The Ice Cloud Imager: retrieval of frozen water column properties
Journal article, 2024

The Ice Cloud Imager (ICI) aboard the second generation of the EUMETSAT Polar System (EPS-SG) will provide novel measurements of ice hydrometeors. ICI is a passive conically scanning radiometer that will operate within a frequency range of 183 to 664 GHz, helping to cover the present wavelength gap between microwave and infrared observations. Reliable global data will be produced on a daily basis. This paper presents the retrieval database to be used operationally and performs a final pre-launch assessment of ICI retrievals.Simulations are performed within atmospheric states that are consistent with radar reflectivities and represent the three-dimensional (3D) variability of clouds. The radiative transfer calculations use empirically based hydrometeor models. Azimuthal orientation of particles is mimicked, allowing for the consideration of polarisation. The degrees of freedom (DoFs) of the ICI retrieval database are shown to vary according to cloud type. The simulations are considered to be the most detailed performed to this date. Simulated radiances are shown to be statistically consistent with real observations.Machine learning is applied to perform inversions of the simulated ICI observations. The method used allows for the estimation of non-Gaussian uncertainties for each retrieved case. Retrievals of ice water path (IWP), mean mass height (Zm), and mean mass diameter (Dm) are presented. Distributions and zonal means of both database and retrieved IWP show agreement with DARDAR. Retrieval tests indicate that ICI will be sensitive to IWP between 10-2 and 101 kgm-2. Retrieval performance is shown to vary with climatic region and surface type, with the best performance achieved over tropical regions and over ocean. As a consequence of this study, retrievals from real observations will be possible from day one of the ICI operational phase.

Author

Eleanor May

Chalmers, Space, Earth and Environment, Geoscience and Remote Sensing

Bengt Rydberg

SMHI

Inderpreet Kaur

Chalmers, Space, Earth and Environment, Geoscience and Remote Sensing

Vinia Mattioli

EUMETSAT

Hanna Hallborn

Chalmers, Space, Earth and Environment, Geoscience and Remote Sensing

Patrick Eriksson

Chalmers, Space, Earth and Environment, Geoscience and Remote Sensing

Atmospheric Measurement Techniques

1867-1381 (ISSN) 1867-8548 (eISSN)

Vol. 17 19 5957-5987

Subject Categories

Aerospace Engineering

Meteorology and Atmospheric Sciences

Probability Theory and Statistics

DOI

10.5194/amt-17-5957-2024

More information

Latest update

11/1/2024