设为首页
加入收藏
Visibility Forecast Based on PhyDNet-ATT Deep Learning Algorithm
-
摘要: 本文采用PhyDNet-ATT深度学习方法建立江苏省能见度预报模型PhyDNet-ATT-VIS, 该模型融合了高时空分辨率地面观测数据和模式产品, 实现了空间分辨率为3 km、时间分辨率为1 h、预报时效为6~18 h的能见度预报, 并且对模型结果进行了检验评估。与ECMWF能见度产品相比, PhyDNet-ATT-VIS预报的均方根误差和平均绝对偏差分别降低201%和310%;对于不同能见度等级, 命中率显著提高, 空报率显著降低, TS评分显示预报技巧优势明显, 但15~18 h低能见度预报仍存在很大提升空间。PhyDNet-ATT-VIS在观测站点密集区域的预报误差显著低于观测站点稀疏区域。在区域性雾过程和局地雾过程预报方面, PhyDNet-ATT-VIS均能较准确地预报雾的落区、强度、生消等关键特征参数。研究为能见度短时临近预报技巧的提升提供了新思路。Abstract: NWP (numerical weather prediction) and statistical methods still have limitations in forecasting low visibility. Therefore, enhancing nowcasting techniques is crucial for ensuring the safety of daily life and industrial activities. A short-term visibility forecast model in Jiangsu Province PhyDNet-ATT-VIS is established based on PhyDNet-ATT (physical dynamics network with attention) deep learning method, using high spatial and temporal resolution ground observations and NWP data. Ground observations contain 1 min average horizontal visibility from automatic weather stations in Jiangsu Province, as well as its upstream regions of Anhui Province, Henan Province, and Shandong Province. NWP data are derived from PWAFS (Precision Weather Analysis and Forecast System) gridded meteorological forecast data. Temporal resolution and spatial resolutions of PWAFS forecast products are 1 h and 3 km, respectively. Due to PhyDNet-ATT-VIS's outstanding ability to handle nonlinear problems, visibility forecast from 6 h to 18 h with a spatial resolution of 3 km and a temporal resolution of 1 h is achieved, and model results are tested and evaluated. The visibility forecasting product of ECMWF (European Centre for Medium-Range Weather Forecasts) is selected to evaluate and compare the forecasting skills with that of PhyDNet-ATT-VIS. The initial forecast time of ECMWF is 0800 BT and 2000 BT every day, with the time interval of 3 h and the spatial resolution of 0.125°×0.125°. Compared to ECMWF visibility product, the rootmean square error (RMSE) and mean absolute error (MAE) of PhyDNet-ATT-VIS are reduced by 201% and 310%, respectively. Across different visibility levels, the probability of detection (POD) significantly improves, while the false alarm ratio (FAR) substantially decreases. Threat score (TS) of the forecast demonstrates a clear advantage, although the model's ability to predict low visibility (defined as visibility less than 0.2 km) in 15 h to 18 h range still requires further improvement. In terms of spatial distribution, the forecast error for visibility in lake and coastal areas is significantly lower than that in other regions for NWP. For PhyDNet-ATT-VIS, the error in areas with dense observation sites is significantly lower than that in regions with sparse observation sites. Compared to ECMWF model, PhyDNet-ATT-VIS can more accurately predict key characteristic parameters, such as affected areas, intensity, onset, and dissipation, in both regional and local fog processes. This study can provide a reliable and operationally replicable reference for improving short-term forecasting skills in visibility.
-
Key words:
- visibility nowcast;
- deep learning;
- fog
-
图 1 不同预报模型的平均绝对偏差和均方根误差分布
Fig. 1 Distribution of mean absolute error and root mean square error for different forecast models
图 2 PhyDNet-ATT-VIS和ECMWF能见度预报评估
Fig. 2 Visibility forecast assessment for PhyDNet-ATT-VIS and ECMWF
图 3 PhyDNet-ATT-VIS和ECMWF不同等级能见度TS评分
Fig. 3 TS for PhyDNet-ATT-VIS and ECMWF at different levels of visibility
图 4 PhyDNet-ATT-VIS在不同能见度等级不同预报时效的TS评分
Fig. 4 TS for PhyDNet-ATT-VIS at different levels of visibility with different lead times
图 5 2020年12月27日20:00起报的2020年12月28日雾过程能见度预报与观测
Fig. 5 Observed and predicted visibility for fog case on 28 Dec 2020 initiated at 2000 BT 27 Dec 2020
图 6 2020年12月28日雾过程PhyDNet-ATT-VIS和ECMWF能见度预报评估
Fig. 6 Visibility test for PhyDNet-ATT-VIS and ECMWF for fog case on 28 Dec 2020
图 7 2023年11月21日20:00起报的2023年11月22日雾过程能见度预报与观测对比
Fig. 7 Observed and predicted visibility for fog case on 22 Nov 2023 initiated at 2000 BT 21 Nov 2023
表 1 能见度评估阈值
Table 1 Visibility assessment threshold
能见度/km 能见度等级 (1, 10] 轻雾 (0.5, 1] 大雾 (0.2, 0.5] 浓雾 (0, 0.2] 强浓雾 
下载: 导出CSV
表 2 测试样本中的预报效果
Table 2 Forecasting effectiveness in testing sample
预报模型 均方根误差/km 平均绝对偏差/km 相关系数 PhyDNet-ATT-VIS预报 2.43 1.46 0.94 ECMWF产品 7.31 5.98 0.4 PWAFS产品 10.87 8.58 0.06
下载: 导出CSV
-
[1] 张小曳, 孙俊英, 王亚强, 等.我国雾-霾成因及其治理的思考.科学通报, 2013, 58(13):1178-1187.Zhang X Y, Sun J Y, Wang Y Q, et al. Factors contributing to haze and fog in China. Chinese Sci Bull, 2013, 58(13): 1178-1187. [2] Gultepe I, Tardif R, Michaelides S C, et al. Fog research: A review of past achievements and future perspectives. Pure Appl Geophys, 2007, 164(6): 1121-1159. [3] Chen H S, Xu Y H, Gao Z Q, et al. Visibility forecast in Jiangsu Province based on the GCN-GRU model. Sci Rep, 2024. DOI: 10.1038/s41598-024-61572-8. [4] Peláez-Rodríguez C, Pérez-Aracil J, Casanova-Mateo C, et al. Efficient prediction of fog-related low-visibility events with machine learning and evolutionary algorithms. Atmos Res, 2023, 295. DOI: 10.1016/j.atmosres.2023.106991. [5] Kim B Y, Belorid M, Cha J W. Short-term visibility prediction using tree-based machine learning algorithms and numerical weather prediction data. Wea Forecasting, 2022, 37(12): 2263-2274. doi: 10.1175/WAF-D-22-0053.1 [6] 丁一汇, 柳艳菊. 近50年我国雾和霾的长期变化特征及其与大气湿度的关系. 中国科学(地球科学), 2014, 44(1): 37-48.Ding Y H, Liu Y J. Analysis of long-term variations of fog and haze in China in recent 50 years and their relations with atmospheric humidity. Science China(Earth Sciences), 2014, 44(1): 37-48. [7] 刘骞, 盛立芳, 王园香, 等. 气象要素对中国大气能见度长期变化影响的定量研究. 气候与环境研究, 2016, 21(1): 47-55.Liu Q, Sheng L F, Wang Y X, et al. Quantitative study of the effect of meteorological variables on the long-term variation of visibility in China. Clim Environ Res, 2016, 21(1): 47-55. [8] 冯蕾, 田华. 国内外雾预报技术研究进展. 南京信息工程大学学报(自然科学版), 2014, 6(1): 74-81.Feng L, Tian H. Progress in fog prediction. J Nanjing Univ Inf Sci Technol(Nat Sci Ed), 2014, 6(1): 74-81. [9] 高山红, 齐伊玲, 张守宝, 等. 利用循环3DVAR改进黄海海雾数值模拟初始场: WRF数值试验. 中国海洋大学学报, 2010, 40: 1-9.Gao S H, Qi Y L, Zhang S B, et al. Initial conditions improvement of sea fog numerical modeling over the Yellow Sea by Using cycling 3DVAR. Part Ⅰ: WRF numerical experiments. Periodical of Ocean University of China, 2010, 40(10): 1-9. [10] 权建农, 徐祥德, 贾星灿, 等. 影响我国霾天气的多尺度过程. 科学通报, 2020, 65(9): 810-824.Quan J N, Xu X D, Jia X C, et al. Multi-scale processes in severe haze events in China and their interactions with aerosols: Mechanisms and progresses. Chinese Sci Bull, 2020, 65(9): 810-824. [11] 章国才. 中国雾的业务预报和应用. 气象科技进展, 2016, 6(2): 42-48.Zhang G C. The progress of fog forecast operation in China. Adv Meteor Sci Tech, 2016, 6(2): 42-48. [12] 高荣珍, 李欣, 时晓曚, 等. 基于WRF模式的青岛近海能见度算法比较研究. 海洋气象学报, 2018, 38(2): 28-35.Gao R Z, Li X, Shi X M, et al. Comparative study on three algorithms of the visibility in Qingdao offshore areas based on WRF model. J Mar Meteor, 2018, 38(2): 28-35. [13] 赵秀娟, 徐敬, 张自银, 等. 北京区域环境气象数值预报系统及PM2.5预报检验. 应用气象学报, 2016, 27(2): 160-172.Zhao X J, Xu J, Zhang Z Y, et al. Beijing regional environmental meteorology prediction system and its performance test of PM2.5 concentration. J Appl Meteor Sci, 2016, 27(2): 160-172. [14] Wang T J, Jiang F, Deng J J, et al. Urban air quality and regional haze weather forecast for Yangtze River Delta region. Atmos Environ, 2012, 58: 70-83. doi: 10.1016/j.atmosenv.2012.01.014 [15] 侯梦玲, 王宏, 赵天良, 等. 京津冀一次重度雾霾天气能见度及边界层关键气象要素的模拟研究. 大气科学, 2017, 41(6): 1177-1190.Hou M L, Wang H, Zhao T L, et al. A modeling study of the visibility and PBL key meteorological elements during a heavy fog-haze episode in Beijing-Tianjin-Hebei of China. Chinese J Atmos Sci, 2017, 41(6): 1177-1190. [16] 赵秀娟, 李梓铭, 徐敬. 霾天能见度参数化方案改进及预报效果评估. 环境科学, 2019, 40(4): 1688-1696.Zhao X J, Li Z M, Xu J. Modification and performance tests of visibility parameterizations for haze days. Environ Sci, 2019, 40(4): 1688-1696. [17] Wang Y, Wu Z J, Ma N, et al. Statistical analysis and parameterization of the hygroscopic growth of the sub-micrometer urban background aerosol in Beijing. Atmos Environ, 2018, 175: 184-191. doi: 10.1016/j.atmosenv.2017.12.003 [18] 姜江, 张国平, 高金兵. 北京大气能见度的主要影响因子. 应用气象学报, 2018, 29(2): 188-199. doi: 10.11898/1001-7313.20180206Jiang J, Zhang G P, Gao J B. Main influencing factors of visibility in Beijing. J Appl Meteor Sci, 2018, 29(2): 188-199. doi: 10.11898/1001-7313.20180206 [19] 张苏平, 任兆鹏. 下垫面热力作用对黄海春季海雾的影响——观测与数值试验. 气象学报, 2010, 68(4): 439-449.Zhang S P, Ren Z P. The influence of the thermal effect of underlaying surface on the spring sea fog over the Yellow Sea: Observations and numerical simulations. Acta Meteor Sinica, 2010, 68(4): 439-449. [20] Schlatter T W, Helms D, Reynolds D, et al. A Phenomenological Approach to the Specification of Observational Requirements. A Report to the Office of Science and Technology. National Weather Service, NOAA, 2005. [21] 胡莹莹, 庞林, 王启光. 基于深度学习的7~15 d温度格点预报偏差订正. 应用气象学报, 2023, 34(4): 426-437. doi: 10.11898/1001-7313.20230404Hu Y Y, Pang L, Wang Q G. Application of deep learning bias correction method to temperature grid forecast of 7-15 days. J Appl Meteor Sci, 2023, 34(4): 426-437. doi: 10.11898/1001-7313.20230404 [22] 袁凯, 李武阶, 庞晶. 基于决策树算法的鄂东地区冰雹识别技术. 应用气象学报, 2023, 34(2): 234-245. doi: 10.11898/1001-7313.20230209Yuan K, Li W J, Pang J. Hail identification technology in eastern Hubei based on decision tree algorithm. J Appl Meteor Sci, 2023, 34(2): 234-245. doi: 10.11898/1001-7313.20230209 [23] 韩念霏, 杨璐, 陈明轩, 等. 京津冀站点风温湿要素的机器学习订正方法. 应用气象学报, 2022, 33(4): 489-500. doi: 10.11898/1001-7313.20220409Han N F, Yang L, Chen M X, et al. Machine learning correction of wind, temperature and humidity elements in Beijing-Tianjin-Hebei Region. J Appl Meteor Sci, 2022, 33(4): 489-500. doi: 10.11898/1001-7313.20220409 [24] 刘冬韡, 穆海振, 贺千山, 等. 一种基于实景图像的低能见度识别算法. 应用气象学报, 2022, 33(4): 501-512. doi: 10.11898/1001-7313.20220410Liu D W, Mu H Z, He Q S, et al. A low visibility recognition algorithm based on surveillance video. J Appl Meteor Sci, 2022, 33(4): 501-512. doi: 10.11898/1001-7313.20220410 [25] 赵琳娜, 卢姝, 齐丹, 等. 基于全连接神经网络方法的日最高气温预报. 应用气象学报, 2022, 33(3): 257-269.Zhao L N, Lu S, Qi D, et al. Daily maximum air temperature forecast based on fully connected neural network. J Appl Meteor Sci, 2022, 33(3): 257-269. [26] 谢舜, 孙效功, 张苏平, 等. 基于SVD与机器学习的华南降水预报订正方法. 应用气象学报, 2022, 33(3): 293-304.Xie S, Sun X G, Zhang S P, et al. Precipitation forecast correction in South China based on SVD and machine learning. J Appl Meteor Sci, 2022, 33(3): 293-304. [27] 黄威, 牛若芸. 基于集合预报和支持向量机的中期强降雨集成预报试验. 气象, 2017, 43(9): 1110-1116.Huang W, Niu R Y. The medium-term multi-model integration forecast experimentation for heavy rain based on support vector machine. Meteor Mon, 2017, 43(9): 1110-1116. [28] 任萍, 陈明轩, 曹伟华, 等. 基于机器学习的复杂地形下短期数值天气预报误差分析与订正. 气象学报, 2020, 78(6): 1002-1020.Ren P, Chen M X, Cao W H, et al. Error analysis and correction of short-term numerical weather prediction under complex terrain based on machine learning. Acta Meteor Sinica, 2020, 78(6): 1002-1020. [29] Baldwin M E, Kain J S, Lakshmivarahan S. Development of an automated classification procedure for rainfall systems. Mon Wea Rev, 2005, 133(4): 844-862. doi: 10.1175/MWR2892.1 [30] McGovern A, Gagne D J 2nd, Williams J K, et al. Enhancing understanding and improving prediction of severe weather through spatiotemporal relational learning. Mach Learn, 2014, 95(1): 27-50. doi: 10.1007/s10994-013-5343-x [31] Zhong J T, Zhang X Y, Gui K, et al. Robust prediction of hourly PM2.5 from meteorological data using LightGBM. Natl Sci Rev, 2021, 8(10). DOI: 10.1093/nsr/nwaa307. [32] Le Guen V, Thome N. Disentangling physical dynamics from unknown factors for unsupervised video prediction. IEEE, 2020. DOI: 10.48550/arXiv.2003.01460. [33] 袁凯, 李武阶, 李明, 等. 四种机器深度学习算法对武汉地区雷达回波临近预报的检验和评估. 气象, 2022, 48(4): 428-441.Yuan K, Li W J, Li M, et al. Examination and evaluation of four machine deep learning algorithms for radar echo nowcasting in Wuhan Region. Meteor Mon, 2022, 48(4): 428-441. [34] 马志峰, 张浩, 刘劼. 基于深度学习的短临降水预报综述. 计算机工程与科学, 2023, 45(10): 1731-1753. doi: 10.3969/j.issn.1007-130X.2023.10.003Ma Z F, Zhang H, Liu J. A survey of precipitation nowcasting based on deep learning. Comput Eng Sci, 2023, 45(10): 1731-1753. doi: 10.3969/j.issn.1007-130X.2023.10.003 [35] Li X, Zou X L, Zhuge X Y, et al. Improved himawari-8/AHI radiance data assimilation with a double cloud detection scheme. J Geophys Res Atmos, 2020, 125(13). DOI: 10.1029/2020JD03631. [36] 苏翔, 刘梅, 康志明, 等. 2020年江苏主汛期短期暴雨预报检验. 气象, 2022, 48(3): 357-371.Su X, Liu M, Kang Z M, et al. Verification of short-range torrential rain forecast during the 2020 Jiangsu main flood season. Meteor Mon, 2022, 48(3): 357-371. [37] Vaswani A, Shazeer N, Parmar N, et al. Attention is All You Need//11th Conference on Neural Information Processing Systems(NIPS), 2017. DOI: 10.48550/arXiv.1706.03762 .[38] 周奕珂, 朱彬, 韩志伟, 等. 长江三角洲地区冬季能见度特征及影响因子分析. 中国环境科学, 2016, 36(3): 660-669. doi: 10.3969/j.issn.1000-6923.2016.03.005Zhou Y K, Zhu B, Han Z W, et al. Analysis of visibility characteristics and connecting factors over the Yangtze River Delta Region during winter time. China Environ Sci, 2016, 36(3): 660-669. doi: 10.3969/j.issn.1000-6923.2016.03.005 [39] Liu X, Zheng Y, Zhuang X R, et al. Spatiotemporal convolutional approach for the short-term forecast of hourly heavy rainfall probability integrating numerical weather predictions and surface observations. Wea Forecasting, 2024, 39(3): 597-612. doi: 10.1175/WAF-D-23-0068.1 [40] 张自银, 赵秀娟, 熊亚军, 等. 基于动态统计预报方法的京津冀雾霾中期预报试验. 应用气象学报, 2018, 29(1): 57-69.Zhang Z Y, Zhao X J, Xiong Y J, et al. The fog/haze medium-range forecast experiments based on dynamic statistic method. J Appl Meteor Sci, 2018, 29(1): 57-69. [41] 殷美祥, 罗瑞婷, 陈荣泉, 等. 基于GRU神经网络的雷州半岛近海岸能见度短临预报研究. 热带气象学报, 2023, 39(2): 267-275.Yin M X, Luo R T, Chen R Q, et al. Research on short-impending forecast of near-coast visibility for Leizhou peninsula based on GRU neural network. J Trop Meteor, 2023, 39(2): 267-275. [42] 庄潇然, 郑玉, 王亚强, 等. 基于深度学习的融合降水临近预报方法及其在中国东部地区的应用研究. 气象学报, 2023, 81(2): 286-303.Zhuang X R, Zheng Y, Wang Y Q, et al. A deep learning-based precipitation nowcast model and its application over East China. Acta Meteor Sinica, 2023, 81(2): 286-303. [43] 中华人民共和国国家质量监督检验检疫总局, 中国国家标准化管理委员会. 雾的预报等级. GB/T 27964—2011. 北京: 中国标准出版社, 2012.General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China. Standardization Administration of the People's Republic of China. Grade of Fog Forecast. GB/T 27964-2011. Beijing: Standards Press of China, 2012. -
图(7) / 表(2)
计量
- 摘要浏览量: 181
- HTML全文浏览量: 41
- PDF下载量: 64
- 被引次数: 0
