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A generalized approach to parameterizing convection combining ensemble and data assimilation techniques
Author(s) -
Grell Georg A.,
Dévényi Dezső
Publication year - 2002
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2002gl015311
Subject(s) - data assimilation , a priori and a posteriori , bayesian probability , computer science , probability density function , convection , ensemble forecasting , meteorology , mathematics , statistics , machine learning , artificial intelligence , physics , philosophy , epistemology
A new convective parameterization is introduced that can make use of a large variety of assumptions previously introduced in earlier formulations. The assumptions are chosen so that they will generate a large spread in the solution. We then show two methods in which ensemble and data assimilation techniques may be used to find the best value to feed back to the larger scale model. First, we can use simple statistical methods to find the most probable solution. Second, the ensemble probability density function can be considered as an appropriate “prior” (a'priori density) for Bayesian data assimilation. Using this “prior”, and information about observation likelihood, measured meteorological or climatological data can be directly assimilated into model fields. Given proper observations, the application of this technique is not restricted to convective parameterizations, but may be applied to other parameterizations as well.