Inversion for atmospheric thermodynamical parameters of IASI data in the principal components space

Abstract

The problem of reducing the dimensionality of infrared atmospheric sounding interferometer (IASI) data space through a suitable transform and performing the retrieval process for thermodynamical parameters within the transformed data space is addressed in this paper. The reduction of dimensionality is performed with the principal components transform, which allows us to represent the full IASI spectrum with a few coefficients of the expansion. This truncated expansion could have a twofold beneficial effect: (i) it could improve the present exploitation and performance of IASI data for the retrieval of temperature and moisture; and (ii) it could save transmission bandwidth, data rate and costs for the dissemination to users of IASI data. A suitable form of the inverse/forward model completely embedded in the transformed space has been derived and applied to simulated and real IASI data. This methodology has allowed us to assess the IASI performance for temperature, water vapor and ozone based on the full IASI spectral coverage. The use of back-transformed spectral radiances (i.e. the filtered radiances obtained by the truncated expansion) instead of expansion coefficients has also been addressed and assessed. Retrieval exercises performed in simulation and with real observations lead us to conclude that the principal components space-based inverse approach is potentially superior over the current practice of using sparse channels. Copyright

An Observing System Simulation Experiment for the Impact of MTG Candidate Infrared Sounding Mission on Regional Forecasts: System Development and Preliminary Results

“…The temperature retrievals are more accurate in the middle levels from 600 to 200 hPa. The RMS error is close or smaller than 1 K, which is comparable to the Atmospheric Infrared Sounder (AIRS) and the Infrared Atmospheric Sounding Interferometer (IASI) retrievals [32,33]. For humidity, the retrieved water vapor mixing ratio has maximum RMS errors near the surface, and dry bias below 800 hPa.…”

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“…Dimension reduction via SVD has been previously used both for satellite retrievals (Masiello et al, 2012;Thompson, 1992;5 Butz et al, 2010), ground-based spectrometers (Tukiainen et al, 2016) and laboratory laser absorption measurements (Bomse and Kane, 2006). The SVD approach described here comes closest to the one applied for satellite methane retrievals (Butz et al, 2010), but performs the retrieval in the principal component basis to eliminate bias originating from the choice of the uninformative prior used (see section 3.5).…”

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