Further investigation on adjoint sensitivity of surface precipitation to initial conditions

ABSTRACT:Variational data assimilation methods and related applications depend on the validity of the tangent-linear approximation, which is truly challenging when applied to deep moist convection. A simple strategy for linearizing complex moist convective schemes in numerical weather prediction models is proposed. This strategy represents a trade-off between code development and maintenance on the one hand and expected benefit on the other hand. The generic linearized scheme described hereafter can be used in conjunction with any nonlinear moist convective scheme, thus eliminating the need to linearize complex codes. The universality of the scheme stems from the fact that conditional triggers and cloud vertical extent are supplied by the trajectory of the nonlinear scheme. In active columns, the convective tendencies cancel the largescale dynamical tendencies. Potential uses of the scheme include variational data assimilation and diagnostic studies such as tropical singular vectors and key analysis errors with precipitation. The relevance of the methodology is examined with the Global Environmental Multiscale model using the Kain-Fritsch mass-flux scheme that is operational at the Canadian Meteorological Centre. It is shown that the tangent-linear approximation is improved when compared to a linearized convection scheme having its own trigger functions. The improvement is particularly noticeable for the partition between stratiform and convective components of surface precipitation. Finally the examination of adjoint sensitivities of surface precipitation with the simplified linearized scheme triggered by the Kain-Fritsch scheme reveals a more pronounced sensitivity to midlatitude baroclinic instability and less predictability for tropical systems when compared to a Kuo-type scheme.

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“…They will be referred to as ML1 and TC1 respectively in the following (i.e. the same notations as in Mahfouf and Bilodeau, 2007b).…”

Section: Experimental Set-upmentioning

confidence: 99%