Article citationsMore>>
Aumann, H.H., Chen, X., Fishbein, E., Geer, A., Havemann, S., Huang, X., et al. (2018) Evaluation of Radiative Transfer Models with Clouds. Journal of Geophysical Research: Atmospheres, 123, 6142-6157.
https://doi.org/10.1029/2017JD028063
has been cited by the following article:
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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.