Observation and numerical prediction of torrential rainfall over Korea caused by Typhoon Rusa (2002)

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1 Click Here for Full Article JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 115,, doi: /2009jd012581, 2010 Observation and numerical prediction of torrential rainfall over Korea caused by Typhoon Rusa (2002) Dong Kyou Lee 1 and Suk Jin Choi 1 Received 2 June 2009; revised 31 January 2010; accepted 16 February 2010; published 17 June [1] On landfall in 2002, Typhoon Rusa caused record breaking rainfall (870.5 mm/d) in Gangneung at the foot of the Taebaek Mountain range, Korea. In this study, the predictability of the torrential rainfall associated with the typhoon and the detailed mesoscale precipitation distribution were investigated through numerical simulation. The weather research and forecasting (WRF) model successfully simulates the mesoscale rainfall distibution and timing. With a 10 km (3.3 km) horizontal grid, the model predicted mm ( mm) of rain with some uncertainty in the observed local maximum in coverage. This study shows that the complexity of forecasting is associated with the interactions between environmental flows, typhoon flow, and topography. In bogus and 1 day earlier initial time experiments, the typhoon track and intensity are substantially improved, although this has little impact on successful rainfall simulation. The interaction between the large scale features and the proper vertical structure of the typhoon is key. The simulated rainfall at the cloud resolving spacing of 3.3 km is greater than that at 10 km because of the increased terrain height and not the greater resolution. Analysis of observed and simulated data shows that the torrential rainfall had two different causes with two peaks in the hourly rainfall at Gangneung. The first peak links to a mesoscale frontal structure, characterized by strong moisture and thermal gradients formed by the intrusion of cold, dry northerly air from a midlatitude trough and moist southerly air from the typhoon. The second peak results from the direct effect of the typhoon and the lifting of the moisture laden typhoon winds. Citation: Lee, D. K., and S. J. Choi (2010), Observation and numerical prediction of torrential rainfall over Korea caused by Typhoon Rusa (2002), J. Geophys. Res., 115,, doi: /2009jd Introduction [2] Typhoon Rusa made landfall at Goheung on the southwestern Korean Peninsula at approximately 0630 UTC, 31 August It was one of the most disastrous tropical cyclone events in the 100 year meteorological history of Korea, producing record breaking rainfall over Gangneung with a daily accumulated precipitation of mm and a maximum hourly rainfall rate of 98 mm. The daily precipitation exceeded 60% of the total annual precipitation ( mm) at Gangneung. [3] A number of studies have examined the track, intensity, structure, and genesis of tropical cyclones [Harr and Elsberry, 1996; Frank and Ritchie, 1999; Liu et al., 1997, 1999; Goerss, 2000; Carr and Elsberry, 2000a, 2000b; Davis and Bosart, 2001, 2002; Goerss et al., 2004; Zhu et al., 2004; Romine and Wilhelmson, 2006; Wu et al., 2006]. For example, Liu et al. [1997, 1999] simulated Hurricane Andrew (1992) and described its inner core structure and 1 Atmospheric Sciences Program, School of Earth and Environmental Sciences, Seoul National University, Seoul, South Korea. Copyright 2010 by the American Geophysical Union /10/2009JD intensification processes. Davis and Bosart [2001, 2002] studied the transformation of a weak baroclinic disturbance into Hurricane Diana (1984). Wu et al. [2006] studied the formation of Hurricane Isabel (2003) by considering the influence of thermodynamic effects. However, few studies have considered the influence of tropical cyclones on quantitative precipitation forecasts [Wu et al., 2002]. [4] Even with accurate forecasts of typhoon track, intensity, and structure, it is difficult to forecast typhoon rainfall. In particular, as a typhoon approaches the midlatitudes, the rainfall resulting from the direct and indirect influence of the typhoon is closely related to the interactions between the synoptic scale environment and the topography. Kitabatake [2002] found that an organized rainfall region was induced in the northern semicircle of a typhoon by an extratropical transformation of the typhoon related to upper troposphere disturbances; the rainfall region was limited southwest of the cyclone center. Atallah and Bosart [2003] showed that the baroclinic zone between the juxtaposition of a cold core potential vorticity (PV) anomaly associated with the midlatitude trough and a warm core PV anomaly associated with a tropical cyclone provided sufficient precipitation in a region favorable for deep isentropic ascent. Through an interaction with the synoptic environment referred to as an extratopical 1of20

2 Figure 1. (a) The computational domains, smoothed topography (shaded; m), the Joint Typhoon Warning Center (JTWC) best track, and central pressure of Typhoon Rusa (circles, every 12 h), and (b) the topography of the 3.3 km horizontal resolution model and the best track. The crosses indicate the location of Korea Meteorological Administration (KMA) surface stations. In Figure 1b the dashed line indicates the Taebaek Mountain Range (TMR). transition, typhoons also can be reintensified or dissipated [Klein et al., 2000;Harr and Elsberry, 2000; Bosart et al., 2000]. [5] As a typhoon approaches land, a large amount of rainfall can occur over mountainous areas due to the strong winds and large amount of moisture brought by the typhoon. Wu et al. [2002] showed that the precipitation distribution associated with a typhoon near Taiwan was highly modulated by the topography effect. On the Korean Peninsula, where about 70% of the land is mountainous, rainfall is usually enhanced by the topographical effect [Seo and Lee, 1996; Lee and Lee, 1998]. The north south oriented Taebaek Mountain Range (TMR) along the eastern coastline of the southern Korean Peninsula plays an important role in producing rainfall. The moisture laden airflows that cross over the TMR are dominant and persistent from the west to the east, or vice versa. Their rapidly developing upslope motion often generates intense rainfall over the eastern Korean Peninsula during the summer monsoon. [6] The main objective of this study is to investigate the predictability of the torrential precipitation caused by Typhoon Rusa, which produced record breaking rainfall of mm/24 h in South Korea and a peak rainfall rate of 98 mm/h. Meteorologists were completely inaccurate in their forecasts of the precipitation for this rainfall event. Previously, Park and Lee [2007] simulated the heavy rainfall caused by Rusa and suggested that the cause of the localized heavy rainfall was a release of a potential instability by a consequent orographic lifting of moist air from the sea. However, in their simulation, the amount and characteristics of the heavy rainfall and the synoptic features associated with the typhoon were not fully examined. Xiao et al. [2006] performed sensitivity experiments on track and intensity prediction for the typhoon using a bogus cyclone data assimilation scheme, but the rainfall was significantly underestimated. In this study, we attempt to quantify the simulated rainfall in terms of the horizontal resolution of the model, the initial state of the typhoon, and the role of topography. For this purpose, we use the weather research and forecasting (WRF) model, a recently developed mesoscale numerical weather prediction system. We also use the observed behavior, data analysis, and simulation results to identify the precipitation processes that maintained the localized torrential rainfall associated with Typhoon Rusa. [7] The paper is organized as follows. In section 2, we describe the characteristics of the heavy rainfall resulting from Typhoon Rusa. The model configuration and experiments are explained in section 3. The simulated results are discussed in section 4, and the conclusions are given in section Typhoon Rusa and Heavy Rainfall [8] On 23 August 2002, Typhoon Rusa formed from a tropical wave in the middle Pacific Ocean and became a tropical depression at the eastern periphery of the monsoon trough, about 600 km southwest of Wake Island. After approximately 7 days, it moved west northwestward to arrive at Okinawa on 30 August (Figure 1a). It then turned 2of20

3 Figure 2. The 24 h accumulated rainfall observed at surface stations from 1500 UTC 30 to 1500 UTC 31 August 2002 with time series of hourly rainfall at Gangneung, Chupungryeong, and Goheung. northward toward the Korean Peninsula and subsequently made landfall near Goheung on the southern Korean Peninsula at approximately 0630 UTC, 31 August. While Typhoon Rusa passed over the southern Korean Peninsula, heavy rainfall occurred in this region over the southern and eastern coastlines and inland. Rusa produced record breaking daily rainfall in Gangneung at the foot of the Taebaek Mountain Range (TMR) in the eastern coastal region of South Korea (Figure 1b). Gangneung is located upwind of the easterly wind originating from Rusa as the typhoon approached and passed over the Korean Peninsula. [9] Figure 2 shows the distribution of the observed 24 h rainfall and the time series of the hourly rainfall at the Goheung, Chupungryeong, and Gangneung surface stations from 1500 UTC 30 August to 1500 UTC 31 August. It is based on hourly rain gauge data from the 73 surface stations of the Korea Meteorological Administration (KMA) with a spatial resolution of approximately 50 km. The maximum local daily rainfall recorded at the three stations is as follows: Goheung, mm; Chupungryeong, mm; and Gangneung, mm. These stations were approximately located in the southern, middle, and eastern regions, respectively, of the southern Korean Peninsula, to the right of the typhoon track. As the typhoon penetrated northeastward through the southern Korean Peninsula from 0600 UTC 31 August to 0000 UTC 1 September, the maximum hourly rainfall (98 mm) directly attributable to the typhoon occurred very near the southern Korean Peninsula at 1700 UTC 31 August. The maximum hourly rainfall at Goheung occurred at 0400 UTC 31 August, 2 h prior to landfall; the maximum hourly rainfall at Chupungryeong took place approximately 4 h later. Both stations showed a single peak. [10] The hourly rainfall series at Gangneung showed two maxima: The first occurred at 0000 UTC 31 August, approximately 4 h before that at Goheung. The second occurred at 1400 UTC 31 August, approximately 14 h after the first at Gangneung and 10 h after that at Goheung. This is one of the reasons why the total rainfall at Gangneung was much larger than that at the other two stations. In addition, the occurrence of two hourly rainfall peaks suggests that two different precipitation processes were involved. Goheung and Chupungryeong had only one peak, so their rainfall was influenced directly by the typhoon; however, the first rainfall maximum at Gangneung (located farther north) was not directly related to the typhoon but was caused by another process. [11] Radar images from the KMA are shown in Figure 3. The rain rate clearly shows the pattern of precipitation systems. At 2030 UTC 30 August (Figure 3a), a narrow rainband that is well developed northwest of Gangneung extends northeastward in the middle Sea of Japan (East Sea). This rainband is stationary and separate from the spiral 3of20

4 Figure 3. Instant column maximum rain rate derived from radar sites in South Korea. Red stars indicate the radar sites. rainfall systems of the typhoon. However, it later merges with a spiral rainband of the typhoon and is intensified over Gangneung at 2330 UTC 30 August (Figure 3b). The merging of rainbands as well as the topographic effect of the TMR, i.e., the north south oriented mountain range that blocks the easterly/southeasterly typhoon induced flows, results in the first rainfall maximum of 78.5 mm/h at 0000 UTC 31 August. After 0300 UTC 31 August, a rainfall system gradually develops from the merged rainbands and forms a ring pattern near Gangneung (Figure 3c). This ring pattern system may also be the result of the blocking of the easterly/ southeasterly flows of the typhoon by the TMR. As the typhoon makes landfall at Goheung at 0600 UTC, the rainfall system of the typhoon and the ring pattern rainfall system near Gangneung are separated from the main precipitation area of the typhoon because of the effect of the TMR (not shown). At 1210 UTC 31 August, the second rainfall maximum is caused by the intensified ring pattern rainfall system (Figure 3d). The radar images clearly indicate that the heavy rainfall at Gangneung results from the merging of two rainbands that are separated from the major rainfall system of the typhoon by the effect of the TMR. [12] We analyzed the National Centers for Environmental Prediction (NCEP) global final (FNL) analysis data to 4of20

5 Figure 4. The 850 hpa geopotential height (gpm, solid lines), temperature ( C, dashed), mixing ratio (g/kg, shaded), and wind vector (m/s) for (a) 1200 UTC 30, (b) 0000 UTC 31, and (c) 1200 UTC 31 August investigate the synoptic environment. Figure 4 shows the 850 hpa geopotential height, temperature, mixing ratio, and wind fields at 1200 UTC 30 August, 0000 UTC 31 August, and 1200 UTC 31 August. The Pacific subtropical high extends toward southern China and a surface trough develops over northwest China (Figure 4a). The northeastward moving typhoon passes through the westward extended Pacific subtropical high, pushing the subtropical high farther north and taking moisture from the ocean. Thus the enhanced pressure gradient between the typhoon and the subtropical high intensifies the southeasterly flows, which convey warm moist air toward the peninsula (Figure 4b). The relatively cold dry continental air mass over northeastern China invades the peninsula to the south of the trough by the mountain range. The two air masses develop a narrow area of convergence over the eastern Korean Peninsula and the middle Sea of Japan (East Sea). As Rusa approaches the peninsula, the 850 hpa thermal trough over the northeastern region of the Korean Peninsula moves rapidly southward via the strong northeasterly airflows formed by the typhoon (Figure 4c). The easterly wind, which is almost perpendicular to the TMR, prevails near Gangneung. After 0000 UTC 31 August, a relatively strong ridge of the Pacific subtropical high over the northern Sea of Japan (East Sea) helps to block the northeastward moving typhoon, causing it to move slowly northeastward into South Korea. 5of20

6 Figure 5. The 500 hpa equivalent potential temperature (K, thick lines) and pressure vertical velocity (Pa/s, contoured and shaded; positive (negative) values solid (dashed)) for (a) 1200 UTC 30, (b) 0000 UTC 31, and (c) 1200 UTC 31 August [13] Figure 5 shows the equivalent potential temperature (EPT; e ) and the vertical velocity fields. The confrontation between the cold dry continental air mass and the warm moist air mass can be clearly seen. The frontal line extends from near Gangneung, in the middle of the Korean Peninsula, to Sakhalin Island in Russia, and remains stationary. The cold dry air over northeast China contrasts well with the warm moist air originating from the typhoon, as shown by the strong temperature and moisture gradients along the coastlines of the Korean Peninsula and Russia. The horizontal gradient of e across the mesoscale front is approximately 15 K/200 km at 500 hpa, which is a mesoscale dimension. The downward (upward) motion of the cold dry (warm moist) air is lined up along the strong gradient of e. The downward motion makes the 850 hpa thermal trough in Figure 4b move rapidly southward over the northeastern region of the Korean Peninsula. As the typhoon approaches the peninsula, the e gradient and the vertical motion fields become clear. Because the upward motion is also supported 6of20

7 ratio of over 13 g/kg. The convective available potential energy (CAPE) has increased from 0 to 124 J kg 1, whereas the convective inhibition has decreased by 4 J kg 1. At 0600 UTC 31 August the severe weather threat index (SWEAT) is and the precipitable water is 49.4 mm. Since the wind profile at 0600 UTC 31 August shows that easterly/northeasterly flow is dominant in the lower troposphere, westward movement of the air parcel is likely. As the easterly/northeasterly low level flow brings in abundant moisture to the TMR, convective cells could be kept stationary while being formed along the slope of the TMR.The stationary convective cells thus formed result in substantial rainfalls [Doswell et al., 1996]. Figure 6. The skew T log p chart at Sokcho for (a) 1200 UTC 30 and (b) 0600 UTC 31 August by the windward lifting of southerly flows in the typhoon, the upward motion is stronger than the downward motion. [14] For further analysis of the descent of the cold air in the midtroposphere, we analyzed the Sokcho sounding data collected 60 km north of Gangneung station. The sounding data come from the Web site of the Department of Atmospheric Science at the University of Wyoming, United States ( Figure 6 shows the skew T log p chart for 1200 UTC 30 August, 12 h prior to the first rainfall peak at Gangneung, and 0600 UTC 31 August, 6 h after the first peak. The layer of relatively lower temperature between 500 and 700 hpa at 1200 UTC 30 August has descended to the lower troposphere between 700 and 1000 hpa. Also, the inversion that limited convection from 1000 to 850 hpa has been dissolved. The decreasing temperature of the lower troposphere reduces the level of free convection (LFC) from 4468 to 559 m, increasing the instability in the lower troposphere with the nearly saturated lower troposphere having a high mixing 3. Model Description and Experiments [15] We used the advanced research weather research and forecasting model (WRF ARW) (Version [Skamarock et al., 2005]) to simulate Typhoon Rusa. The model includes the Kain Fritsch cumulus parameterization scheme for subgrid scale convection [Kain and Fritsch, 1990, 1993; Kain, 2004], and the WRF single moment six class microphysics scheme (WSM6 [Hong et al., 2004; Hong and Lim, 2006]) for moist processes for grid scale cloud and precipitation. We used the Yonsei University (YSU) scheme [Hong et al., 2006] for the planetary boundary layer, the Noah land surface model [Chen and Dudhia, 2001] for the land surface, and the rapid radiative transfer model (RRTM) longwave scheme [Mlawer et al., 1997] together with the Dudhia shortwave scheme [Dudhia, 1989] for the atmospheric radiation processes. [16] The WRF model was run on triple nested domains of 30, 10, and 3.3 km horizontal grid meshes using a one way nesting method; the domains had , , and grid points, respectively (Figure 1a). The 34 vertical layers with the top at 50 hpa were the same in all three domains. The cumulus parameterization scheme was not used in domain 3 of the 3.3 km grid mesh. By interpolating the analysis fields to the model grids, initial and boundary conditions were obtained for the model run of domain 1 from the 6 hourly Final Global Data Assimilation System (FNL) analyses on 1 1 grids from the National Centers for Environmental Prediction (NCEP). The nested domains (2 and 3) used the simulated data of their mother domains for the initial and boundary conditions. The boundary conditions were obtained by linearly interpolating between subsets of the simulation at hourly intervals. [17] Eight numerical experiments were performed to investigate the simulated typhoon and associated rainfall. The experiments are summarized in Table 1. They were designed to examine (1) the effects of the track and intensity of the typhoon on the simulated rainfall, using different initial times and a bogus typhoon, (2) the resultant circulation and rainfall characteristics associated with the typhoon in domain 2, and (3) the effects of high horizontal resolution and topography on simulated precipitation in domain 3. [18] Model integration for EX30C in domain 1 was begun at 0000 UTC 29 August for 72 h forecasts. This initial time is approximately 60 h prior to typhoon landfall on the peninsula. EX10C and EX3.3C were started at 0000 and 1200 UTC 30 August using the forecast outputs of domains 1 and 2, respectively. EX30I was provided a different initial 7of20

8 Table 1. Summary of Numerical Experiments Experiment Horizontal Resolution (km) Initial Time, Date Initial and Boundary Data a Description EX30C UTC, 29 Aug NCEP/NCAR FNL control for mother domain EX10C UTC, 30 Aug EX30C nested in EX30C EX3.3C UTC, 30 Aug EX10C nested in EX10C EX3.3TOPO UTC, 30 Aug EX10C use of 10 km model resolution terrain height EX30I UTC, 30 Aug NCEP/NCAR FNL different initial time (24 h later than in EX30C) EX10I UTC, 30 Aug EX30I nested in EX30I EX30B UTC, 29 Aug NCEP/NCAR FNL use of a bogus typhoon at initial time EX10B UTC, 30 Aug EX30B nested in EX30B a NCEP, National Centers for Environmental Prediction; NCAR, National Center for Atmospheric Research; FNL, Final Global Data Assimilation System. value of 0000 UTC 30 August in the 30 km grid mesh domain to examine the sensitivity of the simulated typhoon track in its curving stage. [19] EX30B was intended to examine the effects of the track and intensity of the typhoon using a bogus typhoon at the initial time of the rainfall simulation. To produce a correctly positioned cyclone vortex with the observed intensity, we used the bogus method of the Japan Meteorological Agency (JMA) developed by Ueno [1995]. Briefly, the bogus vortex was constructed with axis symmetric and asymmetric components and the sum of the two components was merged back into the first guess field at the correct TC position. The symmetric component of the bogus typhoon vortex includes sea level pressure (SLP) and wind profiles. The SLP distribution was calculated by an equation based on the Fujita [1952] formula, and the bogus symmetric wind was based on the gradient wind relation. The geopotential height fields of the bogus typhoon at each analysis level above the surface were obtained based on Frank [1977]. The asymmetric component was calculated by the steering flow, assumed by time extrapolation from the locations of the typhoon at 12 h, 6 h, and the present hour [Davidson et al., 1993]. A more detailed description of the bogus method is given by Ueno [1995] and Davidson et al. [1993]. [20] EX10I and EX10B were nested in EX30I and EX30B, and EX3.3I and EX3.3B were nested in EX10I and EX10B, respectively. To investigate the role of terrain height in the simulation of heavy precipitation, we performed a sensitivity experiment (EX3.3TOPO) in domain 3, which was identical to EX3.3C except for the terrain height. EX3.3TOPO used the terrain height interpolated from EX10C. 4. Simulation Results 4.1. Track, Intensity, and Vertical Structure of Simulated Typhoons [21] Figure 7 shows the track, central pressure, and maximum wind speed of simulated typhoons with the best track analysis from the Joint Typhoon Warning Center (JTWC). The simulated typhoon tracks in the nested model experiments are not shown in this paper because they are similar to those in the 30 km resolution domains. The simulated typhoon tracks in nested models depend strongly on the large scale simulation [Peng and Chang, 2002]. The simulated track of EX30C is shifted eastward compared with the best track. One reason for this is that the typhoon in EX30C takes a turn approximately 12 h earlier than the turn in the observation. However, in EX30C the speed of the northward typhoon movement is in good agreement with the observation. The time averaged position error in the north south direction in EX30C is the smallest among the experiments (Table 2). In addition, the timely landfall of the simulated typhoon on the peninsula provides the nested models (EX10C and EX3.3C) with reasonable environmental circulations that facilitate better simulation of observed hourly rainfall. EX30I starts its model run at 0000 UTC 30 August; at this point the typhoon has almost reached the Korean Peninsula after nearly completing its curved turn. The simulated track in EX30I is close to the best track (Table 2); however, it tends to move slightly faster than the best track data. The simulated typhoon in EX30B is slightly shifted westward and tends to move faster than the best track. [22] EX30B corresponded well with the observation in terms of the minimum SLP and maximum sustained surface wind speed and particularly in modeling their steadiness and weakening before and during landfall. By contrast, EX30C and EX30I did not appropriately simulate the central pressure and maximum wind speed. Thus EX30B demonstrates the effectiveness of using a bogus typhoon to improve the simulated typhoon intensity at the start of the simulation and subsequently. It is noted that the initial deviation of SLP from the observation in EX30B resulted from the calculation of steering flows as an asymmetric component in the bogus typhoon, in which the typhoon position was given by the best track data. Calculating the past motion of the typhoon from the model simulation could reduce the deviation. [23] Figure 8 shows a comparison of the east west cross sections of EPT, relative humidity, and wind trough at the simulated typhoon center at 0000 UTC 31 August. EX30C and EX30I, without the bogus typhoon, show a broader, weaker eye approximately 200 km in size. However, the stronger eye with concentrated high EPT air in the inner 100 km in EX30B corresponds with the observations. However, although the observations and other simulations show a wide rainband with intense precipitation east of the typhoon center, the relative humidity distribution for EX30B indicates that the eyewall convection is more intense on the west. This asymmetry in EX30B affects the simulated rainfall in the nested experiment EX10B, which also has 8of20

9 Figure 7. (a) Tracks of observed and simulated Typhoon Rusa with 3 h intervals from 0000 UTC 29 August to 0000 UTC 1 September 2002 (the relatively larger marks have 12 h intervals). Time evolutions of (b) the minimum surface pressure and (c) maximum wind speed. enhanced precipitation on the western side (Figures 9c and 10e) Simulated Rainfall [24] Figure 9 shows the 36 h accumulated rainfall from 1200 UTC 30 August to 0000 UTC 1 September in the 10 km horizontal resolution experiments (EX10C, EX10I, and EX10B). Although the surface observation data are relatively sparse, the maximum rainfall amount and location in EX10C have the best agreement with the observation. In EX10C, the maximum rainfall is mm and its location error is 42.7 km. In EX10I and EX10B, the maximum rainfall (672.4 and mm, respectively) occurs at points that are shifted northward compared with that in EX10C. In EX10B, the initial intensity of the bogus typhoon is obtained, but in comparison with EX10C, the amount and distribution of the rainfall are not well simulated. EX10B produced heavy rainfall over the western side of Korea and underestimated the rainfall amount. [25] Figure 10 shows the wind, EPT, and 1 h rainfall distribution at the first and second rainfall peaks in EX10C, EX10I, and EX10B. At the first rainfall peak (Figures 10a, 10c, and 10e), EX10C and EX10B simulate the narrow rainbands over the Sea of Japan (East Sea) resulting from the Table 2. Position Error and Position Error in North South Direction a Experiment , 29 Aug Aug to Aug , 30 Aug Aug to Aug , 31 Aug Aug to Sep Average EX30C (26.88) (24.07) (34.34) (53.13) (52.02) (10.77) (33.54) EX30I (23.18) (34.48) (71.40) (88.06) (54.28) EX30B (47.96) (91.72) (97.57) (147.73) (162.22) (145.96) (115.53) a Values are in km. Values in parentheses are the position errors in the north south direction. 9of20

10 Figure 8. East west vertical cross section of equivalent potential temperature (K, thick lines), relative humidity (%, shaded), and wind vector (horizontal wind (m/s) and vertical wind (cm/s)) through the typhoon center at 0000 UTC 31 August for (a) EX30C, (b) EX30I, and (c) EX30B. strong temperature and moisture gradients between the midlatitude disturbance and the typhoon. In these two simulations, the changes in wind direction along the narrow rainband indicate that the band developed from the confrontation of the two air masses. The rainband in EX10C tends to be stationary for several hours, whereas that in EX10B moves northward as the stronger bogus typhoon approaches. Compared with EX10C, EX10B shows the intensifying rainband with enhanced contrast between warm and dry air. However, EX10I does not simulate the observed rainband, although it simulates the best typhoon track. At the second rainfall peak (Figures 10b, 10d, and 10f), the easterly/northeasterly winds developing against the TMR for all experiments are in agreement with the observation, although there are differences in the locations of the maximum rainfall. As the landfall typhoon passes over southern Korea, the coastal area at the foot of the TMR is covered by a spiral rainband from the typhoon. [26] Figure 11 shows the time series of hourly rainfall at the maximum accumulated rainfall points for the observation and the simulations. EX10C simulates the two rainfall peaks and the timings of the hourly rainfall, and the rainfall of the observed second peak is approximately 6 h delayed, which seems attributable to its eastward shifted track. In EX10I and EX10B, although the second peak is well simulated because of a proper track for the simulated typhoon, the observed first peak rainfall is not shown at the maximum accumulated rainfall points. The reason that the first peak is not depicted in EX10B is that the simulated narrow rainband does not coincide with the maximum rainfall points because of the northward shifted rainband at the time of the second peak, as seen in Figures 10e and 10f. In Figure 10e, the 1 h rainfall of the first peak in EX10B is 25.5 mm, compared with 33.5 mm for EX10C. The reason that the first rainfall peak is unresolved in EX10I is that the narrow rainband at the first rainfall peak is not simulated. [27] To highlight the importance of the synoptic scale flows in the midtroposphere in producing the mesoscale front, Figure 12 shows a vertical cross section across the narrow rainband in EX10C at 1800 UTC 30 August (6 h prior to the first peak) and 0000 UTC 31 August (the first peak). The synoptic scale thermal structure across the narrow rainband is well identified from the equivalent potential temperature ( e ) and potential temperature () fields (Figures 12a and 12b). At 1800 UTC 30 August (Figure 12a), the southern warm (northern cold) air mass is identified by a region of high (low) e and. In Figure 12a, the boundary between the warm and cold air masses is well characterized by the strong moisture gradient and the demarcation between the upward and downward motions, so that a potentially unstable distribution of low e air above high e air develops over the location of maximum rainfall [Schultz et al., 2000]. The demarcation between the upward and downward motions begins in the midtroposphere, and results from a confrontation between the continental flows and the typhoon flow. As a consequence, the thermally direct circulation is well characterized. The rainband is delineated by the total hydrometeor mixing ratio consisting of the cloud, graupel, ice, rain, and snow mixing ratio, and the updraft corresponds to the horizontal convergence (Figure 12b). [28] At 0000 UTC 31 August (Figure 12c), the descending low e air from the midtroposphere reaches the surface level 10 of 20

11 Figure 9. The 36 h accumulated total rainfall (mm, lines) from 1200 UTC 30 August to 0000 UTC 1 September with model terrains (m, shaded) and trajectory of the simulated typhoon with 3 h intervals for (a) EX10C, (b) EX10I, and (c) EX10B. The triangle indicates the location of Gangneung. near the location of maximum rainfall, so that upward motion near the surface level is induced. At 1800 UTC 30 August this upward motion is further enhanced by merging with the upward motion related to a spiral band of the typhoon in the southern area of the maximum rainfall location. The merging is also shown in the divergence/ convergence field (Figure 12d). Thus the upward motion extending from near the surface level to the upper troposphere produces a deep cloud band as moist low level air flows toward the cloud band from the east. The deep cloud band is conveyed to the TMR by the easterly flows in the lower troposphere. [29] The above results strongly suggest that the differences among the simulations of the narrow rainband at the first peak in EX10C, EX10I, and EX10B result from differences in the descending cold dry air. The descent of cold dry air is induced 11 of 20

12 Figure 10. The 1 h accumulated rainfall (mm, thick lines with contour interval 10 from 5), 925 hpa wind vector (m/s), and 925 hpa equivalent potential temperature (K, thin lines with interval 5 > 330 shaded) at (left) 0000 UTC 31 and (right) 1200 UTC 31 for (a and b) EX10C, (c and d) EX10I, and (e and f) EX10B. Star denotes the location of the maximum 36 h accumulated rainfall. by a confrontation between the continental flows and the typhoon flows in the midtroposphere. Figure 13 shows the 500 hpa wind and divergence fields at the first peak. In all the experiments, the southeasterly flows conveyed by the approaching typhoon develop a saddle area over the northern Korean Peninsula together with the northwesterly flows from a northern trough in northeastern China. The formation of the frontal structure is initiated south of a saddle area that develops from the northwesterly flows and the southeasterly flows in the northern edge of the typhoon in the midtroposphere over the northern Korean Peninsula. Comparing the convergence fields shows that the maximum convergence in EX10I is significantly weak because of the relatively weak northwesterly, which is almost half the level of that in 12 of 20

13 Figure 11. Time series of observed and simulated hourly precipitation at the grid point where the maximum 36 h accumulated rainfall was recorded. EX10C and EX10B. This means that a proper simulation of the interaction between the synoptic scale environment and the typhoon plays an important role in forecasting the rainfall associated with the landfalling typhoon. [30] The topographic effect is also shown explicitly in the vertical cross section of EX10C across the TMR (Figure 14). At 0000 UTC 31 August, as the airflow impinges on the TMR, the nearly saturated layer below about 4 km altitude is lifted above the mountains (Figure 14a). The horizontal convergence below a 2 km height along the cross section causes vertical motion (Figure 14b). In addition to the vertical motion, the orographic lifting by the TMR enhances the rainfall. At 1200 UTC 31 August, the model simulates intense updrafts and heavy rainfall by the orographic effect over the maximum rainfall point when the typhoon center moves toward the inland of the Korean Peninsula (Figures 14c and 14d). The rain water mixing ratio is enhanced over a windward mountainous region with a prominent feature. The differences between two simulated rainfall peaks at Gangneung are characterized by the simulated horizontal divergence/convergence at the cross section. The simulated divergence/convergence field of the first peak is likely confined in the layer below approximately 5 km, whereas that of the second peak develops almost entirely in the troposphere Effects of Terrain and Its Grid Resolution [31] To investigate the effect of a cloud resolving scale simulation on the torrential rainfall in terms of horizontal grid resolution and increased terrain height of the model, the simulated rainfall in EX3.3C is investigated and compared with that of EX10C and EX3.3TOPO. In this study, the averaged terrain height for South Korea is m in EX10C and m in EX3.3C, and the maximal height of the TMR is m in EX10C and m in EX3.3C. In the observed terrain height derived from a 3 s digital elevation model (DEM), the averaged terrain height for South Korea is m and the maximal height of the TMR is 1942 m. Although the model terrain heights are lower than those observed, the maximal model terrain height of the TMR in EX3.3C increases by m (42%) as the resolution of the model grid mesh increases from 10 km to 3.3 km. [32] Figure 15 shows the 36 h accumulated rainfall and wind fields for EX10C, EX3.3C, and EX3.3TOPO. The 36 h accumulated maximum rainfall for EX3.3C ( mm) is substantially increased, with enhanced rainfall intensity, in comparison with EX10C (830.8 mm) and EX3.3TOPO (824.4 mm). It should be noted that because the observed local maximum itself includes some uncertainty due to the sparse resolution of the observing stations, a larger amount of rainfall may be possible. Although the increased rainfall is simulated in EX3.3C, the horizontal wind vector at 925 hpa is similar in all three simulations. The track and the minimum sea level pressure of the simulated typhoon are also comparable. Comparing the rainfall for EX3.3TOPO with that for EX10C and EX3.3C shows the effects on the simulated precipitation of the horizontal resolution and its resultant topography. The rainfall in EX3.3TOPO is fairly similar to that in EX10C (see also Figure 16a). The simulated maximum rainfall for EX3.3TOPO (824.4 mm) is nearly the same as that for EX10C (830.8 mm). Because EX10C generate significantly more grid resolvable (nonconvective) rain (96%) than subgrid resolvable rain (4%) over the Korean Peninsula, the rainfall amount of EX10C using cumulus parameterization scheme (CPS) could be comparable to that of EX3.3TOPO not using CPS. The locations of the primary and secondary maximum rainfall for EX3.3TOPO are approximately identical to those for EX3.3C. This means that although the larger scale atmospheric motions in EX3.3- TOPO and EX3.3C are similarly simulated, the simulated rainfall can be largely attributed to the local terrain height in the model. This result implies that the effect of topography is likely to be crucial for the simulated rainfall near Gangneung under the conditions of strong wind and sufficient moisture involved with the landfalling typhoon. [33] Figure 16 shows the time series of hourly precipitation, horizontal wind at the lowest model level, and 850 hpa vertical velocity at the maximum rainfall point. EX10C and EX3.3TOPO show similar characteristics for the rainfall and horizontal/vertical wind for the entire period. In EX3.3C, the 13 of 20

14 Figure 12. Vertical cross section of EX10C along A B (see Figure 10a) at (a and b) 1800 UTC 30 and (c and d) 0000UTC 31. Figures 12a and 12c show potential temperature (K, shaded with gray line), equivalent potential temperature (K, black solid lines), relative humidity (black dashed), and wind vector (horizontal wind (m/s) and vertical wind (cm/s)) in the plane of the section. Figures 12b and 12d represent horizontal divergence (10 4 /s, lines; positive (negative) values solid (dashed)) and total hydrometeor mixing ratio (g/kg, shaded with gray line). The triangle indicates the location of maximum rainfall. horizontal wind is similar but the vertical velocity is increased by approximately a double magnitude with a 42% increase in the terrain height compared with those of EX10C and EX3.3TOPO. Thus it is noted that the increased simulated rainfall in EX3.3C results from the increase in vertical velocity due to its increased terrain height. [34] The time series of simulated hydrometeors at the maximum rainfall point for EX3.3C and EX3.3TOPO are shown in Figure 17. The increased precipitation in EX3.3C is mainly caused by the increase in rainwater and cloud water resulting from the explicit moisture scheme of the model. The increased vertical upward motion in EX3.3C induces the increase in the rainwater and cloud water in the lower troposphere, but it does not increase the snow, ice, and graupel in the middle and upper troposphere. The graupel tends to increase to a certain degree because of the 14 of 20

15 increased upward motion, but the snow and ice are insensitive to the increase in terrain height. Figure 13. The 500 hpa wind vector (m/s) and divergence (10 4 /s, lines with < 1 shaded) at 0000 UTC 31 for (a) EX10C, (b) EX10I, and (c) EX10B. 5. Summary and Conclusions [35] Typhoon Rusa made landfall at Goheung, on the southwestern part of the Korean Peninsula, at 0630 UTC 31 August In this study, we simulated the rainfall using the WRF model with 30, 10, and 3.3 km grid resolutions. Our primary goals were to investigate the ability of a mesoscale model to simulate the torrential precipitation associated with the typhoon and to assess the effects of model resolution and topography in a higher resolution simulation of precipitation. Our analysis of the observations and model results was sufficient to identify the mechanisms by which the torrential rainfall at Gangneung was maintained. [36] The WRF model has the potential to predict the torrential rainfall. In EX10C, the simulated maximum rainfall of mm and its location are in better agreement with the observations than other experiments with the same resolution, which simulated the maximum rainfall points farther north. In EX3.3C, the rainfall intensity is enhanced so that the simulated maximum rainfall is mm. It should be noted that the observed local maximum itself includes some uncertainty because of the sparse distribution of observing stations. A larger amount of rainfall may be possible in EX3.3C. [37] This study revealed the complexity of forecasting heavy rainfall associated with typhoon passage and the interactions between the environmental flow, the typhoon itself, and the topography. Comparing the control, the bogus typhoon, and different initial time experiments shows that accurately simulated rainfall is not achieved by accurately simulated typhoon tracks. It is instead achieved by accurately simulated interactions between the typhoon and its environment. In the experiment with the bogus typhoon, the westward biases in the rainfall distribution resulted from the misrepresentative vertical structure. Although the experiments display large variations in the simulated rainfall, all the simulations show that the TMR, a north south backbone in the southern Korean Peninsula, plays an essential role in inducing a stationary lifting mechanism for the atmospheric flows originating from the approaching typhoon. Mountain regions tend to decrease predictability owing to latent heat release, nonlinear processes, and unresolved phenomena, but in this study the opposite occurs as the mountains provide a stationary lifting mechanism that is similar for all the simulations. This information will help forecasters to give adequate warning. [38] In both the observation and the simulations, there are the two peaks in the hourly rainfall at Gangneung, whereas the other locations have only one peak. The two peaks suggest that two distinctive mechanisms are involved in causing the rainfall associated with the landfalling typhoon. The first peak is linked to a narrow rainband with a frontal structure prior to the landfall of the typhoon. The frontal structure develops from the northwesterly flows of cold dry air from the northern trough in northeastern China and the southeasterly flows of warm moist air from the typhoon in the midtroposphere. The frontal structure is characterized by strong moisture ( e ) and temperature () gradients that have developed perpendicularly to the TMR over the Sea of 15 of 20

16 Figure 14. Vertical cross section of EX10C along C D and E F (see Figure 10a and b) at (a and b) 0000 UTC 31 and (c and d) 1200 UTC 31, respectively. Figures 14a and 14c represent equivalent potential temperature (K, solid lines), relative humidity (dashed), and wind vector (horizontal wind (m/s) and vertical wind (cm/s)) in the plane of the section. Figures 14b and 14d show horizontal divergence (10 4 /s, lines; positive (negative) values solid (dashed)) and rainwater mixing ratio (g/kg, shaded). Japan (East Sea). Along the frontal line in the middle to upper troposphere, the downward motion in its northern part and the upward motion in its southern part enhance the north south e gradient. When the upper level sinking air reaches the surface level, strong instability is formed and strong convergence is induced at that level. As a result, the deep upward motion from the surface level to the upper troposphere produces a thick cloud layer that extends toward the TMR by the effect of the surface easterly flows. [39] The second hourly rainfall peak results not only from the direct effect of the typhoon but also from topographic lifting. As the airflow of the typhoon traverses the TMR, the nearly saturated layer is lifted above the mountains. This topographic lifting releases the potential energy of the conditionally unstable air and triggers deep convection. 16 of 20

17 Figure 15. The 36 h accumulated total rainfall (mm, lines) from 1200 UTC 30 August to 0000 UTC 1 September with model terrains (m, shaded) and 925 hpa wind vector (m/s) at 0000 UTC 31 for (a) EX10C, (b) EX3.3C, and (c) EX3.3TOPO. The triangle indicates the location of Gangneung. Radar observation clearly shows a tiny circular reflectivity image around Gangneung; this is evidence of the topographic lifting producing rainfall right after the typhoon landfall from 0400 UTC to 1600 UTC 31 August. The blocking by the TMR of a strong spiral rainband of the typhoon contributes to additional heavy rainfall. [40] This study shows that orographic forcing plays a key role in substantially increasing the total rainfall in the cloudresolving scale rainfall simulation, as seen in the comparison between EX3.3C and EX3.3TOPO. The distribution and maximum of the simulated rainfall in EX3.3TOPO are analogous to those of EX10C and the location of its maximum rainfall is identical to that of EX3.3C. The analogous characteristic in the simulated rainfall between EX3.3TOPO and EX10C seems to result from the analogous resolution of the model terrain. This indicates that in the high resolution simulation of Typhoon Rusa, the terrain height is more effective than the model grid resolution in simulating the amount and location of the torrential rainfall. The increased precipitation in EX3.3C is due to the increase in the rain- 17 of 20

18 Figure 16. Time series of (a) hourly precipitation, (b) horizontal wind at the lowest model level, and (c) vertical velocity (w) at 850 hpa at the grid point where the maximum 36 h accumulated rainfall was recorded. 18 of 20

19 Figure 17. Time series of the vertical integrated hydrometeors (i.e., cloud water (CLW), graupel (GRP), ice (ICE), rainwater (RW), and snow water (SNW)) for (a) EX3.3C and (b) EX3.3TOPO at the grid point where the maximum 36 h accumulated rainfall was recorded. water and cloud water of the explicit moisture scheme. It should be noted that the increased rainfall in EX3.3C could be reduced by using the positive definite (PD) moisture advection method that has been implemented in a later version of the WRF model. Skamarock and Weisman [2009] indicated that because a spurious source of water arises without PD transport of moisture, use of the PD scheme could significantly reduce the large positive bias in surface precipitation forecasts found in non PD model forecasts. Thus future work will include a precipitation sensitivity study of the effect of the PD scheme. Also, the incorporation of advanced data assimilation techniques using improved radar and satellite data may be necessary for a more realistic simulation of the typhoon induced precipitation distribution. [41] Acknowledgments. The authors are grateful for the computing support provided by the Korea Institute of Science and Technology Information (KISTI) through the Strategic Supercomputing Support Program. This work was supported by thekoreameteorological Administration Research and Development Program under grant CATER This research was also supported by the BK21 program of the Korean Government Ministry of Education. References Atallah, E. H., and L. F. Bosart (2003), The extratropical transition and precipitation distribution of Hurricane Floyd (1999), Mon. Weather Rev., 131, , doi: / (2003)131<1063:tetapd> 2.0.CO;2. Bosart, L. F., C. S. Velden, W. E. Bracken, J. Molinari, and P. G. Black (2000), Environmental influences on the rapid intensification of Hurricane Opal (1995) over the Gulf of Mexico, Mon. Weather Rev., 128, , doi: / (2000)128<0322:eiotri>2.0.co;2. Carr, L. E., III, and R. L. Elsberry (2000a), Dynamical tropical cyclone track forecast errors. part I: Tropical region error sources, Weather Forecast., 15, , doi: / (2000)015<0641:dtctfe>2.0. CO;2. Carr, L. E., III, and R. L. Elsberry (2000b), Dynamical tropical cyclone track forecast errors. part II: Midlatitude circulation influences, Weather Forecast., 15, , doi: / (2000)015<0662: DTCTFE>2.0.CO;2. Chen, F., and J. Dudhia (2001), Coupling of an advanced land surfacehydrology with the Penn State NCAR MM5 modeling system. part I: Model implementation and sensitivity, Mon. Weather Rev., 129, , doi: / (2001)129<0569:caalsh>2.0.co;2. Davidson, N. E., J. Wadsley, K. Puri, K. Kurihara, and M. Ueno (1993), Implementation of the JMA Typhoon bogus in the BMRC tropical prediction system, J. Meteorol. Soc. Jpn., 71, Davis, C. A., and L. F. Bosart (2001), Numerical simulations of the genesis of Hurricane Diana (1984). part I: Control simulation, Mon. Weather Rev., 19 of 20