A Statistical Intraseasonal Prediction Model of Extended Boreal Summer Western North Pacific Tropical Cyclone Genesis

Haikun Zhao aKey Laboratory of Meteorological Disaster, Ministry of Education, and Joint International Research Laboratory of Climate and Environment Change, and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster, and Pacific Typhoon Research Center, Nanjing University of Information Science and Technology, Nanjing, China

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https://orcid.org/0000-0002-1771-1461
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Ying Lu bKey Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China

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Xianan Jiang cJoint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, Los Angeles, California/Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California

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Philip J. Klotzbach dDepartment of Atmospheric Science, Colorado State University, Fort Collins, Colorado

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Liguang Wu eDepartment of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences, Fudan University, Shanghai, China

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Jian Cao bKey Laboratory of Meteorological Disaster, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, China

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Abstract

An L2 regularized logistic regression model is developed in this study to predict weekly tropical cyclone (TC) genesis over the western North Pacific (WNP) Ocean and subregions of the WNP including the South China Sea (SCS), the western WNP (WWNP), and the eastern WNP (EWNP). The potential predictors for the TC genesis model include a time-varying TC genesis climatology, the Madden–Julian oscillation (MJO), the quasi-biweekly oscillation (QBWO), and ENSO. The relative importance of the predictors in a constructed L2 regression model is justified by a forward stepwise selection procedure for each region from a 0-week to a 7-week lead. Cross-validated hindcasts are then generated for the corresponding prediction schemes out to a 7-week lead. The TC genesis climatology generally improves the regional model skill, and the importance of intraseasonal oscillations and ENSO is regionally dependent. Over the WNP, there is increased model skill over the time-varying climatology in predicting weekly TC genesis out to a 4-week lead by including the MJO and QBWO, whereas ENSO has a limited impact. On a regional scale, ENSO and then either the MJO or QBWO are the two most important predictors over the EWNP and WWNP after the TC genesis climatology. The MJO is found to be the most important predictor over the SCS. The logistic regression model is shown to have comparable reliability and forecast skill scores to the ECMWF dynamical model on intraseasonal time scales.

Significance Statement

Skillful forecasts of tropical cyclone activity on time scales from short-range to seasonal are now issued operationally. Although there has been great progress in the understanding of physical mechanisms driving tropical cyclone (TC) activity, intraseasonal prediction of TCs remains a significant scientific challenge. This study develops a statistically based intraseasonal model to predict weekly TC genesis over the western North Pacific Ocean basin. The intraseasonal prediction model developed here for TC genesis over the western North Pacific basin shows skill extending out to four weeks. We discuss the regional dependence of the model skill on ENSO and other subseasonal climate oscillations. This approach provides skillful intraseasonal forecasting of TCs over the western North Pacific basin.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Zhao’s ORCID: 0000-0002-1771-1461.

Corresponding author: Haikun Zhao, zhk2004y@gmail.com

Abstract

An L2 regularized logistic regression model is developed in this study to predict weekly tropical cyclone (TC) genesis over the western North Pacific (WNP) Ocean and subregions of the WNP including the South China Sea (SCS), the western WNP (WWNP), and the eastern WNP (EWNP). The potential predictors for the TC genesis model include a time-varying TC genesis climatology, the Madden–Julian oscillation (MJO), the quasi-biweekly oscillation (QBWO), and ENSO. The relative importance of the predictors in a constructed L2 regression model is justified by a forward stepwise selection procedure for each region from a 0-week to a 7-week lead. Cross-validated hindcasts are then generated for the corresponding prediction schemes out to a 7-week lead. The TC genesis climatology generally improves the regional model skill, and the importance of intraseasonal oscillations and ENSO is regionally dependent. Over the WNP, there is increased model skill over the time-varying climatology in predicting weekly TC genesis out to a 4-week lead by including the MJO and QBWO, whereas ENSO has a limited impact. On a regional scale, ENSO and then either the MJO or QBWO are the two most important predictors over the EWNP and WWNP after the TC genesis climatology. The MJO is found to be the most important predictor over the SCS. The logistic regression model is shown to have comparable reliability and forecast skill scores to the ECMWF dynamical model on intraseasonal time scales.

Significance Statement

Skillful forecasts of tropical cyclone activity on time scales from short-range to seasonal are now issued operationally. Although there has been great progress in the understanding of physical mechanisms driving tropical cyclone (TC) activity, intraseasonal prediction of TCs remains a significant scientific challenge. This study develops a statistically based intraseasonal model to predict weekly TC genesis over the western North Pacific Ocean basin. The intraseasonal prediction model developed here for TC genesis over the western North Pacific basin shows skill extending out to four weeks. We discuss the regional dependence of the model skill on ENSO and other subseasonal climate oscillations. This approach provides skillful intraseasonal forecasting of TCs over the western North Pacific basin.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Zhao’s ORCID: 0000-0002-1771-1461.

Corresponding author: Haikun Zhao, zhk2004y@gmail.com

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  • Bakkensen, L. A., D.-S. R. Park, and R. S. R. Sarkar, 2018: Climate costs of tropical cyclone losses also depend on rain. Environ. Res. Lett., 13, 074034, https://doi.org/10.1088/1748-9326/aad056.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bauer, P., A. J. Thorpe, and G. Brunet, 2015: The quiet revolution of numerical weather prediction. Nature, 525, 4755, https://doi.org/10.1038/nature14956.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bender, M. A., T. R. Knutson, R. E. Tuleya, J. J. Sirutis, G. A. Vecchi, S. T. Garner, and I. M. Held, 2010: Modeled impact of anthropogenic warming on the frequency of intense Atlantic hurricanes. Science, 327, 454458, https://doi.org/10.1126/science.1180568.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buizza, R., and T. N. Palmer, 1998: Impact of ensemble size on ensemble prediction. Mon. Wea. Rev., 126, 25032518, https://doi.org/10.1175/1520-0493(1998)126<2503:IOESOE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., and A. H. Sobel, 2005: Western North Pacific tropical cyclone intensity and ENSO. J. Climate, 18, 29963006, https://doi.org/10.1175/JCLI3457.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., A. G. Barnston, and S. E. Zebiak, 2005: A statistical assessment of tropical cyclone activity in atmospheric general circulation models. Tellus, 57A, 589604, https://doi.org/10.3402/tellusa.v57i4.14705.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., K. A. Emanuel, and A. H. Sobel, 2007: Use of a genesis potential index to diagnose ENSO effects on tropical cyclone genesis. J. Climate, 20, 48194834, https://doi.org/10.1175/JCLI4282.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., M. C. Wheeler, and A. H. Sobel, 2009: Diagnosis of the MJO modulation of tropical cyclogenesis using an empirical index. J. Atmos. Sci., 66, 30613074, https://doi.org/10.1175/2009JAS3101.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., and Coauthors, 2019: Tropical cyclone prediction on subseasonal time-scales. Trop. Cyclone Res. Rev., 8, 150165, https://doi.org/10.1016/j.tcrr.2019.10.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chan, J. C. L., J. Shi, and K. S. Liu, 2001: Improvements in the seasonal forecasting of tropical cyclone activity over the western North Pacific. Wea. Forecasting, 16, 491498, https://doi.org/10.1175/1520-0434(2001)016<0491:IITSFO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cheng, Q., P. K. Varshney, and M. K. Arora, 2006: Logistic regression for feature selection and soft classification of remote sensing data. IEEE Geosci. Remote Sens. Lett., 3, 491494, https://doi.org/10.1109/LGRS.2006.877949.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chu, J.-H., C. R. Sampson, A. S. Levine, and E. Fukada, 2002: The Joint Typhoon Warning Center tropical cyclone best-tracks, 1945–2000. Ref. NRL/MR/754002, 16 pp., http://www.usno.navy.mil/NOOC/nmfc-ph/RSS/jtwc/best_tracks/TC_bt_report.html.

    • Search Google Scholar
    • Export Citation
  • Duchon, C. E., 1979: Lanczos filtering in one and two dimensions. J. Appl. Meteor., 18, 10161022, https://doi.org/10.1175/1520-0450(1979)018<1016:LFIOAT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elsberry, R. L., M. S. Jordan, and F. Vitart, 2010: Predictability of tropical cyclone events on intraseasonal timescales with the ECMWF monthly forecast model. Asia-Pac. J. Atmos. Sci., 46, 135153, https://doi.org/10.1007/s13143-010-0013-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elsberry, R. L., H.-C. Tsai, and M. S. Jordan, 2014: Extended-range forecasts of Atlantic tropical cyclone events during 2012 using the ECMWF 32-day ensemble predictions. Wea. Forecasting, 29, 271288, https://doi.org/10.1175/WAF-D-13-00104.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Emanuel, K. A., and D. S. Nolan, 2004: Tropical cyclone activity and global climate. Preprints, 26th Conf. on Hurricanes and Tropical Meteorology, Miami, FL, Amer. Meteor. Soc., 240241.

    • Search Google Scholar
    • Export Citation
  • Goldenberg, S. B., and L. J. Shapiro, 1996: Physical mechanisms for the association of El Niño and West African rainfall with Atlantic major hurricane activity. J. Climate, 9, 11691187, https://doi.org/10.1175/1520-0442(1996)009<1169:PMFTAO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gray, W. M., 1979: Hurricanes: Their formation, structure and likely role in the general circulation. Meteorology over the Tropical Oceans, D. B. Shaw, Ed., Royal Meteorological Society, 155218.

    • Search Google Scholar
    • Export Citation
  • Gray, W. M., 1984: Atlantic seasonal hurricane frequency. Part II: Forecasting its variability. Mon. Wea. Rev., 112, 16691683, https://doi.org/10.1175/1520-0493(1984)112<1669:ASHFPI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gray, W. M., C. W. Landsea, P. W. Mielke, and K. J. Berry, 1992: Predicting Atlantic seasonal hurricane activity 6–11 months in advance. Wea. Forecasting, 7, 440455, https://doi.org/10.1175/1520-0434(1992)007<0440:PASHAM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gregory, P., F. Vitart, R. Rivett, A. Brown, and Y. Kuleshov, 2020: Subseasonal forecasts of tropical cyclones in the Southern Hemisphere using a dynamical multimodel ensemble. Wea. Forecasting, 35, 18171829, https://doi.org/10.1175/WAF-D-20-0050.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ham, Y.-G., J.-H. Kim, and J.-J. Luo, 2019: Deep learning for multi-year ENSO forecasts. Nature, 573, 568572, https://doi.org/10.1038/s41586-019-1559-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, X., H. Zhao, X. Li, G. B. Raga, C. Wang, and Q. Li, 2019: Modulation of boreal extended summer tropical cyclogenesis over the northwest Pacific by the quasi‐biweekly oscillation under different El Niño–Southern Oscillation phases. Int. J. Climatol., 40, 858873, https://doi.org/10.1002/joc.6244.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hansen, K. A., S. J. Majumdar, and B. P. Kirtman, 2020: Identifying subseasonal variability relevant to Atlantic tropical cyclone activity. Wea. Forecasting, 35, 20012024, https://doi.org/10.1175/WAF-D-19-0260.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Henderson, D. A., and D. R. Denison, 1989: Stepwise regression in social and psychological research. Psychol. Rep., 64, 251257, https://doi.org/10.2466/pr0.1989.64.1.251.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hoerl, A. E., and R. W. Kennard, 1970: Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12, 5567, https://doi.org/10.1080/00401706.1970.10488634.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hsu, P.-C., T. Li, L. You, J. Gao, and H.-L. Ren, 2015: A spatial–temporal projection model for 10–30 day rainfall forecast in South China. Climate Dyn., 44, 12271244, https://doi.org/10.1007/s00382-014-2215-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, X., M. Zhao, and D. E. Waliser, 2012: Modulation of tropical cyclones over the eastern Pacific by the intraseasonal variability simulated in an AGCM. J. Climate, 25, 65246538, https://doi.org/10.1175/JCLI-D-11-00531.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, X., B. Xiang, M. Zhao, T. Li, S. J. Lin, Z. Wang, and J. H. Chen, 2018: Intraseasonal tropical cyclogenesis prediction in a global coupled model system. J. Climate, 31, 62096227, https://doi.org/10.1175/JCLI-D-17-0454.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiang, X., A. Adames, D. Kim, E. Maloney, H. Lin, H. Kim, C. Zhang, C. DeMott, and N. Klingaman, 2020: Fifty years of research on the Madden–Julian oscillation: Recent progress, challenges, and perspectives. J. Geophys. Res. Atmos., 125, e2019JD030911, https://doi.org/10.1029/2019JD030911.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kemball-Cook, S., and B. Wang, 2001: Equatorial waves and air–sea interaction in the boreal summer intraseasonal oscillation. J. Climate, 14, 29232942, https://doi.org/10.1175/1520-0442(2001)014<2923:EWAASI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kikuchi, K., B. Wang, and Y. Kajikawa, 2012: Bimodal representation of the tropical intraseasonal oscillation. Climate Dyn., 38, 19892000, https://doi.org/10.1007/s00382-011-1159-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kiladis, G. N., J. Dias, K. H. Straub, M. C. Wheeler, S. N. Tulich, K. Kikuchi, K. M. Weickmann, and M. J. Ventrice, 2014: A comparison of OLR and circulation-based indices for tracking the MJO. Mon. Wea. Rev., 142, 16971715, https://doi.org/10.1175/MWR-D-13-00301.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, H., F. Vitart, and D. E. Waliser, 2018: Prediction of the Madden–Julian oscillation: A review. J. Climate, 31, 94259443, https://doi.org/10.1175/JCLI-D-18-0210.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, H., M. A. Janiga, and K. Pegion, 2019: MJO propagation processes and mean biases in the SubX and S2S reforecasts. J. Geophys. Res. Atmos., 124, 93149331, https://doi.org/10.1029/2019JD031139.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, J.-H., C.-H. Ho, H.-S. Kim, C.-H. Sui, and S. K. Park, 2008: Systematic variation of summertime tropical cyclone activity in the western North Pacific in relation to the Madden–Julian oscillation. J. Climate, 21, 11711191, https://doi.org/10.1175/2007JCLI1493.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kleinen, J., 2007: Historical perspectives on typhoons and tropical storms in the natural and socio-economic system of Nam Dinh (Vietnam). J. Asian Earth Sci., 29, 523531, https://doi.org/10.1016/j.jseaes.2006.05.012.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klotzbach, P. J., and E. C. J. Oliver, 2015: Variations in global tropical cyclone activity and the Madden–Julian oscillation since the midtwentieth century. Geophys. Res. Lett., 42, 41994207, https://doi.org/10.1002/2015GL063966.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klotzbach, P. J., S. G. Bowen, R. Pielke, and M. Bell, 2018: Continental U.S. hurricane landfall frequency and associated damage: Observations and future risks. Bull. Amer. Meteor. Soc., 99, 13591376, https://doi.org/10.1175/BAMS-D-17-0184.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klotzbach, P. J., and Coauthors, 2019: Seasonal tropical cyclone forecasting. Trop. Cyclone Res. Rev., 8, 134149, https://doi.org/10.1016/j.tcrr.2019.10.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Klotzbach, P. J., L.-P. Caron, and M. M. Bell, 2020: A statistical/dynamical model for North Atlantic seasonal hurricane prediction. Geophys. Res. Lett., 47, e2020GL089357, https://doi.org/10.1029/2020GL089357.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lander, M. A., 1994: An exploratory analysis of the relationship between tropical storm formation in the western North Pacific and ENSO. Mon. Wea. Rev., 122, 636651, https://doi.org/10.1175/1520-0493(1994)122<0636:AEAOTR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, C. Y., S. J. Camargo, F. Vitart, A. H. Sobel, and M. K. Tippett, 2018: Subseasonal tropical cyclone genesis prediction and MJO in the S2S dataset. Wea. Forecasting, 33, 967988, https://doi.org/10.1175/WAF-D-17-0165.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, C. Y., S. J. Camargo, F. Vitart, A. H. Sobel, J. Camp, S. Wang, M. K. Tippett, and Q. Yang, 2020: Subseasonal predictions of tropical cyclone occurrence and ACE in the S2S dataset. Wea. Forecasting, 35, 921938, https://doi.org/10.1175/WAF-D-19-0217.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, J.-Y., B. Wang, M. C. Wheeler, X. Fu, D. E. Waliser, and I.-S. Kang, 2013: Real-time multivariate indices for the boreal summer intraseasonal oscillation over the Asian summer monsoon region. Climate Dyn., 40, 493509, https://doi.org/10.1007/s00382-012-1544-4.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leroy, A., and M. C. Wheeler, 2008: Statistical prediction of weekly tropical cyclone activity in the Southern Hemisphere. Mon. Wea. Rev., 136, 36373654, https://doi.org/10.1175/2008MWR2426.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, C.-Y., 1996: Quasi-two weeks oscillation in the tropical atmosphere. Theor. Appl. Climatol., 55, 121127, https://doi.org/10.1007/BF00864707.

  • Li, R. C. Y., and W. Zhou, 2013a: Modulation of western North Pacific tropical cyclone activity by the ISO. Part I: Genesis and intensity. J. Climate, 26, 29042918, https://doi.org/10.1175/JCLI-D-12-00210.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, R. C. Y., and W. Zhou, 2013b: Modulation of western North Pacific tropical cyclone activity by the ISO. Part II: Tracks and landfalls. J. Climate, 26, 29192930, https://doi.org/10.1175/JCLI-D-12-00211.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, R. C. Y., W. Zhou, J. Chan, and P. Huang, 2012: Asymmetric modulation of western North Pacific cyclogenesis by the Madden–Julian oscillation under ENSO conditions. J. Climate, 25, 53745385, https://doi.org/10.1175/JCLI-D-11-00337.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liebmann, B., and C. A. Smith, 1996: Description of a complete (interpolated) outgoing longwave radiation dataset. Bull. Amer. Meteor. Soc., 77, 12751277, https://doi.org/10.1175/1520-0477-77.6.1274.

    • Search Google Scholar
    • Export Citation
  • Lim, Y., S.-W. Son, and D. Kim, 2018: MJO prediction skill of the subseasonal-to-seasonal prediction models. J. Climate, 31, 40754094, https://doi.org/10.1175/JCLI-D-17-0545.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, K. S., and J. C. L. Chan, 2013: Inactive period of western North Pacific tropical cyclone activity in 1998–2011. J. Climate, 26, 26142630, https://doi.org/10.1175/JCLI-D-12-00053.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Madden, R. A., and P. R. Julian, 1971: Detection of a 40–50 day oscillation in the zonal wind in the tropical Pacific. J. Atmos. Sci., 28, 702708, https://doi.org/10.1175/1520-0469(1971)028<0702:DOADOI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Maloney, E. D., and D. L. Hartmann, 2000: Modulation of hurricane activity in the Gulf of Mexico by the Madden–Julian oscillation. Science, 287, 20022004, https://doi.org/10.1126/science.287.5460.2002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mao, J. Y., and J. C. L. Chan, 2005: Intraseasonal variability of the South China Sea summer monsoon. J. Climate, 18, 23882402, https://doi.org/10.1175/JCLI3395.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mason, S. J., and N. E. Graham, 1999: Conditional probabilities relative operating characteristics, and relative operating levels. Wea. Forecasting, 14, 713725, https://doi.org/10.1175/1520-0434(1999)014<0713:CPROCA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Matsuoka, D., M. Nakano, D. Sugiyama, and S. Uchida, 2018: Deep learning approach for detecting tropical cyclones and their precursors in the simulation by a cloud-resolving global nonhydrostatic atmospheric model. Prog. Earth Planet. Sci., 5, 80, https://doi.org/10.1186/s40645-018-0245-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McBride, J. L., and R. Zehr, 1981: Observational analysis of tropical cyclone formation. Part II: Comparison of non-developing versus developing systems. J. Atmos. Sci., 38, 11321151, https://doi.org/10.1175/1520-0469(1981)038<1132:OAOTCF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Merryfield, W. J., and Coauthors, 2020: Current and emerging developments in subseasonal to decadal prediction. Bull. Amer. Meteor. Soc., 101, E869E896, https://doi.org/10.1175/BAMS-D-19-0037.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mundry, R., and C. L. Nunn, 2009: Stepwise model fitting and statistical inference: Turning noise into signal pollution. Amer. Nat., 173, 119123, https://doi.org/10.1086/593303.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murakami, H., T. L. Delworth, W. F. Cooke, M. Zhao, B. Xiang, and P. C. Hsu, 2020: Detected climatic change in global distribution of tropical cyclones. Proc. Natl. Acad. Sci. USA, 117, 10 70610 714, https://doi.org/10.1073/pnas.1922500117.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Murakami, M., 1984: Analysis of deep convective activity over the western Pacific and Southeast Asia. Part II: Seasonal and intraseasonal variations during northern summer. J. Meteor. Soc. Japan, 62, 88108, https://doi.org/10.2151/jmsj1965.62.1_88.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ng, A. Y., 2004: Feature selection, L1 vs. L2 regularization, and rotational invariance. Proc. 21st Int. Conf. on Machine Learning, Banff, AB, Canada, ACM, https://doi.org/10.1145/1015330.1015435.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ogutu, J. O., T. Schulz-Streeck, and H. P. Piepho, 2012: Genomic selection using regularized linear regression models: Ridge regression, lasso, elastic net and their extensions. BMC Proc., 6, S10, https://doi.org/10.1186/1753-6561-6-S2-S10.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Patricola, C. M., S. J. Camargo, P. J. Klotzbach, R. Saravanan, and P. Chang, 2018: The influence of ENSO flavors on western North Pacific tropical cyclone activity. J. Climate, 31, 53955416, https://doi.org/10.1175/JCLI-D-17-0678.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rasmusson, E. M., and T. H. Carpenter, 1982: Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño. Mon. Wea. Rev., 110, 354384, https://doi.org/10.1175/1520-0493(1982)110<0354:VITSST>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, D. P. Rowell, E. C. Kent, and A. Kaplan, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Saunders, M. A., and A. S. Lea, 2005: Seasonal prediction of hurricane activity reaching the coast of the United States. Nature, 434, 10051008, https://doi.org/10.1038/nature03454.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Serra, Y. L., X. Jiang, B. Tian, J. Astua, E. D. Maloney, and G. N. Kiladis, 2014: Tropical intra-seasonal oscillations and synoptic variability. Annu. Rev. Environ. Resour., 39, 189215, https://doi.org/10.1146/annurev-environ-020413-134219.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Slade, S. A., and E. D. Maloney, 2013: An intraseasonal prediction model of Atlantic and East Pacific tropical cyclone genesis. Mon. Wea. Rev., 141, 19251942, https://doi.org/10.1175/MWR-D-12-00268.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, G., 2018: Step away from stepwise. J. Big Data, 5, 32, https://doi.org/10.1186/s40537-018-0143-6.

  • Thompson, B., 1995: Stepwise regression and stepwise discriminant analysis need not apply here: A guidelines editorial. Educ. Psychol. Meas., 55, 525534, https://doi.org/10.1177/0013164495055004001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vecchi, G. A., M. Zhao, H. Wang, G. Villarini, A. Rosati, A. Kumar, I. M. Held, and R. Gudgel, 2011: Statistical–dynamical predictions of seasonal North Atlantic hurricane activity. Mon. Wea. Rev., 139, 10701082, https://doi.org/10.1175/2010MWR3499.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vecchi, G. A., and Coauthors, 2019: Tropical cyclone sensitivities to CO2 doubling: Roles of atmospheric resolution, synoptic variability and background climate changes. Climate Dyn., 53, 59996033, https://doi.org/10.1007/s00382-019-04913-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vitart, F., 2014: Evolution of ECMWF sub‐seasonal forecast skill scores. Quart. J. Roy. Meteor. Soc., 140, 18891899, https://doi.org/10.1002/qj.2256.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vitart, F., and T. N. Stockdale, 2001: Seasonal forecasting of tropical storms using coupled GCM integrations. Mon. Wea. Rev., 129, 25212537, https://doi.org/10.1175/1520-0493(2001)129<2521:SFOTSU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vitart, F., and A. W. Robertson, 2018: The sub-seasonal to seasonal prediction project (S2S) and the prediction of extreme events. npj Climate Atmos. Sci., 1, 3, https://doi.org/10.1038/s41612-018-0013-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vitart, F., A. Leroy, and M. C. Wheeler, 2010: A comparison of dynamical and statistical predictions of weekly tropical cyclone activity in the Southern Hemisphere. Mon. Wea. Rev., 138, 36713682, https://doi.org/10.1175/2010MWR3343.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vitart, F., and Coauthors, 2017: The subseasonal to seasonal (S2S) prediction project database. Bull. Amer. Meteor. Soc., 98, 163173, https://doi.org/10.1175/BAMS-D-16-0017.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vitart, F., and Coauthors, 2019: Sub-seasonal to Seasonal Prediction of Weather Extremes. Elsevier, 365386, https://doi.org/10.1016/B978-0-12-811714-9.00017-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., and J. C. L. Chan, 2002: How strong ENSO events affect tropical storm activity over the western North Pacific. J. Climate, 15, 16431658, https://doi.org/10.1175/1520-0442(2002)015<1643:HSEEAT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, B., R. Wu, and X. Fu, 2000: Pacific–East Asian teleconnection: How does ENSO affect East Asian climate? J. Climate, 13, 15171536, https://doi.org/10.1175/1520-0442(2000)013<1517:PEATHD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, G., J. Su, Y. Ding, and D. Chen, 2007: Tropical cyclone genesis over the South China Sea. J. Mar. Syst., 68, 318326, https://doi.org/10.1016/j.jmarsys.2006.12.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, S., A. H. Sobel, M. K. Tippett, and F. Vitart, 2019: Prediction and predictability of tropical intraseasonal convection: Seasonal dependence and the Maritime Continent prediction barrier. Climate Dyn., 52, 60156031, https://doi.org/10.1007/s00382-018-4492-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wheeler, M. C., and H. H. Hendon, 2004: An all-season real-time multivariate MJO index: Development of an index for monitoring and prediction. Mon. Wea. Rev., 132, 19171932, https://doi.org/10.1175/1520-0493(2004)132<1917:AARMMI>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Whittingham, M. J., P. A. Stephens, R. B. Bradbury, and R. P. Freckleton, 2006: Why do we still use stepwise modelling in ecology and behavior? J. Anim. Ecol., 75, 11821189, https://doi.org/10.1111/j.1365-2656.2006.01141.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilks, D. S., 1995: Statistical Methods in the Atmospheric Sciences: An Introduction. Academic Press, 464 pp.

  • Yu, J., T. Li, Z. Tan, and Z. Zhu, 2016: Effects of tropical North Atlantic SST on tropical cyclone genesis in the western North Pacific. Climate Dyn., 46, 865877, https://doi.org/10.1007/s00382-015-2618-x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, G., H. Murakami, T. R. Knutson, R. Mizuta, and K. Yoshida, 2020: Tropical cyclone motion in a changing climate. Sci. Adv., 6, eaaz7610, https://doi.org/10.1126/sciadv.aaz7610.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, Q., L. Wu, and Q. Liu, 2009: Tropical cyclone damages in China 1983–2006. Bull. Amer. Meteor. Soc., 90, 489495, https://doi.org/10.1175/2008BAMS2631.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, H., and C. Wang, 2016: Interdecadal modulation on the relationship between ENSO and typhoon activity during the late season in the western North Pacific. Climate Dyn., 47, 315328, https://doi.org/10.1007/s00382-015-2837-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, H., and C. Wang, 2019: On the relationship between ENSO and tropical cyclones in the western North Pacific during the boreal summer. Climate Dyn., 52, 275288, https://doi.org/10.1007/s00382-018-4136-0.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, H., L. Wu, and W. Zhou, 2010: Assessing the influence of the ENSO on tropical cyclone prevailing tracks in the western North Pacific. Adv. Atmos. Sci., 27, 13611371, https://doi.org/10.1007/s00376-010-9161-9.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, H., L. Wu, and W. Zhou, 2011: Interannual changes of tropical cyclone intensity in the western North Pacific. J. Meteor. Soc. Japan, 89, 243253, https://doi.org/10.2151/jmsj.2011-305.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, H., X. Jiang, and L. Wu, 2015a: Modulation of Northwest Pacific tropical cyclone genesis by the intraseasonal variability. J. Meteor. Soc. Japan, 93, 8197, https://doi.org/10.2151/jmsj.2015-006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, H., R. Yoshida, and G. B. Raga, 2015b: Impact of the Madden–Julian oscillation on western North Pacific tropical cyclogenesis associated with large-scale patterns. J. Appl. Meteor. Climatol., 54, 14131429, https://doi.org/10.1175/JAMC-D-14-0254.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhao, H., C. Wang, and R. Yoshida, 2016: Modulation of tropical cyclogenesis in the western North Pacific by the quasi-biweekly oscillation. Adv. Atmos. Sci., 33, 13611375, https://doi.org/10.1007/s00376-016-5267-z.

    • Crossref
    • Search Google Scholar