Citation: | Xiaolei MEN, Ruili JIAO, Ding WANG, Chenguang ZHAO, Yakun LIU, Jiangjiang XIA, Haochen LI, Zhongwei YAN, Jianhua SUN, Lizhi WANG. A Temperature Correction Method for Multi-model Ensemble Forecast in North China Based on Machine Learning[J]. Climatic and Environmental Research, 2019, 24(1): 116-124. doi: 10.3878/j.issn.1006-9585.2018.18049 |
[1] |
陈博宇, 代刊, 郭云谦. 2015. 2013年汛期ECMWF集合统计量产品的降水预报检验与分析[J].暴雨灾害, 34 (1):64-73. doi: 10.3969/j.issn.1004-9045.2015.01.009
Chen Boyu, Dai Kan, Guo Yunqian. 2015. Precipitation verification and analysis of ECMWF ensemble statistic products in 2013 flooding season[J]. Torrential Rain and Disasters (in Chinese), 34 (1):64-73, doi:10.3969/j.issn.1004-9045. 2015.01.009.
|
[2] |
范苏丹, 盛春岩, 肖明静, 等. 2015.多模式集合对山东省气象要素预报效果检验[J].气象与环境学报, 31 (6):68-77. doi: 10.3969/j.issn.1673-503X.2015.06.009
Fan Sudan, Sheng Chunyan, Xiao Mingjing, et al. 2015. Forecast effect verification of multi-model ensemble for meteorological elements in Shandong Province[J]. Journal of Meteorology and Environment (in Chinese), 31 (6):68-77, doi: 10.3969/j.issn.1673-503X.2015.06.009.
|
[3] |
冯慧敏, 智协飞, 崔慧慧, 等. 2016.基于多模式集成技术的地面气温精细化预报[J].气象与环境科学, 39 (4):73-79. doi: 10.16765/j.cnki.1673-7148.2016.04.012
Feng Huimin, Zhi Xiefei, Cui Huihui, et al. 2016. Refined forecasting of surface temperature based on multi-model ensemble technology[J]. Meteorological and Environmental Sciences (in Chinese), 39 (4):73-79, doi: 10.16765/j.cnki.1673-7148.2016.04.012.
|
[4] |
Hernández E, Sanchez-Anguix V, Julian V, et al. 2016. Rainfall prediction: A deep learning approach[C]//Proceedings of the 11th International Conference on Hybrid Artificial Intelligence Systems. Seville: Springer, 151-162, doi: 10.1007/978-3-319-32034-2_13.
|
[5] |
Hinton G E, Osindero S, Teh Y W. 2006. A fast learning algorithm for deep belief nets[J]. Neural Computation, 18 (7):1527-1554, doi: 10.1162/neco.2006.18.7.1527.
|
[6] |
黄威, 牛若芸. 2017.基于集合预报和支持向量机的中期强降雨集成预报试验[J].气象, 43 (9):1110-1116. doi: 10.7519/j.issn.1000-0526.2017.09.008
Huang Wei, Niu Ruoyun. 2017. The medium-term multi-model integration forecast experimentation for heavy rain based on support vector machine[J]. Meteorological Monthly (in Chinese), 43 (9):1110-1116, doi:10.7519/j.issn.1000-0526.2017. 09.008.
|
[7] |
焦李成, 杨淑媛, 刘芳, 等. 2016.神经网络七十年:回顾与展望[J].计算机学报, 39 (8):1697-1716. http://d.old.wanfangdata.com.cn/Periodical/jsjxb201608015
Jiao Licheng, Yang Shuyuan, Liu Fang, et al. 2016. Seventy years beyond neural networks:Retrospect and prospect[J]. Chinese Journal of Computers (in Chinese), 39 (8):1697-1716. http://d.old.wanfangdata.com.cn/Periodical/jsjxb201608015
|
[8] |
Krishnamurti T N, Kishtawal C M, LaRow T E, et al. 1999. Improved weather and seasonal climate forecasts from multimodel super ensemble[J]. Science, 285 (5433):1548-1550, doi: 10.1126/science.285.5433.1548.
|
[9] |
李刚, 谢清霞, 魏涛. 2016.集合预报在贵州最低气温中的应用[J].安徽农业科学, 44 (14):229-231. doi: 10.3969/j.issn.0517-6611.2016.14.078
Li Gang, Xie Qingxia, Wei Tao. 2016. Application of multi-model ensemble method for minimum temperature in Guizhou Province[J]. Journal of Anhui Agricultural Sciences (in Chinese), 44 (14):229-231, doi: 10.3969/j.issn.0517-6611.2016.14.078.
|
[10] |
李丽辉, 朱建生, 强丽霞, 等. 2017.基于随机森林回归算法的高速铁路短期客流预测研究[J].铁道运输与经济, 39(9):12-16. doi: 10.16668/j.cnki.issn.1003-1421.2017.09.03
Li Lihui, Zhu Jiansheng, Qiang Lixia, et al. 2017. Study on forecast of high-speed railway short-term passenger flow based on random forest regression[J]. Railway Transport and Economy (in Chinese), 39 (9):12-16, doi:10. 16668/j.cnki.issn.1003-1421.2017.09.03.
|
[11] |
马清. 2008.中尺度集合预报的偏差订正与多模式集成研究[D].南京信息工程大学硕士学位论文, 78pp. http://cdmd.cnki.com.cn/Article/CDMD-10300-2008092054.htm
Ma Qing. 2008. Study of the bias-correction and multi-model combine of mesoscale ensemble forecast[D]. M. S. thesis (in Chinese), Nanjing University of Information Science and Technology, 78pp. http://cdmd.cnki.com.cn/Article/CDMD-10300-2008092054.htm
|
[12] |
牛金龙, 张东方, 姚鹏, 等. 2016.多模式资料在成都地区的温度预报研究应用[J].高原山地气象研究, 36 (3):66-70, 75. http://d.old.wanfangdata.com.cn/Periodical/scqx201603011
Niu Jinlong, Zhang Dongfang, Yao Peng, et al. 2016. Application study of multi-mode data in the forecast of temperaturein Chengdu[J]. Plateau and Mountain Meteorology Research (in Chinese), 36 (3):66-70, 75. http://d.old.wanfangdata.com.cn/Periodical/scqx201603011
|
[13] |
潘留杰, 张宏芳, 朱伟军, 等. 2013. ECMWF模式对东北半球气象要素场预报能力的检验[J].气候与环境研究, 18 (1):111-123. doi: 10.3878/j.issn.1006-9585.2012.11097
Pan Liujie, Zhang Hongfang, Zhu Weijun, et al. 2013. Forecast performance verification of the ECMWF model over the Northeast Hemisphere[J]. Climatic andEnvironmental Research (in Chinese), 18 (1):111-123, doi: 10.3878/j.issn.1006-9585.2012.11097.
|
[14] |
Wang H Z, Li G Q, Wang G B, et al. 2017. Deep learning based ensemble approach for probabilistic wind power forecasting[J]. Applied Energy, 188:56-70, doi: 10.1016/j.apenergy.2016.11.111.
|
[15] |
王奕森, 夏树涛. 2018.集成学习之随机森林算法综述[J].信息通信技术, 12 (1):49-55. doi: 10.3969/j.issn.1674-1285.2018.01.009
Wang Yisen, Xia Shutao. 2018. A survey of random forests algorithms[J]. Information and Communications Technologies (in Chinese), 12 (1):49-55, doi: 10.3969/j.issn.1674-1285.2018.01.009.
|
[16] |
邢彩盈, 张京红, 黄海静. 2016.基于BP神经网络的海口住宅室内气温预测[J].贵州气象, 40 (5):38-42. doi: 10.3969/j.issn.1003-6598.2016.05.007
Xing Caiying, Zhang Jinghong, Huang Haijing. 2016. Forecast of residential indoor temperature based on BP neural network in Haikou[J]. Journal of Guizhou Meteorology (in Chinese), 40 (5):38-42, doi: 10.3969/j.issn.1003-6598.2016.05.007.
|
[17] |
熊国经, 董玉竹, 宗瑾. 2017.基于岭回归法对"三废"排放影响因素的研究——以江西省为例[J].生态经济, 33 (2):103-107. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=671176711
Xiong Guojing, Dong Yuzhu, Zong Jin. 2017. Research on the discharge of "Three Wastes" factors based on ridge regression:Taking Jiangxi Province as an example[J]. Ecological Economy (in Chinese), 33 (2):103-107. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=671176711
|
[18] |
叶笃正, 严中伟, 戴新刚, 等. 2006.未来的天气气候预测体系[J].气象, 32 (4):3-8. doi: 10.3969/j.issn.1000-0526.2006.04.001
Ye Duzheng, Yan Zhongwei, Dai Xingang, et al. 2006. A discussion of future system of weather and climate prediction[J]. Meteorological Monthly (in Chinese), 32 (4):3-8, doi:10.3969/j.issn. 1000-0526.2006.04.001.
|
[19] |
张恒德, 张庭玉, 李涛, 等. 2018.基于BP神经网络的污染物浓度多模式集成预报[J].中国环境科学, 38 (4):1243-1256. doi: 10.19674/j.cnki.issn1000-6923.2018.0147
Zhang Hengde, Zhang Tingyu, Li Tao, et al. 2018. Forecast of air quality pollutants' concentrations based on BP neural network multi-model ensemble method[J]. China Environmental Science (in Chinese), 38 (4):1243-1256, doi: 10.19674/j.cnki.issn1000-6923.2018.0147.
|
[20] |
张禄, 杨志军. 2016.基于神经网络和主分量的日极值气温预测方法[C]//第33届中国气象学会年会S20气象信息化——业务实践与技术应用.西安: 中国气象学会, 5pp.
Zhang Lu, Yang Zhijun. 2016. BP neural network prediction model of the extreme temperature based on principal component analysis[C]//33rd Annual Meeting of China Meteorological Society. Xi'an: Chinese Meteorological Society, 5pp.
|
[21] |
张伟, 王自发, 安俊岭, 等. 2010.利用BP神经网络提高奥运会空气质量实时预报系统预报效果[J].气候与环境研究, 15 (5):595-601. doi: 10.3878/j.issn.1006-9585.2010.05.08
Zhang Wei, Wang Zifa, An Junling, et al. 2010. Update the ensemble air quality modeling system with BP model during Beijing Olympics[J]. Climatic and Environmental Research (in Chinese), 15 (5):595-601, doi: 10.3878/j.issn.1006-9585.2010.05.08.
|