84th AMS Annual Meeting

Thursday, 15 January 2004: 2:00 PM
Room 605/606
Michael C. Morgan, University of Wisconsin, Madison, WI; and D. T. Kleist
Poster PDF (396.9 kB)
The adjoint of a numerical weather prediction (NWP) model can be used to evaluate the forecast sensitivity gradient: how changes to initial conditions of an NWP model forecast will affect some function of the model forecast state (called the response function, R). Given a forecast sensitivity gradient (with respect to initial conditions) for one particular forecast aspect, and given some knowledge of the analysis errors, it may be possible to estimate the likely ranges of values the response function may take. This may be an effective way of generating an ensemble of forecasts, as it requires a single integration of both the NWP model and its corresponding adjoint. In addition, this method allows a forecaster to construct an ensemble which may be tailored for specific forecast needs (site or regionally specific, as well as a wide variety of forecast aspects). In this presentation, an ensemble of short-term forecasts of a particular forecast aspect are generated using forecast sensitivities of that forecast aspect with respect to model initial conditions (using the MM5v2 Adjoint Modeling System). The estimation of initial condition uncertainty is generated by using the differences in MM5 initial conditions derived from different analyses from several operational models. The product of the analysis differences with the sensitivities determines the approximate changes in the response function at the end of the forecast interval. This calculation allows bounds on a particular forecast aspect to be established.

A more traditional method of ensemble generation is used to determine the efficacy in using the previously described methodology, as well as determining the validity of the tangent linear assumption (the basis for the adjoint model). A set of non-linear model integrations forward in time using perturbed initial conditions (generated from the same analysis differences) are being run for this purpose.

A statistical analysis of the results of several months of running this ensemble forecast generation procedure is presented and suggestions regarding the validity of this methodology are discussed.

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