基于MCGAN模型的降水临近预报
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国家自然科学基金 (41575155)


Precipitation Nowcasting Based on MCGAN Model
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    摘要:

    在降水临近预报领域中, 现有基于深度学习的雷达回波外推方法存在一些缺点. 在图像质量方面, 预测图像模糊并缺乏小尺度细节, 在预测精度方面, 降水结果不够准确. 本文提出了一个多尺度生成对抗(MCGAN)模型, 该模型由多尺度卷积生成器和全卷积的判别器组成. 生成器部分采用编码器-解码器架构, 主要包含了多尺度卷积块和下采样门控单元. MCGAN模型使用动态时空变异性损失函数在生成对抗网络(GAN)框架下训练, 以达到更精准和更清晰的回波强度和分布预测效果. 模型的性能在上海市公共雷达数据集上进行了验证, 与主流深度学习模型相比, 本文所提模型在图像质量评估中的MSE指标上下降了11.15%, 在SSIM指标和PSNR指标上分别增加了8.99%、2.95%; 在预测精度评估中, CSI指标、POD指标、HSS指标上平均提高了11.92%、15.89%、9.01%, FAR指标平均降低了14.81%. 此外, 本文通过消融实验证明了MCGAN模型每个部件的作用.

    Abstract:

    In the field of precipitation nowcasting, the existing radar echo extrapolation methods based on deep learning have some shortcomings. In terms of image quality, the prediction images are indistinct and deficient in small-scale details, while in terms of prediction accuracy, the precipitation results are not accurate enough. This study proposes a multi-scale generative adversarial (MCGAN) model, which consists of a multi-scale convolutional generator and a fully convolutional discriminator. The generator part adopts an encoder-decoder architecture, which mainly includes multi-scale convolutional blocks and downsampling gating units. Using the dynamic spatiotemporal variability loss function, the MCGAN model is trained under the generative adversarial network (GAN) framework to achieve more accurate and clearer predictions of echo intensity and distribution. Verified in the Shanghai public radar dataset, the performance of the model in this study decreases by 11.15% in the MSE index in image quality evaluation, and increases by 8.99% and 2.95% in the SSIM index and PSNR index compared with the mainstream deep learning models, respectively. In the evaluation of prediction accuracy, the CSI, POD, and HSS indexes increase by 11.92%, 15.89%, and 9.01% on average, and the FAR index decreases by 14.81% on average. In addition, the role of each component of the MCGAN model is demonstrated by ablation experiments.

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刘蕊,杜景林,许梦.基于MCGAN模型的降水临近预报.计算机系统应用,2024,33(11):68-78

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  • 收稿日期:2024-04-22
  • 最后修改日期:2024-05-20
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  • 在线发布日期: 2024-09-24
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