The SPARC water vapour assessment II: Profile-to-profile comparisons of stratospheric and lower mesospheric water vapour data sets obtained from satellites
Journal article, 2019
This work is distributed under the Creative Commons Attribution 4.0 License. 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 by 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 and 5 hPa. Typically, they range from 0.25 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 increase with altitude overall but this general behaviour is accompanied by considerable variations. Characteristic values vary between 0.3 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 and 10 hPa. Histograms considering results from all altitudes indicate the largest occurrence for drifts between 0.05 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. As 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 the fact 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.
Author
Stefan Lossow
Karlsruhe Institute of Technology (KIT)
F. Khosrawi
Karlsruhe Institute of Technology (KIT)
M. Kiefer
Karlsruhe Institute of Technology (KIT)
K.A. Walker
University of Toronto
Jean Loup Bertaux
Universite de Versailles Saint-Quentin-en-Yvelines
Laurent Blanot
ACRI-ST
J. M. Russell
Hampton University
E. Remsberg
NASA Langley Research Center
John C. Gille
National Center for Atmospheric Research
University of Colorado at Boulder
Takafumi Sugita
National Institute for Environmental Studies of Japan
C. E. Sioris
Environment and Climate Change Canada
B. M. Dinelli
Institute of Atmospheric Sciences and Climate, Bologna
Enzo Papandrea
Institute of Atmospheric Sciences and Climate, Bologna
Serco Group plc
P. Raspollini
Consiglo Nazionale Delle Richerche
M. Garcia-Comas
Institute of Astrophysics of Andalusia (IAA)
G. P. Stiller
Karlsruhe Institute of Technology (KIT)
T. von Clarmann
Karlsruhe Institute of Technology (KIT)
Anu Dudhia
University of Oxford
William G. Read
Jet Propulsion Laboratory, California Institute of Technology
G.E. Nedoluha
Naval Research Laboratory
R. Damadeo
NASA Langley Research Center
J.M. Zawodny
NASA Langley Research Center
K. Weigel
Universität Bremen
A. Rozanov
Universität Bremen
F. Azam
Universität Bremen
K. Bramstedt
Universität Bremen
S. Noël
Universität Bremen
J. Burrows
Universität Bremen
H. Sagawa
Kyoto Sangyo University
Yasuko Kasai
Japan National Institute of Information and Communications Technology
Joachim Urban
Chalmers, Space, Earth and Environment, Microwave and Optical Remote Sensing
Patrick Eriksson
Chalmers, Space, Earth and Environment, Microwave and Optical Remote Sensing
Donal Murtagh
Chalmers, Space, Earth and Environment, Microwave and Optical Remote Sensing
M.E. Hervig
GATS, Inc.
Charlotta Högberg
Stockholm University
D. Hurst
National Oceanic and Atmospheric Administration
Karen H. Rosenlof
National Oceanic and Atmospheric Administration
Atmospheric Measurement Techniques
1867-1381 (ISSN) 1867-8548 (eISSN)
Vol. 12 5 2693-2732Subject Categories
Bioinformatics and Systems Biology
Oceanography, Hydrology, Water Resources
Probability Theory and Statistics
DOI
10.5194/amt-12-2693-2019