Infrared and Laser Engineering, Volume. 51, Issue 8, 20210707(2022)
Atmospheric temperature and humidity profile retrievals using a machine learning algorithm based on satellite-based infrared hyperspectral observations
[1] Ying Wang, Yong Huang, Siyuan Huang. A preliminary study of the retrieval methods for atmosphere temperature and humidity profiles. Remote Sensing for Land & Resources, 20, 23-26(2008).
[2] Hui Liu, Chaohua Dong, Wenjian Zhang. New characteristics of satellite infrared atmospheric detector development over the world. Meteorological Science and Technology, 34, 600-605(2006).
[3] Jinhuan Qiu, Hongbin Chen, Pucai Wang, et al. A prospect on future atmospheric remote sensing. Chinese Journal of Atmospheric Sciences, 29, 131-136(2005).
[4] Sr W L Smith, E Weisz, S V Kireev, et al. Dual-regression retrieval algorithm for real-time processing of satellite ultraspectral radiances. Journal of Applied Meteorology and Climatology, 51, 1455-1476(2012).
[5] Yuanhong Guan, Jie Ren, Yansong Bao, et al. Research of the infrared high spectral (IASI) satellite remote sensing atmospheric temperature and humidity profiles based on the one-dimensional variational algorithm. Trans Atmos Sci, 42, 602-611(2019).
[6] L Zhu, Y Bao, G P Petropoulos, et al. Temperature and humidity profiles retrieval in a plain area from Fengyun-3D/HIRAS sensor using a 1D-VAR assimilation scheme. Remote Sensing, 12, 435(2020).
[7] R Chakraborty, A Maitra. Retrieval of atmospheric properties with radiometric measurements using neural network. Atmospheric Research, 181, 124-132(2016).
[8] Damme M Van, S Whitburn, L Clarisse, et al. Version 2 of the IASI NH3 neural network retrieval algorithm: near-real-time and reanalysed datasets. Atmospheric Measurement Technique, 10, 4905-4914(2017).
[9] J Kolassa, P Gentine, C Prigent, et al. Soil moisture retrieval from AMSR-E and ASCAT microwave observation synergy. Part 2: Product evaluation. Remote Sensing of Environment, 195, 202-217(2017).
[10] Li Guan, Yang Liu, Xuehui Zhang. Application of artificial neural network algorithm in retrieving atmospheric temperature profiles from hyperspectral infrared data. Trans Atmos Sci, 33, 341-346(2010).
[11] Yang Liu, Li Guan. Study on the inversion of clear sky atmospheric humidity profiles with artificial neural network. Meteorological Monthly, 37, 318-324(2011).
[12] P Huang, Q Guo, C Han, et al. An improved method combining ANN and 1D-Var for the retrieval of atmospheric temperature profiles from FY-4A/GIIRS hyperspectral data. Remote Sensing, 13, 481(2021).
[13] A B Milstein, W J Blackwell. Neural network temperature and moisture retrieval algorithm validation for AIRS/AMSU and CrIS/ATMS. Journal of Geophysical Research:Atmospheres, 121, 1414-1430(2016).
[14] Hansen D Mahngren, V Laparra, A A Nielsen, et al. Statistical retrieval of atmospheric profiles with deep convolutional neural networks. ISPRS Journal of Photogrammetry and Remote Sensing, 158, 231-240(2019).
[15] S W Seemann, E E Borbas, R O Knuteson, et al. Development of a global infrared land surface emissivity database for application to clear sky sounding retrievals from multispectral satellite radiance measurements. Journal of Applied Meteorology and Climatology, 47, 108-123(2008).
[16] L Zhu, J Li, Y Zhao, et al. Retrieval of volcanic ash height from satellite-based infrared measurements. Journal of Geophysical Research:Atmospheres, 122, 5364-5379(2017).
[17] R Saunders, J Hocking, E Turner, et al. An update on the RTTOV fast radiative transfer model (currently at version 12). Geoscientific Model Development, 11, 2717-2737(2018).
[18] A Gambacorta, C D Barnet. Methodology and information content of the NOAA NESDIS operational channel selection for the cross-track infrared sounder (CrIS). IEEE Transactions on Geoscience and Remote Sensing, 51, 3207-3216(2013).
[19] P Yu, C Shi, L Yang, et al. A new temperature channel selection method based on singular spectrum analysis for retrieving atmospheric temperature profiles from FY-4A/GIIRS. Advances in Atmospheric Sciences, 37, 735-750(2020).
[20] [20] Wanas N, Auda G, Kamel M S, et al. On the optimal number of hidden nodes in a neural wk [C]Conference Proceedings of IEEE Canadian Conference on Electrical Computer Engineering, 1998, 2: 918921.
[21] Daqi Gao. On structures of supervised linear basis function feedforward three-layered neural networks. Chinese Journal of Computers, 21, 80-86(1998).
[22] Hongqiang Zhou, Lingling Huang, Yongtian Wang. Deep learning algorithm and its application in optics. Infrared and Laser Engineering, 48, 1226004(2019).
[23] Shan Xue, Zhen Zhang, Qiongying Lv, et al. Image recognition method of anti UAV system based on convolutional neural network. Infrared and Laser Engineering, 49, 20200154(2020).
[24] Yaxi Niu, Xiaoping Ji. Image retrieval algorithm based on convolutional neural network. Computer Engineering and Applications, 55, 201-206(2019).
[25] J Zhang, H Chen, Z Li, et al. Analysis of cloud layer structure in Shouxian, China using RS92 radiosonde aided by 95 GHz cloud radar. Journal of Geophysical Research, 115, D00K30(2010).
Get Citation
Copy Citation Text
Shuhan Yao, Li Guan. Atmospheric temperature and humidity profile retrievals using a machine learning algorithm based on satellite-based infrared hyperspectral observations[J]. Infrared and Laser Engineering, 2022, 51(8): 20210707
Category: Optical devices
Received: Sep. 26, 2021
Accepted: Nov. 26, 2021
Published Online: Jan. 9, 2023
The Author Email: