Simultaneous Optimization of 20 Key Parameters of the Integrated Forecasting System of ECMWF Using OpenIFS: Part I: Effect on Deterministic Forecasts

Numerical weather prediction models contain parameters that are inherently uncertain and cannot be determined exactly. It is thus desirable to have reliable objective approaches for estimation of optimal values and uncertainties of these parameters. Traditionally, the parameter tuning has been done manually, which can lead to the tuning process being a maze of subjective choices. In this paper we present how to optimize 20 key physical parameters in the atmospheric model Open Integrated Forecasting System (OpenIFS) that have a strong impact on forecast quality. The results show that simultaneous optimization of O(20) parameters is possible with O(100) algorithm steps using an ensemble of O(20) members; the results also show that the optimized parameters lead to substantial enhancement of predictive skill. The enhanced predictive skill can be attributed to reduced biases in low-level winds and upper-tropospheric humidity in the optimized model. We find that the optimization process is dependent on the starting values of the parameters that are optimized (starting from better-suited values results in a better model). The results show also that the applicability of the tuned parameter values across different model resolutions is somewhat limited because of resolution-dependent model biases, and we also found that the parameter covariances provided by the tuning algorithm seem to be uninformative.

Significance Statement
The purpose of this work is to show how to use algorithmic methods to optimize a weather model in a computationally efficient manner. Traditional manual model tuning is an extremely laborious and time-consuming process, so algorithmic methods have strong potential for saving the model developers’ time and accelerating development. This paper shows that algorithmic optimization is possible and that weather forecasts can be improved. However, potential issues related to the use of the optimized parameter values across different model resolutions are discussed as well as other shortcomings related to the tuning process.