A Low-order Coupled Chemistry Meteorology Model for Testing Online and Offline Advanced Data Assimilation Schemes

Abstract:

Bocquet and Sakov have recently introduced a low-order model based on the coupling of the
chaotic Lorenz-95 model which simulates winds along a mid-latitude circle, with the
transport of a tracer species advected by this wind field. It has been used to test
advanced data assimilation methods with an online model that couples meteorology and
tracer transport. In the present study, the tracer subsystem of the model is replaced
with a reduced photochemistry module meant to emulate reactive air pollution. This
coupled chemistry meteorology model, the L95-GRS model, mimics continental and
transcontinental transport and photochemistry of ozone, volatile organic compounds and
nitrogen dioxides.

The L95-GRS is specially useful in testing advanced data assimilation schemes, such as the
iterative ensemble Kalman smoother (IEnKS) that combines the best of ensemble and
variational methods. The model provides useful insights prior to any implementation of
the data assimilation method on larger models. For instance, online and offline data
assimilation strategies based on the ensemble Kalman filter or the IEnKS can easily be
evaluated with it. It allows to document the impact of species concentration observations
on the wind estimation. The model also illustrates a long standing issue in atmospheric
chemistry forecasting: the impact of the wind chaotic dynamics and of the chemical species
non-chaotic but highly nonlinear dynamics on the selected data assimilation approach.