Special Webinar: The FV3 dynamical core and the Next Generation Global Prediction System

  • 30 November 2016
  • Number of views: 823

 

A special webinar hosted by GMU/COLA on the topic of the FV3 dynamical core and the Next Generation Global Prediction System by SJ Lin (OAR/Geophysical Fluid Dynamics Laboratory) on Wednesday, November 30, 2016 can be viewed here.

Date/Time Title & Presenters


November 30, 2016
1:30 PM - 2:30 PM ET

The FV3 dynamical core and the Next Generation Global Prediction System
Speakers and Topics Shian-Jiann Lin (NOAA/OAR Geophysical Fluid Dynamics Laboratory)
The FV3 dynamical core and the Next Generation Global Prediction System
Remote Access To view the slideshow:
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ABSTRACT:

 

 

 

The fundamental design of the Finite-Volume dynamical core on the cubed-sphere (FV3) will be presented, with focus on FV3's key scientific and computational attributes, such as the vertically Lagrangian discretization, conservative finite-volume advection of potential temperature, air and tracer mass, and therefore, consistent transport of Potential Vorticity. A less known aspect of FV3 is its theoretical "best possible" (in discrete space) representation of Helicity, which is important for the highly non-hydrostatic storm-scale predictions (supercell and tornadoes). FV3 is therefore well suited as the core for a unified modeling system for both weather and climate, with regional (via 2-way nesting) and global domains.

Developed as a prototype for the Next Generation Prediction System (NGGPS), retrospective hindcasts using FV3 with a combination of GFS and GFDL physics will be presented. The initial conditions from GFS and the ECMWF-IFS are used and inter-compared. We will show that the FV3 based NGGPS prototype model, at this early stage of development, is already competitive with the world's best prediction model (the IFS). As we gradually step into the non-hydrostatic gray zone (3-10 km), there exists a golden opportunity for the nation's prediction system to truly become "second to none" for predictions from hours to seasons. To achieve that goal in 3 years, a fast-pace development cycle involving an expert (tiger) team of Data Assimilation, physical parameterization, and the core developers would be needed.

 

 
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