The new spectrometer of the Munich Aerosol Cloud Scanner (specMACS) is a multipurpose hyperspectral cloud and sky imager designated, but is not limited to investigations of cloud–aerosol interactions in Earth's atmosphere. With its high spectral and spatial resolution, the instrument is designed to measure solar radiation in the visible and shortwave infrared region that is reflected from, or transmitted through clouds and aerosol layers. It is based on two hyperspectral cameras that measure in the solar spectral range between 400 and 2500 nm with a spectral bandwidth between 2.5 and 12.0 nm. The instrument was operated in ground-based campaigns as well as aboard the German High Altitude LOng Range (HALO) research aircraft, e.g., during the ACRIDICON-CHUVA campaign in Brazil during summer 2014. This paper describes the specMACS instrument hardware and software design and characterizes the instrument performance. During the laboratory characterization of the instrument, the radiometric response as well as the spatial and spectral resolution was assessed. Since the instrument is primarily intended for retrievals of atmospheric quantities by inversion of radiative models using measured radiances, a focus is placed on the determination of its radiometric response. Radiometric characterization was possible for both spectrometers, with an absolute accuracy of 3 % at their respective central wavelength regions. First measurements are presented which demonstrate the wide applicability of the instrument. They show that key demands are met regarding the radiometric and spectral accuracy which is required for the intended remote sensing techniques.

Remote sensing of cloud effective particle size with passive sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) is an important tool for cloud microphysical studies. As a measure of the radiatively relevant droplet size, effective radius can be retrieved with different combinations of visible through shortwave and midwave infrared channels. In practice, retrieved effective radii from these combinations can be quite different. This difference is perhaps indicative of different penetration depths and path lengths for the spectral reflectances used. In addition, operational liquid water cloud retrievals are based on the assumption of a relatively narrow distribution of droplet sizes; the role of larger precipitation particles in these distributions is neglected. Therefore, possible explanations for the discrepancy in some MODIS spectral size retrievals could include 3-D radiative transport effects, including sub-pixel cloud inhomogeneity, and/or the impact of drizzle formation. For three cloud cases the possible factors of influence are isolated and investigated in detail by the use of simulated cloud scenes and synthetic satellite data: marine boundary layer cloud scenes from large eddy simulations (LES) with detailed microphysics are combined with Monte Carlo radiative transfer calculations that explicitly account for the detailed droplet size distributions as well as 3-D radiative transfer to simulate MODIS observations. The operational MODIS optical thickness and effective radius retrieval algorithm is applied to these and the results are compared to the given LES microphysics. We investigate two types of marine cloud situations each with and without drizzle from LES simulations: (1) a typical daytime stratocumulus deck at two times in the diurnal cycle and (2) one scene with scattered cumulus. Only small impact of drizzle formation on the retrieved domain average and on the differences between the three effective radius retrievals is noticed for both cloud scene types for different reasons. For our, presumably typical, overcast stratocumulus scenes with an optical thickness of 8 to 9 and rain rates at cloud bottom up to 0.05 mm/h clear drizzle impact on the retrievals can be excluded. The cumulus scene does not show much drizzle sensitivity either despite extended drizzle areas being directly visible from above (locally >1 mm/h), which is mainly due to technical characteristics of the standard retrieval approach. 3-D effects, on the other hand, produce large discrepancies between the 1.6 and 2.1 μm channel observations compared to 3.7 μm retrievals in the latter case. A general sensitivity of MODIS particle size data to drizzle formation is not corroborated by our case studies.

In this paper, recent changes to the Meteosat thunderstorm TRacking And Monitoring algorithm (Cb-TRAM) are presented as well as a validation of Cb-TRAM against data from the European ground-based LIghtning NETwork (LINET) of Nowcast GmbH and the South African Weather Service Lightning Detection Network (SAWS LDN). Validation is conducted along the well-known skill measures probability of detection (POD) and false alarm ratio (FAR) on the basis of Meteosat/SEVIRI pixels as well as on the basis of thunderstorm objects. The values obtained demonstrate specific limitations of Cb-TRAM, as well as limitations of satellite methods in general which are based on thermal emission and solar reflectivity information from thunderstorm cloud tops. Although the climatic conditions and the occurrence of thunderstorms are quite different for Europe and South Africa, quality score values are similar. Our conclusion is that Cb-TRAM provides robust results of well-defined quality for very different climatic regimes. The POD for a thunderstorm with intense lightning is about 80% during the day. The FAR for a Cb-TRAM detection which is not even close to intense lightning is about 50%. If only proximity to any lightning activity is required, FAR is much lower at about 15%. Pixel-based analysis shows that detected thunderstorm object size is not indiscriminately large, but well within physical limitations of the satellite method. Night-time POD and FAR are somewhat worse as the detection scheme does not use the high-resolution visible information during night-time hours. Nowcasting scores show useful values up to approximately 30 min in advance.

During the ACRIDICON-CHUVA (Aerosol, Cloud, Precipitation, and Radiation Interactions and Dynamics of Convective Cloud Systems–Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud Resolving Modeling and to the GPM (Global Precipitation Measurement)) aircraft campaign in September 2014 over the Amazon, among other topics, aerosol effects on the development of cloud microphysical profiles during the burning season were studied. Hyperspectral remote sensing with the imaging spectrometer specMACS provided cloud microphysical information for sun-illuminated cloud sides. In order to derive profiles of phase or effective radius from cloud side observations, vertical location information is indispensable. For this purpose, spectral measurements of cloud-side-reflected radiation in the oxygen A absorption band collected by specMACS were used to determine absorption path length between cloud sides and the instrument aboard the aircraft. From these data, horizontal distance and eventually vertical height were derived. It is shown that, depending on aircraft altitude and sensor viewing direction, an unambiguous relationship of absorption and distance exists and can be used to retrieve cloud geometrical parameters. A comparison to distance and height information from stereo image analysis (using data of an independent camera) demonstrates the efficiency of the approach. Uncertainty estimates due to method, instrument and environmental factors are provided. The main sources of uncertainty are unknown in cloud absorption path contributions due to complex 3-D geometry or unknown microphysical properties, variable surface albedo and aerosol distribution. A systematic difference of 3.8 km between the stereo and spectral method is found which can be attributed to 3-D geometry effects not considered in the method's simplified cloud model. If this offset is considered, typical differences found are 1.6 km for distance and 230 m for vertical position at a typical distance around 20 km between sensor and convective cloud elements of typically 1–10 km horizontal and vertical extent.

Clouds are one of the main reasons of uncertainties in the forecasts of weather and climate. In part, this is due to limitations of remote sensing of cloud microphysics. Present approaches often use passive spectral measurements for the remote sensing of cloud microphysical parameters. Large uncertainties are introduced by three-dimensional (3-D) radiative transfer effects and cloud inhomogeneities. Such effects are largely caused by unknown orientation of cloud sides or by shadowed areas on the cloud. Passive ground-based remote sensing of cloud properties at high spatial resolution could be crucially improved with this kind of additional knowledge of cloud geometry. To this end, a method for the accurate reconstruction of 3-D cloud geometry from cloud radar measurements is developed in this work. Using a radar simulator and simulated passive measurements of model clouds based on a large eddy simulation (LES), the effects of different radar scan resolutions and varying interpolation methods are evaluated. In reality, a trade-off between scan resolution and scan duration has to be found as clouds change quickly. A reasonable choice is a scan resolution of 1 to 2\\degree. The most suitable interpolation procedure identified is the barycentric interpolation method. The 3-D reconstruction method is demonstrated using radar scans of convective cloud cases with the Munich miraMACS, a 35 GHz scanning cloud radar. As a successful proof of concept, camera imagery collected at the radar location is reproduced for the observed cloud cases via 3-D volume reconstruction and 3-D radiative transfer simulation. Data sets provided by the presented reconstruction method will aid passive spectral ground-based measurements of cloud sides to retrieve microphysical parameters.

In this study a method is introduced for the retrieval of optical thickness and effective particle size of ice clouds over a wide range of optical thickness from ground-based transmitted radiance measurements. Low optical thickness of cirrus clouds and their complex microphysics present a challenge for cloud remote sensing. In transmittance, the relationship between optical depth and radiance is ambiguous. To resolve this ambiguity the retrieval utilizes the spectral slope of radiance between 485 and 560 nm in addition to the commonly employed combination of a visible and a short-wave infrared wavelength.An extensive test of retrieval sensitivity was conducted using synthetic test spectra in which all parameters introducing uncertainty into the retrieval were varied systematically: ice crystal habit and aerosol properties, instrument noise, calibration uncertainty and the interpolation in the lookup table required by the retrieval process. The most important source of errors identified are uncertainties due to habit assumption: Averaged over all test spectra, systematic biases in the effective radius retrieval of several micrometre can arise. The statistical uncertainties of any individual retrieval can easily exceed 10 µm. Optical thickness biases are mostly below 1, while statistical uncertainties are in the range of 1 to 2.5.For demonstration and comparison to satellite data the retrieval is applied to observations by the Munich hyperspectral imager specMACS (spectrometer of the Munich Aerosol and Cloud Scanner) at the Schneefernerhaus observatory (2650 m a.s.l.) during the ACRIDICON-Zugspitze campaign in September and October 2012. Results are compared to MODIS and SEVIRI satellite-based cirrus retrievals (ACRIDICON – Aerosol, Cloud, Precipitation, and Radiation Interactions and Dynamics of Convective Cloud Systems; MODIS – Moderate Resolution Imaging Spectroradiometer; SEVIRI – Spinning Enhanced Visible and Infrared Imager). Considering the identified uncertainties for our ground-based approach and for the satellite retrievals, the comparison shows good agreement within the range of natural variability of the cloud situation in the direct surrounding.