地球科学 |
|
|
|
|
基于神经网络集合预报的台风路径预报优化 |
周笑天1,2, 张丰1,2, 杜震洪1,2, 刘仁义1,2 |
1.浙江大学 浙江省资源与环境信息系统重点实验室,浙江 杭州 310028 2.浙江大学 地理信息科学研究所, 浙江 杭州 310027 |
|
Optimization of typhoon track prediction based on neural network ensemble prediction |
ZHOU Xiaotian1,2, ZHANG Feng1,2, DU Zhenhong1,2, LIU Renyi1,2 |
1.Zhejiang Provincial Key Lab of GIS, Zhejiang University, Hangzhou 310028, China 2.Department of Geographic Information Science, Zhejiang University, Hangzhou 310027, China |
引用本文:
周笑天, 张丰, 杜震洪, 刘仁义. 基于神经网络集合预报的台风路径预报优化[J]. 浙江大学学报(理学版), 2020, 47(2): 196-202.
ZHOU Xiaotian, ZHANG Feng, DU Zhenhong, LIU Renyi. Optimization of typhoon track prediction based on neural network ensemble prediction. Journal of Zhejiang University (Science Edition), 2020, 47(2): 196-202.
链接本文:
https://www.zjujournals.com/sci/CN/10.3785/j.issn.1008-9497.2020.02.010
或
https://www.zjujournals.com/sci/CN/Y2020/V47/I2/196
|
1 XUB L, LIUC D, WUC D, et al. Typhoon detection of radar improved using ultra low elevation angle[J]. Plateau Meteorology, 2012, 31(1): 251-257. 2 俞兆文,刘健文,钟中,等. 云雨条件下AMSR2微波成像资料同化试验及其在台风预报中的应用[J]. 气象科学,2018, 38(2): 203-211. YUZ W, LIUJ W, ZHONGZ, et al. Assimilation experiment of AMSR2 microwave imaging data under cloudy and rainy condition and its application on the forecast of a typhoon process[J]. Journal of the Meteorological Sciences, 2018, 38(2): 203-211. 3 袁杰颖,陈永平,潘毅,等. 台风路径集合化预报方法的优化[J]. 海洋预报,2017, 34(2): 37-42.DOI:10.1201/9781315116242-26 YUANJ Y, CHENY P, PANY, et al. Improvement of ensemble forecast of typhoon track in the Northwestern Pacific[J]. Marine Forecasts, 2017,34(2):37-42. DOI:10.1201/9781315116242-26 4 WANGC X. Ensemble prediction experiments of typhoon track based on the stochastic total tendency perturbation[J]. Journal of Tropical Meteorology, 2016, 22(3): 305-317. 5 HUOZ H, DUANW S. The application of the orthogonal conditional nonlinear optimal perturbations method to typhoon track ensemble forecasts[J]. Science China Earth Sciences, 2019, 62(2): 376-388.DOI:10.1007/s11430-018-9248-9 6 陈子通,戴光丰,罗秋红,等. 模式动力过程与物理过程耦合及其对台风预报的影响研究[J]. 热带气象学报,2016, 32(1): 1-8. DOI:10.16032/j.issn.1004-4965.2016.01.001 CHENZ T, DAIG F, LUOQ H, et al. Study on the coupling of model dynamics and physical processes and its influence on the forecast of typhoons[J]. Journal of Tropical Meteorology, 2016, 32(1): 1-8.DOI:10.16032/j.issn.1004-4965.2016.01.001 7 黄文锋,周焕林,孙建鹏. 应用台风风场经验模型的台风极值风速预测[J]. 哈尔滨工业大学学报,2016, 48(2): 142-146. HUANGW F, ZHOUH L, SUNJ P, et al. Prediction typhoon design wind speed with empirical typhoon wind field model[J]. Journal of Harbin Institute of Technology, 2016, 48(2): 142-146. 8 CHUNY , MINJ Z, LIUZ Q, et al. The impact of AMSR2 radiance data assimilation on the analysis and forecast of typhoon Son-Tinh[J]. Chinese Journal of Atmospheric Sciences, 2017,41(2):372-384. 9 王向阳,颜琼丹,鄢志波. 热带气旋路径集成预报在业务中使用[C]//中国气象学会年会. 广州:中国气象学会, 2007:127-130. WANGX Y, YANQ D, YANZ B. Application of integrated tropical cyclone track forecasting in operations [C]//Annual Conference China Meteorological Society. Guangzhou:China Meteorological Society ,2007:127-130. 10 黄小燕,金龙. 基于主成分分析的人工智能台风路径预报模型[J]. 大气科学,2013, 37(5): 1154-1164. HUANGX Y, JINL, et al. An artificial intelligence prediction model based on principal component analysis for typhoon tracks[J]. Chinese Journal of Atmospheric Sciences, 2013, 37(5): 1154-1164. 11 邵利民,傅刚,曹祥村,等. BP神经网络在台风路径预报中的应用[J]. 自然灾害学报,2009, 18(6): 104-111. DOI:10.13577/j.jnd.2009.0618 SHAOL M, FUG, CAOX C, et al. Application of BP neural network in typhoon track forecasting[J]. Journal of Natural Disasters, 2009, 18(6): 104-111. DOI:10.13577/j.jnd.2009.0618 12 谭燕, 梁旭东, 黄伟. 集合预报技术在台风路径预报中的应用[C]//海峡两岸气象科学技术研讨会. 北京:中国气象学会,2008: 37-45. TANY, LIANGQ D, HUANGW. Application of ensemble forecasting technology in typhoon track forecasting[C]//Seminar on Meteorological Science and Technology across the Taiwan Strait. Beijing:Chinese Meteorological Society 2008: 37-45. 13 EPSTEINE S. Stochastic dynamic prediction[J]. Tellus, 1969, 21(6): 739-759 .DOI:10.3402/tellusa.v21i6.10143 14 LEITHC E. Theoretical skill of Monte Carlo forecasts[J]. Monthly Weather Review, 1974, 102(6): 409-418. DOI:10.1175/1520-0493(1974)102<0409:tsomcf>2.0.co;2 15 MOLTENIF, BUIZZAR, PALMERT N, et al. The ECMWF ensemble prediction system: Methodology and validation[J]. Quarterly Journal of the Royal Meteorological Society, 2010, 122(529): 73-119. DOI:10.1002/qj.49712252905 16 TOTHZ, KALNAYE. Ensemble forecasting at NCEP and the breeding method[J]. Monthly Weather Review, 1997, 125(12): 3297-3319.DOI:10.1175/1520-0493(1997)125〈3297:efanat〉2.0.co;2 17 TOTHZ. Meeting summary: Ensemble forecasting in WRF[J]. Bulletin of the American Meteorological Society, 2010, 82(4): 695-698.DOI:10.1175/1520-0477(2001)082〈0695:msefiw〉2.3.co;2 18 KRISHNAMURTIT N, KISHTAWALC M, LAROWT E, et al. Improved weather and seasonal climate forecasts from multi-model superensemble[J]. Science, 1999, 285(5433): 1548-1550. 19 ATGERF. Spatial and interannual variability of the reliability of ensemble-based probabilistic forecasts: consequences for calibration[J]. Monthly Weather Review, 2003, 131(8): 1509-1523.DOI:10.1175//1520-0493(2003)131<1509:saivot>2.0.co;2 20 杨学胜. 业务集合预报系统的现状及展望[J]. 气象, 2001, 27(6): 3-9. YANGX S. Current situation and prospect of operational ensemble forecasting system[J]. Meteorological Monthly, 2001, 27(6): 3-9. 21 HANS , LIUX Y, MAOH Z, et al. EIE: Efficient inference engine on compressed deep neural network[J]. ACM SIGARCH Computer Architecture News, 2016, 44(3): 243-254. DOI:10.1109/isca.2016.30 22 SUNW , XUY. Using a back propagation neural network based on improved particle swarm optimization to study the influential factors of carbon dioxide emissions in Hebei province, China[J]. Journal of Cleaner Production, 2016, 112(part 2): 1282-1291. DOI:10.1016/j.jclepro.2015.04.097 23 张少华. 基于深度卷积神经网络的人脸基准点定位研究[D]. 武汉:华中科技大学,2016. ZHANGS H. Research on Face Baseline Location Based on Deep Convolution Neural Network[D]. Wuhan: Huazhong University of Science and Technology, 2016. 24 IOFFES, SZEGEDYC. Batch normalization: Accelerating deep network training by reducing internal covariate shift[C]// Proceedings of the 32nd International Conference on Machine Learning. Lille: ACM, 2015, 37:448-456. 25 KIMY , JERNITEY, SONGTAGD , et al. Character-aware neural language models[C]//30th AAAI Conference on Artificial Intelligence. Phoenix: AAAI Press, 2016. 26 RAYNAUDL, BOUTTIERF. Comparison of initial perturbation methods for ensemble prediction at convective scale[J]. Quarterly Journal of the Royal Meteorological Society, 2016, 142(695): 854-866.DOI:10.1002/qj.2686 27 王皘, 钱传海, 张玲. 2017年西北太平洋和南海台风活动概述[J]. 海洋气象学报, 2018, 38(2): 1-11.DOI:10.19513/j.cnki.issn2096-3599.2018.02.001 WANGQ, QIANC H, ZHANGL, et al. Summary of typhoon activities in the northwest pacific and south china sea in 2017[J]. Journal of Marine Meteorology, 2018, 38(2): 1-11.DOI:10.19513/j.cnki.issn2096-3599.2018.02.001 |
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|