Generalization of the Complete Data Fusion to Multi-Target Retrievals

In order to ensure global and continuous measurements of atmospheric species, an increasingly great number of space-borne missions, airborne and ground-based campaigns have been providing observations of the Earth's atmosphere from over the past two decades. These measurements are extremely important to improve the accuracy in the vertical profiling of atmospheric variables and are used as input to the physical and chemical models predicting the evolution of the atmospheric state. Measurements of two or more instruments that sound the same portion of atmosphere either in different spectral regions or with different observation geometries, 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. The simultaneous retrieval is the most comprehensive way to combine different measurements of the same quantity but this method requires to integrate different forward models in the same retrieval process and this complexity could be a limit to its application. In recent years a great interest in developing innovative techniques to exploit all the available information from independent observations of the same portion of the atmosphere has been growing to face with the great amount of available satellite data. In this context, the Complete Data Fusion (CDF) was proposed as a new method 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 that requires a very simple implementation and uses standard retrieval products characterized by their a priori information, covariance matrix and averaging kernel matrix. In the analysis of remote sensing observations, multi-target retrievals are frequently applied in order to determine simultaneously atmospheric constituents reducing the systematic error caused by interfering species. It was thus crucial to generalize 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 CDF method to multi-target retrieval products showing how to modify the inputs 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 (Infrared Atmospheric Sounding Interferometer - Next Generation) and FORUM (Far Infrared Outgoing Radiation Understanding and Monitoring) 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.