[A-TT35] Machine Learning Techniques in Weather, Climate, Ocean, Hydrology and Disease Predictions

Fri. May 30, 2025 5:15 PM - 7:15 PM Poster Hall (Exhibition Hall 7&8, Makuhari Messe)

convener:Venkata Ratnam Jayanthi(Application Laboratory, JAMSTEC), Patrick Martineau(Japan Agency for Marine-Earth Science and Technology), Takeshi Doi(JAMSTEC), Swadhin Behera(Application Laboratory, JAMSTEC, 3173-25 Showa-machi, Yokohama 236-0001)

Advances in the machine learning techniques such as deep learning have led to an increase in the application of the techniques to a wide range of topics such as weather, climate, ocean, hydrology, and disease predictions. In the recent times, these techniques are being increasingly used to predict extreme events such as malaria outbreaks, heat waves, cold spells, flooding, droughts, tropical cyclones, typhoons, El Nino/Indian Ocean Dipole events among many others. In addition, machine-learning techniques are helping researchers to improve parameterization schemes in numerical prediction models. Machine-learning is also being used to improve numerical model predictions by providing methods to reduce biases and improve the horizontal resolution of the predictions. This session aims to bring together the researchers working on various machine learning techniques to discuss and enhance our understanding of weather, climate, Ocean, hydrology and tropical diseases as well as their predictions and applications for societal benefits and well-being.

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5:15 PM - 7:15 PM

[ATT35-P01] A Hierarchical Multi-scale LSTM Model for Improved Water Level Forecasting in the Han River Basin

*JONGHO KIM¹, Trung Duc Tran¹ (1.University of Ulsan)

5:15 PM - 7:15 PM

[ATT35-P02] Machine Learning Approach to Reconstruct Vertical T/S Profiles from Satellite-Derived SSH.

*Geonmin Lee¹, YoungHo Kim (1.Pukyong University)

5:15 PM - 7:15 PM

[ATT35-P03] Spatiotemporal Attention-Enhanced Deep Learning for Improved ENSO Long-Lead-Time Forecasting

*Wen-Chieh Wu¹, Dong-Lin Li¹, Chih-Chieh Young² (1.Department of Electrical Engineering, National Taiwan Ocean University, 2.Department of Marine Environmental Informatics, National Taiwan Ocean University)

5:15 PM - 7:15 PM

[ATT35-P04] Skillful prediction of Atlantic Niño index using machine learning models

*Venkata Ratnam Jayanthi¹, Ingo Richter¹ (1.Application Laboratory, JAMSTEC)

5:15 PM - 7:15 PM

[ATT35-P05] Machine Learning-Based Bias Correction to Improve Marine Heatwave Forecasts in the Korean Marginal Sea

*NaKyoung Im¹, Hyun Keun Jin², Gyun Do Park², Young Gyu Park², Kyeong Ok Kim², Yong Han Choi³, Young Ho Kim¹ (1.Division of Earth Environmental System Science, Pukyong National University, 2. Ocean Circulation & Climate Research Department, Korea Institute of Ocean Science & Technology, 3. Korea Polar Research Institute, Division of Ocean & Atmosphere Sciences)

5:15 PM - 7:15 PM

[ATT35-P06] Modeling of turbulent pollutants transport in planetary boundary layer expliting large eddy simulation and machine learning

*Ilya Gerasimov¹, Mikhail Krinitskiy^1,3, Evgeniy Mortikov² (1.Moscow Institute of Physics and Technology National Research University, 2.Lomonosov Moscow State University, Moscow, 3. Shirshov Institute of Oceanology, Russian Academy of Sciences)

5:15 PM - 7:15 PM

[ATT35-P07] Bias correction for numerical weather prediction with artificial neural networks using fine-scale preserving loss

*Viktor Artemovich Golikov^1,2, Mikhail Krinitskiy^2,3, Alexander Gavrikov³, Vladimir Vanovskiy^1,2 (1.Skolkovo Institute of Science and Technology, 2.Moscow Institute of Physics and Technology, 3.Shirshov Institute of Oceanology, Russian Academy of Sciences)

Session information

[A-TT35] Machine Learning Techniques in Weather, Climate, Ocean, Hydrology and Disease Predictions

[ATT35-P01] A Hierarchical Multi-scale LSTM Model for Improved Water Level Forecasting in the Han River Basin

[ATT35-P02] Machine Learning Approach to Reconstruct Vertical T/S Profiles from Satellite-Derived SSH.

[ATT35-P03] Spatiotemporal Attention-Enhanced Deep Learning for Improved ENSO Long-Lead-Time Forecasting

[ATT35-P04] Skillful prediction of Atlantic Niño index using machine learning models

[ATT35-P05] Machine Learning-Based Bias Correction to Improve Marine Heatwave Forecasts in the Korean Marginal Sea

[ATT35-P06] Modeling of turbulent pollutants transport in planetary boundary layer expliting large eddy simulation and machine learning

[ATT35-P07] Bias correction for numerical weather prediction with artificial neural networks using fine-scale preserving loss

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