A generalized approach to parameterizing convection combining ensemble and data assimilation techniques

Details A generalized approach to parameterizing convection combining ensemble and data assimilation techniques A generalized approach to parameterizing convection combining ensemble and data assimilation techniques

Jul 2002

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2002georl..29n..38g&link_type=abstract

Geophysical Research Letters, Volume 29, Issue 14, pp. 38-1, CiteID 1693, DOI 10.1029/2002GL015311

Mathematics

Logic

137

Meteorology And Atmospheric Dynamics: Numerical Modeling And Data Assimilation, Meteorology And Atmospheric Dynamics: Convective Processes, Meteorology And Atmospheric Dynamics: Mesoscale Meteorology, Meteorology And Atmospheric Dynamics: Climatology (1620)

Scientific paper

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.

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