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

Mapping global atmospheric CO2 concentration at high spatiotemporal resolution

Satellite measurements of the spatiotemporal distributions of atmospheric CO2 concentrations are a key component for better understanding global carbon cycle characteristics. Currently, several satellite instruments such as the Greenhouse gases Observing SATellite (GOSAT), SCanning Imaging Absorption Spectrometer for Atmospheric CHartographY (SCIAMACHY), and Orbiting Carbon Observatory-2 can be used to measure CO2 column-averaged dry air mole fractions. However, because of cloud effects, a single satellite can only provide limited CO2 data, resulting in significant uncertainty in the characterization of the spatiotemporal distribution of atmospheric CO2 concentrations. In this study, a new physical data fusion technique is proposed to combine the GOSAT and SCIAMACHY measurements. On the basis of the fused dataset, a gap-filling method developed by modeling the spatial correlation structures of CO2 concentrations is presented with the goal of generating global land CO2 distribution maps with high spatiotemporal resolution. The results show that, compared with the single satellite dataset (i.e., GOSAT or SCIAMACHY), the global spatial coverage of the fused dataset is significantly increased (reaching up to approximately 20%), and the temporal resolution is improved by two or three times. The spatial coverage and monthly variations of the generated global CO2 distributions are also investigated. Comparisons with ground-based Total Carbon Column Observing Network (TCCON) measurements reveal that CO2 distributions based on the gap-filling method show good agreement with TCCON records despite some biases. These results demonstrate that the fused dataset as well as the gap-filling method are rather effective to generate global CO2 distribution with high accuracies and high spatiotemporal resolution

Spatiotemporal variability of water vapor investigated using lidar and FTIR vertical soundings above the Zugspitze

Water vapor is the most important greenhouse gas and its spatiotemporal variability strongly exceeds that of all other greenhouse gases. However, this variability has hardly been studied quantitatively so far. We present an analysis of a 5-year period of water vapor measurements in the free troposphere above the Zugspitze (2962 m a.s.l., Germany). Our results are obtained from a combination of measurements of vertically integrated water vapor (IWV), recorded with a solar Fourier transform infrared (FTIR) spectrometer on the summit of the Zugspitze and of water vapor profiles recorded with the nearby differential absorption lidar (DIAL) at the Schneefernerhaus research station. The special geometrical arrangement of one zenith-viewing and one sun-pointing instrument and the temporal resolution of both instruments allow for an investigation of the spatiotemporal variability of IWV on a spatial scale of less than 1 km and on a timescale of less than 1 h. The standard deviation of differences between both instruments sigma IWV calculated for varied subsets of data serves as a measure of variability. The different subsets are based on various spatial and temporal matching criteria. Within a time interval of 20 min, the spatial variability becomes significant for horizontal distances above 2 km, but only in the warm season (sigma IWV = 0.35 mm). However, it is not sensitive to the horizontal distance during the winter season. The variability of IWV within a time interval of 30 min peaks in July and August (sigma IWV > 0.55 mm, mean horizontal distance = 2.5 km) and has its minimum around midwinter (sigma IWV 5 km). The temporal variability of IWV is derived by selecting subsets of data from both instruments with optimal volume matching. For a short time interval of 5 min, the variability is 0.05 mm and increases to more than 0.5 mm for a time interval of 15 h. The profile variability of water vapor is determined by analyzing subsets of water vapor profiles recorded by the DIAL within time intervals from 1 to 5 h. For all altitudes, the variability increases with widened time intervals. The lowest relative variability is observed in the lower free troposphere around an altitude of 4.5 km. Above 5 km, the relative variability increases continuously up to the tropopause by about a factor of 3. Analysis of the covariance of the vertical variability reveals an enhanced variability of water vapor in the upper troposphere above 6 km. It is attributed to a more coherent flow of heterogeneous air masses, while the variability at lower altitudes is also driven by local atmospheric dynamics. By studying the short-term variability of vertical water vapor profiles recorded within a day, we come to the conclusion that the contribution of long-range transport and the advection of heterogeneous layer structures may exceed the impact of local convection by 1 order of magnitude even in the altitude range between 3 and 5 km

Spatio-Temporal Variability of Water Vapor in the Free Troposphere Investigated by Dial and Ftir Vertical Soundings

We report on the free tropospheric spatio-temporal variability of water vapor investigated by the analysis of a five-year period of water vapor vertical soundings above Mt. Zugspitze (2962 m a.s.l., Germany). Our results are obtained from a combination of measurements of vertically integrated water vapor (IWV), recorded with a solar Fourier Transform InfraRed (FTIR) spectrometer and of water vapor profiles recorded with the nearby differential absorption lidar (DIAL). The special geometrical arrangement of one zenith-viewing and one sun-pointing instrument and the temporal resolution of both optical instruments allow for an investigation of the spatiotemporal variability of IWV on a spatial scale of less than one kilometer and on a time scale of less than one hour. We investigated the short-term variability of both IWV and water vapor profiles from statistical analyses. The latter was also examined by case studies with a clear assignment to certain atmospheric processes as local convection or long-range transport. This study is described in great detail in our recent publication [1]