Abstract
Abstract. 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.
Highlights
Several data assimilation methods have been used in the field of atmospheric chemistry and air quality in many studies
We demonstrate the importance of the emission regime for the efficiency of the data assimilation scheme and we assess the impact of the tracer observation network density
Pieces of information contained in concentration observations help estimate the wind back in time before the tracer was advected by that wind. These results stress that variational schemes implemented over large data assimilation window (DAW) have an advantage over the ensemble Kalman filter (EnKF) in that context
Summary
Several data assimilation methods have been used in the field of atmospheric chemistry and air quality in many studies (as exemplified in the reviews of Carmichael et al, 2008; Sandu and Chai, 2011; Zhang et al, 2012; Bocquet et al, 2015a). That is why we believe that the increasing variety of problems encountered in the field of atmospheric chemistry data assimilation puts forwddcwictTdmThmdhth+het+er12ede12ec=eqmd=uqtΦΦ+auΦtm m12amiomt−inos−===nosΦmfo+mtxxΦhf1m m+em t−h1ccme−m m−λom−+cΦdλm1212eomc+ldm+21ae1+iir+lffe−a21tEr+hλxxemocmmEs+m the
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