## 5 Conclusions  
The study investigated the coupling of a very high-resolution NWP model over Auckland, New Zealand, with a grid size of 333 m, to a CFD model. The primary aim was to assess the 333-m model's performance during a high-impact wind event in Auckland on 18 September 2020, and also to carry out a CFD simulation of the airflow around the Auckland Harbour Bridge using a RANS approach. The validation of the NWP forecast data against four stations across the Auckland region showed that the NWP model successfully and accurately predicted the mean and gust wind speeds. The CFD simulations demonstrated that the bridge influences the incoming airflow such that the mean speed on the road can increase by up to 1.2 compared with the undisturbed upstream flow. In addition, while underestimating the observed mean and gust wind speeds at the bridge, coupling the 333-m NWP and CFD models shows promise for better predicting wind speeds around natural and built structures during extreme wind events, since the CFD model can better model the fine-scale effects on local winds that are missing from an NWP model that does not see the finer structures, for various reasons, e.g., computational cost, science design and purpose of the model. It has been demonstrated how small scales of surface roughness and geometrical details (in CFD models) can cause significant variations of local wind conditions (so-called \"local-scale forcing effects\").  
Gaining a better understanding of the detailed airflow patterns over the Auckland Harbour Bridge can aid in developing a warning system to avoid events like the one that happened on 18 September 2020. More work is underway to use large eddy simulation (LES) instead of RANS to study the flow patterns in more detail as well as advancing our capability in coupling NWP outputs to CFD simulations.  
Conceptualisation, A.A.S.P., S.M., R.T. and R.G.J.F.; methodology, A.A.S.P. and S.M.; validation, A.A.S.P. and S.M.; formal analysis, A.A.S.P. and S.M.; investigation, A.A.S.P., S.M., R.T. and R.G.J.F.; data curation, S.M. and A.A.S.P.; writing--original draft preparation, A.A.S.P.; writing--review and editing, A.A.S.P., S.M., R.T. and R.G.J.F.; visualization, A.A.S.P. and S.M.; supervision, R.T. and R.G.J.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Weather and Wildfire Theme of the Resilience to Nature's challenge funded by New Zealand's Ministry of Business Innovation and employment as well as by NIWA through that Climate and Forecasting program of NIWA's Strategic Science Investment Fund (projects CAFS2101 and CAFS2103). The authors wish to acknowledge the use of New Zealand eScience Infrastructure (NeSI) high performance computing facilities, consulting support and/or training services as part of this research. New Zealand's national facilities are provided by NeSI and funded jointly by NeSI's collaborator institutions and through the Ministry of Business, Innovation & Employment's Research Infrastructure programme ([[https://www.nesi.org.nz](https://www.nesi.org.nz)]([https://www.nesi.org.nz](https://www.nesi.org.nz)), accessed on 26 April 2021). The authors would like to acknowledge Total Bridge Services for the wind data from the anemometer mounted on top of the bridge, and New Zealand MetService for SYNOP and maximum gust records from the Auckland Aero and Whenuapai stations.  
**Conflicts of Interest:** The authors declare no conflict of interest.