Validation of Carbon Monoxide Total Column Retrievals from SCIAMACHY Observations with NDACC/TCCON Ground-Based Measurements

Abstract

Abstract:The objective was to validate the carbon monoxide (CO) total column product inferred from Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) full-mission (2003-2011) short-wave infrared (SWIR) nadir observations using the Beer InfraRed Retrieval Algorithm (BIRRA). Globally distributed Network for the Detection of Atmospheric Composition Change (NDACC) and Total Carbon Column Observing Network (TCCON) ground-based (g-b) measurements were used as a true reference. Weighted averages of SCIAMACHY CO observations within a circle around the g-b observing system were utilized to minimize effects due to spatial mismatch of space-based (s-b) and g-b observations, i.e., disagreements due to representation errors rather than instrument and/or algorithm deficiencies. In addition, temporal weighted averages were examined and then the unweighted (classical) approach was compared to the weighted (non-classical) method. The delivered distance-based filtered SCIAMACHY data were in better agreement with respect to CO averages as compared to square-shaped sampling areas throughout the year. Errors in individual SCIAMACHY retrievals have increased substantially since 2005. The global bias was determined to be in the order of −10 parts per billion in volume (ppbv) depending on the reference network and validation strategy used. The largest negative bias was found to occur in the northern mid-latitudes in Europe and North America, and was partly caused by insufficient a priori estimates of CO and cloud shielding. Furthermore, no significant trend was identified in the global bias throughout the mission. The global analysis of the CO columns retrieved by the BIRRA shows results that are largely consistent with similar investigations in previous works.

Impact of Molecular Spectroscopy on Carbon Monoxide Abundances from SCIAMACHY

“…In principle, the retrieval of the vertical column density N m = n m (z) dz of molecule m is equivalent to the problem of finding a scaling factor α m that is relating a (climatological) reference profile (e.g., US-Standard) to the actual profile n m (z) = α m n (ref) m (z). The retrieval setup in BIRRA for this study comprises a state vector x that includes the scaling factors α m of the reference optical depths of the molecules CO, CH 4 and H 2 O, three coefficients for the second order polynomial representing the surface reflectivity and optionally a wavenumber shift and the half width of the Gaussian SRF [37,40]. Atmospheric data for pressure, temperature, and water vapor concentrations were taken from the NCEP reanalysis [65] which provides four profiles per day since 1948 with a 2.5 • latitudinal and longitudinal resolution.…”

“…First, SCIAMACHY and TROPOMI feature very similar spectral characteristics in the 2.3 µm channels-the nominal spectral resolution is 0.26 nm and 0.25 nm, respectively. Second, BIRRA has been validated in terms of accuracy and precision using SCIAMACHY observations regarding NDACC (Network for the Detection of Atmospheric Composition Change) and TCCON (Total Carbon Column Observing Network) [40] and it was found to be largely consistent with findings by Borsdorff et al [35,36]. Furthermore, a coherent time series of CO comprising measurements from different instruments (e.g., TROPOMI is regarded as SCIAMACHY's successor) requires a harmonized and consistent description of physical processes, hence, improved molecular spectroscopy is relevant for SCIAMACHY, too.…”

Evaluation of MOPITT version 7 joint TIR-NIR X<sub>CO</sub> retrievals with TCCON

“…For example, at 60 • SZA for a MOPITT pixel centered on a TCCON site at sea level, the TCCON ray would leave the MOPITT pixel at around 11 km or above 250 hPa. For a comparison of SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric Chartography) with NDACC/TCCON, Hochstaffl et al (2018) found it was necessary to deweight observations that were further away in time and space from points of comparison. This is likely much less of an issue for this study due to differences in retrieval 10 errors and coincidence scales.…”

“…We apply spatial averaging to the MOPITT data typically over areas of 2 • ×4 • (with exceptions noted below). Spatial weighting is not as much of a concern here as for Hochstaffl et al (2018) with SCIAMACHY because they used coincidence criteria of 500-2000 km radii, which are significantly larger in terms of area (about 8-100×). However, despite 15 using smaller areas heterogeneities in CO sources that MOPITT averages over may occasionally introduce bias for real reasons (e.g., Lindenmaier et al, 2014).…”

Intercomparison of three microwave/infrared high resolution line-by-line radiative transfer codes

“…GARLIC [20,39] has been developed for high resolution infrared-microwave atmospheric radiative transfer modeling with a modular approach appropriate for simulation and retrieval in Earth [40,41] and planetary science [42]. Unlike ARTS, GARLIC is not open source, however, Py4CAtS -Python for Computational Atmospheric Spectroscopy [43] a lightweight implementation of GAR-LIC, is publically available at https://atmos.eoc.dlr.…”