Radiative Effects on Tropical Cyclone Development in Different Life Stages

Menggeng Xu aDisaster Prevention Research Institute, Kyoto University, Uji, Japan

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Tetsuya Takemi aDisaster Prevention Research Institute, Kyoto University, Uji, Japan

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Abstract

A tropical cyclone (TC) is a powerful, rotating storm that typically originates over warm tropical oceans and creates strong winds and heavy rain; it is usually a natural disaster with respect to human life and property if it moves over land. This work examines effects of varying radiative forcing on the evolution of two typhoon cases—Typhoon Lionrock (2016) and Typhoon Hagibis (2019)—with the Weather Research and Forecasting (WRF) Model. Hagibis was a rapidly intensifying and quickly moving TC, whereas Lionrock gradually developed and was slow moving. Numerous sensitivity experiments in which shortwave and longwave radiative heating rates were modified were conducted. This study examined latent heating and radiative heating for each experiment. Substantial differences between the sensitivity simulation members indicated that radiative effects can strongly influence TC development. The analysis of diabatic heating sources shows that, before eyewall formation, the differential cooling effect, which indicates that longwave cooling rates between cloud clusters and clear sky differ, can promote low-level inflow and increase relative humidity in the cloud clusters. If the initial relative humidity is low, this effect becomes important because, without differential cooling, the relative humidity remains low, which can promote the generation of cold pools that will prevent cyclone development. After eyewall formation, both the change in temperature lapse rate due to a vertical gradient of radiative heating/cooling and the change in the warm core due to radiative heating/cooling can affect the intensity of a TC; however, the net effect may depend on the magnitude of these influences.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Menggeng Xu, xu_m@storm.dpri.kyoto-u.ac.jp

Abstract

A tropical cyclone (TC) is a powerful, rotating storm that typically originates over warm tropical oceans and creates strong winds and heavy rain; it is usually a natural disaster with respect to human life and property if it moves over land. This work examines effects of varying radiative forcing on the evolution of two typhoon cases—Typhoon Lionrock (2016) and Typhoon Hagibis (2019)—with the Weather Research and Forecasting (WRF) Model. Hagibis was a rapidly intensifying and quickly moving TC, whereas Lionrock gradually developed and was slow moving. Numerous sensitivity experiments in which shortwave and longwave radiative heating rates were modified were conducted. This study examined latent heating and radiative heating for each experiment. Substantial differences between the sensitivity simulation members indicated that radiative effects can strongly influence TC development. The analysis of diabatic heating sources shows that, before eyewall formation, the differential cooling effect, which indicates that longwave cooling rates between cloud clusters and clear sky differ, can promote low-level inflow and increase relative humidity in the cloud clusters. If the initial relative humidity is low, this effect becomes important because, without differential cooling, the relative humidity remains low, which can promote the generation of cold pools that will prevent cyclone development. After eyewall formation, both the change in temperature lapse rate due to a vertical gradient of radiative heating/cooling and the change in the warm core due to radiative heating/cooling can affect the intensity of a TC; however, the net effect may depend on the magnitude of these influences.

© 2022 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Menggeng Xu, xu_m@storm.dpri.kyoto-u.ac.jp

1. Introduction

Tropical cyclones (TCs) are complex systems whose intensities at any given time are affected by various physical processes. Although these physical processes are not fully understood, it is generally agreed that there exists a thermodynamic limit to the intensity of a TC. Under the maximum potential intensity (MPI) hypothesis proposed by Emanuel (1986, 1995), the intensity of TCs is controlled by the sea surface temperature and the upper-level outflow temperature. Because radiation, which is one of the most important diabatic heating sources, can interact with clouds and clear sky and thereby influence the temperature structure, it might be related to TC intensity.

Satellite observations indicate that diurnal variation of TCs in different ocean basins is substantial (Browner et al. 1977; Muramatsu 1983; Lajoie and Butterworth 1984; Steranka et al. 1984; Kossin 2002), which can ultimately be attributed to the diurnal variation of shortwave radiation. Analysis of satellite observations reveals regular radial propagations of the infrared cloud field, which begin forming in the storm’s inner core near the time of sunset and continue to move away from the storm overnight, reaching areas several hundreds of kilometers from the center by the following afternoon (Dunion et al. 2014). A TC diurnal pulse, a term introduced by Dunion et al. (2014), is one of the distinguishing characteristics among the dynamical behaviors of TCs, and the repeatability of TC diurnal pulsing in time and space might influence the structure and intensity change of TCs.

The current hypothesized mechanisms by which radiative processes affect cloud systems can be summarized as follows: 1) differential cooling between the cloudy and clear sky, which enhances low-level convergence (Gray and Jacobson 1977; Craig 1996); 2) large-scale clear-sky cooling, which increases relative humidity (Miller and Frank 1993; Fu et al. 1995; Dudhia 1989; Tao et al. 1996); and 3) radiative destabilization at cloud top overnight, which steepens the lapse rate and leads to activation of nocturnal convection (Webster and Stephens 1980; Hobgood 1986; Xu and Randall 1995). In the tropics, despite regional variations, oceanic deep convection tends to reach its maximum in the early morning, whereas continental convection generally peaks in the evening (Yang and Slingo 2001). An analysis of infrared satellite images of the tropical oceanic warm-pool region (80°E–160°W) suggests that deep convection in very cold clouds peaks before dawn and decreases through the morning. The moderately cold cloud area expands in the afternoon (Mapes and Houze 1993). Numerical simulations have shown that there is a maximum around predawn and a minimum in the late afternoon for convective activity of tropical oceanic convection, and the analysis shows that diurnal variation is primarily attributable to the direct interaction between radiation and convection, whereas the cloud–cloud-free differential heating mechanism plays a secondary role (Liu and Moncrieff 1998). Because a TC is a form of organized cloud systems, these proposed radiative effects likely play important roles in the diurnal variation of TCs. Basic physical processes of the impacts of cloud-radiative feedback and forcing on the track and structure of TCs were investigated by Fovell et al. (2010, 2016). Without cloud-radiative forcing, the variations of tracks in TC simulations with different microphysical schemes were found to be very minor, suggesting that the cloud-radiative forcing affects the dynamics of TC motion (Fovell et al. 2010). Fovell et al. (2016) further indicated that among the cloud-radiative processes the absorption and emission of longwave radiation in the anvil is the most influential; cloud-radiative forcing in the cloudy area leads to the radial expansion of the upper-level outflow, further extending the cloudy area, which moistens the outer core and results in enhanced convection. In this way, cloud-radiative forcing, especially longwave radiative processes, plays an important role in affecting the TC structure and intensity.

In the early stages of TC development, when their intensity is low, radiation strongly affects the TC intensification rate; however, radiation has little effect when the intensity is relatively higher (Nicholls and Montgomery 2013). Radiative–convective feedbacks can aid spontaneous cyclogenesis in radiative–convective equilibrium simulations but are not strictly necessary (Wing et al. 2016). Before an extensive cloud shield develops aloft, large-scale clear-sky cooling leads to an increase in relative humidity that favors the development of moist convection (Nicholls 2015). In the nighttime, radiative cooling leads to an increase in humidity and a decrease in stability, favoring deep moist convection and enhancing the potential for cyclogenesis. Greater latent heating released by deep convection induces stronger low-level inflow, which results in convergence of absolute vorticity and an increase of low-level cyclonic winds. On the contrary, in the daytime, radiative heating is predominant. Radiative heating can increase the temperature and saturation water vapor pressure and reduce the relative humidity, which is less favorable for deep moist convection and storm development (Melhauser and Zhang 2014; Tang and Zhang 2016). Differential radiative cooling or heating between a relatively cloud-free environment and a developing tropical disturbance generates circulations that can strongly influence convective activity in the core of the system (Nicholls 2015). Tall clouds can trap the infrared radiation welling up from the surface, warming the lower–middle troposphere relative to a TC’s surroundings. Therefore, radiation can moisten a tropical disturbance through the differential radiative cooling effect, strengthening the thermally direct transverse circulation and accelerating cyclogenesis (Ruppert et al. 2020; Smith et al. 2020).

After rapid intensification commences, the diurnal radiation cycle mainly affects the storm structure and strength rather than its track and intensity, and nighttime destabilization can promote the development of outer rainbands and increase the size of the storm (Tang and Zhang 2016). Radially propagating diurnal signals related to radiation tendency, thermodynamics, static stability, winds, and precipitation are apparent for an intense TC in simulations (Dunion et al. 2019). Lower upper-tropospheric temperatures yield stronger TCs, as well as greater upper-level vertical mass flux, ice species aloft, and height of the TCs when radiative cooling is included; however, in the absence of radiation, these changes become less significant (Trabing et al. 2019). Shortwave radiation can heat and stabilize the mid- to upper troposphere in the rainbands, leading to a delay of the secondary eyewall formation and eyewall replacement (Trabing and Bell 2021). The clouds at the top of the boundary layer absorb solar shortwave heating during the daytime, which enhances the temperature inversion there and increases the convective inhibition, whereas nighttime destabilization and moistening at low levels through radiative cooling decrease convective inhibition and favor greater convection inside the radius of maximum wind (RMW) than in the daytime phase. The enhanced convection at night inside the RMW causes greater positive radial vorticity flux locally, leading to more RMW contraction (Tang et al. 2019). Diurnal waves of TC can only propagate well beyond the core, and the outflow region is most receptive to near-core diurnal propagation. It is unfavorable for a diurnal wave to propagate when a TC experiences an eyewall replacement cycle (O’Neill et al. 2017). The radiative heating caused by clouds, especially in the upper-level outflow layer of a TC, promotes the formation of storms with more vigorous convection and diabatic heating outside the eyewall, a wider eye, a broader strong-wind field, and stronger secondary circulation (Bu et al. 2014). During the day, anvil clouds absorb solar radiation, leading to strong warming there and promoting lifting and spreading of the anvil clouds (Ruppert and O’Neill 2019). For a balanced TC, periodic heating due to solar radiation near the TC outflow layer can induce an overturning response of the perturbation wind field in the region of heating, which manifests as inertia–buoyancy waves in the storm environment. Periodic heating in the lower troposphere due to latent heat can drive an overturning response of the perturbation wind field throughout the lower to midtroposphere, which is associated with an acceleration of the low-level azimuthal wind 6 h after the maximum in heating (Navarro et al. 2017).

Presently, there is no consensus on the effects of radiation on TCs. Despite numerous observational and numerical studies of the TC diurnal cycle, the mechanisms by which radiation modulates the intensification of TCs remain unclear. The effects of radiative processes can differ at different life stages of TCs because upper-level cloud cover depends on the structure and development of TCs. Additionally, the radiative influences are considered to differ depending on the TC cases because the diurnal cycle features in observations, real-case simulations, and idealized simulations in previous studies are not the same.

To compare TC development in daytime and nighttime, previous related studies have focused mainly on simulations that include and exclude the effects of shortwave radiation. Such sensitivity experiments are conducted by modifying the shortwave-radiation heating rate (Tang and Zhang 2016; Trabing and Bell 2021). Inspired by the modification of the shortwave-radiative heating rate in previous studies, we here carried out numerical experiments by setting weaker or stronger radiative heating rates for different TC cases using a three-dimensional, nonhydrostatic model under realistic configurations. For this study, we chose two contrasting cases of TCs that occurred in the western North Pacific: Typhoon Lionrock (2016; National Institute of Informatics 2016) and Hagibis (2019; National Institute of Informatics 2019). In section 2, these selected typhoon cases are briefly described and the design of the numerical experiments is introduced. In section 3, the results of simulations are analyzed and compared and radiative effects are illustrated in the early stages (before eyewall formation) and in the later stages (after eyewall formation). Discussions and conclusions are presented in section 4.

2. Typhoon cases and design of numerical experiments

a. Typhoon cases

In this study, we selected two typhoons that caused substantial damage in Japan: Typhoons Hagibis (2019) and Lionrock (2016). Hagibis was a rapidly intensifying and quickly moving TC that caused widespread destruction across its path; it was one of the strongest and largest typhoons in decades to strike mainland Japan. According to the best track data of the Regional Specialized Meteorological Center (RSMC) Tokyo, the minimum central surface pressure during its lifetime was 915 hPa. Hagibis generated substantial rainfall in the eastern part of the Japanese mainland and spawned widespread flooding and landslides in the region. According to Takemi and Unuma (2020), a moist absolutely unstable layer (Bryan and Fritsch 2000) continuously developed and led to the rapid generation of precipitating clouds and, hence, greater rainfall. Iizuka et al. (2021) showed that the sea surface temperature anomalies over the Oyashio region played a role in determining the location of strong rainfall.

Lionrock was a slowly developing, long-lived TC that took a meandering track, making landfall on the Pacific side of the Japanese mainland from the east. Lionrock caused extensive flooding and casualties across its path in regions where typhoon landfalls are extremely rare (Nayak and Takemi 2019). The best track data of RSMC Tokyo show that the minimum central surface pressure was 940 hPa. Wada and Oyama (2018) demonstrated that consecutive deep convection (i.e., convective bursts) characterized the evolution of Lionrock, which was strongly influenced by ocean conditions. Among the major typhoons that made landfall over Japan in the summer of 2016, Lionrock produced the greatest amount of accumulated rainfall in northern Japan, which led to devastating damage (Nayak and Takemi 2020).

Hagibis was a rapidly intensifying and quickly moving TC, whereas Lionrock was a slowly developing, long-lived TC. Radiation presumably had less effect on the intensification stage of Hagibis because this period was relatively short and had more effect on Lionrock because the development was slow. Therefore, these two cases were chosen for simulation in this research.

b. Model configuration

The Weather Research and Forecasting (WRF) Model, version 4.1 (Skamarock et al. 2019), was used in this research. The initial and boundary conditions were obtained from the National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) Final (FNL) analysis data on 0.25° × 0.25° grids at a 6-h interval. The simulation time periods were from 0000 UTC 5 October to 0000 UTC 12 October 2019 for Hagibis and from 0000 UTC 22 August to 0000 UTC 29 August 2016 for Lionrock. At the model initialization time, the two TCs were weak vortices with a few convection activities. Sea or land surface temperatures given at the initial time for each case were the mean values at each grid point averaged over the 7 simulation days, and the surface temperatures were fixed throughout the whole simulation period to exclude any possible surface temperature changes that could be caused by the excessive radiative heating rate. The surface temperature data are also from the NCEP FNL analysis data.

The computational domains of the two typhoon cases are shown in Fig. 1. The outermost domain (domain 1) covered areas of 6500 km × 5200 km for Hagibis and 5200 km × 5200 km for Lionrock, both at 50-km grid spacing, which are designed to cover the tracks of the TCs over the lifespan of the simulations. For the inner, two-way-nested domains, the horizontal area of domain 2 was 2000 km × 2000 km with 10-km grid spacing and that of domain 3 was 1000 km × 1000 km with 2-km grid spacing for both typhoon cases. Both domain 2 and domain 3 were vortex-following domains during the simulation periods, with the initial position of the typhoons located within domain 3. The vortex-following configuration was adopted to move the nested domains automatically via an automatic vortex-following algorithm, which is designed to follow the movement of a well-defined TC. With this algorithm, the vortex center location, along with the minimum mean sea level pressure (MSLP) and maximum 10-m winds, were obtained at 15-min intervals. The vortex center was defined as the centroid of the negative deviation of the surface pressure from some reference pressure (Cangialosi et al. 2005). All three domains were run with 40 terrain-following eta levels with the model top at the 20-hPa level. The time steps for domain 1, domain 2, and domain 3 were 100, 20, and 4 s, respectively.

Fig. 1.
Fig. 1.

Computational domains used in the numerical simulations for (a) Typhoon Hagibis and (b) Typhoon Lionrock.

Citation: Monthly Weather Review 150, 12; 10.1175/MWR-D-21-0337.1

All the physics parameterizations applied in this work are commonly used in numerical simulations research. The following physics parameterizations were chosen for all the domains: the WRF single-moment 6-class microphysics scheme (WSM6) (Hong and Lim 2006) for the cloud microphysics scheme, the Rapid Radiative Transfer Model for General Circulation Models (RRTMG) (Iacono et al. 2008) for both shortwave and longwave radiation schemes, which are called every 2 min, the Yonsei University (YSU) scheme (Hong et al. 2006) for the planetary boundary layer scheme, and the revised fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) Monin–Obukhov scheme (Jiménez et al. 2012) for surface exchanges of momentum, heat, and water vapor. In domain 1 only, a modified Tiedtke scheme (Zhang and Wang 2017) cumulus scheme was applied. The radiation physics scheme was called every 2 min.

c. Design of sensitivity experiments

Because this study focuses on the effects of radiative heating processes on the evolution of typhoons, we specifically investigated the sensitivity of the simulated typhoons to radiative parameterizations by intentionally changing the magnitudes of shortwave and longwave heating/cooling. We designed four sensitivity members with different radiative heating rates, in addition to the control experiment (CTL), as listed in Table 1. The radiation heating rates were modified by changing the temperature tendency due to shortwave or longwave radiative heating/cooling. The CTL experiment was a run with standard (i.e., unmodified) settings for radiative processes.

Table 1

List of numerical experiments.

Table 1

The sensitivity experiments included no shortwave radiation (NSW), no longwave radiation (NLW), double the temperature tendency due to longwave radiation (DLW), and double the temperature tendency due to shortwave radiation (DSW) experiments. The NSW experiment was the same as that in the nighttime-only experiment of Tang and Zhang (2016) and is intended to examine how a TC develops in the absence of solar radiative heating. The NLW experiment was designed to examine longwave radiative effects because a differential longwave cooling effect has been suggested in previous studies (Gray and Jacobson 1977; Nicholls 2015); this experiment can verify how this effect works. Both the DLW and DSW experiments apply extremely strong radiative heating rates not investigated in previous studies; we expected shortwave or longwave heating/cooling to be elucidated under these enhanced radiative effects. We analyzed the influences of radiation by comparing the different sensitivity members.

3. Results

a. Typhoons Hagibis and Lionrock analyses: Satellite imagery, best track, and control simulation

In this subsection, the characteristics of the simulated typhoons in the CTL and sensitivity experiments are overviewed. At the same time, the simulated typhoon features in the CTL are compared with the observed features.

The temporal evolutions of the MSLP, the maximum wind speed (MWS), and the track of each simulated typhoon case are compared with the RSMC best track data and are illustrated in Figs. 2 and 3 for Hagibis and Lionrock, respectively. The CTL experiments for Hagibis and Lionrock are first compared with the observations to evaluate the performance of the simulations.

Fig. 2.
Fig. 2.

The temporal change in (a) the minimum sea level pressure, (b) the typhoon track, and (c) the maximum wind speed (smoothed) in the numerical simulations and the RSMC best track data for the Typhoon Hagibis case. The legend for each experiment is shown in the bottom right.

Citation: Monthly Weather Review 150, 12; 10.1175/MWR-D-21-0337.1

Fig. 3.
Fig. 3.

As in Fig. 2, but for the Typhoon Lionrock case.

Citation: Monthly Weather Review 150, 12; 10.1175/MWR-D-21-0337.1

For the Hagibis CTL case, the MSLP in the simulation is very similar to the best track data in the first 1.5 simulation days. Thereafter, the simulated MSLP decreases more slowly than the observation; however, the intensity is stronger than the observation near the end of the simulation period. The track of the CTL is close to the best track and is shifted westward compared with the observation track at later times. The MWS of the CTL is greater than the observation for the first simulation day and then becomes relatively close to the observed value from 0000 UTC 6 October to 0000 UTC 8 October. Thereafter, the value became larger than the observation value.

For the Lionrock CTL case, the MSLP of the CTL remains relatively unchanged in the first simulation day and decreases to the minimum value on 26 August. The simulated MSLP is higher than the observation value in the first 3 simulation days. The tracks of both the CTL and the observation are counterclockwise; however, the CTL track is shifted about 500 km farther eastward than the observation track. The MWS of the CTL is close to the observation value in the first 3 simulation days; however, the maximum value is about 20 m s−1 greater than the observation value and the minimum MSLP is about 20 hPa lower than the observation value.

To further verify the reproducibility of the CTL simulations, the equivalent blackbody temperature (Tbb) from satellite observations and the simulated cloud-top temperatures are compared. The satellite data used here are the measurements by the Himawari-8 geostationary meteorological satellite band 16 (13.3 μm) with 0.02° resolution. This satellite is operated by the Japan Meteorological Agency (JMA), and the data are archived by the Center for Environmental Remote Sensing (CEReS), Chiba University in a geo-corrected, processed format on the latitude–longitude coordinate system (geo-correction algorithm: Takenaka et al. 2020; geo-correction accuracy validation: Yamamoto et al. 2020). Figures 4 and 5 show the temporal evolutions of the satellite-observed Tbb and the simulated cloud-top temperature, respectively, for the Hagibis CTL case; Figs. 6 and 7 show the Tbb and the simulated cloud-top temperature, respectively, for the Lionrock CTL case.

Fig. 4.
Fig. 4.

Snapshot of the equivalent blackbody temperature of Typhoon Hagibis from the Himawari-8 satellite observations [band 16 (13.3 μm) with 0.02° (2 km at nadir) resolution] at (a) 1200 UTC 5 Oct, (b) 0000 UTC 7 Oct, (c) 0000 UTC 9 Oct, and (d) 0000 UTC 12 Oct 2019.

Citation: Monthly Weather Review 150, 12; 10.1175/MWR-D-21-0337.1

Fig. 5.
Fig. 5.

As in Fig. 4, but for the simulated cloud-top temperature in the CTL. The cloud-top temperature algorithm looks from the top down and integrates the optical thickness until the threshold of 1 is met; the cloud-top temperature is then set to the temperature of that level. The white area is where the total integrated optical thickness is less than 1.

Citation: Monthly Weather Review 150, 12; 10.1175/MWR-D-21-0337.1

Fig. 6.
Fig. 6.

As in Fig. 4, but for the Lionrock case at (a) 1200 UTC 22 Aug, (b) 0000 UTC 25 Aug, (c) 1200 UTC 27 Aug, and (d) 0000 UTC 29 Aug 2016.

Citation: Monthly Weather Review 150, 12; 10.1175/MWR-D-21-0337.1

Fig. 7.
Fig. 7.

As in Fig. 5, but for Typhoon Lionrock. The time shown in each panel is as in Fig. 6.

Citation: Monthly Weather Review 150, 12; 10.1175/MWR-D-21-0337.1

For Hagibis, initially, the tropical disturbance was weak and disorganized at 1200 UTC 5 October 2019 (Fig. 4a), during which the CTL experiment also showed similar features (Fig. 5a). From 1200 UTC 6 October to 0000 UTC 7 October, the TC system intensified quickly and the eyewall structure became apparent at 0000 UTC 7 October (Figs. 4b,c); the simulation captured this transition relatively well at this time (Fig. 5b). From 1200 UTC 7 October to 1200 UTC 10 October, the TC system reached its maximum intensity and a clear eyewall structure appeared at 0000 UTC 9 October (Fig. 4c); the CTL also presented a clear eyewall structure (Fig. 5c). Thereafter, the convection weakened, the intensity decreased, and the inner core structure became disorganized (Fig. 4d). The simulated TC was stronger than the observed TC but showed a slightly disorganized inner core at 0000 UTC 12 October (Fig. 5d).

The intensification rate in the Lionrock case (∼6 m s−1 day−1) was smaller than that in the Hagibis case (∼20 m s−1 day−1), and the cloud-cover area was smaller according to the temporal mean of area of cold cloud with Tbb lower than 210 K during the simulation period within 500 km from the vortex center, which is about 198 947 km2 for Hagibis and ∼87 823 km2 for Lionrock. Initially, there were only some scattered cloud clusters near the vortex center (Fig. 6a); the CTL experiment appears to have captured this feature (Fig. 7a). The system developed and reached maximum intensity at 0000 UTC 25 August 2016, and the cloud cover became more extensive; however, the eyewall structure was not clear (Fig. 6b). The CTL captured this feature relatively well, but the eyewall structure was clearer (Fig. 7b). From 0000 UTC 25 August to 1200 UTC 27 August 2016, Lionrock sustained its maximum intensity as the eyewall became increasingly clear (Fig. 6c); the CTL also shows a more apparent eye (Fig. 7c). From 1200 UTC 27 August to 0000 UTC 29 August 2016, the MSLP and MWS of Best Track did not substantially change (Fig. 3); however, the cloud system became weaker (Fig. 6d) according to the area of cold cloud with Tbb lower than 210 K, which is ∼115 092 km2 in Fig. 6c and ∼8928 km2 in Fig. 6d. During this time, the CTL cloud-cover evolution indicated similar features (Fig. 7d); however, the intensity decreased earlier than indicated by the best track (Fig. 3).

The preceding comparison demonstrates that the simulation achieved adequate reproducibility to be used to examine the sensitivity of TC systems to radiative processes in the experiments. On the basis of the performance of the aforementioned CTL experiment, we assessed the results of the sensitivity experiments relative to those of the CTL experiment.

b. Typhoons Hagibis and Lionrock analyses: Sensitivity experiments

In this subsection, the results of the sensitivity experiments are presented.

For the Hagibis simulations, the MSLP of the NLW decreases most slowly, whereas that of the other experiments decreases more rapidly, closer to the best track (Fig. 2a). Compared with the minimum MSLP of the CTL, the values in the NSW and DLW cases are higher, whereas the value of the DSW is lower. The maximum MWS of the CTL and sensitivity experiments except NLW is approximately 65 m s−1, and, consistent with the results for the MSLP, the NLW case shows the smallest value (Fig. 2c). The tracks of all the members are curved clockwise, directed initially northwestward and then northeastward, and the NLW exhibits the easternmost track (Fig. 2b).

In the Lionrock simulations, the deviation of each member is much larger than in the Hagibis simulations. Except for the NLW case, the MSLP decreases and the MWS intensifies as in the best track but are slightly delayed compared with the corresponding changes in the best track data, with the minimum MSLP being approximately 920–940 hPa and the maximum MWS being approximately 60 m s−1 (Figs. 3a,c). The rates of decrease of the members except the NLW are similar to each other in the first three simulation days, during which the MSLP of the DLW decreases the earliest. Similar to the case of Hagibis, the minimum MSLP of the NSW and DLW for Lionrock is higher than the CTL results, whereas the MSLP is lower in the DSW experiment than in the CTL. The MWS of the NLW is the weakest, indicating that the NLW case fails to reproduce a TC. The tracks are highly variable in an earlier stage when the vortex is weak and are directed counterclockwise, as in the best track. In the NLW case, the track is highly erratic over the whole simulation period (Fig. 3b).

From the comparison of the simulated results with the best track observations, the simulated intensities were found to be strongly affected by shortwave and/or longwave radiative heating/cooling. Such intensity changes are further examined with the temporal evolution of the radial distribution of precipitating clouds. Precipitating clouds are examined in terms of radar reflectivity, which is averaged in the azimuthal direction at each radial distance from the typhoon center as well as in the vertical direction from the surface to the 16-km height. Evolutions of reflectivity of the simulated TCs are shown in Figs. 8a–e for the Hagibis case and in Figs. 8f–j for the Lionrock case.

Fig. 8.
Fig. 8.

Radius–time plot of averaged radar reflectivity (dBZ) under 16 km of the (top) Typhoon Hagibis and (bottom) Typhoon Lionrock simulation members: (a),(f) CTL; (b),(g) NSW; (c),(h) NLW; (d),(i) DLW; and (e),(j) DSW. The black line indicates the commencement of eyewall formation, which is defined as the first time the averaged value at r < 10 km is less than −20 dBZ and that at 40–60 km is greater than 10 dBZ.

Citation: Monthly Weather Review 150, 12; 10.1175/MWR-D-21-0337.1

Figure 8 clearly shows that all the members except the Lionrock NLW indicate a TC eyewall structure. In the beginning of the simulation, cloud clusters develop near the vortex center in all the members. The simulated TCs subsequently indicate the gradual development of an eyewall structure except in the NLW case. The region with vertical mean reflectivity greater than 20 dBZ of the simulated TCs is mainly inside the radius of 150 km, and the eye size ranges from ∼30 to ∼50 km. According to this evolution feature, we here separate the simulation period into periods before and after the commencement of eyewall formation, which is shown as black lines in Fig. 8.

For the Hagibis simulations (Figs. 8a–e), the NLW experiment indicates the latest commencement time and the weakest eyewall convection among the sensitivity experiments. The NLW experiment also indicates a slower commencement, whereas the other three experiments show similar commencement times. The size of the eye is the largest for the DLW case. In the Lionrock simulations (Figs. 8f–j), the three cases other than NLW and DLW show similar commencement times, whereas the NLW case shows no eyewall formation and the DLW case shows a different commencement time. The decaying phase in the NSW and DLW cases appears to occur earlier than that in the CTL case, and the radius of the eye is larger in the NSW and DLW cases than in the CTL case.

c. Diabatic processes in the sensitivity experiments

1) Latent heating

In the previous subsection, we showed that differences occur in the tracks, intensities, and structural evolutions of Typhoons Hagibis and Lionrock. The different cloud structure is considered to affect the diabatic heating/cooling because of the phase changes of water substances and the shortwave and longwave radiation processes. In this subsection, we examine the effects of these diabatic heating processes on the intensity and structure of the TCs.

Latent heating is one of the most important diabatic heating sources in a TC system. (Smith and Montgomery 2016) The latent heat released near the vortex center will generate a warm core and lower the pressure to generate a radial pressure gradient. The time–height section of latent heating in the TC inner core (radius r ≤ 150–200 km; Rogers et al. 2012) is examined here. In this work, r = 150 km is chosen as the inner core because the clouds with vertical mean reflectivity greater than 20 dBZ are almost entirely inside 150 km (Fig. 8). The spatial mean within the 150-km radius at each height level is examined for each hourly output.

Figures 9a–e show the temporal changes of the vertical structure of the latent heating for the Hagibis simulations. The heating is predominant for all the members, and cooling patterns occur at low levels near the surface, especially before eyewall formation. The low-level cooling is attributed to the vaporization of the precipitation due to convection within the region. The latent heating of DLW is slightly stronger, and that of NLW is the weakest. The differences among the other members are relatively small.

Fig. 9.
Fig. 9.

Time–height sections of latent heating for the (top) Typhoon Hagibis and (bottom) Lionrock simulation members [(a),(f) CTL; (b),(g) NSW; (c),(h) NLW; (d),(i) DLW; and (e),(j) DSW] in the region r < 150 km. The black vertical lines indicate the commencement of eyewall formation.

Citation: Monthly Weather Review 150, 12; 10.1175/MWR-D-21-0337.1

For the Lionrock simulations, the features appear to be similar to those for the Hagibis simulations (Figs. 9f–j). One difference is that the latent cooling in low levels before eyewall formation is more enhanced in the Lionrock case than in the Hagibis case, especially for the Lionrock NLW. The calculated vertically mass-weighted averaged latent heating in the early stage indicates that the net effect in the NLW case is cooling, which suppresses the pressure drop and prevents intensification.

As shown in Fig. 9h, a relatively pronounced cold pool is observed for the Lionrock NLW; this cold pool prohibits the intensification process, resulting in weak development. However, this phenomenon is not substantial for the Hagibis NLW. A cold pool is generally caused by the evaporation of falling precipitation at low levels, which is strongly related to the relative humidity at low levels. We therefore examined the relative humidity. Table 2 shows the temporal mean of relative humidity within a 150-km radius under 3 km from the beginning of the simulation to the commencement of eyewall formation of the CTL for each member of the two typhoon cases. Generally, the values in the Hagibis simulations are greater than those in the Lionrock simulations, and the differences among the Hagibis members are smaller than those among the Lionrock members. For Lionrock NLW, the value is ∼3% less than that of the CTL, indicating that the air at low level is drier than that in the CTL, which is favorable for low-level evaporation and, hence, the generation of the cold pool in Fig. 9h.

Table 2

Temporal mean of relative humidity within a 150-km radius under 3 km from the beginning of simulation to the commencement of eyewall formation of the CTL for each member of (a) Typhoon Hagibis and (b) Typhoon Lionrock (unit: %).

Table 2

2) Radiative heating and cooling

Radiative heating is another diabatic heating source that can affect the intensity of a TC. From the sensitivity members with different radiative heating rates, the vertical structures and the temporal evolution of the radiative heating/cooling are demonstrated. The time–height sections of radiative heating near the center (i.e., the region of r < 150 km) are shown in Fig. 10 for the Hagibis and Lionrock cases. Because the radiative heating is closely related to cloud cover, the cloud hydrometeor mixing ratios are overlaid in Fig. 10. The cloud hydrometeors here include all five liquid and solid water species (i.e., rain, snow, cloud ice, cloud water, and graupel). The procedure used to compute these time–height sections was the same as that used to compute the sections shown in Fig. 10.

Fig. 10.
Fig. 10.

Time–height plot of radiative heating (color shading) and cloud hydrometeors [contoured at 0.1 (black line), 0.5 (orange), and 0.8 (green) g kg−1] for the (top) Typhoon Hagibis and (bottom) Typhoon Lionrock simulation members [(a),(f) CTL; (b),(g) NSW; (c),(h) NLW; (d),(i) DLW; and (e),(j) DSW] in the region r < 150 km. The black lines indicate the commencement of eyewall formation.

Citation: Monthly Weather Review 150, 12; 10.1175/MWR-D-21-0337.1

For the Hagibis simulations (Figs. 10a–e), the strongest radiative heating/cooling generally appears at cloud tops (near the 15-km height, corresponding to the black contours) for all the members. Beneath the cloud top, the radiative heating is not so strong, which can result in a vertical gradient of radiative heating and likely affect the stability of the atmosphere. For the CTL, the cooling and heating patterns appear periodically both in the cloud top and in the area under the cloud top because of the diurnal variations. For the NSW, longwave radiative cooling occurs over the entire simulation period; however, slight longwave radiative heating also occurs near the surface. These results for the CTL and NSW are consistent with the radiative heating distribution features of the normal radiation and night-only simulations of TC reported by Tang and Zhang (2016; their Fig. 11). For the NLW, the shortwave heating patterns change periodically and no cooling occurs at night. Moreover, almost no radiative heating occurs near the surface. For the DLW and DSW, the general features are similar to those for the CTL except for the stronger longwave-cooling (DLW) and shortwave-heating (DSW) rates. Because DLW and DSW experiments involve doubling the radiative heating/cooling rate, the increase of the radiative heating/cooling rate is stronger in the cloud-top layer than below the cloud top.

In the Lionrock simulations (Figs. 10f–j), the radiative heating/cooling rates among the members indicate features similar to those in the Hagibis simulations. For the NLW (Fig. 10h), because it does not have an eyewall structure and the cloud top height is variable, the strongest radiative heating area near the cloud top also fluctuates.

For the convection development, we identified the area of the total cloud hydrometeor mixing ratio of 0.1, 0.5, and 0.8 g kg−1, which is also shown in Fig. 10. In both the Hagibis and Lionrock simulations, the convection in the NLW appears to be the weakest (Fig. 10c). Another feature common to both typhoon cases is that the convection in the DLW and NSW is more vigorous than that in the CTL, whereas the convection in the DSW appears to be comparable with that in the CTL.

In the Lionrock simulations, the convection activities are generally weaker than those in the Hagibis simulations; however, the differences among the members are similar to those among the members in the Hagibis simulations. Noticeably, convection in the NLW (Fig. 10h) is extremely weak, whereas convection is more vigorous in the DLW than in the other members. The convection in the NSW is slightly stronger than that in the CTL, and convection in the DSW develops later than that in the CTL.

d. Radiative effects before and after eyewall formation

Because the structures of Typhoons Hagibis and Lionrock evolved substantially before and after eyewalls formed in their inner cores (Fig. 8), we expected the radiation tendencies to also evolve over the analysis period. Here, we demonstrate the radius and height features of the radiative effects. The radius–height sections of radiative heating/cooling rates and cloud structures before eyewall formation in the CTL are shown in Figs. 11 and 12 for Hagibis and Lionrock, respectively.

Fig. 11.
Fig. 11.

Radius–height sections of temporal mean of (a) radiative heating rates, (b) longwave radiative heating rates, and (c) shortwave radiative heating rates (shading) and cloud hydrometeors mixing ratio (contours) averaged during the time period before the eyewall formation in the CTL for the Typhoon Hagibis case.

Citation: Monthly Weather Review 150, 12; 10.1175/MWR-D-21-0337.1

Fig. 12.
Fig. 12.

As in Fig. 11, but for the Typhoon Lionrock CTL.

Citation: Monthly Weather Review 150, 12; 10.1175/MWR-D-21-0337.1

Figure 11 shows the radiative heating/cooling and cloud structure for the Hagibis CTL before formation of the eyewall. The longwave cooling is the strongest at and around the cloud-top height, and longwave heating is strong near the surface and at the middle levels (Fig. 11b). The shortwave heating is the strongest near the cloud top, whereas it is relatively weak beneath the cloud top (Fig. 11c). The net effect is cooling near the cloud top and heating near the surface and at the middle levels (Fig. 11a). Figure 12 shows the heating profiles of the Lionrock CTL. In general, the radiative heating/cooling patterns within the cloud are similar to those of the Hagibis CTL; however, the cloud cover is much less extensive. Outside the cloud, which is in the clear sky, the net effect is cooling.

From Figs. 11 and 12, the atmosphere at the cloud base near the surface and at melting levels of 4–5-km height is shown to experience relatively stronger radiative heating; by contrast, outside the cloud area, radiative cooling dominates at the same levels. This differential radiative cooling in the radial direction might enhance the radial pressure contrast and, hence, the pressure gradient toward the TC center, thereby increasing low-level convergence below the cloud base. This low-level convergence near the surface can bring more water vapor to the cloud region. Thus, the vertical profiles of radial wind averaged during the time from the beginning of the simulation to the commencement of eyewall formation in the CTL are demonstrated in Fig. 13. The period for the temporal averaging for the sensitivity members is the same; therefore, the differences among the members imply the effects of radiation on radial wind. For both cases, the inflow is mainly below the 3-km height and outflow is mainly in the 10–16-km layer. For the Hagibis simulations, the values among the members are relatively close; however, the inflow in the low levels is weaker in the NLW case than in the others, which is consistent with a lack of differential longwave cooling (Fig. 13a). In Tang and Zhang (2016), the inflows of normal radiation and night-only simulations of TC are similar, which is consistent with the CTL and NSW of the present work, and the inflow of the constant solar radiation simulation is much weaker [Fig. 6 of Tang and Zhang (2016)].

Fig. 13.
Fig. 13.

Temporal mean of radial wind profile within a 200-km radius at each height level from the beginning of the simulation to the commencement of eyewall formation of the CTL for each member of (a) Typhoon Hagibis and (b) Typhoon Lionrock.

Citation: Monthly Weather Review 150, 12; 10.1175/MWR-D-21-0337.1

For the Lionrock simulations (Fig. 13b), the departure among the members is much larger than that in the Hagibis simulations. In the NLW, the inflow near the surface is much weaker than in the other experiments. In addition, because of the low relative humidity at low levels (Table 2), the low-level cold pool is enhanced (Fig. 9h), preventing the intensification process of the simulated TC.

Surface moisture flux is proportional to surface wind speed, and inflow can carry the moisture obtained from the sea surface in the environment to the cloud system. Therefore, a weak inflow can weaken the water vapor transport to the cloud system. Table 3 shows the temporal mean of the vertical flux of moisture from the surface to the air from the beginning of the simulation to the commencement of eyewall formation in the CTL within a 200-km radius. In both the Hagibis and Lionrock simulations, the value of NLW is the smallest among the members, corresponding to the weakest inflow (Fig. 13). The second smallest one is that of DSW, corresponding to the second weakest inflow (Fig. 13). The values of CTL, NSW, and DLW are similar as a result of the close inflow intensity (Fig. 13). The relative humidity is lower in the Lionrock case than in the Hagibis case (Table 2), which is why the cold pool does not occur in the Hagibis NLW case.

Table 3

Temporal mean of vertical flux of moisture from the surface to the air within 200-km radius from the beginning of simulation to the commencement of eyewall formation of the CTL for each member of (a) Typhoon Hagibis and (b) Typhoon Lionrock (units: 10−5 kg m−2 s−1).

Table 3

Figure 14 shows the radiative heating/cooling and cloud structure for the Hagibis CTL after formation of the eyewall. The longwave cooling is strongest at the cloud top of the eyewall cloud in the radius between ∼40 and ∼150 km and becomes weaker at the outer anvil cloud top. In the eye region, clear-sky longwave cooling occurs. Longwave heating occurs mainly near the surface, at middle levels near ∼5-km height, and under the cloud top of the eyewall cloud (Fig. 14b). The shortwave heating is also strongest at the cloud top of the eyewall cloud and is weaker in the outer anvil cloud top. In the eye region, the clear-sky shortwave heating is also substantial. In total, the radiative heating/cooling within clouds is stronger than that before formation of the eyewall (Fig. 14c). The net effect is cooling at the cloud top and within the eye region and heating under the eyewall cloud top, at the middle levels, and near the surface (Fig. 14a). These azimuthally averaged shortwave, longwave, and total radiation tendency patterns are consistent with those of Dunion et al. (2019; their Figs. 6–8). For the Lionrock CTL, the distribution of heating/cooling within the cloud is similar to that in the Hagibis simulations; the only difference is that the anvil cloud cover is much less than in the Hagibis simulations (not shown).

Fig. 14.
Fig. 14.

As in Fig. 12, but averaged during the time period after the eyewall formation in the CTL for the Typhoon Hagibis simulation.

Citation: Monthly Weather Review 150, 12; 10.1175/MWR-D-21-0337.1

The vertical differences in the radiative heating rates and the resultant changes in the temperature profiles should affect the static stability. The evolution of the mean temperature lapse rate averaged within the radius of 150 km between the levels of 3 and 14 km among the members is shown in Fig. 15. Generally, the evolution of the lapse rate shows a decreasing trend, capturing the shift of the whole profile toward a moist-adiabatic lapse rate with the development of convection. For the Hagibis simulations (Fig. 15a), the values of the lapse rates among the members differ after 9 October, when the eyewall had already been established. Thereafter, the DLW member shows the largest lapse rate, followed by that of the NSW, which is due to stronger cloud-top cooling. Other members show similar lapse rates; however, the NLW and DSW cases show slightly lower values as a result of stronger shortwave heating at the cloud top. For the DLW and NSW cases, the lapse rates are more unstable than in the other cases. In the Lionrock simulation (Fig. 15b), the features are similar to those in the Hagibis simulations. The DLW case exhibits an early increase of the lapse rate, and the higher peak in DLW may indeed indicate stronger destabilization due to cloud-top cooling. The lapse rates of the DLW and NSW cases are also higher than that in the CTL case. For the NLW, the value fluctuates because no stable eyewall cloud structure has yet formed and because the cloud top is also not continuously established. The DSW shows the lowest lapse rate. Moreover, in addition to the stability changes caused by the radiation, the differential radiative cooling affects the TCs; therefore, for the NSW and DLW of both cases, the convections are stronger (Fig. 10).

Fig. 15.
Fig. 15.

The evolution of mean temperature lapse rate between 3 and 14 km within the r < 150-km region for the sensitivity members of (a) Typhoon Hagibis and (b) Typhoon Lionrock.

Citation: Monthly Weather Review 150, 12; 10.1175/MWR-D-21-0337.1

In the eye region, because of the lack of convection and latent heating, the radiative heating becomes a predominant diabatic heating source. Thus, the evolution of mean vertically mass-weighted averaged radiative heating in the region within the radius of 30 km among the members is shown in Fig. 16. The radius of 30 km was chosen on the basis of the evolution features shown in Fig. 8. The radius of the eye is mostly larger than 30 km after the eyewall has formed; therefore, the fixed radius of 30 km approximately indicates the eye region. The vertically mass-weighted averaged value is computed by summing the product of the radiative heating/cooling rate and air density at a certain level, integrated over the entire vertical column, and divided by the sum of the air density of the entire vertical column. In the DLW and NSW cases, strong longwave cooling occurs in the eye region and the central pressure drop is reduced because of the cooling. This effect makes the central pressure higher in the DLW and NSW experiments than in the other experiments for both typhoon cases. However, the DSW case indicates stronger warming in the eye region among the members, which is speculated to enhance the central pressure drop (Figs. 2a and 3a). These two contrasting effects determine the TC intensity as a whole.

Fig. 16.
Fig. 16.

The evolution of mean vertically mass-weighted averaged radiative heating in the eye region (r < 30 km) for each member of (a) Typhoon Hagibis and (b) Typhoon Lionrock.

Citation: Monthly Weather Review 150, 12; 10.1175/MWR-D-21-0337.1

4. Discussion

On the basis of the aforementioned results, radiative effects on the evolution of Typhoons Hagibis and Lionrock can be conceptually summarized as in Fig. 17. Before the eyewall forms, the main effect is the differential longwave cooling between the cloud system and the surrounding clear-sky area (Fig. 17a). After the eyewall has formed, the main effects are related to changes in static stability caused by cloud-top radiation, the still-existing differential longwave cooling effect, and the clear-sky radiation in the eye, which can influence the warm core structure (Fig. 17b).

Fig. 17.
Fig. 17.

Conceptual picture of the radiative effects (a) before eyewall formation and (b) after eyewall formation.

Citation: Monthly Weather Review 150, 12; 10.1175/MWR-D-21-0337.1

The Hagibis NLW develops into a strong TC, whereas the Lionrock NLW just becomes a weak TC whose intensity is less than category 1 of the Saffir–Simpson hurricane wind scale. The differential longwave cooling effect does not occur in the both NLW cases; thus, low-level convergence that brings water vapor is diminished. The humidity is high for the Hagibis case (Table 2), which is considered to favor the development of a TC also in the Hagibis NLW. However, the humidity is lower for the Lionrock NLW, which prevents its development into a TC. Therefore, the water vapor amount and the humidity condition in the cloud system are considered to play a role in determining whether the differential cooling effect is important and in distinguishing the TC effects depending on the case.

For both the Hagibis and Lionrock cases, the convection in the NSW and DLW cases is stronger than in the CTL case (Fig. 10) because the cloud-top cooling is stronger in these cases than in the CTL case, which promotes instability, and the differential cooling effect is also stronger. However, in terms of the central pressure, the NSW and DLW are higher than the CTL (Figs. 2a and 3a) because of stronger longwave cooling in the eye region. Similarly, the convection of the DSW for both cases is slightly weaker than that of the CTL (Fig. 10) because of stronger cloud-top warming, which reduces the instability. Meanwhile, the central pressure of the DSW is lower than that of the CTL (Figs. 2a and 3a) because of stronger shortwave heating in the eye region. For the Hagibis NLW, although the shortwave heating in the eye region is stronger than for the CTL, the instability decreases because of stronger cloud-top shortwave heating; the net effect is that the TC in the Hagibis NLW case becomes less intense than the TC in the CTL. Therefore, contrasting influences from the instability affected by cloud-top radiation and the warm core structure affected by the radiation in the eye region will determine the TC intensity, depending on the magnitude of these influences.

5. Summary and conclusions

Because the effects of radiation on TCs remain not fully understood, we investigated this issue further. Full-physics convective-permitting high-resolution simulations were conducted with WRF Model, version 4.1, to examine the radiative effects on the TC development. Sensitivity experiments of two typhoon cases, Lionrock (2016) and Hagibis (2019), with different shortwave/longwave radiative heating/cooling rates were carried out. A comparison of the CTL and satellite observations indicates that the simulation was sufficiently reproducible to be used to examine the sensitivity to radiative processes in the experiments. The comparison of the sensitivity members shows that the TC could be strongly affected by shortwave and/or longwave heating/cooling.

The difference of these sensitivity members is attributable to different diabatic heating. Therefore, we examined the latent heating and found that the latent heating is predominant for almost all of the members, except the Lionrock NLW, which has a cold pool in the lower levels in the early stage. These cold pools are considered to explain why the Lionrock NLW is extremely weak. A cold pool is generally caused by the evaporation of falling precipitation at low levels, which is closely related to the relative humidity condition at the low levels. The analysis of relative humidity shows that the Hagibis case is moister than the Lionrock case and that the relative humidity of the Lionrock NLW is much lower and can be regarded as the reason for the cold pool.

Radiative heating is another important diabatic heating source. In general, the strongest radiative heating/cooling occurs at cloud tops for all of the investigated members. Beneath the cloud top, the radiative heating is not as strong, which can result in a vertical gradient of radiative heating and likely affect the stability of the atmosphere. The intensity of the convection of the members also differs, which is related to the different temperature profile due to different radiative heating.

Because the cloud structures before and after the eyewall formation differ dramatically, the radiative effects are also considered to differ. According to the analysis of radiative heating and radial wind before eyewall formation, the differential cooling between the cloud and clear-sky environments can increase low-level inflow and the relative humidity in the cloud clusters, favoring further TC development. The analysis of the radiative heating distribution after eyewall formation shows that the different radiative heating rates can cause different temperature lapse rates and different differential radiative cooling, both of which influence the convection. Moreover, the radiative heating/cooling in the eye region can affect the central pressure because of hydrostatic adjustment.

As mentioned in the introduction, the radiative effect on TCs is governed by three main mechanisms. In this research, the differential cooling mechanism appears to be important before eyewall formation. Especially when the water vapor in the cloud system is insufficient, the increased low-level inflow carrying water vapor to the cloud system is important for further development of the TC. The large-scale cooling effect appears to not play an important role in this research. We conjecture that this effect could be important when the convection seldom develops, where the large-scale cooling effects can increase the relative humidity, favoring convection development. In this research, convection activity is already present in the initial state; thus, the large-scale cooling effect does not appear to be important. After eyewall formation, the destabilization effect is important; the temperature lapse rate can be changed by radiation, and, hence, influence the convection intensity, although the differential cooling also plays a role. Moreover, the radiative heating/cooling in the eye region can affect the TC warm core structure, which is also important for TC development. This effect is greatly exaggerated in the doubled longwave/shortwave heating rate experiments, in particular.

The simulation results show that the evolution of TCs is sensitive to radiation. By comparing the differences among the sensitivity members, we analyzed how different radiative heating/cooling rates lead to different simulation results. It is noted that the tracks of sensitivity members vary considerably, especially for Lionrock simulations. Thus, the TCs can experience different large-scale environments that make separating out the local effects of radiation on TC and the large-scale environmental influences complicated. These track differences are likely due to the influence of the effective steering layer, which is tied to the storm intensity (Velden and Leslie 1991), and also due to the beta drift effect (Fang and Zhang 2012; Qian et al. 2013). Radiation can affect the convection and hence affect the track. Conversely, the environment around the TC track can affect the TC development, which can be considered a combined effect of the local radiative effect and the large-scale environment. In the early stage, the track differences are relatively small; therefore, the large-scale differences can be considered a trivial effect compared with radiation differences. Thereafter, both radiation and the environment affect TCs. The similar differences among the sensitivity experiments for Hagibis and Lionrock simulations can be regarded as the radiative effects on TCs. The discrepancies between the two simulation groups can be caused by the different TC cases or by the different tracks of the sensitivity members. Simulations without large-scale environment forcings could be a good method to focus on the intrinsic TC response to radiation. The radiative effects on TC are still not fully understood and should be investigated further.

Acknowledgments.

The comments from three anonymous reviewers are greatly acknowledged in improving the original manuscript. This study is supported by the Japan Society for the Promotion of Science (JSPS) Scientific Research Grants 19H00782, 20H00289, and 21H01591 and by the Environment Research and Technology Development Fund (ERTDF) JPMEERF20192005 of the Environmental Restoration and Conservation Agency (ERCA) of Japan. This work is also supported by the MEXT-Program for the advanced studies of climate change projection (SENTAN) Grant JPMXD0722678534.

Data availability statement.

RSMC best track data can be downloaded from Japan Meteorological Agency website (https://www.jma.go.jp/jma/jma-eng/jma-center/rsmc-hp-pub-eg/besttrack.html). Himawari-8/-9 gridded data can be downloaded from the Center for Environmental Remote Sensing (CEReS) website (http://www.cr.chiba-u.jp/databases/GEO/H8_9/FD/index.html). NCEP FNL operational global analysis and forecast data can be downloaded from Research Data Archive website (https://rda.ucar.edu/datasets/ds083.3/). The Weather Research and Forecasting (WRF) Model can be downloaded from WFR USER PAGE website (https://www2.mmm.ucar.edu/wrf/users/download/get_source.html).

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    • Search Google Scholar
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    • Search Google Scholar
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