Advanced Search

EPP

地球与行星物理

ISSN  2096-3955

CN  10-1502/P

Citation: Yue Wu, Zheng Sheng, and XinJie Zuo, 2022: Application of deep learning to estimate stratospheric gravity wave potential energy, Earth and Planetary Physics. http://doi.org/10.26464/epp2022002

doi: 10.26464/epp2022002

Application of deep learning to estimate stratospheric gravity wave potential energy

1 College of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, China

2 Northwest Institute of Nuclear Technology, Xi’an 710024, China

3 High-tech Institute, Fan Gong-ting South Street on the 12th, Qingzhou 262500, China

Corresponding author: Zheng Sheng, 19994035@sina.com

Fund Project: We would like to thank all the editors and the anonymous reviewers for their help in the development and improvement of this paper. The COSMIC RO data was downloaded from https://cdaac-www.cosmic.ucar.edu/. The GMTED2010 terrain data was downloaded from https://www.usgs.gov/core-science-systems/eros/coastal-changes-and-impacts/gmted2010. The ERA5 hourly data on pressure levels was downloaded from https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels.

As one of the most important dynamic processes in the middle and upper atmosphere, gravity waves (GWs) play a key role in determining the global atmospheric circulation. Gravity wave potential energy (GW Ep) is an important parameter that characterizes GW intensity, so understanding its global distribution is necessary. In this paper, a deep learning algorithm (DeepLab V3+) is used to estimate the stratospheric GW Ep. The deep learning model inputs are ERA5 reanalysis datasets and GMTED2010 terrain data. The output is the estimated GW Ep averaged over 2030 km from 60°S60°N. The GW Ep averaged over 20~30 km calculated by COSMIC radio occultation (RO) data is used as the measured value corresponding to the model output. The results showed that (1) this method can effectively estimate the zonal trend of GW Ep. However, the errors between the estimated and measured value of Ep are larger in low-latitude regions than in mid-latitude regions. The large number of convolution operations used in the deep learning model may be the main reason. Additionally, the measured Ep has errors associated with interpolation to the grid, the error tends to be amplified in low-latitude regions because the GW Ep is larger and the RO data are relatively sparse, which affects the training accuracy. (2) The estimated Ep shows seasonal variations, which are stronger in the winter hemisphere and weaker in the summer hemisphere. (3) The effect of quasi-biennial oscillation (QBO) can be clearly observed in the monthly variation in the estimated GW Ep, and its QBO amplitude may be less than that of the measured Ep.

Key words: deep learning, stratospheric gravity wave, potential energy

Alexander, M. J. (1996). A simulated spectrum of convectively generated gravity waves: propagation from the tropopause to the mesopause and effects on the middle atmosphere. J. Geophys. Res.: Atmos., 101(D1), 1571-1588. https://doi.org/10.1029/95jd02046 Alexander, M. J. (1998). Interpretations of observed climatological patterns in stratospheric gravity wave variance. J. Geophys. Res.: Atmos., 103(D8), 8627-8640. https://doi.org/10.1029/97JD03325 Alexander, M. J., Gille, J., Cavanaugh, C., Coffey, M., Craig, C., Eden, T., Francis, G., Halvorson, C., Hannigan, J., … Dean, V. (2008). Global estimates of gravity wave momentum flux from High Resolution Dynamics Limb Sounder observations. J. Geophys. Res.: Atmos., 113(D15), D15S18. https://doi.org/10.1029/2007JD008807 Bai, W. H., Deng, N., Sun, Y. Q., Du, Q. F., Xia, J. M., Wang, X. Y., Meng, X. G., Zhao, D. Y., Liu, C. L., … Liu, X. X. (2020). Applications of GNSS-RO to numerical weather prediction and tropical cyclone forecast. Atmosphere, 11(11), 1204. https://doi.org/10.3390/atmos11111204 Chen, D., Chen, Z. Y., and Lü, D. R. (2012). Simulation of the stratospheric gravity waves generated by the Typhoon Matsa in 2005. Sci. China Earth Sci., 55(4), 602-610. https://doi.org/10.1007/s11430-011-4303-1 Chen, D., Chen, Z. Y., and Lü, D. R. (2013). Spatiotemporal spectrum and momentum flux of the stratospheric gravity waves generated by a typhoon. Sci. China Earth Sci., 56(1), 54-62. https://doi.org/10.1007/s11430-012-4502-4 Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A. L. (2018). DeepLab: semantic image segmentation with deep convolutional nets, Atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell., 40(4), 834-848. https://doi.org/10.1109/TPAMI.2017.2699184 Chen, Z. P., Li, J. C., Luo, J., and Cao, X. Y. (2018). A new strategy for extracting ENSO related signals in the troposphere and lower stratosphere from GNSS RO specific humidity observations. Remote Sens., 10(4), 503. https://doi.org/10.3390/rs10040503 Cheng, N., Song, S. L., Jiao, G. Q., Jin, X. L., and Li, W. (2021). Global monitoring of geomagnetic storm-induced ionosphere anomalies using 3-D ionospheric modeling with multi-GNSS and COSMIC measurements. Radio Sci., 56(2), e2020RS007074. https://doi.org/10.1029/2020RS007074 Fritts, D. C., and Alexander, M. J. (2003). Gravity wave dynamics and effects in the middle atmosphere. Rev. Geophys., 41(1), 3-1-3-64. https://doi.org/10.1029/2001RG000106 Hamilton, K. (1991). Climatological statistics of stratospheric inertia-gravity waves deduced from historical rocketsonde wind and temperature data. J. Geophys. Res.: Atmos., 96(D11), 20831-20839. https://doi.org/10.1029/91JD02188 Jia, Y., Zhang, S. D., Yi, F., Huang, C. M., Huang, K. M., Gan, Q., and Gong, Y. (2015). Observations of gravity wave activity during stratospheric sudden warmings in the Northern Hemisphere. Sci. China Technol. Sci., 58(6), 951-960. https://doi.org/10.1007/s11431-015-5806-3 John, S. R., and Kumar, K. K. (2012). TIMED/SABER observations of global gravity wave climatology and their interannual variability from stratosphere to mesosphere lower thermosphere. Climate Dyn., 39(6), 1489-1505. https://doi.org/10.1007/s00382-012-1329-9 Kramer, R., Wüst, S., and Bittner, M. (2016). Investigation of gravity wave activity based on operational radiosonde data from 13 years (1997-2009): climatology and possible induced variability. J. Atmos. Solar-Terr. Phys., 140, 23-33. https://doi.org/10.1016/j.jastp.2016.01.014 Liu, X., Xu, J. Y., Yue, J., Vadas, S. L., and Becker, E. (2019). Orographic primary and secondary gravity waves in the middle atmosphere from 16-year SABER observations. Geophys. Res. Lett., 46(8), 4512-4522. https://doi.org/10.1029/2019gl082256 Luo, J., Chen, Z. P., and Xu, X. H. (2018). Specific humidity response in the troposphere and lower stratosphere to ONI revealed by COSMIC observations. Chin. J. Geophys. (in Chinese), 61(2), 466-476. https://doi.org/10.6038/cjg2018L0201 Matsuoka, D., Watanabe, S., Sato, K., Kawazoe, S., Yu, W., and Easterbrook, S. (2020). Application of deep learning to estimate atmospheric gravity wave parameters in reanalysis data sets. Geophys. Res. Lett., 47(19), e2020GL089436. https://doi.org/10.1029/2020GL089436 Meyer, C. I., Ern, M., Hoffmann, L., Trinh, Q. T., and Alexander, M. J. (2018). Intercomparison of AIRS and HIRDLS stratospheric gravity wave observations. Atmos. Meas. Tech., 11(1), 215-232. https://doi.org/10.5194/amt-11-215-2018 Moraux, A., Dewitte, S., Cornelis, B., and Munteanu, A. (2019). Deep learning for precipitation estimation from satellite and rain gauges measurements. Remote Sens., 11(21), 2463. https://doi.org/10.3390/rs11212463 Rasp, S., Pritchard, M. S., and Gentine, P. (2018). Deep learning to represent subgrid processes in climate models. Proc. Natl. Acad. Sci. USA, 115(39), 9684-9689. https://doi.org/10.1073/pnas.1810286115 Ren, Y. Y., Zhang, X. F., Ma, Y. J., Yang, Q. Y., Wang, C. J., Liu, H. L., and Qi, Q. (2020). Full convolutional neural network based on multi-scale feature fusion for the class imbalance remote sensing image classification. Remote Sens., 12(21), 3547. https://doi.org/10.3390/rs12213547 Scher, S. (2018). Toward data-driven weather and climate forecasting: approximating a simple general circulation model with deep learning. Geophys. Res. Lett., 45(22), 12616-12622. https://doi.org/10.1029/2018gl080704 Song, Y. L. Z., and Huang, K. M. (2020). A radiosonde observation study of inertia gravity waves over the high latitudes. J. Wuhan Univ. (Nat. Sci. Ed.) (in Chinese), 66(6), 541-551. https://doi.org/10.14188/j.1671-8836.2020.0028 Stockwell, R. G., Mansinha, L., and Lowe, R. P. (1996). Localization of the complex spectrum: the S transform. IEEE Trans. Signal Process., 44(4), 998-1001. https://doi.org/10.1109/78.492555 Vincent, R. A., and Alexander, M. J. (2000). Gravity waves in the tropical lower stratosphere: an observational study of seasonal and interannual variability. J. Geophys. Res.: Atmos., 105(D14), 17971-17982. https://doi.org/10.1029/2000JD900196 Wang, L., and Alexander, M. J. (2009). Gravity wave activity during stratospheric sudden warmings in the 2007-2008 Northern Hemisphere winter. J. Geophys. Res.: Atmos., 114(D18), D18108. https://doi.org/10.1029/2009JD011867 Wang, L., and Alexander, M. J. (2010). Global estimates of gravity wave parameters from GPS radio occultation temperature data. J. Geophys. Res.: Atmos., 115(D21), D21122. https://doi.org/10.1029/2010JD013860 Xu, X. H., Guo, J. C., and Luo, J. (2015). Analysis of the global distribution of the atmospheric gravity wave parameters using COSMIC radio occultation data. Geomat. Inf. Sci. Wuhan Univ. (in Chinese), 40(11), 1493-1498. https://doi.org/10.13203/j.whugis20130587 Xu, X. H., Yu, D. C., and Luo, J. (2018). The spatial and temporal variability of global stratospheric gravity waves and their activity during sudden stratospheric warming revealed by COSMIC measurements. Adv. Atmos. Sci., 35(12), 1533-1546. https://doi.org/10.1007/s00376-018-5053-1 Yoshiki, M., and Sato, K. (2000). A statistical study of gravity waves in the polar regions based on operational radiosonde data. J. Geophys. Res.: Atmos., 105(D14), 17995-18011. https://doi.org/10.1029/2000jd900204 Yu, D. C., Xu, X. H., Luo, J., and Li, J. (2019). On the relationship between gravity waves and tropopause height and temperature over the globe revealed by COSMIC radio occultation measurements. Atmosphere, 10(2), 75. https://doi.org/10.3390/atmos10020075 Zeng, X. Y., Xue, X. H., and Dou, X. K., Liang, C., and Jia, M. J. (2017). COSMIC GPS observations of topographic gravity waves in the stratosphere around the Tibetan Plateau. Sci. China Earth Sci., 60(1), 188-197. https://doi.org/10.1007/s11430-016-0065-6 Zhang, Y., Xiong, J., Liu, L., and Wan, W. (2012). A global morphology of gravity wave activity in the stratosphere revealed by the 8-year SABER/TIMED data. J. Geophys. Res.: Atmos., 117(D21), D21101. https://doi.org/10.1029/2012jd017676 Zhao, R. C., Dou, X. K., Sun, D. S., Xue, X. H., Zheng, J., Han, Y. L., Chen, T. D., Wang, G. C., and Zhou, Y. J. (2016). Gravity waves observation of wind field in stratosphere based on a Rayleigh Doppler lidar. Opt. Express, 24(6), A581-A591. https://doi.org/10.1364/OE.24.00A581

[1]

GuoChun Shi, Xiong Hu, ZhiGang Yao, WenJie Guo, MingChen Sun, XiaoYan Gong, 2021: Case study on stratospheric and mesospheric concentric gravity waves generated by deep convection, Earth and Planetary Physics, 5, 79-89. doi: 10.26464/epp2021002

[2]

TianJun Zhou, 2019: Toward better watching of the deep atmosphere over East Asia, Earth and Planetary Physics, 3, 85-86. doi: 10.26464/epp2019010

[3]

Wing Ching Jeremy Wong, JinPing Zi, HongFeng Yang, and JinRong Su, 2021: Spatial-temporal Evolution of Injection Induced Earthquakes in Weiyuan Area by Machine-Learning Phase Picker and Waveform Cross-correlation, Earth and Planetary Physics. doi: 10.26464/epp2021055

[4]

Zheng Ma, Yun Gong, ShaoDong Zhang, JiaHui Luo, QiHou Zhou, ChunMing Huang, KaiMing Huang, 2020: Comparison of stratospheric evolution during the major sudden stratospheric warming events in 2018 and 2019, Earth and Planetary Physics, 4, 493-503. doi: 10.26464/epp2020044

[5]

ShengYang Gu, Xin Hou, JiaHui Qi, KeMin TengChen, XianKang Dou, 2020: Reponses of middle atmospheric circulation to the 2009 major sudden stratospheric warming, Earth and Planetary Physics, 4, 472-478. doi: 10.26464/epp2020046

[6]

ZhiGao Yang, XiaoDong Song, 2019: Ambient noise Love wave tomography of China, Earth and Planetary Physics, 3, 218-231. doi: 10.26464/epp2019026

[7]

Xiao Liu, JiYao Xu, Jia Yue, 2020: Global static stability and its relation to gravity waves in the middle atmosphere, Earth and Planetary Physics, 4, 504-512. doi: 10.26464/epp2020047

[8]

XiangHui Xue, DongSong Sun, HaiYun Xia, XianKang Dou, 2020: Inertial gravity waves observed by a Doppler wind LiDAR and their possible sources, Earth and Planetary Physics, 4, 461-471. doi: 10.26464/epp2020039

[9]

Qing Wang, XiaoDong Song, JianYe Ren, 2017: Ambient noise surface wave tomography of marginal seas in east Asia, Earth and Planetary Physics, 1, 13-25. doi: 10.26464/epp2017003

[10]

ZhongLei Gao, ZhenPeng Su, FuLiang Xiao, HuiNan Zheng, YuMing Wang, Shui Wang, H. E. Spence, G. D. Reeves, D. N. Baker, J. B. Blake, H. O. Funsten, 2018: Exohiss wave enhancement following substorm electron injection in the dayside magnetosphere, Earth and Planetary Physics, 2, 359-370. doi: 10.26464/epp2018033

[11]

Kai Fan, XinLiang Gao, QuanMing Lu, and Shui Wang, 2021: Study on electron stochastic motions in the magnetosonic wave field: Test particle simulations, Earth and Planetary Physics. doi: 10.26464/epp2021052

[12]

Yang Li, Zheng Sheng, JinRui Jing, 2019: Feature analysis of stratospheric wind and temperature fields over the Antigua site by rocket data, Earth and Planetary Physics, 3, 414-424. doi: 10.26464/epp2019040

[13]

YuJing Liao, QuanLiang Chen, Xin Zhou, 2019: Seasonal evolution of the effects of the El Niño–Southern Oscillation on lower stratospheric water vapor: Delayed effects in late winter and early spring, Earth and Planetary Physics, 3, 489-500. doi: 10.26464/epp2019050

[14]

Xi Zhang, Peng Wang, Tao Xu, Yun Chen, José Badal, JiWen Teng, 2018: Density structure of the crust in the Emeishan large igneous province revealed by the Lijiang- Guiyang gravity profile, Earth and Planetary Physics, 2, 74-81. doi: 10.26464/epp2018007

[15]

Fidèle Koumetio, Donatien Njomo, Constant Tatchum Noutchogwe, Eric Ndoh Ndikum, Sévérin Nguiya, Alain-Pierre Kamga Tokam, 2019: Choice of suitable regional and residual gravity maps, the case of the South-West Cameroon zone, Earth and Planetary Physics, 3, 26-32. doi: 10.26464/epp2019004

[16]

Kokea Ariane Darolle Fofie, Fidèle Koumetio, Jean Victor Kenfack, David Yemele, 2019: Lineament characteristics using gravity data in the Garoua Zone, North Cameroon: Natural risks implications, Earth and Planetary Physics, 3, 33-44. doi: 10.26464/epp2019009

[17]

Yue Wu, Yuan Gao, 2019: Gravity pattern in southeast margin of Tibetan Plateau and its implications to tectonics and large earthquakes, Earth and Planetary Physics, 3, 425-434. doi: 10.26464/epp2019044

[18]

BinBin Ni, Jing Huang, YaSong Ge, Jun Cui, Yong Wei, XuDong Gu, Song Fu, Zheng Xiang, ZhengYu Zhao, 2018: Radiation belt electron scattering by whistler-mode chorus in the Jovian magnetosphere: Importance of ambient and wave parameters, Earth and Planetary Physics, 2, 1-14. doi: 10.26464/epp2018001

[19]

Jing Huang, XuDong Gu, BinBin Ni, Qiong Luo, Song Fu, Zheng Xiang, WenXun Zhang, 2018: Importance of electron distribution profiles to chorus wave driven evolution of Jovian radiation belt electrons, Earth and Planetary Physics, 2, 371-383. doi: 10.26464/epp2018035

[20]

H. Takahashi, P. Essien, C. A. O. B. Figueiredo, C. M. Wrasse, D. Barros, M. A. Abdu, Y. Otsuka, K. Shiokawa, GuoZhu Li, 2021: Multi-instrument study of longitudinal wave structures for plasma bubble seeding in the equatorial ionosphere, Earth and Planetary Physics, 5, 368-377. doi: 10.26464/epp2021047

Article Metrics
  • PDF Downloads()
  • Abstract views()
  • HTML views()
  • Cited by(0)
Catalog

Figures And Tables

Application of deep learning to estimate stratospheric gravity wave potential energy

Yue Wu, Zheng Sheng, and XinJie Zuo