Ground-based imaging remote sensing of ice clouds: uncertainties caused by sensor, method and atmosphere

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 560aEuro-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 10aEuro-A mu m. 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 (2650aEuro-maEuro-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

How well can brightness temperature differences of spaceborne imagers help to detect cloud phase? A sensitivity analysis regarding cloud phase and related cloud properties

This study investigates the sensitivity of two brightness temperature differences (BTDs) in the infrared (IR) window of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) to various cloud parameters in order to understand their information content, with a focus on cloud thermodynamic phase. To this end, this study presents radiative transfer calculations, providing an overview of the relative importance of all radiatively relevant cloud parameters, including thermodynamic phase, cloud-top temperature (CTT), optical thickness (τ), effective radius (Reff), and ice crystal habit. By disentangling the roles of cloud absorption and scattering, we are able to explain the relationships of the BTDs to the cloud parameters through spectral differences in the cloud optical properties. In addition, an effect due to the nonlinear transformation from radiances to brightness temperatures contributes to the specific characteristics of the BTDs and their dependence on τ and CTT. We find that the dependence of the BTDs on phase is more complex than sometimes assumed. Although both BTDs are directly sensitive to phase, this sensitivity is comparatively small in contrast to other cloud parameters. Instead, the primary link between phase and the BTDs lies in their sensitivity to CTT (or more generally the surface–cloud temperature contrast), which is associated with phase. One consequence is that distinguishing high ice clouds from low liquid clouds is straightforward, but distinguishing mid-level ice clouds from mid-level liquid clouds is challenging. These findings help to better understand and improve the working principles of phase retrieval algorithms

Determination of complex refractive indices and optical properties of volcanic ashes in the thermal infrared based on generic petrological compositions

The spaceborne detection of volcanic ash clouds at infrared wavelengths helps to avoid regions with enhanced volcanic ash concentrations that pose a threat to aviation. Current volcanic ash data retrievals require detailed information on microphysical properties and the refractive index of volcanic ash, which are highly variable. Uncertainties in the latter currently limit the quality of volcanic ash nowcasts. Here, we introduce a novel method to calculate the complex refractive indices of volcanic ashes at wavelengths from 5 to 15 μm from measurements of their individual components based on generic petrological ash compositions. Thereby the refractive indices for volcanic glasses and bulk volcanic ashes of different chemical compositions are derived. The variability of the latter is mainly influenced by the silica content and the porosity and to a minor degree by the glass-to-crystals ratio. Calculating optical properties exhibits an equally large impact of bulk composition and grain size distribution, whereas particle shape is considered less important for particle sizes of the order 1 μm. Using these optical properties to determine brightness temperature differences between the 11 μm and 12 μm channels we show that the effect of ash composition is non-negligible for modern satellite instruments. Particularly, the dependence of the volcanic ash on the silica content (and to a much smaller extent on the glass-to-crystals ratio) is observable in its refractive index, its optical properties and the brightness temperature difference, indicating that composition might be retrievable to some degree by remote sensing methods

Multi-Channel Spectral Band Adjustment Factors for Thermal Infrared Measurements of Geostationary Passive Imagers

The newest and upcoming geostationary passive imagers have thermal infrared channels comparable to those of more established instruments, but their spectral response functions still differ significantly. Therefore, retrievals developed for a certain type of radiometer cannot simply be applied to another imager. Here, a set of spectral band adjustment factors is determined for MSG/SEVIRI, Himawari-8/AHI, and MTG1/FCI from a training dataset based on MetOp/IASI hyperspectral observations. These correction functions allow to turn the observation of one sensor into an analogue observation of another sensor. This way, the same satellite retrieval—that has been usually developed for a specific instrument with a specific spectral response function—can be applied to produce long time series that go beyond one single satellite/satellite series or to cover the entire geostationary ring in a consistent way. It is shown that the mean uncorrected brightness temperature differences between corresponding channels of two imagers can be >1 K, in particular for the channels centered around 13.4 μm in the carbon dioxide absorption band and even when comparing different imager realizations of the same series, such as the four SEVIRI sensors aboard MSG1 to MSG4. The spectral band adjustment factors can remove the bias and even reduce the standard deviation in the brightness temperature difference by more than 80%, with the effect being dependent on the spectral channel and the complexity of the correction function. Further tests include the application of the spectral band adjustment factors in combination with (a) a volcanic ash cloud retrieval to Himawari-8/AHI observations of the Raikoke eruption 2019 and a comparison to an ICON-ART model simulation, and (b) an ice cloud retrieval to simulated MTG1/FCI test data with the outcome compared to the retrieval results using real MSG3/SEVIRI measurements for the same scene

The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development

Volcanic ash clouds are a threat to air traffic security and, thus, can have significant societal and financial impact. Therefore, the detection and monitoring of volcanic ash clouds to enhance the safety of air traffic is of central importance. This work presents the development of the new retrieval algorithm VACOS (Volcanic Ash Cloud properties Obtained from SEVIRI) which is based on artificial neural networks, the thermal channels of the geostationary sensor MSG/SEVIRI and auxiliary data from a numerical weather prediction model. It derives a pixel classification as well as cloud top height, effective particle radius and, indirectly, the mass column concentration of volcanic ash clouds during day and night. A large set of realistic one-dimensional radiative transfer calculations for typical atmospheric conditions with and without generic volcanic ash clouds is performed to create the training dataset. The atmospheric states are derived from ECMWF data to cover the typical diurnal, annual and interannual variability. The dependence of the surface emissivity on surface type and viewing zenith angle is considered. An extensive dataset of volcanic ash optical properties is used, derived for a wide range of microphysical properties and refractive indices of various petrological compositions, including different silica contents and glass-to-crystal ratios; this constitutes a major innovation of this retrieval. The resulting ash-free radiative transfer calculations at a specific time compare well with corresponding SEVIRI measurements, considering the individual pixel deviations as well as the overall brightness temperature distributions. Atmospheric gas profiles and sea surface emissivities are reproduced with a high agreement, whereas cloudy cases can show large deviations on a single pixel basis (with 95th percentiles of the absolute deviations > 30 K), mostly due to different cloud properties in model and reality. Land surfaces lead to large deviations for both the single pixel comparison (with median absolute deviations > 3 K) and more importantly the brightness temperature distributions, most likely due to imprecise skin temperatures. The new method enables volcanic ash-related scientific investigations as well as aviation security-related applications

Can we successfully avoid persistent contrails by small altitude adjustments of flights in the real world?

This paper describes the first-ever operational contrail avoidance trial in the real world, which took place in the region of Maastricht Upper Area Control (including the northwest of Germany, the Benelux countries and part of the North Sea) in the year 2021. Contrail avoidance could be an efficient method for mitigating the climate impact of aviation. Applying a deliberate experiment design, air traffic was deviated every other day by changing the flight altitude by up to 2000 ft up or down if potential persistent contrails were predicted. Whether deviations were successful on average was checked using satellite images of high clouds and by application of a contrail detection algorithm, which makes use of the properties of contrails. Despite the fact that forecasting persistent contrails remains a challenge, the trial was successful at a significance level of 97.5 %, i.e., on average persistent contrails can be avoided for regular flights in the real world with a small intervention in the vertical flight path. The experiment is an important step towards a regular operational reduction of the aviation climate impact by means of air traffic management. Nevertheless, many open questions need to be solved prior to an operational implementation of contrail avoidance or climate optimised flight trajectories in legal ATM procedures

Can we successfully avoid persistent contrails by small altitude adjustments of flights in the real world?

This paper describes the first-ever operational contrail avoidance trial in the real world, which took place in the region of Maastricht Upper Area Control (including the northwest of Germany, the Benelux countries and part of the North Sea) in the year 2021. Contrail avoidance could be an efficient method for mitigating the climate impact of aviation. Applying a deliberate experiment design, air traffic was deviated every other day by changing the flight altitude by up to 2000 ft up or down if potential persistent contrails were predicted. Whether deviations were successful on average was checked using satellite images of high clouds and by application of a contrail detection algorithm, which makes use of the properties of contrails. Despite the fact that forecasting persistent contrails remains a challenge, the trial was successful at a significance level of 97.5 %, i.e., on average persistent contrails can be avoided for regular flights in the real world with a small intervention in the vertical flight path. The experiment is an important step towards a regular operational reduction of the aviation climate impact by means of air traffic management. Nevertheless, many open questions need to be solved prior to an operational implementation of contrail avoidance or climate optimised flight trajectories in legal ATM procedures

Proceedings of the 4th International Conference on Transport, Atmosphere and Climate

The "4th International Conference on Transport, Atmosphere and Climate (TAC-4)" held in Bad Kohlgrub (Germany), 2015, was organised with the objective of updating our knowledge on the impacts of transport on the composition of the atmosphere and on climate, three years after the TAC-3 conference in Prien am Chiemsee (Germany). The TAC-4 conference covered all aspects of the impact of the different modes of transport (aviation, road transport, shipping etc.) on atmospheric chemistry, microphysics, radiation and climate, in particular

VADUGS: a neural network for the remote sensing of volcanic ash with MSG/SEVIRI trained with synthetic thermal satellite observations simulated with a radiative transfer model

After the eruption of volcanoes around the world, monitoring of the dispersion of ash in the atmosphere is an important task for satellite remote sensing since ash represents a threat to air traffic. In this work we present a novel method, tailored for Eyjafjallajökull ash but applicable to other eruptions as well, that uses thermal observations of the SEVIRI imager aboard the geostationary Meteosat Second Generation satellite to detect ash clouds and determine their mass column concentration and top height during the day and night. This approach requires the compilation of an extensive data set of synthetic SEVIRI observations to train an artificial neural network. This is done by means of the RTSIM tool that combines atmospheric, surface and ash properties and runs automatically a large number of radiative transfer calculations for the entire SEVIRI disk. The resulting algorithm is called "VADUGS" (Volcanic Ash Detection Using Geostationary Satellites) and has been evaluated against independent radiative transfer simulations. VADUGS detects ash-contaminated pixels with a probability of detection of 0.84 and a false-alarm rate of 0.05. Ash column concentrations are provided by VADUGS with correlations up to 0.5, a scatter up to 0.6 g m-2 for concentrations smaller than 2.0 g m-2 and small overestimations in the range 5 %-50 % for moderate viewing angles 35-65°, but up to 300 % for satellite viewing zenith angles close to 90 or 0°. Ash top heights are mainly underestimated, with the smallest underestimation of -9 % for viewing zenith angles between 40 and 50°. Absolute errors are smaller than 70 % and with high correlation coefficients of up to 0.7 for ash clouds with high mass column concentrations. A comparison with spaceborne lidar observations by CALIPSO/CALIOP confirms these results: For six overpasses over the ash cloud from the Puyehue-Cordón Caulle volcano in June 2011, VADUGS shows similar features as the corresponding lidar data, with a correlation coefficient of 0.49 and an overestimation of ash column concentration by 55 %, although still in the range of uncertainty of CALIOP. A comparison with another ash algorithm shows that both retrievals provide plausible detection results, with VADUGS being able to detect ash further away from the Eyjafjallajökull volcano, but sometimes missing the thick ash clouds close to the vent. VADUGS is run operationally at the German Weather Service and this application is also presented