[1] |
Bauer P, Thorpe A, Brunet G.The quiet revolution of numerical weather prediction.Nature, 2015, 525(7567):47. http://www.nature.com/articles/nature14956 |
[2] |
Lynch P.The origins of computer weather prediction and climate modeling.Journal of Computational Physics, 2008, 227(7):3431-3444. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=49dfa1cb0f101ab7f782336cee5a8e82 |
[3] |
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. doi: 10.1175-1520-0450(1972)011-1203-TUOMOS-2.0.CO%3b2/ |
[4] |
薛谌彬, 陈娴, 张瑛, 等.ECMWF高分辨率模式2 m温度预报误差订正方法研究.气象, 2019, 45(6):831-842. http://d.old.wanfangdata.com.cn/Periodical/qx201906009 |
[5] |
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 |
[6] |
吴启树, 韩美, 郭弘, 等.MOS温度预报中最优训练期方案.应用气象学报, 2016, 27(4):426-434. doi: 10.11898/1001-7313.20160405 |
[7] |
刘还珠, 赵声蓉, 陆志善, 等.国家气象中心气象要素的客观预报——MOS系统.应用气象学报, 2004, 15(2):181-191. http://qikan.camscma.cn/jamsweb/article/id/20040223 |
[8] |
Overpeck J T, Meehl G A, Bony S, et al.Climate data challenges in the 21st century.Science, 2011, 331(6018):700-702. http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_f8cc95c052940e71464695a84b01f312 |
[9] |
Zurada J M.Introduction to Artificial Neural Systems.St Paul:West Publishing Company, 1992. |
[10] |
Gers F A, Schmidhuber J, Cummins F.Learning to forget:Continual prediction with LSTM.Neural Computation, 12(10):2451-2471. http://d.old.wanfangdata.com.cn/Periodical/jsjyszgc201905028 |
[11] |
Krizhevsky A, Sutskever I, Hinton G E.Imagenet Classification with Deep Convolutional Neural Networks//Advances in Neural Information Processing Systems, 2012: 1097-1105. |
[12] |
Reichstein M, Camps-Valls G, Stevens B, et al.Deep learning and process understanding for data-driven earth system science.Nature, 2019, 566(7743):195. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=92806a1eb1e04a4ce70a528046845561 |
[13] |
Scher S.Toward data-driven weather and climate forecasting:approximating a simple general circulation model with deep learning.Geophys Res Lett, 2018, 45(22):12616-12622. doi: 10.1029/2018GL080704 |
[14] |
Rasp S, Pritchard M S, Gentine P.Deep learning to represent subgrid processes in climate models.Proceedings of the National Academy of Sciences, 2018, 115(39):9684-9689. |
[15] |
Ham Y G, Kim J H, Luo J J.Deep learning for multi-year ENSO forecasts.Nature, 2019, 573(7775):568-572. https://www.nature.com/articles/s41586-019-1559-7 |
[16] |
Pan B, Hsu K, AghaKouchak A, et al.Improving precipitation estimation using convolutional neural network.Water Resources Research, 2019, 55(3):2301-2321. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=10.1029/2018WR024090 |
[17] |
王彦磊, 曹炳伟, 黄兵, 等.基于神经网络的单站雾预报试验.应用气象学报, 2010, 21(1):110-114. http://qikan.camscma.cn/jamsweb/article/id/20100115 |
[18] |
闵晶晶, 孙景荣, 刘还珠, 等.一种改进的BP算法及在降水预报中的应用.应用气象学报, 2010, 21(1):55-62. http://qikan.camscma.cn/jamsweb/article/id/20100107 |
[19] |
杨璐, 韩丰, 陈明轩, 等.基于支持向量机的雷暴大风识别方法.应用气象学报, 2018, 29(6):680-689. doi: 10.11898/1001-7313.20180604 |
[20] |
Runge J, Bathiany S, Bollt E, et al.Inferring causation from time series in earth system sciences.Nature Communications, 2019, 10(1):2553. https://scripps.ucsd.edu/biblio/inferring-causation-time-series-earth-system-sciences |
[21] |
Liu Y, Racah E, Correa J, et al.Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets.arXiv Preprint arXiv: 1605.01156, 2016. |
[22] |
Polikar R.Ensemble Learning.Boston:Springer, 2012:1-34. |
[23] |
Bougeault P, Toth Z, Bishop C, et al.The THORPEX interactive grand global ensemble.Bull Amer Meteor Soc, 2010, 91(8):1059-1072. http://d.old.wanfangdata.com.cn/Periodical/skxjz201902004 |
[24] |
Swinbank R, Kyouda M, Buchanan P, et al.The TIGGE project and its achievements.Bull Amer Meteor Soc, 2016, 97(1):49-67. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=f888d39945f19b48e5de180062eaa989 |
[25] |
Myers D E.Spatial interpolation:An overview.Geoderma, 1994, 62(1-3):17-28. http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ023078433/ |
[26] |
高歌, 龚乐冰, 赵珊珊, 等.日降水量空间插值方法研究.应用气象学报, 2007, 18(5):732-736. doi: 10.11898/1001-7313.20070511 |
[27] |
彭彬, 周艳莲, 高苹, 等.气温插值中不同空间插值方法的适用性分析——以江苏省为例.地球信息科学学报, 2011, 13(4):539-548. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=dqxxkx201104016 |
[28] |
潘留杰, 薛春芳, 王建鹏, 等.一个简单的格点温度预报订正方法.气象, 2017, 43(12):1584-1593. http://d.old.wanfangdata.com.cn/Periodical/qx201712015 |
[29] |
Karlik B, Olgac A V.Performance analysis of various activation functions in generalized MLP architectures of neural networks.International Journal of Artificial Intelligence and Expert Systems, 2011, 1(4):111-122. http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_eb418e6e139bf995bfbbacfcf644491c |
[30] |
Nair V, Hinton G E.Rectified Linear Units Improve Restricted Boltzmann Machines//Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010: 807-814. |
[31] |
Connor J T, Martin R D, Atlas L E.Recurrent neural networks and robust time series prediction.IEEE Transactions on Neural Networks, 1994, 5(2):240-254. http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0224794453/ |
[32] |
韩丰, 龙明盛, 李月安, 等.循环神经网络在雷达临近预报中的应用.应用气象学报, 2019, 30(1):61-69. doi: 10.11898/1001-7313.20190106 |
[33] |
Qing X, Niu Y.Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM.Energy, 2018, 148:461-468. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=d2d346ea1562178ac2662687a590107e |
[34] |
Cao Y, Gui L.Multi-step Wind Power Forecasting Model Using LSTM Networks, Similar Time Series and LightGBM//5th International Conference on Systems and Informatics (ICSAI).IEEE, 2018:192-197. |
[35] |
Shi X, Chen Z, Wang H, et al.Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting//Advances in Neural Information Processing Systems, 2015: 802-810. |
[36] |
Woźniak M, Graña M, Corchado E.A survey of multiple classifier systems as hybrid systems.Information Fusion, 2014, 16:3-17. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=41ed6472bc1ca0e55d4924fd1f21723d |
[37] |
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. doi: 10.1175-1520-0450(1972)011-1203-TUOMOS-2.0.CO%3b2/ |
[38] |
曾晓青, 薛峰, 姚莉, 等.针对模式风场的格点预报订正方案对比.应用气象学报, 2019, 30(1):49-60. doi: 10.11898/1001-7313.20190105 |
[39] |
Marzban C, Sandgathe S, Kalnay E.MOS, perfect prog, and reanalysis.Mon Wea Rev, 2006, 134(2):657-663. http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ028994089/ |
[40] |
Hart K A, Steenburgh W J, Onton D J, et al.An evaluation of mesoscale-model-based model output statistics (MOS) during the 2002 Olympic and Paralympic Winter Games.Wea Forecasting, 2004, 19(2):200-218. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=d5df351bc00d2b7ed904e1dc27abd509 |
[41] |
王秀娟, 陈长胜, 冯旭, 等.一阶卡尔曼滤波方法对EC集合预报气温的订正.气象灾害防御, 2019, 26(1):34-38. http://d.old.wanfangdata.com.cn/Periodical/jlqx201901008 |
[42] |
肖玉华, 赵静, 蒋丽娟.数值模式预报性能的地域性特点初步分析.暴雨灾害, 2010, 29(4):322-327. http://d.old.wanfangdata.com.cn/Periodical/hbqx201004004 |
[43] |
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 |