Abstract
The thermal infrared nadir spectra of IASI (Infrared Atmospheric Sounding Interferometer) are successfully used for retrievals of different atmospheric trace gas profiles. However, these retrievals offer generally reduced information about the lowermost tropospheric layer due to the lack of thermal contrast close to the surface. Spectra of scattered solar radiation observed in the near and/or short wave infrared, for instance by TROPOMI (TROPOspheric Monitoring Instrument) offer higher sensitivity near ground and are used for the retrieval of total column averaged mixing ratios of a variety of atmospheric trace gases. Here we present a method for the synergetic use of IASI profile and TROPOMI total column data. Our method uses the output of the individual retrievals and consists of linear algebra a posteriori calculations (i.e. calculation after the individual retrievals). We show that this approach is largely equivalent to applying the spectra of the different sensors together in a single retrieval procedure, but with the substantial advantage of being applicable to data generated with different individual retrieval processors, of being very time efficient, and of directly benefiting from the high quality and most recent improvements of the individual retrieval processors. We demonstrate the method exemplarily for atmospheric methane (CH4). We perform a theoretical evaluation and show that the a posteriori combination method yields a total column averaged CH4 product (XCH4) that conserves the good sensitivity of the corresponding TROPOMI product while merging it with the upper tropospheric and lower stratospheric (UTLS) CH4 partial column information of the corresponding IASI product. As consequence, the combined product offers in addition sensitivity for the tropospheric CH4 partial column, which is not provided by the individual TROPOMI nor the individual IASI product. The theoretically predicted synergetic effects are verified by comparisons to CH4 reference data obtained from collocated XCH4 measurements at six globally distributed TCCON (Total Carbon Column Observing Network) stations, CH4 profile measurements made by 24 individual AirCore soundings, and lower tropospheric CH4 data derived from continuous ground-based in-situ observations made at two nearby Global Atmospheric Watch (GAW) mountain stations. The comparisons clearly demonstrate that the combined product can reliably detect XCH4 signals and allows to distinguish between tropospheric and UTLS CH4 partial column averaged mixing ratios, which is not possible by the individual TROPOMI and IASI products. We find indications of a weak positive bias of about +1 % of the combined lower tropospheric data product with respect to the references. For the UTLS CH4 partial columns we find no significant bias.
Highlights
Measurements from different ground- or satellite-based sensors target at the observations of the same atmospheric parameters, but with different characteristics
We show that this approach is largely equivalent to applying the spectra of the different sensors together in a single retrieval procedure, but with the substantial advantage of being applicable to data generated with different individual retrieval 10 processors, of being very time efficient, and of directly benefiting from the high quality and most recent improvements of the individual retrieval processors
We present a method for a synergetic use of TROPOMI total column and IASI vertical profile retrieval products
Summary
Measurements from different ground- or satellite-based sensors target at the observations of the same atmospheric parameters (e.g. the same trace gases), but with different characteristics (e.g. sensitivities for different vertical regions). We propose to generate a multi-sensor optimal estimation CH4 profile product by simple a posteriori calculations using available outputs of IASI and TROPOMI (Tropospheric Monitoring Instrument) retrievals. 60 ally very efficient generation of global daily maps of the combined data product and only needs the individually retrieved states, averaging kernels and noise covariances provided by the respective remote sensing experts in the context of their standard retrieval work. Respective data allow a more direct investigation of the CH4 boundary layer source and sink signals than total column averaged mixing ratios (XCH4) provided globally for instance by GOSAT (e.g. Parker et al, 2020) or TROPOMI (Lorente et al, 2020) This is because XCH4 signals are strongly affected by vertical shifts of the tropopause altitude, i.e. their use for investigating CH4 absorption and release at ground depends on the correct consideration of the 70 tropopause altitude by model simulations (Pandey et al, 2016). Appendix C presents the operators used for converting 80 vertical profile data into total and partial column data
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