Articles | Volume 13, issue 2
Atmos. Meas. Tech., 13, 629–644, 2020
Atmos. Meas. Tech., 13, 629–644, 2020

Research article 10 Feb 2020

Research article | 10 Feb 2020

A channel selection method for hyperspectral atmospheric infrared sounders based on layering

Shujie Chang et al.

Related authors

Intercomparison of FY-3 and AIRS Gravity Wave Parameter Extraction Based on Three Methods
Shujie Chang, Zheng Sheng, Wei Ge, Wei Zhang, Yang He, and Zhixian Luo
Ann. Geophys. Discuss.,,, 2019
Manuscript not accepted for further review
Short summary

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Linking rain into ice microphysics across the melting layer in stratiform rain: a closure study
Kamil Mróz, Alessandro Battaglia, Stefan Kneifel, Leonie von Terzi, Markus Karrer, and Davide Ori
Atmos. Meas. Tech., 14, 511–529,,, 2021
Short summary
Classification of lidar measurements using supervised and unsupervised machine learning methods
Ghazal Farhani, Robert J. Sica, and Mark Joseph Daley
Atmos. Meas. Tech., 14, 391–402,,, 2021
Short summary
The development of rainfall retrievals from radar at Darwin
Robert Jackson, Scott Collis, Valentin Louf, Alain Protat, Die Wang, Scott Giangrande, Elizabeth J. Thompson, Brenda Dolan, and Scott W. Powell
Atmos. Meas. Tech., 14, 53–69,,, 2021
Short summary
Retrieved wind speed from the Orbiting Carbon Observatory-2
Robert R. Nelson, Annmarie Eldering, David Crisp, Aronne J. Merrelli, and Christopher W. O'Dell
Atmos. Meas. Tech., 13, 6889–6899,,, 2020
Short summary
Probabilistic analysis of ambiguities in radar echo direction of arrival from meteors
Daniel Kastinen and Johan Kero
Atmos. Meas. Tech., 13, 6813–6835,,, 2020
Short summary

Cited articles

Aires, F., Schmitt, M., Chedin, A., and Scott, N.: The “weighting smoothing” regularization of MLP for Jacobian stabilization, IEEE. T. Neural. Networks., 10, 1502–1510,, 1999. 
Aires, F., Chédin, A., Scott, N. A., and Rossow, W. B.: A regularized neural net approach for retrieval of atmospheric and surface temperatures with the IASI instrument, J. Appl. Meteorol., 41, 144–159,<0144:ARNNAF>2.0.CO;2, 2002. 
Aumann, H. H.: Atmospheric infrared sounder on the earth observing system, Optl. Engr., 33, 776–784,, 1994. 
Aumann, H. H., Chahine, M. T., Gautier, C., and Goldberg, M.: AIRS/AMSU/HSB on the Aqua mission: design, science objective, data products, and processing systems, IEEE. Trans. GRS., 41, 253–264,, 2003. 
Brath, M., Fox, S., Eriksson, P., Harlow, R. C., Burgdorf, M., and Buehler, S. A.: Retrieval of an ice water path over the ocean from ISMAR and MARSS millimeter and submillimeter brightness temperatures, Atmos. Meas. Tech., 11, 611–632,, 2018. 
Short summary
Because a satellite channel’s ability to resolve hyperspectral data varies with height, an improved channel selection method is proposed based on information content. An improved channel selection scheme (ICS) for a hyperspectral atmospheric infrared sounder using AIRS data based on layering is proposed. The accuracy of the retrieval temperature is improved by using our method, which means the ICS method selected in this paper is feasible and shows great promise for various applications.