Aerosol and Cloud Detection Updates


v3.2, February 2024
This update includes support for the Hyperspectral Infrared Atmospheric Sounder-2, (HIRAS-2) and the Infra-Red Interferometer Spectrometer (IRIS), as well as the capability to use VIIRS clustering information as provided with CrIS observations.

v3.1, September 2020
A major upgrade to include the aerosol type classification (over sea only), trace gas detection, flagging for land sensitivity, and support for the Infrared Fourier Spectrometer -2 (IKFS-2) sounder. The source code is also re-organized to emphasize the independence of the four separate detection modules.

v2.4, March 2019
This software update introduces two significant developments. Firstly, processing capability is added for Chinese hyperspectral infrared sounders (HIRAS and GIIRS). Secondly, flagging of contaminated data in the presence of aerosol is made channel-dependent.

v2.3, January 2017
The necessary code changes are made and preliminary processing parameter values are provided for the future European infrared sounders including the Meteosat Third Generation Infra-Red Sounder (MTG-IRS) and the Infrared Atmospheric Sounding Interferometer – Next Generation (IASI-NG).

v2.2, November 2015
The aerosol element of the software has been upgraded to use a new algorithm that works independently from the cloud detection and is focussed on the identification of desert dust (the most important and significant source of contamination in infrared spectra). The new algorithm examines differences between the observed brightness temperature at two spectral locations in the long-wave window. Several channels are averaged around each location to reduce noise effects and if the resulting difference exceeds a pre-defined threshold the entire spectrum is flagged as contaminated.

v2.1, November 2014
A new screening step is introduced to make use of clustered AVHRR radiance data within a collocated IASI field-of-view. If the collocated AVHRR cluster data are available, an initial AVHRR-based cloud flag is derived and used as additional input to the main cloud detection algorithm. Additionally, default processing parameters for CrIS are updated on the basis of experience gathered from assimilation experiments using real radiance data at ECMWF.