Comparison of nitric oxide measurements in the mesosphere and lower thermosphere from ACE-FTS, MIPAS, SCIAMACHY, and SMR
We compare the nitric oxide measurements in the mesosphere and lower thermosphere (60 to 150 km) from four instruments: the Atmospheric Chemistry Experiment-Fourier Transform Spectrometer (ACE-FTS), the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), the SCanning Imaging Absorptio...
| Autores: | , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/133339 |
| Acceso en línea: | http://hdl.handle.net/10261/133339 |
| Access Level: | acceso abierto |
| Palabra clave: | Geomagnetic field Geographical region FTIR spectroscopy Data set Data quality Atmospheric chemistry Annual variations ACE 2 Mesosphere MIPAS Nitric oxide Thermosphere SCIAMACHY Radiometer |
| Sumario: | We compare the nitric oxide measurements in the mesosphere and lower thermosphere (60 to 150 km) from four instruments: the Atmospheric Chemistry Experiment-Fourier Transform Spectrometer (ACE-FTS), the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY), and the Sub-Millimetre Radiometer (SMR). We use the daily zonal mean data in that altitude range for the years 2004-2010 (ACE-FTS), 2005-2012 (MIPAS), 2008-2012 (SCIAMACHY), and 2003-2012 (SMR). <br><br> We first compare the data qualitatively with respect to the morphology, focussing on the major features, and then compare the time series directly and quantitatively. In three geographical regions, we compare the vertical density profiles on coincident measurement days. Since none of the instruments delivers continuous daily measurements in this altitude region, we carried out a multi-linear regression analysis. This regression analysis considers annual and semi-annual variability in the form of harmonic terms and inter-annual variability by responding linearly to the solar Lyman-α radiation index and the geomagnetic Kp index. This analysis helps to find similarities and differences in the individual data sets with respect to the inter-annual variations caused by geomagnetic and solar variability. <br><br> We find that the data sets are consistent and that they only disagree on minor aspects. SMR and ACE-FTS deliver the longest time series in the mesosphere, and they agree with each other remarkably well. The shorter time series from MIPAS and SCIAMACHY also agree with them where they overlap. The data agree within 30 % when the number densities are large, but they can differ by 50 to 100 % in some cases. |
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