Complete Data Fusion of multi-target retrieval products

Over the past two decades, a great number of space-borne missions, airborne and ground-based campaigns have been providing observations of the Earth's atmosphere for the vertical profiling of atmospheric variables aiming at ensuring global and continuous measurements of atmospheric species. These measurements are also used as input to the physical and chemical models that are used to predict the evolution of the atmospheric state. When two or more instruments sound the same portion of atmosphere either in different spectral regions or with different observation geometries, their measurements can be combined in order to obtain a single vertical profile of improved quality with respect to that of the profiles retrieved from the single observations. In the framework of the increasing interest in developing innovative techniques to exploit all the available information from measurements of the same portion of the atmosphere to retrieve the best vertical profile estimate, a new method of data fusion was proposed: The Complete Data Fusion (CDF). This method uses standard retrieval products to combine independent measurements of the same profile into a single estimate for a comprehensive and concise description of the atmospheric state. The CDF is an a posteriori algorithm and requires very simple implementation. In order to determine simultaneously atmospheric constituents reducing the systematic error caused by interfering species, multi-target retrievals are frequently applied to the analysis of remote sensing observations. It is thus crucial to adapt the CDF algorithm to fuse profiles obtained from multi-target retrievals, extending its use to a greater number of remote sensing data. In this work, we present the results of the first application of the complete data fusion to multi-target retrieval products showing how the inputs of the CDF have to be modified to take into account that the state vectors of the fusing measurements may contain only the same atmospheric variables or include different variables as well. We applied the method to simulated measurements in the thermal infrared and in the far infrared spectral ranges, considering the instrumental specifications and performances of IASI-NG and FORUM instruments, respectively. The results obtained demonstrate that the CDF can deal with state vectors from multi target retrievals both when they contain the same variables and when they have only a subset of variables in common, providing outputs of improved quality with respect to the input data.