Abstract. A main limitation today in simulations and inversions of microwave observations of ice hydrometeors (cloud ice, snow, hail, etc.) is the lack of data describing the interaction between electromagnetic waves and the particles. To improve the situation, the development of a comprehensive dataset of such scattering properties has been started. The database aims at giving a broad coverage in both frequency (1 to 886 GHz) and temperature (190 to 270 K), to support both passive and active current and planned measurements, and to provide data corresponding to the full Stokes vector. This first version of the database is restricted to totally random particle orientation. Data for 34 particle sets, i.e. habits, have been generated. About 17 of the habits can be classified as single crystals, three habits can be seen as heavily rimed particles, and the remaining habits are aggregates of different types, e.g. snow and hail. The particle sizes considered vary between the habits, but maximum diameters of 10 and 20 mm are typical values for the largest single crystal and aggregate particles, respectively, and the number of sizes per habit is at least 30. Particles containing liquid water are also inside the scope of the database, but this phase of water is so far only represented by a liquid sphere habit. The database is built upon the netCDF4 file format. Interfaces to browse, extract and convert data for selected radiative transfer models are provided in MATLAB and Python. The database and associated tools are publicly available from Zenodo (https://doi.org/10.5281/zenodo.1175572, Ekelund et al., 2018b), and https://doi.org/10.5281/zenodo.1175588, Mendrok et al., 2018, respectively). Planned extensions include non-spherical raindrops, melting particles and a second orientation case that can be denoted as azimuthally random.

Abstract. Microwave (1–300 GHz) dual-polarization measurements above 100 GHz are so far sparse, but they consistently show polarized scattering signals of ice clouds. Existing scattering databases of realistically shaped ice crystals for microwaves and submillimeter waves (>300 GHz) typically assume total random orientation, which cannot explain the polarized signals. Conceptual models show that the polarization signals are caused by oriented ice particles. Only a few works that consider oriented ice crystals exist, but they are limited to microwaves only. Assuming azimuthally randomly oriented ice particles with a fixed but arbitrary tilt angle, we produced scattering data for two particle habits (51 hexagonal plates and 18 plate aggregates), 35 frequencies between 1 and 864 GHz, and 3 temperatures (190, 230 and 270 K). In general, the scattering data of azimuthally randomly oriented particles depend on the incidence angle and two scattering angles, in contrast to total random orientation, which depends on a single angle. The additional tilt angle further increases the complexity. The simulations are based on the discrete dipole approximation in combination with a self-developed orientation averaging approach. The scattering data are publicly available from Zenodo (https://doi.org/10.5281/zenodo.3463003). This effort is also an essential part of preparing for the upcoming Ice Cloud Imager (ICI) that will perform polarized observations at 243 and 664 GHz. Using our scattering data radiative transfer simulations with two liquid hydrometeor species and four frozen hydrometeor species of polarized Global Precipitation Measurement (GPM) Microwave Imager (GMI) observations at 166 GHz were conducted. The simulations recreate the observed polarization patterns. For slightly fluttering snow and ice particles, the simulations show polarization differences up to 11 K using plate aggregates for snow, hexagonal plates for cloud ice and totally randomly oriented particles for the remaining species. Simulations using strongly fluttering hexagonal plates for snow and ice show similar polarization signals. Orientation, shape and the hydrometeor composition affect the polarization. Ignoring orientation can cause a negative bias for vertically polarized observations and a positive bias for horizontally polarized observations.

Abstract. Satellite microwave remote sensing is an important tool for determining the distribution of atmospheric ice globally. The upcoming Ice Cloud Imager (ICI) will provide unprecedented measurements at sub-millimetre frequencies, employing channels up to 664 GHz. However, the utilization of such measurements requires detailed data on how individual ice particles scatter and absorb radiation, i.e. single scattering data. Several single scattering databases are currently available, with the one by Eriksson et al. (2018) specifically tailored to ICI. This study attempts to validate and constrain the large set of particle models available in this database to a smaller and more manageable set. A combined active and passive model framework is developed and employed, which converts CloudSat observations to simulated brightness temperatures (TBs) measured by the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and ICI. Simulations covering about 1 month in the tropical Pacific Ocean are performed, assuming different microphysical settings realized as combinations of the particle model and particle size distribution (PSD). Firstly, it is found that when the CloudSat inversions and the passive forward model are considered separately, the assumed particle model and PSD have a considerable impact on both radar-retrieved ice water content (IWC) and simulated TBs. Conversely, when the combined active and passive framework is employed instead, the uncertainty due to the assumed particle model is significantly reduced. Furthermore, simulated TBs for almost all the tested microphysical combinations, from a statistical point of view, agree well with GMI measurements (166, 186.31, and 190.31 GHz), indicating the robustness of the simulations. However, it is difficult to identify a particle model that outperforms any other. One aggregate particle model, composed of columns, yields marginally better agreement with GMI compared to the other particles, mainly for the most severe cases of deep convection. Of the tested PSDs, the one by McFarquhar and Heymsfield (1997) is found to give the best overall agreement with GMI and also yields radar dBZ–IWC relationships closely matching measurements by Protat et al. (2016). Only one particle, modelled as an air–ice mixture spheroid, performs poorly overall. On the other hand, simulations at the higher ICI frequencies (328.65, 334.65, and 668.2 GHz) show significantly higher sensitivity to the assumed particle model. This study thus points to the potential use of combined ICI and 94 GHz radar measurements to constrain ice hydrometeor properties in radiative transfer (RT) using the method demonstrated in this paper.

Abstract. The next generation of European polar orbiting weather satellites will carry a novel instrument, the Ice Cloud Imager (ICI), which uses passive observations between 183 and 664 GHz to make daily global observations of cloud ice. Successful use of these observations requires accurate modelling of cloud ice scattering, and this study uses airborne observations from two flights of the Facility for Airborne Atmospheric Measurements (FAAM) BAe 146 research aircraft to validate radiative transfer simulations of cirrus clouds at frequencies between 325 and 664 GHz using the Atmospheric Radiative Transfer Simulator (ARTS) and a state-of-the-art database of cloud ice optical properties. Particular care is taken to ensure that the inputs to the radiative transfer model are representative of the true atmospheric state by combining both remote-sensing and in situ observations of the same clouds to create realistic vertical profiles of cloud properties that are consistent with both observed particle size distributions and bulk ice mass. The simulations are compared to measurements from the International Submillimetre Airborne Radiometer (ISMAR), which is an airborne demonstrator for ICI. It is shown that whilst they are generally able to reproduce the observed cloud signals, for a given ice water path (IWP) there is considerable sensitivity to the cloud microphysics, including the distribution of ice mass within the cloud and the ice particle habit. Accurate retrievals from ICI will therefore require realistic representations of cloud microphysical properties.

Abstract. Remote sensing observations at sub-millimeter wavelengths provide higher sensitivity to small hydrometeors and low water content than observations at millimeter wavelengths, which are traditionally used to observe clouds and precipitation. They are employed increasingly in field campaigns to study cloud microphysics and will be integrated into the global meteorological observing system to measure the global distribution of ice in the atmosphere with the launch of the Ice Cloud Imager (ICI) radiometer on board the second generation of European operational meteorological satellites (Metop-SG). Observations at these novel wavelengths provide valuable information not only on their own but also in combination with complementary observations at other wavelengths. This study investigates the potential of combining passive sub-millimeter radiometer observations with a hypothetical W-band cloud radar for the retrieval of frozen hydrometeors. An idealized cloud model is used to investigate the information content of the combined observations and establish their capacity to constrain the microphysical properties of ice hydrometeors. A synergistic retrieval algorithm for airborne observations is proposed and applied to simulated observations from a cloud-resolving model. Results from the synergistic retrieval are compared to equivalent radar- and passive-only implementations in order to assess the benefits of the synergistic sensor configuration. The impact of the assumed ice particle shape on the retrieval results is assessed for all retrieval implementations. We find that the combined observations better constrain the microphysical properties of ice hydrometeors, which reduces uncertainties in retrieved ice water content and particle number concentrations for suitable choices of the ice particle model. Analysis of the retrieval information content shows that, although the radar contributes the largest part of the information in the combined retrieval, the radiometer observations provide complementary information over a wide range of atmospheric states. Furthermore, the combined observations yield slightly improved retrievals of liquid cloud water in mixed-phase clouds, pointing towards another potential application of combined radar–radiometer observations.