Abstract
Weather forecasting is usually solved through numerical weather prediction
(NWP), which can sometimes lead to unsatisfactory performance due to
inappropriate setting of the initial states. In this paper, we design a
data-driven method augmented by an effective information fusion mechanism to
learn from historical data that incorporates prior knowledge from NWP. We cast
the weather forecasting problem as an end-to-end deep learning problem and
solve it by proposing a novel negative log-likelihood error (NLE) loss
function. A notable advantage of our proposed method is that it simultaneously
implements single-value forecasting and uncertainty quantification, which we
refer to as deep uncertainty quantification (DUQ). Efficient deep ensemble
strategies are also explored to further improve performance. This new approach
was evaluated on a public dataset collected from weather stations in Beijing,
China. Experimental results demonstrate that the proposed NLE loss
significantly improves generalization compared to mean squared error (MSE) loss
and mean absolute error (MAE) loss. Compared with NWP, this approach
significantly improves accuracy by 47.76%, which is a state-of-the-art result
on this benchmark dataset. The preliminary version of the proposed method won
2nd place in an online competition for daily weather forecasting.
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