Validation of stratospheric and mesospheric ozone observed by SMILES from International Space Station

Abstract

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.