2015, Slobodda, J., Hünerbein, A., Lindstrot, R., Preusker, R., Ebell, K., Fischer, J.

Images of measured radiance in different channels of the geostationary Meteosat-9 SEVIRI instrument are analysed with respect to the representativeness of the observations of eight cloud observatories in Europe (e.g. measurements from cloud radars or microwave radiometers). Cloudy situations are selected to get a time series for every pixel in a 300 km × 300 km area centred around each ground station. Then a cross correlation of each time series to the pixel nearest to the corresponding ground site is calculated. In the end a correlation length is calculated to define the representativeness. It is found that measurements in the visible and near infrared channels, which respond to cloud physical properties, are correlated in an area with a 1 to 4 km radius, while the thermal channels, that correspond to cloud top temperature, are correlated to a distance of about 20 km. This also points to a higher variability of the cloud microphysical properties inside a cloud than of the cloud top temperature. The correlation length even increases for the channels at 6.2, 7.3 and 9.7 μm. They respond to radiation from the upper atmospheric layers emitted by atmospheric gases and higher level clouds, which are more homogeneous than low-level clouds. Additionally, correlations at different distances, corresponding to the grid box sizes of forecast models, were compared. The results suggest the possibility of comparisons between instantaneous cloud observations from ground sites and regional forecast models and ground-based measurements. For larger distances typical for global models the correlations decrease, especially for short-wave measurements and corresponding cloud products. By comparing daily means, the correlation length of each station is increased to about 3 to 10 times the value of instantaneous measurements and also the comparability to models grows.

2020, Hartmann, M., Adachi, K., Eppers, O., Haas, C., Herber, A., Holzinger, R., Hünerbein, A., Jäkel, E., Jentzsch, C., van Pinxteren, M., Wex, H., Willmes, S., Stratmann, F.

Ice nucleating particles (INPs) affect the radiative properties of cold clouds. Knowledge concerning their concentration above ground level and their potential sources is scarce. Here we present the first highly temperature resolved ice nucleation spectra of airborne samples from an aircraft campaign during late winter in 2018. Most INP spectra featured low concentration levels (<3 · 10−4 L−1 at −15°C). However, we also found INP concentrations of up to 1.8·10−2 L−1 at −15°C and freezing onsets as high as −7.5°C for samples mainly from the marine boundary layer. Shape and onset temperature of the ice nucleation spectra of those samples as well as heat sensitivity hint at biogenic INP. Colocated measurements additionally indicate a local marine influence rather than long-range transport. Our results suggest that even in late winter above 80°N a local marine source for biogenic INP, which can efficiently nucleate ice at high temperatures, is present. ©2020. The Authors.

2019, Wex, H., Huang, L., Zhang, W., Hung, H., Traversi, R., Becagli, S., Sheesley, R.J., Moffett, C.E., Barrett, T.E., Bossi, R., Skov, H., Hünerbein, A., Lubitz, J., Löffler, M., Linke, O., Hartmann, M., Herenz, P., Stratmann, F.

Number concentrations of ice-nucleating particles (NINP) in the Arctic were derived from ground-based filter samples. Examined samples had been collected in Alert (Nunavut, northern Canadian archipelago on Ellesmere Island), Utqiagvik, formerly known as Barrow (Alaska), Nyalesund (Svalbard), and at the Villum Research Station (VRS; northern Greenland). For the former two stations, examined filters span a full yearly cycle. For VRS, 10 weekly samples, mostly from different months of one year, were included. Samples from Ny-Alesund were collected during the months from March until September of one year. At all four stations, highest concentrations were found in the summer months from roughly June to September. For those stations with sufficient data coverage, an annual cycle can be seen. The spectra of NINP observed at the highest temperatures, i.e., those obtained for summer months, showed the presence of INPs that nucleate ice up to-5 °C. Although the nature of these highly ice-active INPs could not be determined in this study, it often has been described in the literature that ice activity observed at such high temperatures originates from the presence of ice-active material of biogenic origin. Spectra observed at the lowest temperatures, i.e., those derived for winter months, were on the lower end of the respective values from the literature on Arctic INPs or INPs from midlatitude continental sites, to which a comparison is presented herein. An analysis concerning the origin of INPs that were ice active at high temperatures was carried out using back trajectories and satellite information. Both terrestrial locations in the Arctic and the adjacent sea were found to be possible source areas for highly active INPs.

2019, Banks, J.R., Hünerbein, A., Heinold, B., Brindley, H.E., Deneke, H., Schepanski, K.

Infrared "Desert Dust" composite imagery taken by the Spinning Enhanced Visible and InfraRed Imager (SEVIRI), onboard the Meteosat Second Generation (MSG) series of satellites above the equatorial East Atlantic, has been widely used for more than a decade to identify and track the presence of dust storms from and over the Sahara Desert, the Middle East, and southern Africa. Dust is characterised by distinctive pink colours in the Desert Dust false-colour imagery; however, the precise colour is influenced by numerous environmental properties, such as the surface thermal emissivity and skin temperature, the atmospheric water vapour content, the quantity and height of dust in the atmosphere, and the infrared optical properties of the dust itself. For this paper, simulations of SEVIRI infrared measurements and imagery have been performed using a modelling system, which combines dust concentrations simulated by the aerosol transport model COSMO-MUSCAT (COSMO: COnsortium for Small-scale MOdelling; MUSCAT: MUltiScale Chemistry Aerosol Transport Model) with radiative transfer simulations from the RTTOV (Radiative Transfer for TOVS) model. Investigating the sensitivity of the synthetic infrared imagery to the environmental properties over a 6-month summertime period from 2011 to 2013, it is confirmed that water vapour is a major control on the apparent colour of dust, obscuring its presence when the moisture content is high. Of the three SEVIRI channels used in the imagery (8.7, 10.8, and 12.0 μm), the channel at 10.8 μm has the highest atmospheric transmittance and is therefore the most sensitive to the surface skin temperature. A direct consequence of this sensitivity is that the background desert surface exhibits a strong diurnal cycle in colour, with light blue colours possible during the day and purple hues prevalent at night. In dusty scenes, the clearest pink colours arise from high-altitude dust in dry atmospheres. Elevated dust influences the dust colour primarily by reducing the contrast in atmospheric transmittance above the dust layer between the SEVIRI channels at 10.8 and 12.0 μm, thereby boosting red and pink colours in the imagery. Hence, the higher the dust altitude, the higher the threshold column moisture needed for dust to be obscured in the imagery: for a sample of dust simulated to have an aerosol optical depth (AOD) at 550 nm of 2-3 at an altitude of 3-4 km, the characteristic colour of the dust may only be impaired when the total column water vapour is particularly moist ('39 mm). Meanwhile, dust close to the surface (altitude < 1 km) is only likely to be apparent when the atmosphere is particularly dry and when the surface is particularly hot, requiring column moisture/13 mm and skin temperatures '314 K, and is highly unlikely to be apparent when the skin temperature is/300 K. Such low-altitude dust will regularly be almost invisible within the imagery, since it will usually be beneath much of the atmospheric water vapour column. It is clear that the interpretation of satellite-derived dust imagery is greatly aided by knowledge of the background environment.