Forecasting upper atmospheric scalars advection using deep learning: an \(O_3\) experiment. (English) Zbl 07702685
Summary: Weather forecast based on extrapolation methods is gathering a lot of attention due to the advance of artificial intelligence. Recent works on deep neural networks (CNN, RNN, LSTM, etc.) are enabling the development of spatiotemporal prediction models based on the analysis of historical time-series, images, and satellite data. In this paper, we focus on the use of deep learning for the forecast of stratospheric Ozone \((O_3)\), especially in the cases of exchanges between the polar vortex and mid-latitudes known as Ozone Secondary Events (OSE). Secondary effects of the Antarctic Ozone Hole are regularly observed above populated zones on South America, south of Africa, and New Zealand, resulting in abrupt reductions in the total ozone column of more than 10% and a consequent increase in UV radiation in densely populated areas. We study different OSE events from the literature, comparing real data with predictions from our model. We obtained interesting results and insights that may lead to accurate and fast prediction models to forecast stratospheric Ozone and the occurrence of OSE.
MSC:
| 68T05 | Learning and adaptive systems in artificial intelligence |
References:
| [1] | Agrawal, S., Barrington, L., Bromberg, C., Burge, J., Gazen, C., & Hickey, J. (2019). Machine learning for precipitation nowcasting from radar images. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). |
| [2] | Bencherif, H.; Amraoui, LE; Semane, N.; Massart, S.; Charyulu, DV; Hauchecorne, A.; Peuch, VH, Examination of the 2002 major warming in the southern hemisphere using ground-based and ODIN/SMR assimilated data: stratospheric ozone distributions and tropic/mid-latitude exchange, Canadian Journal of Physics, 85, 11, 1287-1300 (2007) · doi:10.1139/p07-143 |
| [3] | Bencherif, H., El Amraoui, L., Kirgis, G., Leclair De Bellevue, J., Hauchecorne, A., Mzé, N., Portafaix, T., Pazmino, A., & Goutail, F. (2011). Analysis of a rapid increase of stratospheric ozone during late austral summer 2008 over kerguelen (49.4° s, 70.3° e). Atmospheric Chemistry and Physics,11(1), 363-373. doi:10.5194/acp-11-363-2011. |
| [4] | Bittencourt, GD; Pinheiro, DK; Bageston, JV; Bencherif, H.; Steffenel, LA; Vaz Peres, L., Investigation of the behavior of the atmospheric dynamics during occurrences of the ozone hole’s secondary effect in southern brazil, Annales Geophysicae, 37, 6, 1049-1061 (2019) · doi:10.5194/angeo-37-1049-2019 |
| [5] | Brewer, A., Evidence for a world circulation provided by the measurements of helium and water vapour distribution in the stratosphere, Quarterly Journal of the Royal Meteorological Society, 75, 326, 351-363 (1949) · doi:10.1002/qj.49707532603 |
| [6] | Canziani, PO; Compagnucci, RH; Bischoff, SA; Legnani, WE, A study of impacts of tropospheric synoptic processes on the genesis and evolution of extreme total ozone anomalies over southern south america, Journal of Geophysical Research: Atmospheres, 107, D24, ACL 2-1-ACL 2-25 (2002) · doi:10.1029/2001JD000965 |
| [7] | Casiccia, C.; Zamorano, F.; Hernandez, A., Erythemal irradiance at the magellan’s region and antarctic ozone hole 1999-2005, Atmosfera, 21, 1, 2-12 (2008) |
| [8] | Davis, SM; Hegglin, MI; Fujiwara, M.; Dragani, R.; Harada, Y.; Kobayashi, C., Assessment of upper tropospheric and stratospheric water vapor and ozone in reanalyses as part of S-RIP, Atmospheric Chemistry and Physics, 17, 20, 12743-12778 (2017) · doi:10.5194/acp-17-12743-2017 |
| [9] | du Preez, D. J., Bencherif, H., Bègue, N., Clarisse, L., Hoffman, R., & Wright, C. (2020). Investigating the large-scale transport of a volcanic plume and the impact on a secondary site. Atmosphere, 11, doi:10.3390/atmos11050548. |
| [10] | Ding, Y.; Zhu, Y.; Feng, J.; Zhang, P.; Cheng, Z., Interpretable spatio-temporal attention LSTM model for flood forecasting, Neurocomputing, 403, 348-359 (2020) · doi:10.1016/j.neucom.2020.04.110 |
| [11] | Dobson, GMB, Forty years’ research on atmospheric ozone at oxford: A history, Applied Optics, 7, 3, 387-405 (1968) · doi:10.1364/AO.7.000387 |
| [12] | Elbern, H., Agusti-Panareda, A., Benedetti, A., et al. (2015). Assimilation of satellite data for atmospheric composition. In Seminar on use of satellite observations in numerical weather prediction, 8-12 September 2014. Shinfield Park, Reading. https://www.ecmwf.int/node/9274. |
| [13] | Errera, Q.; Chabrillat, S.; Christophe, Y.; Debosscher, J.; Hubert, D.; Lahoz, W., Technical note: Reanalysis of aura MLS chemical observations, Atmospheric Chemistry and Physics, 19, 21, 13647-13679 (2019) · doi:10.5194/acp-19-13647-2019 |
| [14] | Eskes, H. (2009). TM3-DAM: Assimilated ozone fields based on GOME data. http://www.temis.nl/gofap/tm3doc/tm3dam.html. |
| [15] | Eskes, HJ; van Velthoven, PFJ; Kelder, HM, Global ozone forecasting based on ERS-2 gome observations, Atmospheric Chemistry and Physics, 2, 4, 271-278 (2002) · doi:10.5194/acp-2-271-2002 |
| [16] | Fan, J.; Li, Q.; Hou, J.; Feng, X.; Karimian, H.; Lin, S., A spatiotemporal prediction framework for air pollution based on deep RNN, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4, 15 (2017) · doi:10.5194/isprs-annals-IV-4-W2-15-2017 |
| [17] | Fan, H.; Zhu, L.; Yang, Y., Cubic LSTMS for video prediction, Proceedings of the AAAI Conference on Artificial Intelligence, 33, 8263-8270 (2019) · doi:10.1609/aaai.v33i01.33018263 |
| [18] | Finn, C., Goodfellow, I., & Levine, S. (2016). Unsupervised learning for physical interaction through video prediction. In Advances in neural information processing systems (pp. 64-72). |
| [19] | Gelaro, R.; McCarty, W.; Suárez, MJ; Todling, R.; Molod, A.; Takacs, L., The modern-era retrospective analysis for research and applications, version 2 (MERRA-2), Journal of Climate, 30, 14, 5419-5454 (2017) · doi:10.1175/JCLI-D-16-0758.1 |
| [20] | Gettelman, A.; Hoor, P.; Pan, LL; Randel, WJ; Hegglin, MI; Birner, T., The extratropical upper troposphere and lower stratosphere, Reviews of Geophysics (2011) · doi:10.1029/2011RG000355 |
| [21] | Godin-Beekmann, S., Spatial observation of the ozone layer, Comptes Rendus Geoscience, 342, 4, 339-348 (2010) · doi:10.1016/j.crte.2009.10.012 |
| [22] | Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Proceedings of the 27th international conference on neural information processing systems (NIPS’14) (Vol. 2, pp. 2672-2680). Cambridge, MA, USA: MIT Press. |
| [23] | Guarnieri, R.; Padilha, L.; Guarnieri, F.; Echer, E.; Makita, K.; Pinheiro, D., A study of the anticorrelations between ozone and UV-B radiation using linear and exponential fits in southern brazil, Advances in Space Research, 34, 4, 764-76 (2004) · doi:10.1016/j.asr.2003.06.040 |
| [24] | Hauchecorne, A.; Godin, S.; Marchand, M.; Heese, B.; Souprayen, C., Quantification of the transport of chemical constituents from the polar vortex to midlatitudes in the lower stratosphere using the high-resolution advection model mimosa and effective diffusivity, Journal of Geophysical Research: Atmospheres, 107, D20, SOL 32-1-SOL 32-13 (2002) · doi:10.1029/2001JD000491 |
| [25] | Hochreiter, S.; Schmidhuber, J., Long short-term memory, Neural computation, 9, 8, 1735-1780 (1997) · doi:10.1162/neco.1997.9.8.1735 |
| [26] | Hofmann, D.; Oltmans, S.; Harris, J.; Johnson, B.; Lathrop, J., Ten years of ozonesonde measurements at the south pole: Implications for recovery of springtime antarctic ozone, Journal of Geophysical Research: Atmospheres, 102, D7, 8931-8943 (1997) · doi:10.1029/96JD03749 |
| [27] | Huijnen, V.; Flemming, J.; Chabrillat, S.; Errera, Q.; Christophe, Y.; Blechschmidt, AM, C-ifs-cb05-bascoe: Stratospheric chemistry in the integrated forecasting system of ECMWF, Geoscientific Model Development, 9, 9, 3071-3091 (2016) · doi:10.5194/gmd-9-3071-2016 |
| [28] | Jia, X., De Brabandere, B., Tuytelaars, T., & Gool, L. V. (2016). Dynamic filter networks. In Advances in neural information processing systems (pp. 667-675). |
| [29] | Jonnakuti, P. K., & Tata Venkata Sai, U. B. (2020). A hybrid cnn-lstm based model for the prediction of sea surface temperature using time-series satellite data. In EGU general assembly 2020. doi:10.5194/egusphere-egu2020-817. |
| [30] | Kalchbrenner, N., van den Oord, A., Simonyan, K., Danihelka, I., Vinyals, O., Graves, A., & Kavukcuoglu, K. (2017). Video pixel networks. In D. Precup, & Y. W. Teh (Eds.,) textitProceedings of the 34th international conference on machine learning Proceedings of machine learning research (Vol. 70, pp. 1771-1779). Sydney, Australia: PMLR, International Convention Centre. |
| [31] | Kirchhoff, VWJH; Schuch, N.; Pinheiro, D.; Harris, J., Evidence for an ozone hole perturbation at \(30^{\circ }\) south, Athmospheric Environment, 33, 9, 1481-1488 (1996) · doi:10.1016/1352-2310(95)00362-2 |
| [32] | Kistler, R.; Kalnay, E.; Collins, W.; Saha, S.; White, G.; Woollen, J., The NCEP-NCAR 50-year reanalysis: Monthly means CD-ROM and documentation, Bulletin of the American Meteorological Society, 82, 2, 247-268 (2001) · doi:10.1175/1520-0477(2001)082<0247:TNNYRM>2.3.CO;2 |
| [33] | Krupa, SV; Jäger, HJ; Bazzaz, WSF, Adverse effects of elevated levels of ultraviolet (UV)-B radiation and ozone (O3) on crop growth and productivity, Global Climate Change and Agricultural Production, 141-169 (1996), Chichester: Wiley, Chichester |
| [34] | Londero, J.E.L., dos Santos, M.B., & Schuch, A.P. (2019). Impact of solar uv radiation on amphibians: Focus on genotoxic stress. Mutation Research/Genetic Toxicology and Environmental Mutagenesis,842, 14 - 21. doi:10.1016/j.mrgentox.2019.03.003. Detection of Genotoxins in Aquatic and Terrestric Ecosystems. |
| [35] | Malardel, S., Wedi, N., Deconinck, W., Diamantakis, M., Kuehnlein, C., Mozdzynski, G., Hamrud, M., & Smolarkiewicz, P. (2016). A new grid for the ifs. ECMWF newsletter (pp. 23-28). doi:10.21957/zwdu9u5i. |
| [36] | Marchand, M.; Bekki, S.; Pazmino, A.; Lefèvre, F.; Godin-Beekmann, S.; Hauchecorne, A., Model simulations of the impact of the 2002 antarctic ozone hole on the midlatitudes, Jounal of the Atmospheric Sciences, 62, 871-884 (2005) · doi:10.1175/JAS-3326.1 |
| [37] | Mathieu, M., Couprie, C., & LeCun, Y. (2016). Deep multi-scale video prediction beyond mean square error. In 4th international conference on learning representations (ICLR 2016). |
| [38] | Mbatha, N.; Bencherif, H., Time series analysis and forecasting using a novel hybrid LSTM data-driven model based on empirical wavelet transform applied to total column of ozone at Buenos Aires, Argentina (1966-2017), Atmosphere, 11, 5, 457 (2020) · doi:10.3390/atmos11050457 |
| [39] | Miao, K.; Han, T.; Yao, YQ; Lu, H.; Chen, P.; Wang, B.; Zhang, J., Application of LSTM for short term fog forecasting based on meteorological elements, Neurocomputing (2020) · doi:10.1016/j.neucom.2019.12.129 |
| [40] | Moradi Kordmahalleh, M., Gorji Sefidmazgi, M., & Homaifar, A. (2016). A sparse recurrent neural network for trajectory prediction of atlantic hurricanes. In Proceedings of the genetic and evolutionary computation conference 2016 (GECCO ’16) (pp. 957-964). New York, NY, USA: Association for Computing Machinery. doi:10.1145/2908812.2908834. |
| [41] | NOAA: Rap chem model fields. https://rapidrefresh.noaa.gov/RAPchem/. |
| [42] | Ohring, G.; Bojkov, R.; Bolle, HJ; Hudson, R.; Volkert, H., Radiation and ozone: Catalysts for advancing international atmospheric science programmes for over half a century, Space Research Today, 177, 16-31 (2010) · doi:10.1016/j.srt.2010.03.004 |
| [43] | Peres, L. V. (2013). Efeito Secundário do Buraco de Ozônio Antártico Sobre o Sul do Brasil. Universidade Federal de Santa Maria, Brazil. |
| [44] | Prabhat, B.; Vishwanath, V.; Dart, E.; Wehner, M.; Collins, WD; Azzopardi, G.; Petkov, N., TECA: Petascale pattern recognition for climate science, Computer analysis of images and patterns, 426-436 (2015), Cham: Springer, Cham · doi:10.1007/978-3-319-23117-4_37 |
| [45] | Pradhan, R.; Aygun, RS; Maskey, M.; Ramachandran, R.; Cecil, DJ, Tropical cyclone intensity estimation using a deep convolutional neural network, IEEE Transactions on Image Processing, 27, 2, 692-702 (2018) · Zbl 1409.94492 · doi:10.1109/TIP.2017.2766358 |
| [46] | Racah, E., Beckham, C., Maharaj, T., Kahou, S. E., & Prabhat, P. C. (2017). Extreme weather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events. In Proceedings of the 31st international conference on neural information processing systems (NIPS’17) (pp. 3405-3416). Red Hook, NY, USA: Curran Associates Inc. |
| [47] | Ranzato, M., Szlam, A., Bruna, J., Mathieu, M., Collobert, R., & Chopra, S. (2014). Video (language) modeling: A baseline for generative models of natural videos. arXiv:1412.6604. |
| [48] | Reboita, M.; Gan, M.; Da Rocha, R.; Ambrizzi, T., Precipitation regimes in south america: A bibliography review, Revista Brasileira de Meteorologia, 25, 2, 185-204 (2010) · doi:10.1590/S0102-77862010000200004 |
| [49] | Research, G. O., & Project, M. (2014). Scientific assessment of ozone depletion: 2018. Tech. Rep. Report No. 55. Geneva, Switzerland: WMO (World Meteorological Organization). |
| [50] | Research, G.O., & Project, M. (2018). Scientific assessment of ozone depletion: 2018. Tech. Rep. Report No. 58. Geneva, Switzerland: WMO (World Meteorological Organization). |
| [51] | Sakaino, H., Spatio-temporal image pattern prediction method based on a physical model with time-varying optical flow, IEEE Transactions on Geoscience and Remote Sensing, 51, 5, 3023-3036 (2013) · doi:10.1109/TGRS.2012.2212201 |
| [52] | Salby, M. L. (1996). Fundamentals of atmospheric physics. In International geophysics (Vol. 61). Academic Press. doi:10.1016/S0074-6142(96)80037-4. |
| [53] | Scaife, AA; Spangehl, T.; Fereday, DR; Cubasch, U.; Langematz, U.; Akiyoshi, H., Climate change projections and stratosphere-troposphere interaction, Climate Dynamics, 38, 2089-2097 (2012) · doi:10.1007/s00382-011-1080-7 |
| [54] | Schuldt, C., Laptev, I., & Caputo, B. (2004). Recognizing human actions: A local SVM approach. In Proceedings 17th international conference on pattern recognition (ICPR’04) (Vol. 3, pp. 32-36). USA: IEEE Computer Society. |
| [55] | Shi, X., Chen, Z., Wang, H., Yeung, D. Y., Wong, W. K., & Woo, W. C. (2015). Convolutional lstm network: A machine learning approach for precipitation nowcasting. In Proceedings of the 28th international conference on neural information processing systems (NIPS’15) (pp. 802-810). Cambridge, MA, USA: MIT Press. |
| [56] | Shi, X.; Gao, Z.; Lausen, L.; Wang, H.; Yeung, D.; Wong, W.; Woo, W.; Guyon, I.; von Luxburg, U.; Bengio, S.; Wallach, HM; Fergus, R.; Vishwanathan, SVN; Garnett, R., Deep learning for precipitation nowcasting: A benchmark and a new model, Advances in neural information processing systems 30: Annual conference on neural information processing systems 2017, 4-9 December 2017, 5617-5627 (2017), USA: Long Beach, CA, USA |
| [57] | Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z., Berner, J., et al. (2019). A description of the advanced research wrf version 4. Tech. Rep. NCAR/TN-556+STR, NCAR. doi:10.5065/1dfh-6p97. |
| [58] | Solomon, S.; Ivy, DJ; Kinnison, D.; Mills, MJ; Neely, RR; Schmidt, A., Emergence of healing in the antarctic ozone layer, Science, 353, 6296, 269-274 (2016) · doi:10.1126/science.aae0061 |
| [59] | Srivastava, N., Mansimov, E., & Salakhudinov, R. (2015). Unsupervised learning of video representations using LSTMs. In International conference on machine learning (pp. 843-852). |
| [60] | Steffenel, L. A., Rasera, G., Begue, N., Damaris Kirsch-Pinheiro, D., & Bencherif, H. (2019). Spatio-temporal LSTM forecasting of ozone secondary events. In: E. G. Assembly (ed.) Geophysical research abstracts (Vol. 21, pp. EGU2019-8075). Vienna, Austria. |
| [61] | Stein, AF; Draxler, RR; Rolph, GD; Stunder, BJB; Cohen, MD; Ngan, F., NOAA’s HYSPLIT atmospheric transport and dispersion modeling system, Bulletin of the American Meteorological Society, 96, 12, 2059-2077 (2016) · doi:10.1175/BAMS-D-14-00110.1 |
| [62] | Sun, J.; Xue, M.; Wilson, JW; Zawadzki, I.; Ballard, SP; Onvlee-Hooimeyer, J., Use of NWP for nowcasting convective precipitation: Recent progress and challenges, Bulletin of the American Meteorological Society, 95, 3, 409-426 (2014) · doi:10.1175/BAMS-D-11-00263.1 |
| [63] | Thomas, A.; Huff, AK; Hu, XM; Zhang, F., Quantifying uncertainties of ground-level ozone within WRF-CHEM simulations in the mid-atlantic region of the united states as a response to variability, Journal of Advances in Modeling Earth Systems, 11, 4, 1100-1116 (2019) · doi:10.1029/2018MS001457 |
| [64] | Vaz Peres, L.; Bencherif, H.; Mbatha, N.; Passaglia Schuch, A.; Toihir, AM; Bègue, N., Measurements of the total ozone column using a brewer spectrophotometer and toms and omi satellite instruments over the southern space observatory in brazil, Annales Geophysicae, 35, 1, 25-37 (2017) · doi:10.5194/angeo-35-25-2017 |
| [65] | Villegas, R., Yang, J., Hong, S., Lin, X., & Lee, H. (2017). Decomposing motion and content for natural video sequence prediction. In ICLR. |
| [66] | Vondrick, C., Pirsiavash, H., & Torralba, A. (2016). Generating videos with scene dynamics. In Advances in neural information processing systems (pp. 613-621). |
| [67] | Wang, Y., Gao, Z., Long, M., Wang, J., & Yu, P. S. (2018). Predrnn++: Towards a resolution of the deep-in-time dilemma in spatiotemporal predictive learning. In 35th international conference on machine learning. |
| [68] | Wang, Y., Jiang, L., Yang, M. H., Li, L. J., Long, M., & Fei-Fei, L. (2019). Eidetic 3d LSTM: A model for video prediction and beyond. In 7th international conference on learning representations (ICLR 2019). |
| [69] | Wang, Y., Long, M., Wang, J., Gao, Z., & Yu, P. S. (2017). Predrnn: Recurrent neural networks for predictive learning using spatiotemporal LSTMS. In I. Guyon, U.V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.,) Advances in neural information processing systems (Vol. 30, pp. 879-888). Curran Associates, Inc. |
| [70] | Wiegerinck, W., Thalmeier, D., & Selten, F. (2019). Deep learning for weather forecasting? a proof of principle. In: E.G. Assembly (Ed.), Geophysical research abstracts (Vol. 21, pp. EGU2019-11443). Vienna, Austria. |
| [71] | Zhang, B., Zhang, H., Zhao, G., & Lian, J. (2020). Constructing a pm2.5 concentration prediction model by combining auto-encoder with bi-lstm neural networks. Environmental Modelling & Software,124, 104600. doi:10.1016/j.envsoft.2019.104600. |
| [72] | Zheng, G.; Li, X.; Zhang, RH; Liu, B., Purely satellite data-driven deep learning forecast of complicated tropical instability waves, Science Advances (2020) · doi:10.1126/sciadv.aba1482 |
| [73] | Zhi Wang, H., Qiang Li, G., Bin Wang, G., Chun Peng, J., Jiang, H., & Tao Liu, Y. (2017). Deep learning based ensemble approach for probabilistic wind power forecasting. Applied Energy, 188, 56-70. doi:10.1016/j.apenergy.2016.11.111. |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
