Authors

Minh Thi Pham 1* , Trung Le Doan 2 , Hang Thi Nguyen 3 , Tuong Hong Thi Tran 4 , Thuy Kim Pham 3

Affiliations

1 Department of Meteorology, Hydrology and Climate change, Ho Chi Minh University of Natural Resources and Environment; minhpt201@gmail.com

2 Student of Department of Meteorology, Hydrology and Climate change, Ho Chi Minh University of Natural Resources and Environment; dltrungphuhoi@gmail.com

3 Department of General Science Ho Chi Minh University of Natural Resources and Environment; hang.nguyen687@gmail.com; pkthuy.math@gmail.com

4 Department of Information Systems and Remote Sensing, Ho Chi Minh University of Natural Resources and Environment; tthtuong@hcmunre.edu.vn

*Corresponding author: minhpt201@gmail.com; Tel.: +84–936069249

Abstracts

This study applicates the multi–physical method in ensemble Kalman filter determining error of WRF models to forecast the track and intensity of storm Damrey in 2017. The study run three experiments with assimilation of satellite data to forecast Damrey in 2017 at the beginning 00 UTC and 12 UTC November 1st and 2nd: (1) 21 ensemble members which are combinated from 11 physics options, no increase in error correlation (MP); (2) Using single set of physical model, 21 ensemble members, inflation factor λ = 6.5 (MI); (3) Using single set of physical model, 21 ensemble members without increase in error correlation (PF). Statistical results of track errors in MP test at the 24, 48, 72–hour is 12–32% reduction in compared with tests MI and PF. For storm intensity, absolute error of Pmin in the MP test at 24 and 72–hour is decreased from 30–47% in compared to the other two tests. And the absolute error of Vmax in the MP test at all forecasting terms is 13–26% reduction in compared with tests MI and PF. Thus, the multi–physical ensemble Kalman filter can forecast the track and intensity of storms affecting Vietnam.

Keywords

Ensemble forecasting; Error model; Typhoon; The Kalman filter.

Cite this paper

Minh, P.T.; Trung, L.D.; Hang, T.N.; Tuong, H.T.T.; Thuy, K.P. Forecasting the track and intensity Damrey storm in 2017 by the multi–physical ensemble Kalman filter. VN J. Hydrometeorol. 2022, 11, 57-71. 

References

1. Holton, J.R. An introduction to dynamic meteorology, 2004.

2. Chanh, K.Q. Estimation of Model Error in the Kalman Filter by Perturbed Forcing. VNU J. Sci.: Nat. Sci. Tehnol. 2010, 26(3S), 310–316.

3. Thomas, M.H.; Jeffrey, S.W. Accounting for the Error due to Unresolved Scales in Ensemble Data Assimilation: A Comparison of Different Approaches. Mon. Weather Rev. 2005, 133, 3132–3147.

4. Pu, Z. Improving Hurricane Intensity Forecasting through Data Assimilation: Environmental Conditions Versus the Vortex Initialization, Recent Hurricane Research – Climate, Dynamics, and Societal Impacts. Anthony Lupo (Ed.), 2011, ISBN: 978-953-307-238-8.

5. Anderson, J.L.; Anderson, S.L. A Monte Carlo implementation of the non–linear filtering problem to produce ensemble assimilations and forecasts. Mon. Wea. Rev. 1999, 127, 27–41.

6. Mitchell, H.L.; Houtekamer, P.L. An adaptive ensemble Kalman filter. Mon. Wea. Rev. 2000, 128, 416.

7. Dee, D.P.; Silva, A.M. Data assimilation in the presence of forecast bias. Quart. J. Roy. Meteor. Soc. 1998, 124, 269.

8. Li, H. Local ensemble transform Kalman filter with realistic observations. Ph.D. dissertation. University of Maryland. 2007, pp. 131.

9. Chanh, K.; Minh, P.T.; Mai, H.T. An Application of the Multi–Physics Ensemble Kalman Filter to Typhoon Forecast. Pure Appl. Geophys. 2013, 170, 745–954.

10. Jun, L.I.; Jun, D.U.; Yu, L.I.U. A comparison of initial condition-, multi-physics- and stochastic physics-based ensembles in predicting Beijing “7.21” excessive storm rain event. Acta Meteorol. Sinica 2014. doi:10.11676/qxxb2015.008.

11. Minh, P.T.; Hang, N.T.; Thuy, P.K.; Gia, C.N.H. An application of the multi-physical method of determining error of WRF models to simulate the track and intensity Usagi storm in 2018. Sci Technol. Dev. J.: Sci. Earth Environ. 2021, 5(1), 298–311.

12. Anderson, J.L.; Anderson, S.L. A Monte Carlo implementation of the non–linear filtering problem to produce ensemble assimilations and forecasts. Mon. Wea. Rev. 1999, 127, 2741–2758.

13. https://en.wikipedia.org/wiki/Typhoon_Damrey_(2017).

14. Phong, N.B.; Hiep, N.V.; Thang, N.V. Application of dynamical vortex initialization scheme on intensity forecast and structure study of typhoon Damrey (2017) during near-shore and landfalling period. J. Clim. Change Sci. 2020, 16, 23–35.

15. Nang, T.Q.; Tien, T.T. Skill validation of probability tropical cyclone track forecast in Bien Dong. VN J. Hydrometeorol. 2020, 717, 11–19.

16. Kulaya, K.; Sujittra, R.; Pakpoom, R. Sensitivity of different physics schemes using WRF model in Typhoon Damrey (2017) over the Indochina region. J. Phys.: Conf. Ser. 2021, 2145, 012046.

17. Hunt, B.R.; Kostelich, E.; Szunyogh, I. Efficient data assimilation for spatiotemporal chaos: a local ensemble transforms Kalman filter. Physica D. 2005, 230, 112–126.

18. https://www2.mmm.ucar.edu/wrf/OnLineTutorial/compilation_tutorial.php.

19. http://homepages.see.leeds.ac.uk/~lecrrb/wrf/aRWUsersGuide.pdf.

20. Wilks, D.S. Statistical Methods in the Atmospheric Sciences, Ithaca New York. 1997, 59, 255.