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Artificial intelligence and machine learning in earth system sciences with special reference to climate science and meteorology in South Asia


Affiliations
1 Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India; Jackson School of Geosciences, The University of Texas at Austin, Austin 78712, USA; IDP in Climate Studies, Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India
2 Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India
3 National Centre for Medium Range Weather Forecasting, Ministry of Earth Sciences, Noida 201 309, India
4 Borehole Geophysics Research Laboratory, Ministry of Earth Sciences, Karad 415 114, India
5 Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India; Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bengaluru 560 012, India; Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, India
 

This study focuses on the current problems in earth system science (ESS), where machine learning (ML) algorithms can be applied. It provides an overview of previous studies, ongoing work at the Ministry of Earth Sciences, Government of India, and future applications of ML algorithms to some significant earth science problems. We compare previous studies, a mind map of multidimensional areas related to ML and Gartner’s hype cycle for ML in ESS. We mainly focus on the cri­tical components in earth sciences, including studies on the atmosphere, oceans, biosphere, hydrogeology, human health and seismology. Various artificial intelligence (AI)/ML applications to problems in the core fields of earth sciences are discussed, in addition to gap areas and the potential for AI techniques.

Keywords

Artificial intelligence, climate science, earth sciences, machine learning, meteorology, mind map.
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  • Chantry, M., Christensen, H., Dueben, P. and Palmer, T., Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI. Philos. Trans. R. Soc. London, Ser. A, 2021, 379, 20200083.

  • Rolnick, D. et al., Tackling climate change with machine learning. arXiv.org, 2019; doi:https://arxiv.org/pdf/1906.05433.pdf.

  • Reichstein, M. et al., Deep learning and process understanding for data-driven earth system science. Nature, 2019, 566, 195–204.

  • Shen, C., A transdisciplinary review of deep learning research and its relevance for water resources scientists. Water Resour. Res., 2018, 54, 8558–8593.

  • Sit, M. et al., A comprehensive review of deep learning applications in hydrology and water resources. Water Sci. Technol., 2020, 82, 2635–2670.

  • Ball, J. E., Anderson, D. T. and Chan, C. S., A comprehensive survey of deep learning in remote sensing: theories, tools and challenges for the community. J. Appl. Remote Sensing, 2017, 11, 042609.

  • Fang, W., Xue, Q., Shen, L. and Sheng, V. S., Survey on the application of deep learning in extreme weather prediction. Atmosphere, 2021, 12.

  • Dong, C., Chen, Loy, C. C., He, K. and Tang, X., Image superresolution using deep convolutional networks. CoRR, abs/1501.00092, 2015.

  • Kumar, B. et al., Deep learning-based downscaling of summer monsoon rainfall data over Indian region. Theor. Appl. Climatol., 2021, 143, 1145–1156.

  • Vandal, T. et al., DeepSD: generating high resolution climate change projections through single image super-resolution. arXiv.org, 2017, 1–9; doi:https://arxiv.org/abs/1703.03126.

  • Saha, M., Mitra, P. and Nanjundiah, R. S., Autoencoder-based identification of predictors of Indian monsoon. Meteorol. Atmos. Phys., 2016, 128, 613–628.

  • Saha, M. and Nanjundiah, R. S., Prediction of the ENSO and EQUINOO indices during June–September using a deep learning method. Meteorol. Appl., 2020, 27, e1826.

  • Lim, B. and Zohren, S., Time-series forecasting with deep learning: a survey. Philos. Trans. R. Soc. London, Ser. A, 2021, 379, 20200209.

  • Kumar, B. et al., Deep learning based forecasting of Indian summer monsoon rainfall. 2021; arXiv:2107.04270.

  • Shi, X. et al., Convolutional LSTM network: a machine learning approach for precipitation nowcasting. arXiv.org, 1506.04214, 2015.

  • Viswanath, S., Saha, M., Mitra, P. and Nanjundiah, R. S., Deep learning based LSTM and SeqToSeq models to detect monsoon spells of India. In International Conference on Computational Science – ICCS 2019, Springer, Cham, 2019, pp. 204–218.

  • Singh, M., Singh, B. B., Singh, R., Upendra, B., Kaur, R., Gill, S. S. and Biswas, M. S., Quantifying COVID-19 enforced global changes in atmospheric pollutants using cloud computing based remote sensing. Remote Sensing Appl.: Soc. Environ., 2021, 22, 100489.

  • Chang, C.-P. et al., The multiscale global monsoon system: research and prediction challenges in weather and climate. Bull. Am. Meteorol. Soc., 2018, 99, ES149–ES153.

  • Gadgil, S., Yadumani and Joshi, N. V., Coherent rainfall zones of the Indian region. J. R. Meteorol. Soc., 1993, 13, 546–566.

  • Gadgil, S., The Indian monsoon and its variability. Annu. Rev. Earth Planet. Sci., 2003, 31, 429–467.

  • Moron, V., Robertson, A. W. and Pai, D. S., On the spatial coherence of sub-seasonal to seasonal Indian rainfall anomalies. Climate Dyn., 2017, 49, 3403–3423.

  • Tripathi, S., Srinivas, V. V. and Nanjundiah, R. S., Downscaling of precipitation for climate change scenarios: a support vector machine approach. J. Hydrol., 2006, 330, 621–640.

  • Harilal, N., Singh, M. and Bhatia, U., Augmented convolutional LSTMs for generation of high-resolution climate change projections. IEEE Access, 2021, 9, 25208–25218.

  • Bergen Karianne, J., Johnson Paul, A., de Hoop Maarten, V. and Beroza Gregory, C., Machine learning for data-driven discovery in solid earth geoscience. Science, 2019, 363, eaau0323.

  • Thibaut, P., Michaël, G. and Marine, D., Convolutional neural network for earthquake detection and location. Sci. Adv., 2018, 4, e1700578.

  • Rouet-Leduc, B., Hulbert, C. and Johnson, P. A., Continuous chatter of the Cascadia subduction zone revealed by machine learning. Nature Geosci., 2019, 12, 75–79.

  • Reynen, A. and Audet, P., Supervised machine learning on a network scale: application to seismic event classification and detection. Geophys. J. Int., 2017, 210, 1394–1409.

  • Qingkai, K., Allen Richard, M., Louis, S. and Young-Woo, K., MyShake: a smartphone seismic network for earthquake early warning and beyond. Sci. Adv., 2016, 2, e1501055.

  • Reddy, R. and Nair, R. R., The efficacy of support vector machines (SVM) in robust determination of earthquake early warning magnitudes in central Japan. J. Earth Syst. Sci., 2013, 122, 1423– 1434.

  • Allen, R. V., Automatic earthquake recognition and timing from single traces. Bull. Seismol. Soc. Am., 1978, 68, 1521–1532.

  • Gibbons, S. J. and Ringdal, F., The detection of low magnitude seismic events using array-based waveform correlation. Geophys. J. Int., 2006, 165, 149–166.

  • Wiszniowski, J., Plesiewicz, B. M. and Trojanowski, J., Application of real time recurrent neural network for detection of small natural earthquakes in Poland. Acta Geophys., 2014, 62, 469–485.

  • Kong, Q. et al., Machine learning in seismology: turning data into insights. Seismol. Res. Lett., 2018, 90, 3–14.

  • Zhu, L. et al., Deep learning for seismic phase detection and picking in the aftershock zone of 2008 Mw 7.9 Wenchuan earthquake. Phys. Earth Planet. Inter., 2019, 293, 106261.

  • Besaw, L. E., Rizzo, D. M., Bierman, P. R. and Hackett, W. R., Advances in ungauged streamflow prediction using artificial neural networks. J. Hydrol., 2010, 386, 27–37.

  • Mudashiru, R. B., Sabtu, N., Abustan, I. and Balogun, W., Flood hazard mapping methods: a review. J. Hydrol., 2021, 603, 126846.

  • Zhang, D. et al., Intensification of hydrological drought due to human activity in the middle reaches of the Yangtze River, China. Sci. Total Environ., 2018, 637–638, 1432–1442.

  • Mukhopadhyay, P. et al., Performance of a very high-resolution global forecast system model (GFS T1534) at 12.5 km over the Indian region during the 2016–2017 monsoon seasons. J. Earth Syst. Sci., 2019, 128, 155.

  • Rao, S. A. et al., Monsoon mission: a targeted activity to improve monsoon prediction across scales. Bull. Am. Meteorol. Soc., 2019, 100, 2509–2532.

  • Deshpande, N. R. and Kulkarni, J. R., Spatio-temporal variability in the stratiform/convective rainfall contribution to the summer monsoon rainfall in India. Int. J. Climatol., 2021.

  • Mukhopadhyay, P. et al., Unraveling the mechanism of extreme (more than 30 sigma) precipitation during August 2018 and 2019 over Kerala, India. Weather Forecast., 2021, 36, 1253–1273.

  • Tirkey, S., Mukhopadhyay, P., Krishna, R. P., Dhakate, A. and Salunke, K., Simulations of monsoon intraseasonal oscillation using Climate Forecast System Version 2: insight for horizontal resolution and moist processes parameterization. Atmosphere, 2019, 10.

  • Lamb, K. D. and Gentine, P., Zero-shot learning of aerosol optical properties with graph neural networks. 2021; doi:arXiv:2107. 10197.

  • Rasp, S., Pritchard, M. S. and Gentine, P., Deep learning to represent subgrid processes in climate models. Proc. Natl. Acad. Sci. USA, 2018, 115, 9684.

  • Brajard, J., Carrassi, A., Bocquet, M. and Bertino, L., Combining data assimilation and machine learning to infer unresolved scale parametrization. Philos. Trans. R. Soc. London, Ser. A, 2021, 379, 20200086.

  • Chattopadhyay, R., Sahai, A. K. and Goswami, B. N., Objective identification of nonlinear convectively coupled phases of monsoon intraseasonal oscillation: implications for prediction. J. Atmos. Sci., 2008, 65, 1549–1569.

  • Martin, Z., Barnes, E. and Maloney, E., Predicting the MJO using interpretable machine-learning models. Earth and Space Science Open Archive, 2021; doi:https://doi.org/10.1002/essoar.10506356.1.

  • Borah, N., Sahai, A. K., Chattopadhyay, R., Joseph, S. and Goswami, B. N., A self-organizing map-based ensemble forecast system for extended range prediction of active/break cycles of Indian summer monsoon. J. Geophys. Res. (Atmos.), 2013, 118, 9022–9034.

  • Giffard-Roisin, S. et al., Tropical cyclone track forecasting using fused deep learning from aligned reanalysis data. Front. Big Data, 2020, 3, 1.

  • Lorenz, E. N., Deterministic nonperiodic flow. J. Atmos. Sci., 1963, 20, 130–141.

  • Chattopadhyay, R. et al., Large-scale teleconnection patterns of Indian summer monsoon as revealed by CFSv2 retrospective seasonal forecast runs. Int. J. Climatol., 2016, 36, 3297–3313.

  • Hoskins, B., The potential for skill across the range of the seamless weather–climate prediction problem: a stimulus for our science. Q. J. R. Meteorol. Soc., 2013, 139, 573–584.

  • Saha, M., Santara, A., Mitra, P., Chakraborty, A. and Nanjundiah, R. S., Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model. Int. J. Forecast., 2021, 37, 58–71.

  • Ham, Y.-G., Kim, J.-H. and Luo, J.-J., Deep learning for multiyear ENSO forecasts. Nature, 2019, 573, 568–572.

  • Nooteboom, P. D., Feng, Q. Y., López, C., Hernández-García, E. and Dijkstra, H. A., Using network theory and machine learning to predict El Niño. Earth Syst. Dyn., 2018, 9, 969–983.

  • Sikka, D. R., Some aspects of the large scale fluctuations of summer monsoon rainfall over India in relation to fluctuations in the planetary and regional scale circulation parameters. Proc. Indian Acad. Sci. – Earth Planet. Sci., 1980, 89, 179–195.

  • Ashok, K., Behera, S. K., Rao, S. A., Weng, H. and Yamagata, T., El Niño Modoki and its possible teleconnection. J. Geophys. Res.: Oceans, 2007, 112.

  • Ashok, K., Guan, Z., Saji, N. H. and Yamagata, T., Individual and combined influences of ENSO and the Indian Ocean dipole on the Indian summer monsoon. J. Climate, 2004, 17, 3141–3155.

  • Goswami, B. N., Venugopal, V., Sengupta, D., Madhusoodanan, M. S. and Xavier, P. K., Increasing trend of extreme rain events over India in a warming environment. Science, 2006, 314, 1442.

  • Krishnan, R. and Sugi, M., Pacific decadal oscillation and variability of the Indian summer monsoon rainfall. Climate Dyn., 2003, 21, 233–242.

  • Singh, M. et al., Fingerprint of volcanic forcing on the ENSO– Indian monsoon coupling. Sci. Adv., 2020, 6, eaba8164.

  • Ayantika, D. C. et al., Understanding the combined effects of global warming and anthropogenic aerosol forcing on the South Asian monsoon. Climate Dyn., 2021, 56, 1643–1662.

  • Fadnavis, S. et al., Atmospheric aerosols and trace gases. In Assessment of Climate Change over the Indian Region (eds Krishnan, R. et al.), A Report of the Ministry of Earth Sciences (MoES), Government of India, Springer, Singapore, 2020, pp. 93–116; doi:10.1007/978-981-15-4327-2_5.

  • de Witt, C. S. and Hornigold, T., Stratospheric aerosol injection as a deep reinforcement learning problem. arXiv.org, 2019; doi:arXiv:1905.07366.

  • Seifert, A. and Rasp, S., Potential and limitations of machine learning for modeling warm-rain cloud microphysical processes. J. Adv. Model. Earth Syst., 2020, 12, e2020MS002301.

  • Singh, B. B. et al., Linkage of water vapor distribution in the lower stratosphere to organized Asian summer monsoon convection. Climate Dyn., 2021; doi:10.1007/s00382-021-05772-2.

  • Geer, A. J., Learning earth system models from observations: machine learning or data assimilation? Philos. Trans. R. Soc. London, Ser. A, 2021, 379, 20200089.

  • Grönquist, P. et al., Deep learning for post-processing ensemble weather forecasts. Philos. Trans. R. Soc. London, Ser. A, 2021, 379, 20200092.

  • Kashinath, K. et al., Physics-informed machine learning: case studies for weather and climate modelling. Philos. Trans. R. Soc. London, Ser. A, 2021, 379, 20200093.

  • Balaji, V., Climbing down Charney’s ladder: machine learning and the post-Dennard era of computational climate science. Philos. Trans. R. Soc. London, Ser. A, 2021, 379, 20200085.

  • Pulkkinen, S. et al., Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0). Geosci. Model Dev., 2019, 12, 4185–4219.

  • Kim, T.-J. and Kwon, H.-H., Development of tracking technique for the short term rainfall field forecasting. Proc. Eng., 2016, 154, 1058–1063.

  • Agarwal, S. et al., Machine learning for precipitation nowcasting from radar images. arXiv.org, 2019; doi:https://arxiv.org/abs/1912.12132.

  • Su, A., Li, H., Cui, L. and Chen, Y., A convection nowcasting method based on machine learning. Adv. Meteorol., 2020, 2020, 5124274.

  • Arulraj, M. and Barros, A. P., Automatic detection and classification of low-level orographic precipitation processes from spaceborne radars using machine learning. Remote Sensing Environ., 2021, 257, 112355.

  • Choubin, B., Borji, M., Mosavi, A., Sajedi-Hosseini, F., Singh, V. P. and Shamshirband, S., Snow avalanche hazard prediction using machine learning methods. J. Hydrol., 2019, 577, 123929.

  • Sarafanov, M., Kazakov, E., Nikolay, N. O. and Kalyuzhnaya, A. V., A machine learning approach for remote sensing data gapfilling with open-source implementation: an example regarding land surface temperature, surface albedo and NDVI. Remote Sensing, 2020, 12, 3865.

  • Cresson, R., Ienco, D., Gaetano, R., Ose, K. and Tong Minh, D. H., Optical image gap filling using deep convolutional autoencoder from optical and radar images. In IEEE International Geoscience and Remote Sensing Symposium, IGARSS, 2019, pp. 218–221; doi:10.1109/IGARSS.2019.8900353.

  • Boukabara, S.-A. et al., Leveraging modern artificial intelligence for remote sensing and NWP: benefits and challenges. Bull. Am. Meteorol. Soc., 2019, 100, ES473–ES491.

  • Rajaee, T., Ebrahimi, H. and Nourani, V., A review of the artificial intelligence methods in groundwater level modeling. J. Hydrol., 2019, 572, 336–351.

  • Adombi, Adoubi Vincent De Paul, Chesnaux, R. and Boucher, Marie-Amélie, Theory-guided machine learning applied to hydrogeology – state of the art, opportunities and future challenges. Hydrogeology, 2021, 29, 2671–2683.

  • Berrang-Ford, L. et al., Systematic mapping of global research on climate and health: a machine learning review. Lancet Planet. Health, 2021, 5(8), e514–e525.


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  • Artificial intelligence and machine learning in earth system sciences with special reference to climate science and meteorology in South Asia

Abstract Views: 353  |  PDF Views: 125

Authors

Manmeet Singh
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India; Jackson School of Geosciences, The University of Texas at Austin, Austin 78712, USA; IDP in Climate Studies, Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India
Bipin Kumar
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India
Rajib Chattopadhyay
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India
K. Amarjyothi
National Centre for Medium Range Weather Forecasting, Ministry of Earth Sciences, Noida 201 309, India
Anup K. Sutar
Borehole Geophysics Research Laboratory, Ministry of Earth Sciences, Karad 415 114, India
Sukanta Roy
Borehole Geophysics Research Laboratory, Ministry of Earth Sciences, Karad 415 114, India
Suryachandra A. Rao
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India
Ravi S. Nanjundiah
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune 411 008, India; Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bengaluru 560 012, India; Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru 560 012, India

Abstract


This study focuses on the current problems in earth system science (ESS), where machine learning (ML) algorithms can be applied. It provides an overview of previous studies, ongoing work at the Ministry of Earth Sciences, Government of India, and future applications of ML algorithms to some significant earth science problems. We compare previous studies, a mind map of multidimensional areas related to ML and Gartner’s hype cycle for ML in ESS. We mainly focus on the cri­tical components in earth sciences, including studies on the atmosphere, oceans, biosphere, hydrogeology, human health and seismology. Various artificial intelligence (AI)/ML applications to problems in the core fields of earth sciences are discussed, in addition to gap areas and the potential for AI techniques.

Keywords


Artificial intelligence, climate science, earth sciences, machine learning, meteorology, mind map.

References





DOI: https://doi.org/10.18520/cs%2Fv122%2Fi9%2F1019-1030