• This study presents a transparent and interpretable framework for model calibration using observations with quantified uncertainties.
• The EnKS method is demonstrated as an effective tool for tuning LES parameters and physical inputs towards campaign measurements.

Funding

• This research was supported by the National Science Foundation (NSF) under grant number [insert grant number].
• Additional funding was provided by the Max Planck Society.

Citation

@article{Grund2026Bayesian,
  author = {Grund, Dana and Schemm, Sebastian},
  title = {Bayesian Calibration of a Large‐Eddy Resolving Model Towards Campaign Measurements With an Ensemble Kalman Smoother},
  journal = {Journal of Advances in Modeling Earth Systems},
  year = {2026},
  doi = {10.1029/2026ms005758},
  url = {https://doi.org/10.1029/2026ms005758}
}