Computer Graphics Laboratory ETH Zurich


Visualization of Neural Network Predictions for Weather Forecasting

I. Roesch, T. Günther

Computer Graphics Forum, Blackwell Publishing, vol. 38, no. 1, 2019, pp. 209-220


Recurrent neural networks are prime candidates for learning evolutions in multi-dimensional time series data. The performance of such a network is judged by the loss function, which is aggregated into a scalar value that decreases during training. Observing only this number hides the variation that occurs within the typically large training and testing data sets. Understanding these variations is of highest importance to adjust network hyperparameters, such as the number of neurons, number of layers or to adjust the training set to include more representative examples. In this paper, we design a comprehensive and interactive system that allows users to study the output of recurrent neural networks on both the complete training data and testing data. We follow a coarse-to-fine strategy, providing overviews of annual, monthly and daily patterns in the time series and directly support a comparison of different hyperparameter settings. We applied our method to a recurrent convolutional neural network that was trained and tested on 25 years of climate data to forecast meteorological attributes, such as temperature, pressure and wind velocity. We further visualize the quality of the forecasting models, when applied to various locations on Earth and we examine the combination of several forecasting models.


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