References
1. Chantry, M., Christensen, H., Dueben, P. & Palmer, T. Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, 20200083 (2021).
2. Rolnick, D. et al. Tackling Climate Change with Machine Learning. arXiv.org (2019) doi:https://arxiv.org/pdf/1906.05433.pdf.
3. Reichstein, M. et al. Deep learning and process understanding for data-driven Earth system science. Nature 566, 195–204 (2019).
4. Shen, C. A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists. Water Resources Research 54, 8558–8593 (2018).
5. Sit, M. et al. A comprehensive review of deep learning applications in hydrology and water resources. Water Science and Technology 82, 2635–2670 (2020).
6. Ball, J. E., Anderson, D. T. & Chan, C. S. A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community. Journal of Applied Remote Sensing 11, 042609 (2017).
7. Fang, W., Xue, Q., Shen, L. & Sheng, V. S. Survey on the Application of Deep Learning in Extreme Weather Prediction. Atmosphere 12, (2021).
8. Dong, C., Chen, Loy, C. C., He, K. & Tang, X. Image Super-Resolution Using Deep Convolutional Networks. CoRR abs/1501.00092, (2015).
9. Kumar, B. et al. Deep learning–based downscaling of summer monsoon rainfall data over Indian region. Theoretical and Applied Climatology 143, 1145–1156 (2021).
10.Vandal, T. et al. DeepSD: Generating High Resolution Climate Change Projections through Single Image SuperResolution. arXiv.org 1–9 (2017) doi:https://arxiv.org/abs/1703.03126. 11.Saha, M., Mitra, P. & Nanjundiah, R. S. Autoencoder-based identification of predictors of Indian monsoon. Meteorology and Atmospheric Physics 128, 613–628 (2016).
12.Saha, M. & Nanjundiah, R. S. Prediction of the ENSO and EQUINOO indices during June–September using a deep learning method. Meteorological Applications 27, e1826 (2020).
13.Lim, B. & Zohren, S. Time-series forecasting with deep learning: a survey. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, 20200209 (2021).
14.Kumar, B. et al. Deep Learning Based Forecasting of Indian Summer Monsoon Rainfall. 1, (2021).
15.Shi, X. et al. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. arXiv.org 1506.04214, (2015).
16.Viswanath, S., Saha, M., Mitra, P. & Nanjundiah, R. S. Deep Learning Based LSTM and SeqToSeq Models to Detect Monsoon Spells of India. in Computational Science – ICCS 2019 (2019).
17.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 Applications: Society and Environment, 22, p.100489 (2021).
18.Chang, C.-P. et al. The Multiscale Global Monsoon System: Research and Prediction Challenges in Weather and Climate. Bulletin of the American Meteorological Society 99, ES149–ES153 (2018).
19.Gadgil, S., Yadumani & Joshi, N. V. Coherent rainfall zones of the Indian region. Royal Meteorologicla Society 13, 546–566 (1993).
20.Gadgil, S. The Indian Monsoon and Its Variability. 31, 429–467 (2003).
21.Moron, V., Robertson, A. W. & Pai, D. S. On the spatial coherence of sub-seasonal to seasonal Indian rainfall anomalies. Climate Dynamics 49, 3403–3423 (2017).
22.Tripathi, S., Srinivas, V. V. & Nanjundiah, R. S. Downscaling of precipitation for climate change scenarios: A support vector machine approach. Journal of Hydrology 330, 621–640 (2006).
23.N. Harilal, M. Singh, & U. Bhatia. Augmented Convolutional LSTMs for Generation of High-Resolution Climate Change Projections. IEEE Access 9, 25208–25218 (2021).
15 24.Bergen Karianne J., Johnson Paul A., de Hoop Maarten V., & Beroza Gregory C. Machine learning for data-driven discovery in solid Earth geoscience. Science 363, eaau0323 (2019).
25.Perol Thibaut, Gharbi Michaël, & Denolle Marine. Convolutional neural network for earthquake detection and location. Science Advances 4, e1700578.
26.Rouet-Leduc, B., Hulbert, C. & Johnson, P. A. Continuous chatter of the Cascadia subduction zone revealed by machine learning. Nature Geoscience 12, 75–79 (2019).
27.Reynen, A. & Audet, P. Supervised machine learning on a network scale: application to seismic event classification and detection. Geophysical Journal International 210, 1394–1409 (2017).
28.Kong Qingkai, Allen Richard M., Schreier Louis, & Kwon Young-Woo. MyShake: A smartphone seismic network for earthquake early warning and beyond. Science Advances 2, e1501055.
29.REDDY, R. & NAIR, R. R. The efficacy of support vector machines (SVM) in robust determination of earthquake early warning magnitudes in central Japan. Journal of Earth System Science 122, 1423–1434 (2013).
30.Allen, R. V. Automatic earthquake recognition and timing from single traces. Bulletin of the Seismological Society of America 68, 1521–1532 (1978).
31.Gibbons, S. J. & Ringdal, F. The detection of low magnitude seismic events using array-based waveform correlation. Geophysical Journal International 165, 149–166 (2006).
32.Wiszniowski, J., Plesiewicz, B. M. & Trojanowski, J. Application of real time recurrent neural network for detection of small natural earthquakes in Poland. Acta Geophysica 62, 469–485 (2014).
33.Kong, Q. et al. Machine Learning in Seismology: Turning Data into Insights. Seismological Research Letters 90, 3–14 (2018).
34.Zhu, L. et al. Deep learning for seismic phase detection and picking in the aftershock zone of 2008 Mw7.9 Wenchuan Earthquake. Physics of the Earth and Planetary Interiors 293, 106261 (2019).
35.Besaw, L. E., Rizzo, D. M., Bierman, P. R. & Hackett, W. R. Advances in ungauged streamflow prediction using artificial neural networks. Journal of Hydrology 386, 27–37 (2010).
36.Mudashiru, R. B., Sabtu, N., Abustan, I. & Balogun, W. Flood hazard mapping methods: A review. Journal of Hydrology 603, 126846 (2021).
37.Zhang, D. et al. Intensification of hydrological drought due to human activity in the middle reaches of the Yangtze River, China. Science of The Total Environment 637–638, 1432–1442 (2018).
38.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. Journal of Earth System Science 128, 155 (2019).
39.Rao, S. A. et al. Monsoon Mission: A Targeted Activity to Improve Monsoon Prediction across Scales. Bulletin of the American Meteorological Society 100, 2509–2532 (2019).
40.Deshpande, N. R. & Kulkarni, J. R. Spatio-temporal variability in the stratiform/convective rainfall contribution to the summer monsoon rainfall in India. International Journal of Climatology n/a, (2021).
41.Mukhopadhyay, P. et al. Unraveling the Mechanism of Extreme (More than 30 Sigma) Precipitation during August 2018 and 2019 over Kerala, India. Weather and Forecasting 36, 1253–1273 (2021).
42.Tirkey, S., Mukhopadhyay, P., Krishna, R. P., Dhakate, A. & Salunke, K. Simulations of Monsoon Intraseasonal Oscillation Using Climate Forecast System Version 2: Insight for Horizontal Resolution and Moist Processes Parameterization. Atmosphere 10, (2019).
43.Lamb, K. D. & Gentine. Zero-Shot Learning of Aerosol Optical Properties with Graph Neural Networks. (2021) doi:arXiv:2107.10197.
44.Rasp, S., Pritchard, M. S. & Gentine, P. Deep learning to represent subgrid processes in climate models. Proc Natl Acad Sci USA 115, 9684 (2018).
45.Brajard, J., Carrassi, A., Bocquet, M. & Bertino, L. Combining data assimilation and machine learning to infer unresolved scale parametrization. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, 20200086 (2021).
46.Chattopadhyay, R., Sahai, A. K. & Goswami, B. N. Objective Identification of Nonlinear Convectively Coupled Phases of Monsoon Intraseasonal Oscillation: Implications for Prediction. Journal of the Atmospheric Sciences 65, 1549–1569 (2008).
47.Martin, Z., Barnes, E. & Maloney, E. Predicting the MJO using interpretable machine-learning models. Earth and Space Science Open Atchive (2021) doi:https://doi.org/10.1002/essoar.10506356.1.
48.Borah, N., Sahai, A. K., Chattopadhyay, R., Joseph, S. & Goswami, B. N. A self-organizing map-based ensemble forecast system for extended range prediction of active/break cycles of Indian summer monsoon. Journal of Geophysical Research (Atmospheres) 118, 9022–9034 (2013).
16 49.Giffard-Roisin, S. et al. Tropical Cyclone Track Forecasting Using Fused Deep Learning From Aligned Reanalysis Data. Frontiers in Big Data 3, 1 (2020).
50.Lorenz, E. N. Deterministic Nonperiodic Flow. Journal of Atmospheric Sciences 20, 130–141 (1963). 51.Chattopadhyay, R. et al. Large-scale teleconnection patterns of Indian summer monsoon as revealed by CFSv2 retrospective seasonal forecast runs. International Journal of Climatology 36, 3297–3313 (2016).
52.Hoskins, B. The potential for skill across the range of the seamless weather-climate prediction problem: a stimulus for our science. Quarterly Journal of the Royal Meteorological Society 139, 573–584 (2013).
53.Saha, M., Santara, A., Mitra, P., Chakraborty, A. & Nanjundiah, R. S. Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model. International Journal of Forecasting 37, 58–71 (2021).
54.Ham, Y.-G., Kim, J.-H. & Luo, J.-J. Deep learning for multi-year ENSO forecasts. Nature 573, 568–572 (2019). 55.Nooteboom, P. D., Feng, Q. Y., López, C., Hernández-García, E. & Dijkstra, H. A. Using network theory and machine learning to predict El Niño. Earth Syst. Dynam. 9, 969–983 (2018).
56.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. Proceedings of the Indian Academy of Sciences - Earth and Planetary Sciences 89, 179–195 (1980).
57.Ashok, K., Behera, S. K., Rao, S. A., Weng, H. & Yamagata, T. El Niño Modoki and its possible teleconnection. Journal of Geophysical Research: Oceans 112, (2007).
58.Ashok, K., Guan, Z., Saji, N. H. & Yamagata, T. Individual and Combined Influences of ENSO and the Indian Ocean Dipole on the Indian Summer Monsoon. Journal of Climate 17, 3141–3155 (2004).
59.Goswami, B. N., Venugopal, V., Sengupta, D., Madhusoodanan, M. S. & Xavier, P. K. Increasing Trend of Extreme Rain Events Over India in a Warming Environment. Science 314, 1442 (2006).
60.Krishnan, R. & Sugi, M. Pacific decadal oscillation and variability of the Indian summer monsoon rainfall. Climate Dynamics 21, 233–242 (2003).
61.Singh, M. et al. Fingerprint of volcanic forcing on the ENSO–Indian monsoon coupling. Sci Adv 6, eaba8164 (2020).
62.Ayantika, D. C. et al. Understanding the combined effects of global warming and anthropogenic aerosol forcing on the South Asian monsoon. Climate Dynamics 56, 1643–1662 (2021).
63.Fadnavis, S. et al. Atmospheric Aerosols and Trace Gases. in Assessment of Climate Change over the Indian Region: A Report of the Ministry of Earth Sciences (MoES), Government of India (eds. Krishnan, R. et al.) 93–116 (Springer Singapore, 2020). doi:10.1007/978-981-15-4327-2_5.
64.de Witt, C. S. & Hornigold, T. Stratospheric Aerosol Injection as a Deep Reinforcement Learning Problem. arXiv.org (2019) doi:arXiv:1905.07366.
65.Seifert, A. & Rasp, S. Potential and Limitations of Machine Learning for Modeling Warm-Rain Cloud Microphysical Processes. Journal of Advances in Modeling Earth Systems 12, e2020MS002301 (2020). 66.Singh, B. B. et al. Linkage of water vapor distribution in the lower stratosphere to organized Asian summer monsoon convection. Climate Dynamics (2021) doi:10.1007/s00382-021-05772-2.
67.Geer, A. J. Learning earth system models from observations: machine learning or data assimilation? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, 20200089 (2021). 68.Grönquist, P. et al. Deep learning for post-processing ensemble weather forecasts. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, 20200092 (2021).
69.Kashinath, K. et al. Physics-informed machine learning: case studies for weather and climate modelling. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, 20200093 (2021).
70.Balaji, V. Climbing down Charney’s ladder: machine learning and the post-Dennard era of computational climate science. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, 20200085 (2021).
71.Pulkkinen, S. et al. Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0). Geosci. Model Dev. 12, 4185–4219 (2019).
72.Kim, T.-J. & Kwon, H.-H. Development of Tracking Technique for the Short Term Rainfall Field Forecasting. Procedia Engineering 154, 1058–1063 (2016).
73.Agrawal, S. et al. Machine Learning for Precipitation Nowcasting from Radar Images. arXiv.org (2019) doi:https://arxiv.org/abs/1912.12132.
74.Su, A., Li, H., Cui, L. & Chen, Y. A Convection Nowcasting Method Based on Machine Learning. Advances in Meteorology 2020, 5124274 (2020).
17 75.Arulraj, M. & Barros, A. P. Automatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning. Remote Sensing of Environment 257, 112355 (2021).
76.Sarafanov, M., Kazakov, E., Nikolay, N. O. & Kalyuzhnaya, A. V. A Machine Learning Approach for Remote Sensing Data Gap-Filling with Open-Source Implementation: An Example Regarding Land Surface Temperature, Surface Albedo and NDVI. 12, 3865 (2020).
77.R. Cresson, D. Ienco, R. Gaetano, K. Ose, & D. H. Tong Minh. Optical image gap filling using deep convolutional autoencoder from optical and radar images. in IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium 218–221 (2019). doi:10.1109/IGARSS.2019.8900353.
78.Boukabara, S.-A. et al. Leveraging Modern Artificial Intelligence for Remote Sensing and NWP: Benefits and Challenges. Bulletin of the American Meteorological Society 100, ES473–ES491 (2019)