Machine learning for Earth System Science (ESS): A survey, status and future directions for South Asia

This survey focuses on the current problems in Earth systems science where machine learning algorithms can be applied. It provides an overview of previous work, ongoing work at the Ministry of Earth Sciences, Gov. of India, and future applications of ML algorithms to some significant earth science problems. We provide a comparison of previous work with this survey, a mind map of multidimensional areas related to machine learning and a Gartner’s hype cycle for machine learning in Earth system science (ESS). We mainly focus on the critical components in Earth Sciences, including atmospheric, Ocean, Seismology, and biosphere, and cover AI/ML applications to statistical downscaling and forecasting problems.

[1]  A. Barros,et al.  Automatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning , 2021, Remote Sensing of Environment.

[2]  Ismail Abustan,et al.  Flood hazard mapping methods: A review , 2021 .

[3]  Dino Ienco,et al.  Optical image gap filling using deep convolutional autoencoder from optical and radar images , 2019, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium.

[4]  Maarten V. de Hoop,et al.  Machine learning for data-driven discovery in solid Earth geoscience , 2019, Science.

[5]  P. Xavier,et al.  Increasing Trend of Extreme Rain Events Over India in a Warming Environment , 2006, Science.

[6]  Muhammed Sit,et al.  A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources , 2020, Water science and technology : a journal of the International Association on Water Pollution Research.

[7]  R. Chattopadhyay,et al.  A self‐organizing map–based ensemble forecast system for extended range prediction of active/break cycles of Indian summer monsoon , 2013 .

[8]  Axel Seifert,et al.  Potential and Limitations of Machine Learning for Modeling Warm‐Rain Cloud Microphysical Processes , 2020, Journal of Advances in Modeling Earth Systems.

[9]  S. Lal,et al.  Atmospheric Aerosols and Trace Gases , 2020, Assessment of Climate Change over the Indian Region.

[10]  Michaël Gharbi,et al.  Convolutional neural network for earthquake detection and location , 2017, Science Advances.

[11]  V. V. Srinivas,et al.  Downscaling of precipitation for climate change scenarios: A support vector machine approach , 2006 .

[12]  E. Lorenz Deterministic nonperiodic flow , 1963 .

[13]  James H. McClellan,et al.  Deep learning for seismic phase detection and picking in the aftershock zone of 2008 Mw7.9 Wenchuan earthquake , 2019, Physics of the Earth and Planetary Interiors.

[14]  H. Kwon,et al.  Development of Tracking Technique for the Short Term Rainfall Field Forecasting , 2016 .

[15]  Henk A. Dijkstra,et al.  Using network theory and machine learning to predict El Niño , 2018, Earth System Dynamics.

[16]  V. S. Prasad,et al.  Unravelling the mechanism of extreme (more than 30 sigma) precipitation during August 2018 and 2019 over Kerala, India , 2021, Weather and Forecasting.

[17]  R. Nanjundiah,et al.  Prediction of the ENSO and EQUINOO indices during June–September using a deep learning method , 2019, Meteorological Applications.

[18]  R. Krishnan,et al.  Understanding the combined effects of global warming and anthropogenic aerosol forcing on the South Asian monsoon , 2021, Climate Dynamics.

[19]  Masato Sugi,et al.  Pacific decadal oscillation and variability of the Indian summer monsoon rainfall , 2003 .

[20]  David Bermbach,et al.  Turning Data into Insights , 2017 .

[21]  F. Ringdal,et al.  The detection of low magnitude seismic events using array-based waveform correlation , 2006 .

[22]  Peter Dueben,et al.  Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI , 2021, Philosophical Transactions of the Royal Society A.

[23]  L. S. Rathore,et al.  Monsoon Mission: A Targeted Activity to Improve Monsoon Prediction across Scales , 2019, Bulletin of the American Meteorological Society.

[24]  R. Nanjundiah,et al.  Autoencoder-based identification of predictors of Indian monsoon , 2016, Meteorology and Atmospheric Physics.

[25]  Nancy P. Kropf,et al.  Benefits and Challenges , 2019, SpringerBriefs in Aging.

[26]  R. Nair,et al.  The efficacy of support vector machines (SVM) in robust determination of earthquake early warning magnitudes in central Japan , 2013, Journal of Earth System Science.

[27]  Qi Zhang,et al.  Intensification of hydrological drought due to human activity in the middle reaches of the Yangtze River, China. , 2018, The Science of the total environment.

[28]  P. Gentine,et al.  Zero-Shot Learning of Aerosol Optical Properties with Graph Neural Networks , 2021, 2107.10197.

[29]  Torsten Hoefler,et al.  Deep learning for post-processing ensemble weather forecasts , 2021, Philosophical Transactions of the Royal Society A.

[30]  Joachim Denzler,et al.  Deep learning and process understanding for data-driven Earth system science , 2019, Nature.

[31]  A. F. Adams,et al.  The Survey , 2021, Dyslexia in Higher Education.

[32]  Donna M. Rizzo,et al.  Advances in ungauged streamflow prediction using artificial neural networks , 2010 .

[33]  Eric Maloney,et al.  Predicting the MJO using interpretable machine-learning models , 2021 .

[34]  Prabhat,et al.  Physics-informed machine learning: case studies for weather and climate modelling , 2021, Philosophical Transactions of the Royal Society A.

[35]  Brian J. Hoskins,et al.  The potential for skill across the range of the seamless weather‐climate prediction problem: a stimulus for our science , 2013 .

[36]  V. Balaji Climbing down Charney’s ladder: machine learning and the post-Dennard era of computational climate science , 2020, Philosophical Transactions of the Royal Society A.

[37]  Andrew Reynen,et al.  Supervised machine learning on a network scale: application to seismic event classification and detection , 2017 .

[38]  V. S. Prasad,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 , 2019, Journal of Earth System Science.

[39]  Pierre Gentine,et al.  Deep learning to represent subgrid processes in climate models , 2018, Proceedings of the National Academy of Sciences.

[40]  S. S. Gill,et al.  Quantifying COVID-19 enforced global changes in atmospheric pollutants using cloud computing based remote sensing , 2021, Remote Sensing Applications: Society and Environment.

[41]  Sangram Ganguly,et al.  DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution , 2017, KDD.

[42]  A. Geer,et al.  Learning earth system models from observations: machine learning or data assimilation? , 2021, Philosophical Transactions of the Royal Society A.

[43]  N. V. Joshi,et al.  Coherent rainfall zones of the Indian region , 1993 .

[44]  Qingkai Kong,et al.  MyShake: A smartphone seismic network for earthquake early warning and beyond , 2016, Science Advances.

[45]  Bryan Lim,et al.  Time-series forecasting with deep learning: a survey , 2020, Philosophical Transactions of the Royal Society A.

[46]  B. Kumar,et al.  Deep learning–based downscaling of summer monsoon rainfall data over Indian region , 2021, Theoretical and Applied Climatology.

[47]  Swadhin K. Behera,et al.  El Niño Modoki and its possible teleconnection , 2007 .

[48]  D. N. Rao,et al.  Large‐scale teleconnection patterns of Indian summer monsoon as revealed by CFSv2 retrospective seasonal forecast runs , 2016 .

[49]  Deep Learning Based Forecasting of Indian Summer Monsoon Rainfall , 2021, 2107.04270.

[50]  Vladimir M. Krasnopolsky,et al.  Leveraging Modern Artificial Intelligence for Remote Sensing and NWP: Benefits and Challenges , 2019, Bulletin of the American Meteorological Society.

[51]  Xiaoou Tang,et al.  Image Super-Resolution Using Deep Convolutional Networks , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[52]  Jason Hickey,et al.  Machine Learning for Precipitation Nowcasting from Radar Images , 2019, ArXiv.

[53]  N. Deshpande,et al.  Spatio‐temporal variability in the stratiform/convective rainfall contribution to the summer monsoon rainfall in India , 2021, International Journal of Climatology.

[54]  Pabitra Mitra,et al.  Prediction of the Indian summer monsoon using a stacked autoencoder and ensemble regression model , 2021 .

[55]  N. Saji,et al.  Individual and Combined Influences of ENSO and the Indian Ocean Dipole on the Indian Summer Monsoon , 2004 .

[56]  D. Sikka 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 , 1980 .

[57]  Derek Anderson,et al.  Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community , 2017 .

[58]  Yoshua Bengio,et al.  Tackling Climate Change with Machine Learning , 2019, ACM Comput. Surv..

[59]  Guillaume Charpiat,et al.  Tropical Cyclone Track Forecasting Using Fused Deep Learning From Aligned Reanalysis Data , 2020, Frontiers in Big Data.

[60]  Jeong-Hwan Kim,et al.  Deep learning for multi-year ENSO forecasts , 2019, Nature.

[61]  D. S. Pai,et al.  On the spatial coherence of sub-seasonal to seasonal Indian rainfall anomalies , 2017, Climate Dynamics.

[62]  B. Goswami,et al.  Fingerprint of volcanic forcing on the ENSO–Indian monsoon coupling , 2020, Science Advances.

[63]  M. Srivastava,et al.  Linkage of water vapor distribution in the lower stratosphere to organized Asian summer monsoon convection , 2021, Climate Dynamics.

[64]  A review , 2019 .

[65]  Dit-Yan Yeung,et al.  Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.

[66]  Marc Bocquet,et al.  Combining data assimilation and machine learning to infer unresolved scale parametrization , 2020, Philosophical Transactions of the Royal Society A.

[67]  U. Germann,et al.  Pysteps: an open-source Python library for probabilistic precipitation nowcasting (v1.0) , 2019, Geoscientific Model Development.

[68]  Paul A. Johnson,et al.  Continuous chatter of the Cascadia subduction zone revealed by machine learning , 2018, Nature Geoscience.

[69]  Nikolay O. Nikitin,et al.  A Machine Learning Approach for Remote Sensing Data Gap-Filling with Open-Source Implementation: An Example Regarding Land Surface Temperature, Surface Albedo and NDVI , 2020, Remote. Sens..

[70]  Mayank Singh,et al.  Augmented Convolutional LSTMs for Generation of High-Resolution Climate Change Projections , 2020, IEEE Access.

[71]  Jan Wiszniowski,et al.  Application of real time recurrent neural network for detection of small natural earthquakes in Poland , 2014, Acta Geophysica.

[72]  R. V. Allen,et al.  Automatic earthquake recognition and timing from single traces , 1978, Bulletin of the Seismological Society of America.

[73]  Christian Schroeder de Witt,et al.  Stratospheric Aerosol Injection as a Deep Reinforcement Learning Problem , 2019, ArXiv.

[74]  Peter Gerstoft,et al.  Machine Learning in Seismology: Turning Data into Insights , 2018, Seismological Research Letters.

[75]  Han Li,et al.  A Convection Nowcasting Method Based on Machine Learning , 2020, Advances in Meteorology.

[76]  Liang Shen,et al.  Survey on the Application of Deep Learning in Extreme Weather Prediction , 2021, Atmosphere.

[77]  P. Mukhopadhyay,et al.  Simulations of Monsoon Intraseasonal Oscillation Using Climate Forecast System Version 2: Insight for Horizontal Resolution and Moist Processes Parameterization , 2019, Atmosphere.

[78]  Sulochana Gadgil,et al.  The Indian monsoon and its variability , 2003 .

[79]  Pabitra Mitra,et al.  Deep Learning Based LSTM and SeqToSeq Models to Detect Monsoon Spells of India , 2019, ICCS.

[80]  R. Chattopadhyay,et al.  Objective Identification of Nonlinear Convectively Coupled Phases of Monsoon Intraseasonal Oscillation: Implications for Prediction , 2008 .