Articles | Volume 10, issue 3
Atmos. Meas. Tech., 10, 955–978, 2017

Special issue: Twenty-five years of operations of the Network for the Detection...

Atmos. Meas. Tech., 10, 955–978, 2017

Research article 10 Mar 2017

Research article | 10 Mar 2017

Investigating differences in DOAS retrieval codes using MAD-CAT campaign data

Enno Peters1, Gaia Pinardi2, André Seyler1, Andreas Richter1, Folkard Wittrock1, Tim Bösch1, Michel Van Roozendael2, François Hendrick2, Theano Drosoglou3, Alkiviadis F. Bais3, Yugo Kanaya4, Xiaoyi Zhao5, Kimberly Strong5, Johannes Lampel6,11, Rainer Volkamer7,8, Theodore Koenig7,8, Ivan Ortega7,8,a, Olga Puentedura9, Mónica Navarro-Comas9, Laura Gómez9, Margarita Yela González9, Ankie Piters10, Julia Remmers11, Yang Wang11, Thomas Wagner11, Shanshan Wang12,13, Alfonso Saiz-Lopez12, David García-Nieto12, Carlos A. Cuevas12, Nuria Benavent12, Richard Querel14, Paul Johnston14, Oleg Postylyakov15, Alexander Borovski15, Alexander Elokhov15, Ilya Bruchkouski16, Haoran Liu17, Cheng Liu17,18,19, Qianqian Hong19, Claudia Rivera20, Michel Grutter21, Wolfgang Stremme21, M. Fahim Khokhar22, Junaid Khayyam22, and John P. Burrows1 Enno Peters et al.
  • 1Institute of Environmental Physics, University of Bremen, Bremen, Germany
  • 2Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium
  • 3Aristotle University of Thessaloniki, Thessaloniki, Greece
  • 4Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, Japan
  • 5Department of Physics, University of Toronto, Ontario, Canada
  • 6Institute of Environmental Physics, University of Heidelberg, Heidelberg, Germany
  • 7Department of Chemistry and Biochemistry, University of Colorado, Boulder, CO, USA
  • 8Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, CO, USA
  • 9National Institute for Aerospace technology, INTA, Madrid, Spain
  • 10Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
  • 11Max Planck Institute for Chemistry, Mainz, Germany
  • 12Department of Atmospheric Chemistry and Climate, Institute of Physical Chemistry Rocasolano, CSIC, Madrid, Spain
  • 13Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP), Department of Environmental Science & Engineering, Fudan University, Shanghai, China
  • 14National Institute of Water and Atmospheric Research (NIWA), Lauder, New Zealand
  • 15A. M. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, Moscow, Russia
  • 16National Ozone Monitoring Research and Education Center BSU (NOMREC BSU), Belarusian State University (BSU), Minsk, Belarus
  • 17School of Earth and Space Sciences, University of Science and Technology of China, Hefei, 230026, China
  • 18CAS Center for Excellence in Regional Atmospheric Environment, Xiamen, 361021, China
  • 19Key Lab of Environmental Optics & Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei, 230031, China
  • 20Facultad de Química, Universidad Nacional Autónoma de México, Mexico City, Mexico
  • 21Centro de Ciencias de la Atmósfera, Universidad Nacional Autónoma de México, Mexico City, Mexico
  • 22Institute of Environmental Sciences and Engineering (IESE), National University of Sciences and Technology (NUST) Islamabad, Islamabad, Pakistan
  • anow at: National Center for Atmospheric Research (NCAR), Boulder, CO, USA

Abstract. The differential optical absorption spectroscopy (DOAS) method is a well-known remote sensing technique that is nowadays widely used for measurements of atmospheric trace gases, creating the need for harmonization and characterization efforts. In this study, an intercomparison exercise of DOAS retrieval codes from 17 international groups is presented, focusing on NO2 slant columns. The study is based on data collected by one instrument during the Multi-Axis DOAS Comparison campaign for Aerosols and Trace gases (MAD-CAT) in Mainz, Germany, in summer 2013. As data from the same instrument are used by all groups, the results are free of biases due to instrumental differences, which is in contrast to previous intercomparison exercises.

While in general an excellent correlation of NO2 slant columns between groups of  >  99.98 % (noon reference fits) and  >  99.2 % (sequential reference fits) for all elevation angles is found, differences between individual retrievals are as large as 8 % for NO2 slant columns and 100 % for rms residuals in small elevation angles above the horizon.

Comprehensive sensitivity studies revealed that absolute slant column differences result predominantly from the choice of the reference spectrum while relative differences originate from the numerical approach for solving the DOAS equation as well as the treatment of the slit function. Furthermore, differences in the implementation of the intensity offset correction were found to produce disagreements for measurements close to sunrise (8–10 % for NO2, 80 % for rms residual). The largest effect of  ≈  8 % difference in NO2 was found to arise from the reference treatment; in particular for fits using a sequential reference. In terms of rms fit residual, the reference treatment has only a minor impact. In contrast, the wavelength calibration as well as the intensity offset correction were found to have the largest impact (up to 80 %) on rms residual while having only a minor impact on retrieved NO2 slant columns.

Short summary
This work is about harmonization of differential optical absorption spectroscopy retrieval codes, which is a remote sensing technique widely used to derive atmospheric trace gas amounts. The study is based on ground-based measurements performed during the Multi-Axis DOAS Comparison campaign for Aerosols and Trace gases (MAD-CAT) in Mainz, Germany, in summer 2013. In total, 17 international groups working in the field of the DOAS technique participated in this study.