Poster
in
Workshop: Tackling Climate Change with Machine Learning: Global Perspectives and Local Challenges
Long-lead forecasts of wintertime air stagnation index in southern China using oceanic memory effects
Chenhong Zhou · Xiaorui Zhang · Meng Gao · Shanshan Liu · Yike Guo · Jie Chen
Keywords: [ Extreme weather ] [ Earth science ] [ Classification, regression, and supervised learning ] [ Time-series analysis ] [ Climate science and climate modeling ]
Stagnant weather condition is one of the major contributors to air pollution as it is favorable for the formation and accumulation of pollutants. To measure the atmosphere’s ability to dilute air pollutants, Air Stagnation Index (ASI) has been introduced as an important meteorological index. Therefore, making long-lead ASI forecasts is vital to make plans in advance for air quality management. In this study, we found that autumn Niño indices derived from sea surface temperature (SST) anomalies show a negative correlation with wintertime ASI in southern China, offering prospects for a prewinter forecast. We developed an LSTM-based model to predict the future wintertime ASI. Results demonstrated that multivariate inputs (past ASI and Niño indices) achieve better forecast performance than univariate input (only past ASI). The model achieves a correlation coefficient of 0.778 between the actual and predicted ASI, exhibiting a high degree of consistency.