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Article

How Hydrometeors Varied with the Secondary Circulation During the Rapid Intensification of Typhoon Nangka (2015)

by
Lin Wang
†,
Hong Huang
†,
Ju Wang
*,
Xinjie Ouyang
,
Xiaolin Ma
and
Zhen Wang
College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Atmosphere 2025, 16(10), 1142; https://doi.org/10.3390/atmos16101142
Submission received: 1 September 2025 / Revised: 20 September 2025 / Accepted: 27 September 2025 / Published: 28 September 2025
(This article belongs to the Special Issue Typhoon/Hurricane Dynamics and Prediction (2nd Edition))
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Abstract

A comprehensive understanding of the evolution and phase transitions of hydrometeors during the development of tropical cyclones (TCs) is essential for advancing research on the mechanisms of TC intensity change. In this study, utilizing the Weather Research and Forecasting numerical model, we simulate the evolution of Super Typhoon Nangka (No. 1511), explore the relationship between the TC intensity variations and the internal hydrometeor distribution, and examine the secondary circulation characteristics. The results indicate that the total content of hydrometeor particles increased during the intensification of Typhoon Nangka. Ice-phase particles expanded outward radially as the typhoon intensified, while liquid-phase particles contracted inward. Ice-phase hydrometeor distributions varied in conjunction with TC intensity variations, whereas liquid-phase hydrometeor variations were closely related to the complex dynamic–thermodynamic–microphysical processes within the typhoon. The spatial pattern of the secondary circulation exhibits high consistency with the distribution of hydrometeor particles. Low-level radial inflow, upper-level radial outflow, and middle-level vertical updrafts played dominant roles in regulating the distribution and transport of particles at different stages. The intensification of Typhoon Nangka was primarily driven by water vapor convergence and the latent heat released by ascending liquid-phase particles near the eyewall, while the stagnation of its intensification was mainly attributed to the resistance exerted by descending ice-phase particles from upper levels and the heat consumption associated with their melting. These findings provide a foundation for better understanding how hydrometeors modulate TC intensity variations and offer valuable insights into energy conversion mechanisms during hydrometeor phase transitions under the influence of secondary circulations.

1. Introduction

Tropical cyclones (TCs) are deep low-pressure systems that originate over warm subtropical or tropical ocean surfaces. They are highly destructive and among the most threatening catastrophic weather systems. During typhoon landfall, extreme weather such as gale-force winds, hailstorms, and torrential rainfall may occur, potentially triggering secondary disasters such as floods, landslides, and debris flows [1]. These hazards not only cause massive economic losses but also endanger human safety. Accurate prediction of the TCs is therefore an important issue in meteorological research. Although significant progress has been made in the forecasting of TC tracks, with relatively high accuracy now achievable, difficulties remain in anomalous track prediction and in extending forecasts to longer timescales [2]. Compared with track forecasts, improvements in TC intensity prediction have been limited. The 24 h forecast errors of TC intensity currently remain in the range of 3.5–4 m s−1 [3,4,5,6], and in some events, they may even exceed 5 m s−1 [7]. The abrupt changes in TC intensity—especially rapid intensification—are of special concern, as they often result in unexpected disasters, making them a key focus and challenge in operational forecasts [8,9,10].
The factors affecting TC intensity vary across multiple temporal and spatial scales, encompassing large-scale background fields and internal structural dynamics such as atmospheric environmental factors, ocean forcing mechanisms, inner-core structural changes, and phase transitions of hydrometeors [11,12]. It is notable that in addition to sea surface temperature, the latent heat released during hydrometeor phase transitions in TC convective systems is the primary source of diabatic energy for cyclone intensification. Changes in precipitation formation processes can alter both the intensity and spatial distribution of rainfall, thereby leading to a reconfiguration of latent heat distribution, which in turn affects TC intensity [13]. The close link between latent heat release and subsequent precipitation provides a physical basis for diagnosing TC intensity and its evolution [14,15,16]. The heating or cooling effects accompanying hydrometeor phase transitions drive energy conversion through specific adjustment processes, ultimately altering the symmetry and intensity of the TCs [17,18,19]. As early as 1985, Willoughby et al. [20], based on the results of the STORMFURY hurricane weakening experiment, argued that the latent heat of condensation released by hydrometeor phase transitions can provide substantial energy for TC development. Subsequently, Gamache et al. [21] and Zhang [22] also confirmed that cloud microphysical processes and the associated latent heat release are crucial in determining TC evolution. In recent years, advances in unconventional observation systems and high-resolution numerical models, especially cloud-resolving models, have enabled detailed analyses of the three-dimensional spatial distribution and evolution characteristics of hydrometeors in the TCs [23].
The phase transition processes of particles such as rain, snow, and hail play a key role in the formation of spiral rain bands and descending airflows [24]. Reductions in the size of graupel particles and their collision coefficients with cloud ice particles favor broader stratiform precipitation [25,26]. Yang [27], using numerical simulation results, diagnosed cloud architectures and precipitation formation mechanisms of TC spiral rainbands. They found that the dominant microphysical mechanism for rainwater production in spiral rainbands is the melting of graupel particles, and the condensation process of cloud water in spiral rainbands exhibits a bimodal pattern. Shi et al. [28] discussed the vertical distribution of cloud microphysical properties in the TC eyewall and surrounding spiral cloud bands. The results indicated that cloud ice primarily exists at altitudes above 5 km, particle effective radius decreases with increasing cloud altitude, particle number concentrations exhibit increasing trends with cloud altitude, and cloud ice content initially increases then decreases with altitude. Yao et al. [29] investigated 236 TC cases using the Tropical Rainfall Measuring Mission Microwave Imager retrievals, and they found that as a TC intensifies, the content of various hydrometeor particles increases, and they gradually move toward the TC center. At the peak of TC intensity, the large-value areas of the number concentrations of hydrometeor particles are concentrated within a 50 km radius centered on TC core. When a TC weakens, the evolution of hydrometeor particles is opposite to that when it strengthens. Pang et al. [30] conducted numerical simulations for Typhoon Ambi (2018) and concluded that among ice-phase hydrometeors, graupel particles were distributed at the lowest levels, cloud ice crystals at upper levels, snow particles in between, with interactions through riming, aggregation, deposition, and accretion. Also, through numerical simulations, Pang et al. [31] pointed out that in a TC, the content of solid hydrometeors exceeds that of liquid hydrometeors, and the distribution height of solid hydrometeors simulated in the Weather Research and Forecasting (WRF) Single-Moment (WSM) 7-Class Microphysics scheme can reach 4 km below.
As for the relationship between hydrometeors and TC intensity variations, Li et al. [32] simulated the rapid intensification process of Typhoon Meranti (2016) and determined that convective bursts and associated cloud microphysical processes considerably contributed to its rapid intensification. Harnos et al. [33] identified in their simulations of Typhoon Ike (2008) and Hurricane Earl (2010) that most of diabatic heating in the radius of maximum wind speed (RMW) occurs at temperatures below the freezing point. This indicates the significance of ice-phase processes in the rapid intensification process of the TCs. Based on numerical simulation, Zhao et al. [34] found that the marked increase in graupel and snow particles in the TC core area coincided with the rapid intensification phase of the TCs. Lai et al. [35] confirmed, through numerical simulation experiments with different microphysics schemes, that cloud microphysics schemes incorporating ice-phase processes exhibit noticeably higher sensitivity to TC intensity variations compared with warm-rain-only schemes [36,37]. These results suggest that ice-phase particle variations can serve as an indicator for the rapid intensification process of the TCs. However, the influence of low-level liquid-phase particles is also important. Heymsfield et al. [37] pointed out that warm-rain processes are the primary microphysical processes responsible for rainfall formation in TC systems. Deng et al. [38] indicated that at the late mature stage of Typhoon Usagi (2013), rainwater in the core area increased markedly as the TC structure became more symmetric. This was related to the intensification of processes such as horizontal convergence, ascending motion, and condensation of cloud water. Through sensitivity experiments, Cai et al. [39] found that rainwater evaporation exerts dual effects—evaporative cooling (intensifying TC intensity) and moistening (weakening TC intensity), which largely offset each other in the TC inner-core region. In simulations of Typhoon Doksuri (2023), Vu et al. [40] showed that latent heat fluxes increased steadily during the TC rapid intensification, and the TC structure became more symmetric. This result indicated that latent heat was effectively released in the TC core region under the influence of strong ascending motion. These energy sources can further intensify the horizontal circulations of the TCs and rapidly strengthen TC vortices. Furthermore, through comparing the differences in simulating Typhoon Doksuri between the NSSL2 and WDM6 parameterization schemes, they also found that the ascending motion simulated in the NSSL2 scheme was stronger than that in the WDM6 scheme. Thus, more hydrometeor particles were transported upward in the NSSL2 scheme, resulting in a higher content of ice-phase particles in the upper atmosphere, more prominent ice-phase processes, and stronger simulated typhoon intensity. Therefore, more accurately simulating ice-phase hydrometeors is crucial for investigating the development of vortices at their rapid intensification stages. Pang et al. [31] compared the simulations with different cloud microphysical schemes for Typhoon Mujigae (2015) and found that all types of hydrometeors increased over time at the TC rapid intensification stage, whereas the simulated typhoon was stronger in schemes with higher total latent heat release rates. Overall, the heating or cooling effects associated with the phase transition of hydrometeors within the TCs can result in energy conversion through one or multiple physical processes, eventually altering TC symmetry and intensity [19].
Although previous studies have provided insights into the distribution characteristics of hydrometeors during TC intensification, there is limited research on how secondary circulations in different regions govern the directional motion of hydrometeor particles in specific areas, and how phase changes induced by the vertical transport of hydrometeor particles at the middle level influence typhoon intensification and weakening. Moreover, there are also relatively few comparative analyses between the weakening and intensifying stages of the same typhoon. Therefore, this study selects Typhoon Nangka (No. 1511). Super Typhoon Nangka (2015) formed and developed primarily over the Western Pacific, and briefly made landfall in Japan twice during its lifecycle. Since the typhoon remained mostly in offshore areas during its main development and mature stages, its cloud system and precipitation characteristics were not affected by topographic interference. This oceanic isolation ensured that the distribution characteristics of hydrometeors did not undergo disturbances due to land interactions. However, limited by the resolution of available observational data, it is challenging to examine the variabilities of hydrometeors and their relationships with typhoon intensity evolution at fine spatio-temporal scales. For this reason, we conduct high-resolution numerical simulations using the Weather Research and Forecasting (WRF) model.
The remainder of this paper is organized as follows. Section 2 describes the data and model configurations employed in this research, introduces Typhoon Nangka, and compares the simulated results with the observations. Section 3 discusses the numerical simulation results of hydrometeor distributions during the development stage of Typhoon Nangka and the azimuthally averaged vertical secondary circulation. The relationships of the hydrometeor distributions and secondary circulation development with the TC intensity evolution are analyzed theoretically. Section 4 summarizes the main conclusions and provides a conceptual diagram of hydrometeor and secondary circulation evolution during the development of Typhoon Nangka. Section 5 offers perspectives for future research.

2. Simulation Setup and Case Description

2.1. Model Configuration

The numerical model used in this study is the WRF version 4.0. The National Centers for Environmental Prediction Final Analysis data (https://gdex.ucar.edu/datasets/, accessed on 20 June 2025) are used as the initial field of the model. A two-way fixed nested grid is adopted, and the numerical simulations started at 1200 BJT (Beijing Time, the same hereafter) on 5 July 2015 and ended at 1200 BJT on 10 July 2015, covering a total of 120 h, which includes the spin-up/down time and the research period. The model domain is illustrated in Figure 1. The outer domain (d01) is the region of 130° E–175° W, 15° S–40° N, with a grid number of 609 × 609 and a grid resolution of 9 km. The inner domain (d02) covers the region of 137.4° E–170.4° E, 4.5° S–27.5° N, with a grid number of 1063 × 1063 and a grid resolution of 3 km.
The selection of cloud microphysical schemes has the most pronounced impact on simulated results [41]. Considering the different types of hydrometeors contained in different cloud microphysical schemes, we select the WSM 6-Class Microphysics (WSM6) scheme in this study. Compared with the five hydrometeor species in the WSM 5-Class Microphysics scheme (snow, cloud ice, cloud water, rainwater, and water vapor), the WSM6 scheme additionally incorporates graupel particles and a series of graupel-related physical processes [42]. With a broader range of hydrometeor species, this scheme provides a more comprehensive representation and is well suited for examining the evolution characteristics of hydrometeors in this study. In addition, the Grell–Devenyi ensemble scheme is employed for cumulus parameterization (only for d01), the Rapid Radiative Transfer Model scheme for longwave radiation, the Goddard shortwave radiation scheme for shortwave radiation, the YSU scheme for boundary layer, the Garratt surface layer scheme for surface layer, and the thermal diffusion scheme for land surface processes. The reason for turning off the cumulus convection parameterization scheme in d02 is that at a resolution of 3 km, the cloud microphysics scheme can already explicitly represent the conversion among water substances, and there is no need for the estimation of the cumulus convection parameterization scheme. In the simulations, we also consider the impacts of clouds and snow cover but exclude urban surface physical processes. The simulated TC track and intensity are shown in Figure 2.

2.2. Overview of Typhoon Nangka (2015)

Typhoon Nangka formed over the northwestern Pacific Ocean at 0200 BJT on 4 July 2015 and subsequently moved northwestward. Around 1400 BJT on 7 July, the minimum sea level pressure at the typhoon center dropped to 930 hPa (a severe typhoon), and Typhoon Nangka slightly weakened subsequently. At 2000 BJT on 9 July, Typhoon Nangka underwent structural reorganization and reached its peak intensity during the lifecycle as the atmospheric environment became more favorable. At this time, the minimum sea level pressure at the typhoon center reached the minimum of 915 hPa during the development process, and the maximum wind speed near the center increased to 62 m s−1. Subsequently, influenced by dry air intrusion and other factors, Typhoon Nangka weakened substantially. From the night of 12 July to the afternoon of 14 July, an eyewall replacement cycle occurred, accompanied by a sharp northward turn of approximately 90° near 19° N, 138° E. Figure 2a demonstrates that the simulated typhoon track is generally consistent with the observations, although the simulated moving speed is slightly slower. Furthermore, the model successfully captures the four characteristic stages of typhoon intensity evolution: the rapid intensification stage from 1200 BJT on the 6th to 1200 BJT on the 7th, the intensification stagnation stage from 1200 BJT on the 7th to 1200 BJT on the 8th, the re-intensification stage from 1200 BJT on the 8th to 1200 BJT on the 9th, and the weakening stage after 1200 BJT on the 9th, although the simulated values are weaker than the observed values, and the peak intensity occurs earlier (Figure 2b).

3. Distribution Characteristics of Hydrometeors Along with the Evolution of the Secondary Circulation

To comprehensively and continuously investigate the distribution characteristics and variations in hydrometeors during the lifecycle of Typhoon Nangka, four representative stages with the according characteristic time are selected: the rapid intensification stage (0600 BJT on the 6th, 1200 BJT on the 7th), the intensification stagnation stage (0600 BJT on the 8th), the re-intensification stage (0600 BJT on the 9th), and the weakening stage (1800 BJT on the 9th, 1000 BJT on the 10th). A detailed study is carried out on the circulation pattern and hydrometeor distributions of Typhoon Nangka during these four consecutive stages.
The infrared cloud images [45] (11.2 μm, blackbody brightness temperature) from the Himawari-8 satellite during the study period (Figure 3) illustrate the evolution of the cloud system and spiral structure during the formation and development of the typhoon. At 0600 BJT on the 7th (Figure 3a), the typhoon center was located at 13.6° N, 154.5° E, with a distinct eyewall structure. There were three outer rainbands surrounding the periphery of the typhoon. By 0600 BJT on the 8th (Figure 3b), the coverage of Typhoon Nangka had expanded markedly. By 0600 BJT on the 9th (Figure 3c), the typhoon eye became smaller and circular, which is one of the key indicators of TC intense development. Meanwhile, the surrounding cloud system became thick and more compact. Except for a spiral rainband located in the fourth quadrant of the typhoon, the overall area of the typhoon was nearly circular. By 0600 BJT on the 10th (Figure 3d), the spiral structure of Typhoon Nangka became more distinct, with several deep spiral cloud bands surrounding the typhoon core. The intensity of Typhoon Nangka weakened slightly compared with the previous time step. Subsequently, the intensity of Typhoon Nangka weakened rapidly, and the typhoon eye began to be filled and disappear.
Based on model outputs of wind speed and hydrometeor mixing ratios, the radial vertical distributions of five hydrometeor species (cloud ice, cloud snow, graupel, cloud water, and rainwater) are analyzed at six characteristic times. Among them, cloud ice, cloud snow, and graupel are ice-phase particles, while cloud water and rainwater are liquid-phase particles. The specific details are presented in Table 1. In order to clearly describe the effect of vertical motion on liquid-phase particles, the vertical velocity is amplified fivefold, and the azimuthally averaged secondary circulation is superimposed.
As shown in Figure 4, cloud ice is widely distributed above 8 km altitude. At the rapid intensification stage of the typhoon, two pronounced large-value centers of cloud ice appear at upper levels: one primary large-value center and one secondary center (Figure 4a), with the primary large-value center displaying a tilted structure. With the strengthening of the typhoon, the cloud ice mixing ratio increases, with values in the secondary large-value region reaching up to 1.5 × 10−4 kg kg−1. Both centers expand outward spatially (Figure 4b). At the intensification stagnation stage, the cloud ice mixing ratio decreases markedly, and the large-value areas contract slightly inward (Figure 4c). At the re-intensification stage, the cloud ice mixing ratio increases markedly, and the two large-value areas merge into one, which expand outward to 150 km (Figure 4d). At the weakening stage, the cloud ice mixing ratio decreases sharply, with overall values dropping below 1.0 × 10−4 kg kg−1. The distribution contracts inward, and the secondary center disappears. Meanwhile, some cloud ice particles appear within a 50 km radius of the eyewall region (Figure 4f).
As an ice-phase particle type, the distribution and evolution of cloud snow are similar to those of cloud ice. As presented in Figure 5, the distribution height of cloud snow particles is approximately 3 km lower than that of cloud ice particles, but their radial distribution at the same distance from the typhoon center is largely consistent with cloud ice. During the rapid intensification stage, cloud snow also exhibits one primary large-value center and one secondary large-value center (Figure 5a). With the strengthening of the typhoon, cloud snow particles expand outward, with the maximum mixing ratio reaching 1.2 × 10−3 kg kg−1 (Figure 5b). From then until the weakening stage, the cloud snow mixing ratio exhibits a variation pattern of decrease–increase–decrease, while its spatial extent shows a contraction–expansion–contraction pattern. Both trends are consistent with those of cloud ice.
Figure 6 shows the distribution of graupel particles. The results indicate that graupel particles are primarily distributed within 5–8 km altitude, concentrated 50–75 km away from the typhoon center. The graupel mixing ratio increases at the intensification stage of the typhoon and decreases with the weakening of Typhoon Nangka, demonstrating a clear response to the TC intensity variation. The evolution of the spatial distribution of graupel particles exhibits similar characteristics to the other ice-phase particles, i.e., outward expansion during intensification and inward contraction during weakening. However, the extent of the expansion and contraction is weaker than that of cloud snow and cloud ice particles; even at the peak intensity of Typhoon Nangka, the distribution of graupel particles remained confined within ~75 km from the typhoon center (Figure 6d). The maximum mixing ratio of graupel particles during the entire study period is approximately 1.5 × 10−3 kg kg−1 (Figure 6d), and the overall mixing ratio of graupel particles is slightly less than the sum of the mixing ratios of cloud snow and cloud ice particles.
As shown in Figure 7, the vertical distribution of cloud water particles markedly differs from the other three ice-phase hydrometeor types. Throughout the study period, cloud water is widely distributed in lower levels (1–5 km), with a small amount in the typhoon eye region. The vertical distribution displays an outward tilt from lower to upper levels, similar to the distribution of cloud ice particles. At the rapid intensification stage, the cloud water mixing ratio increases markedly. Low-level (<3 km) cloud water particles move inward, while upper-level (>5 km) particles remain in their positions. The primary moisture center migrates toward the eye region of Typhoon Nangka. During the intensification stagnation process, despite the weakening of the typhoon intensity, the cloud water mixing ratio continues to increase, with the entire moisture field contracting further inward. At the re-intensification stage, although the typhoon reaches its peak intensity, the cloud water mixing ratio exhibits a decreasing trend, a pattern that is remarkably opposite to the evolution of the storm intensity. This trend differs markedly from the TC intensity-dependent variations in the three ice-phase particle types (Figure 7d). At the weakening stage, although the cloud water mixing ratio continues to decrease, its distribution does not exhibit an evident outward-moving trend (Figure 7e). This phenomenon may be associated with the weakening of low-level inflows.
The variation characteristics of rainwater are similar to those of cloud water. As illustrated in Figure 8, the rainwater mixing ratio increases sharply, and the particles converge toward the eye region of Typhoon Nangka at the rapid intensification stage. At 1200 BJT on the 7th, two parallel large-value centers of the rainwater mixing ratio are observed, respectively, at radial distances of 50 km and 75 km from the typhoon center (Figure 8b). At the intensification stagnation stage, the two large-value centers merge into a single core, while the rainwater mixing ratio continues to increase (Figure 8c). During the re-intensification and subsequent weakening stages, the rainwater mixing ratio decreases markedly, and the distribution of low-level rainwater gradually expands outward (Figure 8d,e). The variation characteristics of rainwater at these two stages are consistent with those of cloud water. These findings demonstrate that the variation trends of these two liquid-phase particles with the typhoon intensity is not “simultaneous increase and decrease”, which is the variation pattern of ice-phase particles. Instead, there is a lag of approximately 12 h: liquid-phase hydrometeors respond to variations in typhoon intensity about 12 h later. This “lagged response” is what we observe from Figure 7 and Figure 8, and specific reasons will be explained in the final paragraph of this section.
Above 1 km altitude, the distribution of the secondary circulation can be divided into five distinct components: low-level radial inflow, low-level tilting updrafts, mid-level vertical updrafts, upper-level tilting updrafts, and upper-level radial outflow. The two ascending branches tilted outward relative to the typhoon core, and the upper-level radial outflow is stronger than the low-level radial inflow. With the strengthening of the typhoon, the large-value areas of low-level radial inflow, low-level tilting updrafts, and vertical updrafts contract toward the center of Typhoon Nangka, while the upper-level tilting updrafts and upper-level radial outflow expand outward. The opposite situation appears during the typhoon weakening period. This evolution corresponds closely with the variations in both ice and liquid particles with the typhoon intensity. The intensities of the inflow, vertical updrafts, and outflow all increase and decrease in tandem with the variations in the typhoon intensity, without exhibiting a lagged response.
The distributions of various hydrometeor particles are strongly correlated with the distribution of the secondary circulation. Specifically, the secondary circulation flows radially inward at altitudes of 1–2 km (low levels) and begins to tilt upward and outward at ~30 km from the typhoon center. The distribution of the tilting updrafts is consistent with that of cloud water, and the regions with the strongest tilting updrafts are also the large-value areas of the cloud water mixing ratio. Rainwater is distributed outside the regions with the low-level tilting updrafts, and the distance from its main large-value area to the typhoon center is comparable to that of the mid-level vertical updrafts. The tilting updrafts of low-level secondary circulation turn to fully vertical updrafts at altitudes of 4 km, and the large-value areas of vertical drafts coincide with those of the graupel particle mixing ratio. At an altitude of 9 km, the secondary circulation again exhibits tilting updrafts toward the outside of Typhoon Nangka, mainly distributed in 9–13 km altitudes and 50–90 km from the typhoon center. The lower part of the tilting ascending-motion region coincides with the large-value area of the cloud snow mixing ratio, while the upper part is consistent with the large-value area of the cloud ice mixing ratio. The upper-level tilting updrafts finally transition into a radial outflow around 13 km altitude, and the distribution of the radial outflow is consistent with that of cloud ice at upper levels. Additionally, the radius of maximum wind speed (RMW) decreases during the intensification of the typhoon and increases during its weakening. The large-value areas of the three ice-phase particles are located inside the RMW area at the early stage of rapid intensification. With the strengthening of the typhoon to its peak strength, these regions shift out of the RMW area. In contrast, throughout the entire study period, rainwater particles are primarily distributed outside the RMW area, while cloud water particles remain mainly inside the RMW area. The outward tilting angle of the cloud water particle distribution is approximately comparable to the tilting angle of the low-level RMW area.
The distribution characteristics of the secondary circulation can explain the variations in different hydrometeor particles with the typhoon intensity. Due to the intensification of the typhoon, both the upper-level outflow and low-level inflow strengthen. Under the influence of these radial flows, cloud ice and cloud snow particles at upper levels move outward, and cloud water and rainwater particles at low levels shift inward. Since cloud snow particles are primarily distributed within the tilting ascending-motion region, the outward expansion of cloud snow distribution is less pronounced than that of cloud ice distribution. Moreover, during the intensification process of Typhoon Nangka, the large-value area of the cloud snow mixing ratio slightly uplifts by approximately 0.5 km. Both the graupel area and the upper portion of the cloud water area are primarily located within the vertical ascending-motion region. Consequently, the hydrometeor particles in these two regions exhibit no distinct horizontal movement as the typhoon intensity varies, and even their positions remain essentially unchanged.
Due to the radial outflow and inflow, cloud ice and rainwater exhibit obvious radial movement as the typhoon intensity varies. Furthermore, since the intensity of the upper-level radial outflow is greater than that of the low-level radial inflow, the movement of cloud ice particles is more pronounced than that of rainwater particles.
To elucidate the physical mechanisms of the observed lagged response of hydrometeors to the variation in the typhoon intensity, we examine the relationship between the total-column liquid-phase hydrometeor mixing ratio and the subsequent anomaly of total-column latent heat flux during the development of Typhoon Nangka (Figure 9). The analysis reveals that when a 12 h time lag is applied, the two fields exhibit a strong correspondence. As demonstrated in Figure 9, the distribution of the total-column liquid-phase hydrometeor mixing ratio shows similar characteristics to that of the anomaly of total-column latent heat flux 12 h later. At the intensification stagnation and re-intensification stages, the large-value areas of these two parameters both appear within the radius of 50–75 km from the typhoon core. This indicates that during the intensification of the typhoon, enhanced vertical updrafts in the eyewall region promote the upward transport of liquid-phase hydrometeors to the middle and upper levels, resulting in a decrease in the liquid-phase hydrometeor mixing ratio in the lower atmosphere. This explains the decreases in liquid-phase hydrometeors observed in Figure 7d and Figure 8d. Subsequently, the liquid-phase particles undergo phase transitions at the middle and upper levels at a 50–75 km distance away from Typhoon Nangka, which releases latent heat of condensation to further enhance the concentration of ice-phase particles at upper levels. This process requires approximately 12 h. As the ice-phase particles increase, they also expand outward from Typhoon Nangka, with their high-concentration regions shifting to a distance of around 100 km. This high-concentration region is controlled by the radial outflow, where vertical updrafts are notably weaker than before the outward expansion of ice-phase particles, resulting in reduced uplifting. Furthermore, once the mixing ratios of ice-phase particles at upper levels reach a certain level, they settle under the influence of gravity, resulting in a drag effect.
Subsequently, ice-phase particles undergo phase transitions at middle levels, absorbing latent heat and promoting the weakening of Typhoon Nangka, while simultaneously increasing liquid-phase particle mixing ratios at lower levels. This explains why, as shown in Figure 7c,e and Figure 8c,e, liquid-phase hydrometeor mixing ratios are greater during periods when the typhoon is weaker. Thus, the evolution of liquid-phase particles can be characterized as a complex process involving lifting, condensation, outward expansion, accumulation, subsidence, melting, and re-subsidence. Moreover, the variation in the typhoon intensity resulted from a coupled dynamic–thermodynamic–microphysical process, and the substantial changes in typhoon intensity were primarily controlled by the large-scale circulation background, while the oscillatory changes in typhoon intensity observed at the intensification stagnation and re-intensification stages were closely linked to the evolution of hydrometeors.

4. Discussion

Based on the WRF model, this study conducts numerical simulations to investigate the distribution characteristics of different hydrometeor particles and the relationships of the typhoon intensity with hydrometeor distribution characteristics and the secondary circulation evolution during the development of Typhoon Nangka. At different development stages of Typhoon Nangka, the relationships among the TC intensity, the secondary circulation, and hydrometeor distributions are illustrated in Figure 10. The main conclusions are as follows.
Radial distribution: Upper-level ice-phase particles expand outward with the intensification of Typhoon Nangka and contract inward with its weakening. Graupel particles vary with the typhoon intensity in a pattern largely consistent with ice-phase particles, but their horizontal movement range is quite small. The movement of liquid-phase particles with the intensity variations in Typhoon Nangka is opposite to that of ice-phase particles, and the upper portion of the large-value area of the cloud water mixing ratio remains unchanged. From the typhoon eye outward, the correspondence between hydrometeor types and different cloud systems is generally consistent with the findings of Ma et al. [45].
Particle mixing ratio variations: During the simulation period, the ice-phase particle mixing ratios increase with the intensification of Typhoon Nangka and decrease with its weakening. The variations in liquid-phase particle mixing ratios are relatively complex, involving an adjustment process of lifting, condensation, outward expansion, accumulation, subsidence, melting, and re-subsidence. This is closely related to the coupled dynamic–thermodynamic–microphysical process within Typhoon Nangka.
Relation to the RMW: With the intensification of Typhoon Nangka, the large-value areas of the mixing ratios of the three ice-phase particle types shift from the inside to the outside of the RMW. The large-value region of the cloud water mixing ratio remains inside the RMW, while that of the rainwater mixing ratio persists outside.
Relationship between hydrometeors and the secondary circulation: The distribution and variations in the secondary circulation of Typhoon Nangka are in good correspondence with those of hydrometeors. Specifically, cloud ice, graupel, rainwater, snow, and cloud water correspond to the upper-level radial outflow, vertical updrafts, low-level radial inflow, upper-level tilting updrafts, and low-level tilting updrafts, respectively. The variation in the secondary circulation distribution in different regions is consistent with the corresponding hydrometeor types. The direction of circulation vectors within the hydrometeor regions governs the radial movement of hydrometeors with the variation in the typhoon intensity.
Through a numerical simulation study of Typhoon Nangka, this research elucidates the interrelationships among typhoon intensity, hydrometeors, and secondary circulation. The findings further validate the studies conducted by researchers such as Yao et al. [29], Zhao et al. [34], and Deng et al. [39]. However, it should be noted that the selected research target, Typhoon Nangka, met the following criteria: its primary development occurred over the ocean without land interaction, it reached super typhoon intensity, and it exhibited fluctuating intensification before attaining its peak strength. Therefore, the conclusions of this study are applicable to typhoons with characteristics similar to Nangka. For typhoons that do not meet these conditions, further research is needed to validate the findings presented herein.

5. Summary and Conclusions

This study investigates the distribution characteristics and temporal evolution of hydrometeors and the secondary circulation during the development of Typhoon Nangka. We also explore the specific correspondence between the secondary circulation in different regions and the radial movement of hydrometeor particles. The results indicate that (1) the strong convergence of liquid-phase hydrometeors toward the typhoon center is essential for the rapid intensification of the typhoon. (2) The latent heat released through condensation in vigorous updrafts near the eyewall serves as a crucial diabatic energy source for typhoon intensification. (3) The subsequent settling of ice-phase hydrometeors exerts a drag effect on the primary circulation of Typhoon Nangka. (4) Combined with the latent heat consumption during the melting of ice-phase particles, this process constitutes a key mechanism for the stagnation of the rapid intensification. (5) The cyclic phase transitions between ice-phase and liquid-phase particles are responsible for the oscillatory intensification of Typhoon Nangka.
These findings can improve the forecasting skills for typhoon intensity and rainfall, and lay a foundation for clarifying the specific mechanisms by which hydrometeors modulate TC intensity. In this study, the WRF model demonstrates good performance on the simulations of Typhoon Nangka that was enhanced over the ocean, and several valuable findings are obtained. However, further observational data and more cases are needed to refine these results.
Moreover, the influence of hydrometeor phase transitions on the TC intensity is complex, involving mutual adjustments and adaptation and feedback mechanisms among the dynamic, thermodynamic, and cloud microphysical fields [17,18,19]. The following four topics will all become key topics in our future research: interconversion mechanisms among different hydrometeor types during TC intensification, associated energy exchanges and their effects on typhoon intensity during hydrometeor phase transitions, and the special characteristics of liquid-phase particles (related to moisture transport, convective self-organization and oceanic feedback) as typhoon intensity varies.

Author Contributions

Conceptualization, L.W.; methodology, H.H.; software, X.O.; formal analysis, J.W., X.M. and Z.W.; supervision, J.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work is funded by National Natural Science Foundation of China Project: “Dynamics of Tropical Cyclone Intensity Modulated by Hydrometeors Changes in Spiral Rainbands”, 42075053.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The 11.2 μm band data from the Himawari-8 satellite (http://www.eorc.jaxa.jp/ptree/index.html (accessed on 19 September 2025)). The National Centers for Environmental Prediction Final Analysis data (https://gdex.ucar.edu/datasets/ (accessed on 20 June 2025)). The best track dataset (http://tcdata.typhoon.org.cn/ (accessed on 19 September 2025)) from the China Meteorological Administration (CMA). The simulated data is derived from the raw data. The simulated data in this study are available on request from the corresponding author. The data are not publicly available due to privacy.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
TCTropical Cyclone
CMAChina Meteorological Administration
RMWRadius of Maximum Wind Speed
RIRapid Intensification
NSSL2National Severe Storms Laboratory 2-moment microphysics scheme
WDM6Weather Research and Forecasting Double-Moment 6-class scheme

References

  1. Yue, C.J. Progress in Application Study of Q Vector, Helicity, Potential Vorticity and its Inversion to Torrential Rainfall Associated with Typhoon. Torrential Rain Disaster 2014, 33, 193–201. (In Chinese) [Google Scholar] [CrossRef]
  2. Emanuel, K. 100 Years of Progress in Tropical Cyclone Research. Meteorol. Monogr. 2018, 59, 15.1–15.68. [Google Scholar] [CrossRef]
  3. Dong, L.; Gao, S.Z.; Xu, Y.L.; Lv, X.Y.; Huang, Y.W. Analysis of Characteristics and Forecast Difficulties of TCs on Western North Pacific in 2017. Meteorol. Mon. 2019, 45, 1322–1334. (In Chinese) [Google Scholar] [CrossRef]
  4. Wang, H.P.; Dong, L.; Xu, Y.L.; Nie, G.Z. Analysis of Characteristics and Forecast Difficulties of TCs on Western North Pacific in 2019. Meteorol. Mon. 2021, 47, 1009–1020. (In Chinese) [Google Scholar] [CrossRef]
  5. Lv, X.Y.; Xu, Y.L.; Dong, L.; Gao, S.Z. Analysis of Characteristics and Forecast Difficulties of TCs on Western North Pacific in 2018. Meteorol. Mon. 2021, 47, 359–372. (In Chinese) [Google Scholar] [CrossRef]
  6. Zhou, L.B.; Liu, L.S.; Dong, L.; Wang, Q.; Xu, Y.L. Analysis of Characteristics and Forecast Difficulties of TCs on Western North Pacific in 2020. Meteorol. Mon. 2022, 48, 504–515. (In Chinese) [Google Scholar]
  7. Gao, S.Z.; Dong, L.; Xu, Y.L.; Qian, Q.F. Analysis of Characteristics and Forecast Difficulties of TCs on Western North Pacific in 2016. Meteorol. Mon. 2018, 44, 284–293. (In Chinese) [Google Scholar] [CrossRef]
  8. Yu, H.; Duan, Y.H. A Statistical Analysis on Intensity Change of Tropical Cyclone over Northwestern Pacific. Acta Meteorol. Sin. 2002, 60, 680–687. (In Chinese) [Google Scholar] [CrossRef]
  9. Demaria, M.; Sanpson, C.R.; Knaff, J.A.; Musgrave, K.D. Is Tropical Cyclone Intensity Guidance Improving? Bull. Am. Meteorol. Soc. 2014, 95, 387–398. [Google Scholar] [CrossRef]
  10. Ma, S.H.; Zhang, J.; Shen, X.S.; Wang, Y.Q. The Upgrade of GRAPE_TYM in 2016 and Its Impacts on Tropical Cyclone Prediction. J. Appl. Meteorol. Sci. 2018, 29, 257–269. (In Chinese) [Google Scholar] [CrossRef]
  11. Wang, Z.; Huang, H.; Zhao, J.; Wang, X.Z. Research Progress on the Intensifying Mechanism of Coastal Tropical Cyclones. Meteorol. Mon. 2023, 49, 525–541. (In Chinese) [Google Scholar] [CrossRef]
  12. Chen, S.S.; Knaff, J.A.; Marks, J.; Frank, D. Effects of Vertical Wind Shear and Storm Motion on Tropical Cyclone Rainfall Asymmetries Deduced from TRMM. Mon. Weather Rev. 2006, 134, 3190–3208. [Google Scholar] [CrossRef]
  13. Rosenfeld, D.; Woodley, W.L.; Khain, A.; Cotton, W.R.; Carri, G.; Ginis, I.; Golden, J.H. Aerosol Effects on Microstructure and Intensity of Tropical Cyclones. Bull. Am. Meteorol. 2012, 93, 987–1001. [Google Scholar] [CrossRef]
  14. Nolan, D.S.; Miyamoto, Y.; Wu, S.S.; Soden, B.J. On the Correlation between Total Condensate and Moist Heating in Tropical Cyclones and Applications for Diagnosing Intensity. Mon. Weather Rev. 2019, 147, 3759–3784. [Google Scholar] [CrossRef]
  15. David, A.; Keith, A.B.; Ralph, J.D.; Hugh, J.S. Automatic Digital Radar Reflectivity Analysis of a Tornadic Storm. J. Appl. Meteorol. 1963, 2, 574–581. [Google Scholar] [CrossRef]
  16. Vernon, F.D. Tropical Cyclone Intensity Analysis and Forecasting from Satellite Imagery. Mon. Weather Rev. 1975, 103, 420–430. [Google Scholar] [CrossRef]
  17. James, J.H.; Wayne, H.S. Nonlinear Response of Atmospheric Vortices to Heating by Organized Cumulus Convection. J. Atmos. Sci. 1986, 43, 1559–1573. [Google Scholar] [CrossRef]
  18. David, S.N.; Yumin, M.; Daniel, P.S. Tropical Cyclone Intensification from Asymmetric Convection: Energetics and Efficiency. J. Atmos. Sci. 2007, 64, 3377–3405. [Google Scholar] [CrossRef]
  19. Didlake, A.C.; Reasor, P.D.; Rogers, R.F.; Wen, C.L. Dynamics of the Transition from Spiral Rainbands to a Secondary Eyewall in Hurricane Earl (2010). J. Atmos. Sci. 2018, 75, 2909–2929. [Google Scholar] [CrossRef]
  20. Willoughby, H.E.; Jorgensen, D.P.; Black, R.A.; Rosenthal, S.L. Project STORMFURY: A Scientific Chronicle 1962–1983. Bull. Am. Meteorol. 1985, 66, 505–514. [Google Scholar] [CrossRef]
  21. Gamache, J.F.; Houze, R.A.; Marks, F.D. Dual-aircraft investigation of the inner core of Hurricane Norbert. III. Water budget. J. Atmos. Sci. 1993, 50, 3221–3243. [Google Scholar] [CrossRef]
  22. Zhang, D.L.; Liu, Y.B.; Yau, M.K. A Multiscale Numerical Study of Hurricane Andrew (1992). Part V: Inner-Core Thermodynamics. Mon. Weather Rev. 2002, 130, 2745–2763. [Google Scholar] [CrossRef]
  23. Stephanie, N.S.; Kristen, L.C.; Mark, D.; Jonathan, L.V. A 10-Year Survey of Tropical Cyclone Inner-Core Lightning Bursts and Their Relationship to Intensity Change. Weather Forecast. 2018, 33, 23–26. [Google Scholar] [CrossRef]
  24. Wang, Y.Q. An Explicit Simulation of Tropical Cyclones with a Triply Nested Movable Mesh Primitive Equation Model: TCM3. II. Model Refinements and Sensitivity to Cloud Microphysics Parameterization. Mon. Weather Rev. 2002, 130, 3022–3036. [Google Scholar] [CrossRef]
  25. Franklin, C.N.; Holland, G.J.; May, P.T. Sensitivity of Tropical Cyclone Rainbands to Ice-Phase Microphysics. Mon. Weather Rev. 2005, 133, 2473–2493. [Google Scholar] [CrossRef]
  26. Cheng, R.; Yu, R.C.; Fu, Y.F.; Xu, Y.P. Numerical Research on Intensity Change and Structure Feature of Typhoon Rananim Near Shore. I: Impact of Cloud Microphysical Parameterization on Cloud Structure and Precipitation Features. Acta Meteorol. Sin. 2009, 67, 764–776. (In Chinese) [Google Scholar] [CrossRef]
  27. Yang, W.X. Study on the Cloud Structure and Precipitation Forming Mechanisms for the Spiral Rain Bands of Typhoon. Meteorol. Mon. 2013, 39, 194–202. (In Chinese) [Google Scholar] [CrossRef]
  28. Shi, L.H.; Cui, L.L.; Zhao, B.K.; Yang, Z.F.; Chen, Y.H.; Wang, W.C.; Zhao, Y.P.; Zhan, L.; Zhang, J.W. Vertical Distribution of Cloud Properties for Typhoon Eyewall and Surrounding Spiral Cloud Bands. J. Trop. Meteorol. 2015, 31, 51–62. (In Chinese) [Google Scholar] [CrossRef]
  29. Yao, X.J.; Li, W.B.; Chen, S.M. Research on Latent Heat Distributions in Tropical Cyclones from Hydrometeor TMI Retrieval Data. Chin. J Atmos. Sci. 2014, 38, 909–923. (In Chinese) [Google Scholar] [CrossRef]
  30. Pang, Y.; Zhu, R.; Liu, H.; Wang, S.Y. Comparison of five cloud microphysical schemes on the rainstorm weather induced by Typhoon Ampil. Adv. Mar. Sci. 2023, 41, 64–75. (In Chinese) [Google Scholar] [CrossRef]
  31. Pang, S.M.; Li, J.N.; Guo, T.Y.; Chen, J.F. Effects of the Species Number of Hydrometeors on the Rapid Intensification of Super Typhoon Mujigae (2015). Atmosphere 2024, 15, 1442. [Google Scholar] [CrossRef]
  32. Li, M.X.; Ping, F.; Tang, X.B.; Yang, S. Effects of Microphysical Processes on the Rapid Intensification of Super-Typhoon Meranti. Atmos. Res. 2019, 219, 77–94. [Google Scholar] [CrossRef]
  33. Harnos, D.S.; Nesbitt, S.W. Varied pathways for simulated tropical cyclone rapid intensification. PartI: Precipitation and environment. Q. J. R. Meteorol. Soc. 2016, 142, 1816–1831. [Google Scholar] [CrossRef]
  34. Zhao, D.J.; Yu, Y.B.; Yin, J.F.; Xu, H.X. Effects of Microphysical Latent Heating on the Rapid Intensification of Typhoon Hato (2017). J. Meteorol. Res. 2020, 34, 368–386. [Google Scholar] [CrossRef]
  35. Lai, Y.L.; Li, X.P.; Zhong, D.L.; Fang, X.Q. Numerical Simulations of Typhoon Vamco and Analyses of its Microphysical Structure. J. Guangdong Meteorol. 2024, 46, 26–31. (In Chinese) [Google Scholar] [CrossRef]
  36. Stephen, J.L.; Hugh, E.W.; Jacqueline, M.P. Role of a Parameterized Ice-Phase Microphysics in an Axisymmetric, Nonhydrostatic Tropical Cyclone Model. J. Atmos. Sci. 1984, 41, 2836–2848. [Google Scholar] [CrossRef]
  37. Heymsfield, A.J.; Bansemer, A.; Durden, S.L.; Herman, R.L.; Bui, T.P. Ice Microphysics Observations in Hurricane Humberto: Comparison with Non-Hurricane-Generated Ice Cloud Layers. J. Atmos. Sci. 2006, 63, 288–308. [Google Scholar] [CrossRef]
  38. Deng, L.; Gao, W.H.; Duan, Y.H.; Wang, Y.Q. Microphysical Properties of Rainwater in Typhoon Usagi (2013): A Numerical Modeling Study. Adv. Atmos. Sci. 2019, 36, 510–526. [Google Scholar] [CrossRef]
  39. Cai, Q.C. Effects of Rainwater Evaporation in Tropical Cyclone Inner Core on Its Intensity and Structure. Master’s Dissertation, Nanjing University, Nanjing, China, 2020. [Google Scholar] [CrossRef]
  40. Vu, D.-H.; Huang, C.-Y.; Nguyen, T.-C. Numerical Investigation of Track and Intensity Evolution of Typhoon Doksuri (2023). Atmosphere 2024, 15, 1105. [Google Scholar] [CrossRef]
  41. Zhu, G.L.; Lin, W.T.; Cao, Y.H. Numerical Simulation of a Rainstorm Event over South China by Using Various Cloud Microphysics Parameterization Schemes in WRF Model and Its Performance Analysis. Chin. J. Atmos. Sci. 2014, 38, 513–523. (In Chinese) [Google Scholar] [CrossRef]
  42. Hong, S.-Y.; Dudhia, J.; Chen, S.-H. A Revised Approach to Ice Microphysical Processes for the Bulk Parameterization of Clouds and Precipitation. Mon. Weather Rev. 2004, 132, 103–120. [Google Scholar] [CrossRef]
  43. Ying, M.; Zhang, W.; Yu, H.; Lu, X.Q.; Feng, J.X.; Fan, Y.X.; Zhu, Y.T.; Chen, D.Q. An Overview of the China Meteorological Administration Tropical Cyclone Database. J. Atmos. Ocean. Technol. 2014, 31, 287–301. [Google Scholar] [CrossRef]
  44. Lu, X.Q.; Yu, H.; Ying, M.; Zhao, B.K.; Zhang, S.; Lin, L.M.; Bai, L.N.; Wan, R.J. Western North Pacific Tropical Cyclone Database Created by the China Meteorological Administration. Adv. Atmos. Sci. 2021, 38, 690–699. [Google Scholar] [CrossRef]
  45. Ma, X.L.; Wang, J.; Huang, H.; Wang, X.Z.; Wang, Z.; Hu, B.H. Comprehensive Analysis of Typhoon Nangka Based on the Satellite Data from the GPM, CloudSat and Himawari-8. Atmosphere 2023, 14, 440. [Google Scholar] [CrossRef]
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Figure 1. Simulated area. The area enclosed by the red solid line represents the d01 domain, while the area enclosed by the blue solid line corresponds to the d02 domain.
Figure 1. Simulated area. The area enclosed by the red solid line represents the d01 domain, while the area enclosed by the blue solid line corresponds to the d02 domain.
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Figure 2. Comparisons of the (a) Typhoon Nangka track and (b) maximum surface wind speed between the observations (black, from CMA [43,44]) and the simulations (red).
Figure 2. Comparisons of the (a) Typhoon Nangka track and (b) maximum surface wind speed between the observations (black, from CMA [43,44]) and the simulations (red).
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Figure 3. Infrared cloud images (K) at the 11.2 μm channel from the Himawari-8 satellite at (a) 0600 BJT on the 7th, (b) 0600 BJT on the 8th, (c) 0600 BJT on the 9th, and (d) 0600 BJT on the 10th [45].
Figure 3. Infrared cloud images (K) at the 11.2 μm channel from the Himawari-8 satellite at (a) 0600 BJT on the 7th, (b) 0600 BJT on the 8th, (c) 0600 BJT on the 9th, and (d) 0600 BJT on the 10th [45].
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Figure 4. Radial vertical distributions of cloud ice (shaded areas; 10−4 kg kg−1) at (a) 0600 BJT on the 7th, (b) 1200 BJT on the 7th, (c) 0600 BJT on the 8th, (d) 0600 BJT on the 9th, (e) 1800 BJT on the 9th, and (f) 0600 BJT on the 10th. The vector arrows represent the vertical secondary circulation distribution (m s−1), and the black dashed lines denote the radius of maximum wind speed. The horizontal axis represents the distance from the center of the typhoon (km).
Figure 4. Radial vertical distributions of cloud ice (shaded areas; 10−4 kg kg−1) at (a) 0600 BJT on the 7th, (b) 1200 BJT on the 7th, (c) 0600 BJT on the 8th, (d) 0600 BJT on the 9th, (e) 1800 BJT on the 9th, and (f) 0600 BJT on the 10th. The vector arrows represent the vertical secondary circulation distribution (m s−1), and the black dashed lines denote the radius of maximum wind speed. The horizontal axis represents the distance from the center of the typhoon (km).
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Figure 5. Radial vertical distributions of cloud snow (shaded areas; 10−4 kg kg−1) at (a) 0600 BJT on the 7th, (b) 1200 BJT on the 7th, (c) 0600 BJT on the 8th, (d) 0600 BJT on the 9th, (e) 1800 BJT on the 9th, and (f) 0600 BJT on the 10th. The vector arrows represent the vertical secondary circulation distribution (m s−1), and the black dashed lines denote the radius of maximum wind speed. The horizontal axis represents the distance from the center of the typhoon (km).
Figure 5. Radial vertical distributions of cloud snow (shaded areas; 10−4 kg kg−1) at (a) 0600 BJT on the 7th, (b) 1200 BJT on the 7th, (c) 0600 BJT on the 8th, (d) 0600 BJT on the 9th, (e) 1800 BJT on the 9th, and (f) 0600 BJT on the 10th. The vector arrows represent the vertical secondary circulation distribution (m s−1), and the black dashed lines denote the radius of maximum wind speed. The horizontal axis represents the distance from the center of the typhoon (km).
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Figure 6. Radial vertical distributions of graupel (shaded areas; 10−4 kg kg−1) at (a) 0600 BJT on the 7th, (b) 1200 BJT on the 7th, (c) 0600 BJT on the 8th, (d) 0600 BJT on the 9th, (e) 1800 BJT on the 9th, and (f) 0600 BJT on the 10th. The vector arrows represent the vertical secondary circulation distribution (m s−1), and the black dashed lines denote the radius of maximum wind speed. The horizontal axis represents the distance from the center of the typhoon (km).
Figure 6. Radial vertical distributions of graupel (shaded areas; 10−4 kg kg−1) at (a) 0600 BJT on the 7th, (b) 1200 BJT on the 7th, (c) 0600 BJT on the 8th, (d) 0600 BJT on the 9th, (e) 1800 BJT on the 9th, and (f) 0600 BJT on the 10th. The vector arrows represent the vertical secondary circulation distribution (m s−1), and the black dashed lines denote the radius of maximum wind speed. The horizontal axis represents the distance from the center of the typhoon (km).
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Figure 7. Radial vertical distributions of cloud water (shaded areas; 10−4 kg kg−1) at (a) 0600 BJT on the 7th, (b) 1200 BJT on the 7th, (c) 0600 BJT on the 8th, (d) 0600 BJT on the 9th, (e) 1800 BJT on the 9th, and (f) 0600 BJT on the 10th. The vector arrows represent the vertical secondary circulation distribution (m s−1), and the black dashed lines denote the radius of maximum wind speed. The horizontal axis represents the distance from the center of the typhoon (km).
Figure 7. Radial vertical distributions of cloud water (shaded areas; 10−4 kg kg−1) at (a) 0600 BJT on the 7th, (b) 1200 BJT on the 7th, (c) 0600 BJT on the 8th, (d) 0600 BJT on the 9th, (e) 1800 BJT on the 9th, and (f) 0600 BJT on the 10th. The vector arrows represent the vertical secondary circulation distribution (m s−1), and the black dashed lines denote the radius of maximum wind speed. The horizontal axis represents the distance from the center of the typhoon (km).
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Figure 8. Radial vertical distributions of rainwater (shaded areas; 10−4 kg kg−1) at (a) 0600 BJT on the 7th, (b) 1200 BJT on the 7th, (c) 0600 BJT on the 8th, (d) 0600 BJT on the 9th, (e) 1800 BJT on the 9th, and (f) 0600 BJT on the 10th. The vector arrows represent the vertical secondary circulation distribution (m s−1), and the black dashed lines denote the radius of maximum wind speed. The horizontal axis represents the distance from the center of the typhoon (km).
Figure 8. Radial vertical distributions of rainwater (shaded areas; 10−4 kg kg−1) at (a) 0600 BJT on the 7th, (b) 1200 BJT on the 7th, (c) 0600 BJT on the 8th, (d) 0600 BJT on the 9th, (e) 1800 BJT on the 9th, and (f) 0600 BJT on the 10th. The vector arrows represent the vertical secondary circulation distribution (m s−1), and the black dashed lines denote the radius of maximum wind speed. The horizontal axis represents the distance from the center of the typhoon (km).
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Figure 9. Distributions of the azimuthally averaged total-column liquid-phase hydrometeor mixing ratio (red lines; 10−2 kg kg−1) and 12 h lagged anomaly of total-column latent heat flux (blue lines; 106 J m−2) during the development of Typhoon Nangka. (a–f) represent the hydrometeor mixing ratio at 1800 BJT on the 6th, 0600 BJT on the 7th, 1800 BJT on the 7th, 0600 BJT on the 8th, 1800 BJT on the 8th, and 0600 BJT on the 9th, and the latent heat flux anomaly at 0600 BJT on the 7th, 1800 BJT on the 7th, 0600 BJT on the 8th, 1800 BJT on the 8th, 0600 BJT on the 9th, and 1800 BJT on the 9th. The horizontal axis represents the distance from the center of the typhoon.
Figure 9. Distributions of the azimuthally averaged total-column liquid-phase hydrometeor mixing ratio (red lines; 10−2 kg kg−1) and 12 h lagged anomaly of total-column latent heat flux (blue lines; 106 J m−2) during the development of Typhoon Nangka. (a–f) represent the hydrometeor mixing ratio at 1800 BJT on the 6th, 0600 BJT on the 7th, 1800 BJT on the 7th, 0600 BJT on the 8th, 1800 BJT on the 8th, and 0600 BJT on the 9th, and the latent heat flux anomaly at 0600 BJT on the 7th, 1800 BJT on the 7th, 0600 BJT on the 8th, 1800 BJT on the 8th, 0600 BJT on the 9th, and 1800 BJT on the 9th. The horizontal axis represents the distance from the center of the typhoon.
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Figure 10. Conceptual diagrams of how the secondary circulation controls the variations in hydrometeor particle distribution at stages of the (a) rapid intensification, (b) peak intensity, and (c) weakening of Typhoon Nangka.
Figure 10. Conceptual diagrams of how the secondary circulation controls the variations in hydrometeor particle distribution at stages of the (a) rapid intensification, (b) peak intensity, and (c) weakening of Typhoon Nangka.
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Table 1. Types of hydrometeors.
Table 1. Types of hydrometeors.
HydrometeorsPhase
Cloud iceIce
Cloud snowIce
GraupelIce
Cloud waterLiquid
RainwaterLiquid
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Wang, L.; Huang, H.; Wang, J.; Ouyang, X.; Ma, X.; Wang, Z. How Hydrometeors Varied with the Secondary Circulation During the Rapid Intensification of Typhoon Nangka (2015). Atmosphere 2025, 16, 1142. https://doi.org/10.3390/atmos16101142

AMA Style

Wang L, Huang H, Wang J, Ouyang X, Ma X, Wang Z. How Hydrometeors Varied with the Secondary Circulation During the Rapid Intensification of Typhoon Nangka (2015). Atmosphere. 2025; 16(10):1142. https://doi.org/10.3390/atmos16101142

Chicago/Turabian Style

Wang, Lin, Hong Huang, Ju Wang, Xinjie Ouyang, Xiaolin Ma, and Zhen Wang. 2025. "How Hydrometeors Varied with the Secondary Circulation During the Rapid Intensification of Typhoon Nangka (2015)" Atmosphere 16, no. 10: 1142. https://doi.org/10.3390/atmos16101142

APA Style

Wang, L., Huang, H., Wang, J., Ouyang, X., Ma, X., & Wang, Z. (2025). How Hydrometeors Varied with the Secondary Circulation During the Rapid Intensification of Typhoon Nangka (2015). Atmosphere, 16(10), 1142. https://doi.org/10.3390/atmos16101142

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