论文标题
MMINR：多帧到型框架推断具有抗噪声性的推断，以降水为雷达
MMINR: Multi-frame-to-Multi-frame Inference with Noise Resistance for Precipitation Nowcasting with Radar
论文作者
论文摘要
基于雷达回声图的降水在气象研究中至关重要。最近，基于卷积RNN的方法主导了该领域，但是无法通过并行计算来求解它们，从而导致推理时间更长。基于FCN的方法采用多帧到单个框架推理（MSI）策略来避免此问题。他们再次反馈到模型中，以预测下一个时间步骤，以在预测阶段获得多帧的幕后结果，这将导致预测错误的积累。此外，由于其不可预测性，降水噪声是导致高预测错误的关键因素。为了解决这个问题，我们提出了一种具有噪声阻力（NR）MMINR的新型多帧到媒体推理（MMI）模型。它避免了误差积累，并在平行计算中抵抗了降水噪声。 NR包含一个噪声辍学模块（NDM）和语义还原模块（SRM）。 NDM故意辍学噪声简单而有效，SRM补充了功能的语义信息，以减轻NDM错误丢失的语义信息问题。实验结果表明，与其他SOTA相比，MMINR可以获得竞争得分。消融实验表明，提出的NDM和SRM可以解决上述问题。
Precipitation nowcasting based on radar echo maps is essential in meteorological research. Recently, Convolutional RNNs based methods dominate this field, but they cannot be solved by parallel computation resulting in longer inference time. FCN based methods adopt a multi-frame-to-single-frame inference (MSI) strategy to avoid this problem. They feedback into the model again to predict the next time step to get multi-frame nowcasting results in the prediction phase, which will lead to the accumulation of prediction errors. In addition, precipitation noise is a crucial factor contributing to high prediction errors because of its unpredictability. To address this problem, we propose a novel Multi-frame-to-Multi-frame Inference (MMI) model with Noise Resistance (NR) named MMINR. It avoids error accumulation and resists precipitation noiseś negative effect in parallel computation. NR contains a Noise Dropout Module (NDM) and a Semantic Restore Module (SRM). NDM deliberately dropout noise simple yet efficient, and SRM supplements semantic information of features to alleviate the problem of semantic information mistakenly lost by NDM. Experimental results demonstrate that MMINR can attain competitive scores compared with other SOTAs. The ablation experiments show that the proposed NDM and SRM can solve the aforementioned problems.
