Abstract
Abstract. Within the project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) ground- and space-based remote sensing as well as in situ data sets of tropospheric water vapour isotopologues are provided. The space-based remote-sensing data set is produced from spectra measured by the IASI (Infrared Atmospheric Sounding Interferometer) sensor and is potentially available on a global scale. Here, we present the MUSICA IASI data for three different geophysical locations (subtropics, midlatitudes, and Arctic), and we provide a comprehensive characterisation of the complex nature of such space-based isotopologue remote-sensing products. The quality assessment study is complemented by a comparison to MUSICA's ground-based FTIR (Fourier Transform InfraRed) remote-sensing data retrieved from the spectra recorded at three different locations within the framework of NDACC (Network for the Detection of Atmospheric Composition Change). We confirm that IASI is able to measure tropospheric H2O profiles with a vertical resolution of about 4 km and a random error of about 10%. In addition IASI can observe middle tropospheric δD that adds complementary value to IASI's middle tropospheric H2O observations. Our study presents theoretical and empirical proof that IASI has the capability for a global observation of middle tropospheric water vapour isotopologues on a daily timescale and at a quality that is sufficiently high for water cycle research purposes.
Highlights
Understanding the geological water cycle is essential for predicting weather and climate, where atmospheric water is affected by evaporation, transport, and condensation and strongly interacts with fundamental thermodynamic processes such as energy transport and radiation
The enrichment of the heavier isotopologue HDO compared to the main isotopologue H2O is called δD and calculated as a deviation of the ratio of both isotopologues compared to the Vienna standard mean ratio in ocean water (VSMOW)
Much better insight is provided by transferring the {ln[H2O], ln[HDO]} state to the {humidity, δD} proxy state. This proxy state concept enables us to characterise the complex MUSICA MetOp/IASI water vapour isotopologue remote-sensing data by means of the well-known Rodgers formalism (Rodgers, 2000). This is done with great similarity to the characterisation of the MUSICA NDACC/FTIR product as presented in Schneider et al (2012), to which we refer throughout this section as S12
Summary
Understanding the geological water cycle is essential for predicting weather and climate, where atmospheric water is affected by evaporation, transport, and condensation and strongly interacts with fundamental thermodynamic processes such as energy transport and radiation. Much better insight is provided by transferring the {ln[H2O], ln[HDO]} state to the {humidity, δD} proxy state This proxy state concept enables us to characterise the complex MUSICA MetOp/IASI water vapour isotopologue remote-sensing data by means of the well-known Rodgers formalism (Rodgers, 2000). This is done with great similarity to the characterisation of the MUSICA NDACC/FTIR product as presented in Schneider et al (2012), to which we refer throughout this section as S12. In the following we will characterise the two types of the IASI water vapour isotopologue products
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