November 15, 2017 - 3:00 pm
November 15, 2017 - 4:00 pm
Address120 David L. Boren Blvd., Room 5930, Norman, OK 73072 View map
In recent decades, there has been a steady increase in the length of skillful forecasts in global models. Much of the improvement can be attributed to the development of higher resolution models, better data assimilation techniques, and more accurate physics parametrization schemes. Even with this steady increase, there are some areas of the globe where model forecasts are lagging in improvement (e.g., Southern Hemisphere, Polar Regions). One potential way to extend the forecast barrier is to better represent processes in the Arctic region. There is large analysis uncertainty here due in part to the lack of conventional observations assimilated with more weight ascribed to derived products from satellite remote sensing observations over the Arctic. Additionally, atmospheric features are smaller in the Arctic due to the Earth’s rotation, which means higher resolution, more computationally expensive NWP model grids are needed to resolve features of equal geographic size in the midlatitudes. Furthermore, knowledge of key polar processes is only in its infancy and therefore not accounted for in current models. This study will focus on the development of a new research tool called Model for Prediction Across Scales (MPAS) with ensemble Kalman Filter (EnKF) data assimilation from the Data Assimilation Research Testbed (DART). MPAS-DART will be used here to investigate the influence of the Arctic region on mid-latitude predictability.
This presentation will start by providing an overview of the predictability of the Arctic region as compared to the Northern Hemisphere. Using a dataset of Rossby wave initiation (RWI) locations and tropopause polar vortex (TPVs) tracks, proximity of TPVs and RWIs is evaluated via composite analysis. Then an overview of the MPAS-DART configuration and an initial evaluation of modeling system is presented. While MPAS has shown promising ability to simulate Arctic features due its variable resolution mesh and smooth transition regions, it has not previously been configured for global data assimilation predictability studies. MPAS-DART assimilates roughly 350,000 observations ranging from radiosonde profiles to satellite derived temperature and moisture profiles. These observations will also serve as comparison data for evaluation of the modeling system with the goal of identifying further improvements to the system. Lastly, MPAS-DART will be compared against other global modeling systems to establish a performance baseline of the system.