Influence of Rain-Rate Initialization, Cloud Microphysics, and Cloud ...

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Influence of Rain-Rate Initialization, Cloud Microphysics, and Cloud Torques on Hurricane Intensity S. PATTNAIK Indian Institute of Tropical Meteorology, Pashan, Pune, India

C. INGLISH AND T. N. KRISHNAMURTI Department of Meteorology, The Florida State University, Tallahassee, Florida (Manuscript received 11 February 2010, in final form 30 August 2010) ABSTRACT This study examines the impact of rain-rate initialization (RINIT), microphysical modifications, and cloud torques (in the context of angular momentum) on hurricane intensity forecasts using a mesoscale model [the Advanced Research Weather Research and Forecasting model (ARW-WRF)] at a cloud-resolving resolution of 2.7 km. The numerical simulations are performed in a triple-nested manner (25, 8.3, and 2.7 km) for Hurricane Dennis of 2005. Unless mentioned otherwise, all the results discussed are from the innermost grid with finest resolution (2.7 km). It is found that the model results obtained from the RINIT technique demonstrated robust improvement in hurricane structure, track, and intensity forecasts compared to the control experiment (CTRL; i.e., without RINIT). Thereafter, using RINIT initial conditions datasets three sensitive experiments are designed by modifying specific ice microphysical parameters (i.e., temperature-independent snow intercept parameter, doubling number of concentrations of ice, and ice crystal diameter) within the explicit parameterization scheme [i.e., the WRF Single-Moment 6-class (WSM6)]. It is shown that the experiment with enhanced ice mass concentration and temperature-independent snow intercept parameter produces the strongest and weakest storms, respectively. The results suggest that the distributions of hydrometeors are also impacted by the limited changes introduced in the microphysical scheme (e.g., the quantitative amount of snow drastically reduced to 0.1–0.2 g kg21 when the intercept parameter of snow is made independent of temperature). It is noted that the model holds ice at a warmer temperature for a longer time with a temperature-independent intercept parameter. These variations in hydrometeor distribution in the eyewall region of the storm affect diabatic heating and vertical velocity structure and modulated the storm intensity. However, irrespective of the microphysical changes the quantitative amount of graupel hydrometeors remained nearly unaffected. Finally, the indirect effect of microphysical modifications on storm intensity through angular momentum and cloud torques is examined. A formulation to predict the short-term changes in the storm intensity using a parcel segment angular momentum budget method is developed. These results serve to elucidate the indirect impact of microphysical modifications on tropical cyclone intensity changes through modulation in cloud torque magnitude.

1. Introduction Accurate prediction of hurricane intensity change remains a great challenge for the meteorological community (Willoughby 2007; National Oceanic and Atmospheric Administration Science Advisory Board Hurricane Intensity Research Working Group 2006; Wang and Wu 2004; Schrope 2005). It is well established that hurricane

Corresponding author address: S. Pattnaik, Indian Institute of Tropical Meteorology, Dr. Homi Bhaba Road, Pashan, Pune, 411008, India. E-mail: [email protected] DOI: 10.1175/2010MWR3382.1 Ó 2011 American Meteorological Society

intensity change depends on many internal as well as external factors. Bender and Ginis (2000) showed the importance of realistic coupling of sea surface temperature (SST) in the Geophysical Fluid Dynamics Laboratory (GFDL) model for predicting the rapid intensification phase of a hurricane. Davis et al. (2001) noted that the hurricane intensity changes are attributed to the difference in the internal dynamics. Important factors such as eyewall replacement cycle (Houze et al. 2007; Zhu et al. 2004), rainbands (Houze et al. 2006), vortical hot towers (Montgomery et al. 2006), boundary layer parameterization (Braun and Tao 2000), angular momentum and scale interactions (Krishnamurti et al. 2005),

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microphysical processes (McFarquhar et al. 2006; Pattnaik and Krishnamurti 2007a,b) and their respective impacts on modulation of hurricane intensity have been comprehensively investigated by these aforementioned authors and many more. Besides elucidating the role of different factors and processes on the intensity changes of the hurricane, there are several published results, where the skill of intensity prediction of the forecast model has been improved using better model configuration, data assimilation, and bogus vortex techniques. Kamineni et al. (2006) showed that incorporating additional field phase data, such as detailed moisture profiling datasets, through threedimensional (3D) variational assimilation in a global spectral model improves the forecasting skill for tracks up to a lead time of 120 h. The positive impacts of bogus vortex data assimilation in improving hurricane intensity skill of a mesoscale model have been discussed by Pu and Braun (2001). Xiao et al. (2006) noted an improvement in forecast of landfall location and timing as well as intensity of typhoons by carrying out a bogus data assimilation procedure in a mesoscale model. Davis et al. (2006) and Tenerelli and Chen (2001) suggest that increasing the resolution of the mesoscale model improves the simulated structural details of the eyewall, rainbands of the storm and positively impact the prediction of intensity. However, the role of precipitation in the modulation of hurricane intensity is somewhat less emphasized in earlier studies, Krishnamurti et al. (2007) showed an improvement in the skill of intensity and rainfall forecasts (up to day 2) by carrying out the rain-rate initialization (RINIT) procedure in a mesoscale model. Kelley et al. (2004, 2005) demonstrated from radar observations that the presence of heavy precipitation within the hurricane eyewall helps its intensification. Droegemeier et al. (2000) emphasized the need for improvement in microphysical parameterization schemes in atmospheric models for better hydrological forecasting. The sensitivity of precipitation forecasts (quantity, distribution) to microphysical parameterization schemes have been discussed by several authors for cloud resolving simulations (Cotton et al. 1982; McCumber et al. 1991). In this article, first, an improvement in prediction of hurricane intensity and precipitation using rain-rate initialization technique is discussed. Thereafter, using these improved initial condition datasets the impacts of specific ice phase cloud microphysical parameters on hurricane intensity are explored at a cloud resolving resolution of 2.7 km. Finally, the impacts of microphysical modifications on cloud torques are examined. The purposes of this study are to 1) investigate the impact of the RINIT technique on the hurricane structure, intensity/track and precipitation forecasts; 2) evaluate the impact of ice microphysical parameters on the hurricane intensity forecast;

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and 3) examine the influence of microphysical changes on cloud torques and subsequent impact of cloud torques on the hurricane intensity forecast. For this study, the mature phase of hurricane Dennis (of 2005) is simulated using the Advanced Research Weather Research and Forecasting model (ARW-WRF) at a cloud resolving resolution of 2.7 km. The manuscript is divided into the following sections; description of Hurricane Dennis (section 2), description of the numerical model (section 3), experiment design (section 4), discussion of results (section 5), and a summary (section 6).

2. Description of Hurricane Dennis (2005) Dennis was the second hurricane of the 2005 Atlantic hurricane season. It reached hurricane strength early on 7 July, thereafter it rapidly intensified into a category-4 hurricane with winds of the order of 62 m s21 before making a landfall near Punta Del Ingles in southeastern Cuba at around 0245 UTC 8 July. Dennis weakened to a category-1 hurricane while passing across southeastern Cuba and gradually reintensified for the next 6–12 h over the Gulf of Mexico, then began a cycle of rapid reintensification near 1800 UTC 9 July and was accompanied with a north-northwestward turn. During this intensification, the pressure fell by nearly 37 hPa in 24 h, including a 20-hPa drop of pressure in 6 h and 11 hPa in 1 h, 35 min. The maximum sustained winds reached a peak of 125 kt near 1200 UTC 10 July. Dennis made landfall on Santa Rosa Island, Florida, between Navarre Beach and Gulf Breeze, at around 1930 UTC 10 July as a category-3 hurricane. Dennis was directly responsible for 42 deaths—22 in Haiti, 16 in Cuba, 3 in the United States, and 1 in Jamaica. Roughly 1.4 million people evacuated their homes prior to the winds of up to 190 km h21 (120 mph) that hit Pensacola Beach, Florida, on Sunday (10 July 2005). The total property loss in the United States was estimated to be $2.23 billion (Beven 2005).

3. Description of the numerical model The ARW-WRF dynamic solver version 2.1.1 model (Skamarock et al. 2005) is used for this study. The following physics options are used for all three nested domains (25, 8.3, and 2.7 km; Fig. 2), except in the innermost domain (2.7 km, domain 3), which is explicitly resolved. The physics schemes include a Rapid Radiative Transfer Algorithm (RRTM; Mlawer et al. 1997) for longwave radiation and the Dudhia scheme (Dudhia 1989) for the shortwave radiation, the Monin–Obukhov and Janjic schemes (Monin and Obukhov 1954; Janjic 1996, 2002) for surface physics, the Mellor–Yamada–Janjic (MYJ) turbulent kinetic energy (TKE) PBL scheme (Janjic

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FIG. 1. Schematic diagram of the experiments.

1996, 2002) for the boundary layer parameterization, the Grell–Devenyi ensemble (Grell and Devenyi 2002) scheme for the convection, and the WRF Single-Moment 6-class (WSM6; Hong et al. 1998, 2004) microphysics scheme. Initial and boundary conditions are interpolated from the National Centers for Environmental Prediction (NCEP) final analysis datasets available at 18 3 18 resolutions for the coarsest domain (i.e., domain 1, 25 km). All inner domains derive boundary conditions from their respective outer domains or parent domains. Rain-rate initialization is invoked in the outermost coarsest domain 25 km following Krishnamurti et al. (2007) using the ARW-WRF model and the Tropical Rainfall Measuring Mission (TRMM) 3B42 datasets (Huffman et al. 1995). The choice of a coarse resolution of 25 km for the rain-rate initialization was dictated by the best resolution of the TRMM datasets.

4. Experiment design At first, impact from RINIT within the ARW-WRF model is assessed by validating the forecast variables (i.e., track, intensity, precipitation, and structural characteristics of the hurricane) with the observed datasets. Next the microphysical sensitivity experiments are carried out at a cloud resolving resolution (2.7 km) using forecast datasets obtained from the RINIT experiment. Finally, hurricane intensity changes are addressed from cloud torques and angular momentum perspectives. Each of these tasks

is discussed in the following sections. Schematic illustration of experiment design is presented in Fig. 1.

a. Rain-rate initialization The model is integrated in a triply nested way having horizontal resolution of 25, 8.3, and 2.7 km, respectively (Fig. 2 and Table 1). The RINIT technique (Krishnamurti et al. 2007) is enforced in the outermost coarsest domain (hereafter RINIT25) for 24 h (i.e., day 21 to day 0, i.e., 0000 UTC 8 July 2005–0000 UTC 9 July 2005) using 3-hourly TRMM 3B42 datasets. A control run with duration of 24 h (i.e., 0000 UTC 8 July 2005–0000 UTC 9 July 2005) is also carried out using the same model but without invoking the RINIT (hereafter CTRL25) technique. Thereafter, the ARW-WRF model is integrated out to 48 h (from 0000 UTC 9 July to 0000 UTC 11 July 2005) in a two-way interactive manner (25 and 8.3 km) once using rain-rate initialized initial conditions and once without using rain-rate initialized datasets (i.e., using NCEP final analysis datasets). The initial and boundary conditions for the innermost domain (i.e., 2.7 km, Fig. 2) for each of these experiments are obtained by nesting down the forecast dataset obtained from their respective parent domains (i.e., 8.3 km, domain 2). The innermost nest (i.e., 2.7 km) is integrated in a 1-way nested fashion. The forecast from the innermost domain (i.e., 2.7 km) using coarser mesh rain-rate initialized initial and boundary conditions is called RINIT and without using the coarser mesh RINIT datasets is called CTRL. The duration of

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FIG. 2. The model domains and respective horizontal resolutions (25, 8.3, and 2.7 km) and 48-h forecast tracks (i.e., 0000 UTC 9 Jul–0000 UTC 11 Jul 2005) for experiments at innermost domain of 2.7 km.

integration for the respective domain is illustrated in Table 1 and a schematic representation of the experiment design is presented in Fig. 1. The acronyms of the experiments are presented in Table 2.

b. Microphysics sensitivity experiments Three microphysical sensitivity experiments are carried out in the innermost domain (2.7 km, Fig. 2) using the initial and boundary conditions from the two-way interactive coarser grid runs (i.e., 25 and 8.3 km) with RINIT. Here our goal is to highlight important microphysical parameters which have influence on ice phase processes in the explicit parameterization scheme. The importance of ice phase physics on genesis, structure, and intensity of hurricane has been well documented in literatures. Lord et al. (1984) and Willoughby et al. (1984) showed that the presence of ice processes delay the storm development but facilitates its intensification. McFarquhar et al. (2006) demonstrated that an increase in fall speed of graupel has

robust impact on reduction of minimum sea level pressure of the storm. Sensitivity of ice phase microphysics on rainbands of hurricanes was discussed by Franklin et al. (2005). Besides this, we feel the number of changes made by Hong et al. (2004) in the WSM6 scheme, which has tangibly impacted ice phase processes on an idealized thunderstorm event, needs to be validated with respect to

TABLE 1. Experiment durations in respective domains. Nest domain

Duration

Domain 1 (25 km)

0000 UTC 8 Aug–0000 UTC 9 Aug 2005 (RINIT phase) 0000 UTC 9 Aug–0000 UTC 11 Aug 2005 (forecast phase, 48 h) 0000 UTC 9 Aug–0000 UTC 11 Aug 2005 (48 h) 0000 UTC 9 Aug–0000 UTC 11 Aug 2005 (48 h)

Domain 2 (8.3 km) Domain 3 (2.7 km)

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c. Cloud torques and angular momentum

TABLE 2. Description of experiments with acronyms. Expt

Description

CTRL25

Control experiment at the coarsest model domain (25 km) Rain-rate initialized experiment at the coarsest domain (25 km) Nest down innermost nest (2.7 km) from control run Nest down innermost nest (2.7 km) from RINIT25 Temperature independent snows intercept parameter in 2.7-km grid Twofold increase in ice concentration in 2.7-km grid Twofold increase in ice crystal diameter in 2.7-km grid

RINIT25 CTRL RINIT M1 M2 M3

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its impact on hurricane intensity forecasts because WSM6 is one of the widely used microphysics schemes in the community for hurricane forecasts. We have carried out limited modifications to three important microphysical parameters (i.e., snow intercept parameter, ice crystal diameter, and ice concentration) in this study. The sensitivity experiments include 1) a temperature-independent snow intercept parameter (hereafter M1), 2) a twofold increase in ice number concentration (hereafter M2), and 3) doubling the ice crystal diameter (hereafter M3). The forecast duration of all these experiment is 48 h (i.e., 0000 UTC 9 July 2005–0000 UTC 11 July 2005) for Hurricane Dennis of 2005. The microphysical parameters such as intercept parameter of snow, number concentration of ice, and ice crystal diameter play principal roles in determining the density, terminal fall speed, and number concentration of frozen hydrometers (i.e., ice, snow, graupel), which later interact with other microphysical processes to influence model precipitation forecast skill. The following are the mathematical representations of the snow intercept parameter, number concentration of ice, and ice crystal diameter (Hong et al. 2004), respectively: nos 5 2 3 106 exp[0.12(T 0  T)],

(1)

N I 5 5.38 3 107 (rqI )0.75 ,

(2)

DI 5 11.9MI0.5 .

(3)

In this expression nos represents the intercept parameter of snow, T is the temperature, T0 is the freezing point (273.15 K), NI (m23) is the number concentration of cloud ice, r is the density of air, qI is the mixing ratio of cloud ice, DI (m) is ice crystal diameter, and MI is the mean mass of the ice crystal.

In line with the third goal of our study, this section evaluates the role of incoming angular momentum flux and inhibiting strong cloud torques within the storm vicinity for each of these microphysics sensitivities (i.e., M1, M2, M3) and the RINIT experiments to investigate their impacts on hurricane intensity modulation. In essence, we would like to examine the indirect effect of cloud microphysical parameters on hurricane intensity from cloud torque perspectives. The primary question addressed here is whether the changes in cloud structural characteristics due to microphysical modifications can affect the cloud torques and lead to a change in angular momentum, which the parcel experiences along its patch following constructed trajectories. To address this, 36-h backward trajectories starting from the region of maximum wind speed of the storm are constructed for each of these experiments following Krishnamurti and Bounoua (1995). Afterward, cloud torque (CT), pressure torque (PT), and changes in angular momentum (nM) are computed at 6-h intervals across segments throughout the 36-h parcel history. The change in angular momentum following parcel motion is given by dM 5 CT 1 PT 1 F T , dt

(4)

where CT is cloud torque, PT is pressure torque, and FT is frictional torque (see appendix A). Following Krishnamurti et al. (2005), the contribution from cloud torques in the x–y–p frame of reference is expressed by 

›M ›t

 5r CT

› W9V u9. ›z

(5)

Following Eq. (4), segments of the parcel trajectories are considered one at a time. The total change in angular momentum the parcel experiences are the sum of the cloud torques, pressure torques, and frictional torques in Eq. (5). The short-term changes in hurricane intensity are computed summing the contributions over different 6-hourly segments along the trajectory following the 36 h of the parcel’s history. For these 36-h durations, changes in angular momentum, cloud toques, frictional torques, and pressure torques the air parcel experiences across different segments of its path (i.e., every 6-h interval) along the constructed trajectories are computed. The formulation in Eq. (6), derived in appendix B, is used to validate the theoretical assumption that the short-term changes of tangential wind over each segment of the parcel motion can be predicted with reasonable accuracy. Final intensity at point A2:

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 V u r1 1 Vu 5 2

1

 f 1 r12 f r2 1 (PT 1 F T 1 CT )Dt  2 2 2 2 . r2

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(6)

5. Discussion of results a. Impacts from rain-rate initialization 1) TRACK AND INTENSITY Figure 2 presents the 48-h forecast tracks of Hurricane Dennis of 2005 for all experiments carried out in this study. However, in this section the discussion is restricted to RINIT and CTRL experiments. All the results presented here are from the innermost domain (i.e., 2.7 km). We notice that the control run (CTRL) forecast track has large westward bias compared to that of the observed (OFCI), whereas the RINIT experiment produced a reasonably better forecast track (average track error for RINIT is 64 km and CTRL is 77 km) with a slight eastward bias compared to that of observed (OFCI). The translational speed of the simulated hurricanes for the RINIT and CTRL experiments appeared to be slightly faster/slower, respectively, compared to that of the observed (OFCI). Figures 3a,b show the intensity for the 48-h forecasts in terms of surface wind speed (kt; 1 kt 5 0.5144 m s21) and minimum sea level pressure (hPa), respectively. These figures exhibit the intensity forecasts of all the experiments but in this section we will only discuss the results for the RINIT and CTRL experiments. We note that at 1200 UTC 10 July 2009 the peak intensity of the storm in terms of minimum sea level pressure (MSLP) obtained from RINIT, CTRL, and OFCI are 940, 980, and 930 hPa, respectively. This shows that the RINIT forecast is closer to the OFCI estimates than CTRL. A similar kind of robust improved pattern in the intensity forecast from RINIT experiment is noted throughout the 48-h integration duration. The largest difference in the RINIT and CTRL forecasts is roughly around 20 kt (10.3 m s21) for the wind speed and around 50 hPa for the minimum sea level pressure. It is interesting to note that as compared to observations (i.e., OFCI) the RINIT experiment overestimated storm intensity in the first 18 h and underestimated it afterward. In general, neither the RINIT nor the CTRL exactly match the observed (OFCI) estimates. All the forcings (i.e., model, resolution, and physics options) were the same for both these experiments except for the RINIT technique. Therefore this positive impact is exclusively attributed to the RINIT procedure. This is mainly due to 24 h (day 0–1) preintegration phase (i.e., from 0000 UTC 8 July to 0000 UTC 9 July 2005) adjustments for accurate distribution of moisture, rainfall, and

FIG. 3. The 48-h forecasted hurricane intensity for experiments and the observed in terms of (a) surface wind speed (kt) and (b) MSLP (hPa).

motion field in and around the storm in the RINIT experiment (Krishnamurti et al. 2007). The initial location of the hurricane in the RINIT experiment is 0.258N of the CTRL. In the RINIT experiment the forecasted convection and rainfall improve during the preintegration phase over the lower troposphere and the resulting motion fields improve. This led to a slight shift in the center of the storm close to the observed location. The differences in translational speed (either fast or slow) might be due to the azimuthal organization of convection and precipitation patterns of the modeled storms (Rogers et al. 2003). These results show that the RINIT experiment produces better track and intensity forecasts compared to the CTRL.

2) RAINFALL Figures 4a,b show accumulated day 1 (0000 UTC 9 July–0000 UTC 10 July 2005) and day 2 (0000 UTC 10 July–0000 UTC 11 July 2005) equitable threat scores (ETS; Schaefer 1990; Gandin and Murphy 1992; Mesinger and Black 1992) for CTRL and RINIT experiments. We have used TRMM 3B42 datasets (Huffman et al. 1995, 2007) at 0.258 3 0.258 resolution for skill score computations. The observed TRMM data are interpolated to the model grid resolutions for the computation of skill scores. The rainfall thresholds, plotted on the abscissa, have units in millimeters, while the skill scores (ETS) are plotted along the ordinate. We note that the rainfall skill scores are better for the RINIT experiment compared to the

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36–42, and 42–48 h, respectively. Because of the slow movement of the storm in the CTRL, its rainfall forecast suffers from the well-known double penalty skill loss problem (Anthes 1983; Mass et al. 2002). This is conspicuous in the CTRL forecast (Figs. 5i–l) because it is not only missing the rain at the right places, but is also producing rain at the wrong places. These errors are minimized in the RINIT forecast (i.e., improvement in skill score); however, an over estimation of precipitation compared to the observed (i.e., TRMM) is seen in the forecasts for both of these experiments (i.e., RINIT and CTRL). The RINIT forecast (middle column, Figs. 5e–h) shows an intense area of precipitation that stays on the northern side of the storm center in close agreement with the observed precipitation (Figs. 5a–d) and the National Weather Service (NWS) radar image during landfall in Fig. 6. The aforementioned results based on forecast skills of track, intensity, and rainfall show that the predictions based on the RINIT experiment are superior to those of the CTRL.

b. Impacts from microphysics sensitivity

FIG. 4. ETS of rainfall thresholds (mm): (a) day 1 (0000 UTC 9 Jul–0000 UTC 10 Jul 2005) and (b) day 2 (0000 UTC 10 Jul– 0000 UTC 11 Jul 2005).

CTRL experiment for both day-1 and day-2 forecasts. In particular, the day-2 skills (Fig. 4b) are greater for the RINIT (ETS 5 0.5 and higher) compared to CTRL (ETS 5 0.4 or less). For day 1 (Fig. 4a), RINIT skills are better than those of the CTRL for most of the rainfall thresholds; however, the magnitude of difference is not large as compared to day 2 results. This is related to the model spinup problem (i.e., the physical initialization does not completely control the balance of the mass and wind fields and as a result sometimes a flare up of rain was seen during the day-1 forecasts). However, in spite of such imbalances, the days 1–3 forecasts from the physical initialization always carry higher skills compared to experiment that do not invoke physical initialization. These improved forecast skills (greater than 0.5) for the RINIT experiment show the impetus for carrying out the RINIT technique procedure for mesoscale models. The left column (Figs. 5a–d), middle column (Figs. 5e–h), and the right column (Figs. 5i–l) exhibit rainfall from TRMM 3B42 (observed), RINIT, and CTRL experiments. These figures present the hourly rain rate (mm h21) for each 6-h interval starting from the 24th and ending at the 48th forecast hour. The 6-hourly interval forecasts for each experiment presented are between 24–30, 30–36,

In this section, effects of microphysical changes introduced in the WSM6 explicit parameterization scheme (i.e., Hong et al. 2004) on the forecasts of hurricane track, intensity, rainfall and hydrometer distribution/evolution patterns are discussed.

1) TRACK AND INTENSITY Figure 2 shows the 48 h (0000 UTC 9 July 2005 to 0000 UTC 11 July 2005) track forecast of Hurricane Dennis. For all three microphysical sensitivity experiments (i.e., M1, M2, and M3) the track forecasts closely follow that of the RINIT experiment (i.e., without any microphysical modifications). These results confirm that track forecasts are not much influenced by microphysical modifications; these findings are in agreement with past research findings (Pattnaik and Krishnamurti 2007b). However, studies such as Fovell and Su (2007), which included different sets of explicit schemes, demonstrated that hurricane track forecasts are sensitive to microphysical parameterization schemes. Present findings suggest the modifications in the microphysical parameters and processes in a single scheme, such as in WSM6 scheme in this study have minimal impact on the storm track forecast. Figures 3a,b show the 48-h intensity forecast of Hurricane Dennis using maximum surface wind (kt) and minimum sea level pressure (hPa). We note that the pattern of intensity changes of the storm in three microphysical sensitivity experiments (i.e., M1, M2, and M3) closely follows the RINIT experiment up to 24 h and thereafter changes in their respective intensities are evident. Experiment M1 produces the weakest storm and M2 produces the most intense storm. It is interesting to note that despite

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FIG. 5. Spatial distribution of forecasted 6-h rain rate (mm h21) for day 2 (i.e., 0000 UTC 10 Jul–0000 UTC 11 Jul 2005) starting at hour 24 and ending at hour 48 for (a)–(d) TRMM, (e)–(h) INIT, and (i)–(l) CTRL.

limited modifications in the microphysical parameters, the intensity fluctuations are evident. The minimum sea level pressures obtained from M1, M2, and M3 during the 72-h forecast period are 945, 940, and 943 hPa at 1200 UTC 10 July 2005. The highlight of Figs. 3a,b is the robust improvement in hurricane intensity prediction

(up to day 2) in the RINIT experiment compared to that of the CTRL.

2) RAINFALL The ETS microphysical sensitivity experiments (i.e., M1, M2, and M3) for day-1 and day-2 accumulated rainfall

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FIG. 6. NWS radar reflectivity snapshot at 1927 UTC 10 Jul 2005.

(mm) are presented in Figs. 7a,b. The ETS scores of these experiments for day-1 forecasts stay very close to one another with values between 0.3 and 0.4; however, improvement in ETS skill scores for M2 are noted for most of the rainfall thresholds in day-2 forecasts. Figures 8a–d show the area-averaged rainfall (i.e., marked ‘‘x’’ in the figure) and the integrated frozen hydrometeors (i.e., snow, graupel, and ice) amounts for the RINIT and three microphysical sensitivity experiments (i.e., M1, M2, and M3) through 48 h of forecast. The units of rainfall rate and hydrometeor mixing ratio are mm h21 and g kg21, respectively. The characteristic fluctuations in the magnitude of rainfall are similar to the pattern of the graupel in these experiments. The correlation coefficient between graupel and surface rainfall for these experiments is between 0.70 and 0.77. As graupel hydrometeors are a good indicator for the vigorous updraft and convective processes it seems to be predominantly contributing to the convective part of the total rain. It also indicates that the primary source of surface rainfall is graupel condensate, and principle mechanisms such as melting of graupel and accretion of cloud water by graupel might be responsible for rain formation. In general, the quantitative

amount of rainfall does not vary much among microphysical sensitivity experiments compared to RINIT (without any microphysics modifications). The rainfall ranges mostly stay between 4 and 6 mm h21. This suggests that limited changes introduced in the parameterization scheme might not be enough to alter the amount of graupel, which is the main source of rainfall. The reduction in snow amount (i.e., less than 0.01 g kg21) and an increase in the ice content in experiment M1 (Fig. 8b) are noted with comparison to that of the RINIT (Fig. 8a). This suggests that once a temperature-independent intercept parameter is introduced for snow (i.e., M1) it resulted in preserving more cloud ice in the model for a longer time at colder temperatures before being converted to snow. The accumulated area-averaged ice, snow, graupel, and rainfall for RINIT are 1, 0.76, 1.74, and 38.9, respectively. Similar values of M1 are 1.07, 0.38, 1.73, and 39.29; for M2, 1.32, 0.86, 1.73, and 39.6; and for M3, 1.34, 0.88, 1.76, and 39.6, respectively. The units for ice, snow, and graupel are grams per kilogram and rainfall is millimeters per hour. This indicates that for M2, the magnitude of the surface rainfall remains unchanged irrespective of the substantial reduction in snow amount. We previously

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also note the rainfall areas over northern Florida and Georgia for experiments RINIT and M1 are largely missing from the M2 and M3 experiments. These results demonstrate that perturbations introduced within the microphysics scheme of a mesoscale model running at a very high resolution can bring about moderate to large variations not only in the amount but also in the spatial distribution of rainfall characteristics. We also note that the spatial distribution pattern of graupel for individual forecast hours closely resembles the intense rainfall location (figure not shown). This is because of the faster fall speed of the graupel, which minimizes its loss due to evaporation and shortens the residence time of the hydrometeor.

3) HYDROMETEOR DISTRIBUTION

FIG. 7. ETS of rainfall thresholds (mm): (a) day 1 (0000 UTC 9 Jul– 0000 UTC 10 Jul 2005) and (b) day 2 (0000 UTC 10 Jul–0000 UTC 11 Jul 2005).These skill scores are for microphysical sensitivity experiments (i.e., M1, M2, and M3) at model resolution of 2.7 km.

noted that the pattern of fluctuation in the quantity of surface rainfall and graupel hydrometeor closely resemble each other. This suggests that snow hydrometeors being less dense and having slower fall speeds are more likely to be advected horizontally by strong winds, whereas graupel hydrometers with higher density and faster fall speeds modulate the surface precipitation (Braun 2006). In experiments M2 and M3 (Figs. 8c,d) where a twofold increase in the ice number concentration and the diameter of ice crystal are introduced, respectively, an increase in the quantity of ice as well as snow is noted in both these experiments compared to the RINIT (Fig. 8a). Overall, these results show that the amount of frozen hydrometeors particularly graupel in the hurricane plays a dominant role in modulating formation and distribution of surface precipitation. The spatial distribution of rainfall (mm h21) for the day-2 forecast (0000 UTC 10 July–0000 UTC 11 July 2005) for RINIT, M1, M2, and M3 experiments are shown in Figs. 9a–d, respectively. Notable differences in intense patches of rainfall over the Gulf of Mexico and the Florida Panhandle areas are present among these experiments. The maximum amount of accumulated rainfall (mm) for day-2 forecast obtained from these experiments are RINIT (567), M1 (541), M2 (591), and M3 (569). We

Figures 9e–h show the spatial distribution of vertical velocity (m s21, shaded) and graupel hydrometeors (g kg21, contoured) for experiments RINIT, M1, M2, and M3, respectively, at 1200 UTC 10 July 2005. From the spatial distribution plots, we note higher concentrations of graupel hydrometeor (3–5 g kg21) over the regions of strong updrafts (4–5 m s21) in the vicinity of the storm. Another interesting feature is the spread of graupel hydrometeors over northern Florida and Georgia for experiments RINIT and M1, which closely correspond with the rainfall patterns over these regions (Figs. 9a,b). This result is complementary to the previous findings that the generation of graupel hydrometeors (main source of surface rainfall) is primarily facilitated by strong updraft cores in the eyewall region. Figures 9i–l are the same as Figs. 9e–h but for snow (shaded) and ice (contoured, 1.0 g kg21 interval) hydrometeors. In experiment M1 (Fig. 9j) the spatial distributions of maximum snow are much less (0.1–0.2 g kg21) as compared to other experiments (i.e., M2, M3, and RINIT) where snow amount varies between 0.4 and 0.6 g kg21. These results are in agreement with the quantitative amount of integrated area-averaged snow (Fig. 8b). For experiment M2 and M3, the intense patches of snow are clearly visible in and around the strong updraft regions (with higher graupel concentration) in the vicinity of the storm center. The distributions of ice are more for experiments M2 and M3 compared to that of RINIT and M1 experiments. The domain-averaged vertical profiles of condensate (i.e., ice, snow, and graupel) for the last 18 forecast hours (i.e., 30–48 forecast hours) for all four experiments (i.e., RINIT, M1, M2, and M3) are shown in Figs. 10a–l. The units are grams per kilogram. Figures on the left column (Figs. 10a–d), middle column (Figs. 10e–h), and the right column (Figs. 10i–l) represent ice, snow, and graupel hydrometeors, respectively. Analyzing ice profiles (Figs. 10a–d), we note that the ice maxima mostly occur in the upper-troposphere region for all experiments. The quantity

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FIG. 8. Integrated domain-averaged hydrometeors ice (ICE), snow (SNW), and graupel (GRA) in g kg21 and rain rate in mm h21 for the 48-h forecast: (a) RINIT, (b) M1, (c) M2, and (d) M3 experiments. The left y axis represents hydrometeors, the right y axis represents rainfall, and the x axis represents forecast hours.

of ice content for experiment M3 is largest among all the experiments. And ice content in M3 is a little higher than that of M2 with largest difference around 0.005 g kg21 (area-averaged value over the domain). It is interesting

to note that for all forecast hours the ice content of M1 is larger than those of the RINIT experiment. This suggests that M1 is holding more ice because of the introduction of a temperature-independent intercept parameter. This

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FIG. 9. (a)–(d) Day 2 (i.e., 0000 UTC 10 Jul–0000 UTC 11 Jul 2005) accumulated spatial distribution rainfall, (e)–(h) vertical velocity (shaded) and graupel (contours), and (i)–(l) snow (shaded), ice (contours) for experiments (from top to bottom) RINIT, M1, M2, and M3; rainfall (mm h21), hydrometeors (g kg21), and vertical velocity (m s21).

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FIG. 10. Domain-averaged vertical profiles of condensates for 30–48-h forecasts: (a)–(d) ice (ICE), (e)–(h) snow (SNW), and (i)–(l) graupel (GRA). The units are in g kg21.

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also shows that once the intercept parameter of the snow is made independent of temperature variation, important microphysical processes for snow formation from ice such as autoconversion (aggregation), accretion, and deposition (which have direct or indirect dependency on temperature) are unable to convert efficiently from ice to snow at certain temperature thresholds leading to less amounts of snow. The snow profiles (Figs. 10e–h) show that experiment M1 has the lowest amount of snow and experiments M2 and M3 have the higher amounts of snow for all forecast hours compared to the RINIT experiment. Therefore it appears that production and sustainability of snow hydrometeors are enhanced in experiments M2 and M3. The reason for the same can be attributed to the enhanced accretion (riming) and aggregation process in ice crystals to form snow as a result of an increase in ice concentration and ice crystal diameter in the M2 and M3 experiments. As far as graupel profiles (Figs. 10i–l) are concerned, the maximum for all experiments resides in the midtroposphere with very little change in their respective quantity. A reduction of ice and snow below 300 hPa is notable because of the rapid autoconversion from ice to snow and onto graupel (Tompkins and Craig 1999). In addition, because of the increase in concentration (M2) and ice crystal diameter (M3) it is possible that accretion and autoconversion processes further facilitated the formation of graupel hydrometeors (Lord et al. 1984) in these experiments. As soon as the riming process starts snow is immediately converted to graupel, which enhances the graupel mixing ratio and decreases the snow mixing ratio. Figures 11a–d show the 48-h time series of domainaveraged vertical distribution of ice, snow, and graupel for all the four experiments. The interesting thing to note is that a sharp decline (maximum 0.01 g kg21) in the amount of snow occurs throughout the model integration for the M2 experiment. The amount of graupel is slightly higher in M3 compared to the others particularly at 24 h of forecast. We also note that the amount of ice in M1 is more than that of RINIT (with maxima of 0.03 g kg21 at 24 h of forecast). The ice content is consistently higher for M2 and M3 throughout the model integration compared to the M1 and RINIT experiments. It is evident that between the M1 and RINIT experiments, M1 has more ice throughout the model integration phase compared to the RINIT. It is also clear that in all the experiments graupel and ice are in greater quantity compared to that of snow. Figure 12 shows a time mean, azimuthal average radius– height (vertical pressure levels) cross section of vertical velocity (Figs. 12a–d), frozen condensate and rainwater (Figs. 12e–h), and diabatic heating (Figs. 12i–l) between 24 and 30 h of forecast duration. We have chosen these forecast hours mainly for two reasons. First, during this time period the storm was offshore, so the eyewall region

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of the storm was not influenced by landmasses and second, these are the most intense phases of the simulated storm’s life cycle. The analysis is confined to a radius within 130 km of the storm center. We note that even though the region of intense upward motion varies among experiments, in general for all the experiments it lies between radial distance of 10–40 km from the storm center (Figs. 12a–d). Most intense updraft cores of 1 m s21 (vertically extended up to the upper levels) and the weakest updraft cores of 0.6 m s21 (vertically extended up to 400 hPa) are seen in experiment RINIT and M1, respectively. However, among sensitivity experiments M2 carries the most intense core of 0.8 m s21 in its eyewall region and updraft cells are vertically extended up to 200 hPa. It has also the largest radial spread of updraft region up to 55 km. For M1, multiple moderate updraft cells are seen throughout the eyewall and neighboring regions (up to 130 km). For M3, a zone of sinking motion with maximum values up to 0.6 m s21 in the upper level between a radius of 70–80 km and a zone of moderate upward motion (up to 0.2 m s21) between a radius of 80–110 km are noted. For RINIT, the outer eyewall region is comprises several individual moderate updraft cells with values up to 0.2 m s21. The middle column (Figs. 12e–h) presents similar plots for the frozen condensate in grams per kilogram (ice 1 snow 1 graupel) in solid contours and rainwater mixing ratio (g kg21) in dash contours. These results clearly show a strong correlation between intense vertical updraft regime and accumulation of frozen condensate. The quantitative amount of frozen condensate with values up to 1.2 g kg21 and radial spreads up to 60 km are noted for the experiment M2. These values of M2 are highest among all simulations (i.e., M1, M3, and RINIT). For M3, condensate is accumulated in two distinct regions (i.e., 10–30- and 70–100-km radial distance) and those regions are well collocated with the zones of moderate to intense updrafts. We also note differences in quantitative amount as well as spatial distribution of rainwater in all these experiments (dashed contours). In general the quantitative amount of rainwater lies in the range from 0.2 to 0.6 g kg21. It is conspicuous that the presence of rainwater in and around the eyewall is well correlated with the frozen hydrometeors in the upper levels. This also demonstrates that frozen hydrometeors are the main source of rainwater formation. In M3, just like frozen hydrometeors, the rainwater also forms in two zones distinctly separated by a stratiform region. These simulated eyewall structures are in agreement with Rogers et al. (2007), where identifications of different regimes are carried out for storms and with Rogers (2010), where model simulated vertical velocity/hydrometeors structures are presented. The rightmost column (Figs. 12i–l) represents time mean (24–30 h) and azimuthal averaged diabatic heating (Zhu and Zhang 2006) in kelvin per hour. It

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is evident from these results that the most intense heating with values up to 0.012 K h21 is noted for M2 and M3 and the weakest heating values up to 0.008 K h21 are noted for M1. The zones of maximum heating (middle and upper levels) remain the same for all the simulations except M3, where the core of maximum heating at the upper level is missing. In addition, the sinking motion zone in the M3 facilitated a cooling tendency within the core at a radial distance of 60–90 km from the storm center. These results suggest that the structure of the hurricane eyewall and the strength of the vertical velocity are quite sensitive to limited microphysical changes introduced in this study. These structural changes in the eyewall region play a dominant role in determining intensity fluctuations by modulating latent heat generation and its distribution within the core. In addition, this also reveals that in addition to the quantitative amount, the spatial distribution pattern of condensate/vertical velocity particularly in the eyewall region might be influencing its intensity. This is because even though the integrated amount of graupel remains unchanged, there are distinct differences in total condensate amount among these experiments (0.75 g kg21). Because of these differences in the distribution pattern of frozen and liquid hydrometeors/ vertical velocity across the eyewall region the heating/ cooling distribution patterns are getting modulated and as a result their respective intensities are affected because of the vortex response (Vigh and Schubert 2009; Rogers 2010).

c. Role of cloud torques and angular momentum The influence of microphysical changes on cloud torques and its subsequent impact on hurricane intensity modulation is now evaluated. We used the relationship between cloud torque and angular momentum following Krishnamurti et al. (2005). In Fig. 13, 42-h backward trajectories in a storm-relative frame of reference following Krishnamurti and Bounoua (1995) are constructed for microphysics experiments (i.e., M1, M2, and M3). Along these segments CTs, PTs, frictional torques (FTs), and changes in angular momentum (DM) are computed for each experiment. We explicitly compute cloud torque and the changes of angular momentum using Eq. (5) and compare these with the contributions from pressure and frictional torques.

FIG. 11. 48-h domain-averaged vertical distribution of hydrometeors for experiments (a) RINIT, (b) M1, (c) M2, and (d) M3. Ice (solid contours in black), snow (dash contours in green), and graupel (shaded). The units are g kg21. The x axis is the forecast hours and the y axis is the pressure level.

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FIG. 12. Time mean (24–30-h forecast) radius–height (pressure level) distribution of azimuthal mean (a)–(d) vertical velocity (m s21), (e)–(h) frozen condensate (ice 1 snow 1 graupel, g kg21) in solid contour and rainwater in dashed contour, and (i)–(l) diabatic heating (K h21). (from top to bottom) RINIT, M1, M2, and M3. (a)–(d) The contour intervals are 0.2 m s21, solid contours are positive, and dotted contours are negative; (e)–(h) the solid contours (frozen hydrometeors) are in interval 0.15 g kg21 and dashed contours (rainwater) are in intervals of 0.1 g kg21; (i)–(l) the contour intervals are 0.002 K h21.

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FIG. 13. 42-h backward trajectories for microphysical sensitivity experiments (M1, M2, and M3).The domain resolution is 2.7 km. The shaded area is the region of maximum wind (m s21). Every individual parcel’s respective forecast hours and pressure level are mentioned at 6-hourly intervals.

In Fig. 13, each of these trajectories carries seven 6-hourly parcel segments. Individual parcel’s positions and their pressure levels are noted at the beginning and end of each of these segments. It is noted that the parcels mostly stay between 800 and 900 hPa for all forecast hours. Figures 14a–c show the changes in angular momentum (DM, DELM), CT, and pressure and frictional torques combined (PT 1 FT) across the path of parcel trajectories for M1, M2, and M3 experiments. Analyzing Figs. 14a–c it seems that the microphysical modifications introduced in the cloud-resolving model have impacted the magnitude of cloud torques and related angular momentum of each parcel. In general it is conspicuous that the combined contributions from pressure and frictional torques are small compared to those of cloud torques regardless of the microphysical modifications. In Fig. 13 the trajectories of each air parcel gradually tend to move toward the center

of the storm as the forecast hour progresses. Following these air parcels, we note an increase in cloud torque magnitude as the parcel moves nearer to the storm’s center. The largest reductions in cloud torque magnitude (0.0–0.5) are noted in M2 (i.e., the most intense storm) compared to the other two microphysical experiments (i.e., M1 and M3). This implies that the smallest depletion of angular momentum has occurred for the parcel in the M2 experiment as the parcel moves in toward the center. This is an important feature of the M2 experiment where an influx of angular momentum is large near the storm core and contributes to the amplification of the storm’s intensity. Similarly, the least intense storm (i.e., M1) has higher cloud torques compared to that of M2 and M3 experiments throughout the forecast period and increases substantially as parcels are converging toward the storm’s center in the later part of the forecast hours. This feature in M1 will

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FIG. 14. Changes in angular momentum (DELM), CT, and PT 1 FT across the path of the parcel for three microphysical sensitivity experiments (a) M1, (b) M2, and (c) M3. Units: cloud torques (m2 s22) and angular momentum (m2 s21). The values are scaled by 106.

prohibit influx of angular momentum into the storm leading to reduction in its intensity. These findings are quite compelling in suggesting that the changes in microphysical parameters indeed led to changes in the magnitude of cloud torques and that modulated the storm’s intensity.

d. Parcel segment budget Figures 15a–c show both model forecasted (denoted by MODEL) and predicted (denoted by PRE) tangential winds for experiments M1, M2, and M3. Here we see the model forecasted wind (i.e., MODEL) obtained from the respective model runs and the predicted wind (i.e., PRE) obtained using the angular momentum budget in Eq. (6) (see appendix B). Both PRE and MODEL are computed for every 6-h segment following the trajectories (Fig. 13). These short 6-hourly line segments are well suited to cover the length of mesoconvective cloud elements along the

traverse of a parcel. Along these segments the angular momentum of parcels is altered by the torques that they encounter. In general, the PRE winds are greater in magnitude compared to MODEL winds throughout the initial 18 h of forecast duration. Overall, we do note that the predicted tangential winds (PRE) are close to the model winds (MODEL) for experiments M2 and M1, whereas for M3 differences are higher in wind magnitudes particularly after 24 h of forecast. Since the life cycle of the individual clouds is generally much less than 6 h, the rapid fluctuations in the respective magnitudes of the torques are expected. Though difference are noted in PRE and MODEL winds, predicting and validating winds from angular momentum budgets can give us a better understanding of the important torques that modify the angular momentum and tangential motions result in changes in storm intensity.

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FIG. 15. Model forecasted tangential wind (m s21) and predicted tangential wind using parcel segment budget for microphysical sensitivity experiments (a) M1, (b) M2, and (c) M3, respectively.

6. Summary This study has addressed specific issues related to factors influencing hurricane intensity prediction using the ARW-WRF model. They are 1) improving the intensity and precipitation forecast using initial conditions obtained from RINIT technique, 2) assessing the impact of ice phase microphysical modifications on hurricane intensity

forecasts, and 3) assessing the indirect impact of microphysical modifications on short term intensity fluctuations through cloud torque and angular momentum modulation. There is a clear improvement in hurricane intensity prediction skills (up to 48 h) in the RINIT experiment compared to the CTRL. The minimum sea level pressure from observed (i.e., OFCI) is 930 hPa, whereas in the RINIT experiment the minimum sea level pressure reached

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is 940 hPa. The RINIT experiment (i.e., with RINIT) shows robust improvements in forecast skills in important aspects of the hurricane characteristics such as its track, structure, precipitation, and movement compared to CTRL (i.e., model run without rain-rate initialized initial conditions). Though the improvements in the intensity forecast from the RINIT experiment do not exactly match the observed intensity, it shows a robust positive impact throughout the total model integration duration compared to CTRL. The rapid intensification phases of the hurricane life cycle are well captured in the RINIT experiment. Also, improvement is noted for track prediction in RINIT experiment. There is an eastward bias of the CTRL track forecasts compared to those of the observed (i.e., OFCI), and this bias is corrected by the RINIT forecasts. The quantitative rainfall skill scores for the RINIT experiment are better than CTRL, particularly for the second day forecasts. The simulated spatial rainfall distribution obtained from the RINIT experiment exhibits close resemblance to observed estimates. Features such as intense rainfall regions on the northern side of the storm center as it makes landfall are well captured by RINIT simulations. However, both the RINIT and CTRL experiments overestimated the rainfall amounts. Overall at a cloud resolving model’s resolution (i.e., 2.7 km) the prediction of storm intensity, track, speed, and rainfall carries less skill in the CTRL experiment compared to RINIT. Next we discuss the impacts of specific ice microphysical modifications on hurricane intensity. Three important microphysical parameters such as intercept parameter for snow (M1), ice concentration (M2), and diameter of ice crystal (M3) are examined in this section through sensitivity experiments. All of these microphysical sensitivity experiments have been carried out at the cloud resolving resolution of 2.7 km. The experiment M2, where ice concentration was doubled, produced the most intense storm, and M1, where the intercept parameter of snow made temperature independent, produced the weakest storm. The impact on the track and accumulated rainfall forecasts from microphysical perturbations are minimal. One of the highlights in hydrometeor distributions has been the drastic reduction of snow in M1 as a consequence of the introduction of a temperature independent snow intercept parameter in the model, which holds the ice condensate for a longer time before it converts to snow. In addition, it appears that because of the dependency of snow forming processes such as riming, accretion, and autoconversion on temperature variations, there is a reduction in total frozen condensate amount in M1 leading to reduction in the condensational heating rate in the core resulting in a weaker storm. It is interesting to note that irrespective of changes made in ice and snow hydrometeor, the quantitative amount of graupel more or less remains unchanged

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in these three experiments. However, distinct differences are noted in spatial distribution of graupel particularly in the eyewall region. Here we have emphasized that more than quantitative amount, spatial distribution of frozen hydrometeors might be modulating the storm intensity by channelizing the heating distribution in the inner core of the storm. In general these results also suggest that the processes responsible for graupel formation such as riming/ collisions between snow, cloud ice/water and rainwater are not strongly influenced by limited changes carried out in microphysical parameters. The bulk of the graupel forms at lower levels as a result of the dominant role played by cloud water/ice and rainwater processes (i.e., collision and riming) in its formation. Large amounts of graupel hydrometeors are seen in and around strong updraft regions for these experiments, suggesting that a large portion of the graupel formation is facilitated by conducive, strong vertical velocity and weaker horizontal advection that leads to an accumulation of graupel hydrometeors. The model results presented here also suggest that accumulation of graupel in and around the vicinity of the inner core might have played a dominant role in hurricane intensification. It is found that modification in microphysical parameterization strongly influences the spatial distribution of surface rainfall. The domain-averaged surface rainfall trend closely follows the trend of graupel formation. Variations in spatial distribution of intense patches of heavy rainfall are clearly noted among sensitivity experiments. This suggests that the increase or decrease in specific frozen condensate amount can strongly modify the surface rainfall distribution. In experiment M3 (i.e., crystal diameter is doubled), an intense double banding structure in the inner core of the storm is noted. This result hints that effective choices of microphysical parameter/processes can provide better intensity and quantitative rainfall forecasts of hurricanes when models are used at cloud-resolving resolutions. We do understand that factors such as warm SST anomaly pockets can easily overwhelm the impact of microphysics on storm intensity; however, SST variations for these experiments are not the cause of concern. As far as M1 is concern, it seems the combined effect of reduction in frozen hydrometeors (due to less snow) and increase in rainwater perhaps leads to decrease in heating tendency and increase in evaporative cooling effect in the eyewall region. This increase in cooling tendency in the storm core might have prohibited the storm’s intensification. It also implies that the increase in accumulation of frozen hydrometeors and intense updraft cells in the inner-core region of M2 facilitates additional release of condensational heating and intensification of the storm. The third component of this work addresses the role of cloud torques in the change of angular momentum and its influence on hurricane intensity following air parcels

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along the constructed trajectories. In a nutshell, this section illustrates an indirect impact of cloud microphysics on storm intensity through modulating cloud torques. We also note that distinct differences in rainband structures from microphysical modifications suggest that these structural changes might also influence the storm intensity by modulating the cloud torques. An equation has been formulated to objectively assess the short-term changes of tangential velocity along segments of parcel motion. The results presented here do indicate that the magnitudes of cloud torque and angular momentum are impacted by the microphysical modifications introduced. Experiment M2 shows that the angular momentum of a parcel is obstructed by the least amount of resistance from cloud torques while moving toward the center of the storm. This helps the parcels to import more angular momentum through inflowing channels into the storm core and flares up its intensity. It is also noted that the cloud torques in M1 and M3 are larger than those of M2 (i.e., strongest storm). These results suggest the importance of cloud torques, the depletion of angular momentum and their impact on the storm’s intensity. In addition, we have demonstrated that it is possible to objectively predict the short-term changes in intensity by carrying out studies on two key parameters such as angular momentum and cloud torques durin a storm’s life cycle. Using the segment budget equation, the tangential wind speed for each of the 6-h segments of parcel path up to 36th forecast hour has been determined. The predicted tangential velocities obtained from the budget formulation have been compared to those from the model forecasts and it is noted that they are in better agreement with each other with few exceptions where differences are large and this might be attributed to the temporal interval (i.e., 6 h) of the forecast datasets. It is felt that more frequent forecast data needs to be stored for these segmentation budgets. Further work to elucidate the interplay between angular momentum, cloud torques, and cloud microphysical processes is warranted. Acknowledgments. This work is supported by NSF Grants ATM 0553491 and ATM 0533108, NASA Grant NNX07AD39G, NOAA SAIC Subcontract 4400105430, and NOAA Florida International University (FIU) Subcontract 120000586. We are thankful to the director at the Indian Institute of Tropical Meteorology (IITM), Pune, India, for the support to carry out this research work. We gratefully acknowledge the anonymous reviewers for their valuable comments and suggestions. We are also thankful to NCEP, NCAR, and NASA for free access to their data sets, the Mesoscale and Microscale Meteorology Division of NCAR for the WRF-ARW model used in this study, and the Grid Analysis and Display System (GrADS) software for preparing figures in this manuscript.

APPENDIX A Angular Momentum Budget Angular momentum can be written as M 5 uu r 1

f u r2 , 2

(A1)

where uu is the tangential velocity and fu is the Coriolis parameter. Starting from tangential equation of motion, upon multiplication by r and noting that ur 5 dr/dt, we obtain ! d fr2 1 ›p u r1 1 F u r. 5 dt u r ›u 2

(A2)

Equation (A2) is the angular momentum per unit mass of air [M 5 uur 1 ( f0r2)/2], which is modified by pressure torques [(1/r)(›p/›u)] and by frictional torques (Fur). The frictional torques carry two parts those related to surface friction and those related to internal friction (i.e., cloud torques). If we were to consider finite segments of a parcel motion we can write dM dM › 1 ›p 5 1 M9W9 5  1 F u r. dt dt ›z r ›u

(A3)

The term M9W9 is the contribution from cloud torques in the cloud layers. Here the bar denotes segment scale averaged and the primes denote subsegment-scale motions. Here we have neglected effects of horizontal eddy motions.

APPENDIX B Parcel Segment Budget of Angular Momentum The angular momentum equation: dM 5 PT 1 F T 1 CT dt

(B1)

applied to a segment point A1 to A2 (Fig. B1): M2 5 M1 1 (PT 1 F T 1 CT )Dt,

(B2)

hence V u r2 1 2

f 2 r22 f r2 5 V u r1 1 1 1 1 (PT 1 F T 1 CT )Dt. 1 2 2 (B3)

Final intensity at point A2

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FIG. B1. Segment points from A1 to A2.

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