• Aberson, S. D., 2010: 10 years of hurricane synoptic surveillance (1997–2006). Mon. Wea. Rev., 138, 15361549, https://doi.org/10.1175/2009MWR3090.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., A. W. Robertson, S. J. Gaffney, P. Smyth, and M. Ghil, 2007a: Cluster analysis of typhoon tracks. Part I: General properties. J. Climate, 20, 36353653, https://doi.org/10.1175/JCLI4188.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., A. W. Robertson, S. J. Gaffney, P. Smyth, and M. Ghil, 2007b: Cluster analysis of typhoon tracks. Part II: Large-scale circulation and ENSO. J. Climate, 20, 36543676, https://doi.org/10.1175/JCLI4203.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Camargo, S. J., A. W. Robertson, A. G. Barnston, and M. Ghil, 2008: Clustering of eastern North Pacific tropical cyclone tracks: ENSO and MJO effects. Geochem. Geophys. Geosyst., 9, Q06V05, https://doi.org/10.1029/2007GC001861.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carr, L. E., III, and R. L. Elsberry, 2000a: Dynamical tropical cyclone track forecast errors. Part I: Tropical region error sources. Wea. Forecasting, 15, 641661, https://doi.org/10.1175/1520-0434(2000)015<0641:DTCTFE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Carr, L. E., III, and R. L. Elsberry, 2000b: Dynamical tropical cyclone track forecast errors. Part II: Midlatitude circulation influences. Wea. Forecasting, 15, 662681, https://doi.org/10.1175/1520-0434(2000)015<0662:DTCTFE>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cha, D. H., C. S. Jin, D. K. Lee, and Y. H. Kuo, 2011: Impact of intermittent spectral nudging on regional climate simulation using Weather Research and Forecasting model. J. Geophys. Res., 116, D10103, https://doi.org/10.1029/2010JD015069.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chan, J. C., 2005: The physics of tropical cyclone motion. Annu. Rev. Fluid Mech., 37, 99128, https://doi.org/10.1146/annurev.fluid.37.061903.175702.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chan, J. C., and W. M. Gray, 1982: Tropical cyclone movement and surrounding flow relationships. Mon. Wea. Rev., 110, 13541374, https://doi.org/10.1175/1520-0493(1982)110<1354:TCMASF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, G., X. Zhang, L. Bai, and R. Wan, 2019: Verification of tropical cyclone operational forecast in 2018. 51st Session of ESCAP/WMO Typhoon Committee, Guangzhou, China, 20 pp.

  • Chen, S. S., J. F. Price, W. Zhao, M. A. Donelan, and E. J. Walsh, 2007: The CBLAST-Hurricane program and the next-generation fully coupled atmosphere–wave–ocean models for hurricane research and prediction. Bull. Amer. Meteor. Soc., 88, 311318, https://doi.org/10.1175/BAMS-88-3-311.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davis, C., and et al. , 2008: Prediction of landfalling hurricanes with the Advanced Hurricane WRF Model. Mon. Wea. Rev., 136, 19902005, https://doi.org/10.1175/2007MWR2085.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Davis, C., and et al. , 2011: High-resolution hurricane forecasts. Comput. Sci. Eng., 13, 2230, https://doi.org/10.1109/MCSE.2010.74.

  • Dong, K., and C. J. Neumann, 1986: The relationship between tropical cyclone motion and environmental geostrophic flows. Mon. Wea. Rev., 114, 115122, https://doi.org/10.1175/1520-0493(1986)114<0115:TRBTCM>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dudhia, J., 1989: Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J. Atmos. Sci., 46, 30773107, https://doi.org/10.1175/1520-0469(1989)046<3077:NSOCOD>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Elsner, J. B., 2003: Tracking hurricanes. Bull. Amer. Meteor. Soc., 84, 353356, https://doi.org/10.1175/BAMS-84-3-353.

  • Elsner, J. B., and K. Liu, 2003: Examining the ENSO-typhoon hypothesis. Climate Res., 25, 4354, https://doi.org/10.3354/cr025043.

  • Feser, F., and H. von Storch, 2008: A dynamical downscaling case study for typhoons in Southeast Asia using a regional climate model. Mon. Wea. Rev., 136, 18061815, https://doi.org/10.1175/2007MWR2207.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fierro, A. O., R. F. Rogers, F. D. Marks, and D. S. Nolan, 2009: The impact of horizontal grid spacing on the microphysical and kinematic structures of strong tropical cyclones simulated with the WRF-ARW model. Mon. Wea. Rev., 137, 37173743, https://doi.org/10.1175/2009MWR2946.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Galarneau, T. J., Jr., and C. A. Davis, 2013: Diagnosing forecast errors in tropical cyclone motion. Mon. Wea. Rev., 141, 405430, https://doi.org/10.1175/MWR-D-12-00071.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gentry, M. S., and G. M. Lackmann, 2010: Sensitivity of simulated tropical cyclone structure and intensity to horizontal resolution. Mon. Wea. Rev., 138, 688704, https://doi.org/10.1175/2009MWR2976.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • George, J. E., and W. M. Gray, 1976: Tropical cyclone motion and surrounding parameter relationships. J. Appl. Meteor., 15, 12521264, https://doi.org/10.1175/1520-0450(1976)015<1252:TCMASP>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gopalakrishnan, S. G., F. Marks Jr., X. Zhang, J.-W. Bao, K.-S. Yeh, and R. Atlas, 2011: The experimental HWRF system: A study on the influence of horizontal resolution on the structure and intensity changes in tropical cyclones using an idealized framework. Mon. Wea. Rev., 139, 17621784, https://doi.org/10.1175/2010MWR3535.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hall, T. M., and S. Jewson, 2007: Statistical modelling of North Atlantic tropical cyclone tracks. Tellus, 59A, 486498, https://doi.org/10.1111/j.1600-0870.2007.00240.x.

    • Search Google Scholar
    • Export Citation
  • Harr, P. A., and R. L. Elsberry, 1991: Tropical cyclone track characteristics as a function of large-scale circulation anomalies. Mon. Wea. Rev., 119, 14481468, https://doi.org/10.1175/1520-0493(1991)119<1448:TCTCAA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harr, P. A., and R. L. Elsberry, 1995a: Large-scale circulation variability over the tropical western North Pacific. Part I: Spatial patterns and tropical cyclone characteristics. Mon. Wea. Rev., 123, 12251246, https://doi.org/10.1175/1520-0493(1995)123<1225:LSCVOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harr, P. A., and R. L. Elsberry, 1995b: Large-scale circulation variability over the tropical western North Pacific. Part II: Persistence and transition characteristics. Mon. Wea. Rev., 123, 12471268, https://doi.org/10.1175/1520-0493(1995)123<1247:LSCVOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hartigan, J. A., and M. A. Wong, 1979: Algorithm AS 136: A k-means clustering algorithm. J. Roy. Stat. Soc., 28C, 100108, https://doi.org/10.2307/2346830.

    • Search Google Scholar
    • Export Citation
  • Hodanish, S., and W. M. Gray, 1993: An observational analysis of tropical cyclone recurvature. Mon. Wea. Rev., 121, 26652689, https://doi.org/10.1175/1520-0493(1993)121<2665:AOAOTC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hodges, K. I., and R. Emerton, 2015: The prediction of Northern Hemisphere tropical cyclone extended life cycles by the ECMWF ensemble and deterministic prediction systems. Part I: Tropical cyclone stage. Mon. Wea. Rev., 143, 50915114, https://doi.org/10.1175/MWR-D-13-00385.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, S.-Y., and J.-O. J. Lim, 2006: The WRF single-moment 6-class microphysics scheme (WSM6). J. Korean Meteor. Soc., 42, 129151.

  • Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Wea. Rev., 134, 23182341, https://doi.org/10.1175/MWR3199.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jin, H., M. S. Peng, Y. Jin, and J. D. Doyle, 2014: An evaluation of the impact of horizontal resolution on tropical cyclone predictions using COAMPS-TC. Wea. Forecasting, 29, 252270, https://doi.org/10.1175/WAF-D-13-00054.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43, 170181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kehoe, R. M., M. A. Boothe, and R. L. Elsberry, 2007: Dynamical tropical cyclone 96-and 120-h track forecast errors in the western North Pacific. Wea. Forecasting, 22, 520538, https://doi.org/10.1175/WAF1002.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, H.-S., J.-H. Kim, C.-H. Ho, and P.-S. Chu, 2011: Pattern classification of typhoon tracks using the fuzzy c-means clustering method. J. Climate, 24, 488508, https://doi.org/10.1175/2010JCLI3751.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krzanowski, W. J., and Y. Lai, 1988: A criterion for determining the number of groups in a data set using sum-of-squares clustering. Biometrics, 44, 2334, https://doi.org/10.2307/2531893.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lander, M. A., 1996: Specific tropical cyclone track types and unusual tropical cyclone motions associated with a reverse-oriented monsoon trough in the western North Pacific. Wea. Forecasting, 11, 170186, https://doi.org/10.1175/1520-0434(1996)011<0170:STCTTA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Liu, B., and L. Xie, 2012: A scale-selective data assimilation approach to improving tropical cyclone track and intensity forecasts in a limited-area model: A case study of Hurricane Felix (2007). Wea. Forecasting, 27, 124140, https://doi.org/10.1175/WAF-D-10-05033.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • MacQueen, J., 1967: Some methods for classification and analysis of multivariate observations. Proc. Fifth Berkeley Symp. on Mathematical Statistics and Probability, Oakland, CA, University of California, 281297.

  • Magnusson, L., J. D. Doyle, W. A. Komaromi, R. D. Torn, C. K. Tang, J.C.L. Chan, M. Yamaguchi, and F. Zhang, 2019: Advances in understanding difficult cases of tropical cyclone track forecasts. Trop. Cyclone Res. Rev., 8, 109122, https://doi.org/10.1016/j.tcrr.2019.10.001.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, 16 66316 682, https://doi.org/10.1029/97JD00237.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moon, J., D. H. Cha, M. Lee, and J. Kim, 2018: Impact of spectral nudging on real-time tropical cyclone forecast. J. Geophys. Res. Atmos., 123, 12 64712 660, https://doi.org/10.1029/2018JD028550.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nakamura, J., U. Lall, Y. Kushnir, and S. J. Camargo, 2009: Classifying North Atlantic tropical cyclone tracks by mass moments. J. Climate, 22, 54815494, https://doi.org/10.1175/2009JCLI2828.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Neumann, C. J., and J. M. Pelissier, 1981: Models for the prediction of tropical cyclone motion over the North Atlantic: An operational evaluation. Mon. Wea. Rev., 109, 522538, https://doi.org/10.1175/1520-0493(1981)109<0522:MFTPOT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Noh, Y., W. Cheon, S. Hong, and S. Raasch, 2003: Improvement of the K-profile model for the planetary boundary layer based on large eddy simulation data. Bound.-Layer Meteor., 107, 401427, https://doi.org/10.1023/A:1022146015946.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nystrom, R. G., F. Zhang, E. B. Munsell, S. A. Braun, J. A. Sippel, Y. Weng, and K. Emanuel, 2018: Predictability and dynamics of Hurricane Joaquin (2015) explored through convection-permitting ensemble sensitivity experiments. J. Atmos. Sci., 75, 401424, https://doi.org/10.1175/JAS-D-17-0137.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pielke, R. A., Jr., J. Gratz, C. W. Landsea, D. Collins, M. A. Saunders, and R. Musulin, 2008: Normalized hurricane damage in the United States: 1900–2005. Nat. Hazards Rev., 9, 2942, https://doi.org/10.1061/(ASCE)1527-6988(2008)9:1(29).

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Powell, M. D., and S. D. Aberson, 2001: Accuracy of United States tropical cyclone landfall forecasts in the Atlantic basin (1976–2000). Bull. Amer. Meteor. Soc., 82, 27492768, https://doi.org/10.1175/1520-0477(2001)082<2749:AOUSTC>2.3.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raju, P., J. Potty, and U. Mohanty, 2011: Sensitivity of physical parameterizations on prediction of tropical cyclone Nargis over the Bay of Bengal using WRF model. Meteor. Atmos. Phys., 113, 125137, https://doi.org/10.1007/s00703-011-0151-y.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Short, C. J., and J. Petch, 2018: How well can the Met Office Unified Model forecast tropical cyclones in the western North Pacific? Wea. Forecasting, 33, 185201, https://doi.org/10.1175/WAF-D-17-0069.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Skamarock, W. C., and et al. , 2008: A description of the Advanced Research WRF version 3. NCAR Tech. Note NCAR/TN-475+STR, 113 pp., https://doi.org/10.5065/D68S4MVH.

    • Crossref
    • Export Citation
  • Sun, Y., and et al. , 2017: Impact of ocean warming on tropical cyclone track over the western North Pacific: A numerical investigation based on two case studies. J. Geophys. Res. Atmos., 122, 86178630, https://doi.org/10.1002/2017JD026959.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Torn, R. D., T. J. Elless, P. P. Papin, and C. A. Davis, 2018: Tropical cyclone track sensitivity in deformation steering flow. Mon. Wea. Rev., 146, 31833201, https://doi.org/10.1175/MWR-D-18-0153.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Velden, C. S., and L. M. Leslie, 1991: The basic relationship between tropical cyclone intensity and the depth of the environmental steering layer in the Australian region. Wea. Forecasting, 6, 244253, https://doi.org/10.1175/1520-0434(1991)006<0244:TBRBTC>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Waldron, K. M., J. Paegle, and J. D. Horel, 1996: Sensitivity of a spectrally filtered and nudged limited-area model to outer model options. Mon. Wea. Rev., 124, 529547, https://doi.org/10.1175/1520-0493(1996)124<0529:SOASFA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yamada, H., T. Nasuno, W. Yanase, and M. Satoh, 2016: Role of the vertical structure of a simulated tropical cyclone in its motion: A case study of Typhoon Fengshen (2008). SOLA, 12, 203208, https://doi.org/10.2151/sola.2016-041.

    • 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, 489496, https://doi.org/10.1175/2008BAMS2631.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 136 136 79
Full Text Views 38 38 17
PDF Downloads 49 49 22

Five-Day Track Forecast Skills of WRF Model for the Western North Pacific Tropical Cyclones

View More View Less
  • 1 a School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
  • | 2 b Department of Atmospheric Sciences, University of Washington, Seattle, Washington
© Get Permissions Rent on DeepDyve
Restricted access

Abstract

In this study, the characteristics of simulated tropical cyclones (TCs) over the western North Pacific by a regional model (the WRF Model) are verified. We utilize 12-km horizontal grid spacing, and simulations are integrated for 5 days from model initialization. A total of 125 forecasts are divided into five clusters through the k-means clustering method. The TCs in the cluster 1 and 2 (group 1), which includes many TCs moving northward in the subtropical region, generally have larger track errors than for TCs in cluster 3 and 4 (group 2). The optimal steering vector is used to examine the difference in the track forecast skill between these two groups. The bias in the steering vector between the model and analysis data is found to be more substantial for group 1 TCs than group 2 TCs. The larger steering vector difference for group 1 TCs indicates that environmental fields tend to be poorly simulated in group 1 TC cases. Furthermore, the residual terms, including the storm-scale process, asymmetric convection distribution, or beta-related effect, are also larger for group 1 TCs than group 2 TCs. Therefore, it is probable that the large track forecast error for group 1 TCs is a result of unreasonable simulations of environmental wind fields and residual processes in the midlatitudes.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dong-Hyun Cha, dhcha@unist.ac.kr

Abstract

In this study, the characteristics of simulated tropical cyclones (TCs) over the western North Pacific by a regional model (the WRF Model) are verified. We utilize 12-km horizontal grid spacing, and simulations are integrated for 5 days from model initialization. A total of 125 forecasts are divided into five clusters through the k-means clustering method. The TCs in the cluster 1 and 2 (group 1), which includes many TCs moving northward in the subtropical region, generally have larger track errors than for TCs in cluster 3 and 4 (group 2). The optimal steering vector is used to examine the difference in the track forecast skill between these two groups. The bias in the steering vector between the model and analysis data is found to be more substantial for group 1 TCs than group 2 TCs. The larger steering vector difference for group 1 TCs indicates that environmental fields tend to be poorly simulated in group 1 TC cases. Furthermore, the residual terms, including the storm-scale process, asymmetric convection distribution, or beta-related effect, are also larger for group 1 TCs than group 2 TCs. Therefore, it is probable that the large track forecast error for group 1 TCs is a result of unreasonable simulations of environmental wind fields and residual processes in the midlatitudes.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dong-Hyun Cha, dhcha@unist.ac.kr
Save