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Le Marshall, J., Jung, J., Derber, J., Chahine, M., Treadon, R., Lord, S.J., et al. (2006) Improving Global Analysis and Forecasting with AIRS. Bulletin of the American Meteorological Society, 87, 891-894.
http://www.jstor.org/stable/26217190

https://doi.org/10.1175/BAMS-87-7-891

has been cited by the following article:

• TITLE: Impacts on Initial Condition Modification from Hyperspectral Infrared Sounding Data Assimilation: Comparisons between Full-Spectrum and Channel-Selection Scheme Based on Two-Month Experiments Using CrIS and IASI Observation

  AUTHORS: Qi Zhang

  KEYWORDS: Hyperspectral Infrared, Remote Sensing, Data Assimilation, Performance Evaluation, Numerical Weather Prediction

  JOURNAL NAME: International Journal of Geosciences, Vol.12 No.9, September 13, 2021

  ABSTRACT: This paper discusses the performance difference between full-spectrum and channel-selection assimilation scheme of hyperspectral infrared observation, e.g. CrIS and IASI, on improving the accuracy of initial condition in numerical weather prediction. To accomplish this, we develop a 3D-Variational data assimilation system whose observation operator is a principal-component based fast radiative transfer model, which equips the direct assimilation of full-channel radiance from hyperspectral infrared sounders with high computational efficiency. This project’s primary goal is to demonstrate that assimilation of infrared observation in a full-channel mode could improve the accuracy of initial condition compared to selected-channel assimilation. Results show that full-channel assimilation performs better than selected-channel assimilation in modifying low and middle troposphere (1000 - 700 hPa, 700 - 400 hPa) temperature and water vapor field, while marginal improvements from temperature and water vapor field could be found over upper troposphere (400 - 100 hPa). This research also proves the feasibility of an alternative path to data assimilation for the full usage of hyperspectral infrared sounding observation in numerical weather prediction.