Interference errors in infrared remote sounding of the atmosphere

International audienceMore and more profiles of atmospheric state parameters are being retrieved from remote soundings in the infrared spectral domain. Classical error analysis, which was originally applied to microwave sounding systems, distinguishes between "smoothing errors," "forward model errors," "forward model parameter errors," and "retrieval noise errors". We show that for infrared soundings "interference errors", which have not been treated up to now, can be significant. Interference errors originate from "interfering species" that introduce signatures into the spectral measurement which overlap with the spectral features used for retrieval of the target species. This is a frequent situation in infrared atmospheric spectra where the vibration-rotation bands of different species often overlap; it is not the case in the microwave region. This paper presents a full theoretical formulation of interference errors. It requires a generalized state vector including profile entries for all interfering species. This leads to a generalized averaging kernel matrix made up of classical averaging kernels plus here defined "interference kernels". The latter are used together with climatological covariances for the profiles of the interfering species in order to quantify the interference errors. To illustrate the methods we apply them to a real sounding and show that interference errors have a significant impact on standard CO profile retrievals from ground-based mid-infrared solar absorption spectra. We also demonstrate how to minimize overall error, which is a trade-off between minimizing interference errors and the smoothing error. The approach used in this paper can be applied to soundings of all infrared-active atmospheric species, which includes more than two dozen different gases relevant to climate and ozone. And this holds for all kind of infrared remote sounding systems, i.e., retrievals from ground-based, balloon-borne, airborne, or satellite spectroradiometers

Technical Note: Interference errors in infrared remote sounding of the atmosphere

Classical error analysis in remote sounding distinguishes between four classes: "smoothing errors," "model parameter errors," "forward model errors," and "retrieval noise errors". For infrared sounding "interference errors", which, in general, cannot be described by these four terms, can be significant. Interference errors originate from spectral residuals due to "interfering species" whose spectral features overlap with the signatures of the target species. A general method for quantification of interference errors is presented, which covers all possible algorithmic implementations, i.e., fine-grid retrievals of the interfering species or coarse-grid retrievals, and cases where the interfering species are not retrieved. In classical retrieval setups interference errors can exceed smoothing errors and can vary by orders of magnitude due to state dependency. An optimum strategy is suggested which practically eliminates interference errors by systematically minimizing the regularization strength applied to joint profile retrieval of the interfering species. This leads to an interfering-species selective deweighting of the retrieval. Details of microwindow selection are no longer critical for this optimum retrieval and widened microwindows even lead to reduced overall (smoothing and interference) errors. Since computational power will increase, more and more operational algorithms will be able to utilize this optimum strategy in the future. The findings of this paper can be applied to soundings of all infrared-active atmospheric species, which include more than two dozen different gases relevant to climate and ozone. This holds for all kinds of infrared remote sounding systems, i.e., retrievals from ground-based, balloon-borne, airborne, or satellite spectroradiometers

Intercomparison of atmospheric water vapor soundings from the differential absorption lidar (DIAL) and the solar FTIR system on Mt. Zugspitze

We present an intercomparison of three years of measurements of integrated water vapor (IWV) performed by the mid-infrared solar FTIR (Fourier Transform Infra-Red) instrument on the summit of Mt. Zugspitze (2964 m a.s.l.) and by the nearby near-infrared differential absorption lidar (DIAL) at the Schneefernerhaus research station (2675 m a.s.l.). The solar FTIR was shown to be one of the most accurate and precise IWV sounders in recent work (Sussmann et al., 2009) and is taken as the reference here. By calculating the FTIR-DIAL correlation (22 min coincidence interval, 15 min integration time) we derive an almost ideal slope of 0.996 (10), a correlation coefficient of <i>R</i> = 0.99, an IWV intercept of −0.039 (42) mm (−1.2 % of the mean), and a bias of −0.052 (26) mm (−1.6 % of the mean) from the scatter plot. By selecting a subset of coincidences with an optimum temporal and spatial matching between DIAL and FTIR, we obtain a conservative estimate of the precision of the DIAL in measuring IWV which is better than 0.1 mm (3.2 % of the mean). We found that for a temporal coincidence interval of 22 min the difference in IWV measured by these two systems is dominated by the volume mismatch (horizontal distance: 680 m). The outcome from this paper is twofold: (1) the IWV soundings by FTIR and DIAL agree very well in spite of the differing wavelength regions with different spectroscopic line parameters and retrieval algorithms used. (2) In order to derive an estimate of the precision of state-of-the-art IWV sounders from intercomparison experiments, it is necessary to use a temporal matching on time scales shorter than 10 min and a spatial matching on the 100-m scale

Insights into Tikhonov regularization: application to trace gas column retrieval and the efficient calculation of total column averaging kernels

Insights are given into Tikhonov regularization and its application to the retrieval of vertical column densities of atmospheric trace gases from remote sensing measurements. The study builds upon the equivalence of the least-squares profile-scaling approach and Tikhonov regularization method of the first kind with an infinite regularization strength. Here, the vertical profile is expressed relative to a reference profile. On the basis of this, we propose a new algorithm as an extension of the least-squares profile scaling which permits the calculation of total column averaging kernels on arbitrary vertical grids using an analytic expression. Moreover, we discuss the effective null space of the retrieval, which comprises those parts of a vertical trace gas distribution which cannot be inferred from the measurements. Numerically the algorithm can be implemented in a robust and efficient manner. In particular for operational data processing with challenging demands on processing time, the proposed inversion method in combination with highly efficient forward models is an asset. For demonstration purposes, we apply the algorithm to CO column retrieval from simulated measurements in the 2.3 μm spectral region and to O₃ column retrieval from the UV. These represent ideal measurements of a series of spaceborne spectrometers such as SCIAMACHY, TROPOMI, GOME, and GOME-2. For both spectral ranges, we consider clear-sky and cloudy scenes where clouds are modelled as an elevated Lambertian surface. Here, the smoothing error for the clear-sky and cloudy atmosphere is significant and reaches several percent, depending on the reference profile which is used for scaling. This underlines the importance of the column averaging kernel for a proper interpretation of retrieved column densities. Furthermore, we show that the smoothing due to regularization can be underestimated by calculating the column averaging kernel on a too coarse vertical grid. For both retrievals, this effect becomes negligible for a vertical grid with 20–40 equally thick layers between 0 and 50 km