A posteriori fusion of atmospheric profiles: real data applications

Remote sensing observations of the composition of Earth's atmosphere are performed with instruments operating on space-borne and airborne platforms and from ground-based stations. In this context, vertical profiles of atmospheric variables are often obtained with an inversion procedure (retrieval) from the observed radiances. When one or more instruments observe the same portion of the atmosphere, the information obtained from the different measurements can be combined in order to get a unique vertical profile of improved quality with respect to that of the profiles retrieved from the single observations. The most comprehensive way to combine different measurements of the same quantity is considered to be synergistic retrieval, which jointly inverts all the observations and produces a single output profile. However, recently a new method referred to as Complete Data Fusion (CDF) was proposed that, in linear approximation conditions, provides products equivalent to that of the synergistic retrieval with simpler implementation requirements. Using a series of examples based on real data, we will present the different contexts in which the CDF can be used and the key benefits that can be achieved. This is particularly interesting considering the forthcoming operation of atmospheric Sentinels. In fact, the examples deal with precursor instruments to those operating on the new platforms or others that could provide complementary information to them. Another essential aspect to be underlined is that the CDF can modify the characteristics of a product (spatial and vertical resolution, a priori information), making it more compatible with other tasks such as assimilation, source points detection and time-series calculation. In particular, here, we present some results of the CDF application to measurements of vertical profiles of Ozone, Temperature, Water Vapour and eventually other trace gases, performed by different instruments (GOME2 and IASI at least, but eventually also TROPOMI, MIPAS and others). The method's inputs are the profiles retrieved directly from the single measurements, characterized by their a priori information, covariance matrices and averaging kernel matrices. The output consists of a single profile also characterized by an a priori information, a covariance matrix and an averaging kernel matrix, which collects the information content of the input profiles. The fused product is compared with the input ones in terms of errors and number of degrees of freedom (DOFs). We will see that, in general, the fused product has lower errors and higher DOFs if compared with the L2 ones, so we will analyse the mechanism that provokes this quality improvement, also considering the shape of the individual averaging kernels rows. We will discuss the strategies to allow the fusion of non-coincident measurements and the relative implications in terms of information content. We will also focus on the actual open problems and the desirable future developments and applications.