An Overview of the NAVGEM Global Hybrid 4D-Var System

This presentation will give an overview of the Navy’s high-resolution global weather prediction system. NAVGEM consists of the Navy Global Environmental Model (Hogan et al., 2014) and a hybrid four-dimensional variational data assimilation system (NAVDAS-AR; NRL Atmospheric Variational Data Assimilation System) (Rosmond and Xu 2006, Xu et al. 2005). NAVGEM includes a semi-lagrangian/semi-implicit dynamical core, along with cloud liquid water, cloud ice water, and ozone as fully predicted constituents. The hybrid NAVDAS-AR uses a linear combination of a “static” background error covariance formulation combined with an ensemble background error covariance generated from the using the NAVGEM ensemble forecast system (McLay et al., 2008 and 2010) along with the Ensemble Transform (Bishop and Toth, 1999) to generate an eighty member ensemble at the inner loop resolution of T119 (~111 km). Microwave and IR satellite radiometers are assimilated using the Community Radiative Transfer Model (CRTM) with variational bias correction.

An important component of NAVGEM is the Forecast Sensitivity Observation Impact (FSOI). FSOI is a mathematical method to quantify the contribution of individual observations or sets of observations to the reduction in the 24-hr forecast error (Baker and Daley, 2000; Langland and Baker, 2004). The measure of error used in the NRL FSOI is a moist energy norm, which is sensitive to such factors as errors in position and intensity of mid-latitude cyclones, high-pressure centers, and jet streams. The FSOI is routinely computed for each 6-hr update cycle to allow for dynamic monitoring of the relative quality and value of the observations assimilated by NAVGEM, and the relative ability of the data assimilation system to effectively use the observation information to generate an improved forecast. Over the past 10 years, the number of observations assimilated has increased from around 1 million observations to nearly 5 million observations per update cycle. Satellite observations now comprise around 85-90% of the total observations, and contribute around 65% of the total forecast error reduction as measured by the FSOI.