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Derber, J.C., Van Delst, P., Su, X., Li, X., Okamoto, K. and Treadon, R. (2003) Enhanced Use of Radiance Data in the NCEP Data Assimilation System. 13th International TOVS Study Conference, Adele, 15 October 2003, 1-8.

https://cimss.ssec.wisc.edu/itwg/itsc/itsc13/proceedings/session1/1_8_derber.pdf

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.