Articles | Volume 9, issue 9
Research article
08 Sep 2016
Research article |  | 08 Sep 2016

Uncertainty budgets of major ozone absorption cross sections used in UV remote sensing applications

Mark Weber, Victor Gorshelev, and Anna Serdyuchenko

Abstract. Detailed uncertainty budgets of three major ultraviolet (UV) ozone absorption cross-section datasets that are used in remote sensing application are provided and discussed. The datasets are Bass–Paur (BP), Brion–Daumont–Malicet (BDM), and the more recent Serdyuchenko–Gorshelev (SG). For most remote sensing application the temperature dependence of the Huggins ozone band is described by a quadratic polynomial in temperature (Bass–Paur parameterization) by applying a regression to the cross-section data measured at selected atmospherically relevant temperatures. For traceability of atmospheric ozone measurements, uncertainties from the laboratory measurements as well as from the temperature parameterization of the ozone cross-section data are needed as input for detailed uncertainty calculation of atmospheric ozone measurements. In this paper the uncertainty budgets of the three major ozone cross-section datasets are summarized from the original literature. The quadratic temperature dependence of the cross-section datasets is investigated. Combined uncertainty budgets is provided for all datasets based upon Monte Carlo simulation that includes uncertainties from the laboratory measurements as well as uncertainties from the temperature parameterization. Between 300 and 330 nm both BDM and SG have an overall uncertainty of 1.5 %, while BP has a somewhat larger uncertainty of 2.1 %. At temperatures below about 215 K, uncertainties in the BDM data increase more strongly than the others due to the lack of very low temperature laboratory measurements (lowest temperature of BDM available is 218 K).

Short summary
Ozone absorption cross sections measured in the laboratory using spectroscopic means can be a major source of uncertainty in atmospheric ozone retrievals. In this paper we assess the overall uncertainty in three published UV ozone cross-section datasets that are most popular in the remote sensing community. The overall uncertainties were estimated using Monte Carlo simulations. They are important for traceability of atmospheric ozone measuring instruments to common metrological standards.