Hierarchical LSTM Framework for LongTerm Sea Surface Temperature Forecasting Multistep prediction of sea surface temperature (SST) is a challenging problem because small errors in its shortrange forecasts can be compounded to create large errors at longer ranges. In this paper, we propose a hierarchical LSTM framework to improve the accuracy for longterm SST prediction. Our framework alleviates the error accumulation problem in multistep prediction by leveraging outputs from an ensemble of physicallybased dynamical models. Unlike previous methods, which simply take a linear combination of the outputs to produce a single deterministic forecast, our framework learns a nonlinear relationship among the ensemble member forecasts. In addition, its multilevel structure is designed to capture the temporal autocorrelation between forecasts generated for the same lead time as well as those generated for different lead times. Experiments performed using SST data from the tropical Pacific ocean region show that the proposed framework outperforms various baseline methods in more than 70% of the grid cells located in the study region.

A Novel Datadriven Approach for Tropical Cyclone Tracks Prediction Based on Granger Causality and GRU The strong tropical cyclones will make a drastic effect on human life and natural environment. In these days, as meteorological data and monitoring data accumulated to a large amount, traditional methods of predicting tropical cyclones tracks face many challenges about the prediction efficiency and accuracy. Recently, deep learning method has proven to be an efficient and accurate way to forecast time series data. Therefore, this paper proposes a novel datadriven deep learning model based on Granger causality and Gated Recurrent unit(GRU) to predict the tropical cyclones tracks by selecting the meteorological factors that affect the tropical cyclone locations. The model comprises several aspects including data preprocessing, the feature selection layer, and the GRU with a customized batch process. The model was trained using a realworld tropical cyclones dataset from the year 1945 to 2017, and the results proved that our proposed model can improve the prediction accuracy in the experimental scenario.