Authors

Affiliations

1 VNU University of Science, Vietnam National University; hoadao@vnu.edu.vn; tientt@vnu.edu.vn

*Corresponding author: hoadao@vnu.edu.vn; Tel.: +84–857758771

Abstracts

In this study, a logistic regression model is developed to forecast tropical storm (TS) genesis in the Vietnam East Sea from 2012 to 2019. The model incorporates seven potential predictors including dynamic and thermodynamic parameters at formation time retrieved from the WRF-LETKF outputs. After rigorous testing, six predictors are selected, excluding minimum sea-level pressure. In a broader context, the logistic regression model performs promisingly, generating forecast probabilities that enhance the accuracy of TS genesis predictions, particularly in early forecast cycles. The model’s regression coefficients and forecast outcomes align well with test dataset results, affirming its stability and validity. As a result, the forecast probability from this model can be effectively employed as a probabilistic forecast value for predicting TS genesis status.

Keywords

Cite this paper

Hoa, D.N.Q.; Tien, T.T. Development of an ensemble dynamic-probabilistic prediction model for tropical storm genesis in the Vietnam East Sea using the Logistic Regression approach. J. Hydro-Meteorol. 2023, 17, 19-30.

References

1. Halperin, D.J.; Fuelberg, H.E.; Hart, R.E.; Cossuth, J.H. Verification of tropical cyclone genesis forecasts from global numerical models: Comparisons between the North Atlantic and eastern North Pacific basins. Wea. Forecasting 2016, 31, 947–955.

2. Liang, M.; Chan, J.C.L.; Xu, J.; Yamaguchi, M. Numerical prediction of tropical cyclogenesis Part I: Evaluation of model performance. Quart. J. Roy. Meteor. Soc. 2021, 147, 1626–1641.

3. Jaiswal, N.; Kishtawal, C.M.; Bhomia, S.; Pal, P.K. Multi-model ensemble-based probabilistic prediction of tropical cyclogenesis using TIGGE model forecasts. Meteor. Atmos. Phys. 2016, 128, 601–611.

4. Pedlosky, J. Geophysical fluid dynamics. 1979.

5. Zhang, X.; Yu, H. A probabilistic tropical cyclone track forecast scheme based on the selective consensus of ensemble prediction systems. Wea. Forecasting 2017, 32, 2143–2157.

6. Zhang, X.; Fang, J.; Yu, Z. The forecast skill of tropical cyclone genesis in two global ensembles. Wea. Forecasting 2023, 38(1), 83–97. https://doi.org/10.1175/WAF-D-22-0145.1.

7. Hunt, B.R.; Kostelich, E.J.; Szunyogh, I. Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter. Physica D: Nonlinear Phenomena 2007, 230, 112–126.

8. Miyoshi, T.; Kunii, M. Using AIRS retrievals in the WRF-LETKF system to improve regional numerical weather prediction. Tellus A 2012, 64(1), 18408. https://doi.org/10.3402/tellusa.v64i0.18408.

9. Kieu, C.Q.; Truong, N.M.; Mai, H.T.; Ngo-Duc, T. Sensitivity of the track and intensity forecasts of Typhoon Megi (2010) to satellite-derived atmospheric motion vectors with the ensemble Kalman filter. J. Atmos. Oceanic Technol. 2012, 29(12), 1794–1810. https://doi.org/10.1175/jtech-d-12-00020.1.

10. Liu, J.; Fertig, E.; Li, H.; Kalnay, E.; Hunt, B.; Kostelich, E.; Szunyogh, I.; Todling, R. Comparison between local ensemble transform Kalman filter and PSAS in the NASA finite volume GCM - Perfect model experiments. Nonlinear Processes Geophys. 2007, 15, 645–659. https://doi.org/10.5194/npg-15-645-2008.

11. Kwon, H.; Lee, W.; Won, S.H.; Cha, E.J. Statistical ensemble prediction of the tropical cyclone activity over the western North Pacific. Geophys. Res. Lett. 2007, 34, 24805. https://doi.org/10.1029/2007GL032308.

12. Leroy, A.; Wheeler, M. Statistical prediction of weekly tropical cyclone activity in the Southern Hemisphere. Mon. Weather Rev. 2008, 136, 3637–3654. https://doi.org/10.1175/2008MWR2426.1.

13. Mestre, O.; Hallegatte, S. Predictors of Tropical Cyclone Numbers and Extreme Hurricane Intensities over the North Atlantic Using Generalized Additive and Linear Models. J. Clim. 2009, 22, 633–648. https://doi.org/10.1175/2008JCLI2318.1.

14. Chan, J.; Shi, J.E.; Lam, C.M. Seasonal forecasting of tropical cyclone activity over the western North Pacific and the South China Sea. Wea. Forecasting 1998, 13, 997–1004. https://doi.org/10.1175/1520-0434(1998)013<0997:SFOTCA>2.0.CO;2.

15. Wijnands, J.; Qian, G.; Kuleshov, Y. Variable selection for tropical cyclogenesis predictive modeling. Mon. Weather Rev. 2016, 144, 4605–4619. https://doi.org/10.1175/MWR-D-16-0166.1.

16. Wilks, D.S. Statistical methods in the atmospheric sciences. Int. Geophys. Series 2006, 59, xi. https://doi.org/10.1016/S0074-6142(06)80036-7.

17. Choi, J.W.; Kang, K.R.; Kim, D.W.; Kim, T.R. Development of a probability prediction model for tropical cyclone genesis in the northwestern pacific using the logistic regression method. J. Korean Earth Sci. Soc. 2010, 31(5), 454–464. https://doi.org/10.5467/JKESS.2010.31.5.454.

18. Tien, T.T.; Hoa, D.N.Q.; Thanh, C.; Kieu, C. Assessing the impacts of augmented observations on the forecast of Typhoon Wutip (2013)’s formation using the ensemble Kalman filter. Wea. Forecasting 2020, 35(4), 1483–1503. https://doi.org/10.1175/waf-d-20-0001.1.

19. Knapp, K.R.; Kruk, M.C.; Levinson, D.H.; Diamond, H.J.; Neumann, C.J. The international best track archive for climate stewardship (IBTrACS): Unifying tropical cyclone data. Bull. Am. Meteorol. Soc. 2010, 91(3), 363–376.

20. Fu, B.; Peng, M.S.; Li, T.; Stevens, D.E. Developing versus nondeveloping disturbances for tropical cyclone formation. Part II: Western North Pacific. Mon. Weather Rev. 2012, 140(4), 1067–1080. https://doi.org/10.1175/2011MWR3618.1.

21. Kerns, B.W.; Chen, S.S. Cloud clusters and tropical cyclogenesis: Developing and nondeveloping systems and their large-scale environment. Mon. Weather Rev. 2013, 141(1), 192–210. https://doi.org/10.1175/MWR-D-11-00239.1.

22. Gray, W. Environmental influences on tropical cyclones. Aust. Meteorol. Mag.     1988, 36, 127–139.

23. Brier, G.W. Verification of forecasts expressed in terms of probability. Mon. Weather Rev. 1950, 78(1), 1–3.

24. Swets, J.A. The relative operating characteristic in psychology. Science 1973, 182, 990–1000.

25. Buizza, R.; Miller, M.; Palmer, T.N. Stochastic representation of model uncertainties in the ECMWF Ensemble Prediction System. 1999, pp. 2887-2908.