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Proceedings Paper

Comparison of the RTTOV-12 ice cloud models for hyperspectral IR instruments using the A-Train
Author(s): Jérôme Vidot; Pascal Brunel
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Paper Abstract

The fast radiative transfer model RTTOV offers different ice cloud models to simulate observations from hyperspectral infrared instruments such as AIRS, IASI or CrIS. Since the last RTTOV version 12 there are two ice cloud models: the first one is an ice water content and temperature parameterization of a large dataset of ice cloud optical properties coming from the Met Office, and the second one is based on the ice crystal size dependent ice cloud optical properties database provided by SSEC. In order to compare these two ice cloud models, we used collocated data from the A-Train during two weeks of global observations. The ice cloud profiles description (ice water content and effective size of ice crystals) used as inputs for RTTOV is provided by the synergetic retrieval from active sensors (CloudSat and CALIOP/CALIPSO) named the 2C-ICE product. The RTTOV top of atmosphere simulations using these ice cloud profiles are then compared with collocated infrared observations from the IIR radiometer that is onboard CALISPO and has three infrared window channels. We found that RTTOV-12 is able to reproduce observations with nearly no biases and standard deviation below 7 K in window channels. The results show also the strong impact of the knowledge of the ice crystal size that is not currently information predicted by NWP models. By using hyperspectral infrared instruments, the spectral consistency of these two ice cloud models is also preliminary discussed.

Paper Details

Date Published: 23 October 2018
PDF: 7 pages
Proc. SPIE 10780, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VII, 1078004 (23 October 2018); doi: 10.1117/12.2324673
Show Author Affiliations
Jérôme Vidot, Météo-France (France)
Pascal Brunel, Météo-France (France)

Published in SPIE Proceedings Vol. 10780:
Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VII
Allen M. Larar; Makoto Suzuki; Jianyu Wang, Editor(s)

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