A correct and reliable forecast of volcanic plume dispersion is vital for aviation safety. This can only be achieved by representing all responsible physical and chemical processes (sources, sinks, and interactions) in the forecast models. The representation of the sources has been enhanced over the last decade, while the sinks and interactions have received less attention. In particular, aerosol dynamic processes and aerosol–radiation interaction are neglected so far. Here we address this gap by further developing the ICON-ART (ICOsahedral Nonhydrostatic – Aerosols and Reactive Trace gases) global modeling system to account for these processes. We use this extended model for the simulation of volcanic aerosol dispersion after the Raikoke eruption in June 2019. Additionally, we validate the simulation results with measurements from AHI (Advanced Himawari Imager), CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization), and OMPS-LP (Ozone Mapping and Profiling Suite-Limb Profiler). Our results show that around 50 % of very fine volcanic ash mass (particles with diameter d<30 µm) is removed due to particle growth and aging. Furthermore, the maximum volcanic cloud top height rises more than 6 km over the course of 4 d after the eruption due to aerosol–radiation interaction. This is the first direct evidence that shows how cumulative effects of aerosol dynamics and aerosol–radiation interaction lead to a more precise forecast of very fine ash lifetime in volcanic clouds.

In June 2019, the Raikoke volcano, Kuril Islands, emitted 0.4–1.8×10⁹ kg of very fine ash and 1–2×10⁹ kg of SO₂ up to 14 km into the atmosphere. The eruption was characterized by several eruption phases of different duration and height summing up to a total eruption length of about 5.5 h. Resolving such complex eruption dynamics is required for precise volcanic plume dispersion forecasts. To address this issue, we coupled the atmospheric model system ICON-ART (ICOsahedral Nonhydrostatic with the Aerosols and Reactive Trace gases module) with the 1D plume model FPlume to calculate the eruption source parameters (ESPs) online. The main inputs are the plume heights for the different eruption phases that are geometrically derived from satellite data. An empirical relationship is used to derive the amount of very fine ash (particles <32 µm), which is relevant for long-range transport in the atmosphere. On the first day after the onset of the eruption, the modeled ash loading agrees very well with the ash loading estimated from AHI (Advanced Himawari Imager) observations due to the resolution of the eruption phases and the online treatment of the ESPs. In later hours, aerosol dynamical processes (nucleation, condensation, and coagulation) explain the loss of ash in the atmosphere in agreement with the observations. However, a direct comparison is partly hampered by water and ice clouds overlapping the ash cloud in the observations. We compared 6-hourly means of model and AHI data with respect to the structure, amplitude, and location (SAL method) to further validate the simulated dispersion of SO₂ and ash. In the beginning, the structure and amplitude values for SO₂ differed largely because the dense ash cloud leads to an underestimation of the SO₂ amount in the satellite data. On the second and third day, the SAL values are close to zero for all parameters (except for the structure value of ash), indicating a very good agreement of the model and observations. Furthermore, we found a separation of the ash and SO₂ plume after 1 d due to particle sedimentation, chemistry, and aerosol–radiation interaction. The results confirm that coupling the atmospheric model system and plume model enables detailed treatment of the plume dynamics (phases and ESPs) and leads to significant improvement of the ash and SO₂ dispersion forecast. This approach can benefit the operational forecast of ash and SO₂ especially in the case of complex and noncontinuous volcanic eruptions like that of Raikoke in 2019.

The presence of volcanic clouds in the atmosphere affects air quality, the environment, climate, human health and aviation safety. The importance of the detection and retrieval of volcanic SO₂ lies with risk mitigation as well as with the possibility of providing insights into the mechanisms that cause eruptions. Due to their intrinsic characteristics, satellite measurements have become an essential tool for volcanic monitoring. In recent years, several sensors, with different spectral, spatial and temporal resolutions, have been launched into orbit, significantly increasing the effectiveness of the estimation of the various parameters related to the state of volcanic activity. In this work, the SO₂ total masses and fluxes were obtained from several satellite sounders—the geostationary (GEO) MSG-SEVIRI and the polar (LEO) Aqua/Terra-MODIS, NPP/NOAA20-VIIRS, Sentinel5p-TROPOMI, MetopA/MetopB-IASI and Aqua-AIRS—and compared to one another. As a test case, the Christmas 2018 Etna eruption was considered. The characteristics of the eruption (tropospheric with low ash content), the large amount of (simultaneously) available data and the different instrument types and SO₂ columnar abundance retrieval strategies make this cross-comparison particularly relevant. Results show the higher sensitivity of TROPOMI and IASI and a general good agreement between the SO₂ total masses and fluxes obtained from all the satellite instruments. The differences found are either related to inherent instrumental sensitivity or the assumed and/or calculated SO₂ cloud height considered as input for the satellite retrievals. Results indicate also that, despite their low revisit time, the LEO sensors are able to provide information on SO₂ flux over large time intervals. Finally, a complete error assessment on SO₂ flux retrievals using SEVIRI data was realized by considering uncertainties in wind speed and SO₂ abundance.

Current Earth Observation (EO) satellites provide excellent spatial, temporal and spectral coverage for passive measurements of atmospheric volcanic emissions. Of particular value for ash detection and quantification are the geostationary satellites that now carry multispectral imagers. These instruments have multiple spectral channels spanning the visible to infrared (IR) wavelengths and provide 1 × 1 km² to 4 × 4 km² resolution data every 5–15 min, continuously. For ash detection, two channels situated near 11 and 12 $\mathsf{\mu }$ m are needed; for ash quantification a third or fourth channel also in the infrared is useful for constraining the height of the ash cloud. This work describes passive EO infrared measurements and techniques to determine volcanic cloud properties and includes examples using current methods with an emphasis on the main difficulties and ways to overcome them. A challenging aspect of using satellite data is to design algorithms that make use of the spectral, temporal (especially for geostationary sensors) and spatial information. The hyperspectral sensor AIRS is used to identify specific molecules from their spectral signatures (e.g., for SO₂) and retrievals are demonstrated as global, regional and hemispheric maps of AIRS column SO₂. This kind of information is not available on all sensors, but by combining temporal, spatial and broadband multi-spectral information from polar and geo sensors (e.g., MODIS and SEVIRI) useful insights can be made. For example, repeat coverage of a particular area using geostationary data can reveal temporal behaviour of broadband channels indicative of eruptive activity. In many instances, identifying the nature of a pixel (clear, cloud, ash etc.) is the major challenge. Sophisticated cloud detection schemes have been developed that utilise statistical measures, physical models and temporal variation to classify pixels. The state of the art on cloud detection is good, but improvements are always needed. An IR-based multispectral cloud identification scheme is described and some examples shown. The scheme is physically based but has deficiencies that can be improved during the daytime by including information from the visible channels. Physical retrieval schemes applied to ash detected pixels suffer from a lack of knowledge of some basic microphysical and optical parameters needed to run the retrieval models. In particular, there is a lack of accurate spectral refractive index information for ash particles. The size distribution of fine ash (1–63 $\mathsf{\mu }$ m, diameter) is poorly constrained and more measurements are needed, particularly for ash that is airborne. Height measurements are also lacking and a satellite-based stereoscopic height retrieval is used to illustrate the value of this information for aviation. The importance of water in volcanic clouds is discussed here and the separation of ice-rich and ash-rich portions of volcanic clouds is analysed for the first time. More work is required in trying to identify ice-coated ash particles, and it is suggested that a class of ice-rich volcanic cloud be recognized and termed a ‘volcanic ice’ cloud. Such clouds are frequently observed in tropical eruptions of great vertical extent (e.g., 8 km or higher) and are often not identified correctly by traditional IR methods (e.g., reverse absorption). Finally, the global, hemispheric and regional sampling of EO satellites is demonstrated for a few eruptions where the ash and SO ${}_2$ dispersed over large distances (1000s km).

Previous work suggests that general circulation (global climate) models have excess net radiation at land surfaces, apparently due to overestimates in downwelling shortwave flux and underestimates in upwelling long-wave flux. Part of this excess, however, may be compensated for by an underestimate in downwelling longwave flux. Long term observations of the downwelling longwave component at several land stations in Europe, the United States, Australia, and Antarctica suggest that climate models (four are used, as in previous studies) underestimate this flux component on an annual basis by up to 10 W m⁻², yet with low statistical significance. It is probable that the known underestimate in boundary-layer air temperature contributes to this, as would low model cloudiness and neglect of minor gases such as methane, nitrogen oxide, and the freons. The bias in downwelling longwave flux, together with those found earlier for downwelling shortwave and upwlling long-wave fluxes, are consistent with the model bias found previously for net radiation. All annually averaged fluxes and biases are deduced for global land as a whole.