用深度神经网络提升单点降水预测精度，优于传统方法。
Towards Location-Specific Precipitation Projections Using Deep Neural Networks
- 设计两种神经网络，融合地形、位置及气象数据
- 在1980-2019年数据上训练，五年报验证表现更优
- 适合气象建模与精准气候分析人员使用
精确的单点降水估计对天气预报和空间分析至关重要。本研究提出范式转变，利用深度神经网络（DNN）超越传统方法如克里金插值，实现站点级降水估算。我们提出了两种创新的神经网络架构：一种结合降水、高程和位置信息，另一种还引入湿度、温度和风速等气象参数。模型基于1980-2019年大规模数据集训练，在五年期验证集上，各项评估指标（相关系数、均方根误差、偏差和技巧评分）均优于克里金法。这一有力证据表明深度学习在空间预测中的变革潜力，为站点级降水估算提供了稳健且精确的新方案。
原文摘要 · Abstract (English)
Accurate precipitation estimates at individual locations are crucial for weather forecasting and spatial analysis. This study presents a paradigm shift by leveraging Deep Neural Networks (DNNs) to surpass traditional methods like Kriging for station-specific precipitation approximation. We propose two innovative NN architectures: one utilizing precipitation, elevation, and location, and another incorporating additional meteorological parameters like humidity, temperature, and wind speed. Trained on a vast dataset (1980-2019), these models outperform Kriging across various evaluation metrics (correlation coefficient, root mean square error, bias, and skill score) on a five-year validation set. This compelling evidence demonstrates the transformative power of deep learning for spatial prediction, offering a robust and precise alternative for station-specific precipitation estimation.
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