
Forschungszentrum Karlsruhe, Institut für Meteorologie und Klimaforschung, Karlsruhe, Germany Abstract. Profiles of atmospheric state parameters retrieved from remote measurements often contain a priori information which causes complication in the use of data for validation, comparison with models, or data assimilation. For such applications it often is desirable to remove the a priori information from the data product. If the retrieval involves an illposed inversion problem, formal removal of the a priori information requires resampling of the data on a coarser grid, which, however, is a prior constraint in itself. The fact that the trace of the averaging kernel matrix of a retrieval is equivalent to the number of degrees of freedom of the retrieval is used to define an appropriate informationcentered representation of the data where each data point represents one degree of freedom. Since regridding implies further degradation of the data and thus causes additional loss of information, a reregularization scheme has been developed which allows resampling without additional loss of information. For a typical ClONO_{2} profile retrieved from spectra as measured by the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), the constrained retrieval has 9.7 degrees of freedom. After application of the proposed transformation to a coarser informationcentered altitude grid, there are exactly 9 degrees of freedom left, and the averaging kernel on the coarse grid is unity. Pure resampling on the informationcentered grid without reregularization would reduce the degrees of freedom to 7.1. Citation: von Clarmann, T. and Grabowski, U.: Elimination of hidden a priori information from remotely sensed profile data, Atmos. Chem. Phys. Discuss., 6, 67236751, doi:10.5194/acpd667232006, 2006. 
