[E] Oral

A (Atmospheric and Hydrospheric Sciences ) » A-AS Atmospheric Sciences, Meteorology & Atmospheric Environment

[A-AS04] Machine Learning Techniques in Weather, Climate, Hydrology and Disease Predictions

Fri. Jun 4, 2021 1:45 PM - 3:15 PM Ch.10 (Zoom Room 10)

convener:Venkata Ratnam Jayanthi(Application Laboratory, JAMSTEC), Rajib Maity(Indian Institute of Technology Kharagpur), Swadhin Behera(Application Laboratory, JAMSTEC, 3173-25 Showa-machi, Yokohama 236-0001), Takeshi Doi(JAMSTEC), Chairperson:Swadhin Behera(Application Laboratory, JAMSTEC, 3173-25 Showa-machi, Yokohama 236-0001), Takeshi Doi(JAMSTEC), Venkata Ratnam Jayanthi(Application Laboratory, JAMSTEC)

The machine learning techniques have found a wide range of applications in weather, climate, Hydrology and Disease predictions. In the recent times, these techniques are being widely used to forecast extreme events such as malaria outbreaks, heat waves, cold waves, flooding and droughts. The techniques are helping the researchers improve the parameterization schemes in the numerical models. The techniques are also being used to improve the numerical model predications by providing methods to reduce the biases and also to improve the horizontal resolution of the forecasts. The aim of the session is to bring together the researchers working on various techniques of machine learning to enhance the understanding and predictions of weather, climate, Hydrology and Disease predictions for the benefit of the society.

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1:45 PM - 2:00 PM

[AAS04-01] Prediction of the ice season timing in the Sea of Okhotsk: Tipping element approach

★Invited Papers

*Elena Surovyatkina^1,2 (1.Potsdam Institute for Climate Impact Research, Potsdam, Germany, 2.Space Research Institute of Russian Academy of Sciences, Moscow, Russian Federation )

2:00 PM - 2:15 PM

[AAS04-02] Accelerating Climate Model Computation by Neural Networks: A Comparative Study

*Maha Mdini¹, Takemasa Miyoshi¹, Shigenori Otsuka¹ (1.RIKEN Center for Computational Science)

2:15 PM - 2:30 PM

[AAS04-03] Potential of Deep Learning Approach to Predict Multi-step ahead Daily Rainfall

*Mohd Imran Khan¹, Rajib Maity¹ (1.Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, India.)

2:30 PM - 2:45 PM

[AAS04-04] An early warning malaria prediction system based on climate predictors using machine learning

*Patrick Martineau^1,2, Swadhin Behera¹, Venkata Ratnam Jayanthi¹, Masami Nonaka¹, Qavanisi E. Mabunda³, Philip Kruger³, Noboru Minakawa⁴ (1.Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan, 2.Research Center for Advanced Science and Technology, The University of Tokyo, Japan, 3.Malaria Control Programme, Limpopo Department of Health, Tzaneen, South Africa, 4.Institute of Tropical Medicine, Nagasaki University, Japan)

2:45 PM - 3:00 PM

[AAS04-05] Retrieval of ice cloud properties from Himawari-8 measurement with a deep neural network method

*Xinyue Wang¹, Hironobu Iwabuchi¹, Takaya Yamashita¹ (1.Tohoku University)

3:00 PM - 3:15 PM

[AAS04-06] Seasonal prediction of land surface air temperature in mid-latitudes: The value of machine learning to improve prediction skills

*Pascal Oettli¹, Ingo Richter¹, Masami Nonaka¹, Hiroyuki Koshiba², Yosuke Tokiya², Itsumi Hoshino², Swadhin Behera¹ (1.Japan Agency for Marine-Earth Science and Technology, 2.JERA)

Session information