Global Modeling (Code 7532)
Section Head: 7532@nrlmry.navy.mil

The Global Modeling Section develops and applies computer models for the dynamic prediction of atmospheric and oceanographic variables on a global scale, and uses these models to improve our understanding of atmospheric behavior.  In March 2013 a new model, NAVGEM (Navy Global Environmental Model) replaced the Navy Operational Global Atmospheric Prediction System (NOGAPS) as the global atmospheric forecast model used operationally at Fleet Numerical Meteorology and Oceanography Center. Almost all Navy environmental prediction and support systems require global atmospheric forecast products; therefore, nearly every meteorological and oceanographic requirement benefits from continued global model development and improvement. The global ensemble prediction system, currently run with 20 members, twice daily, out to 16 days, is part of the National Unified Operational Prediction Capability (NUOPC) ensemble. NAVGEM is currently being coupled to the HYbrid-Coordinate Ocean Model (HYCOM) with the goal of extending current prediction capabilities to the monthly and seasonal time scales.

The Global Modeling Section is working in these main areas:

Global Atmospheric Model Development.

• Advanced physical parameterizations (land surface, clouds, convection, radiation).
• Advanced numerical methods for global models (high order finite element methods, semi-Lagrangian formulation).
• Identification of systematic errors in the global prediction system.
• Development of a global coupled atmosphere/ocean data assimilation/modeling system.
• Extension of NAVGEM into the middle atmosphere.
• Transition of NAVGEM improvements to FNMOC.

Predictability Studies.

• Adaptive observing techniques using adjoint sensitivity and singular vectors.
• Forecast sensitivity to observations using the forecast model and data assimilation adjoints .
• Mechanisms for rapid perturbation growth.
• Seasonal and interannual variations in predictability.
• Transient and asymptotic perturbation growth.
• Observing network design with applications for field programs
  
  

Ensemble forecasting techniques.

• Ensemble diagnostics
• Accounting of model uncertainty (stochastic methods, parameter variations)
• Use of ensembles in data assimilation.