Articles | Volume 21, issue 17
https://doi.org/10.5194/acp-21-13149-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-21-13149-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Forecasting and identifying the meteorological and hydrological conditions favoring the occurrence of severe hazes in Beijing and Shanghai using deep learning
Laboratoire d'Aerologie, CNRS and University Toulouse III – Paul Sabatier,
14 Avenue Edouard Belin, 31400 Toulouse, France
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Cited articles
Chan, C. K. and Yao, X.: Air pollution in mega cities in China, Atmos. Environ., 42, 1–42,
2008.
Chattopadhyay, A., Nabizadeh, E., and Hassanzadeh, P.: Analog forecasting of
extreme-causing weather patterns using deep learning, J. Adv. Model. Earth Sy., 12, e2019MS001958,
https://doi.org/10.1029/2019MS001958, 2020.
Forest, D.: Generative Deep Learning, O'Reilly Media, Inc., Sebastopol, CA, 2019.
Gagne, D., Haupt, S., and Nychka, D.: Interpretable deep learning for spatial
analysis of severe hailstorms, Mon. Weather Rev., 147, 2827–2845,
https://doi.org/10.1175/MWR-D-18-0316.1, 2019.
Gilbert, G. K.: Finley's tornado predictions, Amer. Meteor. J., 1, 166–172, 1884.
Goodfellow, I., Bengio, Y. and Courville, A.: Deep Learning, MIT Press, Cambridge, MA, 800 pp., 2017.
Grover, A. Kapoor, A., and Horvitz, E.: A deep hybrid model for weather
forecasting, Proc. 21st ACM SIGKDD Intern'l Conf. KDD, 10 August 2015, Sydney, Australia, ACM, 379–386, https://doi.org/10.1145/2783258.2783275, 2016.
He, K., Zhang, X., Ren, S., and Sun, J.: Deep residual learning for image
recognition, arXiv:1512.03385, 2015.
Heidke, P.: Calculation of the success and goodness of strong wind forecasts
in the storm warning service, Geogr. Ann. Stockholm, 8, 301–349, 1926.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková,
M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay,
P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5
global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, 2020.
Ioffe, S. and Szegedy, C.: Batch normalization: Accelerating deep network
training by reducing internal covariate shift, arXiv:1502.03167, 2015.
Jiang, G.-Q., Xu, J., and Wei, J.: A deep learning algorithm of neural
network for the parameterization o typhoon-ocean feedback in typhoon
forecast models, Geophys. Res. Lett., 45, https://doi.org/10.1002/2018GL077004,
2018.
Kiehl, J. T. and Briegleb, B. P.: The relative roles of sulfate aerosols and
greenhouse gases in climate forcing, Science, 260, 311–314, 1993.
Kurth, T., Treichler, S., Romero, J., Mudigonda, M., Luehr, N., Phillips,
E., Mahesh, A., Matheson, M., Deslippe, J., Fatica, M., Prabhat, and Houston,
M.: Exascale deep learning for climate analytics, arXiv:1810.01993, 2018.
Lagerquist, R., McGovern, A., and Gagne II, D.: Deep learning for spatially
explicit prediction of synoptic-scale fronts, Weather Forecast., 34, 1137–1160,
https://doi.org/10.1175/WAF-D-18-0183.1, 2019.
LeCun, Y., Bengio, Y., and Hinton, G.: Depp learning, Nature, 521, 436–444,
https://doi.org/10.1038/nature14539, 2015.
Lee, H.-H., Bar-Or, R. Z., and Wang, C.: Biomass burning aerosols and the low-visibility events in Southeast Asia, Atmos. Chem. Phys., 17, 965–980, https://doi.org/10.5194/acp-17-965-2017, 2017.
Lee, H.-H., Iraqui, O., Gu, Y., Yim, S. H.-L., Chulakadabba, A., Tonks, A. Y.-M., Yang, Z., and Wang, C.: Impacts of air pollutants from fire and non-fire emissions on the regional air quality in Southeast Asia, Atmos. Chem. Phys., 18, 6141–6156, https://doi.org/10.5194/acp-18-6141-2018, 2018.
Lee, H.-H., Iraqui, O., and Wang, C.: The impacts of future fuel consumption
on regional air quality in Southeast Asia, Sci. Rep.-UK, 9, 2648,
https://doi.org/10.1038/s41598-019-39131-3, 2019.
Lin, Y., Wijedasa, L. S., and Chisholm, R. A.: Singapore's willingness to pay
for mitigation of transboundary forest-fire haze from Indonesia, Environ. Res. Lett., 12,
024017, https://doi.org/10.1088/1748-9326/aa5cf6, 2016.
Liu, M., Huang, Y., Ma, Z., Jin, Z., Liu, X., Wang, H., Liu, Y., Wang, J.,
Jantunen, M., Bi, J., and Kinney, P. L.: Spatial and temporal trends in the
mortality burden of air pollution in China: 2004–2012, Environ. Int., 98,
75–81, 2017.
Liu, Y., Racah, E., Prabhat, Correa, J., Khosrowshahi, A., Lavers, D.,
Kunkel, K., Wehner, M., and Collins, W.: Application of deep convolutional
neural networks for detecting extreme weather in climate datasets,
arXiv:1605.01156, 2016.
McGovern, A., Lagerquist, R., Gagne II, D. J., Jergensen, G. E., ElmLMore,
K. L., Homeyer, C. R., and Smith, T.: Making the black box more transparent:
Understanding the physical implications of machine learning, B. Am. Meteorol. Soc., 100,
2175–2199, 2019.
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional networks for
biomedical image segmentation, arXiv:1505.04597, 2015.
Shi, X., Chen, Z., Wang, H., and Yeung, D.-Y.: Convolutional LSTM network: A
machine learning approach for precipitation nowcasting, arXiv:1506.04214,
2015.
Silva, R. A., West, J. J., Zhang, Y., Anenberg, S. C., Lamarque, J.-F.,
Shindell, D. T., Collins, W. J., Dalsoren, S., Faluvegi, G., Folberth, G.,
Horowitz, L. W., Nagashima, T., Naik, V., Rumbold, S., Skeie, R., Sudo, K.,
Takemura, T., Bergmann, D., Cameron-Smith, P., Cionni, I., Doherty, R. M.,
Eyring, V., Josse, B., MacKenzie, I. A., Plummer, D., Righi, M., Stevenson,
D. S., Strode, S., Szopa, S., and Zeng, G.: Global premature mortality due to
anthropogenic outdoor air pollution and the contribution of past climate
change, Environ. Res. Lett., 8, 034005, https://doi.org/10.1088/1748-9326/8/3/034005, 2013.
Simonyan, K. and Zisserman, A.: Very deep convolutional networks for
large-scale image recognition, arXiv:1409.1556, 2015.
Smith, A., Lott, N., and Vose, R.: The integrated surface database: Recent
developments and partnerships, B. Am. Meteorol. Soc., 92, 704–708, https://doi.org/10.1175/2011BAMS3015.1,
2011.
Steinhaus, H.: Sur la division des corps matériels en parties, Bull. Acad. Polon. Sci., 4,
801–804, 1957.
Swets, J.: Measuring the accuracy of diagnostic systems, Science, 240, 1285–1293,
1988.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z.: Rethinking
the inception architecture for computer vision, arXiv:1512.00567, 2015.
van Rijsbergen, C.: Foundation of evaluation, J. Documentation, 30, 365–373, 1974.
Wang, C.: Exploiting deep learning in forecasting the occurrence of severe
haze in Southeast Asia, arXiv:2003.05763, 2020.
Weyn, J. A., Durran, D. R., and Caruana, R.: Improving data-driven global
weather prediction using deep convolutional neural networks on a cubed
sphere, J. Adv. Model. Earth Sy., e2020MS002109, https://doi.org/10.1029/2020MS002109,
2020.
Short summary
Haze caused by abundant atmospheric aerosols has become a serious environmental issue in many countries. An innovative deep-learning machine has been developed to forecast the occurrence of hazes in two Asian megacities (Beijing and Shanghai) and has achieved good overall accuracy. Using this machine, typical regional meteorological and hydrological regimes associated with haze and non-haze events in the two cities have also been, arguably for the first time, successfully categorized.
Haze caused by abundant atmospheric aerosols has become a serious environmental issue in many...
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