Abstract
The task of precipitation nowcasting is significant in the operational weather forecast. The radar echo map extrapolation plays a vital role in this task. Recently, deep learning techniques such as Convolutional Recurrent Neural Network (ConvRNN) models have been designed to solve the task. These models, albeit performing much better than conventional optical flow based approaches, suffer from a common problem of underestimating the high echo value parts. The drawback is fatal to precipitation nowcasting, as the parts often lead to heavy rains that may cause natural disasters. In this paper, we propose a novel interaction dual attention long short-term memory (IDA-LSTM) model to address the drawback. In the method, an interaction framework is developed for the ConvRNN unit to fully exploit the short-term context information by constructing a serial of coupled convolutions on the input and hidden states. Moreover, a dual attention mechanism on channels and positions is developed to recall the forgotten information in the long term. Comprehensive experiments have been conducted on CIKM AnalytiCup 2017 data sets, and the results show the effectiveness of the IDA-LSTM in addressing the underestimation drawback. The extrapolation performance of IDA-LSTM is superior to that of the state-of-the-art methods.
Highlights
Precipitation nowcasting refers to predicting the future rainfall intensity within a relatively short period (e.g., 0∼6 h) based on the observation of radar
We can see that the proposed model including DA-Long Short-Term Memory (LSTM) and IDA-LSTM achieves the best performance in terms of the Heidke Skill Score (HSS), Critical Success Index (CSI) at all thresholds
We propose a novel radar echo map extrapolation method, namely, IDA-LSTM
Summary
Precipitation nowcasting refers to predicting the future rainfall intensity within a relatively short period (e.g., 0∼6 h) based on the observation of radar. (1) optical flow-based models [2,3] and (2) deep learning-based algorithms [4,5,6,7,8] The former calculates a motion field between the adjacent maps based on the assumption that the brightness of pixels is constant. The generation of movement only uses a few recent radar images, which suggests the type of methods cannot utilize the valuable historical observations The latter builds a mapping from previous radar observations to future echo maps by constructing a neural network, for example, the convolution neural network (CNN) [9,10], recurrent neural network (RNN) [11], and the Spatial Transformer Networks (STN) [12].
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