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The increase in satellite instruments sounding the atmosphere will increase the frequency of several instruments simultaneously measuring either the same vertical profile or vertical profiles related to nearby geo-locations, and users will consult fused products rather than individual measurements. Therefore, the retrieval products should be optimized for use in data fusion operations, rather than for the representation of the profile. This change in paradigm raises the question of whether a more functional representation of the retrieval products exists. New variables for the retrieval products are proposed that have several advantages with respect to the standard retrieval products. These variables, in the linear approximation of the forward model, are independent of the a priori information used in the retrieval, allow us to represent the profile with any a priori information and can be used directly to perform the data fusion of a set of measurements. Furthermore, the use of these variables allows us to reduce the stored data to about one third of its volume with respect to the use of standard retrieval products. [ABSTRACT FROM AUTHOR]

The increase of satellite instruments sounding the atmosphere will make it very likely that several instruments simultaneously measure either the same vertical profile or vertical profiles related to nearby geo-locations, and users will consult fused products rather than individual measurements. Therefore, the retrieval products should be optimized for the use in data fusion operations, rather than for the representation of the profile. This change of paradigm raises the question if a more functional representation of the retrieval products exists. New variables for the retrieval products are proposed that have several advantages with respect to the standard retrieval products. These variables, in the linear approximation of the forward model, are independent of the a priori information used in the retrieval, allow to represent the profile with any a priori information and can directly be used to perform the data fusion of a set of measurements. Furthermore, the use of these variables allows to reduce to about one third the stored data volume with respect to the use of standard retrieval products. [ABSTRACT FROM AUTHOR]

The validation of atmospheric remote-sensing measurements involves the comparison of vertical profiles of atmospheric constituents obtained by different instruments. This operation is a complex one because it has to take into account the measurement errors that are described by the variance--covariance matrices and the different features of the two observing systems that are described by the averaging kernels. The procedure is discussed and a method of comparison that is rigorous and does not involve degradation of the available information is developed by use of the formalism of functional spaces. The functional spaces that can be used for representation of the two profiles are reviewed, and criteria are determined for the choice of the most convenient functional space to minimize degradation of the measurements. Once the functional spaces are chosen, the components of the profiles are compared in the intersection space of these two functional spaces. If the intersection space coincides with the null vector, a pseudointersection space with useful geometrical properties can be used instead. A test of the method is made with a realistic simulation. In the test the profiles retrieved by two real instruments are simulated and quantitatively compared. OCIS codes: 010.1280, 120.0280, 100.3190.

In the frame of Earth observation remote sensing data analysis, Synergistic Retrieval (SR) and Complete Data Fusion (CDF) are techniques used to exploit the complementarity of the information carried by different measurements sounding the same air mass and / or ground pixel. While more difficult to implement due to the required simultaneous access to measurements originating from different instruments / missions, the SR method is sometimes preferred over the CDF method as the latter relies on a linear approximation of the retrieved states as functions of the true atmospheric and / or surface state. In this work, we study the performance of the SR and CDF techniques when applied to simulated measurements of the FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) and the IASI-NG (Infrared Atmospheric Sounding Interferometer - New Generation) missions that will be operational in a few years, from two polar orbiting satellites. The study is based on synthetic measurements generated for the two missions, in clear-sky Antarctic atmospheres. The target parameters of the inversion are the vertical profiles of temperature, water vapour and ozone mixing ratios, surface temperature and spectral emissivity. We find that for exact matching of the measurements, the results of the SR and CDF techniques differ by less than 1/10 of their errors estimated trough the propagation of measurement noise. For measurements with a realistic mismatch in space and time, the two methods provide more different results. Still in this case, however, the differences between the results are within the error bars due to measurement noise. We conclude that, when applied to FORUM and IASI-NG missions, the two methods are equivalent from the accuracy point of view. [ABSTRACT FROM AUTHOR]

High quality long-term data sets of altitude-resolved measurements of the atmospheric composition are important because they can be used both to study the evolution of the atmosphere and as a benchmark for future missions. For the final ESA reprocessing of MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) on ENVISAT (ENViromental SATellite) data, numerous improvements were implemented in the level 2 (L2) processor Optimised Retrieval Model (ORM) version 8.22 (V8) and its auxiliary data. The implemented changes involve all aspects of the processing chain, from the modelling of the measurements with the handling of the horizontal inhomogeneities along the line of sight to the use of the Optimal Estimation for retrieving the minor species, from a more sensitive approach to detect the spectra affected by clouds to a refined method for identifying low quality products. Improvements in the modelling of the measurements were obtained also with an update of the used spectroscopic data and of the databases providing the a priori knowledge of the atmosphere. The HITRAN_mipas_pf4.45 spectroscopic database was finalised with new spectroscopic data verified with MIPAS measurements themselves, while recently measured cross-sections were used for the heavy molecules. The so-called IG2 data set, containing the climatology used by MIPAS L2 processor to generate the initial guess and interfering species profiles when the retrieved profiles from previous scans are not available, was improved taking into account the diurnal variation of the profiles defined using climatologies from both measurements and models. Horizontal gradients were generated using ECMWF ERA-Interim data closest in time and space to the MIPAS data. Further improvements in the L2 V8 products derived from the use of the L1b V8 products, which were upgraded to reduce the instrumental temporal drift and to handle the abrupt changes in the calibration gain. The improvements introduced into the ORM V8 L2 processor and its upgraded auxiliary data, together with the use of the L1b V8 products, lead to the generation of the MIPAS L2 V8 products that are characterised by an increased accuracy, better temporal stability, and a greater number of retrieved species. [ABSTRACT FROM AUTHOR]

A great interest is growing about methods that combine measurements from two or more instruments that observe the same species either in different spectral regions or with different geometries. Recently, a method based on the Kalman filter has been proposed to combine IASI and TROPOMI methane products. We show that this method is equivalent to the Complete Data Fusion method. Therefore, the choice between these two methods is driven only by the advantages of the different implementations. From the comparison of the two methods a generalization of the Complete Data Fusion formula, which is valid also in the case that the noise error covariance matrices of the fused products are singular, is derived. [ABSTRACT FROM AUTHOR]