| 1. |
- Echle, Georg, et al.
(författare)
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Optimized spectral microwindows for data analysis of the Michelson Interferometer for Passive Atmospheric Sounding on the Environmental Satellite
- 2000
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Ingår i: Applied Optics. - 1559-128X .- 2155-3165. ; 39:30, s. 5531-
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Tidskriftsartikel (refereegranskat)abstract
- For data analysis of the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) atmospheric limb emission spectroscopic experiment on Environmental Satellite microwindows, i.e., small spectral regions for data analysis, have been defined and optimized. A novel optimization scheme has been developed for this purpose that adjusts microwindow boundaries such that the total retrieval error with respect to measurement noise, parameter uncertainties, and systematic errors is minimized. Dedicated databases that contain optimized microwindows for retrieval of vertical profiles of pressure and temperature, H2O, O3, HNO3, CH4, N2O, and NO2 have been generated. Furthermore, a tool for optimal selection of subsets of predefined microwindows for specific retrieval situations has been provided. This tool can be used further for estimating total retrieval errors for a selected microwindow subset. It has been shown by use of this tool that an altitude-dependent definition of microwindows is superior to an altitude-independent definition. For computational efficiency a dedicated microwindow-related list of spectral lines has been defined that contains only those spectral lines that are of relevance for MIPAS limb sounding observations.
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| 2. |
- Lossow, Stefan, 1977, et al.
(författare)
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The SPARC water vapour assessment II: Profile-to-profile comparisons of stratospheric and lower mesospheric water vapour data sets obtained from satellites
- 2019
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Ingår i: Atmospheric Measurement Techniques. - : Copernicus GmbH. - 1867-1381 .- 1867-8548. ; 12:5, s. 2693-2732
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Tidskriftsartikel (refereegranskat)abstract
- This work is distributed under the Creative Commons Attribution 4.0 License. Within the framework of the second SPARC (Stratosphere-troposphere Processes And their Role in Climate) water vapour assessment (WAVAS-II), profile-to-profile comparisons of stratospheric and lower mesospheric water vapour were performed by considering 33 data sets derived from satellite observations of 15 different instruments. These comparisons aimed to provide a picture of the typical biases and drifts in the observational database and to identify data-set-specific problems. The observational database typically exhibits the largest biases below 70 hPa, both in absolute and relative terms. The smallest biases are often found between 50 and 5 hPa. Typically, they range from 0.25 to 0.5 ppmv (5 % to 10 %) in this altitude region, based on the 50 % percentile over the different comparison results. Higher up, the biases increase with altitude overall but this general behaviour is accompanied by considerable variations. Characteristic values vary between 0.3 and 1 ppmv (4 % to 20 %). Obvious data-set-specific bias issues are found for a number of data sets. In our work we performed a drift analysis for data sets overlapping for a period of at least 36 months. This assessment shows a wide range of drifts among the different data sets that are statistically significant at the 2 σ uncertainty level. In general, the smallest drifts are found in the altitude range between about 30 and 10 hPa. Histograms considering results from all altitudes indicate the largest occurrence for drifts between 0.05 and 0.3 ppmv decade-1. Comparisons of our drift estimates to those derived from comparisons of zonal mean time series only exhibit statistically significant differences in slightly more than 3 % of the comparisons. Hence, drift estimates from profile-to-profile and zonal mean time series comparisons are largely interchangeable. As for the biases, a number of data sets exhibit prominent drift issues. In our analyses we found that the large number of MIPAS data sets included in the assessment affects our general results as well as the bias summaries we provide for the individual data sets. This is because these data sets exhibit a relative similarity with respect to the remaining data sets, despite the fact that they are based on different measurement modes and different processors implementing different retrieval choices. Because of that, we have by default considered an aggregation of the comparison results obtained from MIPAS data sets. Results without this aggregation are provided on multiple occasions to characterise the effects due to the numerous MIPAS data sets. Among other effects, they cause a reduction of the typical biases in the observational database.
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