| description abstract | Classic formulations of variational data assimilation in amplitude space are not able to directly handle observations that measure the geographical positions of meteorological features like fronts and vortices. These observations can be derived from satellite images, as is already the case for tropical cyclones. Although some advanced data assimilation algorithms have been specifically designed to tackle the problem, a widespread way of dealing with this information is to use so-called bogussing pseudo-observations: user-specified artificial observations are inserted in a traditional data assimilation scheme. At the midlatitudes, there is a relationship between dry intrusions in water vapor images and upper-level potential vorticity structures. Some prior work has also shown that it was possible to automatically identify dry intrusions with tracking algorithms. The difference of positions between model and image dry intrusions could therefore be used as observations of the misplacement of potential vorticity structures. One strategy to achieve the displacement of potential vorticity anomalies is to sample them, and assimilate the values at displaced locations. The uncertainty of these pseudo-observations is left as a tuning parameter to try to make the displacement both effective and robust. A simple one-dimensional assimilation model is used to study the displacement of curves defined by Gaussian humps. The concept is then illustrated in realistic examples from real synoptic systems, where pseudo-observations of potential vorticity are incorporated in a global variational data assimilation scheme. Overall and despite reasonable optimization, the results contain artifacts. This suggests that the use of pseudo-observations to displace identifiable structures is not an effective strategy. | |
