Selection of IASI channels for use in numerical weather prediction

By Andrew Collard. 2007

170 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/5ec169e02b1bac8b2d6575058dc473b17d21953a

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In Atmospheric Science

In Global and Planetary Change

Citations in the corpus

On the interaction of observation and prior error correlations in data assimilation (2018, unknown): “The dfs has previously been used for objectively choosing a subset of the most useful channels for IASI, which measures more channels than can practically be stored, transmitted and assimilated (Rabier et al. , 2002; Collard, 2007; Eresmaa et al. , 2014; Ventress and Dudhia, 2014).”
Estimating interchannel observation‐error correlations for IASI radiance data in the Met Office system † (2014, methodology): “IASI has the potential to provide observations in 8461 channels, but at the time of the research only observations from a subset of 314 derived in Collard (2007) were used.”
Satellite-Based Atmospheric Infrared Sounder Development and Applications (2017, background): “…more independent pieces of information and channel selection out of the thousands available, which becomes important in order to reduce the computation load while maintaining the information content (Li and Huang 1994; Rodgers 2000, Rabier et al. 2002; Collard 2007; Ventress and Dudhia 2014).”
The assimilation of Infrared Atmospheric Sounding Interferometer radiances at ECMWF (2009, methodology): “These comprise the 300 channels chosen in Collard (2007) plus an additional 43 channels from the 15 µm CO2 band that peak in the lower stratosphere and 23 chosen to allow intercomparison of monitoring statistics between operational NWP centres and CNES.”
On the representation error in data assimilation (2018, unknown): “Such studies, combined with examples demonstrating that ignoring correlation structure hinderstheuseofsatellitedata(e.g. constrainingchannelselection algorithms:Collard,2007),suggestthaterrorcorrelationsfor certainobservationtypeshaveanimportantroletoplayin improvingnumericalweatherforecasting.”