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Open AccessArticle

Denoising Algorithm for the FY-4A GIIRS Based on Principal Component Analysis

by Sihui Fan 1, Wei Han 2,3,*, Zhiqiu Gao 1,4, Ruoying Yin 2,4 and Yu Zheng 1
1
Climate and Weather Disasters Collaborative Innovation Center, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China
2
Numerical Weather Prediction Center of Chinese Meteorological Administration, Beijing 100081, China
3
National Meteorological Center, Beijing 100081, China
4
State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(22), 2710; https://doi.org/10.3390/rs11222710
Received: 27 August 2019 / Accepted: 14 November 2019 / Published: 19 November 2019
(This article belongs to the Collection Feature Papers for Section Atmosphere Remote Sensing)
The Geostationary Interferometric Infrared Sounder (GIIRS) is the first high-spectral resolution advanced infrared (IR) sounder onboard the new-generation Chinese geostationary meteorological satellite FengYun-4A (FY-4A). The GIIRS has 1650 channels, and its spectrum ranges from 700 to 2250 cm−1 with an unapodized spectral resolution of 0.625 cm−1. It represents a significant breakthrough for measurements with high temporal, spatial and spectral resolutions worldwide. Many GIIRS channels have quite similar spectral signal characteristics that are highly correlated with each other in content and have a high degree of information redundancy. Therefore, this paper applies a principal component analysis (PCA)-based denoising algorithm (PDA) to study simulation data with different noise levels and observation data to reduce noise. The results show that the channel reconstruction using inter-channel spatial dependency and spectral similarity can reduce the noise in the observation brightness temperature (BT). A comparison of the BT observed by the GIIRS (O) with the BT simulated by the radiative transfer model (B) shows that a deviation occurs in the observation channel depending on the observation array. The results show that the array features of the reconstructed observation BT (rrO) depending on the observation array are weakened and the effect of the array position on the observations in the sub-center of the field of regard (FOR) are partially eliminated after the PDA procedure is applied. The high observation and simulation differences (O-B) in the sub-center of the FOR array notably reduced after the PDA procedure is implemented. The improvement of the high O-B is more distinct, and the low O-B becomes smoother. In each scan line, the standard deviation of the reconstructed background departures (rrO-B) is lower than that of the background departures (O-B). The observation error calculated by posterior estimation based on variational assimilation also verifies the efficiency of the PDA. The typhoon experiment also shows that among the 29 selected assimilation channels, the observation error of 65% of the channels was reduced as calculated by the triangle method. View Full-Text
Keywords: FY-4A; Geostationary Interferometric Infrared Sounder (GIIRS); principal component analysis (PCA); denoising FY-4A; Geostationary Interferometric Infrared Sounder (GIIRS); principal component analysis (PCA); denoising
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MDPI and ACS Style

Fan, S.; Han, W.; Gao, Z.; Yin, R.; Zheng, Y. Denoising Algorithm for the FY-4A GIIRS Based on Principal Component Analysis. Remote Sens. 2019, 11, 2710.

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