Feature Selection and Spatial-Temporal Forecast of Oceanic Niño Index Using Deep Learning

Abstract

El Niño-Southern Oscillation (ENSO) is a climate phenomenon caused due to irregular periodic oscillation in easterly winds and sea surface temperature (SST) over the tropical Pacific Ocean. ENSO is one of the main drivers of Earth’s inter-annual climate variability, which causes climate anomalies in the form of tropical cyclones, severe storms, heavy rainfalls and droughts. Due to the impact of ENSO on global climate, forecasting ENSO is of great importance. However, forecast accuracy of ENSO for a lead time of one year is low. ENSO events are forecasted through Oceanic Niño Index (ONI), which is the three-month running mean of SST anomalies over the Niño 3.4 region (5∘N-5∘S, 120∘W-170∘W). Features, such as SST, sea level pressure, zonal wind speed and meridional wind speed that contribute in determining ONI are mapped on spatial or geographical grids, where each spatial or geographical grid represents the values of one feature at a snapshot. Juxtaposing the spatial grids of all features creates a layered map at a snapshot. The layered spatial feature map is constructed at different snapshots, and they all are fed to the CLSTM to forecast ONI at lead times of 1, 3, 6, 9 and 12 months. This study employs backward stepwise feature selection based on generalization accuracy to find the most effective features. SST showed to be the best feature for forecasting ONI. Experiments showed that the CLSTM outperforms Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Standard Neural Network (SNN) in terms of coefficient of determination ([Formula: see text]), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). More specifically, an improvement in [Formula: see text] values by 27.6%, 20.9%, 25% and 15.2% over CNN is observed for lead times of 3, 6, 9 and 12 months, respectively.

Long Lead ENSO Forecast Using an Adaptive Graph Convolutional Recurrent Neural Network

“…Recent studies applied artificial neural networks [19][20][21] convolutional neural networks (CNNs) [22][23][24], and recurrent neural networks [25][26][27] to ENSO forecasting. For example, in [22], CNN outperformed state-of-the-art dynamical models and achieved a CC of 0.5 for forecasting ONI for lead times of up to 17 months.…”

Ocean Turbulence Observation Techniques and Data Processing Algorithms based on SAR Sequence Images

“…(2) Oceanic internal waves can be divided into descending internal waves and ascending internal waves according to their types [13][14]. The changes in the surface flow field caused by descending internal waves are in front of the troughs of the waves to produce irradiated areas, and behind them to produce irradiated areas, along the propagation direction in the SAR remote sensing images show a light and then dark streak distribution; rising internal waves show a dark and then bright distribution pattern.…”

“…From Equation (13), it follows that the Rayleigh distribution is obeyed and E(I) = √Π/2 and var(I) = (4 -Π)/4.…”

Quality-Aware Conditional Generative Adversarial Networks for Precipitation Nowcasting

“…Both theoretical [5,6] and practical [7][8][9][10][11] application of ML in forecasting and predicting spatial-temporal phenomena has been underscored in the literature. Random forest and decision tree performed better in rainfall forecasting for shorter lead times [12,13].…”