Bi-Weekly to Monthly Typhoon Forecast Using a Global Model with Variable Mesoscale Resolutions

MPAS (Model for Prediction Across Scales) is a new-generation global nonhydrostatic model which allows a convenient higher mesoscale resolution for a specific domain of interest. We will attempt to fully explore and extend the feasibility of MPAS to at least bi-weekly typhoon prediction as most regional models (like WRF) will be significantly impacted by lateral boundary conditions at later stages. In the three years, we will develop a MPAS framework of global 60-15 km variable resolutions to conduct bi-weekly prediction for Typhoon Morakot (2009), Typhoon Megi (2010), Typhoon Haiyan (2013) and/or others, which will commence from an earlier spin-up stage to the final dissipation stage. This task will allow a long-term track prediction with a possible projection of heavy rainfall over local regions like Taiwan as the steep topography can be reasonably resolved by the global model. In the first year (2015 phase), we will focus on investigation for the impacts of various cloud microphysical schemes, cumulus parameterizations and PBL parameterizations on prediction of typhoon tracks and local rainfall. In the second year (2016 phase), with a 30-6 km MPAS, we will develop typhoon a suitable vortex initialization method based on model dynamics and investigate various scenarios for exploring track variability due to varying synoptic moisture, topography and sea surface temperature to highlight the roles of terrain and oceanic fluxes in the prediction of typhoons impinging Taiwan. In the last year (2017 phase), the influence of various monsoon varieties on typhoon evolution will be investigated. This study will employ low-pass filter to construct various scenarios for large-scale environmental flow (including southwesterly monsoon flow) and devote to understanding and task development of long-term (bi-weekly to monthly) typhoon prediction in relation to model physics as well as specific environmental changes that may be caused by climate variability. This project will provide global MPAS high-resolution ensemble forecasts for track and rainfall associated with typhoons impinging Taiwan.