The aim of this work is to study the vertical distribution of microphysical cloud properties, in particular the thermodynamic phase partitioning and the cloud droplet size, in low-level mixed-phase clouds during marine cold air outbreaks in the Arctic. For this purpose, high resolution observations of the initial phase of a strong marine cold air outbreak in the Fram Strait collected with the hyperspectral and polarized imaging system specMACS during the airborne HALO-(AC)3 campaign are analyzed. Pseudo-vertical profiles of the cloud thermodynamic phase generally showed increasing ice fractions with increasing height and decreasing temperature, except for a geometrically thin layer at the cloud top, which was more liquid-dominated. The measurements indicated that ice formation occurred preferentially at the coldest temperatures. In addition, the effective radius of the liquid cloud droplets increased with height, as expected. The observed vertical evolution of the liquid cloud droplets could be successfully modeled by an entraining parcel model. The good agreement between measured and calculated vertical profiles of the cloud droplet effect radius and additional information based on in situ measurements indicated that the influence of collision-coalescence and ice processes, such as riming, the Wegener-Bergeron-Findeisen mechanism, and ice formation through heterogeneous freezing, on the liquid cloud droplets was small for the observed clouds. The analyses and data presented can help to improve the representation of low-level Arctic mixed-phase clouds in models and to further our understanding of these clouds and the related microphysical processes.

In May 2024, the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) satellite was launched. For the first time a satellite payload combines two active instruments, i.e., the Atmospheric Lidar and the Cloud Profiling Radar, together with two passive instruments, a multi-spectral imager and a broad-band radiometer, on one single spacecraft platform. EarthCARE is thus the most complex satellite mission to date for collocated aerosol, cloud, radiation and precipitation measurements. To utilize the data collected by the EarthCARE mission to its full extent and to support and quantify the data quality and measurement uncertainty, careful and holistic validation activities are needed. For this purpose, we set up an airborne instrument payload on the German High Altitude and LOng-range research aircraft (HALO), which is similar to the EarthCARE instrumentation. We used this payload during an extensive measurement campaign in summer and fall 2024 in the tropic and mid- to high-latitudes to validate the EarthCARE measurements and data products early in its commissioning phase. Here we aim to give a detailed overview of the PERCUSION (Persistent Earth CARE underflight studies of the ITCZ and organized convection) mission, and to advertise the use of its data in future more detailed validation studies. We give examples of how to use PERCUSION data to approach the validation of all four instruments of EarthCARE as well as of higher level (i.e. multi-sensor) products, and give first confidence in the quality of EarthCARE data.

Abstract. Radiative transfer is an inherently three-dimensional (3D) process that, for computational reasons, is still approximated as one-dimensional (1D) in most atmospheric models. To address this limitation, Maier et al. (2024) introduced the dynamic TenStream solver, which reduces the cost of 3D radiative transfer calculations through incomplete solves. Here, we investigate how coupling dynamic TenStream to the large-eddy simulation model PALM affects cloud development compared to simulations using conventional 1D and full 3D radiation. Results show that during daytime, clouds driven by either of the 3D solvers organize into cloud streets oriented perpendicular to the solar incidence angle, whereas with 1D radiation they remain more or less randomly distributed. Moreover, daytime clouds grow larger, become thicker, and contain more liquid water with 3D radiative transfer. It is shown that these differences arise because, unlike in the 1D case, clouds coupled to 3D radiation are not positioned directly above their own shadows. Instead, they are located over areas of enhanced net surface irradiance, where values even exceed those in the clear-sky columns of the 1D simulation, strengthening rather than weakening the associated updrafts. Additionally, 3D radiation is shown to reduce the domain-averaged net thermal emission at the surface, which affects the surface energy budget and is primarily balanced by an increase in the domain-averaged latent heat flux, resulting in a greater release of water vapor into the atmosphere. Both effects are captured by dynamic TenStream, demonstrating its ability to represent 3D radiative effects on cloud development at a substantially lower computational cost.

The effect of clouds on radiation remains a critical source of uncertainty in climate and weather prediction models. Moreover, the 3D structure of the clouds, including horizontal heterogeneity along with cloud vertical placement, further affects the radiation fields. Herein we utilize the 3D cloud scenes provided by EarthCARE to quantify the effect of the cloud 3D structure on radiation. Monte Carlo radiative transfer (RT) simulations from the MYSTIC/libRadtran model are employed to calculate the 1D vs 3D radiation fields. Airborne observations are also utilized, acquired during the ORCESTRA/PERCUSION EarthCARE Cal/Val campaign in the tropical Atlantic.Simulated top-of-atmosphere 1D and 3D radiances and irradiances are compared with EarthCARE Broadband Radiometer (BBR) observations, along with collocated radiation observations from the Munich Aerosol Cloud Scanner (specMACS) onboard the HALO aircraft during the ORCESTRA/PERCUSION campaign. The 1D vs 3D RT simulations are performed to investigate the importance of the 3D cloud structure on the cloud radiation fields, for different types of clouds.This analysis is part of the Obs3RvE EarthCARE+ project, which aims to develop new realistic 3D cloud scenes, combining EarthCARE and Meteosat Third Generation (MTG) observations, employing machine learning tools. These new 3D cloud scenes are expected to improve estimates of the cloud radiative effect from EarthCARE, as well as extend its suite of products to solar energy applications. Acknowledgements:This work has been financially supported by the Obs3RvE (Optimising 3D RT Earthcare product using geostationary observations and AI) project, funded from the European Space Agency under Contract No. 4000147848/25/I/AG, the PANGEA4CalVal project (Grant Agreement 101079201) funded by the European Union , the CERTAINTY project (Grant Agreement 101137680) funded by Horizon Europe program, the EarthCARE DISC project, funded by the European Space Agency under Contract No. 4000144997/24/I-NS and the AIRSENSE (Aerosol and aerosol cloud Interaction from Remote SENSing Enhancement) project, funded from the European Space Agency under Contract No. 4000142902/23/I-NS. It is also based upon work from COST Action EARLICOST, CA24135, supported by COST (European Cooperation in Science and Technology). DK, ΑΤ and SK would like to acknowledge COST Action HARMONIA (International network for harmonization of atmospheric aerosol retrievals from ground-based photometers), CA21119, supported by COST (European Cooperation in Science and Technology).

Forecasting solar irradiance is essential for grid stability and solar power integration. This study evaluates the performance of ensemble-based numerical weather prediction (NWP) models, which incorporate stochastic per turbations to represent forecast uncertainty. Five operational ensemble prediction systems (EPS) and a reference model are assessed using ground-based observations from 27 stations in Germany over summer of 2024. Forecasts with lead times under 24 hours are evaluated using metrics such as the Continuous Ranked Probability Score (CRPS), rank histograms, and spread-skill relationships. All NWP models outperform the reference in accuracy, with ICON-D2-EPS being the most accurate and MOGREPS-G and IFS-ENS the most reliable. A case study using WRF-Solar EPS explores the impact of different configurations and stochastic schemes, revealing mixed results. This work introduces a visual representation of the CRPS decomposition adapted for solar irradiance forecasts, and presents a comparison of nested WRF-Solar ensemble setups. The findings highlight the value of ensem ble forecasts for solar irradiance and the importance of post-processing to improve reliability in operational applications.

In 2018, the CoMet series of airborne missions was launched under the auspices of the German Aerospace Center (DLR). From this point onward, large-scale field campaigns are organized regularly every few years, in collaboration with partner institutions, to understand the anthropogenic and natural fluxes of the greenhouse gases CO2 and CH4, using the German research aircraft HALO. In addition to scientific objectives, the CoMet field campaigns aim to promote technological developments necessary for new and future Earth observation satellites, validate current greenhouse gas satellite measurements, and prepare new satellite missions, in particular the upcoming German-French methane lidar mission MERLIN. One of the core instruments of CoMet is the IPDA lidar CHARM-F, which was developed at DLR as an airborne demonstrator, testbed, and validation tool for MERLIN.

This work aims to quantify the macrophysical and microphysical properties of Arctic mixed-phase clouds and their temporal and spatial evolution during marine cold air outbreaks in the Arctic. In particular, cloud thermodynamic phase partitioning and phase transitions are discussed. To this end, high-resolution observations from the airborne hyperspectral and polarized imaging system specMACS during the HALO-(AC)3 campaign are analyzed within a quasi-Lagrangian framework based on backward airmass trajectories. Six flights targeting marine cold air outbreaks of different intensity are compared to investigate the variability of cloud evolution. With increasing time the airmass spent above open, sea ice-free ocean, rising cloud top heights, increasing horizontal cloud extents, and growing effective radii of liquid cloud droplets are observed for all cases. In addition, a phase transition from the liquid water to the mixed-phase cloud regime is detected and the ice fraction increases with time. The variability between the observed cloud properties during the cold air outbreaks is large. Larger and faster increasing cloud top heights and effective radii of liquid cloud droplets are observed during stronger events. In addition, the phase transition from the liquid water to the mixed phase occurs earlier and larger ice fractions are reached during the more intense events. The presented data and analyses provide unique observational data, which can be used to improve the representation of low-level Arctic mixed-phase clouds and their evolution during marine cold air outbreaks in models in the future.

Neglecting three-dimensional (3D) cloud–radiation interactions in weather and climate models can significantly distort surface irradiance estimates. Understanding how these effects depend on factors such as cloud properties, surface albedo and solar angle is the central goal of the DFG research unit C3SAR. Accurate representation of surface irradiance is becoming increasingly important as numerical weather prediction (NWP) and cloud-resolving models advance toward ever higher spatial resolutions.Traditionally, analyses have focused on mean bias, which is computationally efficient - especially when using Monte Carlo methods - since pixel-to-pixel noise cancels out. This allows large-scale studies of realistic, high-resolution cloud scenes. However, relying solely on mean bias can obscure important 3D effects, such as sharper and displaced cloud shadows (Gristey, 2019).We present a method to go beyond bias by computing pixel-wise root-mean-square differences (RMSD) between 1D and 3D simulations, as well as metrics of surface flux variability. Using novel statistical techniques applicable to general Monte Carlo simulations, this approach retains the computational efficiency of traditional bias calculations. This enables large-scale analysis not only of mean bias but also of pixel-wise differences and variability across statistically representative ensembles of high-resolution surface irradiance fields derived from physically consistent radiative transfer simulations.We apply this method to systematically investigate how 3D bias, local deviations, and variability depend on solar angle and cloud properties, using MYSTIC (Mayer, 2009) radiative transfer simulations on large-eddy simulation (LES) datasets at kilometer-scale domains with 10 m horizontal resolution. Furthermore, by coarsening the LES fields, we assess how cloud resolution influences 3D effects and determine the resolutions required to capture them accurately. Comparing fully 3D radiative transfer with independent-column approximations (ICA), we quantify the limitations of ICA in representing 3D cloud–radiation interactions, providing valuable guidance for next-generation cloud-resolving models.

Clouds are the largest source of uncertainty in the Earth's energy budget and in operational weather and climate models. However, because radiative transfer is among the most computationally expensive physical parameterisations it typically relies on the independent column approximation (ICA). This neglects horizontal photon transport and therefore introduces systematic biases in surface shortwave CRE up to 120% depending on solar zenith angle (Hogan et al. 2016), particularly beneath and around spatially heterogeneous broken cumulus clouds. The radiative impact of these conditions is moreover highly transient and evolves over the lifetime of a single cumulus cell. As the cloud grows into a thicker, more reflective cloud, ICA cannot correctly capture shadow position and extent on hectometer scales and entirely misses the surface irradiance enhancements above clear-sky levels caused by side-scattering at the edges or neighboring thin clouds. The DFG funded research unit C3SAR (Cloud 3d Structure And Radiation) combines ground-based, in situ, and satellite observations with ICON-NWP simulations and the Monte Carlo radiative transfer model MYSTIC to investigate how 3D cloud variability alters radiative fluxes and the Earth's energy budget. We use ICON cloud microphysics and atmospheric profiles as offline input to MYSTIC to compare ICA and fully 3D radiative calculations against clear-sky simulations. The tobac (Tracking and Object-Based Analysis of Clouds) package is used to track single cumulus cells and their (3D)CRE throughout the simulation. This framework enables physically consistent 3D radiative studies on regional scales larger than most large-eddy simulations and with more realistic cloud scenes than previous idealised studies. In the presented case study we analyse the temporal evolution of individual cumulus cells to quantify cloud-induced and three-dimensional radiative effects on surface solar irradiance. The preliminary results demonstrate the value of ICON-MYSTIC synergy for closure studies combing pyranometer network, cloud radar and allsky imager observations from the C3SAR 2026 campaign.

In May 2024 the EarthCARE satellite mission EarthCARE was launched. For the first time, the satellite combines a high spectral resolution lidar and a cloud radar with doppler capability as key instruments on one single platform. In addition, it is equipped with a multi spectral imager and a broadband radiometer. This unique combination makes EarthCARE the most complex satellite mission to study aerosol, clouds, precipitation, and radiation. To fully use these new and advanced data for science applications, a careful validation of the measurements and data products is required. We have implemented an EarthCARE-like payload onboard the German research aircraft HALO (High Altitude and LOng range) to prepare and validate the EarthCARE data. This instrumentation was flown during PERCUSION (Persistent EarthCARE underflight studies of the ITCZ and organized convection) as a contribution to ORCESTRA (Organized Convection and EarthCARE Studies over the Tropical Atlantic).ORCESTRA is a network of different campaigns conducted to better understand the organized tropical convection at the mesoscale, e.g. including the interaction of convective organization with tropical waves and air-sea interaction, and the impact of convective organization on the Earth’s climate and radiation budget. In addition, ORCESTRA helps to validate satellite remote sensing (especially EarthCARE). To achieve these objectives, ORCESTRA combines several sub-campaigns taking place on the Cape Verde Islands and Barbados in August and September 2024.One of the campaigns within ORCESTRA is the PERCUSION campaign. PERCUSION aims to test factors hypothesized to influence the organization of deep maritime convection in the tropics and the influence of convective organization on its larger-scale environment. One focus of PERCUSION was to establish confidence in the EarthCARE measurements and products. For this purpose, we conducted one EarthCARE underpass within each research flight HALO measurements were performed during the EarthCARE commissioning phase in August 2024 out of Sal, Cape Verde, and out of Barbados in September 2024. In addition, we performed flights out of Oberpfaffenhofen, Germany in November 2024 for validation of conditions that could not be captured in the two first campaign parts. Altogether, 33 EarthCARE underpasses were carried out in different aerosol and cloud situations. Some of the flights were coordinated with in-situ measurements onboard other aircrafts (e.g. the French ATR42), with shipborne measurements onboard the German research vessel METEOR, or with ground-based radar and lidar measurements at Mindelo (Cape Verde), Barbados, and the ACTRIS stations Antikythera, Leipzig, Lindenberg and Munich. Four underpasses under NASA’s PACE mission were also performed.In our presentation we will give an overview of ORCESTRA with the main focus on PERCUSION. We will present the HALO PERCUSION measurements and will show first comparisons of HALO lidar and radar and EarthCARE lidar and radar measurements.

The impact of stratospheric aerosols on Earth’s climate, particularly through atmospheric heating and ozone depletion, remains a critical area of atmospheric research. While satellite data provide valuable insights, independent validation methods are necessary for ensuring accuracy. Twilight near-infrared (NIR) radiometry offers a promising approach for investigating aerosol properties, such as optical depth and layer height, at high altitudes. This study aims to evaluate the effectiveness of twilight radiometry in corroborating satellite data and assessing aerosol characteristics. Two methods based on twilight radiometry—the color ratio and the derivative method—are employed to derive the aerosol layer height and optical depth. Radiances at 450, 550, 762, 775, and 1050 nm wavelengths are analyzed at varying solar zenith angles, using zenith viewing geometry for consistency. Comparisons of aerosol optical depths (AODs) between Research Pandora (ResPan) and AErosol RObotic NETwork (AERONET) data (R = 0.99) and between ResPan and Modern-Era Retrospective analysis for Research and Applications (MERRA-2) data (R = 0.86) demonstrate a strong correlation. Twilight ResPan data are also used to estimate the aerosol layer height, with results in good agreement with SAGE and lidar measurements, particularly following the Hunga Tonga eruption in Lauder, New Zealand. The simulation database, created using the libRadtran DISORT and Monte Carlo packages for daylight and twilight calculations, is capable of detecting AODs as low as 10−3 using the derivative method. This work highlights the potential of twilight radiometry as a simple, cost-effective tool for atmospheric research and satellite data validation, offering valuable insights into aerosol dynamics at stratospheric altitudes.

Clouds generally have a complex three-dimensional geometry. However, realistic three-dimensional radiative transfer simulations of clouds are computationally expensive, so most retrievals of cloud properties assume one-dimensional clouds, which introduces retrieval biases. In this work, a fast forward operator for polarized 3D radiative transfer in the visible wavelength range is presented. To this end, a new approximation for 3D radiative transfer, the InDEpendent column local halF-sphere ApproXimation (IDEFAX), is introduced. The basic idea behind this approximation is similar to the independent column approximation assuming plane-parallel clouds. However, every column is approximated by an independent field of 3D half-spherical clouds instead of a plane-parallel homogeneous cloud. This field of half-spherical clouds is defined by the local cloud surface orientation angles and the cloud fraction. Thus, the IDEFAX has only three more parameters compared to the plane-parallel approximation. To obtain a fast forward operator, artificial neural networks are trained for both the plane-parallel and the half-spherical cloud assumptions. The IDEFAX and the neural network forward operators are validated against polarized 3D radiative transfer simulations with MYSTIC for low-level Arctic mixed-phase clouds using a realistic cloud field simulated with the WRF model. The use of the IDEFAX significantly improves the representation of 3D radiative effects in the simulated radiance fields compared to the plane-parallel independent column approximation. Due to the implementation of the forward operator with neural networks, the computation time for both approximations is comparable and about 5 orders of magnitude faster than full 3D radiative transfer simulations for the shown example. The introduced neural network forward operators are constructed to be used in retrievals of cloud properties with the specMACS instrument. However, the methods are also applicable to other measurements in the visible wavelength range and to model data.

We present CrystalTrace, a Monte Carlo raytracing algorithm designed for radiative transfer computations involving ice crystals with both random and preferred orientations. Integrated into the three-dimensional radiative transfer solver MYSTIC, which is part of libRadtran, CrystalTrace enables multiple-scattering simulations of absolute radiances for realistic atmospheric scenes, including clouds with different mixtures of ice crystals and water droplets, aerosols, molecules, and surface properties. It seamlessly extends MYSTIC's macroscopic raytracing down to the microscopic scale of individual ice crystals. By computing single-scattering properties online, CrystalTrace removes the need for precomputed look-up tables, which are also subject to limited angular resolution. The current version of CrystalTrace is implemented for the visible spectral range and supports individual hexagonal prisms with different sizes and aspect ratios. It currently does not account for diffraction and ice absorption. CrystalTrace, integrated with MYSTIC, fills a critical gap by providing a computationally efficient forward radiative transfer simulator for ice clouds containing oriented crystals, thereby enabling retrievals of ice crystal properties from ground-based, airborne, and satellite imaging observations. CrystalTrace is a Monte-Carlo raytracing algorithm for radiative transfer simulations of oriented ice crystals.

This paper explores the influence of the presence of clouds on sky radiances. It also analyses their impact on the retrieval of aerosol properties when using an inversion algorithm whose radiative transfer model (RTM) is designed for cloud-free atmospheres. For that, synthetic observations are simulated for 9 partially cloudy skies and for their equivalent cloud-free skies, considering 16 different aerosol scenarios. A parameter named cloud enhancement factor (CEF) has been used to determine the modifications induced in the sky radiances by each partially cloudy scenario with respect to the cloud-free sky. This parameter indicates that the sky radiances remaining after applying a cloud-screening are affected by the presence of clouds. In general, they show enhancements between 0 and 20 % with respect to the cloud-free radiances, depending on the cloudy conditions and the scattering angle. The synthetic observations used as input for the retrieval of aerosol properties are the ones required by the inversion strategy used, GRASPpac: the aerosol optical depth (AOD) and sky radiances at 4 different wavelengths together with the ceilometer range corrected signal (RCS). In partially cloudy scenarios with low CEFs, the aerosol properties do not present significant changes with respect to the cloud-free conditions. However, for partially cloudy scenarios with higher CEFs, a clear differentiation between the aerosol optical properties retrieved with and without clouds is observed. In these scenarios, the precision of the retrieval is similar for both conditions, but the accuracy is lower for the cloudy conditions. In particular, under partially cloudy conditions, it is observed an overestimation of the real refractive index (RRI) and the single scattering albedo (SSA) between 0.05 and 0.06 and between 0.03 and 0.06 respectively, and an underestimation of the asymmetry factor (g) and the imaginary refractive index (IRI) of about-0.02 and- 0.005, respectively. These values slightly vary with the aerosol load and wavelength for the RRI and SSA. The effects on the size distribution parameters are very small, concluding that the impact of clouds is noticeable in the optical properties but not so much in the microphysical part.

Mixed-phase clouds are frequently observed in the Arctic and still not well represented in climate and general circulation models. The spatial distribution of cloud thermodynamic phase and its partitioning are important quantities since they affect the radiative effect of clouds as well as cloud life time. In this work, a new quantitative retrieval method of cloud thermodynamic phase partitioning based on multi-angle polarimetry is presented. The polarization signal is sensitive to cloud thermodynamic phase since liquid water and ice particles have different shapes and different optical properties. The basic idea of the retrieval is to fit simulations obtained from a forward operator to measurements in the cloudbow range between 135 and 165° scattering angle and the slope range between 60 and 110° to determine a quantitative ice fraction. Either plane-parallel clouds or three-dimensional (half-spherical) clouds are assumed in the simulations. The retrieval was validated using synthetic data. 3D radiative transfer simulations were performed for different idealized cloud cases as well as for a realistic field of low-level Arctic mixed-phase clouds. As the retrieval is based on polarization, it is sensitive to cloud top. The retrieved ice fraction is here defined as the ratio of the ice optical thickness to the liquid plus ice optical thickness and corresponds to the mean ice fraction of the uppermost cloud layer from cloud top to an optical thickness of about 1 to 2, depending on the solar zenith angle. In addition, the retrieval was applied to measurements of the polarization-resolving cameras of the specMACS instrument during the HALO–(𝒜𝒞)3 campaign, providing two-dimensional fields of cloud thermodynamic phase partitioning with a high spatial resolution of about 100 m.

Accurately determining and reducing the climate impact of aviation and its uncertainties is one of the pressing challenges of our times. Contrail cirrus are estimated to contribute more than half of the total effective radiative forcing from aviation, yet the uncertainties in their optical and radiative properties are large. In contrast to midlatitude cirrus, high-latitude cirrus are less anthropogenically influenced; thus, they are more pristine. However, little is known about Arctic cirrus properties and their role in the amplified warming of this region. The Cirrus in High Latitudes (CIRRUS-HL) mission using the High Altitude and Long Range Research Aircraft (HALO) provides measurements in mid-and high latitudes during summer (June/July) 2021, exploiting HALO's capabilities and a comprehensive cloud-aerosol-trace gas and radiation instrumentation. The results of 24 HALO flights provide new insights into both natural cirrus and contrail cirrus properties in high (60 degrees-76 degrees N) and midlatitudes (38 degrees-60 degrees N). In particular, we find lower ice water content (-42%) and lower number concentrations (-88%) of cirrus particles with larger mean diameters (+22%) in high latitudes. Ice supersaturated regions were frequently observed in mid-and high latitudes, with median in-cloud relative humidity over ice between 105% and 122%. Mean aerosol number concentrations in the midlatitudes were reduced by up to 80% compared to pre-COVID-19 times. Less air traffic during the COVID-19 lockdowns, reduced contrail cirrus coverage, and lower ice nucleating particle concentrations in high latitudes help to explain the observed differences in cirrus properties. The extensive dataset will be used to improve weather and climate models. SIGNIFICANCE STATEMENT: In contrast to Arctic cirrus, midlatitude cirrus are more often modi-fied by human activities, of which air traffic is a significant contributor through the formation of contrails and contrail cirrus. These man-made cirrus warm Earth, but to constrain their effects on climate, in situ and remote sensing measurements were conducted with the German research aircraft High Altitude and Long Range Research Aircraft (HALO). During 24 flights, we used HALO's exceptional altitude and distance range to sample and contrast different cirrus types from the dense air traffic regions to the remote Arctic regions. We find that microphysical properties of high-and midlatitude cirrus differ substantially, related to their formation pathway, the abundance of air traffic, and the availability of ice nucleating particles. The measurements will help to validate contrail cirrus and climate models.

We present a method to retrieve cloud thermodynamic phase from multi-angle polarimetric and spectral imaging. Spectral absorption differences between water and ice in the near infrared are commonly used to discriminate between liquid, mixed, and ice clouds. For example, the spectral slope between 1500 and 1700 nm increases with decreasing liquid cloud fraction. These methods are very sensitive to small amounts of ice in liquid clouds. On the other hand, the polarization signal of clouds shows different features depending on the cloud thermodynamic phase. The cloudbow is formed by single scattering on liquid cloud droplets. Observation of the cloudbow indicates the presence of liquid water while its absence indicates pure ice clouds. In addition the slope of the Q component of the Stokes vector for scattering angles in the range of 60 to 100 degree depends on the partitioning between liquid and ice phase. The polarimetric method is much more sensitive to small amounts of liquid water compared to the spectral method and represents cloud thermodynamic phase at cloud top. In addition, polarization is dominated by single scattering and thus does not suffer from 3D radiative effects. Both methods are applied to data of the airborne hyperspectral and polarized imaging system specMACS measured during the HALO-(AC)3 campaign. specMACS provides wide-field and high spatial resolution data with a horizontal resolution down to a few 10m. By a combination of the spectral and multi-angle polarimetric observations we will retrieve cloud thermodynamic phase partitioning of single layer mixed-phase clouds and investigate spatial and temporal scales of phase transitions in low-level arctic mixed-phase clouds.

Aerosols significantly attenuate solar radiation and influence atmospheric thermodynamic stability, particularly over regions like the Atlantic, impacting Earth's energy budget and climate through radiative heating or cooling. Quantifying these effects is challenging due to aerosol diversity and complexity. For desert dust particles, the difficulty lies in defying their optical properties and accurately monitoring their extensive distribution.This study aims to assess the radiative effects of dust aerosols and water vapor (WV), and their impact on atmospheric heating rates, by adopting non-spherical particle shapes and their intrinsic microphysical and optical properties during severe dust events. To achieve this, ground-based, airborne, and satellite observations are employed along with Radiative Transfer (RT) modeling, and more precisely the libRadtran RT package (Mayer and Kylling, 2005; Emde et al., 2016). The study utilizes data from two experimental campaigns – ASKOS and ORCESTRA/PERCUSION – both conducted in the Atlantic region during peak trans-Atlantic dust transport periods, in summers of 2022 and 2024.In the frame of the ASKOS ESA Joint Aeolus Tropical Atlantic Campaign (JATAC), we utilized ground-based remote sensing and airborne in-situ observations, including solar radiation and airborne meteorological profiles. Microphysical properties from UAVs, MOPSMAP (Gasteiger and Wiegner, 2018) and TAMUdust2020 (Saito et al., 2021) scattering databases were used to derive dust optical properties considering a mixture of spheroidal and irregular-hexahedra shapes. Multi-wavelength lidar measurements contributed to the validation of the optical properties and dust vertical distribution. RT simulations incorporated WV concentration, to investigate dust-WV-solar radiation interactions under clear sky conditions. The simulated broadband shortwave radiation was, finally, compared with the ground-based solar radiation measurements.A second case study was performed, leveraging ORCESTRA/PERCUSION campaign (https://orcestra-campaign.org/percusion.html) synergistic airborne measurements. This campaign incorporated a comprehensive suite of airborne instruments, providing, amongst others, radiation measurements, meteorological profiles, and extensive lidar measurements. Radiation at the top of the atmosphere (TOA) from the EarthCARE ESA mission supported comprehensive closure studies at TOA and at aircraft level.Acknowledgements This research was financially supported by the PANGEA4CalVal project (Grant Agreement 101079201) funded by the European Union, the CERTAINTY project (Grant Agreement 101137680) funded by Horizon Europe program and the AIRSENSE project which is part of Atmosphere Science Cluster of ESA’s EO Science for Society programme. DK, ΑΤ, ΚP, PR and SK would like to acknowledge COST Action HARMONIA (International network for harmonization of atmospheric aerosol retrievals from ground-based photometers), CA21119, supported by COST (European Cooperation in Science and Technology).References Mayer, B., Kylling, A.: Technical note: The libRadtran software package for radiative transfer calculations - description and examples of use. Atmos. Chem. Phys., 5(7), 1855–1877, 2005.Emde, C., et al.: The libRadtran software package for radiative transfer calculations (version 2.0.1), Geoscientific Model Development, 9(5), 1647–1672, 2016.Gasteiger, J. and Wiegner, M.: MOPSMAP v1.0: a versatile tool for the modeling of aerosol optical properties, Geosci. Model Dev., 11, 2739–2762, https://doi.org/10.5194/gmd-11-2739-2018, 2018.Saito, M., et al.: A comprehensive database of the optical properties of irregular aerosol particles for radiative transfer simulations, J. Atmos. Sci., in press, https://doi.org/10.1175/JAS-D-20-0338.1, 2021.

Most of the dust models underestimate the load of the large dust particles, consider spherical shapes instead of irregular ones, and have to deal with a wide range of the dust refractive index (RI) to be used. This leads to an incomplete assessment of the dust radiative effects and dust-related impacts on climate and weather. The current work aims to provide an assessment, through a sensitivity study, of the limitations of models to calculate the dust direct radiative effect (DRE) due to the underrepresentation of its size, RI, and shape. We show that the main limitations stem from the size and RI, while using a more realistic shape plays only a minor role, with our results agreeing with recent findings in the literature. At the top of the atmosphere (TOA) close to dust sources, the underestimation of size issues an underestimation of the direct warming effect of dust of ∼ 18–25 W m−2, for DOD = 1 (dust optical depth) at 0.5 µm, depending on the solar zenith angle (SZA) and RI. The underestimation of the dust size in models is less above the ocean than above dust sources, resulting in an underestimation of the direct cooling effect of dust above the ocean by up to 3 W m−2, for aerosol optical depth (AOD) of 1 at 0.5 µm. We also show that the RI of dust may change its DRE by 80 W m−2 above the dust sources and by 50 W m−2 at downwind oceanic areas for DOD = 1 at 0.5 µm at TOA. These results indicate the necessity of including more realistic sizes and RIs for dust particles in dust models, in order to derive better estimations of the dust DRE, especially near the dust sources and mostly for studies dealing with local radiation effects of dust aerosols.

Cloud-radiative heating (CRH) within the atmosphere affects the dynamics and predictability of extratropical cyclones. However, CRH is uncertain in numerical weather prediction and climate models, and this could affect model predictions of extratropical cyclones. In this paper, we present a systematic quantification of CRH uncertainties. To this end, we study an idealized extratropical cyclone simulated at a convection-permitting resolution of 2.5 km and combine large-eddy-model simulations at a 300 m resolution with offline radiative transfer calculations. We quantify four factors contributing to the CRH uncertainty in different regions of the cyclone: 3D cloud-radiative effects, parameterization of ice optical properties, cloud horizontal heterogeneity, and cloud vertical overlap. The last two factors can be considered essentially resolved at 300 m but need to be parameterized at a 2.5 km resolution. Our results indicate that parameterization of ice optical properties and cloud horizontal heterogeneity are the two factors contributing most to the mean uncertainty in CRH at larger spatial scales and can be more relevant for the large-scale dynamics of the cyclone. On the other hand, 3D cloud-radiative effects are much smaller on average, especially for stratiform clouds within the warm conveyor belt of the cyclone. Our analysis in particular highlights the potential to improve the simulation of CRH by better representing ice optical properties. Future work should, in particular, address how uncertainty in ice optical properties affects the dynamics and predictability of extratropical cyclones.