Global cloud-resolving models enable weather prediction from local to planetary scales and are therefore often expected to transform the weather prediction enterprise. This potential however depends on the predictability of the atmosphere, which was explored through identical twin experiments using the Model for Prediction Across Scales. Two MPAS simulations that differed by a small, randomly distributed temperature perturbation were carried out on a quasi-uniform 4-km mesh covering the entire globe. The figure below shows the growth in the difference total energy (called 'error') illustrating the concentration of error in small convective scales at short times which subsequently gradually grows upscale until the error covers the entire globe at approximately 480h (20 days).

WRF & MPAS (Ensemble) fig
WRF-MPAS group photo
Attendees of the WRF Users Tutorial during the WRF workshop held at NCAR in June 2017.

The Weather Research and Forecasting (WRF) Model is a next-generation mesoscale numerical weather prediction system designed for both atmospheric research and operational forecasting applications. The Model for Prediction Across Scales (MPAS) is a global nonhydrostatic atmospheric model that uses unstructured spherical centroidal Voronoi meshes allowing for uniform and variable horizontal resolution in weather, regional climate and climate applications. Through the WRF Pre-Processing System (WPS) Version 3.9 release, WRF can now use native output from MPAS for initial and boundary conditions.  WPS has new interpolation methods to handle MPAS’s unstructured horizontal meshes so that now no mesh pre-processing is needed.  This capability allows for the downscaling of MPAS simulations for WRF, and it is a concrete step towards the goal of interoperability of NCAR community models.  This capability was covered in the MPAS for WRF Users tutorial that was given during the WRF workshop in June 2017.  The tutorial presentations are available at http:/mpas-dev.github.io