What is the ClusterTech Platform for Atmospheric Simulation?
The ClusterTech Platform for Atmospheric Simulation (CPAS) is a cloud-based service platform which implements Customizable Unstructured Mesh Generation (CUMG) and Hierarchical Time-Stepping (HTS) on Model for Prediction Across Scales - Atmosphere (MPAS-A (v6.3)) to better serve the computational needs of numerical atmospheric model users.
Background - CPAS is developed based on MPAS-A and WRF.
To achieve a balance between accuracy and computational cost, many Numerical Weather Prediction models (NWPs), such as Weather Research and Forecasting Model (WRF), adopt a set of rectangular grids where local refinement is achieved by nesting a high-resolution domain inside coarser grids. MPAS-A adopts spherical centroidal Voronoi tessellations (SCVTs) covering the globe, while local refinement is achieved using variable-resolution meshes. MPAS-A is a promising model for practical usages. However, MPAS-A uses a constant global timestep, determined by the size of the smallest mesh cell, which limits the resolution variability. This is because the existence of a high resolution region would make the computational resource requirements prohibitively large.
HTS - Arbitrary resolution variation
CPAS has relaxed this restriction by implementing an optional Hierarchical Time-Stepping (HTS) treatment in the MPAS-A dynamic core. Cells of different sizes can have different time step levels and hence the computational requirements are substantially reduced, particularly for large variations in resolution. This allows MPAS-A to be used for high-resolution regional/local forecast, instead of limited area models such as WRF.
CUMG - 100% well-staggered mesh, zero obtuse Delaunay triangle
Bespoke mesh generation can be done by simply drawing polygons on a map panel and entering the resolutions. Our mesh generation algorithm have solved a well-known problem in generating SCVT with arbitrarily shaped refinement for MPAS’ use - no obtuse Delaunay triangle. We guarantee the resulting meshes have perfect SCVT staggering.
Logistics, Maritime & Aviation
- Route weather prediction (maritime, road, airway) and optimization
- Port management optimization
- Extreme weather event prediction
- Fuel consumption prediction
- Air quality prediction and analysis
- Wind farm power generation prediction
- Hydropower operation optimization
- Solar power forecasting
- Electricity demand forecast
- Alert for power facility damage due to icing and landslide
- First cloud-based service for meteorological model
- Freedom in “domain configuration”
- Automatic resolution boost for orography and coastline
- Optimized load-balancing and extreme scalability
- Multi-resolution geographical source data support
- Modified scale-aware features