大気水圏科学(A)
セッション小記号 計測技術・研究手法(TT)
セッションID A-TT30
タイトル 和文 Machine Learning Techniques in Weather, Climate, Ocean, Hydrology and Disease Predictions
英文 Machine Learning Techniques in Weather, Climate, Ocean, Hydrology and Disease Predictions
タイトル短縮名 和文 Machine learning techniques
英文 Machine learning techniques
代表コンビーナ 氏名 和文 Jayanthi Venkata Ratnam
英文 Venkata Ratnam Jayanthi
所属 和文 Application Laboratory, JAMSTEC
英文 Application Laboratory, JAMSTEC
共同コンビーナ 1 氏名 和文 Patrick Martineau
英文 Patrick Martineau
所属 和文 Japan Agency for Marine-Earth Science and Technology
英文 Japan Agency for Marine-Earth Science and Technology
共同コンビーナ 2 氏名 和文 土井 威志
英文 Takeshi Doi
所属 和文 JAMSTEC
英文 JAMSTEC
共同コンビーナ 3 氏名 和文 Behera Swadhin
英文 Swadhin Behera
所属 和文 Climate Variation Predictability and Applicability Research Group, Application Laboratory, JAMSTEC, 3173-25 Showa-machi, Yokohama 236-0001
英文 Application Laboratory, JAMSTEC, 3173-25 Showa-machi, Yokohama 236-0001
発表言語 E
スコープ 和文 
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.
英文 
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.
発表方法 口頭および（または）ポスターセッション
招待講演 Philippe Baron (National Institute of Information and Communications Technology)
石崎 紀子 (国立環境研究所)
時間 講演番号 タイトル 発表者
口頭発表 5月29日 PM2
15:30 - 15:45 ATT30-01 Using supervised neural networks to upscale conventional weather radar 4D-resolution Philippe Baron
15:45 - 16:00 ATT30-02 Artificial Intelligence Technology to Retrieve Cloud Properties Using Geostationary Satellite Measurements Feng Zhang
16:00 - 16:15 ATT30-03 Acquisition of wind wave characteristics from X-band Maritime Radar Images via Artificial Neural Networks Mikhail Krinitskiy
16:15 - 16:30 ATT30-04 Utilizing a Causal Discovery to Identify Robust Tropical Cyclone Predictors across the North Indian Ocean Akshay Kumar Sagar
16:30 - 16:45 ATT30-05 Predictability of El Niño-Southern Oscillation (ENSO) Index Beyond 2-Year Lead Time Using Seasonally Optimized Deep Learning Models Kalpesh Ravindra Patil
口頭発表 5月30日 AM1
09:00 - 09:15 ATT30-06 Estimation of past and future snow depth over Japan using machine learning techniques 石崎 紀子
09:15 - 09:30 ATT30-07 Forecasting Dengue Outbreaks in Vietnam: A Machine Learning Approach Utilizing Climate Data Patrick Martineau
09:30 - 09:45 ATT30-08 Machine learning regression for building climate and weather-driven scenarios Damiani Alessandro
09:45 - 10:00 ATT30-09 Improving Data Assimilation Using Machine Learning: Insights from the Lorenz-96 Model and Ensemble Kalman Filter Namal Rathnayake
10:00 - 10:15 ATT30-10 A MACHINE LEARNING APPROACH FOR RELIABLE NEAR-REAL-TIME PREDICTION OF SOLAR IRRADIANCE FROM GEOSTATIONARY SATELLITE IMAGERY Nifat Sultana Nima
講演番号 タイトル 発表者
ポスター発表 5月29日 PM3
ATT30-P01 変分オートエンコーダによる急潮の分類 青木 邦弘
ATT30-P02 A Study on the Establishment of Open Channel Water Level Recognition Method Using Artificial Intelligence Model in the Absence of Historical Water Level Imagery JIAN-MING Wang
ATT30-P03 Diagnostics of intense precipitation from large-scale atmospheric fields for the interpretation of climate modeling in Moscow region Yulia Yarinich
ATT30-P04 Subsurface temperature estimation using artificial neural networks in the East/Japan sea Eun-Joo Lee
ATT30-P05 Skillful prediction of the Indian Ocean Dipole using machine learning techniques Jayanthi Venkata Ratnam