Preprints
https://doi.org/10.5194/egusphere-2023-2260
https://doi.org/10.5194/egusphere-2023-2260
10 Oct 2023
 | 10 Oct 2023

A new approach to the crystal habit retrieval from far infrared spectral radiance measurements

Gianluca Di Natale, Marco Ridolfi, and Luca Palchetti

Abstract. To generate reliable climate predictions, global models need accurate estimates of all the energy fluxes contributing to the Earth Radiation Budget (ERB). Clouds in general, and more specifically ice clouds play a key role in the determination of the ERB, as they may exert either a feedback or a forcing action, depending on their optical and microphysical properties and physical state (solid/liquid). To date, accurate statistics and climatologies of cloud parameters are not available. The ice cloud composition in terms of ice crystal shape (or habit) is one of the parameters with the largest uncertainty. This is because crystal shape estimates are generally hard to extract from space measurements. Satellite spectral radiance measurements available to date, are actually limited to the middle-infrared spectral region, therefore their information on ice cloud crystal habits is not sufficient to attempt a retrieval. 

The Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) experiment, foreseen to be the 9th Earth Explorer mission of the European Space Agency, will measure, for the first time spectrally resolved from space, the entire upwelling spectrum emitted by the Earth, from 100 to 1600 cm-1. The far-infrared portion of the Earth spectrum, especially from 200 to 600 cm-1, is very sensitive to cloud ice crystal shapes, thus FORUM measurements could also represent an opportunity to study the ice cloud composition in terms of ice crystal habit mixtures. 

To investigate this possibility, we developed an accurate and advanced scheme allowing to model ice cloud optical properties also in cases of clouds composed of mixed ice crystal habits. This feature is in fact necessary, because also in situ measurements acquired over the years point out that the shape of ice cloud crystals varies depending on the crystal size range. In our model, the resulting cloud optical properties are also determined by the input habit fractions. Thus, the retrieval of these fractions from spectral radiance measurements can be attempted. Using 375 different cloudy scenarios, we assess the performance of our retrieval scheme in the determination of crystal habit mixtures starting from FORUM simulated measurements. The most relevant error components affecting the retrieved cloud parameters are not very large and are of random nature, thus FORUM measurements will allow to set up an accurate climatology of cloud parameters.

To provide an example of the benefit that one could get out of the habit mixture retrievals, we also show the improved accuracy of the thermal outgoing fluxes calculations as compared to using assumed mixtures.

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Gianluca Di Natale, Marco Ridolfi, and Luca Palchetti

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2260', Anonymous Referee #1, 07 Nov 2023
  • RC2: 'Comment on egusphere-2023-2260', Anonymous Referee #2, 07 Feb 2024

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2260', Anonymous Referee #1, 07 Nov 2023
  • RC2: 'Comment on egusphere-2023-2260', Anonymous Referee #2, 07 Feb 2024
Gianluca Di Natale, Marco Ridolfi, and Luca Palchetti
Gianluca Di Natale, Marco Ridolfi, and Luca Palchetti

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Short summary
This work aims to define a new approach to retrieve  from spectral infrared measurements the distribution of the main ice crystal shapes occurring inside ice and cirrus clouds. The capability of retrieving from satellites these shapes of the ice crystals in clouds will allow to extend the current available climatologies to be used as physical constrains in the global circulation models. This could could allow to improve their accuracy and prediction performance.