Thesis of Lucie Leonarski

Amphithéâtre Pierre Glorieux (CERLA) Thesis defense of Lucie Leonarski - laboratory LOA Abstract : It is now well established that the highest errors concerning the Earth radiative balance come from the misknowledge of both the microphysics and the vertical and horizontal distribution of the condensed water in ice clouds. If the information about the vertical profile provided by the active instruments comprised in the A-train has allowed a significant advance, the spatial coverage, compared to passive measurements, in not sufficient to be
systematically used to constraint the climate models. Besides their strong contribution to weather forecast improvement through data assimilation in clear-sky conditions, thermal infrared sounders on board polar orbiting platforms are now playing a key role in monitoring changes in atmospheric composition. However, it is known that clear sky observations are only a small part of the entire set of measurements, the remaining part is not being
used as it is contaminated by aerosols and/or clouds which are more difficult to handle. The objective of this work is to study the capability of hyperspectral measurements in the infrared (such as the ones of IASI and IASI-NG) to (1) improve our knowledge about the vertical and horizontal distribution of condensed water in ice clouds and (2) pave the way to the assimilation of such kind of measurements to constraint the ice cloud representation in weather forecast models. An information content analysis based on Shannon’s formalism has been used to determine the level and the spectral repartition of the information about the ice cloud properties in the IASI and IASI-NG spectra. Based on this analysis, we have developed and tested an algorithm which allows to retrieve from an optimal estimation approach the
cloud integrated ice water content together with the cloud layer altitude. We have taken into account the Signal-to-Noise ratio of each specific instrument and the uncertainties due to the non-retrieved atmospheric and surface parameters. The forward model is the fast radiative transfer model RTTOV which has been developed for satellite data assimilation in Numerical Weather Prediction models. The ice cloud microphysical model is based on the ensemble model of Baran and Labonnote (2007), where the bulk ice optical properties have been parameterized as a function of the ice water content (expressed in g=m3) and in-cloud temperature. The results of this work showed that hyperspectral measurements in the infrared allow
to retrieve the layer top altitude and integrated water content in ice clouds with good accuracy in both mono-layer and multi-layer cases. They also bring partial information about the geometrical thickness of the clouds. Keywords : remote sensing,infrared,hyperspectral,IASI,ice clouds