An Objective Prediction Model for Tropical Cyclone Genesis in the Northwest Pacific

Zheng Qian; Gao Meng

doi:10.11898/1001-7313.20220507

Vol.33, NO.5, 2022

Turn off MathJax

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2022 > 33(5): 594-603

Zheng Qian, Gao Meng. An objective prediction model for tropical cyclone genesis in the Northwest Pacific. J Appl Meteor Sci, 2022, 33(5): 594-603. DOI: 10.11898/1001-7313.20220507.

Citation:

Zheng Qian, Gao Meng. An objective prediction model for tropical cyclone genesis in the Northwest Pacific. J Appl Meteor Sci, 2022, 33(5): 594-603. DOI: 10.11898/1001-7313.20220507.

Zheng Qian, Gao Meng. An objective prediction model for tropical cyclone genesis in the Northwest Pacific. J Appl Meteor Sci, 2022, 33(5): 594-603. DOI: 10.11898/1001-7313.20220507.

Citation:

Zheng Qian, Gao Meng. An objective prediction model for tropical cyclone genesis in the Northwest Pacific. J Appl Meteor Sci, 2022, 33(5): 594-603. DOI: 10.11898/1001-7313.20220507.

PDF( 3956 KB)

An Objective Prediction Model for Tropical Cyclone Genesis in the Northwest Pacific

DOI: 10.11898/1001-7313.20220507

Zheng Qian^{1, 2},
Gao Meng^{3
,
,}

1.
Key Laboratory of Coastal Environmental Processes and Ecological Restoration, Yantai Institute of Coastal Zone, Chinese Academy of Sciences, Yantai 264003
2.
School of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049
3.
School of Mathematics and Information Science, Yantai University, Yantai 264003

Received Date: 2022-07-15
Rev Recd Date: 2022-08-19
Publish Date: 2022-09-15

Abstract

Abstract

At present, the maximum predictable time of tropical cyclone using numerical model is limited to 2 weeks. Statistical forecasting methods have substantial advantages in mining the potential value of massive meteorological and oceanographic observations, surpassing the limit of numerical forecast, and providing a new way to solve the bottlenecks of tropical cyclone forecasts. A novel statistical prediction scheme is proposed for tropical cyclone annual frequency and genesis location in the Northwest Pacific. The effect of large-scale meteorological factors including sea surface temperature, the geopotential height, the humidity, the vorticity, the wind shear, the Nio3.4 index, the QBO index and the SO index on the annual frequency of tropical cyclone in Northwest Pacific are considered. Correlations between the annual frequency of tropical cyclone and the large-scale environmental variables are analyzed and 14 highly correlated predictors are selected to predict tropical cyclone frequency. The least absolute shrinkage and selection operator method is used to select 8 factors from 14 initial predictors. Then, a prediction model based on random forest is established using training samples (1979-2015) for calibration and testing samples (2016-2020) for validation. In addition, the impact of environmental conditions including the vorticity, the wind shear, the humidity, the potential intensity, the sea surface temperature anomaly and the Nio3.4 index on the formation location of tropical cyclone is also investigated. The stepwise regression algorithm is used to choose a set of independent predictive variables by an automatic procedure. The local Poisson regression is performed on training datasets using count data inside data circles whose size is determined by the method of likelihood cross validation maximation. The seasonality of tropical cyclone genesis location is added to Poisson model. Results show that the random forest model presents a major variation and trend of tropical cyclone annual frequency though there are some deviations from the fitted data. The rank importance of influence indicates the primary effect of sea surface temperature and secondary effect of atmospheric variables on tropical cyclone frequency, which further reveals the applicability of the random forest model. The local Poisson regression model predicts where the tropical cyclone is most likely to occur. This model performs well when tropical cyclone occurs in the region of the Philippine and has some deviation in some months when tropical cyclone occurs in the region of the South China Sea. This model has good performance in predicting tropical cyclone genesis location but is weak in predicting abnormal situations. Finally, these two models are used to simulate tropical cyclone genesis activity in 1979-2020. The distribution of simulated tropical cyclone genesis points is consistent with the observations. This new prediction scheme can provide support for tropical cyclone risk analysis.
- random forest;
- local Poisson regression;
- tropical cyclone;
- frequency;
- genesis location

FullText(HTML)

References(44)

Figures and Tables

图 1 西北太平洋热带气旋年生成频次与海表温度相关分析

(填色，仅显示达到0.05显著性水平的相关系数)(方框表示预测因子的位置)

Fig. 1 Correlations between the annual frequency of tropical cyclone in the Northwestern Pacific and sea surface temperature

(the shaded, only correlations passing the test of 0.05 level are given) (the box denotes the predictor location)

DownLoad: Full-Size Img PowerPoint

图 2 西北太平洋热带气旋年生成频次与夏秋季大尺度环境因子相关分析

(填色，仅显示达到0.05显著性水平的相关系数)(方框表示预测因子的位置)

Fig. 2 Correlations between the annual frequency of tropical cyclone in the Northwestern Pacific and the large-scale environmental factors from Jun to Nov

(the shaded, only correlations passing the test of 0.05 level are given)(the box denotes the predictor location)

DownLoad: Full-Size Img PowerPoint

Fig. 3 The predicted tropical cyclone annual frequency in the Northwest Pacific

DownLoad: Full-Size Img PowerPoint

Fig. 4 Observed and predicted probability of tropical cyclone genesis location

DownLoad: Full-Size Img PowerPoint

Fig. 5 Observed and simulated tropical cyclone genesis position location

DownLoad: Full-Size Img PowerPoint

Table 1 Selected predictors

预测因子	相关系数	描述
X₁	-0.53	12°~22°N, 35°~60°W区域平均春季海温异常
X₂	-0.50	3°~12°S, 45°~52°E区域平均春季海温异常
X₃	-0.48	20°~30°S, 155°~175°E区域平均夏秋季海温异常
X₄	0.45	30°~40°N，130°~140°W区域平均夏秋季海表温度异常
X₅	-0.57	10°~23°N，130°~150°E区域平均夏秋季500 hPa高度场异常
X₆	-0.56	24°~32°N，180°~210°W区域平均夏秋季500 hPa高度场异常
X₇	-0.61	20°~30°N，127°~140°E区域平均夏秋季600 hPa相对湿度异常
X₈	0.57	16°~26°N，155°~165°W区域平均夏秋季600 hPa相对湿度异常
X₉	0.63	16°~22°N，128°~152°E区域平均春季850 hPa相对涡度异常
X₁₀	-0.62	4°~12°N，130°~140°E区域平均夏秋季850 hPa与250 hPa纬向风垂直切变
X₁₁	0.45	10°~17°N，157°E~177°W区域平均夏秋季850 hPa与250 hPa纬向风垂直切变
X₁₂	-0.09	春季Nio3.4海温
X₁₃	0.09	夏秋季平流层准两年振荡指数
X₁₄	0.06	前年夏秋季南方涛动指数

DownLoad: Download CSV

Table 2 Importance ranking of predictors

因子	重要性
X₂	0.247933
X₁₀	0.174951
X₇	0.170453
X₅	0.120027
X₉	0.113977
X₁₃	0.067993
X₁₁	0.061185
X₄	0.043481

DownLoad: Download CSV

Table 3 Optimized scale

月份	最优半径/km
1	1200
2	1500
3	1400
4	1300
5	1200
6	1100
7	900
8	800
9	800
10	900
11	1100
12	1200

DownLoad: Download CSV

References

[1]	Li X, Zhang L. Formation mechanism and microphysics characteristics of heavy rainfall caused by northward-moving typhoons. J Appl Meteor Sci, 2022, 33(1): 29-42. doi: 10.11898/1001-7313.20220103
[2]	Jing X, He W B, Bi X, et al. Analysis of the abrupt rainstorm in north Shaanxi in relation to typhoon far away. J Appl Meteor Sci, 2005, 16(5): 655-662. doi: 10.3969/j.issn.1001-7313.2005.05.012
[3]	Yang S N, Duan Y H. Extremity analysis on the precipitation and environmental field of Typhoon Rumbia in 2018. J Appl Meteor Sci, 2020, 31(3): 290-302. doi: 10.11898/1001-7313.20200304
[4]	He L F, Chen S, Guo Y Q. Observation characteristics and synoptic mechanisms of Typhoon Lekima extreme rainfall in 2019. J Appl Meteor Sci, 2020, 31(5): 513-526. doi: 10.11898/1001-7313.20200501
[5]	Noy I. The socio-economics of cyclones. Nature Clim Change, 2016, 6: 343-345. doi: 10.1038/nclimate2975
[6]	Zhang Q, Wu L, Liu Q. Tropical cyclone damages in China 1983-2006. Bull Amer Meteor Soc, 2009, 90(4): 489-496. doi: 10.1175/2008BAMS2631.1
[7]	Zhang Y H, Fan G Z, Ma Q Y, et al. The evaluation model of typhoon disaster influence on Zhejiang Province. J Appl Meteor Sci, 2009, 20(6): 772-776. doi: 10.3969/j.issn.1001-7313.2009.06.017
[8]	Jiao M Y, Gong J D, Zhou B, et al. An overview of the development of weather forecasting. J Appl Meteor Sci, 2006, 17(5): 594-601. doi: 10.3969/j.issn.1001-7313.2006.05.009
[9]	Chen L S. The evolution on research and operational forecasting techniques of tropical cyclones. J Appl Meteor Sci, 2006, 17(6): 672-681. doi: 10.3969/j.issn.1001-7313.2006.06.005
[10]	Wang S W. Advances on typhoon numerical model of NMC and applied experiments. J Appl Meteor Sci, 1999, 10(3): 347-353. doi: 10.3969/j.issn.1001-7313.1999.03.013
[11]	Chou J F. Predictability of weather and climate. Adv Meteor Sci Tech, 2011, 1(2): 11-14. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKZ201102007.htm
[12]	Li M, Zhang S Q, Wu L X, et al. An examination of the predictability of tropical cyclone genesis in high-resolution coupled models with dynamically downscaled coupled data assimilation initialization. Adv Atmos Sci, 2020, 37(9): 939-950. doi: 10.1007/s00376-020-9220-9
[13]	Tao Z Y, Zhao C G, Chen M. The necessity of statistical forecasts. Adv Meteor Sci Tech, 2016, 6(1): 6-13. https://www.cnki.com.cn/Article/CJFDTOTAL-QXKZ201601006.htm
[14]	Sun J, Cao Z, Li Heng, 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
[15]	Chen R, Zhang W M, Wang X. Machine learning in tropical cyclone forecast modeling: A review. Atmosphere, 2020, 11(7): 676-704. doi: 10.3390/atmos11070676
[16]	Wang Z, Zhao J, Huang H, et al. A review on the application of machine learning methods in tropical cyclone forecasting. Front Earth Sci, 2022. DOI: 10:10.3389/feart.2022.902596.
[17]	Vickery P J, Skerlj P F, Steckley A C, et al. Hurricane wind field model for use in hurricane simulations. J Struct Eng, 2000, 126(10): 1203-1221. doi: 10.1061/(ASCE)0733-9445(2000)126:10(1203)
[18]	Rumpf J, Weindl H, Höppe P, et al. Stochastic modeling of tropical cyclone tracks. Math Method Oper Res, 2007, 66: 475-490. doi: 10.1007/s00186-007-0168-7
[19]	Rumpf J, Weindl H, Höppe P, et al. Tropical cyclone hazard assessment using model-based track simulation. Nat Hazards, 2009, 48: 383-398. doi: 10.1007/s11069-008-9268-9
[20]	Emanuel K, Ravela S, Vivant E, et al. A statistical deterministic approach to hurricane risk assessment. Bull Amer Meteor Soc, 2006, 87(3): 299-314. doi: 10.1175/BAMS-87-3-299
[21]	Fang W H, Shi X W. A review of stochastic modeling of tropical cyclone track and intensity for disaster risk assessment. Adv Earth Sci, 2012, 27(8): 866-875. https://www.cnki.com.cn/Article/CJFDTOTAL-DXJZ201208005.htm
[22]	Chan J C L, Shi J. Long-term trends and interannual variability in tropical cyclone activity over the western North Pacific. Geophys Res Lett, 1996, 23(20): 2765-2767. doi: 10.1029/96GL02637
[23]	Chan J C L, Liu K S. Global warming and western North Pacific typhoon activity from an observational perspective. J Climate, 2004, 17(23): 4590-4602. doi: 10.1175/3240.1
[24]	Wang B, Chan J C L. How strong ENSO events affect tropical storm activity over the western North Pacific. J Climate, 2002, 15(13): 1643-1658. doi: 10.1175/1520-0442(2002)015<1643:HSEEAT>2.0.CO;2
[25]	Chen G H, Tam C Y. Different impacts of two kinds of Pacific Ocean warming on tropical cyclone frequency over the western North Pacific. Geophys Res Lett, 2010, 37(1): L01803.
[26]	Tan J K, Liu H X, Li M Y, et al. A prediction scheme of tropical cyclone frequency based on lasso and random forest. Theor Appl Climatol, 2018, 133: 973-983. doi: 10.1007/s00704-017-2233-3
[27]	Zhao J P, Wu L G, Zhao H K, et al. Improvement of tropical cyclone genesis potential index in the western North Pacific Basin. J Meteor Sci, 2012, 32(6): 591-599. doi: 10.3969/2012jms.0110
[28]	Tippett M K, Camargo S J, Sobel A H. A poisson regression index for tropical cyclone genesis and the role of large-scale vorticity in genesis. J Climate, 2011, 24(9): 2335-2357. doi: 10.1175/2010JCLI3811.1
[29]	Wu T T. Optimization and Verification of the Statistical Dynamics-full Track Synthesis Method for Typhoon Hazard Analysis. Shenzhen: Harbin Institute of Technology, 2020.
[30]	Wijnands J S, Shelton K, Kuleshov Y. Improving the operational methodology of tropical cyclone seasonal prediction in the Australian and the South Pacific Ocean regions. Adv Meteor, 2014, 2014(1): 1-8.
[31]	Richman M B, Leslie L M, Ramsay H A, et al. Reducing tropical cyclone prediction errors using machine learning approaches. Procedia Comput Sci, 2017, 114: 314-323. doi: 10.1016/j.procs.2017.09.048
[32]	Liu H, Zhang D L, Chen J, et al. Prediction of tropical cyclone frequency with a wavelet neural network model incorporating natural orthogonal expansion and combined weights. Nat Hazards, 2013, 65(1): 63-78. doi: 10.1007/s11069-012-0343-x
[33]	Hai Y. Using Artificial Neural Network Model to Predict the Frequency and Probability of Tropical Cyclones in the Northwest Pacific. Chengdu: Chengdu University of Information Technology, 2019.
[34]	Vickery P J, Skerlj P F, Twisdale L A. Simulation of hurricane risk in the US using empirical track model. J Struct Eng ASCE, 2000, 47(10): 2497-2517.
[35]	Yonekura E, Hall T M. A statistical model of tropical cyclone tracks in the western North Pacific with ENSO-dependent cyclogenesis. J Appl Meteor Climatol, 2011, 50(8): 1725-1739. doi: 10.1175/2011JAMC2617.1
[36]	Breiman L. Random forests. Mach Learn, 2001, 45(1): 5-32. doi: 10.1023/A:1010933404324
[37]	Breiman L. Bagging predictors. Mach Learn, 1996, 26(2): 123-140.
[38]	Ho T K. Random Decision Forests. The 3rd International Conference on Document Analysis and Recognition, Montreal, QC, Canada, 1995.
[39]	Ho T K. The random subspace method for constructing decision forests. IEEE Trans Pattern Anal, 1998, 20(8): 832-844. doi: 10.1109/34.709601
[40]	Trenberth K E, Hurrell J W. Decadal atmosphere-ocean variations in the Pacific. Climate Dyn, 1994, 9(6): 303-319. doi: 10.1007/BF00204745
[41]	Ault T R, Cole J E, Evans M N, et al. Intensified decadal variability in tropical climate during the late 19th century. Geophys Res Lett, 2009, 36(8): 134-150.
[42]	Chen G H, Huang R H. The effect of warm pool thermal states on tropical cyclone in west Northwest Pacific. J Trop Meteor, 2006, 22(6): 527-532. doi: 10.3969/j.issn.1004-4965.2006.06.002
[43]	Gray W M. Global view of the origin of tropical disturbances and storms. Mon Wea Rev, 1968, 96(10): 87.
[44]	Lander M. An exploratory analysis of the relationship between tropical storm formation in the western North Pacific and ENSO. Mon Wea Rev, 1994, 122(4): 636-651. doi: 10.1175/1520-0493(1994)122<0636:AEAOTR>2.0.CO;2