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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 14 Nov 2018

Research article | 14 Nov 2018

Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Atmospheric Measurement Techniques (AMT) and is expected to appear here in due course.

The SPARC water vapour assessment II: Profile-to-profile comparisons of stratospheric and lower mesospheric water vapour data sets obtained from satellites

Stefan Lossow1, Farahnaz Khosrawi1, Michael Kiefer1, Kaley A. Walker2, Jean-Loup Bertaux3, Laurent Blanot4, James M, Russell5, Ellis E. Remsberg6, John C. Gille7,8, Takafumi Sugita9, Christopher E. Sioris10, Bianca M. Dinelli11, Enzo Papandrea11,12, Piera Raspollini13, Maya Garcia-Comas14, Gabriele P. Stiller1, Thomas von Clarmann1, Anu Dudhia15, William G. Read16, Gerald E. Nedoluha17, Robert P. Damadeo6, Joseph M. Zawodny6, Katja Weigel18, Alexei Rozanov18, Faiza Azam18, Klaus Bramstedt18, Stefan Noël18, John P. Burrows18, Hideo Sagawa19, Yasuko Kasai20, Joachim Urban21,†, Patrick Eriksson21, Donal P. Murtagh21, Mark E. Hervig22, Charlotta Högberg23, Dale F. Hurst24, and Karen H. Rosenlof24 Stefan Lossow et al.
  • 1Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Hermann-von-Helmholtz-Platz 1, 76344 Leopoldshafen, Germany
  • 2University of Toronto, Department of Physics, 60 St. George Street, Toronto, ON M5S 1A7, Canada
  • 3LATMOS, CNRS/UVSQ/IPSL, Quartier des Garennes, 11 Boulevard d’Alembert, 78280 Guyancourt, France
  • 4ACRI-ST, 260 Route du Pin Montard, 06904 Sophia-Antipolis Cedex, France
  • 5Hampton University, Center for Atmospheric Sciences, 23 Tyler Street, Hampton, VA 23669, USA
  • 6NASA Langley Research Center, 21 Langley Boulevard, Hampton, VA 23681, USA
  • 7National Center for Atmospheric Research, Atmospheric Chemistry Observations & Modeling Laboratory, P.O. Box 3000, Boulder, CO 80307-3000, USA
  • 8University of Colorado, Atmospheric and Oceanic Sciences, Boulder, CO 80309-0311, USA
  • 9National Institute for Environmental Studies, Center for Global Environmental Research, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
  • 10Environment and Climate Change Canada, 4905 Dufferin Street, Toronto, ON M3H 5T4, Canada
  • 11stituto di Scienze dell’Atmosfera e del Clima del Consiglio Nazionale delle Ricerche (ISAC-CNR), Via Gobetti, 101, 40129 Bologna, Italy
  • 12Serco SpA, Via Sciadonna, 24–26, 00044 Frascati, Italy
  • 13Istituto di Fisica Applicata del Consiglio Nazionale delle Ricerche (IFAC-CNR), Via Madonna del Piano, 10, 50019 Sesto Fiorentino, Italy
  • 14Instituto de Astrofísica de Andalucía (IAA-CSIC), Glorieta de la Astronomía, 18008 Granada, Spain
  • 15University of Oxford, Atmospheric Physics, Clarendon Laboratory, Parks Road, Oxford OX1 3PU, United Kingdom of Great Britain and Northern Ireland
  • 16Jet Propulsion Laboratory, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
  • 17Naval Research Laboratory, Remote Sensing Division, 4555 Overlook Avenue Southwest, Washington, DC 20375, USA
  • 18University of Bremen, Institute of Environmental Physics, Otto-Hahn-Allee 1, 28334 Bremen, Germany
  • 19Kyoto Sangyo University, Faculty of Science, Motoyama, Kamigamo, Kita-ku, Kyoto 603-8555, Japan
  • 20National Institute of Information and Communications Technology (NICT), 20 THz Research Center, 4-2-1 Nukui-kita, Koganei, Tokyo 184-8795, Japan
  • 21Chalmers University of Technology, Department of Space, Earth and Environment, Hörsalsvägen 11, 41296 Göteborg, Sweden
  • 22GATS Inc., 65 South Main Street #5, Driggs, ID 83442, USA
  • 23Stockholm University, Department of Physical Geography, Svante-Arrhenius-väg 8, 10691 Stockholm, Sweden
  • 24NOAA Earth System Research Laboratory, Global Monitoring Division, 325 Broadway, Boulder, CO 80305, USA
  • deceased on 14 August 2014

Abstract. Within the framework of the second SPARC (Stratosphere-troposphere Processes And their Role in Climate) water vapour assessment (WAVAS-II), profile-to-profile comparisons of stratospheric and lower mesospheric water vapour were performed considering 33 data sets derived from satellite observations of 15 different instruments. These comparisons aimed to provide a picture of the typical biases and drifts in the observational database and to identify data set specific problems. The observational database typically exhibits the largest biases below 70 hPa, both in absolute and relative terms. The smallest biases are often found between 50 hPa and 5 hPa. Typically, they range from 0.25 ppmv to 0.5 ppmv (5 % to 10 %) in this altitude region, based on the 50 % percentile over the different comparison results. Higher up, the biases are overall increasing with altitude but this general behaviour is accompanied by considerable variations. Characteristic values vary between 0.3 ppmv and 1 ppmv (4 % to 20 %). Obvious data set specific bias issues are found for a number of data sets. In our work we performed a drift analysis for data sets overlapping for a period of at least 36 months. This assessment shows a wide range of drifts among the different data sets that are statistically significant at the 2σ uncertainty level. In general, the smallest drifts are found in the altitude range between about 30 hPa to 10 hPa. Histograms considering results from all altitudes indicate the largest occurrence for drifts between 0.05 ppmv decade−1 and 0.3 ppmv decade−1. Comparisons of our drift estimates to those derived from comparisons of zonal mean time series only exhibit statistically significant differences in slightly more than 3 % of the comparisons. Hence, drift estimates from profile-to-profile and zonal mean time series comparisons are largely interchangeable. Like for the biases, a number of data sets exhibit prominent drift issues. In our analyses we found that the large number of MIPAS data sets included in the assessment affects our general results as well as the bias summaries we provide for the individual data sets. This is because these data sets exhibit a relative similarity with respect to the remaining data sets, despite that they are based on different measurement modes and different processors implementing different retrieval choices. Because of that, we have by default considered an aggregation of the comparison results obtained from MIPAS data sets. Results without this aggregation are provided on multiple occasions to characterise the effects due to the numerous MIPAS data sets. Among other effects, they cause a reduction of the typical biases in the observational database.

Stefan Lossow et al.
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Interactive discussion
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Status: closed
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Stefan Lossow et al.
Stefan Lossow et al.
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