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With CPAS' unique and self-developed technologies including the Customisable Unstructured Mesh Generation (CUMG) and Hierarchical Time-Stepping (HTS), we are able to provide customised and ultra high resolution (100m-scale, minute-scale) weather forecasts so as to optimise the port operation and route planning.

In this paper, we introduce a retrospective weather data set for Hong Kong for the two years July 2019 to June 2021 with 200m resolution using CPAS. We verified that the result is better than the fifth-generation ECMWF atmospheric reanalysis of the global climate data (ERA5), which is of about 28km resolution over Hong Kong. The reduction of mean absolute error for hourly temperature and 10-minute instantaneous wind speed over ERA5 are 19.4% and 28.2% respectively.

CPAS 128-to-5 km mesh contains 43,580 horizontal grid cells. It includes 5 regions covering ‘Asia’, ‘Himalaya’, ‘Southern China’, ‘Guangdong’ and ‘HongKong’ where the resolution transits from 128 km (outside) to 5 km (Hong Kong) with the ‘Maximum Resolution Gradient’ equals to 0.0833 km/km.

CPAS 128-to-1 km mesh contains 343,226 horizontal grid cells. It includes 5 regions covering ‘Asia’, ‘Himalaya’, ‘Southern China’, ‘Guangdong’ and ‘HongKong’ where the resolution transits from 128 km (outside) to 1 km (Hong Kong) with the ‘Maximum Resolution Gradient’ equals to 0.0833 km/km.

MPAS-A uses a global timestep (determined by the CFL condition on the smallest mesh cell) that substantially limits the resolution variation. The hierarchical time-stepping (HTS) feature implemented in ClusterTech Platform for Atmospheric Simulation (CPAS) relaxes this restriction by applying different time-steps to mesh cells of different sizes. This new feature enables extreme variable-resolution meshes and higher computational efficiency. This study evaluates the model results using HTS.

The ClusterTech Platform for Atmospheric Simulation (CPAS) is a cloud-based service platform which implements customized mesh generation and hierarchical time-stepping on Model for Prediction Across Scales - Atmosphere (MPAS-A (v6.3)) to better serve the computational needs of numerical atmospheric model users.