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Discussion papers
https://doi.org/10.5194/amt-2019-293
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-293
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 19 Aug 2019

Submitted as: research article | 19 Aug 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

Using passive and active microwave observations to constrain ice particle models

Robin Ekelund, Patrick Eriksson, and Simon Pfreundschuh Robin Ekelund et al.
  • Department of Space, Earth and Environment, Chalmers University of Technology, Gothenburg, Sweden

Abstract. Satellite microwave remote sensing is an important tool for determining the distribution of atmospheric ice globally. The upcoming Ice Cloud Imager (ICI) sensor 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 GPM Microwave Imager (GMI) and ICI. Simulations covering about one month in the tropic pacific ocean are performed, assuming different microphysical settings realized as combinations of particle model and particle size distribution (PSD).

Firstly, it is found that when the CloudSat inversions and passive forward model are considered separately, 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 assumed particle model is significantly reduced, essentially due to a compensation effect between bulk extinction at passive frequencies and radar reflectivity. Furthermore, simulated TBs for almost all the tested microphysical combinations, from a statistical point of view, agree well with GMI measurements (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 to GMI compared to the other particles, mainly for the most severe cases of deep convection. Of tested PSDs, the one by McFarquhar and Heymsfield (1997) is found to give the best overall agreement to 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.

Robin Ekelund et al.
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Short summary
Atmospheric ice particles (e.g., snow and ice cloud crystals) are an important part of weather, climate and the hydrological cycle. This study investigates if combined satellite measurements by radar and radiometers at microwave wavelengths can be used to identify the shape of such ice particles. It is found that this concept is limited when using currently operating sensors (e.g, CloudSat and GPM Microwave Imager), while promising if the upcoming Ice Cloud Imager sensor is considered as well.
Atmospheric ice particles (e.g., snow and ice cloud crystals) are an important part of weather,...
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