Harmonization and comparison of vertically resolved atmospheric state observations: methods, effects, and uncertainty budget

Abstract

Abstract. Many applications of atmospheric composition and climate data involve the comparison or combination of vertically resolved atmospheric state variables. Calculating differences and combining data require harmonization of data representations in terms of physical quantities and vertical sampling at least. If one or both datasets result from a retrieval process, knowledge of prior information and averaging kernel matrices in principle allows retrieval differences to be accounted for as well. Spatiotemporal mismatch of the sensed air masses and its contribution to the data discrepancies can be estimated with chemistry transport modeling support. In this work an overview of harmonization or matching operations for atmospheric profile observations is provided. The effect of these manipulations on the information content of the original data and on the uncertainty budget of data comparisons is examined and discussed.

Overview: Estimating and reporting uncertainties in remotely sensed atmospheric composition and temperature

“…Most other limb-sounding retrieval schemes use the spherical homogeneity approximation, although this approach can be challenged for limb sounders. For example, Kiefer et al (2010) provided evidence of biases in trace gas retrievals from MIPAS limb emission spectra due to horizontal temperature gradients. Thus, neglect of the horizontal variation of the atmospheric state needs either to be corrected or to be considered in the error budget.…”

Validation of Aura-OMI QA4ECV NO<sub>2</sub> climate data records with ground-based DOAS networks: the role of measurement and comparison uncertainties

“…Data assimilation in TM5-MP (Boersma et al, 2018) Surface albedo from Kleipool et al (2008) 5-year climatology at 0.5 • × 0.5 • ; clouds from OMI O 2 -O 2 algorithm (OMCLDO2 data product, Veefkind et al, 2016); a priori NO 2 profiles from daily TM5-MP at…”

“…OMI STREAM a Weighted (observations with a negligible tropospheric contribution -clean regions and cloudy pixels) convolution (Beirle et al, 2016) OMNO2 v3.1 Marchenko et al 2015Three-step (interpolation, filtering and smoothing) stratospheric field reconstruction to fill in the tropospheric contaminated scenes (Bucsela et al, 2013) Surface albedo from Kleipool et al (2008) 5-year climatology at 0. and a priori profile shape on the retrieval in this QA4ECV approach, the horizontal location of the centre of the effectively probed air mass is close to the instrument location (typically within 1 km). The NO 2 AMF LUTs are produced using the bePRO/LIDORT radiative transfer suite (Clémer et al, 2010;Spurr, 2008).…”

TROPOMI tropospheric ozone column data: Geophysical assessment and comparison to ozonesondes, GOME-2B and OMI

“…A common approach is to reduce the uncertainty from nuisance parameters as much as possible, e.g., by using a wellcalibrated data record as a reference and/or by harmonising the representation of the data records (Keppens et al, 2019).…”