Abstract
Accurate prediction of tropical cyclone (TC) intensity remains a challenge due to the complex physical processes involved in TC intensity changes. A seven-day TC intensity prediction scheme based on the logistic growth equation (LGE) for the western North Pacific (WNP) has been developed using the observed and reanalysis data. In the LGE, TC intensity change is determined by a growth term and a decay term. These two terms are comprised of four free parameters which include a time-dependent growth rate, a maximum potential intensity (MPI), and two constants. Using 33 years of training samples, optimal predictors are selected first, and then the two constants are determined based on the least square method, forcing the regressed growth rate from the optimal predictors to be as close to the observed as possible. The estimation of the growth rate is further refined based on a step-wise regression (SWR) method and a machine learning (ML) method for the period 1982–2014. Using the LGE-based scheme, a total of 80 TCs during 2015–17 are used to make independent forecasts. Results show that the root mean square errors of the LGE-based scheme are much smaller than those of the official intensity forecasts from the China Meteorological Administration (CMA), especially for TCs in the coastal regions of East Asia. Moreover, the scheme based on ML demonstrates better forecast skill than that based on SWR. The new prediction scheme offers strong potential for both improving the forecasts for rapid intensification and weakening of TCs as well as for extending the 5-day forecasts currently issued by the CMA to 7-day forecasts.
摘 要
热带气旋 (TC) 强度变化涉及复杂的物理过程, 因此对其进行准确预报是一个极具挑战性的议题. 本文利用观测资料和再分析数据, 建立了一个基于逻辑生长方程 (LGE) 的西北太平洋 TC 强度7天预报模型. 在 LGE 中, TC 强度变化由增长项和衰减项决定. 这两项由四个自由参数组成, 包括随时间变化的增长率、 最大可能强度 (MPI) 及两个常数. 基于 33 年训练集, 本文首先选择了最优预报因子, 根据最小二乘法, 通过使基于最优预报因子回归得到的增长率尽可能逼近观测的增长率的方法来确定两个常数. 继而, 基于 1982–2014 年的逐步回归方法和机器学习方法, 进一步估算了增长率. 利用研制的 LGE 新模型, 对 2015–17 年期间共 80 个 TC 进行独立预测. 结果表明, 基于逐步回归和机器学习的 LGE 模型的强度预报均方根误差均较中国气象局 (CMA) 的官方预报误差要小, 特别是对位于东亚沿海地区的 TC, LGE 模型能够显示出较好的性能. 此外, 基于机器学习的 LGE 模型表现出比基于逐步回归的模型更好的预报性能. 新的强度预报模型在 TC 快速增强和快速减弱阶段也显示了一定的预报能力, 并具有将 CMA 当前的 5 天预报延长至 7 天的潜力.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Baik, J. J., and H. S. Hwang, 1998: Tropical cyclone intensity prediction using regression method and neural network. J. Meteor. Soc. Japan, 76, 711–717, https://doi.org/10.2151/jmsj1965.76.5_711.
Baik, J.-J., and J.-S. Paek, 1998: A climatology of sea surface temperature and the maximum intensity of western North Pacific tropical cyclones. J. Meteor. Soc. Japan, 76, 129–137, https://doi.org/10.2151/jmsj1965.76.l_129.
Bender, M. A., I. Ginis, R. Tuleya, B. Thomas, and T. Marchok, 2007: The operational GFDL coupled hurricane-ocean prediction system and a summary of its performance. Mon. Wea. Rev., 135, 3965–3989, https://doi.org/10.1175/2007MWR2032.1.
Cangialosi, J. P., 2020: National hurricane center forecast verification report: 2019 hurricane season. National Hurricane Center, Miami, Fla.
Cangialosi, J. P., E. Blake, M. DeMaria, A. Penny, A. Latto, E. Rappaport, and V. Tallapragada, 2020: Recent progress in tropical cyclone intensity forecasting at the National Hurricane Center. Wea. Forecasting, 35, 1913–1922, https://doi.org/10.1175AVAF-D-20-0059.1.
Chen, G. M., X. P. Zhang, L. N. Bai, and R. J. Wan, 2019: Verification on forecasts of tropical cyclones over western North Pacific and South China Sea in 2017. Meteorological Monthly, 45, 577–586, https://doi.org/10.7519/j.issn.1000-0526.2019.04.012. (in Chinese with English abstract)
Chen, P. Y., H. Yu, and J. C. L. Chan, 2011: A western North Pacific tropical cyclone intensity prediction scheme. Acta Meteorologica Sinica, 25, 611–624, https://doi.org/10.1007/s13351-011-0506-9.
Cloud, K. A., B. J. Reich, C. M. Rozoff, S. Alessandrini, W. E. Lewis, and L. D. Monache, 2019: A feed forward neural network based on model output statistics for short-term Hurricane intensity prediction. Wea. Forecasting, 34, 985–997, https://doi.org/10.1175/WAF-D-18-0173.l.
DeMaria, M., 2009: A simplified dynamical system for tropical cyclone Intensity prediction. Mon. Wea. Rev., 137, 68–82, https://doi.org/10.1175/2008MWR2513.1.
DeMaria, M., and J. Kaplan, 1994: A statistical hurricane intensity prediction scheme (SHIPS) for the Atlantic basin. Wea. Forecasting, 9, 209–220, https://doi.org/10.1175/1520-0434(1994)009<0209:ASHIPS>2.0.CO;2.
DeMaria, M., and J. Kaplan, 1997: An operational evaluation of a statistical hurricane intensity prediction scheme (SHIPS). Preprints, 22nd Conf. on Hurricanes and Tropical Meteorology, Boston, American Meteorological Society.
Dong, L., S. Z. Gao, Y. L. Xu, X. Y. Lü, and Y. W. Huang, 2019: Analysis of characteristics and forecast difficulties of TCs on western North Pacific in 2017. Meteorological Monthly, 45, 1322–1334, https://doi.org/10.7519/j.issn.1000-0526.2019.09.012. (in Chinese with English abstract)
Duan Y. H., H. Yu, and R. S. Wu, 2005: Review of the research in the intensity change of tropical cyclone. Acta Meteorologica Sinica, 63, 636–645, https://doi.org/10.3321/j.issn:0577-6619.2005.05.009. (in Chinese with English abstract)
Dvorak, V. F., 1975: Tropical cyclone intensity analysis and forecasting from satellite imagery. Mon. Wea. Rev., 103, 420–430, https://doi.org/10.1175/1520-0493(1975)103<0420:TCIAAF>2.0.CO;2.
Elsberry, R. L., L. S. Chen, J. Davidson, R. Rogers, Y. Q. Wang, and L. G. Wu, 2013: Advances in understanding and forecasting rapidly changing phenomena in tropical cyclones. Tropical Cyclone Research and Review, 2, 13–24, https://doi.org/10.6057/2013TCRR01.02.
Emanuel, K., C. DesAutels, C. Holloway, and R. Korty, 2004: Environmental control of tropical cyclone intensity. J. Atmos. Sci., 61(7), 843–858, https://doi.org/10.1175/1520-0469(2004)061<0843:ECOTCI>2.0.CO;2.
Groemping, U., 2006: Relative importance for linear regression in R: The package relaimpo. Journal of Statistical Software, 17, 1–27, https://doi.org/10.18637/jss.v017.i01.
Heming, J. T., and Coauthors, 2019: Review of recent progress in tropical cyclone track forecasting and expression of uncertainties. Tropical Cyclone Research and Review, 8, 181–218, https://doi.org/10.1016/j.tcrr.2020.01.001.
Huang, X. Y., Z. Y. Guan, L. He, Y. Huang, and H. S. Zhao, 2016: A PNN prediction scheme for local tropical cyclone intensity over the South China Sea. Natural Hazards, 81, 1249–1267, https://doi.org/10.1007/s11069-015-2132-9.
Jin, Q. W., X. T. Fan, J. Liu, Z. X. Xue, and H. D. Jian, 2019: Using eXtreme gradient BOOSTing to predict changes in tropical cyclone intensity over the western North Pacific. Atmosphere, 10, 341, https://doi.org/10.3390/atmos10060341.
Ju, Y., G. Y. Sun, Q. H. Chen, M. Zhang, H. X. Zhu, and M. U. Rehman, 2019: A model combining convolutional neural network and LightGBM algorithm for Ultra-Short-Term wind power forecasting. IEEE Access, 7, 28309–28318, https://doi.org/10.1109/ACCESS.2019.2901920.
Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year reanalysis project. Bull. Amer. Meteor. Soc., 77, 437–472, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.
Ke, G. L., Q. Meng, T. Finley, T. F. Wang, W. Chen, W. D. Ma, Q. W. Ye, and T. Y. Liu, 2017: LightGBM: A highly efficient gradient boosting decision tree. Proc. 31st Int. Conf. on Neural Information Processing Systems, Long Beach, California, USA, NIPS.
Knaff, J. A., C. R. Sampson, and M. DeMaria, 2005: An operational statistical typhoon intensity prediction scheme for the western North Pacific. Wea. Forecasting, 20, 688–699, https://doi.org/10.1175/WAF863.1.
Knutson, T. R., and Coauthors, 2010: Tropical cyclones and climate change. Nature Geoscience, 3, 157–163, https://doi.org/10.1038/ngeo779.
Kurihara, Y., M. A. Bender, and R. J. Ross, 1993: An initialization scheme of hurricane models by vortex specification. Mon. Wea. Rev., 121, 2030–2045, https://doi.org/10.1175/1520-0493(1993)121<2030:AISOHM>2.0.CO;2.
Li, Z. C., L. Zhang, Q. F. Qian, S. H. Ma, J. Xu, K. Dai, Y. Chen, and D. Y. Wang, 2020: The development and consideration of typhoon forecast operation of national Meteorological Center. Transactions of Atmospheric Sciences, 43, 10–19, https://doi.org/10.13878/j.cnki.dqkxxb.20200110015. (in Chinese with English abstract)
Lindeman, R. H., 1980: Introduction to bivariate and multivariate analysis. J. Am. Stat. Assoc., 76, 752.
Ma, L. M., and Z. M. Tan, 2009: Improving the behavior of the cumulus parameterization for tropical cyclone prediction: Convection trigger. Atmospheric Research, 92, 190–211, https://doi.org/10.1016/j.atmosres.2008.09.022.
R Core Team, 2013: R: A language and environment for statis-ticalcomputing. R Core Team, Vienna, Austria
Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes, and W. Q. Wang, 2002: An improved in situ and satellite SST analysis for climate. J. Climate, 15, 1609–1625, https://doi.org/10.1175/1520-0442(2002)015<1609:AIISAS> 2.0.CO;2.
Su, H., L. T. Wu, J. H. Jiang, R. Pai, A. Liu, A. J. Zhai, P. Tavallali, and M. DeMaria, 2020: Applying satellite observations of tropical cyclone internal structures to rapid intensification forecast with machine learning. Geophys. Res. Lett., 47, e2020GL089102, https://doi.org/10.1029/2020GL089102.
Velden, C. S., T. L. Olander, and R. M. Zehr, 1998: Development of an objective scheme to estimate tropical cyclone intensity from digital geostationary satellite infrared imagery. Wea. Forecasting, 13, 172–186, https://doi.org/10.1175/1520-0434(1998)013<0172:DOAOST>2.0.CO;2.
Wang, X. D., C. Z. Wang, L. P. Zhang, and X. Wang, 2015a: Multi-decadal variability of tropical cyclone rapid intensification in the western North Pacific. J. Climate, 28, 3806–3820, https://doi.org/10.1175/JCLI-D-14-00400.1.
Wang, Y., and C.-C. Wu, 2004: Current understanding of tropical cyclone structure and intensity changes-A review. Meteor. Atmos. Phys., 87, 257–278, https://doi.org/10.1007/S00703-003-0055-6.
Wang, Y. Q., Y. J. Rao, Z. M. Tan, and D. Schönemann, 2015b: A statistical analysis of the effects of vertical wind shear on tropical cyclone intensity change over the western North Pacific. Mon. Wea. Rev., 143, 3434–3453, https://doi.org/10.1175/MWR-D-15-0049.1.
Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. 2nd ed. Academic Press, 627 pp.
Xu, Y. L., L. Zhang, and S. Z. Gao, 2010: The advances and discussions on China operational typhoon forecasting. Meteorological Monthly, 36, 43–49. (in Chinese with English abstract)
Yang, F. L., H. L. Pan, S. K. Krueger, S. Moorthi, and S. J. Lord, 2006: Evaluation of the NCEP global forecast system at the ARM SGP Site. Mon. Wea. Rev., 134, 3668–3690, https://doi.org/10.1175/MWR3264.1.
Zeng, Z. H., Y. Q. Wang, and C.-C. Wu, 2007: Environmental dynamical control of tropical Cyclone intensity-An observational study. Mon. Wea. Rev., 135, 38–59, https://doi.org/10.1175/MWR3278.1.
Zhang, J., D. Mucs, U. Norinder, and F. Svensson, 2019: LightGBM: An effective and scalable algorithm for prediction of chemical toxicity-application to the Tox21 and mutagenicity data sets. Journal of Chemical Information and Modeling, 59, 4150–4158, https://doi.org/10.1021/acs.jcim.9b00633.
Acknowledgements
This study is supported by the National Key R&D Program of China (Grant Nos. 2017YFC1501604 and 2019YFC1509101) and the National Natural Science Foundation of China (Grant Nos. 41875114, 41875057, and 91937302). The CMA best track TC dataset was downloaded from http://tcdata.typhoon.org.cn/. The official real-time forecast data of the CMA and the GFS forecast fields were derived from the TC operational database at the STI. The NCEP-NCAR reanalysis data were downloaded from https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html. The weekly OISST V2 data were downloaded from http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html.
Author information
Authors and Affiliations
Corresponding author
Additional information
Article Highlights
• A 7-day TC intensity prediction scheme based on the LGE for the WNP has been developed using the SWR and LightGBM schemes.
• The LGE-based scheme has better forecast skills than the CMA official forecasts, especially for TCs in the coastal regions of East Asia.
• The new prediction scheme exhibits strong potential for an extension of the CMA’s current 5-day forecasts to 7-day forecasts.
This paper is a contribution to the special issue on the Key Dynamic and Thermodynamic Processes and Prediction of Typhoon.
Rights and permissions
About this article
Cite this article
Zhou, Y., Zhao, J., Zhan, R. et al. A Logistic-growth-equation-based Intensity Prediction Scheme for Western North Pacific Tropical Cyclones. Adv. Atmos. Sci. 38, 1750–1762 (2021). https://doi.org/10.1007/s00376-021-0435-1
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00376-021-0435-1