ALBERT

Description: With the increasing availability of observations from different space-borne sensors, the joint analysis of observational data from multiple sources becomes more and more attractive. For such an analysis – oftentimes with little prior knowledge about local and global interactions between the different observational variables available – an explorative data-driven analysis of the remote sensing data may be of particular relevance. In the present work we used generalized additive models (GAM) in this task, in an exemplary study of spatio-temporal patterns in the tropospheric NO2-distribution derived from GOME satellite observations (1996 to 2001) at global scale. We modelled different temporal trends in the time series of the observed NO2, but focused on identifying correlations between NO2 and local wind fields. Here, our nonparametric modelling approach had several advantages over standard parametric models: While the model-based analysis allowed to test predefined hypotheses (assuming, for example, sinusoidal seasonal trends) only, the GAM allowed to learn functional relations between different observational variables directly from the data. This was of particular interest in the present task, as little was known about relations between the observed NO2 distribution and transport processes by local wind fields, and the formulation of general functional relationships to be tested remained difficult. We found the observed temporal trends – weekly, seasonal and linear changes – to be in overall good agreement with previous studies and alternative ways of data analysis. However, NO2 observations showed to be affected by wind-dominated processes over several areas, world wide. Here we were able to estimate the extent of areas affected by specific NO2 emission sources, and to highlight likely atmospheric transport pathways. Overall, using a nonparametric model provided favourable means for a rapid inspection of this large spatio-temporal data set,with less bias than parametric approaches, and allowing to visualize dynamical processes of the NO2 distribution at a global scale.

Description: International shipping is recognized as a pollution source of growing importance, in particular in the remote marine boundary layer. Nitrogen dioxide originating from ship emissions has previously been detected in satellite measurements. This study presents the first satellite measurements of formaldehyde (HCHO) linked to shipping emissions as derived from observations made by the Global Ozone Monitoring Experiment (GOME) instrument. We analyzed enhanced HCHO tropospheric columns from shipping emissions over the Indian Ocean between Sri Lanka and Sumatra. This region offers good conditions in term of plume detection with the GOME instrument as all ship tracks follow a single narrow track in the same east-west direction as used for the GOME pixel scanning. The HCHO signal alone is weak but could be clearly seen in the high-pass filtered data. The line of enhanced HCHO in the Indian Ocean as seen in the 7-year composite of cloud free GOME observations clearly coincides with the distinct ship track corridor from Sri Lanka to Indonesia. The observed mean HCHO column enhancement over this shipping route is about 2.0×1015 molec/cm2. Compared to the simultaneously observed NO2 values over the shipping route, those of HCHO are substantially higher; also the HCHO peaks are found at larger distance from the ship routes. These findings indicate that direct emissions of HCHO or degradation of emitted NMHC cannot explain the observed enhanced HCHO values. One possible reason might be increased CH4 degradation due to enhanced OH concentrations related to the ship emissions, but this source is probably too weak to fully explain the observed values. The observed HCHO pattern also agrees qualitatively well with results from the coupled earth system model ECHAM5/MESSy applied to atmospheric chemistry (EMAC). However, the modelled HCHO values over the ship corridor are two times lower than in the GOME high-pass filtered data. This might indicate uncertainties in the satellite data and used emission inventories and/or that the in-plume chemistry taking place in the narrow path of the shipping lanes are not well represented at the rather coarse model resolution.

Description: With the increasing availability of observational data from different sources at a global level, joint analysis of these data is becoming especially attractive. For such an analysis – oftentimes with little prior knowledge about local and global interactions between the different observational variables at hand – an exploratory, data-driven analysis of the data may be of particular relevance. In the present work we used generalized additive models (GAM) in an exemplary study of spatio-temporal patterns in the tropospheric NO2-distribution derived from GOME satellite observations (1996 to 2001) at global scale. We focused on identifying correlations between NO2 and local wind fields, a quantity which is of particular interest in the analysis of spatio-temporal interactions. Formulating general functional, parametric relationships between the observed NO2 distribution and local wind fields, however, is difficult – if not impossible. So, rather than following a model-based analysis testing the data for predefined hypotheses (assuming, for example, sinusoidal seasonal trends), we used a GAM with non-parametric model terms to learn this functional relationship between NO2 and wind directly from the data. The NO2 observations showed to be affected by wind-dominated processes over large areas. We estimated the extent of areas affected by specific NO2 emission sources, and were able to highlight likely atmospheric transport "pathways". General temporal trends which were also part of our model – weekly, seasonal and linear changes – showed to be in good agreement with previous studies and alternative ways of analysing the time series. Overall, using a non-parametric model provided favorable means for a rapid inspection of this large spatio-temporal NO2 data set, with less bias than parametric approaches, and allowing to visualize dynamical processes of the NO2 distribution at a global scale.

Description: In this study, we analyse the sensitivity of nadir viewing satellite observations in the visible range to freshly produced lightning NOx. This is a particular challenge due to the complex and highly variable conditions of meteorology, (photo-) chemistry, and radiative transfer in and around cumulonimbus clouds. For the first time, such a study is performed accounting for photo-chemistry, dynamics, and radiative transfer in a consistent way: A one week episode in the TOGA COARE/CEPEX region (Pacific) in December 1992 is simulated with a 3-D cloud resolving chemistry model. The simulated hydrometeor mixing ratios are fed into a Monte Carlo radiative transfer model to calculate box-Air Mass Factors (box-AMFs) for NO2. From these box-AMFs, together with model NOx profiles, slant columns of NO2 (SNO2), i.e. synthetic satellite measurements, are calculated and set in relation to the actual model NOx vertical column (VNOx), yielding the "sensitivity" SNO2/VNOx. From this study, we find a mean sensitivity of 0.46. NOx below the cloud bottom is mostly present as NO2, but shielded from the satellites' view, whereas NOx at the cloud top or above is shifted to NO due to high photolysis and low temperature, and hence not detectable from space. However, a significant fraction of the lightning produced NOx in the middle part of the cloud is present as NO2 and has a good visibility from space. Due to the resulting total sensitivity being quite high, nadir viewing satellites provide a valuable additional platform to quantify NOx production by lightning; strong lightning events over "clean" regions should be clearly detectable in satellite observations. Since the observed enhancement of NO2 column densities over mesoscale convective systems are lower than expected for current estimates of NOx production per flash, satellite measurements can in particular constrain the upper bound of lightning NOx production estimates.

Description: In 1997/1998 a severe smoke episode due to extensive biomass burning, especially of peat, was observed over Indonesia. September 1997 was the month with the highest aerosol burden. This month was simulated using the limited area model REMOTE driven at its lateral boundaries by ERA40 reanalysis data. REMOTE was extended by a new convective cloud parameterization mimicking individual clouds competing for instability energy. This allows for the interaction of aerosols, convective clouds and precipitation. Results show that in the monthly mean convective precipitation is diminished at nearly all places with high aerosol loading, but at some areas with high background humidity precipitation from large-scale clouds may over-compensate the loss in convective rainfall. The simulations revealed that both large-scale and convective clouds' microphysics are influenced by aerosols. Since aerosols are washed and rained out by rainfall, high aerosol concentrations can only persist at low rainfall rates. Hence, aerosol concentrations are not independent of the rainfall amount and in the mean the maximum absolute effects on rainfall from large scale clouds are found at intermediate aerosol concentrations. The reason for this behavior is that at high aerosol concentrations rainfall rates are small and consequently also the anomalies are small. For large-scale as well as for convective rain negative and positive anomalies are found for all aerosol concentrations. Negative anomalies dominate and are highly statistically significant especially for convective rainfall since part of the precipitation loss from large-scale clouds is compensated by moisture detrained from the convective clouds. The mean precipitation from large-scale clouds is less reduced (however still statistically significant) than rain from convective clouds. This effect is due to detrainment of cloud water from the less strongly raining convective clouds and because of the generally lower absolute amounts of rainfall from large-scale clouds. With increasing aerosol load both, convective and large scale clouds produce less rain. At very few individual time steps cases were found when polluted convective clouds produced intensified rainfall via mixed phase microphysics. However, these cases are not unequivocal and opposite results were also simulated, indicating that other than aerosol-microphysics effects have important impact on the results. Overall, the introduction of the new cumulus parameterization and aerosol-cloud interaction reduced some of the original REMOTE biases of precipitation patterns and total amount.

Description: The Absorbing Aerosol Index (AAI) is a useful tool for detecting aerosols that absorb UV radiation – especially in cases where other aerosol retrievals fail, such as over bright surfaces (e.g. desert) and in the presence of clouds. The AAI does not, however, consider contributions from scattering (hardly absorbing) aerosols and clouds: they cause negative AAI values and are usually disregarded. In this paper, we demonstrate the use of the AAI's negative counterpart, the SCattering Index (SCI) to detect scattering aerosols. Consideration of the full UV Aerosol Index scale is of importance if the Aerosol Index is to be used for the quantification of aerosol absorption in the future. Maps of seasonally averaged SCI show significantly enhanced values in summer in Southeast USA and Southeast Asia, pointing to a high production of scattering aerosols (presumably mainly sulphate aerosols and secondary organic aerosols) in this season. The application of a cloud filter makes the presence of scattering aerosols even more clear. Radiative transfer calculations were performed to investigate the sensitivity of AAI and SCI to cloud parameters, and it is demonstrated that clouds cause significant SCI, in some special cases even small AAI values. The results from cloud modelling imply that cloud effects need to be taken into account when AAI and SCI are used in a quantitative manner. The paper concludes with a comparison of aerosol parameters from AERONET and our Aerosol Indices (AAI and SCI) from SCIAMACHY, where reasonable agreement was found for six AERONET stations in Southeast USA, Southeast Asia, and Africa. These findings corroborate the suitability of SCI as a tool to detect scattering aerosols.

Description: We present a new technique for the quantitative simulation of the "Ring effect" for scattered light observations from various platforms and under different atmospheric situations. The method is based on radiative transfer calculations at only one wavelength λ0 in the wavelength range under consideration, and is thus computationally fast. The strength of the Ring effect is calculated from statistical properties of the photon paths for a given situation, which makes Monte Carlo radiative transfer models in particular appropriate. We quantify the Ring effect by the so called rotational Raman scattering probability, the probability that an observed photon has undergone a rotational Raman scattering event. The Raman scattering probability is independent from the spectral resolution of the instrument and can easily be converted into various definitions used to characterise the strength of the Ring effect. We compare the results of our new method to the results of previous studies and in general good quantitative agreement is found. In addition to the simulation of the Ring effect, we developed a detailed retrieval strategy for the analysis of the Ring effect based on DOAS retrievals, which allows the precise determination of the strength of the Ring effect for a specific wavelength while using the spectral information within a larger spectral interval around the selected wavelength. Using our new technique, we simulated synthetic satellite observation of an atmospheric scenario with a finite cloud illuminated from different sun positions.

Description: A DOAS (Differential Optical Absorption Spectroscopy) instrument was implemented and operated onboard a long-distance passenger aircraft within the framework of the CARIBIC project (Civil Aircraft for the Regular Investigation of the atmosphere Based on an Instrument Container). The instrument was designed to keep weight, size and power consumption low and to comply with civil aviation regulations. It records spectra of scattered light from three viewing directions (nadir, 10° above and below horizon) using a miniaturized telescope system. The telescopes are integrated in the main pylon of the inlet system which is mounted at the belly of the aircraft. Fibre bundles transmit light from the telescopes to spectrograph-detector units inside the DOAS container instrument. The latter is part of the removable CARIBIC instrument container, which is installed monthly on the aircraft for a series of measurement flights. During 30 flight operations within three years, measurements of HCHO, HONO, NO2, BrO, O3 and the oxygen dimer O4 were conducted. All of these trace gases except BrO could be analysed with a 30 s time resolution. HONO was detected for the first time in a deep convective cloud over central Asia, while BrO, NO2 and O3 could be observed in tropopause fold regions. Biomass burning signatures over South America could be seen and measurements during ascent and descent provided information on boundary layer trace gas profiles (e.g. NO2 or HCHO).

Description: We present a new technique for the quantitative simulation of the "Ring effect" for scattered light observations from various platforms and under different atmospheric situations. The method is based on radiative transfer calculations at only one wavelength λ0 in the wavelength range under consideration, and is thus computationally fast. The strength of the Ring effect is calculated from statistical properties of the photon paths for a given situation, which makes Monte Carlo radiative transfer models in particular appropriate. We quantify the Ring effect by the so called rotational Raman scattering probability, the probability that an observed photon has undergone a rotational Raman scattering event. The Raman scattering probability is independent from the spectral resolution of the instrument and can easily be converted into various definitions used to characterise the strength of the Ring effect. We compare the results of our method to the results of previous studies and in general good quantitative agreement is found. In addition to the simulation of the Ring effect, we developed a detailed retrieval strategy for the analysis of the Ring effect based on DOAS retrievals, which allows the precise determination of the strength of the Ring effect for a specific wavelength while using the spectral information within a larger spectral interval around the selected wavelength. Using our technique, we simulated synthetic satellite observation of an atmospheric scenario with a finite cloud illuminated from different sun positions. The strength of the Ring effect depends systematically on the measurement geometry, and is strongest if the satellite points to the side of the cloud which lies in the shadow of the sun.