Dense-Cast：用于临近降水预报的轻量级深度学习架构集成模型
Dense-Cast: A lightweight ensemble of deep learning architectures for precipitation nowcasting
浏览论文内容
中文总结 AI 辅助
本文针对降水预报难题，提出融合DenseNet、残差连接与Transformer编码器的轻量级模型，在印度东北地区的GPM IMERG数据集上取得了0.235mm MAE等优异预报性能。
中文摘要 AI 辅助
短期降水预报对灾害管理和防灾准备至关重要，但降水的变异性与非线性特征使得气象学家开展短期预报颇具挑战，且在临近降水预报中捕捉时空数据的时间依赖关系也是一项难题。本文提出一种用于半小时级临近降水预报的轻量级深度学习模型，该模型融合了DenseNet架构、残差连接与Transformer编码器，可在减少模型参数的同时实现有效的临近降水预报。本研究选取印度东北地区作为研究区域，该区域因季风季在6-9月迎来最高降水量。所提模型以过去5个半小时的降水时间步长数据作为输入，预测未来两个半小时的降水量；研究采用时间分辨率为30分钟的GPM IMERG降水数据集对模型进行训练与测试。该架构在30分钟时间间隔下，取得了0.235毫米的最佳平均绝对误差（MAE）、0.735毫米的均方根误差（RMSE）以及0.816的KGE评分。
英文摘要
Proper short-term forecasting of precipitation is crucial in disaster management and preparedness. Nonetheless, the variability and nonlinearity of precipitation make short-term forecasting challenging for meteorologists. Moreover, capturing temporal dependencies in spatiotemporal data is a challenge in precipitation nowcasting. In this article, we introduce a lightweight deep learning model for half-hourly precipitation nowcasting. This model has been designed by incorporating the DenseNet architecture, residual connections, and transformer encoders for effective precipitation nowcasting with reduced model parameters. The North-Eastern region of India has been selected as the area of interest for our study. The region receives the highest precipitation during the months of June-September due to the monsoon season. The proposed model takes the previous five time-steps of half-hourly precipitation as inputs and predicts the precipitation in the next two half-hours. The GPM IMERG precipitation dataset with a 30-minute cadence has been used in this study for training and testing the model. The proposed architecture achieves best MAE of 0.235 millimetres, RMSE of 0.735 millimetres, and KGE score of 0.816 at an interval of 30 minutes.
