| When stochastic parameterizations in a numerical model are physically based, choice of integration scheme is crucial to quantitative accuracy. Since numerical modelers are unlikely to rewrite a model's dynamical core to accommodate this problem, ways around it must be found. A method analogous to data assimilation, but involving random numbers applied in forecast mode, is shown theoretically and practically to give accurate results in common situations, independent of integrations scheme. |
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