| Aires, F., V. Pellet, C. Prigent, and J. L. Moncet, 2016: Dimension reduction of satellite observations for remote sensing. Part 1: A comparison of compression, channel selection and bottleneck channel approaches. Quart. J. Roy. Meteorol. Soc., 142, 2658−2669, https://doi.org/10.1002/qj.2855. |
| Arthur, D., and S. Vassilvitskii, 2007: K-means++: The advantages of careful seeding. Proc. 18th Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, Louisiana, SIAM, https://dl.acm.org/doi/10.5555/1283383.1283494. |
| Beale, M. H., M. T. Hagan, and H. B. Demuth, 2018: Neural Network ToolboxTM: User's Guide. The MathWorks, Inc., 558 pp. |
| Biondi, F., A. Gershunov, and D. R. Cayan, 2001: North Pacific decadal climate variability since 1661. J. Climate, 14, 5−10, https://doi.org/10.1175/1520-0442(2001)014<0005:NPDCVS>2.0.CO;2. |
| Blackwell, W. J., 2005: A neural-network technique for the retrieval of atmospheric temperature and moisture profiles from high spectral resolution sounding data. IEEE Trans. Geosci. Remote Sens., 43, 2535−2546, https://doi.org/10.1109/TGRS.2005.855071. |
| Chahine, M. T., 1970: Inverse problems in radiative transfer: Determination of atmospheric parameters. J. Atmos. Sci., 27, 960−967, https://doi.org/10.1175/1520-0469(1970)027<0960:IPIRTD>2.0.CO;2. |
| Chakraborty, R., and A. Maitra, 2016: Retrieval of atmospheric properties with radiometric measurements using neural network. Atmospheric Research, 181, 124−132, https://doi.org/10.1016/j.atmosres.2016.05.011. |
| CMA (China Meteorological Administration), 2010: Specification of Routine Upper-Air Observation. China Meteorological Press, 77 pp. (in Chinese) |
| Collard, A. D., 2007: Selection of IASI channels for use in numerical weather prediction. Quart. J. Roy. Meteorol. Soc.:, 133, 1977−1991, https://doi.org/10.1002/qj.178. |
| Divakarla, M. G., C. D. Barnet, M. D. Goldberg, L. M. McMillin, E. Maddy, W. Wolf, L. H. Zhou, and X. P. Liu, 2006: Validation of atmospheric infrared sounder temperature and water vapor retrievals with matched radiosonde measurements and forecasts. J. Geophys. Res., 111, D09S15, https://doi.org/10.1029/2005JD006116. |
| Feng, Y., Y. Peng, N. B. Cui, D. Z. Gong, and K. D. Zhang, 2017: Modeling reference evapotranspiration using extreme learning machine and generalized regression neural network only with temperature data. Computers and Electronics in Agriculture, 136, 71−78, https://doi.org/10.1016/j.compag.2017.01.027. |
| Gambacorta, A., and C. D. Barnet, 2013: Methodology and information content of the NOAA NESDIS operational channel selection for the Cross-Track Infrared Sounder (CrIS). IEEE Trans. Geosci. Remote Sens., 51, 3207−3216, https://doi.org/10.1109/TGRS.2012.2220369. |
| Gao, Y. Y., C. Qu, and K. Q. Zhang, 2016: A hybrid method based on singular spectrum analysis, firefly algorithm, and BP neural network for short-term wind speed forecasting. Energies, 9, 757, https://doi.org/10.3390/en9100757. |
| Ge, L. L., R. L. Hang, Y. Liu, and Q. S. Liu, 2018: Comparing the performance of neural network and deep convolutional neural network in estimating soil moisture from satellite observations. Remote Sensing, 10, 1327, https://doi.org/10.3390/rs10091327. |
| Ghafarian Malamiri, H. R., I. Rousta, H. Olafsson, H. Zare, and H. Zhang, 2018: Gap-Filling of MODIS time series land surface temperature (LST) products using singular spectrum analysis (SSA). Atmosphere, 9, 334, https://doi.org/10.3390/atmos9090334. |
| Golyandina, N., and A. Zhigljavsky, 2013: Singular Spectrum Analysis for Time Series. Springer, 118pp, https://doi.org/10.1007/978-3-642-34913-3. |
| Hansen, J. W., S. J. Mason, L. Q. Sun, and A. Tall, 2011: Review of seasonal climate forecasting for agriculture in sub-Saharan Africa. Experimental Agriculture, 47, 205−240, https://doi.org/10.1017/S0014479710000876. |
| Hassani, H., 2007: Singular spectrum analysis: methodology and comparison. Journal of Data Science, 5, 239−257. |
| Huang, H.-L., and P. Antonelli, 2001: Application of principal component analysis to high-resolution infrared measurement compression and retrieval. J. Appl. Meteorol., 40, 365−388, https://doi.org/10.1175/1520-0450(2001)040<0365:AOPCAT>2.0.CO;2. |
| Iglovikov, V., S. Mushinskiy, and V. Osin, 2017: Satellite imagery feature detection using deep convolutional neural network: A kaggle competition. arXiv: 1706.06169. |
| Kolassa, J., P. Gentine, C. Prigent, F. Aires, and S. H. Alemohammad, 2017: Soil moisture retrieval from AMSR-E and ASCAT microwave observation synergy. Part 2: Product evaluation. Remote Sensing of Environment, 195, 202−217, https://doi.org/10.1016/j.rse.2017.04.020. |
| Liu, H., X. W. Mi, and Y. F. Li, 2018: Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM. Energy Conversion and Management, 159, 54−64, https://doi.org/10.1016/j.enconman.2018.01.010. |
| Luers, J. K., and R. E. Eskridge, 1998: Use of radiosonde temperature data in climate studies. J. Climate, 11, 1002−1019, https://doi.org/10.1175/1520-0442(1998)011<1002:UORTDI>2.0.CO;2. |
| Menzel, W. P., T. J. Schmit, P. Zhang, and J. Li, 2018: Satellite-based atmospheric infrared sounder development and applications. Bull. Amer. Meteorol. Soc., 99, 583−603, https://doi.org/10.1175/BAMS-D-16-0293.1. |
| Moré, J. J., 1978: The levenberg-marquardt algorithm: Implementation and theory. Numerical Analysis, G. A. Watson, Ed., Springer, 105-116, https: //doi.org/10.1007/BFb0067700. |
| Noh, Y.-C., B.-J. Sohn, Y. Kim, S. Joo, W. Bell, and R. Saunders, 2017: A new infrared atmospheric sounding interferometer channel selection and assessment of its impact on Met Office NWP forecasts. Adv. Atmos. Sci., 34, 1265−1281, https://doi.org/10.1007/s00376-017-6299-8. |
| Serio, C., G. Masiello, C. Camy-Peyret, E. Jacquette, O. Vandermarcq, F. Bermudo, D. Coppens, and D. Tobin, 2018: PCA determination of the radiometric noise of high spectral resolution infrared observations from spectral residuals: Application to IASI. Journal of Quantitative Spectroscopy and Radiative Transfer, 206, 8−21, https://doi.org/10.1016/j.jqsrt.2017.10.022. |
| Shibata, K., and Y. Ikeda, 2009: Effect of number of hidden neurons on learning in large-scale layered neural networks. Proc. 2009, ICCAS-SICE, Fukuoka, Japan, IEEE, 5008−5013. |
| Smith, W. L., 1970: Iterative solution of the radiative transfer equation for the temperature and absorbing gas profile of an atmosphere. Appl. Opt., 9, 1993−1999, https://doi.org/10.1364/AO.9.001993. |
| van Damme, M., S. Whitburn, L. Clarisse, C. Clerbaux, D. Hurtmans, and P.-F. Coheur, 2017: Version 2 of the IASI NH3 neural network retrieval algorithm: Near-real-time and reanalysed datasets. Atmospheric Measurement Techniques, 10(12), 4905−4914, https://doi.org/10.5194/amt-10-4905-2017. |
| van Gerven, M., and S. Bohte, 2017: Editorial: Artificial neural networks as models of neural information processing. Frontiers in Computational Neuroscience, 11, 114, https://doi.org/10.3389/fncom.2017.00114. |
| Ventress, L., and A. Dudhia, 2014: Improving the selection of IASI channels for use in numerical weather prediction. Quart. J. Roy. Meteorol. Soc., 140, 2111−2118, https://doi.org/10.1002/qj.2280. |
| Wanas, N., G. Auda, M. S. Kamel, and F. Karray, 1998: On the optimal number of hidden nodes in a neural network. Proc. IEEE Canadian Conf. on Electrical and Computer Engineering, Waterloo, Ontario, IEEE, 918−921, https://doi.org/10.1109/CCECE.1998.685648. |
| Wang, J. S., and F. Wei, 2012: Impact of 500hpa height field anomaly on precipitation and temperature change over arid Central Asia over the past 100 years. Proc. 2012 IEEE Int. Geoscience and Remote Sensing Symposium, Munich, Germany, IEEE, 868−871, https://doi.org/10.1109/IGARSS.2012.6351423. |
| Wark, D. O., and H. E. Fleming, 1966: Indirect measurements of atmospheric temperature profiles from satellites: I. Introduction. Mon. Wea. Rev., 94, 351−362, https://doi.org/10.1175/1520-0493(1966)094<0351:IMOATP>2.3.CO;2. |
| Whitburn, S., and Coauthors, 2016: A flexible and robust neural network IASI‐NH3 retrieval algorithm. J. Geophys. Res.:, 121, 6581−6599, https://doi.org/10.1002/2016JD024828. |
| Wu, C. L., and K. W. Chau, 2011: Rainfall-runoff modeling using artificial neural network coupled with singular spectrum analysis. J. Hydrol., 399, 394−409, https://doi.org/10.1016/j.jhydrol.2011.01.017. |
| Yang, J., Z. Q. Zhang, C. Y. Wei, F. Lu, and Q. Guo, 2017: Introducing the new generation of Chinese geostationary weather satellites, Fengyun-4. Bull. Amer. Meteorol. Soc., 98, 1637−1658, https://doi.org/10.1175/BAMS-D-16-0065.1. |
| Yuan, F., S. Huang, H. Wang, Q. Li, J. Liao, and H. K, 2016: Evaluation report of China's high-altitude L-band second-level observation basic data set(V1. 0). CMA, 21pp. |
| Zabalza, J., J. C. Ren, J. B. Zheng, J. W. Han, H. M. Zhao, S. T. Li, and S. Marshall, 2015: Novel two-dimensional singular spectrum analysis for effective feature extraction and data classification in hyperspectral imaging. IEEE Trans. Geosci. Remote Sens., 53, 4418−4433, https://doi.org/10.1109/TGRS.2015.2398468. |
| Zeng, Z. L., and Coauthors, 2019: Preliminary evaluation of the atmospheric infrared sounder water vapor over China against high-resolution radiosonde measurements. J. Geophys. Res., 124, 3871−3888, https://doi.org/10.1029/2018JD029109. |