| 基于深度学习的京津冀地区精细尺度降水临近预报研究 |
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| 引用本文： | 曹伟华,南刚强,陈明轩,程丛兰,杨璐,吴剑坤,宋林烨,刘瑞婷.基于深度学习的京津冀地区精细尺度降水临近预报研究[J].气象学报,2022,80(4):546-564. |
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| 作者姓名： | 曹伟华 南刚强 陈明轩 程丛兰 杨璐 吴剑坤 宋林烨 刘瑞婷 |
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| 作者单位： | 北京城市气象研究院，北京，100089 |
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| 基金项目： | 北京市自然基金项目（8192016、8204060）、国家自然科学基金项目（41801022）、国家重点研发计划项目（2017YFC1502104、2018YFC1507504） |
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| 摘 要： | 精细尺度降水的临近预报对于提升现代城市内涝和山洪地质灾害预警能力具有重要意义。深度学习作为一种新兴方法，在挖掘数据内部特征及物理规律方面更具优势，近年来在天气雷达图像领域的应用已初见成效。为进一步提升精细尺度降水的临近预报能力，基于深度学习网络模型RainNet，研究建立了两种滚动预报方式，开展了京津冀地区1 km分辨率精细尺度降水滚动式临近预报试验和对比分析。 试验结果表明:与传统基于交叉相关的外推预报相比，深度学习网络模型RainNet总体可以明显改进降水1 h临近预报的绝对误差和相关系数；两个RainNet相结合的滚动预报方式对1.04 mm/（10 min）及以下阈值降水，在10—50 min预报性能一致优于传统的交叉相关外推预报。深度学习模型对降水消亡过程的时、空演变趋势刻画更好，尤其更适用于降水消亡过程的临近预报。采用两个RainNet模型相结合的滚动式预报方式优于单一模型滚动预报方式。
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| 关 键 词： | 降水 临近预报 深度学习 交叉相关 外推 |
| 收稿时间： | 2021-08-02 |
| 修稿时间： | 2022-03-07 |
A study on fine scale precipitation nowcasting in Beijing-Tianjin-Hebei region based on deep learning |
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| Institution: | Institute of Urban Meteorology，China Meteorological Administration，Beijing 100089，ChinaBeijing Urban Meteorological Engineering Research Center，Beijing 100089，China |
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| Abstract: | Precipitation nowcasting on fine scale is of great significance to improve the ability of early warning of flood and waterlogging disasters in modern cities. As a new method, deep learning has more advantages in mining the internal characteristics and physical laws of data. In recent years, the application of deep learning in the field of meteorological radar image has achieved preliminary results. In order to improve the effectiveness of nowcasting on fine scale, a deep convolutional neural network-RainNet is used to propose two ways of rolling approach for precipitation nowcasting. Experiments and comparative analysis are carried out in Beijing-Tianjin-Hebei region on 1 km resolution. Compared with the traditional extrapolation based on Tracking Radar Echoes by Correlation (TREC), the results show that the mean absolute error and correlation coefficient of 1 h nowcasting can be improved. The prediction in 10—50 min in thresholds of 1.04 mm/(10 min) and below is better than that of traditional prediction. Temporal and spatial evolution of precipitation extinction process is better described by deep learning compared with that by traditional extrapolation. The rolling approach with two RainNet models combined outperforms one single model in precipitation nowcasting. |
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