ANALYSIS OF WIND FIELD FOR TYPHOON RUSA IN SOUTH KOREA: BASED ON WIND DIRECTION OF TYPHOON MODEL

Chan-Su Yang (1), Antonio Reppucci (2), Kicheon Jun (3), Susanne Lehner (4)

(1) Ocean Satellite Research Group, Korea Ocean Research & Development Institute (KORDI), Ansan, 425-600, Korea –

yangcs@kordi.re.kr (2) Institute for Remote Sensing Technology, German Aerospace Center, Oberpfaffenhofen, D 82234 Wessling, Germany–

antonio.reppucci@dlr.de (3) Ocean Satellite Research Group, Korea Ocean Research & Development Institute, Ansan, 425-600, Korea –

kcjun@kordi.re.kr (4) Institute for Remote Sensing Technology, German Aerospace Center, Oberpfaffenhofen, D 82234 Wessling, Germany–

susanne.lehner@dlr.de

ABSTRACT Synthetic Aperture Radar (SAR) images are more and more widely used in ocean wind monitoring. We present a method for the estimation of wind directions from synthetic aperture radar (SAR) images of the ocean. The method is based on winds of QuikSCAT scatterometer and Typhoon Surface Wind Model (PVM, KORDI) for the region of eye wall that the wind direction does not appear as streaks in SAR images of the ocean. Within the eye wall, the wind speed reaches its maximum and the most damaging winds and intense rainfall is found. A RADARSAT-SAR image of TY RUSA acquired on August 30, 2002 at 2112 UTC seems like the center where the NRCS is quite low, but belongs to the eye wall. To retrieve wind speeds, CMOD5 was originally developed for the scatterometer aboard ERS-1 and 2 operating at VV polarization and consequently requires modification if applied to HH-polarized SAR data. However, because the Extended High mode used here has comparatively high incidence angles of 54.24° – 57.31°, the polarization ratio in a hybrid model function is supposed to be 1. The retrieved SAR wind is compared with the result of the typhoon model with a fine computing grid of 1/60 degrees. It is shown that wind structure of eye wall can be described by using SAR based on wind directions from QuikSCAT. 1. INTRODUCTION

When Typhoon Rusa, which peaked at 60 m/s winds earlier in its lifetime, hit South Korea, it caused torrential flooding, causing 113 casualties (with 71 missing) and nearly $6 billion in damage. Rusa formed as a tropical depression (TD) north of Bikini Island at 06UTC 22 August 2002. It developed into a typhoon (TY) at 18UTC 25 August and then reached its peak intensity at 12UTC on the following day and kept almost the same intensity until 15UTC 30 August. After changing its direction to the north gradually, Rusa made landfall on the Korean Peninsula at around 08UTC 31 August (Figure 1).

Figure 1. Tracks of Typhoon Rusa during the period from

August 23 to September 3, 2002.

Figure 2. GMS-5 Visible image acquired on 30 August

2002 at 21:00 UTC.

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Proc. ‘Envisat Symposium 2007’, Montreux, Switzerland 23–27 April 2007 (ESA SP-636, July 2007)

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scatterometer and Typhoon Surface Wind Model (PVM, KORDI). In the second part wind speeds are derived from the normalized radar cross section (NRCS) and image geometry of the calibrated SAR images, together with the local wind direction resulting from the first step. For the wind speed retrieval the semi empirical C-band scatterometer model CMOD5 is used. CMOD5 was originally developed for the scatterometer aboard ERS-1 and 2 operating at VV polarization and consequently requires modification if applied to HH-polarized SAR data. In case of HH-polarization the CMOD5 model is extended by considering the polarization ratio (PR). A hybrid model for application of CMOD5 to HH polarization is given by [Thompson et al. 1998]:

(1)

where α is a constant and set to 0.6, fitting the measurements of Unal et al.. This form is closely related to theoretical forms of the PR. Several different values for α have been suggested in the past considering RADARSAT-1 SAR data, they vary between 0.4 and 1.2. However, because EH4 mode has comparatively very high incidence angle of 54.24° – 57.31°, it is considered that the polarization ratio is approximately 1. Figure 6 shows an approach for retrieving wind fields from SAR images. 2.2 Typhoon Surface Wind Model (PVM, KORDI) Typhoon Surface Wind Model (KORDI) is based on Primitive Vortex Model (PVM) as follows;

(2)

(3)

where

Figure 6. Analysis approach for retrieving wind fields from SAR images.

Figure 7. Nested moving grid system for PVM simulation.

3. RESULTS AND CONCLUDING REMARKS

Wind filds of Typhoon Model are shown in Figs. 8 & 9 to each grid systems, compared to wind distribution based on QuickSCAT.

Figure 8. Two model grid results of wind fields on August 30, 2002 at 2100 UTC.

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Figure 9. Wind fields from QuickSCAT on August 30, 2002 at 2000-2100 UTC.

Figure 10. High-resolution wind map of TY Rusa based on the wind direction from QuickSCAT. QS data(fig. 9) has 90% of ice-free ocean every day a low spatial resolution of app. 25 km, compared to SAR data. Therefore, a linear interpolation method was used to get the same size as SAR. Figures 10 and 11 are high-resolution wind maps of TY Rusa by using wind directions based on both Quick SCAT and typhoon mode, respectively. It seems that QS-based wind directions could be more useful than those results from typhoon model which does not consider a vertical variation of air.

Much work remains to be done to retrieve wind direction and speed in and around the eye wall of a typhoon. In the future, a combined method of winds from both QuickSCAT and typhoon model will be applied to the SAR wind retrieval.

Figure 11. High-resolution wind map of TY Rusa based on the wind direction from typhoon model.

ACKNOWLEDGEMENTS

This work was supported by the Basic Research Project, “Development of Management and Restoration Technologies for Estuaries” of KORDI and the Public Benefit Project of Remote Sensing, “Satellite Remote Sensing for Marine Environment” of Korea Aerospace Research Institute.

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126oE 127oE 128oE 129oE

32oN

33oN

34oN

12

31

m/s

127oE 30'

30'

33oN

QS Wind-based

4

33

m/s

4

33

m/s

127oE 30'

30'

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Typhoon Model Wind-based

D.R. Thompson, T.M. Elfouhaily, and B. Chapron, Polarization Ratio for Microwave Backscattering from the Ocean Surface at Low to Moderate Incidence Angles, Proc. Int. Geosci. Remote Sens. Symp., Seattle, USA, 1998.