Variational Data Assimilation for the Global Ocean - Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications - page 328

13.7 Future Capabilities

The NCODA 3DVAR and Navy global ocean forecasting systems continue to be
developed and improved. These new developments and capabilities are summarized
in this section.

13.7.1 HYCOM GOFS

1=12 ı global HYCOM/NCODA system is the first step towards a 1=25 ı

The present

1=12 ı
model. In this phase the simple thermodynamic ice model will be replaced by the
Los Alamos Community Ice CodE (CICE). CICE is the result of an effort to develop
a computationally efficient sea ice component for a fully coupled forecast system.
CICE has several interacting components: a thermodynamic model that computes
local growth rates of snow and ice due to vertical conductive, radiative and turbulent
fluxes, along with snowfall; a model of ice dynamics, which predicts the velocity
field of the ice pack based on a model of the material strength of the ice; a transport
model that describes advection of the areal concentration, ice volumes and other
state variables; and a ridging parameterization that transfer ice among thickness cat-
egories based on energetic balances and rates of strains. HYCOM and CICE will be
fully coupled via the Earth System Modeling Framework (ESMF: Hill et al. 2004 ).
An interim, fully coupled, real time Arctic Cap HYCOM/CICE/NCODA-3DVAR
forecast system has been set up until CICE is implemented in the global model
( Posey et al. 2010 ). The second phase of the upgrade includes the implementation
of a fully coupled

global forecast system. The first phase of the upgrade will continue to use the

1=25 ı HYCOM/CICE model that includes tidal forcing and uses
NCODA 3DVAR as the data assimilation component for both HYCOM and CICE.
Preliminary experiments with the assimilative

1=25 ı

model are under way. This

model will have 3 km mid latitude resolution.

13.7.2 Satellite SST Radiance Assimilation

At the present time, SST retrievals are empirically derived using stored regressions
between cloud cleared satellite SST radiances and drifting buoy SSTs. The regres-
sions are global, calculated once, and held constant. The coefficients represent a very
broad range of atmospheric conditions with the result that subtle systematic errors
are introduced into the empirical SST when the method is uniformly applied to new
radiance data. In the 3DVAR, work is underway to develop an observation operator
for direct assimilation of satellite SST radiances. This new physical SST algorithm
uses an incremental approach. It takes as input prior estimates of SST and short-term
predictions of air temperature and water vapor profiles from NWP. The algorithm is