基于深度学习的7~15 d温度格点预报偏差订正

胡莹莹; 庞林; 王启光

doi:10.11898/1001-7313.20230404

基于深度学习的7~15 d温度格点预报偏差订正

DOI: 10.11898/1001-7313.20230404

胡莹莹^{1, 2},
庞林³,
王启光^3, ,

1.
中国气象科学研究院，北京 100081
2.
中国科学院大学，北京 100190
3.
中国气象局气象干部培训学院，北京 100081

资助项目:

国家自然科学基金项目 41975100

详细信息

通信作者:
王启光, 邮箱：photon316@163.com

计量
- 摘要浏览量: 1161
- HTML全文浏览量: 185
- PDF下载量: 150
- 被引次数: 0
出版历程
- 收稿日期: 2023-03-10
- 修回日期: 2023-06-06
- 刊出日期: 2023-07-31

Application of Deep Learning Bias Correction Method to Temperature Grid Forecast of 7-15 Days

Hu Yingying^{1, 2},
Pang Lin³,
Wang Qiguang^{3
, ,}

1.
Chinese Academy of Meteorological Sciences, Beijing 100081
2.
University of Chinese Academy of Sciences, Beijing 100190
3.
China Meteorological Administration Training Center, Beijing 100081

摘要

摘要: 为了提高模式对于7~15 d温度格点预报准确性，基于U-Net模型以及U-Net残差连接模型，采用2018年12月25日—2022年7月5日多种组合气象数据作为输入数据特征，针对TIGGE数据中心提供的全球集合预报CMA-GEPS 2 m气温控制预报，开展168 ~360 h时效的格点预报误差订正试验。结果表明：对于240 h预报时效，两种深度学习模型中，U-Net模型表现较好；对于不同输入数据特征，加入起报时刻ERA5 2 m气温产品的U-Net模型表现最佳，在多个预报时效上有较好的订正效果，均方根误差减小率为10%~25%，可有效改善模式对于15.75°~55.25°N，73°~136.5°E区域北部的蒙古高原、西部的青藏高原及部分山地的预报误差较大的不足；而加入CMA-GEPS控制预报10 m风预报产品后改进不明显。总体上，基于U-Net模型构建的模式格点预报偏差订正模型可有效降低7~15 d温度格点预报误差，进一步提升复杂地形下格点预报的准确性。
- 7~15 d温度格点预报;
- 偏差订正;
- 深度学习方法
Abstract: The forecast error of numerical weather forecasting is inevitable, and there are still difficulties in temperature forecast of 7-15 days. To improve forecast accuracy and timeliness, the deviation correction technique is often used in operation. In recent years, deep learning methods have shown great potential in statistical post-processing of model forecasts. To improve the accuracy of Global Ensemble Prediction System of China Meteorological Administration (CMA-GEPS) for 7-15 days, error characteristics of 2 m temperature and 10 m wind products of CMA-GEPS control forecast provided by TIGGE data center from 25 December 2018 to 5 July 2022, and ERA5 data provided by ECMWF are analyzed. The U-Net model and residual connection model is used to conduct 2 m temperature lattice forecast error revision experiment for the lead time of 168-360 h in the region 15.75°-55.25°N, 73°-136.5°E. The experiments are designed with various data features to explore differences of the deep learning methods for longer lead time with different sample characteristics and model parameters, and performances of models are examined by comparing the bias, mean absolute bias and root mean square error. The results show that 2 m temperature forecast errors of 7-15 days become larger as the lead time increases. The model forecast skill gradually decreases, and in the target area, performance in eastern and southern marine and offshore areas is better than in western and northern the plateaus and mountains. The differences in the spatial distribution of errors are more prominent. Among the revised models, the effect of the U-Net model is better than that of the U-Net residual connection model, and adding the initial 2 m temperature data of ERA5 can greatly improves the performance, but the effect of adding CMA-GEPS control forecast 10 m wind product of CMA-GEPS control forecast is not apparent. For 9 lead times, the revised root mean square errors are reduced by 10%-25%, and the model can effectively reduce the large forecast errors for the northern Mongolian Plateau and the western Tibetan Plateau, and some mountainous areas in the target area.
- temperature grid forecast of 7-15 days;
- bias correction;
- deep learning method

HTML全文

图 1 2022年4月8日—7月5日CMA-GEPS模式控制预报不同预报时效2 m气温产品均方根误差

Fig. 1 Root mean square error of CMA-GEPS model control forecast for 2 m temperature with different lead times from 8 Apr to 5 Jul in 2022

下载: 全尺寸图片幻灯片

图 2 2022年4月8日—7月5日研究区域内不同预报时效2 m气温产品均方根误差

Fig. 2 Root mean square error of different lead times for 2 m temperature in the target area from 8 Apr to 5 Jul in 2022

下载: 全尺寸图片幻灯片

图 3 CMA-GEPS模式控制预报与U-Net模型订正2 m气温平均绝对偏差

折线为基于数据集Ⅱ的订正结果相对CMA-GEPS控制预报的平均绝对偏差减小率

Fig. 3 Average absolute errors of 2 m temperature for CMA-GEPS model control forecast and U-Net model correction

the line denotes average absolute error reduction rate of U-Net model correction based on dataset Ⅱ relative to CMA-GEPS control forecast

下载: 全尺寸图片幻灯片

图 4 同图 3，但为均方根误差

折线为基于数据集Ⅱ的订正结果相对CMA-GEPS控制预报的均方根误差减小率

Fig. 4 The same as in Fig. 3, but for root mean square error

the line denotes root mean square error reduction rate of U-Net model correction based on dataset Ⅱ relative to CMA-GEPS control forecast

下载: 全尺寸图片幻灯片

图 5 2022年4月8日—7月5日168 h时效CMA-GEPS模式控制预报及U-Net模型订正2 m气温逐日均方根误差

Fig. 5 Daily root mean square error of 2 m temperature for 168 h lead time from 8 Apr to 5 Jul in 2022 by CMA-GEPS control forecast and U-Net model correction

下载: 全尺寸图片幻灯片

图 6 同图 5，但为360 h时效

Fig. 6 The same as in Fig. 5, but for 360 h lead time

下载: 全尺寸图片幻灯片

图 7 研究区域内360 h时效2 m气温CMA-GEPS模式控制预报及U-Net模型订正结果均方根误差

Fig. 7 Root mean square error of 2 m temperature in the target area for 360 h lead time by CMA-GEPS control forecast and U-Net model correction

下载: 全尺寸图片幻灯片

表 1 CMA-GEPS模式控制预报2 m气温240 h预报时效与基于数据集Ⅰ订正结果评价指标(单位：℃)

Table 1 Evaluation indicaters of CMA-GEPS model control forecast of 2 m temperature for 240 h lead time and deep learning model correction based on dataset Ⅰ (unit:℃)

预报与订正结果	评价指标
预报与订正结果	偏差	平均绝对偏差	均方根误差
CMA-GEPS控制预报产品	0.41	2.57	3.56
U-Net模型订正	0.51	2.40	3.16
U-Net残差连接模型订正	0.10	2.73	3.68

下载: 导出CSV

表 2 同表 1，但为基于数据集Ⅱ订正结果(单位：℃)

Table 2 The same as in Table 1, but based on dataset Ⅱ (unit:℃)

预报与订正结果	评价指标
预报与订正结果	偏差	平均绝对偏差	均方根误差
CMA-GEPS控制预报产品	0.41	2.57	3.56
U-Net模型订正	-0.21	1.67	2.29
U-Net残差连接模型订正	-0.03	1.77	2.44

下载: 导出CSV

参考文献(44)

[1]	代刊, 曹勇, 钱奇峰, 等.中短期数字化天气预报技术现状及趋势.气象, 2016, 42(12):1445-1455. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201612003.htm Dai K, Cao Y, Qian Q F, et al. Situation and tendency of operation technologies in short-and medium-range weather forecast. Meteor Mon, 2016, 42(12): 1445-1455. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201612003.htm
[2]	李泽椿, 毕宝贵, 金荣花, 等. 近10年中国现代天气预报的发展与应用. 气象学报, 2014, 72(6): 1069-1078. doi: 10.3969/j.issn.1004-4965.2014.06.007 Li Z C, Bi B G, Jin R H, et al. The development and application of the modern weather forecast in China for the recent 10 years. Acta Meteor Sinica, 2014, 72(6): 1069-1078. doi: 10.3969/j.issn.1004-4965.2014.06.007
[3]	端义宏, 金荣花. 我国现代天气业务现状及发展趋势. 气象科技进展, 2012, 2(5): 6-11. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKZ201205004.htm Duan Y H, Jin R H. The status quo of modern weather operations in China and its future. Adv Meteor Sci Tech, 2012, 2(5): 6-11. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKZ201205004.htm
[4]	沈学顺, 王建捷, 李泽椿, 等. 中国数值天气预报的自主创新发展. 气象学报, 2020, 78(3): 451-476. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202003008.htm Shen X S, Wang J J, Li Z C, et al. China's independent and innovative development of numerical weather prediction. Acta Meteor Sinica, 2020, 78(3): 451-476. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202003008.htm
[5]	丑纪范. 数值天气预报的创新之路——从初值问题到反问题. 气象学报, 2007, 65(5): 673-682. doi: 10.3321/j.issn:0577-6619.2007.05.003 Chou J F. An innovative road to numerical weather prediction—From initial value problem to inverse problem. Acta Meteor Sinica, 2007, 65(5): 673-682. doi: 10.3321/j.issn:0577-6619.2007.05.003
[6]	曾庆存. 天气预报——由经验到物理数学理论和超级计算. 物理, 2013, 42(5): 300-314. https://www.cnki.com.cn/Article/CJFDTOTAL-WLZZ201305001.htm Zeng Q C. Weather forecast—From empirical to physicomathematical theory and super-computing system engineering. Physics, 2013, 42(5): 300-314. https://www.cnki.com.cn/Article/CJFDTOTAL-WLZZ201305001.htm
[7]	章大全, 郑志海, 陈丽娟, 等. 10~30 d延伸期可预报性与预报方法研究进展. 应用气象学报, 2019, 30(4): 416-430. doi: 10.11898/1001-7313.20190403 Zhang D Q, Zheng Z H, Chen L J, et al. Advances on the predictability and prediction methods of 10-30 d extended range forecast. J Appl Meteor Sci, 2019, 30(4): 416-430. doi: 10.11898/1001-7313.20190403
[8]	康志明, 鲍媛媛, 周宁芳. 我国中期和延伸期预报业务现状以及发展趋势. 气象科技进展, 2013, 3(1): 18-24. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKZ201301008.htm Kang Z M, Bao Y Y, Zhou N F. Current situation and development of medium-range and extended-range weather forecast in China. Adv Meteor Sci Tech, 2013, 3(1): 18-24. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKZ201301008.htm
[9]	金荣花, 马杰, 任宏昌, 等. 我国10~30天延伸期预报技术进展与发展对策. 地球科学进展, 2019, 34(8): 814-825. https://www.cnki.com.cn/Article/CJFDTOTAL-DXJZ201908007.htm Jin R H, Ma J, Ren H C, et al. Advances and development countermeasures of 10-30 days extended-range forecasting technology in China. Adv Earth Science, 2019, 34(8): 814-825. https://www.cnki.com.cn/Article/CJFDTOTAL-DXJZ201908007.htm
[10]	沈学顺, 陈起英, 孙健, 等. 中央气象台全球中期数值预报业务系统的发展. 气象, 2021, 47(6): 645-654. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202106001.htm Shen X S, Chen Q Y, Sun J, et al. Development of operation global medium-range forecast system in National Meteorological Centre. Meteor Mon, 2021, 47(6): 645-654. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202106001.htm
[11]	吴曼丽, 陆忠艳, 王瀛. 未来8~17 d中期预报方法研究//中国气象学会2006年年会中尺度天气动力学、数值模拟和预测分会场论文集, 2006: 929-935. Wu M L, Lu Z Y, Wang Y. The Method for Mid-range Forecast in Future 8-17 Days//Proceedings of the 2006 Annual Conference of the Chinese Meteorological Society on Mesoscale Weather Dynamics, Numerical Simulation and Prediction, 2006: 929-935.
[12]	沈学顺, 苏勇, 胡江林, 等. GRAPES_GFS全球中期预报系统的研发和业务化. 应用气象学报, 2017, 28(1): 1-10. doi: 10.11898/1001-7313.20170101 Shen X S, Su Y, Hu J L, et al. Development and operation transformation of GRAPES global middle-range forecast system. J Appl Meteor Sci, 2017, 28(1): 1-10. doi: 10.11898/1001-7313.20170101
[13]	尹姗, 李勇, 马杰, 等. 延伸期温度预报误差订正技术初探. 气象, 2020, 46(3): 412-419. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202003012.htm Yin S, Li Y, Ma J, et al. Preliminary study on bias correction for the extended-range temperature forecast. Meteor Mon, 2020, 46(3): 412-419. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX202003012.htm
[14]	Bauer P, Thorpe A, Brunet G. The quiet revolution of numerical weather prediction. Nature, 2015, 525(7567): 47-55.
[15]	Lorenz E N. Deterministic nonperiodic flow. J Atmos Sci, 1963, 20(2): 130-141.
[16]	赵声蓉, 赵翠光, 赵瑞霞, 等. 我国精细化客观气象要素预报进展. 气象科技进展, 2012, 2(5): 12-21. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKZ201205005.htm Zhao S R, Zhao C G, Zhao R X, et al. The development of objective meteorological element forecast in China. Adv Meteor Sci Tech, 2012, 2(5): 12-21. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKZ201205005.htm
[17]	纪立人, 陈嘉滨, 张道民, 等. 数值预报模式动力框架发展的若干问题综述. 大气科学, 2005, 29(1): 120-130. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK200501013.htm Ji L R, Chen J B, Zhang D M, et al. Review of some numerical aspects of the dynamic framework of NWP model. Chinese J Atmos Sci, 2005, 29(1): 120-130. https://www.cnki.com.cn/Article/CJFDTOTAL-DQXK200501013.htm
[18]	黄嘉佑. 气象统计分析与预报方法. 北京: 气象出版社, 1990. Huang J Y. Meteorological Statistical Analysis and Forecasting Methods. Beijing: China Meteorological Press, 1990.
[19]	施能. 气象统计预报. 北京: 气象出版社, 2009. Shi N. Meteorological Statistical Forecasting. Beijing: China Meteorological Press, 2009.
[20]	史久恩, 瞿栋根, 孙山泽, 等. 统计学长期天气预报方法的若干研究(一)——逐步回归技术的应用. 气象学报, 1964, 34(4): 507-518. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB196404010.htm Shi J E, Qu D G, Sun S Z, et al. Studies on long-range forecasting by statistical methods: Part Ⅰ—Application of stepwise multiple regression technique. Acta Meteor Sinica, 1964, 34(4): 507-518. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB196404010.htm
[21]	张玉涛, 佟华, 孙健. 一种偏差订正方法在平昌冬奥会气象预报的应用. 应用气象学报, 2020, 31(1): 27-41. doi: 10.11898/1001-7313.20200103 Zhang Y T, Tong H, Sun J. Application of a bias correction method to meteorological forecast for the Pyeongchang Winter Olympic Games. J Appl Meteor Sci, 2020, 31(1): 27-41. doi: 10.11898/1001-7313.20200103
[22]	Klein W H, Lewis B M, Enger I. Objective prediction of five-day mean temperatures during winter. J Meteor, 1959, 16(6): 672-682.
[23]	Glahn H R, Lowry D A. The use of model output statistics (MOS) in objective weather forecasting. J Appl Meteor, 1972, 11(8): 1203-1211.
[24]	丁士晟. 中国MOS预报的进展. 气象学报, 1985, 43(3): 332-338. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB198503008.htm Ding S S. The advance of model output statistics method in China. Acta Meteor Sinica, 1985, 43(3): 332-338. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB198503008.htm
[25]	陈豫英, 陈晓光, 马金仁, 等. 风的精细化MOS预报方法研究. 气象科学, 2006, 26(2): 210-216. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKX200602013.htm Chen Y Y, Chen X G, Ma J R, et al. A study on subtle MOS forecasting method of wind. Sci Meteor Sinica, 2006, 26(2): 210-216. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKX200602013.htm
[26]	罗菊英, 周建山, 闫永财. 基于数值预报及上级指导产品的本地气温MOS预报方法. 气象科技, 2014, 42(3): 443-450. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKJ201403016.htm Luo J Y, Zhou J S, Yan Y C. Local temperature MOS forecast method based on numerical forecast products and superior guidance. Meteor Sci Technol, 2014, 42(3): 443-450. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKJ201403016.htm
[27]	佟华, 郭品文, 朱跃建, 等. 基于大尺度模式产品的误差订正与统计降尺度气象要素预报技术. 气象, 2014, 40(1): 66-75. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201401008.htm Tong H, Guo P W, Zhu Y J, et al. Bias correction and statistical downscaling meteorological parameters forecast technique based on large-scale numerical model products. Meteor Mon, 2014, 40(1): 66-75. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201401008.htm
[28]	孙健, 曹卓, 李恒, 等. 人工智能技术在数值天气预报中的应用. 应用气象学报, 2021, 32(1): 1-11. doi: 10.11898/1001-7313.20210101 Sun J, Cao Z, Li H, et al. Application of artificial intelligence technology to numerical weather prediction. J Appl Meteor Sci, 2021, 32(1): 1-11. doi: 10.11898/1001-7313.20210101
[29]	许小峰. 从物理模型到智能分析——降低天气预报不确定性的新探索. 气象, 2018, 44(3): 341-350. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201803001.htm Xu X F. From physical model to intelligent analysis: A new exploration to reduce the uncertainty of weather forecast. Meteor Mon, 2018, 44(3): 341-350. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201803001.htm
[30]	马雷鸣. 天气预报中的人工智能技术进展. 地球科学进展, 2020, 35(6): 551-560. https://www.cnki.com.cn/Article/CJFDTOTAL-DXJZ202006001.htm Ma L M. Development of artificial intelligence technology in weather forecast. Adv Earth Science, 2020, 35(6): 551-560. https://www.cnki.com.cn/Article/CJFDTOTAL-DXJZ202006001.htm
[31]	Han L, Chen M X, Chen K K, et al. A deep learning method for bias correction of ECMWF 24-240 h forecasts. Adv Atmos Sci, 2021, 38(9): 1444-1459.
[32]	张延彪, 陈明轩, 韩雷, 等. 数值天气预报多要素深度学习融合订正方法. 气象学报, 2022, 80(1): 153-167. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202201011.htm Zhang Y B, Chen M X, Han L, et al. Multi-element deep learning fusion correction method for numerical weather prediction. Acta Meteor Sinica, 2022, 80(1): 153-167. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXB202201011.htm
[33]	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: 10.11898/1001-7313.20220409 Han 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
[34]	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. doi: 10.11898/1001-7313.20220301 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. doi: 10.11898/1001-7313.20220301
[35]	尹晓燕, 胡志群, 郑佳锋, 等. 利用深度学习填补双偏振雷达回波遮挡. 应用气象学报, 2022, 33(5): 581-593. doi: 10.11898/1001-7313.20220506 Yin X Y, Hu Z Q, Zheng J F, et al. Filling in the dual polarization radar echo occlusion based on deep learning. J Appl Meteor Sci, 2022, 33(5): 581-593. doi: 10.11898/1001-7313.20220506
[36]	金子琪, 王新敏, 鲍艳松, 等. 基于卷积神经网络的飑线识别算法. 应用气象学报, 2021, 32(5): 580-591. doi: 10.11898/1001-7313.20210506 Jin Z Q, Wang X M, Bao Y S, et al. Squall line identification method based on convolution neural network. J Appl Meteor Sci, 2021, 32(5): 580-591. doi: 10.11898/1001-7313.20210506
[37]	陈永义, 俞小鼎, 高学浩, 等. 处理非线性分类和回归问题的一种新方法(Ⅰ)——支持向量机方法简介. 应用气象学报, 2004, 15(3): 345-354. http://qikan.camscma.cn/article/id/20040344 Chen Y Y, Yu X D, Gao X H, et al. A new method for non-linear classify and non-linear regression Ⅰ: Introduction to support vector machine. J Appl Meteor Sci, 2004, 15(3): 345-354. http://qikan.camscma.cn/article/id/20040344
[38]	冯汉中, 陈永义. 处理非线性分类和回归问题的一种新方法(Ⅱ)——支持向量机方法在天气预报中的应用. 应用气象学报, 2004, 15(3): 355-365. http://qikan.camscma.cn/article/id/20040345 Feng H Z, Chen Y Y. A new method for non-linear classify and non-linear regression Ⅱ: Application of support vector machine to weather forecast. J Appl Meteor Sci, 2004, 15(3): 355-365. http://qikan.camscma.cn/article/id/20040345
[39]	张延彪, 宋林烨, 陈明轩, 等. 基于卷积神经网络的京津冀地区高分辨率格点预报偏差订正试验. 大气科学学报, 2022, 45(6): 850-862. https://www.cnki.com.cn/Article/CJFDTOTAL-NJQX202206005.htm Zhang Y B, Song L Y, Chen M X, et al. A study of error correction for high-resolution gridded forecast based on a convolutional neural network in the Beijing-Tianjing-Hebei Region. Trans Atmos Sci, 2022, 45(6): 850-862. https://www.cnki.com.cn/Article/CJFDTOTAL-NJQX202206005.htm
[40]	陈昱文, 黄小猛, 李熠, 等. 基于ECMWF产品的站点气温预报集成学习误差订正. 应用气象学报, 2020, 31(4): 494-503. doi: 10.11898/1001-7313.20200411 Chen Y W, Huang X M, Li Y, et al. Ensemble learning for bias correction of station temperature forecast based on ECMWF products. J Appl Meteor Sci, 2020, 31(4): 494-503. doi: 10.11898/1001-7313.20200411
[41]	金荣花, 代刊, 赵瑞霞, 等. 我国无缝隙精细化网格天气预报技术进展与挑战. 气象, 2019, 45(4): 445-457. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201904001.htm Jin R H, Dai K, Zhao R X, et al. Progress and challenge of seamless fine gridded weather forecasting technology in China. Meteor Mon, 2019, 45(4): 445-457. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201904001.htm
[42]	Richardson D, Buizza R, Hagedom R. First Workshop on the THORPEX Interactive Grand Global Ensemble(TIGGE). WMO/TD-No. 1273, WWRP/THORPEX-No. 05, 2005.
[43]	李嘉晨. 基于TIGGE资料的洪安涧河流域径流预报研究. 郑州: 华北水利水电大学, 2019. Li J C. Study on Runoff Forecasting of Honganjian River Basin Based on TIGGE Data. Zhengzhou: North China University of Water and Electric Power, 2019.
[44]	Ronneberger O, Fischer P, Brox T. U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention(MICCAI), 2015, 9351: 234-241.

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基于深度学习的7~15 d温度格点预报偏差订正

DOI: 10.11898/1001-7313.20230404

通信作者:
王启光, 邮箱：photon316@163.com

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Application of Deep Learning Bias Correction Method to Temperature Grid Forecast of 7-15 Days

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基于深度学习的7~15 d温度格点预报偏差订正

DOI: 10.11898/1001-7313.20230404

通信作者: 王启光, 邮箱：photon316@163.com

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Application of Deep Learning Bias Correction Method to Temperature Grid Forecast of 7-15 Days

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通信作者:
王启光, 邮箱：photon316@163.com