Using adjoint sensitivity as a local structure function in variational data assimilation
Utiliser la sensibilité de l'adjoint comme une fonction de structure locale dans l'assimilation variationnelle des données
Hello, G. ; Bouttier, F.
Année de publication
2001
<span class="pb_abstract"><span class="pb_abstract_title">Abstract.</span> One approach<br> recently proposed in order to improve the forecast of weather events, such<br> as cyclogenesis, is to increase the number of observations in areas<br> depending on the flow configuration. These areas are obtained using, for<br> example, the sensitivity to initial conditions of a selected predicted<br> cyclone. An alternative or complementary way is proposed here. The idea is<br> to employ such an adjoint sensitivity field as a local structure function<br> within variational data assimilation, 3D-Var in this instance. Away from<br> the sensitive area, observation increments project on the initial fields<br> with the usual climatological (or weakly flow-dependent, in the case of<br> 4D-Var) structure functions. Within the sensitive area, the gradient<br> fields are projected using all the available data in the zone,<br> conventional or extra, if any. The formulation of the technique is given<br> and the approach is further explained by using a simple 1D scheme. The<br> technique is implemented in the ARPEGE/IFS code and applied to 11 FASTEX<br> (Fronts and Atlantic Storm-Track Experiment) cyclone cases, together with<br> the targeted observations performed at the time of the campaign. The new<br> approach is shown to allow for the desired stronger impact of the<br> available observations and to systematically improve the forecasts of the<br> FASTEX cyclones, unlike the standard 3D-Var.</span><span class="pb_toc_link"></span><br><span class="pb_toc_link"></span></div>
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