The Impact of Forcing Datasets on the High ... - AMS Journals

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The Impact of Forcing Datasets on the High-Resolution Simulation of Tropical Storm Ivan (2004) in the Southern Appalachians XIAOMING SUN AND ANA P. BARROS Department of Civil and Environmental Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina (Manuscript received 30 November 2011, in final form 6 March 2012) ABSTRACT The influence of large-scale forcing on the high-resolution simulation of Tropical Storm Ivan (2004) in the southern Appalachians was investigated using the Weather Research and Forecasting model (WRF). Two forcing datasets were employed: the North American Regional Reanalysis (NARR; 32 km 3 32 km) and the NCEP Final Operational Global Analysis (NCEP FNL; 18 3 18). Simulated fields were evaluated against rain gauge, radar, and satellite data; sounding observations; and the best track from the National Hurricane Center (NHC). Overall, the NCEP FNL forced simulation (WRF_FNL) captures storm structure and evolution more accurately than the NARR forced simulation (WRF_NARR), benefiting from the hurricane initialization scheme in the NCEP FNL. Further, the performance of WRF_NARR is also negatively affected by a previously documented low-level warm bias in NARR. These factors lead to excessive precipitation in the Piedmont region, delayed rainfall in Alabama, as well as spatially displaced and unrealistically extreme rainbands during its passage over the southern Appalachians. Spatial filtering of the simulated precipitation fields confirms that the storm characteristics inherited from the forcing are critical to capture the storm’s impact at local places. Compared with the NHC observations, the storm is weaker in both NARR and NCEP FNL (up to Dp ; 5 hPa), yet it is persistently deeper in all WRF simulations forced by either dataset. The surface wind fields are largely overestimated. This is attributed to the underestimation of surface roughness length over land, leading to underestimation of surface drag, reducing low-level convergence, and weakening the dissipation of the simulated cyclone.

1. Introduction Spawned from a large tropical wave that moved off the west coast of Africa, Hurricane Ivan (2004) made landfall for the first time as a category-3 storm at approximately 0650 UTC 16 September, just west of Gulf Shores, Alabama (Stewart 2005). Thereafter, and before transitioning to an extratropical low over the Delmarva Peninsula around 1800 UTC 18 September, the southeast progression of a cold front northwest of Lake Michigan steered Ivan north-northeastward and along the Appalachian Mountains. As documented by Stewart (2005), the observed rainfall totals generally ranged from 75 to 175 mm (3 to 7 in.) along a northeastward swath ranging from Alabama and the Florida panhandle to New England, with rainfall accumulations over 175 mm as far north as New Hampshire. In Macon County, North Carolina (area delimited by bold boundary in Fig. 1c), rain

Corresponding author address: Ana P. Barros, Duke University, Box 90287, 2457 CIEMAS Fitzpatrick Bldg., Durham, NC 27708. E-mail: [email protected] DOI: 10.1175/MWR-D-11-00345.1 Ó 2012 American Meteorological Society

gauges reported precipitation totals ranging from 76 mm at low elevations and up to 305 mm in the vicinity of Peeks Creek (the white star in Fig. 1c; http://www.geology.enr. state.nc.us/Landslide_Info/Landslides_hurricanes_frances_ ivan_sept_2004.htm). Elsewhere, and despite the lack of observations at high elevations, even larger values were registered, including 425 mm at Cruso, North Carolina (the white dot in Fig. 1a). Because of the previous passages of Tropical Storm Bonnie and Hurricane Frances in August and early September, respectively, the land surface was already saturated, and precipitation from Ivan induced major flooding and numerous landslides and debris flows in the southern Appalachians (Wooten et al. 2008). The lack of high-resolution rainfall observations and reanalysis datasets remains as a major challenge in understanding the hydrometeorology of the southern Appalachian Mountains, especially for extreme rainfall events that trigger natural hazards. During the passage of Ivan over the region (16 and 17 September 2004), hourly rain gauge measurements from the National Climatic Data Center (NCDC) are available at only 21 locations, far apart from each other and predominantly

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FIG. 1. (a) The 6-hourly best track of Hurricane Ivan (2004) from the NHC and NARR accumulated precipitation (mm) from 0600 UTC 15 Sep to 0600 UTC 18 Sep. The dashed squares indicate the three domains involved in this study; Cruso, North Carolina, is marked by the white dot. (b) The sounding observations from the University of Wyoming as detailed in Table 1. (c) The hourly NCDC rain gauges in the innermost domain. In total, 21 gauges were available during 16 and 17 Sep 2004. The white star denotes Peeks Creek, with Macon County, North Carolina, emphasized by its bolded boundary in white. The contours in (b) and (c) represent topography (m).

at low elevations (Fig. 1c). The spatial resolution (approximately at 32 km 3 32 km horizontal grid spacing) of the 3-hourly model-based North America Regional Reanalysis (NARR; Mesinger et al. 2006) is too coarse to capture the complex terrain effects at the characteristic scales (,250 km2) of flash floods and earth flows in southern Appalachian catchments (Fig. 1c). Furthermore, the intensity of heavy rainfall is also considerably underestimated (see Sun and Barros 2010, their Figs. 7 and 8, and Fig. 1a in this manuscript). Despite imperfect parameterizations and numerics, the value added by high-resolution simulations, especially over regions of complex terrain, is well documented in

the literature (Kuligowski and Barros 1999; Mass et al. 2002; Kim and Lee 2003; Antic et al. 2006; Barros et al. 2006; J. Li et al. 2008; among others). Similarly, numerous previous studies have detailed the considerable benefit in forecast skill gained by the high-resolution simulation of heavy rainfall events (e.g., Colle and Mass 2000; Colle et al. 2000; Bindlish and Barros 2000; Zˇagar et al. 2006; Litta et al. 2007), which are often associated with mesoscale systems and/or involve detailed interactions between convection and topography as well as local environment conditions. A number of previous studies relied on high-resolution simulations to examine the evolution of hurricanes and tropical storms. In a study

TABLE 1. The sounding observations in Fig. 1b. For each entry under Abbreviation, the first two letters refer to the county and the last two letters denote the state. The station identifier denotes the station label from the University of Wyoming (available online at http:// weather.uwyo.edu/upperair/sounding.html). Abbreviation

Station identifier

Latitude (8)

Longitude (8)

Elevation (m)

State

JNFL VLFL PTGA SBAL JNMS SLLA CHSC NPNC GBNC NATN BKVA WLVA STVA PIPA ABMD WLOH LIIL

JAX VPS FFC BMX JAN LIX CHS MHX GSO BNA RNK WAL IAD PIT APG ILN ILX

30.50 30.48 33.36 33.16 32.31 30.33 32.90 34.78 36.08 36.25 37.20 37.93 38.98 40.53 39.47 39.41 40.15

281.70 286.51 284.56 286.76 290.08 289.77 280.03 276.88 279.95 286.57 280.41 275.48 277.46 280.23 276.07 283.81 289.33

9 29 244 178 101 8 15 11 270 180 654 12 93 357 5 317 178

Florida Florida Georgia Alabama Mississippi Louisiana South Carolina North Carolina North Carolina Tennessee Virginia Virginia Virginia Pennsylvania Maryland Ohio Illinois

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of Hurricane Danny’s landfall (1997) on the coast of Louisiana and Alabama on 18 and 19 July 1997, Kuo et al. (2001) reported that the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) at 9-km grid increment could not simulate the propagation of a rainband that was well observed by Doppler radar (Blackwell 2000), and argued that a cloud-resolving grid was needed to capture the detailed storm structure. Zhang et al. (2005) investigated the two-eyewall structure of Typhoon Winnie (1997) at large scale using a nested implementation of MM5 with inner grid spacing of 9 km. The model reproduced an outer eyewall with a diameter around 350 km, which compared well with the observed 370 km. Feser and von Storch (2008) performed nested ensemble dynamical downscaling of typhoons in Southeast Asia using the climate version of the Lokal Modell (CLM) with grid spacing of 18 km in their inner domain. Spectral nudging was applied to prevent the CLM from deviating significantly from the large-scale system. They showed that tropical storms were correctly identified and tracked with considerably deeper core pressure and higher wind speeds in the CLM than in the (coarser resolution) National Centers for Environmental Prediction (NCEP)–NCAR reanalysis. Q. Li et al. (2008) simulated Typhoon Rananim (2004) using MM5 with the finer nest at 2-km grid increment, and showed that the temporal evolution of intensification, maintenance, landfall, and inner-core structure agreed well with a variety of observations, including changes in surface winds and pressure near the storm center. In this study, we focus on the evolution of the spatial and temporal organization of rainfall during Tropical Storm Ivan (2004) in the southern Appalachians. The specific objective is to investigate the impact of alternative forcing1 datasets and physical parameterizations of the Advanced Research Weather Research and Forecasting model version 3.1 (ARW-WRF3.1; Skamarock et al. 2008) on the simulation of a storm over complex terrain at high resolution. Both the NARR and NCEP Final Operational Global Analysis (NCEP FNL; Kalnay et al. 1996) were used as forcing to evaluate the role of large-scale processes on high-resolution simulation and examine the sources of model bias. In this manuscript, the sensitivity of the simulations to the forcing dataset is examined in detail. An examination of the sensitivity associated with various model configurations [mainly focusing on the planetary boundary layer (PBL) and

1

If not explicitly stated, the term ‘‘forcing’’ in this manuscript refers to the initial and boundary conditions derived from the forcing dataset rather than physical processes.

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cumulus parameterizations] is ongoing, and will be presented in a follow-up publication. Future work will focus on the analysis of simulated rainfall fields (1 km 3 1 km) to characterize the relationships between spatial and temporal patterns of rainfall and hydrometeorological hazards (flashfloods, landslides, and debris flows) that are associated with extreme events, specifically tropical storms (Fuhrmann et al. 2008). The manuscript is organized as follows. Section 2 describes the model configurations for the simulations. The forcing and validation datasets are introduced in detail in section 3. Evaluation of the simulated results and intercomparison against observations are documented in section 4, with the conclusions and discussion presented in section 5.

2. Model configuration The ARW-WRF3.1 (Skamarock et al. 2008) was employed as the research tool. The ARW-WRF3.1 dynamical core is fully compressible, Euler nonhydrostatic, and conservative for scalar variables. The model was implemented on three grids, with the first one consisting of 90 3 90 grid cells and the two inner domains including 271 3 271 grid points, respectively (Fig. 1a). All of these grids are centered at Cruso (elevation 895 m), North Carolina, where the maximum rainfall accumulation (425 mm, ;17 in.) was reported (marked by the white dot in Fig. 1a). The purpose of the outermost domain at 15-km grid spacing (domain 1) is to reduce spurious behavior or noise along the lateral boundaries (i.e., the Gibbs phenomena; see also Castro et al. 2005) due to large ratios of spatial resolution between the nesting field and the nested model, especially when the NCEP FNL (18 3 18) is employed as the forcing. For the two inner grids, the grid increment is 3 km (domain 2) and 1 km (domain 3), respectively, such that the second domain is large enough to avoid the potential damping by the lateral boundary conditions (LBCs) derived from the forcing data, and to allow the model simulated small-scale features to be fully developed. This intermediate grid, encompassing much of the southeastern United States, also enables systematic tracking of the temporal and spatial evolution of the simulated storm. In addition, the lateral boundaries between the innermost and second domains are separated in each direction by a distance larger than one-half the size of the innermost domain, guaranteeing that the average properties of the flow in the inner domain become independent of the distance to the boundaries of the outer domain (Antic et al. 2006). In the vertical direction, each grid consists of 35 sigma levels, except for the experiment WRF_FNL_Vert, which has 60 levels (see Table 2).

Dudhia (1989) Rapid Radiative Transfer Model (RRTM) (Mlawer et al. 1997) Noah (Chen and Dudhia 2001)

SR LR

WRF_FNL

Noah (Chen and Dudhia 2001)

Lin et al. (Lin et al. 1983; Rutledge and Hobbs 1984; Tao et al. 1989) Dudhia (1989) RRTM (Mlawer et al. 1997)

NA (D03)

35 NCEP FNL NCEP FNL YSU (Hong et al. 2006) MM5 (Zhang and Anthes 1982) Kain–Fritsch (D01; Kain 2004) NA (D02)

WRF_FNL_MYJ


Dudhia (1989) RRTM (Mlawer et al. 1997)

Lin et al. (Lin et al. 1983; Rutledge and Hobbs 1984; Tao et al. 1989)

NA (D03)

NA (D02)

Kain–Fritsch (D01; Kain 2004)

35 NCEP FNL NCEP FNL MYJ (Janjic´ 1990, 2002) MYJ (Janjic´ 1990, 2002)

WRF_FNL_CumuFdON



Kain–Fritsch (D02; Kain 2004) Kain–Fritsch (D03; Kain 2004) Lin et al. (Lin et al. 1983; Rutledge and Hobbs 1984; Tao et al. 1989)


MM5 (Zhang and Anthes 1982)

35 NCEP FNL NCEP FNL YSU (Hong et al. 2006)

WRF_FNL_CumuFdOFF



Kain–Fritsch (D02; Kain 2004) Kain–Fritsch (D03; Kain 2004) Lin et al. (Lin et al. 1983; Rutledge and Hobbs 1984; Tao et al. 1989)




WRF_FNL_Vert




NA (D03)

NA (D02)




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LSM


Microphysics

NA (D03)

NA (D02)


SL

Cumulus

WRF_NARR

35 NARR NARR YSU (Hong et al. 2006) MM5 (Zhang and Anthes 1982)

VLs IC LBC PBL

Configuration

Experiment

TABLE 2. Model configuration [vertical levels (VLs), initial condition (IC), LBC, PBL, surface layer (SL), cumulus, microphsysics, shortwave radiation (SR), longwave radiation (LR), and land surface model (LSM)]. Compared with WRF_FNL_CumuFdON, feedback from the innermost grid to the intermediate one was not enabled in the WRF_FNL_CumuFdOFF.

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The physics options used for all simulations are described in Table 2. We focus the discussion however on the experiments using the Yonsei University PBL scheme (YSU; Hong et al. 2006), since the Mellor–Yamada–Janjic´ parameterization (MYJ; Janjic´ 1990, 2002) can cause ‘‘premature’’ rainbands (not shown here) due to its limitations in representing counter gradient fluxes, which detracts from the focus on sensitivity to forcing in this manuscript. One-way nesting was applied between the outermost (domain 1) and the intermediate domain to avoid physical incompatibility due to cumulus parameterization. Twoway nesting was applied between the two inner grids to transfer the feedbacks of the finely resolved features in the innermost grid (domain 3) on the relatively coarser and larger intermediate domain (domain 2). The numerical experiments for the two inner domains were carried out from 0600 UTC 15 September to 0600 UTC 18 September, while the outermost grid was initialized at 0600 UTC 14 September, when the storm was far away from the southern Appalachians, at 2 a.m. local time. Model spinup was assessed by monitoring the spatial spectrum of the column-averaged total kinetic energy. After further confirmation by daily reinitialized runs, the first 6 h of the model integration were not analyzed to ensure consistency with a highresolution limited-area model (LAM) application and to avoid spinup issues.

3. Datasets a. Forcing data The two forcing datasets investigated are the widely used 32 km 3 32 km NARR (Mesinger et al. 2006; available online at http://nomads.ncdc.noaa.gov/data.php? name5access#narr_datasets) and the 18 NCEP FNL (Kalnay et al. 1996; available online at http://dss.ucar. edu/datasets/ds083.2/). In NARR, orographic precipitation effects are introduced by the assimilation of rainfall data corrected by the Parameter-elevation Regressions on Independent Slopes Model (PRISM; Daly et al. 1994), which is optimized for monthly time scales. Moreover, precipitation rates with magnitude larger than 100 mm day21 and rainfall near tropical storm centers are not assimilated because of concerns regarding their reliability and the inadequate temporal and spatial resolutions of the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) data. This leads to weaker cyclonic circulations over the northern Atlantic (Mesinger et al. 2006) and the underestimation of heavy rainfall over land as pointed out by Sun and Barros (2010).

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The NCEP FNL, though at much coarser resolution, benefits from the implementation of a hurricane initialization scheme (Kurihara et al. 1993, 1995). One advantage of this scheme is that it preserves features in the vicinity of the storm through its strictly defined hurricane region, and enables a smooth transition to the surrounding environment by an optimum interpolation method (Gandin 1963). Further, during the assimilation process, the hurricane vortex in the NCEP FNL is relocated to match the observed location from the National Hurricane Center (NHC). As shown in Fig. 2, although the NCEP FNL is at much coarser resolution than the NARR, the analyzed Ivan vortex is deeper and larger in the NCEP FNL analysis (cf. row 1 and row 2, Fig. 2). In contrast, NARR shows stronger wind fields in the vicinity of the storm but weaker circulation in the vortex center (row 3, Fig. 2), inducing smaller radial gradients for the horizontal winds. Another known feature of NARR pointed out by Mesinger et al. (2006) and reported by various subsequent studies (e.g., J. Li et al. 2008; Bennington et al. 2010; Royer and Poirier 2010; Jiang and Yang 2012, manuscript submitted to Climate Res.) is the low-level warm bias. As shown in Fig. 2 (row 4), the NARR is up to several degrees Celsius warmer over land than the NCEP FNL up to at least 950 hPa. This feature prevails even at 925 hPa (not shown). By comparing NARR soundings to the sounding observations from the University of Wyoming (available online at http:// weather.uwyo.edu/upperair/sounding.html), it can be seen that this warm bias is real in the case of Ivan, and not just a relative difference with regard to the NCEP FNL (Fig. 3). Note that although we only show the temperature discrepancy at the initialization of the outermost domain (i.e., at 0600 UTC 14 September), this warm feature persists in the next few days in NARR (not shown). Jiang and Yang (2012, manuscript submitted to Climate Res.) attributed this warm bias to the lack of assimilation of ground-based temperature measurements in the NARR three-dimensional variational data assimilation (3DVAR) scheme. This scheme requires the specification of the residuals between the observations and model background, which can be very large over land because of diurnal variations. A significant difference at the surface can potentially impact the wind field far beyond the boundary layer, instead of being constrained within it. Thus, rather than being assimilated, the surface air temperature in NARR is simulated by the Noah LSM, which tends to overestimate sensible heat fluxes at the cost of underestimating latent heat fluxes (Jiang and Yang 2012, manuscript submitted to Climate Res.).

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FIG. 2. The 6-hourly NHC best track and the sea level pressure (hPa) from (top) NARR and (top middle) NCEP FNL. The 997.5- and 1015-hPa contours are in black. (bottom middle) The horizontal wind residual (m s21) between NARR and NCEP FNL at 925 hPa, with the 2 m s21 contours in solid black. The corresponding time of each panel in (top)–(bottom middle) is documented in their title. (bottom) The temperature residual (K) between NARR and NCEP FNL at different levels at model initialization (0600 UTC 14 Sep), where the solid thick contours denote zero values. The gray squares in each panel indicate the three domains of this study.

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FIG. 3. Temperature profiles of sounding observations (solid black), WRF_NARR (dashed black), and WRF_FNL (dashed gray) at model initialization (0600 UTC 14 Sep) of the outermost domain. The location of each sounding is illustrated in Fig. 1b and documented in Table 1. Note that the initial temperature in WRF is directly derived from the forcing data and only nine soundings were available at 0600 UTC 14 Sep.

b. Observational data To evaluate the simulated spatial patterns of precipitation, the NCEP stage IV analysis (STIV; available online at http://www.emc.ncep.noaa.gov/mmb/ylin/pcpanl/ stage4/) was used. This dataset is based on combined multisensor analysis of radar and gauge observations produced by the 12 River Forecast Centers (RFCs) over the continental United States. Note that the rain gauges used to produce this combined product are sparse and at relatively low elevations away from the inner mountain region. Also, the utility of the two relevant radars (Knoxville, Tennessee, and Greenville, South Carolina) is strongly affected by terrain blocking in the southern

Appalachians. Nevertheless, the hourly STIV data at 4-km resolution provide a very useful overview of the overall evolution of the storm and reliable rainfall estimates at low elevations, and are widely used for evaluation of precipitation products (e.g., Tao and Barros 2010). For assessments at individual locations, hourly NCDC rain gauge measurements (see Fig. 1c for their locations) were employed. Microwave and infrared satellite data from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) profile 2A25 dataset (available online at http:// trmm.gsfc.nasa.gov) from the National Aeronautics and Space Administration (NASA) and Geostationary Operational Environmental Satellite-8 (GOES-8) satellite (available online at http://www.goes.noaa.gov) from the

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National Oceanic and Atmospheric Administration (NOAA) were also used for model evaluation. For TRMM 2A25, the averaged rain rate between 2- and 4-km height was used for evaluation purposes based on previous work in the region (Prat and Barros 2010b). The GOES-8 cloud-top temperature (CTT) data were used as a proxy indicator of convective activity and potential rain-producing cloud systems (e.g., Barros et al. 2004; Giovannettone and Barros 2009). This dataset has an absolute accuracy up to 1 K with horizontal and temporal resolutions of 4 km and in less than 26 min, respectively. In addition to evaluating the character of the NARR warm bias, atmospheric sounding data obtained from the University of Wyoming are also employed to evaluate the model output profiles at individual locations. Finally, the 6-hourly best track of Ivan by the NHC (Stewart 2005; McAdie et al. 2009; available online at http://www.aoml.noaa.gov/hrd/hurdat/) was utilized to evaluate the tropical cyclone position and intensity from each simulation.

4. Results The skill and utility of quantitative precipitation estimates depend on accuracy of both simulated precipitation amounts and precipitation timing (time of arrival and duration). The simulation’s ability to capture storm progression is therefore critical. Thus, before focusing on the skill of simulated precipitation fields in the innermost nest, storm evolution is first analyzed in the multistate-scale grids (mainly domain 2).

a. Simulated Ivan in the multistate-scale grids 1) OVERALL PERFORMANCE (i) Storm position and intensity To determine the storm center when Ivan was not directly over the southern Appalachians, either in the forcing datasets or in the simulations, a 1–2–1 filter was applied to the sea level pressure fields in both the x and y directions to smooth out anomalies associated with convective features and small-scale vortices—the same procedure as in Nolan et al. (2009). When the storm was over the southern Appalachians, the geopotential height at 750 hPa, instead of the sea level pressure, was used in the determination of the storm center to address the impact of topography (Tao et al. 2011a). The position of the cyclone center was next subjectively confirmed by inspecting the low-level wind fields as per Chavas and Emanuel (2010). According to Fig. 4a and the first two rows of Table 3, the storm center in the NCEP FNL is always closer to the NHC track than NARR. This is not unexpected, since, as described in section 3a, the NCEP FNL benefits

FIG. 4. (a) Storm center from the NHC (large gray circle), NARR (black square), NCEP FNL (black diamond), WRF_NARR (black square), and WRF_FNL (black diamond). (b) Derived from the simulations in the outermost grid; (c) derived from the intermediate domain.

from a hurricane initialization scheme that includes vortex relocation to match NHC. The storm position error in the original forcing data is retained in both simulations (see Figs. 4b,c as well as rows 3–6 of Table 2), and could be partially responsible for the timing error as discussed in detail in section 4a(2)(ii). As documented in Fig. 5, although Ivan is weaker than in the NHC data for both reanalyses (up to ;5 hPa), the simulated storm is persistently deeper (column 1, Fig. 5)

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TABLE 3. Storm center deviation (km) from the 6-hourly NHC best track. Positive deviation means to the east in the west-east direction and to the north in the south-north orientation. Time (UTC) Forcing data/Simulation NARR

NCEP FNL

WRF_NARR (outermost domain) WRF_FNL (outermost domain) WRF_NARR (intermediate domain) WRF_FNL (intermediate domain)

West–east South–north Absolute West–east South–north Absolute West–east South–north Absolute West–east South–north Absolute West–east South–north Absolute West–east South–north Absolute

0600 16 Sep

1200 16 Sep

1800 16 Sep

0000 17 Sep

0600 17 Sep

1200 17 Sep

1800 17 Sep

0000 18 Sep

0600 18 Sep

230 260 67.1 215 260 61.8 230 260 67.1 215 260 61.8 NA NA NA NA NA NA

230 0 30.0 215 215 21.2 230 245 54.1 215 230 33.5 NA NA NA NA NA NA

230 0 30.0 15 215 21.2 215 230 33.5 15 230 33.5 23 275 75.1 23 260 60.1

260 245 75.0 0 230 30.0 245 230 54.1 0 215 15.0 293 218 94.7 212 212 17.0

230 0 30.0 30 0 30.0 230 15 33.5 15 15 21.2 251 0 51.0 3 0 3.0

245 15 47.4 230 215 33.5 245 215 47.4 215 15 21.2 245 239 59.5 227 0 27.0

15 60 61.8 215 30 33.5 230 245 54.1 0 215 15.0 215 281 82.4 15 242 44.6

45 0 45.0 30 0 30.0 215 260 61.8 15 0 15.0 9 287 87.5 30 224 38.4

215 230 33.5 0 245 45.0 245 230 54.1 230 0 30.0 227 15 30.9 6 23 6.7

and surface winds are overestimated (column 2, Fig. 5) in the second grid. Note that the large differences of minimum sea level pressure and 10-m maximum wind speed between the simulations and observations are during the period when the storm center is still outside of the intermediate nest (domain 2, approximately within the first 40-h model integration). However, during that time window, the sea level pressure was higher in the highresolution simulations than in either forcing dataset, indicating there is no systematic shift to lower pressure by WRF at high resolution for this set of simulations. Recently, Bryan et al. (2010) showed that the simulated hurricane intensity in WRF is sensitive to the horizontal turbulence scale (lh ). When the Smagorinsky first-order closure is employed in WRF, this scale depends on the horizontal grid spacing (Dh ) as lh 5 0:25 3 Dh . Because this closure scheme was used in this study, the horizontal turbulence scale for the outermost (domain 1, at 15-km grid spacing) and the intermediate (domain 2, at 3-km grid increment) grids are respectively 3750 and 750 m. According to Fig. 2 (the black line) in Bryan et al. (2010), the maximum wind velocity in domain 2 should be significantly larger than in domain 1. Further, Gentry and Lackmann (2010), also for Hurricane Ivan (2004), reported that the simulated intensity was sensitive to model resolution. Their simulated peak intensity is 12 hPa stronger than observed when the grid spacing is 1 km, but up to 18 hPa weaker in their 8-km runs. They hypothesized that this is explained by the ability to resolve a greater range of wavenumbers in high-resolution simulations. By contrast, in this study, the simulated

Ivan is weaker in the outermost domain than in the intermediate grid as expected, but it is not weaker than the NHC observations (Figs. 5a,b). This behavior is independent of model configuration in terms of the various physics options, as well as in the experiment with 60 vertical model levels as illustrated in Fig. 5. Although the variation of microphysics (MP) schemes was not investigated, Yang and Ching (2005) reported underestimated hurricanes at 6.667-km grid increment for simulations of Typhoon Toraji (2001) using MM5 with five different MP schemes. In another modeling study (Li and Pu 2008) of Hurricane Emily (2005) using WRF, it was found that WRF simulations underestimated intensity down to 3-km grid spacing for six different MP schemes, including the Lin et al. (Lin et al. 1983; Rutledge and Hobbs 1984; Tao et al. 1989) microphysics. These results suggest that other nonlinear processes in the model may counteract the expected weakening of the storm due to coarse resolution, for the 15-km outermost domain at least. May and Holland (1999) proposed that the stratiform rainbands may play a positive role on the intensification of tropical cyclones by propagating their associated positive potential vorticity (PV) anomalies into the eyewall region. More recently and according to radar observations, Hence and Houze (2008) suggested the existence of an upscale positive feedback to hurricanes from the convective-scale elements in the principal rainbands. Didlake and Houze (2009) further proposed that this feedback is realized by inward flux of angular momentum to the core region at low levels. They

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FIG. 5. (left) The minimum sea level pressure (hPa) from the NHC, NARR, NCEP FNL, and various simulations using different physics options. (right) As in (left), but for maximum 10-m wind speed (m s21). (top) The outermost grid; (bottom) the intermediate one.

hypothesized that this angular momentum anomaly is induced by the low-level negative (potential) vorticity associated with the convective-scale structure within the rainbands. PV anomalies are present in the stratiform region as well as at the location of the principal rainbands in this study (not shown). However, under the midlevel westerlies in the midlatitudes (not shown) and because of the eastward deviation of the east and northeast flanks of Ivan by the southern Appalachians through the conservation of PV, the high-level PV anomalies associated with stratiform rainfall (rows 1 and 2 of Fig. 6) and the low-level negative PV maximum (rows 3 and 4 of Fig. 6), which itself is even not substantial, are not propagated to the center of the storm as shown by the radial flow in Fig. 6 (column 2). More importantly, as illustrated in column 3 of Fig. 6, the equivalent potential temperature

is still highest at low levels in the core region when Ivan is over land within the time window of this study. Given that the downdrafts associated with the overpredicted rainbands tend to advect the high level low equivalent potential temperature downward to where inflow is still prevalent, the storm should weaken (see Tao et al. 2011b for more details on the effect of downdrafts on hurricane intensity). In addition, the presence of overestimated rainbands may also reduce the radial pressure gradients and thus weaken the tropical cyclone more rapidly (Barnes et al. 1983; Powell 1990a,b; Wang 2002). Therefore, the evidence in this study does not support the hypothesis that rainfall overestimation leads to storm intensity overestimation. One critical difference between the aforementioned studies and this research is that the former are simulations

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FIG. 6. (left) Radar reflectivity (dBZ) at 3000 m with the center of Ivan marked by the black dot. (middle) The wind (m s21) and potential vorticity [potential vorticity unit (PVU) where 1 PVU 5 1026 K m2 kg21 s21, z] along the cross section denoted by the dashed line in (left). (right) As in (middle), but for wind (m s21) and equivalent potential temperature (K, ue). The dashed lines in (middle) and (right) illustrate the location of the storm center. All panels are for 0600 UTC 17 Sep and from the intermediate domain. The corresponding experiment is indicated in the title. The vertical velocity is enlarged 10 times.

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FIG. 7. Rainfall accumulation (mm) from (a) NARR, (b) WRF_NARR, (c) WRF_FNL, and (d) STIV during the last 66-h model integration in domain 2. The contour lines are for topography (m) at 400-m interval with the areas higher than 1000 m filled by pluses.

over the ocean, whereas here the focus is on the terrestrial path of the storm. A significant issue related to the nature of the lower boundary condition (i.e., water versus land) is the treatment of the surface roughness length in the model. Over water, the surface roughness length in WRF is calculated by a variation of the Charnock’s relation and depends on the frictional velocity in the surface layer parameterization (MM5 scheme; Zhang and Anthes 1982); over land, it is based on the vegetation property’s table (see VEGPARM.TBL in the standard WRF release). The roughness lengths specified in this table are smaller compared with those reported elsewhere (e.g., Steyaert and Knox 2008, their Table 2; Garratt 1992, his Table A6). Further, as a function of plant height (e.g., Zeng and Wang 2007), roughness length is typically specified as ;10% of the canopy height (e.g., Molder and Lindroth 1999). According to the recent observations of the global canopy height (e.g., Lefsky 2010, his Fig. 1) as well as the vegetation height specified in some other models (e.g., in the Community Land Surface Model; see Oleson et al. 2010, their Table 2.2), the roughness length used in WRF 3.1 is underestimated. Therefore, the excessive storm intensity in both simulations is tentatively linked here to the underestimation of the land surface roughness, leading to excessively low values of surface drag. This effect can in turn reduce low-level convergence, which otherwise dissipates the tropical storm by filling up the low-pressure center, thus leading to weaker dissipation or overestimated storm intensity.

(ii) Precipitation The simulated rainfall accumulation from the WRF_ NARR and WRF_FNL in the intermediate grid (domain 2) is shown in Figs. 7b and 7c, respectively. Compared to the STIV observations (Fig. 7d), there is a clear improvement on capturing known patterns of orographic precipitation in the southern Appalachians (Wooten et al. 2008; Prat and Barros 2010a,b) as compared to the

original NARR dataset (Fig. 7a), though not in south Alabama and southwest Georgia. The underestimation in this area is mainly associated with the underestimation of precipitation when Ivan was entering into the intermediate domain through the lateral boundaries (especially in the south). As indicated by the radar reflectivity at different levels (not shown) and since the horizontal wind was strong during that time period, the hydrometeors formed upwind in the primary storm vortex are responsible for the precipitation downstream. Without covering such upstream regions, the corresponding ground precipitation cannot be produced. Although both simulations generally reproduce the accumulated precipitation patterns quite well compared with STIV, the WRF_NARR fields show larger deviations from observed patterns than the WRF_FNL: 1) it overestimates light-to-moderate rainfall across central North Carolina (e.g., in the Piedmont region), 2) it produces unrealistic intense rainfall in Alabama, and 3) it fails to produce observed heavy precipitation over east Tennessee and along the north border of Georgia and Alabama. In addition, although unrealistic high precipitation to the northwest of the Appalachians is present in both simulations, especially over Kentucky, this artifact is substantially smaller in the WRF_FNL. These differences will be further explored in section 4a(2). The TRMM overpass around 0630 UTC 17 September (Fig. 8a) detected three separated rainbands south of the southern Appalachians, including one across the Carolinas. There is a good agreement between the TRMM data and both simulations (Figs. 8c–f), although rainfall upstream of the mountains and at low elevations is overestimated as suggested by the heavy rainband going across northeast Georgia and the extensive regions of stratiform rain with embedded convection in the Carolinas. This confirms the overestimation over east Tennessee in both simulations, though the WRF_FNL

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FIG. 8. (a) Averaged rain rate (mm h21) between 2- and 4-km height from the TRMM overpass around 0630 UTC 17 Sep. (b) Cloud-top brightness temperature (K) from GOES at 0645 UTC 17 Sep. (c) WRF_NARR precipitation (mm h21) from 0620 to 0630 UTC 17 Sep. (d) As in (c), but from 0630 to 0640 UTC 17 Sep. (e),(f) As in (c),(d), respectively, but for WRF_FNL. The simulated rainfall is from the intermediate grid.

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FIG. 9. Scatterplot of accumulated rainfall during the last 66-h model integration for (a) STIV vs WRF_NARR and (b) STIV vs WRF_FNL. The grid cells are classified into three categories according to elevation (black circles: higher than 1000 m; light gray dots: below 500 m; dark gray pluses: between 500 and 1000 m). The correlation coefficients between the STIV and WRF_NARR (WRF_FNL) are 0.58, 0.72, and 0.81 (0.75, 0.78, and 0.77) for these three classes, respectively. The simulated precipitation is from domain 2 with the STIV data rearranged to the WRF mesh by inverse distance interpolation.

performs better. Regarding the spatial patterns of brightness temperatures from GOES imagery (Fig. 8b), note the homogeneous CTT patterns around 240 K that suggest stratiform rainfall, consistent with light rainfall in South Carolina. Likewise, the very high CTTs at the west corner of South Carolina (Fig. 8b) are consistent with the light and negligible rainfall in TRMM 2A25 (Fig. 8a), which is reasonably well described in both simulations. For an overall quantitative assessment, scatterplots of pixel-based STIV rainfall against simulated rainfall in domain 2 were organized according to three elevation classes (below 500 m, between 500 and 1000 m, and higher than 1000 m) as shown in Fig. 9. Although the simulations indicate overestimation at high-elevation grid cells (over 1000 m, 128.6-mm average in WRF_NARR, and 153.4-mm average in WRF_FNL) as compared to STIV (99.4-mm average), independent rain gauge observations (Wooten et al. 2008) reported significantly higher values (up to 300 mm) at high elevations than those reported from STIV on the eastern slopes, which is consistent with the simulated rainfall. Indeed, because of ground clutter affecting both radars and the lack of ground-based observations in the mountains, STIV rainfall is not representative of actual rainfall in the inner mountain area and over the ridges. Further, the WRF_NARR and WRF_FNL rainfall fields were filtered to separate the wavelengths below and above 60 km (the shortest wavelength that can be possibly resolved in the outermost domain) using the method proposed by Denis et al. (2002). The short wavelength fields exhibit similar rain patterns and magnitudes

consistent with local physical forcing (cf. Figs. 10a and 10b). However, for the long wavelengths, there are very large differences in the precipitation fields (cf. Figs. 10c and 10d). This suggests that the signature of the interaction between the forcing data and the betterresolved regional forcing (e.g., topography, land use– land cover, coastal lines, etc.) is still dominant in the high-resolution simulation of Ivan.

2) SPACE–TIME EVOLUTION (i) Excessive precipitation in the Piedmont The overestimation of accumulated rainfall in the Piedmont region across North Carolina in the WRF_NARR mainly results from the excess precipitation early in the simulation (from 1200 UTC 15 September to 0000 UTC 16 September), during which the STIV data shows almost no rainfall over land. By contrast, the WRF_FNL only produces moderate precipitation over the southern slopes of the Appalachians in Virginia and very light rainfall (below 3 mm h21) along the border between North Carolina and Virginia within the same period, and thus is in closer agreement with observations (not shown). According to the sounding observations at Greensboro, North Carolina (GBNC, the only one available in the Piedmont region; see Fig. 1b for its location), the wind veers (turns anticyclonically) with increasing height from the surface to nearly ;700 hPa (column 1, Fig. 11), which is indicative of warm advection. Consequently, an elevated capping inversion formed at the level where this

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FIG. 10. Accumulated rainfall (mm) after short wavelength (,60 km) filtering for (a) WRF_NARR and (b) WRF_FNL; and the rest of the total rainfall accumulation (mm) for (c) WRF_NARR and (d) WRF_FNL during the last 66-h model integration in the second nest. The contour lines are for topography (m) at 400-m interval with the areas higher than 1000 m filled by pluses.

warm advection occurred as shown in Figs. 11a,d. Although both the WRF_NARR and WRF_FNL reproduced the warm advection generally well as illustrated by the low-level veering shown in columns 2 and 3 of Fig. 11, the capping inversion was missed in the NARR forced run (column 2, Fig. 11). This is attributed to the warm bias discussed earlier as the low-level temperatures are even higher than the ‘‘warm’’ air advected to this area. Further, the highly overestimated low-level temperature in the WRF_NARR deforms the parcel moist adiabatic trajectory by bending to the right in the skew-T plot, resulting in an unrealistically large value of convective available potential energy (CAPE; 1262 J kg21, Fig. 11h) as compared to the observations (0 J kg21, Fig. 11g). By contrast, this feature is closely reproduced in the WRF_FNL (14 J kg21, Fig. 11i). Actually, the CAPE in the WRF_ NARR over the Piedmont region within this time period is around 1200 ; 1700 J kg21 and thus is adequate to support moderate convection, whereas only 200 ; 800 J kg21 CAPE is available in the WRF_FNL (not shown).

(ii) Timing offset in Alabama The timing error in the WRF_NARR was most prominent between 0800 and 0900 UTC 16 September. As shown in Fig. 12a, while precipitation was already widely developed in the second grid as per the STIV dataset, the WRF_NARR (Fig. 12b) did not show any rainfall in Alabama, although it was in phase with observations around 1100 UTC 16 September and kept pace thereafter (not shown). By contrast, the WRF_FNL (Fig. 12c) captures the timing of this early rainfall much better. The WRF_NARR timing offset can be attributed to two aspects: 1) storm features inherited from NARR and 2) the low-level warm layer in the WRF_NARR. As shown in column 1 of Fig. 13, a capping inversion did show up in both simulations over the area marked by the rectangle in Fig. 12b. However, although the convective

inhibition (CIN) is greater in WRF_NARR, the difference with regard to WRF_FNL does not exceed 10 J kg21, and the magnitude varies between 50 and 70 J kg21 in both simulations (not shown). More importantly, at 0600 UTC 16 September, the storm is smaller and weaker in the NARR than in the NCEP FNL as illustrated in Figs. 2a,d. This leads to a stronger storm and greater pressure gradient in the WRF_FNL at later times of the model integration (cf. Figs. 13b and 13e). Consequently, a relatively more intense secondary circulation, through the adjustment of the primary vortex toward gradient wind and hydrostatic balance (Emanuel 1986), triggers elevated convection in the WRF_FNL at the top of the capping inversion (cf. Figs. 13c and 13f). Moreover, because of the low-level higher temperature, atmospheric relative humidity in the WRF_NARR in this region was actually lower (row 1, Fig. 14). Thus, more evaporation is allowed as indicated by the higher precipitable water vapor (PWV) in the WRF_NARR (cf. rows 2 and 3 of Fig. 14), which further reduces the chance for the hydrometeors to reach the ground surface as precipitation.

(iii) Unrealistic principal rainbands Figure 12e illustrates how the shifts of the spiral rainbands are responsible for the unrealistic heavy rainfall in central Alabama of up to 75 mm from 1500 to 1600 UTC 16 September in the WRF_NARR that is absent in the STIV (Fig. 12d). Note the better agreement between the NCEP FNL forced simulation and the observations (Fig. 12f). Based on an intercomparison of hourly rainfall accumulation between these two experiments and the STIV data (not shown), the WRF_NARR starts to produce extreme artificial rainfall as early as 1200–1900 UTC 16 September. The missed rainfall at the intersection of Tennessee, Georgia, and Alabama but overestimation in central Tennessee in the WRF_NARR can also be traced to the time offset and localization of the rainbands.

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FIG. 11. (left) Observed and soundings with simulated soundings in (middle) WRF_NARR and (right) WRF_FNL at GBNC at different times (UTC) as indicated in the panel titles. The simulated soundings are from the intermediate grid.

As shown in the third-row panels of Fig. 12, although the major rainband in the WRF_FNL (Fig. 12i) is somewhat behind (southwestward) of the observed rainband (Fig. 12g) with approximately 15-mm rainfall excess in the time window from 2200 to 2300 UTC 16 September, the major spiral rainband in the WRF_NARR is much narrower and about 100 km away (Fig. 12h). In fact, problems in the simulation of this spiral rainband start at 1800 UTC 16 September and continue through 0200 UTC 17 September, impairing the overall skill of the WRF_NARR.

Since these rainbands were more or less stationary with respect to the storm center both in the simulations and the STIV product, they can be termed as principal rainbands (see Willoughby et al. 1984). Willoughby et al. (1984) suggested that these quasi-stationary rainbands result from the interaction between the vortex wind field and the environment, and tend to occur in the region where the Rossby number is close to unity. However, this hypothesis has not been conclusively verified (Houze 2010), and it appears not applicable to this

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FIG. 12. Hourly rainfall (mm) from (left) STIV, (middle) WRF_NARR, and (right) WRF_FNL in the intermediate grid. The contour lines are for topography (m) at 400-m interval, with the areas higher than 1000 m filled by pluses. The corresponding time window is indicated in the title of each panel.

study (not shown). Houze (2010) argues that the proximate cause of the principal rainband has not been firmly established, and a consensus interpretation is still lacking (e.g., Kurihara 1976; Willoughby 1978; Willoughby et al. 1984; Montgomery and Kallenbach 1997; Chow et al. 2002). Although achieving consensus is beyond the

scope of this manuscript, some attention is directed next toward elucidating the physical mechanisms that explain the inferior simulation of principal rainbands in the WRF_NARR. The most prominent feature of the principal rainbands in the WRF_NARR is their unrealistic proximity

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FIG. 13. (left) The skew-T plot for the region marked by the rectangle in Fig. 12b. (middle) The sea level pressure (hPa). (right) The wind (m s21) and radar reflectivity (dBZ) in the plane of the cross section as indicated by the dashed lines in Figs. 12b and 12c. (top) WRF_NARR; (bottom) WRF_FNL. Time is 0830 UTC 16 Sep and all the results are from the second nest. The vertical velocity is enlarged 10 times.

to the core region and extreme magnitude. Compared with the NCEP FNL simulation, the WRF_NARR storm is relatively weaker (see Figs. 5a,c as well as Figs. 13b,e), and therefore the transverse slantwise secondary circulation is weaker. This overturning circulation dominates the overall vertical motion of the air in the cyclone above the planetary boundary layer, which is different from traditional convective activity (Houze 2010). As shown in Fig. 15a, the relatively weak overturning circulation in the WRF_NARR leads to less activity of ice processes in the upper layer of the troposphere and colder temperatures because of less production of latent heating through condensation. Further, even though the presence of a low-level warm bias in NARR induces high lowlevel moisture (Fig. 15b) through evaporative cooling (not shown), the WRF_NARR underestimates upperlevel moisture (Fig. 15b) as confirmed by the sounding observations at Shelby, Alabama (SBAL; row 1 of Fig. 16) and Peachtree, Georgia (PTGA; row 2 of Fig. 16). This WRF_NARR thermodynamic structure is conducive to

the development of localized deep convection. Near the storm center but outside the core, the overestimated low-level moisture in the WRF_NARR was released because of the strong vertical motion therein, leading to deep convection up to 12 km in height (see Fig. 17 for example) and, subsequently, extreme rainbands as illustrated in rows 2 and 3 of Fig. 12. Note that, in realworld conditions, the vertical motion, especially for the region just outside the core area, tends to be confined below the radial outflow emanated from the eyewall, and it usually remains below ;(8–10) km (Houze 2010). The deep convection close to the cyclone center in the WRF_NARR appears therefore unrealistic, and indeed is absent in the WRF_FNL.

(iv) Overestimation to the northwest of the southern Appalachians Compared with the WRF_FNL, the stronger overestimation of precipitation in the northwest sector of the

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FIG. 14. (top) Temperature (K) (black) and relative humidity (%) (gray) profiles of WRF_NARR (solid) and WRF_FNL (dashed) from 0800 to 0900 UTC 16 Sep at every 10-min interval over the area marked by the rectangle in Fig. 12b. These profiles are horizontally averaged at every vertical level. The PWV (mm) at different times for (middle) WRF_NARR and (bottom) WRF_FNL. These are from the intermediate grid.

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FIG. 15. (a) Hovmo¨ller diagram of the residual of solid water (g kg21, ice 1 snow 1 graupel) between WRF_NARR and WRF_FNL. (b) As in (a), but for water vapor (g kg21). The solid thick lines denote the zero contours, while the dashed lines indicate negative values. They are from the intermediate domain and horizontally averaged at each vertical level.

southern Appalachians (mainly from 0300 to 2000 UTC 17 September) in the WRF_NARR is attributed to the unrealistic earlier arrival of the cold front from the northwest, whose progression steered Ivan northnortheastward and along the Appalachian Mountains. Because of limited observations, we rely on the sounding observations outside the intermediate nest but within the outermost domain for the analysis presented next. At 0000 UTC 17 September, the model sounding at Wilmington, Ohio (WLOH; see Fig. 1b for its location) in the WRF_NARR (Fig. 16h) shows backing from 900 to 850 hPa and veering from 850 to 720 hPa, indicating lowlevel cold advection with warm advection above. However, this feature was still absent in the observations (Fig. 16g) at that time as well as in the WRF_FNL model sounding (Fig. 16i), which reproduced the observed profile reasonably well (cf. Figs. 16g and 16i). With the low-level flow climbing on the simulated cold front, more precipitation is predicted in the NARR forced run.

b. Comparison against local observations: The simulated rainfall in the innermost domain The comparison of the simulated precipitation from the innermost grid against the NCDC rain gauges and the STIV radar–rain gauge composites is presented in Fig. 18. At each location, the rainfall time series at the grid cell containing the rain gauge as well as at the eight surrounding grid cells are included to account for localized space–time offsets. Note that the NCDC (point data) and STIV (4-km pixel) time series are shifted with respect to each other, showing the distinct nature of

these two types of products—that is, the point-based NCDC rain gauge and the pixel-scale-averaged STIV. However, the substantial rainfall at gauge 21 between 1200 and 1800 UTC 16 September may not be realistic, since during this time period only very little precipitation was received at its neighboring stations (i.e., rain gauges 10, 19, and 20; see Fig. 1c) whose distance to gauge 21 is less than the width of a typical principal rainband. Gauge 4 (at the foothill of the southern Appalachians; see Fig. 1c) is isolated and its topographic setting is different from nearby stations (i.e., rain gauges 2, 3, and 5, in the inner region or at high elevations; see Fig. 1c), and therefore is not representative of regional conditions besides the difficulty on determining its reliability and will not be considered for further discussion. To quantitatively benchmark the simulation, the rootmean-square error (RMSE) was employed. However, this measure is greatly influenced by timing or location offsets and tends to heavily weigh outliers. For example, at gauge 10, the RMSE between the NCDC and STIV is not smaller than the RMSE between either of these observations and any simulations (Table 4), whereas visual inspection of the time series plot (Fig. 18) indicates that the best agreement is actually between these two observations. It is therefore more meaningful to rely on RMSE only for the cases where no clear conclusions can be made merely according to the time series structure (e.g., timing, number of peaks, extreme values, etc.). Similar concerns apply to other widely used skill scores (e.g., Tao and Barros 2010; Gilleland et al. 2009).

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FIG. 16. As in Fig. 11, but for different soundings at different times (UTC) as indicated in the panel titles. The simulated soundings are from (top),(middle) the second grid and (bottom) the outermost domain.

Generally, both simulations provide reasonable results—especially for the location of rain gauges 13, 14, 16, 17, and 18—with the corresponding RMSE not exceeding 4.7 mm h21. The multiple peak modes in rain gauges 3, 6, 9, 10, 11, 13, and 20 are all captured in both simulations, which is in agreement with the two major mechanisms responsible for the rainfall in this study: orographic enhancement and the passage of individual rainbands. The last artificial rainfall intensity peaks at rain gauges 2 (31.2 mm h21), 3 (23.3 mm h21),

5 (30.1 mm h21), 6 (36.8 mm h21), 8 (37.3 mm h21), and 12 (29.2 mm h21) in the WRF_FNL are associated with the overestimation of the magnitude of the rainbands from 0700 through 1300 UTC 17 September, which are actually very well reproduced in terms of position. This leaves comparatively smaller RMSE for the WRF_NARR at these gauges (except at gauge 12). Nevertheless, the spatial layout of these relatively light rainbands is in fact not captured very well in the NARR forced run (not shown). The larger RMSE at gauge 1 in

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FIG. 17. Wind (m s21) and radar reflectivity (dBZ) in the plane of the cross section marked by the dashed line in Figs. 12e and 12f. (a) WRF_NARR; (b) WRF_FNL. Both are from the simulation in the second nest. The vertical velocity is enlarged 10 times.

the WRF_FNL results from an exaggerated extension of the principal rainband in its early stage of model integration, leading to precipitation up to 36.5 mm h21 (see panel 1 of Fig. 18). Overall, although the WRF_NARR performs better at rain gauge 20 according to RMSE, the WRF_FNL is generally more robust with respect to timing and rainfall amounts associated with rainbands (ahead of time at gauges 2, 7, and 9, and delayed at gauges 10 and 16 in the WRF_NARR), number of rainfall peaks (gauge 3: three peaks in NCDC, STIV, and WRF_FNL, but two in WRF_NARR; gauge 8: one peak in NCDC, STIV, and WRF_FNL, but two light ones in WRF_NARR; and gauge 19: three peaks in NCDC and STIV and two peaks in WRF_FNL, but only a weak one in WRF_NARR), magnitude of extreme values (gauge 5: 22.4 mm h21 in NCDC, 22.6 mm h21 in STIV, 33.4 mm h21 in WRF_ NARR, and 30.12 mm h21 in WRF_FNL), or smaller RMSE (gauges 7, 9, 11, 12, 15, and 17). For gauges 13, 14, 16, 18, and 19, it is not clear which simulation is superior either according to time series structure or the RMSE, depending on which dataset (NCDC or STIV) is deemed as the reference. These results, synthesized in the Hovmo¨ller diagram of hourly rain rate (Fig. 19) at the location of the NCDC stations documented in Fig. 1c, are consistent with the better storm evolution simulated in the intermediate grid of the WRF_FNL, especially the representation of the principal rainbands.

5. Conclusions and discussion The impact of alternative forcing datasets (NARR and NCEP FNL) on high-resolution simulations of

Tropical Cyclone Ivan (2004) in the southern Appalachians using ARW-WRF3.1 was investigated. Sensitivity analysis to various physical parameterizations (mainly on the planetary boundary layer and cumulus schemes) will be presented in a subsequent manuscript. Compared with satellite-based observations (TRMM 2A25 and GOES), station measurements (NCDC rain gauge), combined radar–rain gauge products (STIV), and the best track from NHC, it was found that both simulations forced by NARR and NCEP FNL reproduce the precipitation patterns reasonably well. However, despite previously reported successful applications using NARR for high-resolution simulations or dynamical downscaling for nonhurricane cases (e.g., J. Li et al. 2008), the simulation forced by the NCEP FNL, though much coarser (18 3 18) than the NARR (32 km 3 32 km), captures the overall storm structure better. This is confirmed by intercomparison of atmospheric thermodynamic structure between model and observational soundings. The inferior performance of WRF_NARR is attributed to the storm structure and low-level warm bias inherited from its forcing data (NARR), leading to excessive precipitation in the Piedmont region and delayed rainfall in Alabama as well as spatially displaced and unrealistically extreme rainbands during its passage over the southern Appalachians. These results suggest that substantial care and attention are required on the selection of the forcing data for similar high-resolution simulations or dynamical downscaling of multiple tropical cyclones, where the accurate representation of the principal rainbands is of significance and can be highly influenced by the forcing. Independently of forcing, both simulations overestimate storm intensity at low elevations away from the

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FIG. 18. Hourly rain rate (mm h21) of each NCDC station (black) as documented in Fig. 1c and the retrieved rainfall from STIV (red), WRF_NARR (blue), and WRF_FNL (orange). The simulated precipitation is from the innermost grid. To account for the uncertainties of spatial distribution, either from STIV or model output (WRF_NARR and WRF_FNL), the rainfall from the nearest nine grid points to each NCDC rain gauge is included. Only the precipitation records from 0600 UTC 16 Sep to 0000 UTC 18 Sep are shown.

mountains. The authors hypothesize this is due to the underestimation of surface drag associated with the underestimated roughness length over the land in the WRF model. The underestimated surface drag weakens low-level convergence, slowing storm dissipation and leading to overestimation of the storm intensity. It is

suspected that the similarity theory–based surface layer scheme (MM5 scheme; Zhang and Anthes 1982), which is developed by the data collected under normal weather conditions, may not be appropriate for strong wind regimes. Further analysis, however, indicates that the surface layer scheme is as desired not sensitive to the

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TABLE 4. RMSE (mm h21). The rain gauges higher than 1000 m (i.e., gauge 5) are in italic and the ones between 500 and 1000 m (i.e., gauges 2, 3, 9, 10, 14, and 20) are boldface. RMSE (mm h21) Rain gauge No.

Elevation (m)

NCDC and STIV

NCDC and WRF_NARR

NCDC and WRF_FNL

STIV and WRF_NARR

STIV and WRF_FNL

1 2 3 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

371.86 630.94 682.14 1316.74 201.17 251.16 287.43 762.00 502.92 354.18 313.94 457.20 524.26 293.22 407.82 275.84 413.00 263.96 656.84

2.9 2.8 2.2 2.4 3.0 1.4 1.6 8.2 12.1 6.2 3.4 1.9 4.3 1.3 3.0 2.0 3.5 8.1 10.9

3.3 4.8 3.8 4.4 3.8 6.5 3.7 10.3 10.1 10.6 6.2 3.9 4.2 6.5 3.3 4.0 3.0 6.7 9.1

4.7 7.6 5.9 6.0 5.9 6.1 7.4 8.9 12.1 8.4 5.6 3.6 3.0 4.6 3.1 2.7 1.7 8.3 13.0

2.5 4.7 4.2 5.5 2.3 6.4 4.0 8.3 10.4 9.9 6.5 4.0 3.4 6.5 3.2 4.0 3.0 7.1 8.4

5.7 7.4 5.6 5.8 5.8 6.2 7. 7 5.3 9.7 7.6 5.9 4.6 4.1 4.4 4.7 2.6 4.3 4.8 11.5

stability of the lowest model layers for high-wind regimes with very strong mechanical shear such as a tropical depression (not shown). According to earlier studies (e.g., Tuleya 1994; Evans et al. 2011), the simulated storm intensity could also be related to soil moisture initialization. Indeed, the initial soil moisture conditions in the WRF_NARR and WRF_FNL are different by about 10%–20% at various locations, with the NARR tending to be drier than the FNL soil moisture fields (not shown). Because of the lack of soil moisture observations in the southeastern United States generally, it is not feasible to determine

conclusively which one is more realistic. Nevertheless, in order to investigate the impact of soil moisture initialization on storm intensity, one additional simulation was conducted with the initial and lateral boundary conditions derived from the NCEP FNL but the initial soil moisture from the NARR. The results show negligible difference between the two simulations, either for storm structure or precipitation (not shown). The impact of soil moisture initialization for the numerical simulations presented in this manuscript is therefore not significant. Although the rainfall distribution is dominated by forcing as illustrated in Fig. 10, the representation of the

FIG. 19. Hovmo¨ller diagram of hourly rain rate (mm h21) from (a) NCDC, (b) STIV, (c) WRF_NARR, and (d) WRF_FNL at the location of the NCDC stations documented in Fig. 1c. Note that gauges 4 and 21 are not included as discussed in section 4b. The simulated precipitation is from the innermost domain.

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topography is critical to resolve the localized extreme rainfall features that drive natural hazards (cf. Figs. 7a with Figs. 7b,c). Different from previous dynamical downscaling studies on climate scales, there is no question of the value added by high-resolution topography in order to capture the high spatial and temporal variability of precipitation during the passage of the storm over the mountains. Nevertheless, note that there were no precipitation observations at high elevations and in the inner regions of the southern Appalachians, and therefore an objective quantitative evaluation of the orographic enhancement of WRF precipitation estimates for either experiment cannot be conducted. Relying on a recently installed high-elevation rain gauge network (installed after 2007), observed orographic enhancement factors for other (weaker) tropical storms in the inner ridge–valley region of the Great Smoky Mountains between the eastern and western slopes of the southern Appalachians suggest that actual precipitation during Ivan may have been substantially higher than that simulated here (Prat and Barros 2010a,b). Nevertheless, all-around better performance of the simulation vis-a`-vis the original forcing fields is found at high elevations (black circles in Fig. 9), which is of great interest to meet the quantitative precipitation estimation (QPE) objectives for diagnosing hydrometeorological hazards in mountainous regions, specifically flash floods and earth flows. Acknowledgments. The authors thank two anonymous reviewers for their helpful suggestions and comments, as well as the Computational and Information Systems Laboratory (CISL) at the National Center for Atmospheric Research (NCAR) for providing computational resources. This research was supported by the National Aeronautics and Space Administration (NASA) under Grants NNX07AK40G and NNX10AH66G and National Science Foundation (NSF) Grant EAR-0711430. REFERENCES Antic, S., R. Laprise, B. Denis, and R. de Elı´a, 2006: Testing the downscaling ability of a one-way nested regional climate model in regions of complex topography. Climate Dyn., 26, 305–325. Barnes, G. M., E. J. Zipser, D. Jorgensen, and F. Marks Jr., 1983: Mesoscale and convective structure of a hurricane rainband. J. Atmos. Sci., 40, 2125–2137. Barros, A. P., G. Kim, E. Williams, and S. W. Nesbitt, 2004: Probing orographic controls in the Himalayas during the monsoon using satellite imagery. Nat. Hazards Earth Syst. Sci., 4, 29–51. ——, S. Chiao, T. J. Lang, D. Burbank, and J. Putkonen, 2006: From weather to climate—Seasonal and interannual variability of storms and implications for erosion processes in the

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