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An examination of the accuracy of the linearization of a mesoscale model with moist physics
Author(s) -
Errico Ronald M.,
Raeder Kevin D.
Publication year - 1999
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.49712555310
Subject(s) - linearization , parametrization (atmospheric modeling) , tangent , nonlinear system , diabatic , perturbation (astronomy) , mathematics , mesoscale meteorology , data assimilation , convection , meteorology , physics , adiabatic process , geometry , radiative transfer , quantum mechanics , thermodynamics
The accuracy of tangent linear and adjoint versions of a primitive‐equation model with moist physics is examined with respect to growing perturbations having significant initial magnitudes. the Jacobians for the convective parametrizations are approximated using a perturbation method. These Jacobians are then quality controlled to ensure that the approximations are suitable. Results show that: (1) linearization of the diabatic moist physics can have a significant impact; (2) even where such impacts are large, the linearized versions of the model can yield good approximations to the nonlinear behaviour for significant perturbations, especially if there is sufficient dynamical influence; (3) poor approximations can be obtained when convection dominates the results; and (4) a straightforward linearization of some parametrization schemes may be inadequate. the results are encouraging for quantitative applications of some moist adjoint models to extratropical cyclones in the winter, but suggest some tangent linear approximations may be unsuitable in the tropics or over continents in the summer, except if qualitative agreements with nonlinear results are suficient. Detailed comparisons of linear and nonlinear results should be made, particularly using optimal perturbations, prior to any applications of tangent linear or adjoint models.