Except for the average reconstruction loss, we additionally employ adversarial loss and perceptual similarity loss to further improve the visual quality. We separate the extrapolation task into coarse- and fine- two levels which can reduce the down-sampling loss and retain echo fine details. Moreover, to resolve the blurry prediction problem and improve forecast accuracy, we also adopt a coarse-fine hierarchical extrapolation strategy and compositive loss function. To complement this paradigm, we propose to incorporate a novel long-term evolution regularity memory (LERM) module into the network, which can memorize long-term echo-evolution regularities during training and be recalled for guiding extrapolation. However, existing extrapolation methods mainly focus on a defective echo-motion extrapolation paradigm based on finite observational dynamics, neglecting that the actual echo sequence has a more complicated evolution process that contains both nonlinear motions and the lifecycle from initiation to decay, resulting in poor prediction precision and limited application ability. Weather radar echo extrapolation, which predicts future echoes based on historical observations, is one of the complicated spatial-temporal sequence prediction tasks, and plays a prominent role in severe convection and precipitation nowcasting. To evaluate the model, we conduct experiments on extrapolating 0.5h, 1h, and 1.5h imminent future echoes, the results show that our proposed AENN can achieve the expected effect and outperforms other models significantly, which has a powerful potential application value for short-term weather forecasting.
Adversarial network radar generator#
The generator and discriminators are trained alternately in an adversarial way to make the final extrapolation results be realistic and accurate. To address this problem and make a more realistic and accurate extrapolation, we propose a deep learning model called Adversarial Extrapolation Neural Network (AENN), which is a Generative Adversarial Network (GAN) structure and consist of a conditional generator and two discriminators, echo-frame discriminator and echo-sequence discriminator. However, traditional extrapolation methods cannot offer accurate enough extrapolation results since their modeling capacity is limited, the recent deep learning based methods make some progress but still remains a problem of blurry prediction when making deeper extrapolation, which may due to they choose the mean square error as their loss function and that will lead to losing echo details. Through the technique of weather radar echo extrapolation, the future short-term weather conditions can be predicted and severe convection storms can be warned. Weather radar echo is one of the fundamental data for meteorological workers to weather systems identification and classification.
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