Abstract
Short-term precipitation forecast in local areas based on radar reflectance images has become a hot spot issue in the meteorological field, which has an important impact on daily life. Recently, deep learning techniques have been applied to this field, and the effect is promoted remarkably compared with traditional methods. However, existing deep learning-based methods have not considered the problem that different areas and channels exert different influence on precipitation. In this paper, we propose to incorporate the multihead attention into a dual-channel neural network to highlight the key areas for precipitation forecast. Furthermore, to solve the problem of excessive loss of global information caused by the attention mechanism, the residual connection is introduced into the proposed model. Quantitative and qualitative results demonstrate that the proposed method achieves the state-of-the-art precipitation forecast accuracy on the radar echo dataset.
Highlights
Precipitation forecast refers to providing a very short range (e.g., 0–2 hours) forecast of the rainfall intensity in the local region as accurate as possible based on the radar echo map, rain gauge, or other observation data [1]
One is the echo extrapolation technique represented by the optical flow method [3,4,5], as shown in Figure 1. is kind of method estimates convective cloud movements by radar echo maps and predicts the future radar echo maps by Semi-Lagrangian Advection Scheme
When the echo happens to split or merge, the accuracy of the prediction will quickly decrease. e other kind of methods are based on the numerical weather prediction [6]
Summary
Precipitation forecast refers to providing a very short range (e.g., 0–2 hours) forecast of the rainfall intensity in the local region as accurate as possible based on the radar echo map, rain gauge, or other observation data [1]. En, the future atmospheric motion and weather phenomenon are predicted according to the numerical results This method is limited by the spin-up time; the first two hours of precipitation prediction by the mesoscale numerical model are invalid, especially in the application of nowcasting, which has low accuracy and requires complex physical equation calculation. Attention mechanism can focus on the key areas, some global information may be lost Addressing this issue, Chu et al [16] created a multicontext model based on a stacked hourglass network by implementing a global representation of the feature; Wang et al [17] proposed a nonlocal block for video classification, which considers the contribution of other regions in the image to the target by introducing a residual link. In this paper, aiming to precipitation forecast, we propose a dual-channel deep learning model, called multihead attention residual convolutional neural network (MARCNN). To the best of our knowledge, this work is the first attempt for precipitation forecast by jointly using residual structure and multiattention mechanism
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