TY - GEN
AU - Annelize van Niekerk
AU - Birgit Suetzl
AU - Nina Raoult
AU - Martin Janousek
AU - Ivan Bastak Duran
AB - Calibrating uncertain parameters is a necessary step in the development of comprehensive numerical weather prediction models such as the ECMWF Integrated Forecasting System (IFS), particularly<br />
when scientific changes have been made. We present a Bayesian optimisation tool and workflow that semi-automates parameter calibration within the fully coupled IFS using perturbed parameter ensemble forecasts and Gaussian Process (GP) emulators. The workflow uses the GP emulator to map from parameter values to a combined forecast-error metric. The tool then proposes new parameter values to sample with the IFS based on minimising the error metric using the GP emulator predictions, following a Bayesian optimisation approach. By iteratively sampling parameter combinations using the IFS, minimisation of the GP emulator yields several plausible optimal parameter sets. This helps avoid local minima and exposes parameter interdependencies.<br />
We demonstrate the approach by calibrating five orographic drag parameters, after changes to the parametrization scheme led to initially degraded large-scale scores. We show that the method is able<br />
to identify parameter sets that deliver neutral to improved upper-air skill and significant improvements in near-surface variables. The workflow is user configurable and applicable to many other<br />
parameters in the IFS. While choices of metrics and priors remain subjective and compensating errors can mask regional trade-offs, Bayesian optimisation offers a practical, systematic alternative to<br />
manual calibration and accelerates the path from scientific change to operationally acceptable performance.
BT - Technical Memoranda
CY - Reading
DA - 12/2025
DO - 10.21957/3febe9bc59
M1 - 934
N2 - Calibrating uncertain parameters is a necessary step in the development of comprehensive numerical weather prediction models such as the ECMWF Integrated Forecasting System (IFS), particularly<br />
when scientific changes have been made. We present a Bayesian optimisation tool and workflow that semi-automates parameter calibration within the fully coupled IFS using perturbed parameter ensemble forecasts and Gaussian Process (GP) emulators. The workflow uses the GP emulator to map from parameter values to a combined forecast-error metric. The tool then proposes new parameter values to sample with the IFS based on minimising the error metric using the GP emulator predictions, following a Bayesian optimisation approach. By iteratively sampling parameter combinations using the IFS, minimisation of the GP emulator yields several plausible optimal parameter sets. This helps avoid local minima and exposes parameter interdependencies.<br />
We demonstrate the approach by calibrating five orographic drag parameters, after changes to the parametrization scheme led to initially degraded large-scale scores. We show that the method is able<br />
to identify parameter sets that deliver neutral to improved upper-air skill and significant improvements in near-surface variables. The workflow is user configurable and applicable to many other<br />
parameters in the IFS. While choices of metrics and priors remain subjective and compensating errors can mask regional trade-offs, Bayesian optimisation offers a practical, systematic alternative to<br />
manual calibration and accelerates the path from scientific change to operationally acceptable performance.
PB - ECMWF
PP - Reading
PY - 2025
T2 - Technical Memoranda
TI - Bayesian optimisation of parameters in the ECMWF IFS
UR - &nbsp;
ER -