A low-order coupled chemistry meteorology model for testing online and offline data assimilation schemes: L95-GRS (v1.0)
Bocquet and Sakov (2013) introduced a low-order model based on the coupling of the chaotic Lorenz-95 (L95) model, which simulates winds along a mid-latitude circle, with the transport of a tracer species advected by this zonal wind field. This model, named L95-T, can serve as a playground for testin...
Main Authors: | , |
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Format: | Article |
Language: | English |
Published: |
Copernicus Publications
2016-01-01
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Series: | Geoscientific Model Development |
Online Access: | http://www.geosci-model-dev.net/9/393/2016/gmd-9-393-2016.pdf |
Summary: | Bocquet and Sakov (2013) introduced a low-order model based on the coupling of the
chaotic Lorenz-95 (L95) model, which simulates winds along a mid-latitude
circle, with the transport of a tracer species advected by this zonal wind
field. This model, named L95-T, can
serve as a playground for testing data assimilation schemes with an online
model. Here, the tracer part of the model is extended to a reduced
photochemistry module. This coupled chemistry meteorology model (CCMM), the
L95-GRS (generic reaction
set) model, mimics continental and transcontinental transport and the
photochemistry of ozone, volatile organic compounds and nitrogen oxides. Its
numerical implementation is described. The model is shown to reproduce the
major physical and chemical processes being considered. L95-T and L95-GRS are
specifically designed and useful for testing advanced data assimilation
schemes, such as the iterative ensemble Kalman smoother (IEnKS), which
combines the best of ensemble and variational methods. These models provide
useful insights prior to the implementation of data assimilation methods into
larger models. We illustrate their use with data assimilation schemes on
preliminary yet instructive numerical experiments. In particular, online and
offline data assimilation strategies can be conveniently tested and discussed
with this low-order CCMM. The impact of observed chemical species
concentrations on the wind field
estimate can be quantitatively assessed. The impacts of the wind chaotic
dynamics and of the chemical species non-chaotic but highly nonlinear
dynamics on the data assimilation strategies are illustrated. |
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ISSN: | 1991-959X 1991-9603 |