**Sensitivity Analysis in
Atmospheric Data Assimilation**

**Ron Gelaro**

**NASA Global Modeling and
Assimilation Office**

**Goddard**** ****Space**** ****Flight**** ****Center**

**Greenbelt****, ****MD**** ****20771**

Mathematical adjoints derived
from the complex equations comprising a data
assimilation and forecast model system have proven to be effective tools for
estimating sensitivities in many contexts.
With the adjoint of the analysis component of a data assimilation
system, sensitivities of aspects of either forecasts or the analyses themselves
can be efficiently estimated. These can
be determined with respect to observational data, background fields or
assimilation parameters, all computed simultaneously. This permits arbitrary aggregation of the
sensitivities, e.g., by data type, data channel, data location, etc. It also allows for estimation of the impacts
of *any* subset of data on standard
forecast measures and has proven useful for monitoring observation
quality. The results so far show great
promise, but also raise interesting questions about how best to design future
strategies for intelligent data selection and utilization. We’ll examine observation impact results
from the adjoint of the Navy’s 3DVAR system (NAVDAS), as well as show
preliminary test results from the development of the adjoint of NCEP’s GSI
system.