Johanna Mayer

The mesoscale organisation of marine stratocumulus clouds into closed and open cells strongly affects cloud albedo and thus their cooling effect on climate, yet the processes governing transitions between these regimes remain incompletely understood. The EarthCARE satellite provides collocated observations of cloud mesoscale structure from the Multi-Spectral Imager (MSI) together with vertically resolved cloud and precipitation measurements from the Atmospheric Lidar (ATLID) and Cloud Profiling Radar (CPR), enabling detailed characterization of stratocumulus cloud microphysics. We apply a convolutional neural network to MSI scenes to identify closed and open cells and relate these classifications to EarthCARE microphysical retrievals from the synergy of ATLID, CPR, and MSI. Open cells exhibit substantially lower droplet number concentrations ( N _d ), greater variability in liquid water path (LWP) and droplet sizes ( r _e ), and more frequent and heavier precipitation, although light drizzle is also common in closed cells. To investigate closed-to-open cell transitions, we combine EarthCARE overpasses with GOES/ABI geostationary imagery and ERA5-driven trajectories to track cloud scenes and determine transition timing. This combined approach allows us to reconstruct the temporal evolution of cloud properties around transitions. We find that LWP and rain amounts increase in closed cells up to 25 h before transitions, followed by decreasing N _d and increasing r _e , while cloud vertical structure remains largely unchanged. These findings support a precipitation-linked transition pathway, potentially triggered by enhanced boundary-layer moisture and amplified by aerosol scavenging–rain feedback. This new observational evidence advances our understanding of stratocumulus breakup with implications for the cooling effect of these clouds.

The mesoscale organisation of marine stratocumulus clouds into closed and open cells strongly affects cloud albedo and thus their cooling effect on climate, yet the processes governing transitions between these regimes remain incompletely understood. The EarthCARE satellite provides collocated observations of cloud mesoscale structure from the Multi-Spectral Imager (MSI) together with vertically resolved cloud and precipitation measurements from the Atmospheric Lidar (ATLID) and Cloud Profiling Radar (CPR), enabling detailed characterization of stratocumulus cloud microphysics. We apply a convolutional neural network to MSI scenes to identify closed and open cells and relate these classifications to EarthCARE microphysical retrievals from the synergy of ATLID, CPR, and MSI. Open cells exhibit substantially lower droplet number concentrations (N_d), greater variability in liquid water path (LWP) and droplet sizes (r_e), and more frequent and heavier precipitation, although light drizzle is also common in closed cells. To investigate closed-to-open cell transitions, we combine EarthCARE overpasses with GOES/ABI geostationary imagery and ERA5-driven trajectories to track cloud scenes and determine transition timing. This combined approach allows us to reconstruct the temporal evolution of cloud properties around transitions. We find that LWP and rain amounts increase in closed cells up to ~25 hours before transitions, followed by decreasing N_d and increasing r_e, while cloud vertical structure remains largely unchanged. These findings support a precipitation-linked transition pathway, potentially triggered by enhanced boundary-layer moisture and amplified by aerosol scavenging–rain feedback. This new observational evidence advances our understanding of stratocumulus breakup with implications for the cooling effect of these clouds.

Ice-nucleating particles (INPs) play a crucial role in Earth's weather and climate by influencing cloud properties and precipitation. However, their abundance in the free troposphere, vertical distribution, and transport mechanisms are not well-characterized. This study presents immersion INP measurements from filter samples collected aboard the High Altitude and LOng range research aircraft (HALO) in the troposphere and the lower stratosphere (up to 14.5 km) over Europe during the CIRRUS-HL (Cirrus in High Latitudes) campaign in summer 2021. By sampling cloud particle residuals and aerosol particles in the inflow and outflow of deep convective clouds (DCCs), and performing offline INP analysis, we shed light on the vertical transport and segregation of INPs in DCCs. INP-temperature spectra of convective inflow included both INPs active at high ( T > −15 °C) and low temperatures ( T < −20 °C). In contrast the outflow spectra only featured INPs active at low temperatures. We explain the observed INP segregation in the updraft with precipitation scavenging of INPs active at high temperatures. INPs active at lower temperatures ( T < −20 °C), however, are efficiently transported upwards into the free troposphere. There, ambient temperatures are below −40 °C, i.e., temperatures far below the temperatures at which these INPs initiate immersion freezing. In the DCC outflow, INP concentrations exceed the upper tropospheric background concentration by at least two orders of magnitude. These INPs are then available for ice formation in mid and upper tropospheric clouds.

The mesoscale organisation of marine stratocumulus clouds into closed and open cells strongly affects cloud albedo and thus their cooling effect on climate, yet the processes governing transitions between these regimes remain incompletely understood. The EarthCARE satellite provides collocated observations of cloud mesoscale structure from the Multi-Spectral Imager (MSI) together with vertically resolved cloud and precipitation measurements from the Atmospheric Lidar (ATLID) and Cloud Profiling Radar (CPR), enabling detailed characterization of stratocumulus cloud microphysics. We apply a convolutional neural network to MSI scenes to identify closed and open cells and relate these classifications to EarthCARE microphysical retrievals from the synergy of ATLID, CPR, and MSI. Open cells exhibit substantially lower droplet number concentrations (N_d), greater variability in liquid water path (LWP) and droplet sizes (r_e), and more frequent and heavier precipitation, although light drizzle is also common in closed cells. To investigate closed-to-open cell transitions, we combine EarthCARE overpasses with GOES/ABI geostationary imagery and ERA5-driven trajectories to track cloud scenes and determine transition timing. This combined approach allows us to reconstruct the temporal evolution of cloud properties around transitions. We find that LWP and rain amounts increase in closed cells up to ~25 hours before transitions, followed by decreasing N_d and increasing r_e, while cloud vertical structure remains largely unchanged. These findings support a precipitation-linked transition pathway, potentially triggered by enhanced boundary-layer moisture and amplified by aerosol scavenging–rain feedback. This new observational evidence advances our understanding of stratocumulus breakup with implications for the cooling effect of these clouds.

Lightning activity reflects mixed-phase microphysical and dynamical processes in convective storms, but observations linking electrical activity to vertical cloud structure remain limited. The EarthCARE satellite now provides vertically resolved observations of clouds and precipitation, including Doppler velocity measurements, while the Meteosat Third Generation Lightning Imager (MTG-LI) and the GOES-R Geostationary Lightning Mapper (GLM) continuously observe total lightning from geostationary orbit, enabling collocated observations of electrically active storms. Here we present Lightning2EarthCARE, a collection of datasets and a lightning storm catalogue constructed from collocated EarthCARE overpasses and geostationary lightning observations, available through the European Space Agency Open Science Catalogue under DOI https://doi.org/10.57780/esa-7c530aa (Piskala Gvoždíková et al., 2026b). The collection includes three linked products: frame-level lightning observations within EarthCARE Multispectral Imager-like frames, track-level lightning counts along EarthCARE’s nadir vertical sampling, and corresponding storm-level records with summary lightning characteristics and the temporal evolution of storm lightning activity within ±1 hour of the EarthCARE overpass. The first version of the dataset covers the first year and a half of EarthCARE operations, from August 2024 to January 2026 within the geostationary lightning observing domain between 60° S and 60° N. During this period, EarthCARE sampled 6071 lightning storms along its narrow nadir track. The sampled cases include lightning associated with convective cores as well as stratiform or anvil regions. In cases dominated by convective cores, lightning maxima are generally aligned with EarthCARE signatures of strong convection, including multiple scattering effects in the radar signal and strongly variable Doppler velocities. This demonstrates the physical consistency between the collocated lightning activity and the EarthCARE cloud and precipitation observations. Lightning2EarthCARE provides a resource for linking geostationary lightning observations with EarthCARE vertical cloud profiling. The catalogue supports analyses of how lightning occurrence and intensity vary with storm structure, evolution stage, and observing environment, and provides a foundation for model evaluation and future integration with additional satellite or ground-based observations.

The mesoscale organisation of marine stratocumulus clouds into closed and open cells strongly affects cloud albedo and thus their cooling effect on climate, yet the processes governing transitions between these regimes remain incompletely understood. The EarthCARE satellite provides collocated observations of cloud mesoscale structure from the Multi-Spectral Imager (MSI) together with vertically resolved cloud and precipitation measurements from the Atmospheric Lidar (ATLID) and Cloud Profiling Radar (CPR), enabling detailed characterization of stratocumulus cloud microphysics. We apply a convolutional neural network to MSI scenes to identify closed and open cells and relate these classifications to EarthCARE microphysical retrievals from the synergy of ATLID, CPR, and MSI. Open cells exhibit substantially lower droplet number concentrations (N_d), greater variability in liquid water path (LWP) and droplet sizes (r_e), and more frequent and heavier precipitation, although light drizzle is also common in closed cells. To investigate closed-to-open cell transitions, we combine EarthCARE overpasses with GOES/ABI geostationary imagery and ERA5-driven trajectories to track cloud scenes and determine transition timing. This combined approach allows us to reconstruct the temporal evolution of cloud properties around transitions. We find that LWP and rain amounts increase in closed cells up to ~25 hours before transitions, followed by decreasing N_d and increasing r_e, while cloud vertical structure remains largely unchanged. These findings support a precipitation-linked transition pathway, potentially triggered by enhanced boundary-layer moisture and amplified by aerosol scavenging–rain feedback. This new observational evidence advances our understanding of stratocumulus breakup with implications for the cooling effect of these clouds.

Ice-nucleating particles (INPs) play a crucial role in Earth’s weather and climate by influencing cloud properties and precipitation. However, their abundance in the free troposphere, vertical distribution, and transport mechanisms are not well-characterized. This study presents immersion INP measurements from filter samples collected aboard the HALO research aircraft in the troposphere and the lower stratosphere (up to 14.5 km) over Europe during the CIRRUS-HL (Cirrus in High Latitudes) campaign in summer 2021. By sampling cloud particle residuals and aerosol particles in the inflow and outflow of deep convective clouds (DCCs) and performing offline INP analysis, we shed light on the vertical transport and segregation of INPs in DCCs. While INP-temperature spectra of convective inflow included both INPs active at high (T > -15 °C) and low temperatures (T < -20 °C), the in-cloud and outflow spectra only featured INPs active at low temperatures. We explain the observed INP segregation in the updraft with precipitation scavenging of INPs active at high temperatures. In contrast, INPs active at lower temperatures (T < -20 °C) are efficiently transported upwards into the free troposphere with ambient temperatures below -40 °C, i.e., temperatures far below the temperatures at which these INPs initiate immersion freezing. In the DCC outflow, INP concentrations exceed the upper tropospheric background concentration by at least two orders of magnitude. These INPs are then available for ice formation in mid and upper tropospheric clouds.

The Alpine region experiences frequent deep convection during summer, driven by thermal and mechanical forcing associated with the complex terrain. Deep convection transports moisture into the upper troposphere and lower stratosphere, which affects the climate through its radiative interactions. It is poorly represented in models that rely on parameterized convection and lack adequate representation of boundary layer turbulence and microphysics. In this study, we investigate the evolution of moist deep convection observed on 8 July 2021 over the Alps using ICON simulations with explicitly resolved convection (horizontal resolution of 1 km). The simulations use two turbulence parameterizations: the default turbulence kinetic energy (TKE) and the newly developed two turbulence energies (2 TE) scheme combined with single moment (SM) and double moment (DM) microphysics parameterizations. The simulations are evaluated using cloud properties derived from MSG/SEVIRI satellite measurements. The sensitivity of cross tropopause transport to the choice of turbulence and microphysics scheme is examined. Although, the ICON simulations capture the observed diurnal cycle of convection and successfully simulate the overshooting cloud tops during peak convective activity, our results show that the choice of the turbulence scheme influences the temporal evolution and spatial extent of deep convection, while the microphysics parameterization has a larger impact on the hydrometeor distribution and on the cross tropopause transport.

The mesoscale organisation of marine stratocumulus clouds into closed and open cells strongly affects cloud albedo and thus their cooling effect on climate, yet the processes governing transitions between these regimes remain incompletely understood. The EarthCARE satellite provides collocated observations of cloud mesoscale structure from the Multi-Spectral Imager (MSI) together with vertically resolved cloud and precipitation measurements from the Atmospheric Lidar (ATLID) and Cloud Profiling Radar (CPR), enabling detailed characterization of stratocumulus cloud microphysics. We apply a convolutional neural network to MSI scenes to identify closed and open cells and relate these classifications to EarthCARE microphysical retrievals from the synergy of ATLID, CPR, and MSI. Open cells exhibit substantially lower droplet number concentrations (Nd), greater variability in liquid water path (LWP) and droplet sizes (re), and more frequent and heavier precipitation, although light drizzle is also common in closed cells. To investigate closed-to-open cell transitions, we combine EarthCARE overpasses with GOES/ABI geostationary imagery and ERA5-driven trajectories to track cloud scenes and determine transition timing. This combined approach allows us to reconstruct the temporal evolution of cloud properties around transitions. We find that LWP and rain amounts increase in closed cells up to ~25 hours before transitions, followed by decreasing Nd and increasing re, while cloud vertical structure remains largely unchanged. These findings support a precipitation-linked transition pathway, potentially triggered by enhanced boundary-layer moisture and amplified by aerosol scavenging–rain feedback. This new observational evidence advances our understanding of stratocumulus breakup with implications for the cooling effect of these clouds.

Ice-nucleating particles (INPs) play a crucial role in Earth’s weather and climate by influencing cloud properties and precipitation. However, their abundance in the free troposphere, vertical distribution, and transport mechanisms are not well-characterized. This study presents immersion INP measurements from filter samples collected aboard the HALO research aircraft in the troposphere and the lower stratosphere (up to 14.5 km) over Europe during the CIRRUS-HL (Cirrus in High Latitudes) campaign in summer 2021. By sampling cloud particle residuals and aerosol particles in the inflow and outflow of deep convective clouds (DCCs) and performing offline INP analysis, we shed light on the vertical transport and segregation of INPs in DCCs. While INP-temperature spectra of convective inflow included both INPs active at high (T > -15 °C) and low temperatures (T < -20 °C), the in-cloud and outflow spectra only featured INPs active at low temperatures. We explain the observed INP segregation in the updraft with precipitation scavenging of INPs active at high temperatures. In contrast, INPs active at lower temperatures (T < -20 °C) are efficiently transported upwards into the free troposphere with ambient temperatures below -40 °C, i.e., temperatures far below the temperatures at which these INPs initiate immersion freezing. In the DCC outflow, INP concentrations exceed the upper tropospheric background concentration by at least two orders of magnitude. These INPs are then available for ice formation in mid and upper tropospheric clouds.