Strong convection nowcasting has been gaining importance in operational weather forecasting. Recently, deep learning methods have been used to meet the increasing requirement for precise and timely nowcasting. One of the promising deep learning models is the convolutional gated recurrent unit (ConvGRU), which has been proven to perform better than traditional methods in strong convection nowcasting. Despite its encouraging performance, ConvGRU tends to produce blurry radar echo images and fails to model radar echo intensities that have multi-modal and skewed distributions. To overcome these disadvantages, we tested the structural similarity (SSIM) and multiscale structural similarity (MS-SSIM) indexes as loss functions. The SSIM and MS-SSIM loss functions are composed of luminance, contrast, and structure and provide more information about the intensity, grade, and shape of the radar echo, which can reduce blurring. Due to multi-layer downscaling, MS-SSIM extracted more radar echo characteristics, and its extrapolation was the most realistic and accurate among all of the loss function schemes. Only the MS-SSIM scheme successfully predicted strong radar echoes after 2 h, especially those at the rainstorm level.

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基于雷达回波外推的深度学习方法在强对流临近预报中的应用

强对流临近预报在业务天气预报中越来越重要。最近，深度学习方法已被用于满足对精确和及时临近预报日益增长的要求。一种有前途的深度学习模型是卷积门控循环单元 (ConvGRU)，它已被证明在强对流临近预报中比传统方法表现更好。尽管其性能令人鼓舞，但 ConvGRU 往往会产生模糊的雷达回波图像，并且无法对具有多模态和偏斜分布的雷达回波强度进行建模。为了克服这些缺点，我们测试了结构相似性（SSIM）和多尺度结构相似性（MS-SSIM）指标作为损失函数。SSIM 和 MS-SSIM 损失函数由亮度、对比度、和结构并提供有关雷达回波的强度、等级和形状的更多信息，这可以减少模糊。由于多层降尺度，MS-SSIM提取了更多的雷达回波特征，其外推是所有损失函数方案中最真实、最准确的。只有 MS-SSIM 方案成功预测了 2 小时后的强雷达回波，尤其是暴雨级别的回波。