Highlights Assimilation of cloud-affected brightness temperature using simulated data for AIRS. Generalised variational combines both advantage of classical one and M-estimators. Huber estimator was considered the most robust estimation for various distributions.

    Abstract Assimilated channel brightness temperature data from infrared sounders accounting for cloud effects have a positive effect on weather forecasting, especially in weather-sensitive areas. When cloud effects are included, the channel brightness temperature deviations follow a non-Gaussian distribution. However, classical variational data assimilation follows a Gaussian distribution. When processing the cloud-affected brightness temperature, useful data are lost through the cloud detection process, thus assimilating some channel brightness temperatures with weight function peaks above the cloud top. Furthermore, strict quality control of brightness temperature removes outliers. By adopting the generalised variational assimilation method, which assumes that errors follow a non-Gaussian distribution, this paper assimilates the cloud-affected brightness temperature using simulated data for the hyper-spectral atmospheric infrared sounder (AIRS). A channel set is formed by dynamically selecting AIRS channels. The experiments for retrieving temperature and humidity data demonstrate that the generalised variational assimilated cloud-affected brightness temperature method performs better than the classical method.


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    Titel :

    Generalised variational assimilation of cloud-affected brightness temperature using simulated hyper-spectral atmospheric infrared sounder data


    Beteiligte:
    Wang, Gen (Autor:in) / Zhang, Jianwei (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2014-03-05


    Format / Umfang :

    10 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch