Tangent linear and adjoint models for the MPAS-Atmosphere is available!
The MPAS-A is an advanced global numerical weather prediction model with a hexagonal mesh that can be compressed for higher resolutions in some targeted regions of interest and smoothly transitioned to coarse resolutions in others. In this study, a Python-driven MPAS-A model is first developed, combining a flexible Python driver and Fortran’s fast computation, making the MPAS-A model exceedingly user- and platform- friendly. The tangent linear and adjoint models of the MPAS-A dynamical core are then developed, both of which are required for various sensitivity studies. They are also indispensable components of a future MPAS- based global four-dimensional variational (4D-Var) data assimilation system. Finally, the relative sensitivity of a baroclinic instability wave development is obtained and shown using the MPAS-A adjoint model.
The MPAS-Atmosphere tangent linear model can well approximate the evolutions of perturbations of small magnitudes (10^-3) in the figure below.
Sensitivity analyses to a wide variety of user-defined quantities at the forecast time is accurate and efficient with the adjoint model.
