Optimising orography for global high-resolution simulations

The model’s mean orography acts as the boundary condition for the model dynamics and the drag from resolved orographic gravity waves can have a significant impact on the large-scale atmospheric circulation in weather and climate models. As we approach km-scale horizontal resolutions in global models, more of the orographic spectrum and the impact of orography becomes resolved. Benefits of increased resolution, for example better prediction of orographic rain, can only be harvested if the resolution of the mean orography field is also increased. However, even at kilometre scale, some of the orographic variance will not be represented on the model grid and must be parameterised.

Initial simulations for Destination Earth’s global Digital Twin at 4.4 km horizontal resolution have shown a negative wind bias over Eastern Asia and increasing forecast error compared to the operational 9 km model, indicating that the higher resolution of resolved orography causes additional small horizontal-scale orographic gravity waves breaking above the mid-latitude jet, which affects the global circulation. Therefore, we focused on improving the mean orography processing and sub-grid scale orography parameterisations with the aim to find a scale-independent formulation that maintains good forecast skill at operational model resolutions and profits from increased resolution at kilometre scale.

The processing of the source data was simplified and harmonised across resolutions using conservative interpolation of the source dataset.  A new source dataset for surface elevation with 30 m resolution is used. A small increase to the spectral filtering of mean orography showed an improvement in forecast skill over the Tibetan plateau, and the updated fields describing sub-grid orographic features yield a more consistent behaviour across different resolutions. However, the sub-grid orography has significantly changed, and the parameters of the orographic parameterisation schemes needed to be optimised again considering an appropriate formulation across resolutions. We present a new approach to this parameter re-tuning, using Bayesian parameter optimisation, which enables an efficient workflow for simultaneously optimising several interdependent parameters.