5A.6 Ensemble Kalman Filter data assimilation for the MPAS system

Ha, So-Young, Chris Snyder, Bill Skamarock, Jeffrey Anderson, Nancy Collins, Michael Duda, Laura Fowler, and Tim Hoar, National Center for Atmospheric Research

The Model for Prediction Across Scales (MPAS; http://mpas-dev.github.io/) is a global nonhydrostatic numerical atmospheric model based on unstructured centroidal Voronoi meshes that allow both uniform and variable resolutions.

Recently we established an interface between the MPAS and the Data Assimilation Research Testbed (DART; http://www.image.ucar.edu/DAReS/DART) system, and successfully completed analysis/forecast cycling experiments with real observations for one summer month of 2008. Assimilated observations are all conventional data as well as satellite winds and GPS radio occultation refractivity data. Through the retrospective study, we will examine issues specific to the MPAS grid, such as smoothing in the interpolation and the update of horizontal wind fields, and show their impact on the Ensemble Kalman Filter (EnKF) analysis and the following short-range forecast. Because interfaces for several other models are available within DART, we compare the MPAS results to those from cycling experiments with an identical EnKF and identical observations using the

Community Atmosphere Model (CAM) on the comparable grid mesh.