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.


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


J. M. Russell

Hampton University Center for Atmospheric Sciences

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

Serco Group plc

Institute of Atmospheric Sciences and Climate, Bologna

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 2693-2732

Subject Categories

Bioinformatics and Systems Biology

Oceanography, Hydrology, Water Resources

Probability Theory and Statistics



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