Japanese

Atmospheric and Hydrospheric Sciences (A)
Session Sub-categoryTechnology &Techniques (TT)
Session IDA-TT30
Session Title Machine Learning Techniques in Weather, Climate, Ocean, Hydrology and Disease Predictions
Short Title Machine learning techniques
Date & Time Oral
Session		 PM2 Wed, 29 MAY
On-site
Poster
Coretime		 PM3 Wed, 29 MAY
Main Convener Name Venkata Ratnam Jayanthi
Affiliation Application Laboratory, JAMSTEC
Co-Convener 1 Name Patrick Martineau
Affiliation Japan Agency for Marine-Earth Science and Technology
Co-Convener 2 Name Takeshi Doi
Affiliation JAMSTEC
Co-Convener 3 Name Swadhin Behera
Affiliation Application Laboratory, JAMSTEC, 3173-25 Showa-machi, Yokohama 236-0001
Session Language E
Scope (Session Description) 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.
Presentation Format Oral and Poster
Collaboration Joint with -
Co-sponsoring
Society		 The Oceanographic Society of Japan