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Forecast sensitivity analysis provides an objective means of evaluating how initial
condition errors affect a forecast and where to gather additional observations to
reduce forecast errors. Most sensitivity studies use the adjoint of a linearized
forecast model to determine the gradient of a forecast metric with respect to the
initial conditions. Adjoints suffer from a number of difficulties including
coding, linearity assumptions, and moist processes. Ensemble-based sensitivity
analysis provides an attractive alternative to adjoint-based methods because
it combines data assimilation and sensitivity analysis in a consistent manner.
This image illustrates the effect of zonal winds aloft on the position of
Hurricane Katrina.
[link to more information]
Ryan Torn, torn@atmos.albany.edu |