Articles | Volume 17, issue 15
https://doi.org/10.5194/amt-17-4613-2024
https://doi.org/10.5194/amt-17-4613-2024
Research article
 | 
09 Aug 2024
Research article |  | 09 Aug 2024

A new non-linearity correction method for the spectrum from the Geostationary Inferometric Infrared Sounder on board Fengyun-4 satellites and its preliminary assessments

Qiang Guo, Yuning Liu, Xin Wang, and Wen Hui

Related subject area

Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Determination of high-precision tropospheric delays using crowdsourced smartphone GNSS data
Yuanxin Pan, Grzegorz Kłopotek, Laura Crocetti, Rudi Weinacker, Tobias Sturn, Linda See, Galina Dick, Gregor Möller, Markus Rothacher, Ian McCallum, Vicente Navarro, and Benedikt Soja
Atmos. Meas. Tech., 17, 4303–4316, https://doi.org/10.5194/amt-17-4303-2024,https://doi.org/10.5194/amt-17-4303-2024, 2024
Short summary
Unfiltering of the EarthCARE Broadband Radiometer (BBR) observations: the BM-RAD product
Almudena Velázquez Blázquez, Edward Baudrez, Nicolas Clerbaux, and Carlos Domenech
Atmos. Meas. Tech., 17, 4245–4256, https://doi.org/10.5194/amt-17-4245-2024,https://doi.org/10.5194/amt-17-4245-2024, 2024
Short summary
Variance estimations in the presence of intermittent interference and their applications to incoherent scatter radar signal processing
Qihou Zhou, Yanlin Li, and Yun Gong
Atmos. Meas. Tech., 17, 4197–4209, https://doi.org/10.5194/amt-17-4197-2024,https://doi.org/10.5194/amt-17-4197-2024, 2024
Short summary
A clustering-based method for identifying and tracking squall lines
Zhao Shi, Yuxiang Wen, and Jianxin He
Atmos. Meas. Tech., 17, 4121–4135, https://doi.org/10.5194/amt-17-4121-2024,https://doi.org/10.5194/amt-17-4121-2024, 2024
Short summary
A multi-instrument fuzzy logic boundary-layer-top detection algorithm
Elizabeth N. Smith and Jacob T. Carlin
Atmos. Meas. Tech., 17, 4087–4107, https://doi.org/10.5194/amt-17-4087-2024,https://doi.org/10.5194/amt-17-4087-2024, 2024
Short summary

Cited articles

Chase, D.: Nonlinear detector response in FT-IR, Appl. Spectrosc., 38, 491-494, 1984. 
Datla, R., Shao, X., Cao, C., and Wu, X.: Comparison of the calibration algorithms and SI traceability of MODIS, VIIRS, GOES, and GOES-R ABI sensors, Remote Sens., 8, 126, https://doi.org/10.3390/rs8020126, 2016. 
Guo, Q. and Feng, X.: In-orbit spectral response function correction and its impact on operational calibration for the long-wave split-window infrared band (12.0 ìm) of FY-2G satellite, Remote Sens., 9, 553, https://doi.org/10.3390/rs9060553, 2017. 
Guo, Q., Chen, F., Li, X., Chen, B., Wang, X., Chen, G., and Wei, C.: High-accuracy source-independent radiometric calibration with low complexity for infrared photonic sensors, Light: Science Appl., 10, 163, https://doi.org/10.1038/s41377-021-00597-4, 2021a. 
Guo, Q., Yang, J., Wei, C., Chen, B., Wang, X., Han, C., Hui, W., Xu, W., Wen, R., and Liu, Y.: Spectrum calibration of the first hyperspectral infrared measurements from a geostationary platform: Method and preliminary assessment, Q. J. Roy. Meteorol. Soc., 147, 1562–1583, https://doi.org/10.1002/qj.3981, 2021b. 
Download
Short summary
Non-linearity (NL) correction is a critical procedure to guarantee that the calibration accuracy of a spaceborne sensor approaches a reasonable level. Different from the classical method, a new NL correction method for a spaceborne Fourier transform spectrometer is proposed. To overcome the inaccurate linear coefficient from two-point calibration influencing NL correction, an iteration algorithm is established that is suitable for NL correction of both infrared and microwave sensors.