Mesoscale prediction systems are playing an increasingly vital role in the US Navy’s mission planning and execution. Current research and development focuses on systems that resolve very localized phenomena, both in mid-latitudes and in the tropics. NRL’s atmospheric mesoscale analysis/prediction systems include a nonhydrostatic model with explicit moist physics, which is capable of running on nested grids with inner grid spacing as low as 1 km. Research will continue on improving all components of this system, particularly the data assimilation, surface representation, soil model, boundary layer parameterizations, moist physics, and radiation, as well as pushing the model toward higher resolutions for applications to urban areas. Such a high-resolution application will require advanced numerical techniques and improved treatment of the lower boundary condition for effective treatment of steep terrain and building aggregates, and the incorporation of nonlocal processes for turbulence and/or radiation. Modeling the wide range of complex interactions that can arise between clouds and their physical environment will represent an enormous challenge requiring an advanced coupling between the parameterized physical processes (cloud physics, radiation, and mixing) and the model dynamics. With an enhanced microphysical capability, we will need advanced numerical techniques to treat the mixing and advection of the moist species carried by the model. We are also working on coupling the atmospheric model to a coastal ocean model, investigating the important time and space scales of such coupling, and developing methods for performing data assimilation for the coupled mesoscale system.

 

key words

Cloud models; Coupled models; Moist physics; Nonhydrostatic models; Numerical weather prediction; Surface process;

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

Level:  Open to Postdoctoral applicants