首页|Data on Machine Learning Detailed by Researchers at China Meteorological Administration (The Efficacy of Tropical and Extratropical Predictors for Long-lead El Nino-southern Oscillation Prediction: a Study Using a Machine Learning Algorithm)
Data on Machine Learning Detailed by Researchers at China Meteorological Administration (The Efficacy of Tropical and Extratropical Predictors for Long-lead El Nino-southern Oscillation Prediction: a Study Using a Machine Learning Algorithm)
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NETL
NSTL
Wiley
Current study results on Machine Learning have been published. According to news reporting from Beijing, People's Republic of China, by NewsRx journalists, research stated, "This study illustrates the considerable improvement in accuracy achievable for long-lead forecasts (18 months) of the Ocean Nino Index (ONI) through the utilization of a long short-term memory (LSTM) machine learning algorithm. The research assesses the predictive potential of eight predictors from both tropical and extratropical regions constructed based on sea surface temperature, outgoing longwave radiation, sea surface height and zonal and meridional wind anomalies." Funders for this research include National Natural Science Foundation of China (NSFC), NSF's Climate and Large-Scale Dynamics Program of USA, Open Fund of State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography.
BeijingPeople's Republic of ChinaAsiaAlgorithmsCyborgsEmerging TechnologiesMachine LearningChina Meteorological Administration
NETL
NSTL
Wiley
