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
Received: 28 Oct 2016 – Discussion started: 11 Nov 2016
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.
Revised: 31 Jan 2017 – Accepted: 10 Feb 2017 – Published: 10 Mar 2017
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.
Peters, E., Pinardi, G., Seyler, A., Richter, A., Wittrock, F., Bösch, T., Van Roozendael, M., Hendrick, F., Drosoglou, T., Bais, A. F., Kanaya, Y., Zhao, X., Strong, K., Lampel, J., Volkamer, R., Koenig, T., Ortega, I., Puentedura, O., Navarro-Comas, M., Gómez, L., Yela González, M., Piters, A., Remmers, J., Wang, Y., Wagner, T., Wang, S., Saiz-Lopez, A., García-Nieto, D., Cuevas, C. A., Benavent, N., Querel, R., Johnston, P., Postylyakov, O., Borovski, A., Elokhov, A., Bruchkouski, I., Liu, H., Liu, C., Hong, Q., Rivera, C., Grutter, M., Stremme, W., Khokhar, M. F., Khayyam, J., and Burrows, J. P.: Investigating differences in DOAS retrieval codes using MAD-CAT campaign data, Atmos. Meas. Tech., 10, 955-978, doi:10.5194/amt-10-955-2017, 2017.