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COPYRIGHT: © Author(s) 2013. This work is distributed under the Creative Commons Attribution 3.0 License.
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Copyright Copernicus GmbH 2013
Abstract
We observed ozone (O3 ) in the vertical region between 250 and 0.0005 hPa (~ 12-96 km) using the Superconducting Submillimeter-Wave Limb-Emission Sounder (SMILES) on the Japanese Experiment Module (JEM) of the International Space Station (ISS) between 12 October 2009 and 21 April 2010. The new 4 K superconducting heterodyne receiver technology of SMILES allowed us to obtain a one order of magnitude better signal-to-noise ratio for the O3 line observation compared to past spaceborne microwave instruments. The non-sun-synchronous orbit of the ISS allowed us to observe O3 at various local times. We assessed the quality of the vertical profiles of O3 in the 100-0.001 hPa (~ 16-90 km) region for the SMILES NICT Level 2 product version 2.1.5. The evaluation is based on four components: error analysis; internal comparisons of observations targeting three different instrumental setups for the same O3 625.371 GHz transition; internal comparisons of two different retrieval algorithms; and external comparisons for various local times with ozonesonde, satellite and balloon observations (ENVISAT/MIPAS, SCISAT/ACE-FTS, Odin/OSIRIS, Odin/SMR, Aura/MLS, TELIS). SMILES O3 data have an estimated absolute accuracy of better than 0.3 ppmv (3%) with a vertical resolution of 3-4 km over the 60 to 8 hPa range. The random error for a single measurement is better than the estimated systematic error, being less than 1, 2, and 7%, in the 40-1, 80-0.1, and 100-0.004 hPa pressure regions, respectively. SMILES O3 abundance was 10-20% lower than all other satellite measurements at 8-0.1 hPa due to an error arising from uncertainties of the tangent point information and the gain calibration for the intensity of the spectrum. SMILES O3 from observation frequency Band-B had better accuracy than that from Band-A. A two month period is required to accumulate measurements covering 24 h in local time of O3 profile. However such a dataset can also contain variation due to dynamical, seasonal, and latitudinal effects.
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