Downloaded 10/02/21 10:10 PM UTC 3078 MONTHLY WEATHER REVIEW VOLUME 126 Mode Initialization (NMI; E.G., Puri and Miller 1990A; Satility in the Tropics

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Impact of Initial Conditions, Rainfall Assimilation, and Cumulus Parameterization on Simulations of Hurricane Florence (1988)

V. M OHAN KARYAMPUDI* AND GEORGE S. LAI* Laboratory for Atmospheres, NASA/Goddard Space Flight Center, Greenbelt, Maryland

JOHN MANOBIANCO ENSCO, Inc., Cocoa Beach, Florida (Manuscript received 14 May 1997, in ®nal form 14 November 1997)

ABSTRACT Numerical simulations were performed with the Pennsylvania State University/National Center for Atmospheric Research Mesoscale Model Version 5 (MM5) to study the impact of initial conditions, satellite-derived rain assimilation, and cumulus parameterization on Hurricane Florence (1988). A few modi®cations were made to the J. Manobianco et al. (MKKN) rain assimilation scheme, which was developed originally for midlatitude weather systems, to successfully simulate organized tropical weather systems such as Florence. These changes consist of replacing latent heating scaling with convective rainfall in the Kuo±Anthes scheme in areas where both the model-predicted and satellite-derived rainfall coincide, and specifying a normalized parabolic heating pro®le in deep convective regions where there is satellite rain but no model rain. Restoration of the original Kuo±Anthes heating distribution function in lieu of the ®xed heating pro®le speci®ed in the MM5 model is another change implemented in the Kuo±Anthes scheme. Results from the sensitivity simulations made with the modi®ed rain assimilation scheme show that 1) the enhanced initial conditions with the omega dropsonde data yield a positive impact on the development of Florence for both the Betts±Miller and the modi®ed Kuo±Anthes schemes, 2) the effect of ingesting continuous (Special Sensor Microwave/Imager and Geostationary Operational Environmental Satellite Infrared) satellite-derived rainfall rates as latent heating by the modi®ed rain assimilation scheme is much greater with the modi®ed Kuo± Anthes scheme than with the Betts±Miller scheme, and 3) the combined impact of enhanced initial conditions and rain assimilation yields a superior simulation of Florence, particularly with the Kuo±Anthes scheme. The weak response of the Betts±Miller scheme to rain assimilation, compared to the large impact with the Kuo± Anthes scheme, appears to be related mainly to the differences in the treatment of convective rainfall and its latent heat release in respective cumulus parameterization schemes. Since the MKKN scheme mainly invokes latent heat scaling to ingest satellite rainfall, the Kuo±Anthes scheme responds to increased latent heating from satellite rainfall rates more favorably through conditional instability of the second kind (CISK)-type feedback effects than the Betts±Miller scheme. The latter result clearly suggests that the success of the modi®ed rain assimilation scheme on development of organized tropical systems such as Hurricane Florence depends to a large extent on the choice of cumulus parameterization scheme.

1. Introduction quately resolve the mesoscale circulations of these tropical systems. The second problem is dependent not only Numerical simulations of tropical cyclones are on the initial speci®cation of divergence and moisture plagued by 1) poor speci®cation of the initial state of ®eld but also, to a large extent, on the parameterization the atmosphere, and 2) improper evolution of convective of physical processes of the hydrological cycle. These heating, particularly the timing and occurrence of rain- de®ciencies often lead to inaccurate simulations of the fall. The ®rst problem is more serious over data sparse regions where conventional data networks cannot ade- timing, location, and intensity of rainfall within these convective precipitation systems, which is known as the spinup problem (e.g., Tiedtke et al. 1988; Krishnamurti et al. 1988). * Additional af®liation: Science Systems and Applications, Inc., Various methods have been proposed to alleviate the Lanham, Maryland spinup problem in limited-area models (e.g., Donner and Rasch 1989; Davidson and Puri 1992) as well as in global models (e.g., Mohanty et al. 1986; Krishnamurti Corresponding author address: Dr. V. Mohan Karyampudi, Code 912, NASA/Goddard Space Flight Center, Greenbelt, MD 20771. et al. 1988; Puri and Miller 1990a). In global models, E-mail: [email protected] the most widely used schemes are the diabatic normal

᭧ 1998 American Meteorological Society

Unauthenticated | Downloaded 10/02/21 10:10 PM UTC 3078 MONTHLY WEATHER REVIEW VOLUME 126 mode initialization (NMI; e.g., Puri and Miller 1990a; satility in the Tropics. To improve the simulations of Heckley et al. 1990; Kasahara and Mizzi 1992) and the organized tropical convective systems, development and physical initialization proposed by Krishnamurti et al. application of satellite-based rain assimilation schemes (1984). In limited-area models, direct assimilation of are part of ongoing efforts here at the Goddard Space diabatic heating from convective heat sources is com- Flight Center (GSFC) through utilization of rainfall de- monly used (Molinari 1982; Davidson and Puri 1992; rived from existing platforms such as the Special Sensor Chang and Holt 1994). The diabatic NMI has its limi- Microwave Imager (SSM/I) as well as from future plat- tations such as the use of time-®ltered heating to dampen forms such as the Tropical Rainfall Measuring Mission diabatic forcing during the ®rst few hours of initiali- (TRMM) satellite (Simpson et al. 1996). The major goal zation (e.g., Puri and Miller 1990a), which may not be of this study, however, is to test the effects of initial totally representative of strong convective heating with- conditions, assimilation of satellite-derived rainfall in rapidly developing cyclones. The physical initiali- rates, and cumulus parameterization schemes on the zation, on the other hand, does not suffer from this simulated development of Hurricane Florence (1988). drawback since it provides a humidity analysis consis- Recent hurricane modeling studies have shown that en- tent with the imposed heating ®elds at every time step. hanced initial conditions obtained from either bogussed- However, it requires a diabatic initialization, which is vortex (Kurihara et al. 1993) or dropwindsonde data accomplished through Newtonian relaxation of model (Tuleya and Lord 1997) improve the hurricane track and variables within a preintegration period, to obtain a con- intensity predictions. However, we hypothesize that sat- sistency between the divergent wind and imposed sur- ellite rain assimilation alone can yield improved sim- face ¯uxes and condensational heating. ulations of tropical cyclones comparable to those made Assimilation of diabatic heating within the thermo- with enhanced initial conditions with a responsive cu- dynamic equation circumvents some of these problems mulus parameterization scheme. Furthermore, rain as- since divergence, which predominates the mesoscale similation studies are potentially useful to ®nd out convective areas in the Tropics, is imposed indirectly whether the utilization of satellite rain rates over data- by the model itself. However, the spinup problem cannot sparse oceanic regions can reduce the spinup problem be eliminated entirely without model initialization since and hence the intensity forecasts without the need for it takes a certain amount of time for the divergence to arti®cially bogussing the vortex in the initial conditions evolve in response to the imposed diabatic heating ®eld. of hurricane prediction models. Therefore, this study Another limitation of diabatic assimilation and the dia- will address speci®c questions such as 1) whether four- batic dynamic assimilation scheme is the external spec- dimensional assimilation of satellite-derived rainfall i®cation of latent heating pro®le in the imposed satellite- alone over data-sparse regions will generate tropical cy- derived rainfall regions. clone development comparable to the simulation made Although numerous studies have shown that tropical with enhanced initial conditions, 2) whether a combi- simulations are strongly sensitive to diabatic heating nation of enhanced initial conditions and rain assimi- (e.g., Fiorino and Warner 1981; Molinari 1982; Heckley lation will provide a superior simulation of tropical cy- et al. 1990; Puri and Miller 1990a; Davidson and Puri clone development than either enhanced initial condi- 1992; Raymond et al. 1995), previous work on rainfall tions or rain assimilation alone, and 3) whether a rain assimilation has concentrated mainly on developing var- assimilation scheme is sensitive to the choice of a cu- ious methods for assimilation of moisture to reduce the mulus parameterization scheme. spinup problem. Furthermore, many studies have spec- Recently, Shi et al. (1996) examined the impact of i®ed, a priori, vertical latent heating pro®les in the areas SSM/I rain rates and dropwindsonde data on Hurricane of observed precipitation (e.g., Fiorino and Warner Florence using the Navy Research Laboratory Meso- 1981; Molinari 1982). Most often these heating pro®les scale Model. Although the present investigation paral- may not resemble the model-generated heating at the lels their study, there are signi®cant differences. In par- end of the assimilation period. It is well known that ticular, our rain assimilation study (i) utilizes continu- vertical heating pro®les vary in location as well as dur- ously varying rain rates (over a 12-h period) derived ing various stages of convective system life cycles from two satellites [i.e., SSM/I and the Geostationary (Houze 1982). To avoid specifying constant heating pro- Operational Environmental Satellite Infrared (GOES/ ®les during assimilation, Manobianco et al. (1994) pro- IR) satellites] for assimilation compared to the inter- posed a novel approach in which internally consistent, mittent rain rates from a single polar orbiting satellite model-generated heating pro®les are utilized for assim- (SSM/I only) used by Shi et al. (1996); (ii) examines ilating latent heating obtained from satellite-rain esti- the response of the rain assimilation to cumulus param- mates. They used this technique to simulate the evo- eterization schemes [i.e., Betts±Miller (Betts and Miller lution of a rapidly developing extratropical cyclone that 1986) and Kuo±Anthes (Kuo 1974; Anthes 1977a) occurred during the Experiment on Rapidly Intensifying schemes] and omega dropwindsonde (ODW) data, in Cyclones over the Atlantic ®eld experiment. contrast to the rain assimilation and ODW data impact The purpose of this paper is to apply this rain assim- study carried out by Shi et al. (1996); (iii) assimilates ilation technique to Hurricane Florence to test its ver- the satellite rain as convective and stratiform rain in

Unauthenticated | Downloaded 10/02/21 10:10 PM UTC DECEMBER 1998 KARYAMPUDI ET AL. 3079 proportion to the model-predicted rain type, whereas Shi Dudhia (1993) and Grell et al. (1993). The model phys- et al. (1996) assumed all the satellite rain is convective; ics selected for this study include the Kuo±Anthes (Kuo and (iv) faithfully reproduces all the satellite rain, sup- 1974; Anthes 1977a) and the Betts±Miller (Betts and pressing model rain in regions where satellites see no Miller 1986) cumulus parameterization schemes, the rain. Therefore, this study not only examines the sen- grid-scale precipitation, and the Blackadar high-reso- sitivity of continuous rain rates to Florence development lution planetary boundary layer (PBL) model (Zhang but also investigates the sensitivity of the Manobianco and Anthes 1982). The Davies relaxation boundary con- et al. (1994) rain assimilation scheme to the Betts±Miller ditions are used for the lateral boundary conditions (Da- and the Kuo±Anthes cumulus parameterization schemes. vies and Turner 1977). Furthermore, unlike Shi et al.'s study, which examined the impact of ODW data in reducing intensity and track b. Initial conditions forecast errors of Florence, the ODW simulation results will be used only to compare them with rain assimilation The initial and boundary conditions for Hurricane simulations in the context of obtaining improved sim- Florence were obtained from the National Meteorolog- ulations of Florence using a cumulus parameterization ical Center's [i.e., NMC, now known as the National scheme sensitive to imposed latent heating. Center for Environmental Prediction (NCEP)] 2.5Њ lat Hurricane Florence (1988) was selected because of ϫ 2.5Њ long gridded analyses (available on 13 manda- its short-lived development and the availability of spe- tory levels), which were interpolated to the MM5 mesh cial datasets such as the ODW data collected by the of 112 ϫ 102 ϫ 23 grid points with a horizontal spacing Hurricane Research Division (HRD) of the Atlantic of 40 km using a 16-point, two-dimensional parabolic Oceanographic Meteorological Laboratory (AOML), function. More vertical levels are placed within the low- Miami, Florida, as well as the SSM/I and the GOES/IR er troposphere (with decreasing resolution above) to bet- satellite data during 9±10 September 1988 over the Gulf ter resolve the PBL and the moist processes. The sea of Mexico. This case has attracted attention recently surface temperatures (SST), obtained from the naval through a few diagnostic and modeling studies (e.g., climatological SST data, are objectively analyzed to the Rodgers et al. 1991; Kaplan and Franklin 1991; Beven MM5 grid. The initial conditions at 0000 UTC 9 Sep- 1995; Shi et al. 1996). Florence, which formed on 7 tember 1988 are enhanced with the ODW and conven- September over the south-central portion of the Gulf of tional rawinsonde data using a two-pass Cressman ob- Mexico, became a hurricane on 9 September just before jective analysis scheme (Cressman 1959). Although the landfall over southeastern Louisiana as it accelerated ODW dataset, collected by AOML/HRD on 8±9 Sep- toward the northern Gulf Coast. Although Florence was tember 1989 over the Gulf of Mexico, consisted of 51 a hurricane for only 12 h with a minimum pressure of soundings, only 42 ODWs (shown in Fig. 1) were se- 982 mb and a highest sustainable surface wind of 36 m lected for use in the objective analysis due to the im- sϪ1, it caused considerable damage in terms of property position of a 6-h time window (i.e., centered on 0000 losses and one fatality [see Lawrence and Gross (1989) UTC 9 September 1989). ODW soundings falling out- for a brief history of Florence]. side of this time window are assumed to be asynoptic In the following section, we will describe the meth- and hence rejected for data enhancement. This Ϯ3-h odology used in this study, which includes brief de- cutoff time is the same as that used by Shi et al. (1996) scriptions of the mesoscale model, satellite-rainfall and by many operational forecast models. The objec- rates, and experimental design. Section 3 gives an over- tively analyzed, ODW-enhanced initial conditions are view of the modi®cations made to the Manobianco et then subjected to integrated mean divergence removal al. (1994) rain assimilation scheme (hereafter abbrevi- to eliminate gravity wave noise in the model simula- ated as the MKKN scheme) and the Kuo±Anthes cu- tions, which used a time step of 2.5 min. mulus parameterization scheme. Section 4 discusses Inclusion of ODW data in the initial conditions re- sensitivity simulation results from enhanced initial con- sulted not only in the decrease of Florence central pres- ditions, rain assimilation, and cumulus parameterization sure from 1003 to 1000 mb but also in a slight im- schemes. Summary and discussion are presented in sec- provement in the position of the disturbance center (see tion 5. Fig. 2). However, the analyzed disturbance center location does not coincide exactly with the best track cyclone position because of the 40-km grid resolution used 2. Methodology in this study. The objectively analyzed central pressure a. The mesoscale model of 1000 mb is higher than the best track (hereafter referred to as the observed) central mean sea level pres- The mesoscale model used for simulations of Hur- sure (MSLP) of 992 mb. Further reduction in the ana- ricane Florence development is the nonhydrostatic ver- lyzed central pressure has been limited most likely by sion of the Pennsylvania State University (PSU)/Na- the choices of horizontal resolution and the objective tional Center for Atmospheric Research (NCAR) Me- analysis scheme. Since the major emphasis of this study soscale Model Version 5 (MM5) described in detail by is to examine the impact of rain assimilation, and to a

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FIG. 1. The PSU/NCAR Mesoscale Model Version 5 (MM5) model domain for the numerical simulations of Hurricane Florence. The circles indicate the locations of the ODW soundings, which are used for the enhancement of initial conditions at 0000 UTC 9 September 1988. The large circle with gray shading indicates the initial position of Hurricane Florence. lesser extent to reduce the track errors associated with 3a) and at the end (1151 UTC; Fig. 3d) are based solely enhanced initial conditions as mentioned earlier, special on the SSM/I retrievals, whereas at the intermediate efforts such as those made by Shi et al. (1996) have not times (i.e., at 0400 UTC and 0800 UTC) they are largely been made to further reduce the initial central pressure. based on IR data. The initial (i.e., 0000 UTC 9 Sep- tember 1988) rainfall ®eld in Fig. 2 shows no signi®cant precipitation within the immediate vicinity of the Flor- c. Satellite-derived rainfall rates ence center, which agrees well with the National Oce- The satellite-derived rainfall rates for assimilation anic and Atmospheric Administration (NOAA) WP-3D purposes are obtained using the synthesized SSM/I- aircraft radar data showing very little convection ex- GOES/IR technique described by MKKN. The multi- tending above the 500-mb level (Kaplan and Franklin channel, physical approach of Kummerow and Giglio 1991). The maximum rainfall, however, is located (1994) is applied to retrieve rainfall rates (over ocean roughly 2Њ to the south of the vortex center, which has implications for the evolution of Florence in the rain areas only) from brightness temperatures (TB) obtained from SSM/I data at ϳ0033 UTC 9 September and assimilation simulations as discussed in section 4. In ϳ1151 UTC 9 September 1988. The GOES/IR data that fact, the IR imagery analysis shows that an organized coincide most closely with the SSM/I overpasses (i.e., deep convective area, collocated with the maximum 0035 UTC and 1135 UTC 9 September) are used for rainfall to the south of the vortex gradually shifted to calibration purposes. The IR data, available at every 30 the southeast while a second area of deep convection min between these two SSM/I orbits, are used to obtain evolved rapidly to the northwest of the low center in rainfall rates at the intermediate times. the 12-h period ending at 1200 UTC [e.g., see Fig. 4 The combined SSM/I and IR-derived retrievals thus in Rodgers et al. (1991)]. Such explosive convective provide rainfall ®elds at 30-min intervals between 0000 development preceding the intensi®cation of Florence UTC and 1200 UTC 9 September 1988. These rainfall strongly suggests the important role played by diabatic rates are spatially interpolated from 25-km satellite res- heating in the growth of this hurricane (Rodgers et al. olution to 40-km model grid resolution, and temporally 1991, their Fig. 12). interpolated to each model time step from the 30-min rainfall ®elds. Rainfall ®elds derived from this proce- d. Experiment design dure are shown in Fig. 3 at approximately 4-h intervals. Simulations were conducted with the modi®ed rain The rainfall ®elds at the beginning (i.e., 0033 UTC; Fig. assimilation scheme (see section 3a for description) to

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FIG. 2. Objectively analyzed MSLP (mb; solid lines) and satellite-derived rainfall rates (shaded regions in mm hϪ1 with the intervals given by the scale on the lhs of each panel) at 0000 UTC 9 September 1988: (a) without ODW enhancement and (b) with ODW enhancement. The cross mark indicates the location of minimum central pressure and the gray dot indicates the best-track location of Florence central pressure.

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FIG. 3. Rainfall rates (mm hϪ1; scale given at the side of each panel) derived from the combined SSM/I and GOES/IR data at (a) 0033 UTC, (b) 0400 UTC, (c) 0800 UTC, and (d) 1151 UTC 9 September 1988.

test its effectiveness on the development of Hurricane ies (e.g., Bengtsson et al. 1982; Krishnamurti et al. 1989; Florence. All simulations are initialized at 0000 UTC 9 Baik et al. 1991). In all the rain assimilation experi- September 1988 and integrated for 24 h. These simu- ments, satellite-derived rainfall rates are assimilated lation experiments, listed in Table 1, fall under two sets during the ®rst 12 h of integration. However, since SSM/ of simulationsÐeach carried out separately with the I satellite-derived rainfall rates are not available prior Betts±Miller (Betts and Miller 1986, hereafter referred to 0033 UTC, rainfall rates during the period of 0000± to as BM) and the modi®ed Kuo±Anthes [Anthes 0033 UTC were kept the same as those available at 0033 (1977a) and Kuo (1974), hereafter abbreviated as MKA] UTC. No rain assimilation is performed after 1151 UTC cumulus parameterization schemes. These two schemes (i.e., the time at which the last SSM/I data are available) have been selected because of their prior success in through 0000 UTC 10 September 1988. simulating tropical cyclones in previous modeling stud- Sensitivity runs with the BM and MKA schemes have been performed not only to test their effectiveness in assimilating ``observed rainfall'' but also to evaluate their impact on successfully simulating the dynamical evolution and structure of Hurricane Florence. Each set consists of runs made with and without enhanced initial conditions (BMC, BMD, MKAC, and MKAD), continuous rainfall assimilation (BMNA and MKANA), and continuous rain assimilation combined with enhanced

FIG. 4. A schematic representation of the model (region 2), satellite initial conditions (BMDNA and MKADNA). These sen- (region 3), and overlapping model-satellite rain regions (region 1). sitivity simulations executed with and without enhanced

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TABLE 1. Summary of the model simulations performed with the modi®ed rain assimilation scheme (see text for details) including the simulation number, simulation description, cumulus parameterization scheme used, and abbreviation of the simulation. Simulation Cumulus no. Description parameterization Abbreviation 1 Control (C) Betts±Miller (BM) BMC 2 Enhanced initial conditions with Betts±Miller BMD ODW (D) 3 Modi®ed rain assimilation using Betts±Miller BMNA SSM/I and IR data (NA) 4 ODW with modi®ed rain assimila- Betts±Miller BMDNA tion (DNA) 5 Control (C) Modi®ed Kuo±Anthes (MKA) MKAC 6 Enhanced initial conditions with Modi®ed Kuo±Anthes MKAD ODW (D) 7 Modi®ed rain assimilation using Modi®ed Kuo±Anthes MKANA SSM/I and IR data (NA) 8 ODW with modi®ed rain assimila- Modi®ed Kuo±Anthes MKADNA tion (DNA) 9 Modi®ed rain assimilation using Modi®ed Kuo±Anthes MKASA SSM/I data only (SA) 10 ODW with modi®ed rain assimila- Modi®ed Kuo±Anthes MKADSA tion using SSM/I only (DSA)

(ODW) data, and rain rates with and without ODW data, are described brie¯y in this section. These modi®cations were primarily designed, respectively, to test the in¯u- were necessary to rectify some of the de®ciencies re- ence of enhanced initial conditions and assimilation of vealed by preliminary model simulations of Hurricane satellite-derived rainfall rates on the development of Florence performed with the Kuo±Anthes cumulus pa- Florence. In addition, simulations with SSM/I rain rates rameterization and the MKKN assimilation scheme, only were performed with the MKA scheme to test the which have been developed primarily for application to effect of assimilating intermittent rainfall rates (MKA- midlatitude cyclones as pointed out earlier. Some of the SA and MKADSA). The purpose is to determine wheth- MKKN rain assimilation de®ciencies were found to be er rain rates obtained from only one satellite have any related to the fundamental differences in the behavior signi®cant impact on cyclone development, in contrast of convective processes between the Tropics and the to assimilating continuous rain rates obtained from two midlatitudes. satellites (i.e., SSM/I and GOES/IR). Examining the impact of SSM/I rainfall rates would be useful for assimilation of hydrometeors or vertical latent heating pro®les a. Modi®cation to the rain assimilation scheme derived from special microwave platforms such as the The MKKN rain assimilation scheme [see Mano- TRMM satellite. bianco et al. (1994) for details], which is largely based Rain rates derived from the SSM/I satellite were as- on scaling the model-predicted condensational heating sumed to be valid only during a 3-h time window cen- pro®les, considers three regions (see Fig. 4). The ®rst tered at the time of the SSM/I image. This narrow time region de®nes an area where both the satellite (Ps) and window is selected due to rapid changes in Florence's model-predicted (Pm) rainfall rates are greater than zero movement and rainfall patterns (Fig. 3). This constraint (i.e., Ps Ͼ 0 and Pm Ͼ 0). In the second region, Ps ϭ results in a 1.5-h rain assimilation period at the begin- 0 but P Ͼ 0, whereas in region 3, P Ͼ 0 but P ϭ ning of the simulation (i.e., due to the unavailability of m s m 0. In the third region, where Ps Ͼ 0 but Pm ϭ 0, the the SSM/I rain rates prior to 0033 UTC 9 September scheme searches for a model-predicted heating pro®le 1988) and another 3-h assimilation period starting from corresponding to Ϯ20% of the satellite rainfall (Ps) 10.5 to 13.5 h (which is centered on 1200 UTCÐclosest within a 320-km radius. A summary of the MKKN to the 1151 UTC SSM/I satellite overpass). Intermittent scheme is given in Table 2 and the modi®cations are rain assimilation with SSM/I has only been tested with described below. the MKA scheme and not with the BM scheme since In region 1 (see Fig. 4), the model-predicted con- the MKA scheme appears to have the most impact with vective rainfall was replaced directly with the satellite- rain assimilation as described in section 4b. derived rainfall in the temperature tendency equation of the Kuo±Anthes cumulus parameterization scheme in- 3. Modi®cations to the rain assimilation and the stead of scaling the condensational heating by (1 ϩ ␣), Kuo±Anthes schemes where ␣ ϭ (Ps Ϫ Pm)/Pm (see Table 2). This modi®- Modi®cations made to the MKKN scheme as well as cation, which follows Shi et al. (1996), is equivalent to to the Kuo±Anthes cumulus parameterization scheme the scaling of latent heat in the MKKN rain assimilation

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scheme for negligible values of moistening parameter in regions where convective precipitation dominates the total rainfall, such as in the Tropics. Therefore, this minor modi®cation which has been made to make it consistent with other rain assimilation schemes in the Tropics, such as Shi et al.'s, should give similar results only as in region IIIout with- grid saturation with grid saturation to those obtained from the MKKN scheme. Although Latent heating scaling Specify heating pro®le Parabolic heating pro®le replacement of model rain with satellite rain may resemble the well-known ``reverse-Kuo'' scheme (e.g., Krishnamurti et al. 1994), vertical moisture distribution is not altered in this scheme and therefore sharply contrasts with the reverse-Kuo methods. Instead, latent heating is modi®ed only in proportion to the ratio of moisture convergence from satellite rain to the model- calculated moisture convergence (as in the MKKN Betts±Miller only as in region IIInonconvective for rain without grid saturation

with grid saturation scheme), where the estimated moisture convergence convective scheme Nonconvective Latent heating scaling Specify heating pro®le Specify heating pro®le (M t* ) from the satellite-derived rainfall (Ps) is calculated from the expression: Modi®ed rain assimilation scheme M ts* ϭ (P*␳w)/(1 Ϫ b)g, (1)

where g is the gravitational constant and ␳w is the density of liquid water. In the Kuo scheme (1 Ϫ b)gMt/␳w de- ®nes precipitation, whereas bgMt gives the amount of moistening that goes into moisture storage of the at- Kuo±Anthes

proportional to satellite rain as in region IIInonconvective for rain without grid saturation mosphere (e.g., Anthes 1977a). with grid saturation convective scheme Moisture convergence Specify heating pro®le Specify heating pro®le This modi®cation is applied only if Ps Յ 2Pm, which follows that of MKKN. This constraint was set by Man- obianco et al. (1994) to limit excessive temperature changes at any given time step that may lead to model

Ϯ instability. Another justi®cation for this limit is that the

is derived heating pro®le from satellite rain may differ m within 320 P s substantially from the model heating pro®le if the dif- P ference between the satellite rain and the model rain

becomes large (i.e., when Ps Ͼ 2Pm). For example, Song only pro®le as in region III without grid saturation 20% of pro®le with grid saturation (if km radius) and Frank (1983) show from heat budgets of GARP Latent heating scaling Neighborhood heating Neighborhood heating Atlantic Tropical Experiment (GATE) data that the shape of the vertical heating pro®le changes signi®- cantly for different mean rainfall rates; for a mean rainfall rate of 0.1±0.5 mm hϪ1, the convective heating pro- Ϯ

is ®le has a maximum at low levels, which shifts to mid- m within 320 Ϫ1 P s levels for rainfall rates of 0.5±1 mm h . Their results P

MKKN rain assimilation scheme suggest that the shape of the heating pro®le changes when the mean rainfall rates differ by more than roughly Convective only pro®le as in region III without grid saturation 20% of km radius) pro®le with grid saturation (if a factor of 2 (i.e., from the ratio of average rainfall rates (Kuo-type scheme) Nonconvective Latent heating scaling Neighborhood heating in each category), which implies that it is not justi®able to use the same shape of the model-predicted heating pro®le for latent heat scaling when the satellite rain rate m m

P P exceeds the model-predicted rain rate by a factor of 2. 2 2

2. Summary of the Manobianco et al. (1994) and modi®ed rain assimilation schemes for both convective and nonconvective rain regions. Based on these arguments, the model-predicted con- Յ Ͼ s s P P vective heating pro®le is assumed to be unreliable in ABLE

T areas where Ps Ͼ 2Pm, and therefore, a normalized par- 0) Neighborhood heating

0) No heatingabolic heating No heating pro®le (with No heating a midlevel maximum) No heating is No heating ϭ 0) Ͼ speci®ed as in region 3 without saturation as an upper m m P Ͼ

P limit (see Table 2 and further explanation below). The m 0, P 0, normalized parabolic convective heating pro®le is then Ͼ ϭ s and

s used to vertically distribute the total latent heating from P Region s P P the satellite rain through the temperature tendency equa- III. ( I. ( II. ( tion:

Unauthenticated | Downloaded 10/02/21 10:10 PM UTC DECEMBER 1998 KARYAMPUDI ET AL. 3085 p*TL model simulations initialized with Florence ODWץ ϭ p* g␳ PN, (2) tCwsh soundings. Further justi®cation in specifying a parabolicץ p heating pro®le with grid saturation is based on the fact where Nh is the normalized parabolic heating function, that normalized heating pro®les of tropical cloud clus- Ps is the satellite-derived rainfall, and T is the model- ters generally show a maximum near 500 mb during the predicted temperature from latent heat forcing only. On system life cycle within a nearly saturated environment the other hand, rain assimilation with the BM scheme that may support convective and stratiform rain simul- followed the same procedure as in the MKKN assimi- taneously (Frank and McBride 1989). Accounting for lation scheme since replacement of rainfall for latent both types of rain (i.e., convective and stratiform) is heat scaling involves variationally adjusting both tem- necessary since tropical cyclone precipitation consists perature and moisture, in contrast to equating rainfall of both convective (40%) and stratiform (60%) portions to moisture convergence (which is equivalent to latent (Marks 1985; Marks and Houze 1987). [Note that the heat scaling) in the MKA scheme. Since the purpose of parabolic heating pro®le also resembles the conditional this study is to test the MKKN scheme and not to im- instability of the second kind (CISK) type diabatic heat- plement an entirely different rain assimilation scheme, ing pro®le that yields solutions with growth rates and we limit the rainfall rate assimilation to latent heat scal- length scales characteristic of hurricanes (Koss 1976).] ing (or an equivalent function) only. However, in areas However, the use of an externally based ®xed heating where Ps Ͼ 2Pm a normalized parabolic heating pro®le pro®le, which is a major departure from the MKKN is speci®ed for the BM scheme, as in the MKA scheme scheme, may not be the best choice since it is known without moisture change. from observational studies that the heating pro®les vary In region 2, the model-predicted latent heating is not in space and time depending upon the life cycle of the allowed as in the MKKN scheme; therefore, no change convective systems. The vertical distribution of latent has been made to the rain assimilation scheme (Table heating remains an outstanding problem that plagues

2). However, in region 3 where Ps Ͼ 0 but Pm ϭ 0, a initialization and assimilation studies (e.g., Fiorino and normalized parabolic heating pro®le is speci®ed (as in Warner 1981; Molinari 1982; Puri and Miller 1990a). region 2 for Ps Ͼ 2Pm) instead of the model-predicted Nevertheless, speci®cation of external heating pro®le is neighborhood heating pro®le from an adjacent grid point appropriate since satellite-derived (SSM/I) latent heat- (within a 320-km radius) used by Manobianco et al. ing pro®les such as those obtained by Rodgers et al. (1994). This change was necessitated by the fact that (1998) for Hurricane Opal (1995) will be available in preliminary model simulations (not shown) have indi- the future for direct insertion into the assimilation cated that there was very little overlap between the sat- scheme without the need for using either the neighbor- ellite rain and the model-predicted rain, particularly dur- hood heating pro®les or arbitrarily speci®ed heating pro- ing the ®rst few hours of integration. This discrepancy ®les. Indeed, preliminary assimilation simulations have occurred as the model produced rain mostly to the north been performed for Hurricane Opal using the satellite- of the cyclone center in contrast to the satellite rain derived latent heating pro®les in lieu of the ®xed par- maximum located about 200 km to the south of the abolic heating pro®le used in this study (Karyampudi cyclone center as discussed previously in section 2. As et al. 1998). a result, the search algorithm failed to locate suitable heating pro®les within a radius of 320 km; therefore, b. Modi®cations to the Kuo±Anthes scheme 85% of grid points within the satellite rain region were left unaffected by the latent heating. As recognized by Preliminary model simulations (not shown) have in- Manobianco et al. (1994), the major de®ciency of the dicated that the poor response of the MKKN rain as- MKKN scheme is that at those grid points where the similation scheme in MM5 during simulation of Flor- aforementioned criteria failed, no change is made to ence's development is partly due to the treatment of either the latent heating or to the moisture. moist convection in the Kuo±Anthes scheme. This is To avoid the limitation in ®nding a suitable model- found to be related to the vertical distribution of latent predicted heating pro®le in region 3, we specify a nor- heating. In MM5 (Grell et al. 1993) as well as in the malized parabolic heating pro®le (with midlevel heating earlier versions of the PSU/NCAR model (Anthes et al. maximum at 500 mb) in a deep layer extending from 1987), the convective latent heating in the Kuo±Anthes 950 to 150 mb to mimic tropical cloud cluster condi- scheme is distributed vertically via a normalized, par- tions, with imposition of saturation as an upper limit as abolic vertical pro®le between the cloud top and base. in Krishnamurti et al. (1994). Imposition of saturation Since the parabolic pro®le is a function of the sigma for heating pro®les selected outside the model rain but coordinate, this heating function is maximized in the within the satellite rain area is consistent with the ap- upper-half of the cloud near 300 mb for deep tropical proach taken by Manobianco et al. (1994). The speci®ed cloud systems. midlevel (ϳ500 mb) heating maximum is based on mod- Sensitivity runs made with the maximum heating el-generated heating pro®les from MM5 simulations of shifted from upper levels to midlevels showed a much Florence with the MKA scheme and also from 1D cloud better response of the Kuo±Anthes scheme in devel-

Unauthenticated | Downloaded 10/02/21 10:10 PM UTC 3086 MONTHLY WEATHER REVIEW VOLUME 126 oping Hurricane Florence compared to the MM5 version with the maximum at upper levels (not shown). This ®nding suggested that some of the problems involving the Kuo±Anthes scheme in MM5 may be related to the ®xed upper-level heating. The deepening with midlevel heating occurs as lower-tropospheric higher ␪e is drawn to the center of the disturbance in response to midlevel warming. In contrast, no signi®cant development occurs when upper-level warming causes only lower ␪e air from midlevels to converge. Because of these contrasting effects resulting from uncertainties in specifying a heating maximum for hurricane simulations, the original Kuo (1974) vertical heating distribution formulation, which is a function of the temperature difference between cloud and environment, was implemented in subsequent MM5 simulations for this study. We refer to this scheme as the MKA. Anthes (1977b) compared the evolution of the Kuo heating function [i.e., the cloud temperature excess heating function, (Tc Ϫ T), where Tc and T are the cloud and environmental temperatures, respectively] with the cloud-scale condensation rate in idealized hurricane simulations and found that it is very similar to the cloud- scale condensation rate for deep clouds at the developing stage but differs signi®cantly at the mature stage. Despite this limitation, the varying heating function at FIG. 5. Time series plot of central MSLP (mb) from the best track least allows the model to determine its own heating record and model simulations of Hurricane Florence with the modi®ed pro®le, which is an improvement over the ®xed heating rain assimilation scheme beginning from 0000 UTC 9 September (0 h) through 0000 UTC 10 September 1988 (24 h):(a) with BM ad- pro®le approach used in the standard MM5 model. justment scheme, and (b) with the MKA scheme. (Note that satellite rain is assimilated during the ®rst 12-h period only; see Table 1 for description of simulation abbreviations.) 4. Numerical simulations Two sets of simulations, one with the BM and the other with the MKA cumulus parameterization scheme, in their control simulation despite the fact that both have been performed to determine the impact of en- studies used Kuo-type schemes. These contrasting re- hanced initial conditions and assimilation of rainfall sults may be attributed to different initialization pro- rates on the development of Florence. These simulations cedures (i.e., removal of integrated mean divergence in are listed in Table 1. The evolution of central pressures this study versus normal mode initialization in Shi et and tracks from the simulations of Hurricane Florence al.). Regardless of these differences in the control sim- are shown in Figs. 5 and 6, respectively. The observed ulations, both the MKA and BM schemes have a positive central MSLP from the best-track data obtained from impact on the development of Florence with enhanced the National Hurricane Center [now known as the Trop- initial conditions (MKAD and BMD runs, respectively), ical Prediction Center (TPC)], Miami, Florida (Law- which helps to reduce the spinup problem consistent rence and Gross 1989), is also shown in Fig. 5. with results from Shi et al. (1996). Although there is less rapid development in the ®rst few hours of the MKAD run compared to BMD, the a. Effect of enhanced initial conditions MKA scheme with ODW data produces cyclone de- The effect of enhanced initial conditions on the development between 6 and 18 h with some weakening velopment of Florence has been tested by running the thereafter, yielding a minimum central pressure of 989 model with and without ODW data for 24 h using the mb at the end of 24 h. This central pressure at the end MKA scheme (MKAC and MKAD, respectively; Table of 24 h is nearly identical to that from BM enhanced 1) as well as the BM scheme (BMC and BMD). It is initial conditions run (BMD) and also agrees well with apparent from these simulations that both the control the 987-mb central pressure obtained by Shi et al. runs (BMC and MKAC) fail to deepen the Florence (1996). The cyclone track with the MKA scheme is central pressure; the BMC performs better than MKAC parallel to the observed track in the ®rst few hours as in both the intensity and track forecasts (Figs. 5 and 6, with the BM scheme but produces a slightly faster respectively). No deepening in MKAC sharply contrasts movement later in the forecast compared to the BM with the slight deepening obtained by Shi et al. (1996) scheme.

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FIG. 6. Storm tracks of Florence from the model simulations including the best-track data with (a) the BM scheme and (b) the MKA scheme. Data are plotted at 6-h intervals. Open circles indicate the initial positions of the cyclone. (see Table 1 for description of simulation abbreviations.)

Predicted intensity and track errors (i.e., departures schemes improves the intensity and track errors signif- of the predicted cyclone MSLPs and positions from best- icantly compared to the control runs. Although the BM track data) from simulations with the BM and MKA scheme produced a better track forecast than the MKA schemes are given in Tables 3 and 4, respectively. These scheme, the MKA scheme gave a better intensity fore- tables clearly show that data enhancement for both cast than the BM scheme during the latter part of the

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TABLE 3. Hurricane Florence central MSLP (mb), locations (lat/lon), intensity errors (mb), and position errors (km) at 3-h intervals from simulations with the BM scheme (described in Table 2) including the best track (central MSLPs and locations) data from TPC. The intensity errors (top numbers in each row) and position errors (bottom numbers in parentheses) are determined from taking the difference of cyclone MSLPs and locations between model simulations and the best-track data. Note the central MSLPs at intermediate times (i.e., at 3, 9, 15, and 21 h) are interpolated values from the best-track data.

SSM/IϩIR rain SSM/IϩIR rain assimilation with Enhanced initial assimilation enhanced initial Date/Time Control conditions only conditions (UTC) Forecast hour Best track (BMC) (BMD) (BMNA) (BMDNA) 9/0000 0 992 11.2 8.3 11.2 8.3 (24.2; Ϫ89.2) (116.9) (49.4) (116.9) (49.4) 9/0300 3 991.5 10.8 4.8 11.3 9.6 (24.6; Ϫ89.2) (94) (11) (286) (45.4) 9/0600 6 991 11.7 3.2 10.7 7.7 (25.2; Ϫ89.2) (52.8) (26.4) (186.9) (107.5) 9/0900 9 989.5 11.7 2.3 10.3 6.6 (25.5; Ϫ89.2) (45.4) (22) (155.2) (113.3) 9/1200 12 988 13.5 3.1 11.6 8 (26.1; Ϫ89.2) (59.2) (15.3) (158.6) (113.3) 9/1500 15 986.5 16 4.4 13.5 9.2 (26.7; Ϫ89.3) (49.2) (11) (179.1) (95.4) 9/1800 18 985 17.4 4.6 13.7 8.8 (27.4; Ϫ89.2) (59.5) (15.3) (188.2) (165) 9/2100 21 984 19.2 6 14 9.5 (28.0; Ϫ89.3) (49.2) (11) (242.6) (211.3) 10/0000 24 983 19.6 6 14 10 (28.7; Ϫ89.3) (55) (62.2) (263) (238)

simulation. In fact, the 24-h average intensity error with pee et al. (1996), who reported that ODW data en- the MKA scheme (4.52 mb) is less than that of the BM hancement produced statistically signi®cant reductions scheme (4.74 mb), which implies that the MKA scheme in 24±60-h model consensus forecasts of tropical cy- gives a better intensity forecast overall than the BM clone track errors relative to control runs. scheme. Nevertheless, the improvement of track forecast The impact of ODW data on simulations with BM with the inclusion of ODW data is consistent with Bur- and MKA schemes can be better discerned by compar-

TABLE 4. Same as Table 3 except for the MKA scheme (see Table 1 for simulation notations). SSM/I rain SSM/IϩIR rain assimilation assimilation with Enhanced SSM/IϩIR with enhanced SSM/I rain enhanced initial rain initial assimilation initial Date/Time Forecast Control conditions assimilation conditions only conditions (UTC) hour Best track (MKAC) (MKAD) (MKANA) (MKADNA) (MKASA) (MKADSA) 9/0000 0 992 11.2 8.3 11.2 8.3 11.2 8.3 (24.2; Ϫ89.2) (116.9) (49.4) (118.1) (49.4) (116.9) (49.2) 9/0300 3 991.5 11.5 5.5 10.9 9.7 11.3 9.2 (24.6; Ϫ89.2) (209) (11) (95.4) (55) (213.6) (11) 9/0600 6 991 13.7 5.8 10.2 8.5 13.4 11.1 (25.2; Ϫ89.2) (176.3) (26.4) (183.7) (71.6) (202.1) (119.2) 9/0900 9 989.5 14.8 4.8 8.3 5.4 14.4 11.2 (25.5; Ϫ89.2) (188) (80.1) (113.3) (67.3) (94) (49.2) 9/1200 12 988 16.3 3.7 8.5 5.6 14.1 9.8 (26.1; Ϫ89.2) (165) (124.2) (113.3) (86.5) (56.1) (108.3) 9/1500 15 986.5 19.7 2.6 8.9 2.9 16.9 9.9 (26.7; Ϫ89.3) (257.7) (122.2) (70.4) (22) (45.4) (55) 9/1800 18 985 21.3 0.2 6.8 Ϫ1.3 17.7 9 (27.4; Ϫ89.2) (273.3) (113) (57.2) (53.4) (49.4) (43.8) 9/2100 21 984 24.3 3.5 5.5 Ϫ2.6 18.9 8.7 (28.0; Ϫ89.3) (257.7) (128.8) (78.3) (78.3) (83.8) (77) 10/0000 24 983 25.7 6.3 4.8 Ϫ0.6 18.9 8.6 (28.7; Ϫ89.3) (454.7) (118.5) (126.7) (126.7) (83.8) (88.7)

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FIG. 7. Model-predicted MSLP (mb) and total (convective and nonconvective) rainfall rates (mm hϪ1; scale given at the side of each panel) at 12 and 24 h from control simulations with (top) the BM scheme and (bottom) the MKA scheme. ing horizontal ®elds of simulated (MSLPs) with those move northward as observed, in contrast to the MKA derived from NMC analysis. The predicted MSLP and scheme, which failed to contribute to any development instantaneous total rainfall rates (convective plus strat- of the disturbance as the initial vortex ®lled rapidly as iform) at 12 and 24 h from BMC, MKAC, BMD, and it slowly progressed westward (see Fig. 7). This may MKAD are shown in Figs. 7 and 8, respectively. These be due to the fact that the BM scheme may have allowed can be compared with the NMC analysis of MSLP1 and the coupling of the lower and upper tropospheres to be the SSM/I-derived rainfall rates (available at the closest more in¯uenced than the MKA scheme by the deep- times to the NMC analysis) shown in Fig. 9. The most layer mean ¯ow, which appears to advect the storm in notable features in the SSM/I-derived rainfall ®eld at a northerly direction. However, inclusion of ODW data 12 h are the bent back rainband, which has several max- in the initial conditions has a stronger impact on cyclone ima embedded within it, and the narrow north±south- development for both the BM and MKA schemes (Fig. oriented broken rainband within the warm southerly 8). The simulated intensities in the ODW-enhanced runs ¯ow. for both schemes are closer to each other than to ob- From the control simulations, it is quite apparent that servations. The predicted locations, however, differ sig- the BM scheme allowed the vortex to strengthen and ni®cantly, particularly at 12 and 24 h; location predicted by the BM scheme is closer to the observed track than that from the MKA scheme, especially at 24 h (cf. Tables 3 and 4). However, the BM scheme produced unreal- 1 Although the NMC analysis may not be the best choice for com- istically wider rain areas but comparable rainfall rates parison due to its weaker intensity of the storm, the main purpose in presenting the NMC analysis here is to show an isobaric ®eld of (with respect to SSM/I rain rates in Fig. 9), particularly Hurricane Florence in conjunction with the SSM/I-derived rainfall along the bent back rainband at 12 h, around the cyclone rates for verifying cyclone location and rainfall patterns only. center, and along the in¯ow rainband at 24 h than either

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FIG. 8. Same as in Fig. 7 except from simulations with ODW-enhanced initial conditions. the MKA scheme (Fig. 8c and 8d) or observations (Fig. ences (1003 mb in this study vs the 1005 mb in their 9). study) as well as from the rain assimilation differences including duration of assimilation (i.e., continuous assimilation over a 12-h period in this study versus 9 h b. Effect of rain assimilation of intermittent rain assimilation performed by Shi et al.). The rain assimilation simulations with and without The predicted track by MKANA is also much closer to ODW data enhancement have been conducted utilizing the observed track than that from the BMNA run (see both the BM (BMNA and BMDNA runs, respectively) Fig. 6). Note that the 24-h predicted intensity and track and MKA (MKANA and MKADNA runs, respectively) errors are larger for the BM scheme (14 mb and 263 schemes (see Table 1). Figures 5 and 6 show that the km) than for the MKA scheme (4.8 mb and 127 km) most impact of continuous (SSM/I ϩ IR) rain assimi- (see Tables 3 and 4). The reduced track and intensity lation without ODW enhancement is achieved by the errors in the MKA runs can be attributed to a stronger MKA scheme (MKANA) compared to the BM scheme vortex generated by the MKA scheme due to stronger (BMNA) in terms of both the intensity and track. The response to imposed latent heating (from rain assimi- minimum central pressure at the end of 24 h obtained lation) than the BM scheme, as explained in the fol- from the BM scheme is only 997 mb in contrast to 988 lowing section. mb from the MKA scheme (Fig. 5). The central pressure The ®rst 6-h predicted tracks from both schemes show of 988 mb obtained in the MKANA run is slightly lower a south-southeastward movement instead of the north- than the 992-mb MSLP reported by Shi et al. (1996). ward track shown by the best track during the same time Since the cumulus parameterization schemes in both (Fig. 6). This initial southward movement in the assim- these studies are similar (i.e., Kuo-type schemes), the ilation runs (i.e., BMNA and MKANA) appears to result slightly stronger pressure fall in MKANA may be ex- from the diabatic forcing induced by the satellite rainfall plained partially by the initial central pressure differ- maximum, which is located to the south of the vortex

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FIG. 9. NMC analyses of MSLP (mb) and SSM/I-derived rainfall rates (mm hϪ1; scale given at the side of each panel) at (top) 1200 UTC (or 1151 UTC for the SSM/I data) 9 September 1988 and (bottom) 0000 UTC (0030 UTC for the SSM/I data) 10 September 1988. center initially (Fig. 2). In other words, the original cated asymmetrically away from the vortex center ap- cyclonic circulation has shifted southward as the rota- pears to be present in other rain assimilation schemes tional part of the wind ®eld adjusted to the mass ®eld as well (e.g., see Peng and Chang 1996).] This south- imposed by the diabatic heating to the south of the orig- ward movement could be minimized if the cyclonic cir- inal vortex center. [Note that such shifting of vortex culations in control simulations are stronger than pre- circulation to the satellite rainfall maximum that is lo- dicted (see Fig. 7). It is argued that a stronger northward

Unauthenticated | Downloaded 10/02/21 10:10 PM UTC 3092 MONTHLY WEATHER REVIEW VOLUME 126 moving cyclonic circulation would not be in¯uenced so assimilation runs ®lled the initial vortex during the ®rst strongly by the development of a diabatically forced 6 h, which indicates that the 1.5-h constant rainfall as- circulation away from its center (to the south) since it similation after initial time has no signi®cant impact on takes a certain amount of time for the diabatically forced the development of the cyclone. However, there appears convergence to turn into rotational circulation (which to be a weak impact, most signi®cantly in the ODW- is a function of latitude). This argument has some va- enhanced MKADSA run, from assimilating rainfall dur- lidity if we compare the control and rain assimilation ing the later 3-h period centered on 1200 UTC. The simulations to those from Shi et al. (1996). In their positive impact translates to a stronger pressure fall in simulations, no southward cyclone tracks from rain as- the MKADSA run compared to MKASA (i.e., intensity similation can be seen as their control simulation yielded errors are 8.6 mb and 18.9 mb, respectively, after 24 h; a stronger cyclone than that from the corresponding sim- see Table 4). The cyclone tracks in intermittent assim- ulation in this study, which explains the large track dif- ilation runs (Fig. 6) followed a northerly course as in ferences given by respective rain assimilation schemes enhanced initial condition runs, even though in MKASA during the ®rst few hours. Furthermore, the enhanced the cyclone had a large track error during the ®rst 6 h ODW run with rain assimilation (MKADNA) does not of simulation (see Table 4). This result suggests that the show such southward movement since the initial ODW- diabatic heating from the cloud cluster located to the enhanced vortex circulation is stronger than that in the south of the vortex center had no signi®cant in¯uence control run. on cyclone development and track, possibly due to the Rain assimilation with ODW-enhanced initial con- shorter duration of rain assimilation (1.5 h) at the be- ditions has a stronger impact on cyclogenesis for both ginning of simulation. One should note that the weak the BM and MKA schemes (BMDNA and MKADNA) impact of intermittent rain assimilation sharply contrasts than without ODW enhancement (BMNA and with the signi®cant impact obtained by Shi et al. (1996), MKANA) (see Fig. 5). The impact with the BM scheme, who used longer assimilation periods (i.e., 3 h after 0000 however, is marginal compared to the MKA scheme. In UTC and another 6 h centered on 1200 UTC, in contrast fact, among all simulations the MKA scheme has pro- to 1.5 h and 3 h, respectively, used in this study). Their duced the best minimum central pressure of 982 mb at result implies that at least a 6-h assimilation period is 24 h, which is very close to the observed central pres- needed for achieving a signi®cant impact from rain as- sure of 983 mb, and slightly lower than the simulated similation. However, their assumption of constant (i.e., central pressure of 985 mb reported by Shi et al. (1996) time and space invariant) rain rates over a 6-h assimi- from their corresponding (ODW ϩ SSM/I) run. The lation period is not justi®able since Florence underwent intensity error for the MKA scheme is only Ϫ0.6 mb rapid changes in rain intensities, patterns, and locations compared to 10 mb for the BM scheme (see Tables 3 (see Fig. 3). and 4). The rain assimilation impact on predicted cyclone Figure 6 clearly shows the westward displacement of positions, intensities, and rainfall ®elds can be further the predicted tracks from BMDNA and MKADNA runs illustrated by examining the horizontal plots of MSLP due to the initial (i.e., during the ®rst few hours) south- and total rainfall rates taken from the runs without ward movement of the cyclone arising from the satellite (BMNA and MKANA; Fig. 10) and with ODW data rainfall maximum located to the south of the vortex enhancement (BMDNA and MKADNA; Fig. 11). First center as mentioned earlier. Nevertheless, the track pre- of all, one should note that the rain assimilation scheme dicted by the MKA scheme is far better than that by reproduces most of the satellite-derived rainfall rates the BM scheme since the southward movement is min- by the end of the 12-h assimilation period in all the imized in the MKA scheme compared to that with the assimilation runs irrespective of the cumulus parame- BM scheme (Fig. 6), which resulted in reduced track terization scheme used (cf. Figs. 10 and 11 with Fig. errors for the MKA scheme (see Tables 3 and 4). The 9). The minor difference between the satellite-derived ®nal position error given by the MKA scheme is only and assimilated rainfall rates may be due to the small 127 km in contrast to the 238 km error obtained from time difference in depicting the rainfall rates; the ``ob- the BM scheme. This large error from the BM runs served'' rain rates are valid at 1151 UTC, whereas the appears to be related to a weaker cyclone resulting from assimilated rain rates are applicable at 1200 UTC. Fur- weak response to rain assimilation by the BM scheme ther differences between the BM and MKA scheme as discussed in the next section. (Figs. 10 and 11) can be explained by the slightly dif- The impact of intermittent (SSM/I-derived rain only) ferent approaches used in assimilating the rainfall as assimilation with and without ODW data utilizing the discussed in section 3b. The assimilated precipitation is MKA scheme (i.e., MKASA and MKADSA runs, re- dominated by the convective type, as expected in the spectively) is much weaker than those from correspond- Tropics. Regardless of the minor discrepancies, an over- ing runs with continuous rain assimilation (MKANA all agreement between the observed and assimilated and MKADNA) as can be seen in Fig. 5. In fact, the precipitation ®elds gives con®dence in the performance MKASA run performed only slightly better than the of the improved rain assimilation scheme. control run. As in the control run, both the intermittent The predicted rainfall patterns in both the unenhanced

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FIG. 10. Same as Fig. 7 except from modi®ed rain assimilation runs without ODW-enhanced initial conditions.

and the ODW-enhanced runs at 24 h from the MKA Also, the simulated rainfall pattern resembles the ``ob- scheme (MKANA and MKADNA; Figs. 10d and 11d, served'' precipitation shown in Fig. 9b. respectively) resemble the ``observed'' rainfall ®eld (re- In summary, continuous (SSM/I ϩ GOES/IR) rain fer to Fig. 9b) more closely than that from the BM assimilation had a better impact on the MKA scheme scheme (BMNA and BMDNA; Figs. 10b and 11b). The than on the BM scheme in terms of predicting the cy- BM scheme tends to produce wider rainfall areas than clone intensity, track, and rainfall patterns, even though the MKA scheme. In the assimilation runs, the wide- the rain assimilation without the enhanced initial con- spread rainfall produced by the BM scheme may have ditions gave an initial southward track due to the dia- affected the intensity and movement of the cyclone as batic heat source being located asymmetrically away the BM scheme consistently predicted a weaker cyclone from the vortex center. Rain assimilation with enhanced with a more westward track than that produced by the initial conditions produced the best impact for the MKA MKA scheme (see Fig. 6). The rainfall intensity in the scheme (with the intensity, track, and rainfall patterns MKADNA run (Fig. 11d) in a small area just to the being closer to the observations), whereas intermittent north of the cyclone center is greater than 24 mm hϪ1, rain (SSM/I only) assimilation produced a lesser impact which is much higher than the satellite-derived rainfall than the continuous rain assimilation with the MKA rates (Ͼ12 mm hϪ1) shown in Fig. 9b. However, these scheme. predicted higher rainfall rates are not unreasonable for a hurricane given that the rainfall rates derived from COMPARISON WITH SHI ET AL. (1996) RESULTS NOAA WP-3D research aircraft's airborne Doppler radar show a maximum rainfall (Ͼ50 mm hϪ1) area around The positive impact of the modi®ed rain assimilation 28 NЊ and 90Њ W between 2012 and 2341 UTC 9 Sep- with the MKA scheme can be better discerned by com- tember 1988 (Frank Marks, personal communication). paring the results from this study with those made by

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FIG. 11. Same as Fig. 7 except from modi®ed rain assimilation runs with ODW-enhanced initial conditions.

Shi et al. (1996), who also applied the Kuo scheme to track (i.e., lower intensity and track errors in the last their rain assimilation in simulations of Florence, as 12 h) than the corresponding run from Shi et al. (SSM/ pointed out earlier. Table 5 shows the intensity (mb) and I only)Ðin fact, rain assimilation alone gives smaller track (km) errors, including absolute mean errors for track errors than the ODW run (cf. last 12-h track errors the ®rst 12-h (i.e., rain assimilation period), last 12-h between MKANA and MKAD runs), if not lower in- (i.e., model forecast), and for the entire 24-h simulations tensity errors; and (e) the spinup problem (as evident (note the values at 6-h intervals follow Shi et al.Ðsee in the ®rst 12-h intensity errors) is less in all three runs their Tables 2 and 3). By examining this table, one can (i.e., MKAD, MKANA, and MKADNA) compared to make the following inferences: (a) the 24-h (absolute) those from Shi et al. (i.e., ODW, SSM/I, and ODW ϩ mean intensity and track errors from rain assimilation SSM/I, respectively). From these inferences, one can with ODW run (MKADNA) are, respectively, equal and conclude that the modi®ed rain assimilation scheme slightly lower than the ODW run (MKAD); (b) 24-h with ODW-enhanced initial conditions gives a positive mean intensity errors of ODW (MKAD) and rain as- impact on both the intensity and track forecasts, in con- similation (MKADNA and MKANA) simulations are substantially less than the corresponding runs from Shi trast to positive impact on track forecast only in Shi et et al., although the track errors are higher in the assim- al. (1996). Overall, the modi®ed rain assimilation results ilation runs; (c) the ODW rain assimilation (MKADNA) in smaller 24-h intensity errors (and a smaller spinup impact (based on last 12 h errors) on intensity and track problem) but larger track errors than those from Shi et is positive (with respect to the ODW run) in contrast al. Most signi®cantly, these rain assimilation results ap- to the negative (positive) impact on intensity (track) pear to validate our hypothesis that satellite rain assim- from Shi et al. (cf. their ODW and ODW ϩ SSM/I ilation alone can yield improved simulations of Florence runs); (d) modi®ed rain assimilation without ODW (i.e., positive impact with respect to the control run) (MKANA) produces a better impact on intensity and similar to those made with the enhanced initial condi-

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TABLE 5. Comparison of intensity (mb) and track (km) errors between this study and Shi et al. (1996) from 24-h simulations with the Kuo Scheme, at 6-h intervals. The intensity errors (top numbers in each row) and position errors (bottom numbers in parentheses) are calculated as in Table 3. Last three columns, respectively, represent the mean absolute (intensity and track) errors for the ®rst 12-h (averages of 0, 6, and 12 h), second 12-h (averages of 18 and 24 h), and the entire 24-h periods. Last 12-h First 12-h mean 24-h mean mean absolute absolute absolute Case 0 h 6 h 12 h 18 h 24 h error error error MKAD 8.3 5.8 3.7 0.2 6.3 5.9 3.3 4.9 (49.4) (26.4) (124.2) (113.0) (118.5) (66.7) (115.8) (86.4) ODW only 13.0 10.0 9.0 3.0 4.0 10.7 3.5 7.8 (from Shi et al.) (24.5) (79.3) (118.5) (121.5) (99.0) (74.1) (110.3) (88.6) MKADNA 8.3 8.5 5.6 Ϫ1.3 Ϫ0.6 7.5 0.95 4.9 (49.4) (71.6) (86.5) (53.4) (126.7) (69.2) (90.1) (77.5) ODW ϩ SSM/I 13.0 12 11 7 2 12.0 4.5 9.0 (from Shi et al.) (24.6) (34.8) (49.2) (11.0) (46.7) (36.2) (28.9) (33.3) MKANA 11.2 10.2 8.5 6.8 4.8 10.0 5.8 8.3 (118.1) (183.7) (113.3) (57.2) (126.7) (138.4) (92) (119.8) SSM/I only 14.0 13.0 14.0 14.0 9.0 13.7 11.5 12.8 (from Shi et al.) (108.9) (24.6) (24.6) (77.0) (213.6) (52.7) (145.3) (89.7) tions and a responsive cumulus parameterization Knowing these fundamental differences between the scheme. two schemes, it is possible to explain the large differences obtained from the rain assimilation simulations. Revisiting Fig. 5, the deepening of the tropical storm c. Effect of cumulus parameterization obtained from the MKA scheme in all the continuous As noted above, the MKA scheme has a better overall rain assimilation runs can be attributed to additional response to the assimilation of rainfall than the BM latent heat release from the satellite rainfall, which in- scheme and the impact of assimilated rain with the MKA ¯uences the low-level moisture convergence (and hence scheme produces a more intense tropical cyclone than further deepening from CISK). In the BM scheme, how- with the BM scheme. The explanation for these differ- ever, the additional latent heating contributes to vertical ences apparently lies in the intricacies of these two dis- stability change, which has no direct linkage to moisture parate cumulus parameterization schemes, mainly in accession as a result of its dependence on moist con- their contrasting approaches in parameterizing the convective adjustment and, therefore, has a minor in¯uence vective rainfall and its latent heat release. on surface pressure fall. The BM scheme parameterizes the latent heat effects These contrasting effects of both schemes can be in- indirectly through a soft convective adjustment ap- ferred from examining the area-averaged vertical latent proach, which adjusts the environmental lapse rate to- heating pro®les (Fig. 12) from both BM and MKA rain ward a wet virtual adiabat over a convective timescale assimilation runs with ODW data (i.e., BMDNA and ␶ given by MKADNA) at 12 h. The heating rates are categorized according to precipitation type (convective vs stable) TTϪ T Ϫ1 Ϫ1ץ ϭ r , (3) and rate (0.1±2 mm h and Ͼ6mmh ), and are then -t ␶ area-averaged over a subdomain encompassing the tropץ ical cyclone within a rectangular area of 20 ±35 N and where T is reference pro®le temperature and T is en- Њ Њ r 98Њ±78Њ W. The categorization for low and high rainfall vironmental temperature. On the other hand, the MKA rates is based on histograms, which show a large number scheme treats the latent heat effects directly as a function of grid points (280 for BM and 323 for MKA schemes) of fractional moisture convergence distributed vertically at low rain rates and signi®cantly less (73 and 54 for by a normalized heating function in proportion to the BM and MKA schemes, respectively) for higher rain difference between the actual and moist-adiabatic po- rates. By examining these heating pro®les, one can infer tential temperature pro®les, that: 1) there is no signi®cant difference in convective -p*TL heating rate maximum (located at midlevels) or in proץ ϭ p* N (1 Ϫ b)gM . (4) -tcht ®le shape for low-rainfall rates, whereas the stable heatץ p ing rates from the MKA scheme are slightly higher at By examining these two convective temperature ten- lower levels than from the BM scheme, and 2) the con- dency equations, one can infer that in the BM scheme vective heating rates with a maximum at ϳ500 mb are there is no direct dependency of the convective latent signi®cantly higher in the MKA scheme (Ͼ150 K heating on the large-scale moisture convergence, where- dayϪ1) than in the BM scheme (Ͼ50 K dayϪ1) despite as in the MKA scheme latent heating effects are directly the fact that stable heating rates in both schemes are linked to the moisture supplied by the large-scale ®elds. quite similar for high rainfall rates (Ͼ6mmhϪ1).

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FIG. 12. Subdomain-averaged (over a rectangular area of 20Њ±35Њ N and 98Њ±78Њ W) convective and nonconvective latent heating pro®les for low (0.1±2 mm hϪ1) and high rain rates (Ͼ6mmhϪ1) after 12 h of simulation from modi®ed rain assimilation with enhanced initial conditions: (top) with the BM scheme and (bottom) with the MKA scheme.

Further examination of horizontal ®elds of (convec- located off-center to the cyclone vortex in the BM tive and stable) precipitation and heating rates at 500 scheme, most likely did not contribute signi®cantly to mb every 3 h during the 12-h assimilation period in- the vortex intensi®cation. Without signi®cant in¯uence dicates that the convective rain-rate pattern from the of either stable or convective heating, low-level con- BM scheme prior to invoking the satellite-rain algorithm vergence remained weaker within the vortex resulting during the integration is wider but weaker than the MKA in a minimal growth of the cyclone (not shown). Stron- scheme by 5±6 mm hϪ1 (not shown). This translated ger mid- to low-level grid-scale heating in the MKA into smaller and broader convective heating rates in the scheme, on the other hand, appears to organize low-

BM scheme with the maximum mostly located outside level moisture convergence of higher ␪e air, just to the of the vortex center (Ͻ110 K dayϪ1 in Fig. 13) than in south of the initial cyclone center (refer to Fig. 2) but the MKA scheme (Ͼ250 K dayϪ1 in Fig. 14), which within the region of the satellite rain area. Increased depicts a well-organized compact rainband structure moisture convergence, in turn, allows the MKA scheme during the evolution of the storm at 500 mb. The stable to trigger convective rainfall, which renders a stronger heating rates at midlevels in the BM scheme (not and a more well-organized convective heating very shown), on the other hand, are almost nonexistent in the close to the vortex center contributing to further deep- vicinity of the cyclone center in contrast to the MKA ening of the cyclone through CISK. Thus, a rapid tran- scheme, which shows larger areas of nonconvective sition from gridscale heating to convective heating lead- heating (maximum Ͼ100 K dayϪ1; not shown) just to ing to a nonlinear feedback between moisture conver- the southwest of the vortex center that extends to low gence and convective heating facilitates cyclone growth. levels during the early stages of the storm evolution. Therefore, the inherent ability of the MKA scheme to The implication of the above results is that wide- feed on the moisture convergence initiated by the grid- spread and weaker convective heating rates, which were scale heating appears to be the most critical factor for

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FIG. 13. Convective heating rates (K dayϪ1; scale given on the side of each panel) at 500 mb from the BM scheme in the modi®ed rain assimilation run with ODW-enhanced initial conditions prior to invoking the rain assimilation algorithm in the model at (a) 0300 UTC, (b) 0600 UTC, (c) 0900 UTC, and (d) 1200 UTC 9 September 1988. the cyclone intensi®cation in the rain assimilation sim- SSM/I and GOES/IR satellite data at 30-min intervals, ulations. Absence of such close linkage between mois- were assimilated into the model within the ®rst 12 h ture convergence and convective rain in the BM scheme using the MKKN rain assimilation scheme, which was results in much weaker cyclone growth since either con- originally developed for midlatitude cyclogenesis. The vective or grid-scale heating only contributes to stability Kuo±Anthes and the Betts±Miller (BM) schemes were change in the vertical and to a net warming of the col- utilized to compare the sensitivity of Florence devel- umn (if the heating rates are large). Hence, one can opment to cumulus parameterization details. The major conclude that the response of rain assimilation depends emphasis of this study is to test the MKKN scheme in to a large extent not only on the choice of cumulus the context of obtaining improved simulations of Flor- parameterization scheme but also on the method of as- ence through a cumulus parameterization scheme sen- similating the observed rainfall. sitive to imposed latent heating. The MKKN rain assimilation and Kuo±Anthes cumulus parameterization schemes were modi®ed to im- 5. Summary and discussion prove the simulations of Florence's development since Numerical simulations of 24-h duration (with a grid preliminary simulations using the MKKN scheme with resolution of 40 km) were performed with the PSU/ either the BM or the Kuo±Anthes scheme indicated NCAR Mesoscale Model (MM5) to study the impact of some de®ciencies in capturing the development of Flor- initial conditions, satellite-derived rain assimilation, and ence. These modi®cations mainly consist of replacing cumulus parameterization on Hurricane Florence latent heating scaling with satellite rainfall in the Kuo± (1988). Initial conditions over the Gulf of Mexico were Anthes scheme, in areas where both the model-predicted enhanced with ODW data at 0000 UTC 9 September and satellite-derived rainfall coincide, and specifying a 1988. Rainfall estimates, obtained from the combined normalized parabolic heating pro®le in deep convective

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FIG. 14. Same as in Fig. 13 except from the MKA scheme. regions where there is satellite rain but no model rain. data is in agreement with previous studies (e.g., Burpee Restoration of the original Kuo heating distribution et al. 1996; Shi et al. 1996). function in lieu of the ®xed heating pro®le speci®ed in 2) The effect of ingesting continuous satellite-de- the MM5 model is another change implemented in the rived rainfall rates, which were obtained from combined Kuo±Anthes scheme. SSM/I and GOES/IR data, on the development of Hur- Several sensitivity simulations were performed with ricane Florence during the ®rst 12 h, is far greater with the modi®ed Kuo±Anthes (MKA) and the BM schemes the MKA scheme than with the BM scheme in all as- to test the impact of rain assimilation and enhanced similation runs. Signi®cant impact is achieved with the initial conditions on the development of Hurricane Flor- MKA scheme in the ODW-enhanced rain assimilation ence. The major ®ndings from these simulations are the simulation both in terms of cyclone intensity and track, following: particularly during the latter half of the simulation. The 1) ODW-enhanced initial conditions made a positive MKA scheme with enhanced initial conditions gave the impact on the development of Florence for both the BM smallest track and intensity errors at 24 h among all the and the MKA schemes; the latter sharply contrasts with assimilation runs, with a predicted central pressure and the weak (or no) response given by the original Kuo± rainfall pattern that agreed closely with observations. Anthes scheme. Although both schemes produced sim- However, the impact of intermittent rain assimilation ilar tropical cyclone intensities and improved track fore- with SSM/I data is much weaker than the continuous casts at the end of 24-h simulations compared to control rain assimilation. Overall, the modi®ed rain assimilation runs, overall the BM scheme gave a better track forecast scheme produces smaller intensity errors, reduced spin- whereas the MKA scheme performed better in reducing up problems, and larger track errors in Florence com- the intensity errors. These results suggest that the en- pared to those from Shi et al. Most signi®cantly, assim- hanced initial conditions signi®cantly improve the lo- ilation results from the MKA scheme appear to validate cation and intensity of the tropical cyclone in both our hypothesis that satellite rain assimilation alone can schemes. The positive impact obtained with the ODW yield improved simulations of Florence (positive impact

Unauthenticated | Downloaded 10/02/21 10:10 PM UTC DECEMBER 1998 KARYAMPUDI ET AL. 3099 with respect to the control run) comparable to those 1991). The modi®ed rain assimilation scheme through made with the enhanced initial conditions with a re- latent heat scaling of satellite rain appears to favor Kuo- sponsive cumulus parameterization scheme. type schemes more than the convective adjustment 3) The greater impact of rain assimilation with the schemes such as BM. An entirely different result from MKA scheme than with the BM scheme appears to be the BM scheme can be obtained if the observed rainfall related mainly to the differences in the treatment of rates are assimilated through an energy conserving ap- convective rainfall and its latent heat release. The BM proach in which both moisture and temperature are var- scheme produced weaker but widespread convective iationally adjusted. Indeed this may be a better approach heating with reduced grid-scale heating around the cen- for a rain assimilation with the BM scheme since latent ter of the vortex. In contrast, the MKA scheme yielded heat scaling in the BM scheme warms (and dries) the a stronger and well-organized convective heating re- environment, which effectively reduces the convective sulting from low-level moisture convergence initiated rainfall contributing to weakening of the vortex. by grid-scale heating near the vortex center. The inher- Since one case study may not be suf®cient to judge ent ability of the MKA scheme to feed on the moisture the effectiveness of any rainfall assimilation scheme, convergence initiated by the grid-scale heating appears attempts are currently under way to apply this rainfall to be the most critical factor for the cyclone develop- assimilation technique (with MKA scheme only) to oth- ment in the rain assimilation simulations. er tropical cyclone cases such as Hurricane Opal (1995), Intensity change forecast remains a major problem in the latent heating pro®les of which were derived by operational tropical cyclone predictions (Elsberry et al. Rodgers et al. (1998.) One of the aspects that needs 1992). Currently, ingestion of ODW data in operational further investigation is the impact of intermittent rainfall models such as the Geophysical Fluid Dynamics Lab- rates on tropical cyclone development since our result oratory model indicate a signi®cant positive impact on sharply contrasts with those reported by Shi et al. track forecasts but small positive impact on intensity (1996). However, assimilation of continuous rainfall forecasts (Tuleya and Lord 1997). One major implica- rates derived from a single satellite (e.g., SSM/I) using tion of this study is that continuous rain assimilation interpolation techniques appears to be better than using within the 12-h preforecast period in operational models intermittent constant rain-rate assimilation since prelim- will have a potential impact on improving tropical cy- inary simulation results of Hurricane Opal show a strong clone intensity forecasts signi®cantly in the following positive impact with the assimilation of digitally 12±24 h even in regions where special observations such morphed (Pratt 1977) SSM/I rainfall (not shown). These as ODW data are not available. In regions where ODW tentative results show that rain assimilation over a 24- data are available, forecasts obtained from ODW-en- h period yielded a surface pressure fall of over 50 mb hanced rain assimilation (with MKA scheme) will be far superior to those obtained with enhanced initial con- in a 36-h period (with a minimum pressure of 936 mb ditions alone. However, one of the limitations of the vs 915 mb observed pressure) compared to only a 30- MKKN rain assimilation is that it may yield preforecast mb pressure drop in the control run (not shown). More track errors during the ®rst few hours, particularly in signi®cantly, no southward movement of the assimilated situations where the satellite rain is asymmetrically lo- cyclone occurred during the early period of integration cated with respect to a weakly de®ned cyclone center. due to a stronger vortex in the control run, in sharp This de®ciency, which may be present in other rain contrast to the southward movement in the Florence assimilation schemes (e.g., Peng and Chang 1996), ap- case. This result reaf®rms our hypothesis that a stronger pears to result from the fact that diabatic assimilation control vortex is the key to arrest an undue southward provides the divergent component of the wind and not movement when satellite convective precipitation is dis- the rotational wind. This problem may be alleviated if placed from the initial vortex center. the rotational component of the observed wind ®eld is One of the limitations of the modi®ed rain assimi- assimilated into the model using a dynamic assimilation lation scheme is the external speci®cation of latent heat- scheme such as the Newtonian relaxation, also known ing pro®le in certain regions where the model does not as dynamic nudging, during a preforecast integration predict rain but the satellite shows rain. One way to period (e.g., Krishnamurti et al. 1988; Davidson and overcome this limitation is to specify the vertical dis- Puri 1992). However, the limitation with this approach tribution and partitioning of latent heating a priori from is that tropical cyclone structures need to be imposed the remotely sensed rainfall data. Recently, attempts are since the large-scale analysis often lacks the mesoscale being made at GSFC in obtaining latent heating pro®les details of cyclonic circulations. associated with various types of convective systems us- One should exercise caution, however, in interpreting ing retrieval algorithms based on passive microwave rain assimilation results with different cumulus param- data such as SSM/I (Tao et al. 1993; Olson et al. 1998.) eterization schemes since previous studies have shown Assimilating such externally determined vertical heating a better performance of the BM scheme than the MKA pro®les will improve the present rain assimilation scheme in simulating tropical cyclone structure and de- scheme and make it one of the truly self-consistent rain velopment (e.g., Puri and Miller 1990b; Baik et al. assimilation schemes that can be used to assimilate trop-

Unauthenticated | Downloaded 10/02/21 10:10 PM UTC 3100 MONTHLY WEATHER REVIEW VOLUME 126 ical rainfall and vertical latent heating pro®les derived Chang, S. W., and T. R. Holt, 1994: Impact of assimilating SSM/I from future microwave satellites such as the TRMM. rainfall rates on numerical prediction of winter cyclones. Mon. Wea. Rev., 122, 151±164. Cressman, G. P., 1959: An operational analysis system. Mon. Wea. Acknowledgments. The authors thank Dr. Chris Kum- Rev., 87, 367±374. merow of GSFC for providing the rainfall rates derived Davidson, N. E., and K. Puri, 1992: Tropical prediction using dy- from the SSM/I data and Mr. Harold Pierce of SSAI, namical nudging, satellite-de®ned convective heat sources, and Inc. for processing the GOES/IR data. We are indebted a cyclone bogus. Mon. Wea. Rev., 120, 2501±2522. Davies, H. C., and R. E. Turner, 1977: Updating prediction models to Mr. Andy Negri of GSFC for his assistance in pixel by dynamical relaxation: An examination of the technique. matching of the IR precipitation rates with the SSM/I Quart. J. Roy. Meteor. Soc., 103, 225±245. rain rates, and to Dr. Brad Ferrier of SSAI, Inc. for Donner, L. J., and P. J. Rasch, 1989: Cumulus initialization in a global providing 1D cloud model simulation results from ODW model for numerical weather prediction. Mon. Wea. Rev., 117, soundings to examine the vertical structure of heating 2654±2671. Dudhia, J., 1993: A nonhydrostatic version of the Penn State/NCAR pro®les within the hurricane environment. We are also mesoscale model: Validation tests and simulation of an Atlantic grateful to Dr. Steve Koch of N. C. State University, cyclone and cold front. Mon. Wea. Rev., 121, 1493±1513. Raleigh, North Carolina, for his suggestions and interest Elsberry, R. L., G. J. Holland, H. Gerrish, M. DeMaria, and C. P. during the initial phase of this research. We acknowl- Guard, 1992: Is there any hope for tropical cyclone intensity edge Dr. David Stauffer from The Pennsylvania State prediction?ÐA panel discussion. Bull. Amer. Meteor. Soc., 73, 264±275. University for sharing his insights on the BM cumulus Fiorino, M., and T. T. Warner, 1981: Incorporating surface winds and parameterization scheme. We thank Mr. James Franklin rainfall rates into the initialization of a mesoscale hurricane mod- and Dr. Frank Marks of HRD/AOML, Miami, Florida, el. Mon. Wea. Rev., 109, 1914±1929. respectively, for, providing the ODW and aircraft Dopp- Frank, W. M., and J. L. McBride, 1989: The vertical distribution of ler radar data. We extend our gratitude to Dr. Robert heating in AMEX and GATE cloud clusters. J. Atmos. Sci., 46, 3464±3478. Adler of GSFC not only for his keen interest and support Grell, A. G., J. Dudhia, and D. R. 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