Application of an artificial neural network for a direct estimation of atmospheric instability from a next-generation imager

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• 作者：Su Jeong Lee ; Myoung-Hwan Ahn ; Yeonjin Lee
• 关键词：CAPE ; artificial neural network ; instability ; geostationary imager
• 刊名：Advances in Atmospheric Sciences
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：33
• 期：2
• 页码：221-232
• 全文大小：3,647 KB
• 参考文献：Berk, A., G. P. Anderson, P. K. Acharya, and E. P. Shettle, 2011: MODTRAN® 5.2.2 User’s Manual. Spectral Sciences, INC., Burlington, MA, 69 pp.
  Blackwell, W. J., and F. W. Chen, 2009: Introduction to multilayer perceptron neural networks. Neural Networks in Atmospheric Remote Sensing, Massachusetts Institute of Technology, 73–96.
  Botes, D., J. R. Mecikalski, and G. J. Jedlovec, 2012: Atmospheric Infrared Sounder (AIRS) sounding evaluation and analysis of the pre-convective environment. J. Geophys. Res., 117(D9), doi: 10.1029/2011JD016996.
  Craven, J. P., R. E. Jewell, and H. E. Brooks, 2002: Comparison between observed convective cloud-base heights and lifting condensation level for two different lifted parcels. Wea. Forecasting, 17, 885–890.CrossRef
  EUMETSAT, 2013: ATBD for the MSG GII/TOZ product. EUM/MET/DOC/11/0247, 32 pp.
  Gardner, M. W., and S. R. Dorling, 1998: Artificial neural networks (the multilayer perceptron): A review of applications in the atmospheric sciences. Atmos. Environ., 32, 2627–2636.CrossRef
  Hilton, F., and Coauthors, 2012: Hyperspectral earth observation from IASI: Five years of accomplishments. Bull. Amer. Meteor. Soc., 93(3), 347–370.CrossRef
  Jin, X., and J. Li, 2010: Improving moisture profile retrieval from broadband infrared radiances with an optimized firstguess scheme. Remote Sensing Letters, 1(4), 231–238, doi: 10.1080/01431161003762322.CrossRef
  Jin, X., J. Li, T. J. Schmit, J. L. Li, M. D. Goldberg, and J. J. Gurka, 2008: Retrieving clear-sky atmospheric parameters from SEVIRI and ABI infrared radiances. J. Geophys. Res., 113, D15310, doi: 10.1029/2008JD010040.CrossRef
  Kitzmiller, D. H., and W. E. McGovern, 1989: VAS retrievals as a source of information for convective weather forecasts: An objective assessment and comparison with other sources of upper-air observations. Mon. Wea. Rev., 117, 2095–2110.CrossRef
  Kim, D. H., and M. H. Ahn, 2014: Introduction of the in-orbit test and its performance for the first meteorological imager of the Communication, Ocean, and Meteorological Satellite. Atmospheric Measurement Techniques, 7, 2471–2485.CrossRef
  Koenig, M., and E. de Coning, 2009: The MSG global instability indices product and its use as a nowcasting tool. Wea. Forecasting, 24, 272–282.CrossRef
  Krasnopolsky, V. M., 2007: Neural network emulations for complex multidimensional geophysical mappings: Applications of neural network techniques to atmospheric and oceanic satellite retrievals and numerical modeling. Rev. Geophys., 45, RG3009, doi: 10.1029/2006RG000200.CrossRef
  Lee, S. J., M. H. Ahn, and Y. Lee, 2013: Application of artificial neural network for direct estimation of atmospheric instability Index (CAPE) from geostationary satellite. Proceedings of Autumn Meeting of KMS, 544–545, Gwang-ju, Korea, Kor. Meteo. Soc.
  Lee, S. J., M. H. Ahn, and Y. Lee, 2014a: Application of artificial neural network for the direct estimation of atmospheric instability from a geostationary satellite imager. 10pp, Proceedings of the 19th ITSC, Jeju Island, South Korea.
  Lee, Y.-K., Z. L. Li, J. Li, and T. J. Schmit, 2014b: Evaluation of the GOES-R ABI LAP retrieval algorithm using the GOES-13 sounder. J. Atmos. Oceanic Technol., 31, 3–19, doi: 10.1175/JTECH-D-13-00028.1.CrossRef
  Li, J., C.-Y. Liu, P. Zhang, and T. J. Schmit, 2012: Applications of full spatial resolution space-based advanced infrared soundings in the preconvection environment. Wea. Forecasting, 27, 515–524.CrossRef
  Li, Z. L., J. Li, W. P. Menzel, T. J. Schmit, J. P. Nelson III, J. Daniels, and S. A. Ackerman, 2008: GOES sounding improvement and applications to severe storm nowcasting. Geophys. Res. Lett., 35, L03806, doi: 10.1029/2007GL032797.
  Liu, H., C. Wu, J. Li, and Q. Chengli, 2014: Deriving atmospheric instability indices directly from Geostationary Interferometric Infrared Sounder (GIIRS) radiances. poster presentation in the 19th ITSC, Jeju Island, South Korea.
  Ma, X. L., T. J. Schmit, and W. L. Smith, 1999: A nonlinear physical retrieval algorithm-its application to the GOES-8/9 sounder. J. Appl. Meteor., 38, 501–513.CrossRef
  Martinez, M. A., M. Velazquez, M. Manso, and I. Mas, 2007: Application of LPW and SAI SAFNWC/MSG satellite products in pre-convective environments. Atmospheric Research, 83, 366–379.CrossRef
  Menzel, W. P., F. C. Holt, T. J. Schmit, R. M. Aune, A. J. Schreiner, G. S. Wade, and D. G. Gray, 1998: Application of GOES-8/9 soundings to weather forecasting and nowcasting. Bull. Amer. Meteor. Soc., 79(10), 2059–2077.CrossRef
  Oolman, L., 2014: Upper Air Data Soundings. University of Wyoming, College of Engineering, Department of Atmospheric Science. [Available online at http://​weather.​uwyo.​edu/​], accessed in July 2014.
  Romero, R., M. Gayà, and C. A. Dowsell III, 2007: European climatology of severe convective storm environmental parameters: a test for significant tornado events. Atmospheric Research, 83, 389–404.CrossRef
  Schmit, T. J., J. Li, J. L. Li, W. F. Feltz, J. J. Gurka, M. D. Goldberg, and K. J. Schrab, 2008: The GOES-R advanced baseline imager and the continuation of current sounder products. Journal of Applied Meteorology and Climatology, 47, 2696–2711, doi: 10.1175/2008JAMC1858.1.CrossRef
  Seemann, S. W., J. Li, W. P. Menzel, and L. E. Gumley, 2003: Operational retrieval of atmospheric temperature, moisture, and ozone from MODIS infrared radiances. J. Appl. Meteor., 42, 1072–1091.CrossRef
  Seidel, D. J., B. Sun, M. Pettey, and A. Reale, 2011: Global radiosonde balloon drift statistics. J. Geophys. Res., 116, D07102, doi: 10.1029/2010JD014891.
  Setvák, M., and J. Müller, 2013: 2.5-minute rapid scan experiments with the MSG satellites. 7th European Conference on Severe Storms, Helsinki, Finland, 3-7 June 2013.
  Taravat, A., S. Proud, S. Peronaci, F. de Frate, and N. Oppelt, 2015: Multilayer perceptron neural networks model for meteosat second generation SEVIRI daytime cloud masking. Remote Sensing, 7, 1529–1539.CrossRef
  Zhang, G. J., 2002: Convective quasi-equilibrium in midlatitude continental environment and its effect on convective parameterization. J. Geophys. Res., 107, ACL 12-1-ACL 12-16, doi: 10.1029/2001JD001005.
  
• 作者单位：Su Jeong Lee (1)
  Myoung-Hwan Ahn (1)
  Yeonjin Lee (1)
  
  1. Department of Atmospheric Science and Engineering, Ewha Women’s University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul, 120-750, Republic of Korea
  
• 刊物主题：Atmospheric Sciences; Meteorology; Geophysics/Geodesy;
• 出版者：Springer Berlin Heidelberg
• ISSN：1861-9533

文摘

Atmospheric instability information derived from satellites plays an important role in short-term weather forecasting, especially the forecasting of severe convective storms. For the next generation of weather satellites for Korea’s multi-purpose geostationary satellite program, a new imaging instrument has been developed. Although this imaging instrument is not designed to perform full sounding missions and its capability is limited, its multi-spectral infrared channels provide information on vertical sounding. To take full advantage of the observation data from the much improved spatiotemporal resolution of the imager, the feasibility of an artificial neural network approach for the derivation of the atmospheric instability is investigated. The multi-layer perceptron model with a feed-forward and back-propagation training algorithm shows quite a sensitive response to the selection of the training dataset and model architecture. Through an extensive performance test with a carefully selected training dataset of 7197 independent profiles, the model architectures are selected to be 12, 5000, and 0.3 for the number of hidden nodes, number of epochs, and learning rate, respectively. The selected model gives a mean absolute error, RMSE, and correlation coefficient of 330 J kg-1, 420 J kg-1, and 0.9, respectively. The feasibility is further demonstrated via application of the model to real observation data from a similar instrument that has comparable observation channels with the planned imager. Keywords CAPE artificial neural network instability geostationary imager