Data assimilation research is greatly enhanced by co-location of NRL with the Fleet Numerical Meteorological and Oceanographic Center (FNMOC), one of the world’s leading operational forecast centers. Researchers have ready access to operational global and regional data assimilation systems developed at NRL and complete real-time global data bases maintained by FNMOC.  The NRL Atmospheric Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR) was transitioned to operations in 2009 and further upgraded to Hybrid 4D-Var in 2016.  Research opportunities exist to contribute to further improvement of the mesoscale and global data assimilation systems as well as the development of coupled and next generation data assimilation systems. Research areas include treatment of nonlinearity for both prediction and observation models,  efficient computational algorithms for high resolution data assimilation suitable for operational use, direct assimilation of all sky radiances, use of artificial intelligence, initialization of tropical cyclones, coupled data assimilation, observation bias and covariance estimation, forecast and model error estimation, preconditioning techniques, Kalman filters/smoothers, adaptive ensemble covariance localization, inflation and hybridization, particle filters, and Monte-Carlo-Markov-Chains.

 

Strong emphasis is placed on developing techniques for

 

(1) utilizing new types of atmospheric data (e.g., satellite, Doppler radar, high resolution mesoscale observations, unmanned platforms, observations of opportunity), (2) initializing models with cloud and moisture information (including soil moisture and precipitation), (3) analyzing mesoscale features in the coastal zone, (4) directly assimilating all-sky (cloudy radiance) assimilation MW and IR, (5) the use of artificial intelligence in detecting/correcting errors in conventional and satellite observations, (6) improving the initialization of tropical cyclones including examining data selection for dropsonde data, (7) data assimilation in the polar (especially Arctic) regions, (8) observation bias and covariance estimation, forecast and model error estimation, (9) aerosol and trace constituent assimilation, (10) coupled data assimilation for Earth System of system that includes atmosphere, ocean, land, sea-ice, waves, upper-atmosphere, aerosols and trace gases, and improved land surface components 

 

key words

Data assimilation; Data quality control; Meteorology; Moisture initialization; 4D optimal estimation; Representer methods; Ensemble data assimilation methods; Satellite radiance assimilation; Variational analysis; Ensemble; Nonlinearity; Kalman filter;

Citizenship:  Open to U.S. citizens and permanent residents

Level:  Open to Postdoctoral applicants