Tropical Cyclone Temperature Profiles and Cloud Macro-/Micro-Physical Properties Based on AIRS Data

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Article Tropical Cyclone Temperature Proﬁles and Cloud Macro-/Micro-Physical Properties Based on AIRS Data

1,2, 1, 3 3, 1, 1 Qiong Liu †, Hailin Wang †, Xiaoqin Lu , Bingke Zhao *, Yonghang Chen *, Wenze Jiang and Haijiang Zhou 1

1 College of Environmental Science and Engineering, Donghua University, Shanghai 201620, China; [email protected] (Q.L.); [email protected] (H.W.); [email protected] (W.J.); [email protected] (H.Z.) 2 State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China 3 Shanghai Typhoon Institute, China Meteorological Administration, Shanghai 200030, China; [email protected] * Correspondence: [email protected] (B.Z.); [email protected] (Y.C.) The authors have the same contribution. † Received: 9 October 2020; Accepted: 29 October 2020; Published: 2 November 2020

Abstract: We used the observations from Atmospheric Infrared Sounder (AIRS) onboard Aqua over the northwest Pacific Ocean from 2006–2015 to study the relationships between (i) tropical cyclone (TC) temperature structure and intensity and (ii) cloud macro-/micro-physical properties and TC intensity. TC intensity had a positive correlation with warm-core strength (correlation coefficient of 0.8556). The warm-core strength increased gradually from 1 K for tropical depression (TD) to >15 K for super typhoon (Super TY). The vertical areas affected by the warm core expanded as TC intensity increased. The positive correlation between TC intensity and warm-core height was slightly weaker. The warm-core heights for TD, tropical storm (TS), and severe tropical storm (STS) were concentrated between 300 and 500 hPa, while those for typhoon (TY), severe typhoon (STY), and Super TY varied from 200 to 350 hPa. Analyses of the cloud macro-/micro-physical properties showed that the top of TC cloud systems mainly consisted of ice clouds. For TCs of all intensities, areas near the TC center showed lower cloud-top pressures and lower cloud-top temperatures, more cloud fractions, and larger ice-cloud effective diameters. With the increase in TC intensity, the levels of ice clouds around the TC center became higher and the spiral cloud-rain bands became larger. When a TC developed into a TY, STY, or Super TY, the convection in the clouds was stronger, releasing more heat, thus forming a much warmer warm core.

Keywords: tropical cyclone; typhoon intensity; warm core; vertical distribution; ice clouds

1. Introduction A tropical cyclone (TC) is a strong cyclonic vortex with a warm-core structure formed on the tropical ocean surface [1]. Since the 1970s, some achievements have been made in studying TCs; especially with the development of satellite technology, people have a new understanding of the origin, development process, and internal structure of TCs [2–14]. Chen et al. [15] suggested that the triggering mechanisms of TC extreme rainfall events were not only determined by the characteristics of the TCs, but also restricted by factors such as topography, interactions between multi-scale circulation systems, and the formation and propagation of vortex Rossby waves and gravity inertia waves. Brand [16] found that two or more TCs could interact when they existed concurrently at a distance less than approximately 1450 km, while in recent years, the ongoing development of observational techniques

Atmosphere 2020, 11, 1181; doi:10.3390/atmos11111181 www.mdpi.com/journal/atmosphere Atmosphere 2020, 11, 1181 2 of 16

(especially the application of satellite and remote sensing technology) has allowed meteorologists to develop a more in-depth understanding of variations in the structure and intensity of TCs. For example, Stern and Zhang [17] used dropsondes deployed by the DC-8 as part of the Genesis and Rapid Intensification Processes (GRIP) throughout the lifetime of Hurricane Earl to investigate the evolution of the inner-core temperature structure and whether or not any relationship existed between the height and intensity evolution of the maximum disturbance temperature. Besides, the Advanced Microwave Sounding Unit (AMSU) has long been used to analyze the TC warm-core structure, estimate TC intensity, and determine TC center position and size, in combination with Joint Geostationary Satellite [1,3,18–23]. The warm-core structure is important for monitoring TC intensity, studying TC inner-core dynamics, and constructing the initial vortex for a TC simulation [24–28]. The two most common variables used to characterize the warm core were strength (the magnitude of the maximum temperature anomaly) and height (the altitude where the maximum anomaly is located) [29]. In this context, previous research found that TC intensity was positively correlated with the warm-core strength [30,31]. Meanwhile, a TC had a stronger warm core, the density contrast between the eye (the center of the cyclone with the lowest surface atmospheric pressure) and the outside of the TC was greater [32]. Velden et al. [33] suggested that the warm-core height decreased with the weakened TC and the strongest TC warm core usually appeared at 250 hPa. Wang and Jiang [34] reported that typical warm-core height was at the upper level around 300–400 hPa for all TCs and increased with TC intensity using AIRS data. TC intensity showed a robust positive correlation with the warm-core strength and had a weaker positive correlation with the warm-core height due to the scattered warm-core heights of weak TCs [35]. In contrast, Zhu and Weng [36] believed that the warm-core height was regulated by the environmental conditions in which a storm was embedded and it did not have a strong correlation with the storm intensity. For example, the height of Sandy’s warm-core was located around the 400 hPa level, which was much lower than that of a typical TC. The TC cloud system is also a key factor in the atmospheric water circulation and Earth’s radiative energy balance [37–41]. Wu et al. [42] used CloudSat and other A-Train satellite data to show that the size of ice particles and the content of ice water in the cloud system near the TC eyewalls gradually decreased with increasing height; their results were consistent with those of Black et al. [43,44]. Zhao and Zhou [45] used Fengyun-2C (FY-2C), Tropical Rainfall Measuring Mission (TRMM), and Cloudsat satellite data to analyze the evolution of Typhoon Ewiniar as well as the mesoscale and microscale structural characteristics of the typhoon’s eye area. Similarly, Mitrescu et al. [46] used Cloud Profiling Radar (CPR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data to quantitatively study the macroscopic and microscopic physical parameters of clouds inside TCs, while also analyzing the vertical distribution of the radar reflectivity, microphysical parameters of ice clouds in the eyewall, and stratified cloud precipitation area. Zhou et al. [47] reported a new perspective of optical properties of TC cloud systems over the years from 2010 to 2014 for the 35 TCs in East Asia by using TRMM and MODIS measurements. On the other hand, the impact of radiation on the warm-core development remains an open question. Cloud-radiative forcing can enhance convective activity in the TC’s outer core, leading to a wider eye, a broader tangential wind field, and a stronger secondary circulation [48]. It also can lead directly to stronger upper-tropospheric radial outflow as well as a slow, yet sustained, ascent throughout the outer core [49]. The development of a double warm core in intense TCs was accompanied by a thin inflow layer above the typical upper outflow layer, which was associated with an inward pressure gradient force induced by cooling at the cloud top [50]. Due to the importance of clouds in warm-core formation, we not only focus on TC temperature profiles but also on cloud macro-/micro-physical properties for different TC intensities in this study. Specifically, we will address the following questions: (1) What is the relationship between warm-core strength and TC intensity? (2) Is warm-core height related to TC intensity, and if so, to what degree? (3) What are the macro-/micro-physical properties of clouds for TCs of different intensities? Section2 defines the time span and methods applied in this study; Section3 considers the TC temperature structure, Atmosphere 2020, 11, 1181 3 of 16 cloud macro-/micro-physical characteristics, and their relationships to TC intensity; Section4 presents the conclusions.

2. Experiments

2.1. Data

2.1.1. AIRS Data AIRS, an infrared spectrometer, launched in 2002, with 2378 spectral channels and a 12.5-km footprint at nadir, is currently the most advanced instrument system for detecting atmospheric vertical profiles from infrared to microwave bands, providing more accurate multi-spectral and high-resolution infrared spectral data from the land, ocean, and atmosphere. These data have been widely used in global climate research and weather forecasting to detect atmospheric temperature and humidity as well as clouds, surfaces, and ozone. AIRS is capable of producing retrievals of temperature and water vapor with high accuracy under clear and partly cloudy (cloud fraction up to 80%) conditions [51]. Wang and Jiang [34] found that the biases and root-mean-square errors (RMSEs) for the AIRS best group data between 200- and 700-hPa levels (where warm-cores were typically located) were within 60.5 K and less than 1 K. Therefore, the data named AIRS/Aqua L2 Support Retrieval (AIRS + AMSU) V006 (AIRX2SUP) used in this study were to obtain the relationship between (i) TC temperature structure and intensity and (ii) cloud macro-/micro-physical properties and TC intensity over the northwestern Pacific from 2006 to 2015. The AIRS data were downloaded from the Goddard Earth Science Data and Information Services Center (GES DISC; http://disc.gsfc.nasa.gov). The spatial resolution is 50 km 50 km. We extracted × atmospheric temperature data with 100 layers from 0.01 to 1100 hPa and cloud macro-/micro-physical properties data, including cloud fraction, cloud-top temperature, cloud-top pressure, and effective particle diameter of cirrus. All data were selected with good quality control (the product quality flag is equal to 0 or 1; Table1).

Table 1. Quality control (QC) for AIRS temperature products.

QC Quality Flag 0 Best 1 Good 2 Do not use

The estimated errors of AIRS average temperature profiles were calculated by the inversion estimation errors of the temperature data. As shown in Figure1, the higher error levels were near 0 hPa and below 700 hPa, which were mainly affected by the precipitation clouds based on satellite observations. We assessed the TC thermal structure by the temperature data from 83.23 to 1013.94 hPa. The error range of the selected data was <1.4 K, especially from 200 to 700 hPa. The average error of the temperature data was <1 K, sufficiently accurate to describe the TC temperature structure. Atmosphere 2020, 11, x FOR PEER REVIEW 4 of 17

0 100 200 300 400 500 600

Pressure (hPa) 700 800 900 1000 1100 01234567 Temperature estimation error (K) Figure 1. Estimation error profile of AIRS temperature data. Figure 1. Estimation error proﬁle of AIRS temperature data. 2.1.2. TC Best Track Data In this paper, the TC best track data were obtained from the tropical cyclone dataset of the China Meteorological Administration [52], which can be downloaded from the website: http://tcdata.typhoon.org.cn/zjljsjj_zlhq.html; these included the longitude, latitude, maximum wind speed, and the lowest pressure of the TC center at 00:00, 06:00, 12:00, and 18:00 (Greenwich mean time).

2.2. Methods

2.2.1. Data Matching TC intensity is represented by the maximum sustained wind speed near the TC center and the minimum sea-level pressure. The greater the wind speed near the ground and the lower the central pressure, the stronger the TC. The China Meteorological Administration classifies TCs as tropical depression (TD), tropical storm (TS), severe tropical storm (STS), typhoon (TY), severe typhoon (STY), and super typhoon (Super TY). The maximum wind speed near the center of each TC class is level 6– 7 (10.8–17.1 m/s), 8–9 (17.2–24.4 m/s), 10–11 (24.5–32.6 m/s), 12–13 (32.7–41.4 m/s), 14–15 (41.5–50.8 m/s), and >16 (>50.8 m/s), respectively. Not all TCs can be well-observed by the AIRS. Therefore, in order to study the variabilities of TC temperature profiles and cloud macro-/micro-physical properties, we need to select the TC cases captured by AIRS. According to the TC best track data, in terms of temporal scale, the AIRS overpassed the TC center within ±2 h and we considered that the TC was captured by AIRS and selected the AIRS data. Then, in terms of spatial scale, the AIRS data within a window size of 10° × 10° centered on the TC center were chosen. A total of 1027 TC samples (excluding tropical disturbances and degeneration) from 2006 to 2015 were identified based on these criteria, as shown in Table 2.

Table 2. Number of tropical cyclone (TC) samples by intensity. Tropical depression (TD), tropical storm (TS), severe tropical storm (STS), typhoon (TY), severe typhoon (STY).

Sample Number TD TS STS TY STY Super TY 1027 384 299 139 99 73 33

2.2.2. Warm-Core Calculation The warm core was defined as the maximum temperature anomaly near the TC center. The temperature anomaly area of each layer was within a 10° × 10° range around the center. This was calculated as follows:

∆𝑇(𝑖, 𝑗) =𝑇(𝑖,𝑗)−𝑇

Atmosphere 2020, 11, 1181 4 of 16

2.1.2. TC Best Track Data In this paper, the TC best track data were obtained from the tropical cyclone dataset of the China Meteorological Administration [52], which can be downloaded from the website: http://tcdata.typhoon. org.cn/zjljsjj_zlhq.html; these included the longitude, latitude, maximum wind speed, and the lowest pressure of the TC center at 00:00, 06:00, 12:00, and 18:00 (Greenwich mean time).

2.2. Methods

2.2.1. Data Matching TC intensity is represented by the maximum sustained wind speed near the TC center and the minimum sea-level pressure. The greater the wind speed near the ground and the lower the central pressure, the stronger the TC. The China Meteorological Administration classifies TCs as tropical depression (TD), tropical storm (TS), severe tropical storm (STS), typhoon (TY), severe typhoon (STY), and super typhoon (Super TY). The maximum wind speed near the center of each TC class is level 6–7 (10.8–17.1 m/s), 8–9 (17.2–24.4 m/s), 10–11 (24.5–32.6 m/s), 12–13 (32.7–41.4 m/s), 14–15 (41.5–50.8 m/s), and >16 (>50.8 m/s), respectively. Not all TCs can be well-observed by the AIRS. Therefore, in order to study the variabilities of TC temperature profiles and cloud macro-/micro-physical properties, we need to select the TC cases captured by AIRS. According to the TC best track data, in terms of temporal scale, the AIRS overpassed the TC center within 2 h and we considered that the TC was captured by AIRS and selected the AIRS ± data. Then, in terms of spatial scale, the AIRS data within a window size of 10 10 centered on the ◦ × ◦ TC center were chosen. A total of 1027 TC samples (excluding tropical disturbances and degeneration) from 2006 to 2015 were identified based on these criteria, as shown in Table2.

Table 2. Number of tropical cyclone (TC) samples by intensity. Tropical depression (TD), tropical storm (TS), severe tropical storm (STS), typhoon (TY), severe typhoon (STY).

Sample Number TD TS STS TY STY Super TY 1027 384 299 139 99 73 33

2.2.2. Warm-Core Calculation The warm core was deﬁned as the maximum temperature anomaly near the TC center. The temperature anomaly area of each layer was within a 10 10 range around the center. This was ◦ × ◦ calculated as follows: ∆T(i, j) = T(i, j) Tmean − where ∆T is the temperature anomaly of the ith row and jth column of the current layer, T is the lattice temperature of the same row and column, and Tmean is the average temperature within the range. This allowed the maximum temperature anomaly in all layers and grid points that were taken to be determined.

2.2.3. Synthetic Method The synthetic method is a common statistical approach used in the study of climate diagnosis and analysis [53]. To analyze changes in the environmental field around TCs, we adopted this method for dynamic circulation. Compared with simple arithmetic means, synthetic analysis has a significant difference in its physical meaning; it reduces the mutual canceling effect of the average physical quantity of the samples so that the TC structure remains relatively intact and its relative position with the surrounding circulation system remains fairly constant [53]. Atmosphere 2020, 11, x FOR PEER REVIEW 5 of 17 where ∆T is the temperature anomaly of the ith row and jth column of the current layer, T is the lattice temperature of the same row and column, and Tmean is the average temperature within the range. This allowed the maximum temperature anomaly in all layers and grid points that were taken to be determined.

2.2.3. Synthetic Method The synthetic method is a common statistical approach used in the study of climate diagnosis and analysis [53]. To analyze changes in the environmental field around TCs, we adopted this method for dynamic circulation. Compared with simple arithmetic means, synthetic analysis has a significant difference in its physical meaning; it reduces the mutual canceling effect of the average physical quantity of the samples so that the TC structure remains relatively intact and its relative position with Atmosphere 2020, 11, 1181 5 of 16 the surrounding circulation system remains fairly constant [53].

2.2.4.2.2.4. Lattice Lattice Processing Processing ToTo avoid mismatches in the data volume between different diﬀerent satellite scanning areas during the the syntheticsynthetic analysis, wewe standardizedstandardized the the research research region. region. The The survey survey region region of 10of 10°10 × 10°around around the the TC ◦ × ◦ TCcenter center was was divided divided into into 50 5050 × 50 grid grid points, points, each each point point having having a a range range of of 0.2 0.2° × 0.20.2°.. The longitude × ◦ × ◦ andand latitude latitude of of the the central central region region were were defined deﬁned as the longitude longitude and and latitude latitude of the the grid grid point point and and the the averageaverage value value of of the elements in the grid point was the element value of the grid point.

3.3. Results Results and and Discussion Discussion

3.1.3.1. Relationship Relationship Between Between Warm Warm-Core-Core Strength Strength and TC Intensity ThereThere is abundantabundant water water vapor vapor in in the the updraft updraft flowing flowing toward toward the centerthe center of a TC, of whicha TC, which can release can releaselarge amounts large amounts of latent of heat latent when heat it when condenses. it condenses. This updraft This alsoupdraft produces also produces outflow afteroutflow reaching after reachinga certain a height, certain causing height, thecausing eye areathe eye to sinkarea andto sink warm. and Therefore,warm. Therefore, mature mature TCs generally TCs generally have a havewarm-core a warm-core structure; structure; the stronger the stronger the TC, the the TC, more the obvious more obvious the warm the core warm [54 core,55]. [54,55]. ToTo reveal the relationship between warm-core warm-core stre strengthngth and and TC TC intensity, intensity, we developed a a scatter scatter plot of all samples and performed a linear fit fit to calculate the correlation coefficient coefficient between them (Figure(Figure 22).). TC intensity was positively correlated withwith warm-corewarm-core strength,strength, withwith anan R-valueR-value ofof 0.85560.8556 andand a a fitting fitting line slope of 3.09; this matches the results of Zhao [[54].54].

70 Y = 3.09x + 8.92 R = 0.8556 60 N = 1027 50

30 TC intensity (m/s) intensity TC 20

0 0 5 10 15 20 Warm core strength (K) FigureFigure 2. 2. ScatterScatter plot plot of of warm warm core core strength strength and and TC TC intensity intensity with with linear linear fit ﬁt (red (red line).

InIn order order to to understand understand the the relationship relationship between between warm-core warm-core intensity intensity and and wind wind speed speed for for the the same same TC intensity, intensity, we we calculated calculated the the correlation correlation coefficien coefficientsts for for six six TC TC intensity intensity classes classes (Table (Table 3). 3For). ForTD, TD,the p the correlation coefficient between the warm core strength and TC intensity was 0.064 and the -value was 0.21 (>0.01), meaning that the F test was not passed and these data were irrelevant. Because the development of convection around the TC center was not strong enough, the nearby sinking and warming effect and latent heat heating were weak, such that the temperature anomalies between the center and the surrounding area differed only slightly. Inversion errors in AIRS temperature data may also have erased weak warm cores. The correlation coefficient was 0.185 for TS and STS, 0.469 for TY, and 0.334 for STY. Their p-values were all less than 0.01, indicating that they were correlated under this intensity. However, Super TY had a correlation coefficient of only 0.073, and the F test result did not pass the 0.01 reliability test. When the TC intensity and the convection are both strong, the sinking warming effect and the latent heat heating reach their apex, such that further internal temperature increases become small and re-intensification of the TC becomes difficult. Atmosphere 2020, 11, x FOR PEER REVIEW 6 of 17

correlation coefficient between the warm core strength and TC intensity was 0.064 and the P-value was 0.21 (>0.01), meaning that the F test was not passed and these data were irrelevant. Because the development of convection around the TC center was not strong enough, the nearby sinking and warming effect and latent heat heating were weak, such that the temperature anomalies between the center and the surrounding area differed only slightly. Inversion errors in AIRS temperature data may also have erased weak warm cores. The correlation coefficient was 0.185 for TS and STS, 0.469 for TY, and 0.334 for STY. Their P-values were all less than 0.01, indicating that they were correlated under this intensity. However, Super TY had a correlation coefficient of only 0.073, and the F test result did not pass the 0.01 reliability test. When the TC intensity and the convection are both strong, the sinking Atmospherewarming2020 effect, 11 ,and 1181 the latent heat heating reach their apex, such that further internal temperature6 of 16 increases become small and re-intensification of the TC becomes difficult. Table 3. Correlation between warm core strength and wind speed for the same TC intensity. Table 3. Correlation between warm core strength and wind speed for the same TC intensity. TC Class Sample Number Correlation Coeﬃcient p-Value(F Test) TC Class Sample Number Correlation Coefficient P-Value（F Test） TDTD 384 384 0.064 0.064 0.210 0.210 TSTS 299 299 0.185 0.185 0.001 0.001 STSSTS 140 140 0.185 0.185 0.003 0.003 TY 99 0.469 8.719 10 7 TY 99 0.469 8.719 × 10−×7 − STY 73 0.334 0.003 STY 73 0.334 0.003 Super TY 33 0.073 0.6834 Super TY 33 0.073 0.6834

3.2. Warm Core Height Distribution Figure3 3 shows shows thethe altitudealtitude distributiondistribution ofof thethe warmwarm corecore inin allall 10271027 samples,samples, whichwhich waswas mainlymainly concentrated from from 200 200 to to 500 500 hPa hPa (76.1% (76.1% of ofall allsamp samples),les), consistent consistent with with previous previous results results [34,54]. [34 ,The54]. Thehighest highest individual individual ranges ranges were were 300–350 300–350 hPa hPa (33% (33% of oftall tall samples) samples) and and 400–450 400–450 hPa hPa (19% (19% of of all samples), while all others werewere <<10%.10%.

0.35

0.30 N = 1027

0.25

0.20

0.15 Frequency 0.10

0.05

0.00

50 00 00 50 50 00 00 50 50 0~1 0~2500~3 0~4 0~4 0~5 0~7 0~8 0~8 0~9 50~200 00~350 50~500 50~600 00~650 00~750 50~900 0~1000 10 1 20 25 3 35 40 4 50 5 6 65 7 75 80 8 90 95 Height (hpa) Figure 3. Warm core height distribution.

The distribution of the warm core height for diffe diﬀerentrent TC intensities was quite variable (Figure (Figure 44).). For TD, the strongeststrongest warm corecore waswas distributeddistributed throughoutthroughout the layerslayers butbut waswas mainlymainly concentratedconcentrated from 300 to 500 hPa. The largest height was from 400 to 450 hPa (25%), followed by 300–350 hPa (16%) and 450–500 hPa (12%). The warm core height distributiondistribution was similar for TS, for which all the heights were again represented represented but but mainly mainly concentrated concentrated from from 300 300 to 500 to 500hPa. hPa. The warm The warm core height core height in the inrange the rangeof 400–450 of 400–450 hPa was hPa the was largest, the largest, accounting accounting for 27%. for Fo 27%.r STS, For the STS, dominant the dominant range of range warm of core warm height core heightwas 300–450 was 300–450 hPa, with hPa, the with maximum the maximum from 300 from to 300350 tohPa 350 (36%), hPa (36%),followed followed by 400–450 by 400–450 hPa (20%). hPa (20%). For TY, 64% of samples were concentrated from 300 to 350 hPa, 15% from 200 to 250 hPa, and the rest from 150 to 200 hPa, 250 to 300 hPa, 350 to 550 hPa, and 900 to 950 hPa. No warm core occurred from 550 to 900 hPa and 950 to 1000 hPa. For STY, almost all warm cores were distributed from 300 to 350 hPa (>80%). For Super TY, all warm core heights occurred above 350 hPa. In summary, for TD, TS, and STS, the warm core heights were mainly distributed from 300 to 500 hPa, while those for TY, STY, and Super TY were concentrated from 200 to 350 hPa, indicating that the warm core height increased with TC intensity [34]. There were two main reasons why a certain number of samples were distributed below 700 hPa when the TC was classiﬁed at or below STS. First, the rising movement of water vapor could be too weak to form a warm core in the upper layer. Second, the AIRS temperature data had larger errors at this level. Atmosphere 2020, 11, x FOR PEER REVIEW 7 of 17

For TY, 64% of samples were concentrated from 300 to 350 hPa, 15% from 200 to 250 hPa, and the rest from 150 to 200 hPa, 250 to 300 hPa, 350 to 550 hPa, and 900 to 950 hPa. No warm core occurred from 550 to 900 hPa and 950 to 1000 hPa. For STY, almost all warm cores were distributed from 300 to 350 hPa (>80%). For Super TY, all warm core heights occurred above 350 hPa. In summary, for TD, TS, and STS, the warm core heights were mainly distributed from 300 to 500 hPa, while those for TY, STY, and Super TY were concentrated from 200 to 350 hPa, indicating that the warm core height increased with TC intensity [34]. There were two main reasons why a certain number of samples were distributed below 700 hPa when the TC was classified at or below AtmosphereSTS. First,2020 the, 11 rising, 1181 movement of water vapor could be too weak to form a warm core in the upper7 of 16 layer. Second, the AIRS temperature data had larger errors at this level.

1.0 1.0

TS 0.8 TD 0.8 N = 384 N = 298

0.6 0.6

0.4 0.4 Frequency Frequency

0.2 0.2

0.0 0.0

00 50 50 0 00 0 0 0 0 0 150 200 00 50 400 50 00 50 0 50 00 0 5 900 ~250 ~500 ~750 ~900 ~3 ~5 100 0~200 50~3000~350 00~450 0~600 0~3 0~5 0~7 100~15015 200 2 30 350~44 450 500~555 600~6650~70700 750~8800~850850 900~9550~1000 00~4 00~6 00~8 9 100~150~200~25025 300 350~4 45 500 550~66 65 700~750750~88 850~900~950950~ Height (hpa) Height (hpa) (a) (b)

1.0 1.0

STS 0.8 0.8 TY N = 139 N = 100

0.6 0.6 y

0.4 0.4 Frequency Frequenc

0.2 0.2

0.0 0.0 0 0 0 0 0 0 0 0 0 50 5 0 0 5 5 0 5 00 00 700 900 2 3 7 7 8 9 ~150~200~250 ~450~500~550 ~ ~750~800~850~ ~9 0~1 0~400 0~5000~5 0~6000~6 0~ 0 5 0 5 0 0 100 150 200 250~300300~350350~400400 450 500 550~600600~650650 700 750 800 850 900 50~100 1 150~200~250250~300~35035 400~4504 5 5 6 650~7 750~800~850850~900~95050~100 9 9 Height (hpa) Height (hpa) (c) (d)

1.0 1.0

Super TY 0.8 STY 0.8 N = 73 N = 33

0.6 0.6

0.4 0.4 Frequency Frequency

0.2 0.2

0.0 0.0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 00 5 ~8 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 ~1 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 0 100~150150~200200~250250~300300~350350~400400~450450~500500~550550~600600~650650~700700~750750 800~850850~900900~950 50 50 00 00 50 50 00 00 50 50 00 950~1000 10 1 200 2 3 350 4 4 500 5 6 650 7 7 800 8 9 950~1000 Height (hpa) Height (hpa) (e) (f)

Figure 4. Warm-core height for different TC intensities: (a) TD, (b) TS, (c) STS, (d) TY, (e) STY, and (f) Figure 4. Warm-core height for different TC intensities: (a) TD, (b) TS, (c) STS, (d) TY, (e) STY, and (f) Super TY. Super TY. 3.3. Analysis of Warm Core Structure with Different TC Intensities The vertical temperature anomaly distributions of all 1027 samples were analyzed synthetically with the TC center as the axis. Figure5 shows vertical profiles of the temperature anomaly for each TC class after synthesis (transect along the center of the TC latitude from east to west), where point 0 of the X-axis is the TC center. At different TC intensities, the shape of the warm core structure in the TC’s upper layer changed significantly. For TD, the shape of the warm core was approximately elliptical, with a long axis in the vertical direction, the warm core height extending up to 200 hPa, and the warm core strength ranging from 1 to 1.5 K. For TS, the warm core extended up to 160 hPa and down to 900 hPa, while its strength increased to 3.5–4 K. For STS, the warm core strength continued to increase, Atmosphere 2020, 11, x FOR PEER REVIEW 8 of 17

3.3. Analysis of Warm Core Structure with Different TC Intensities The vertical temperature anomaly distributions of all 1027 samples were analyzed synthetically with the TC center as the axis. Figure 5 shows vertical profiles of the temperature anomaly for each TC class after synthesis (transect along the center of the TC latitude from east to west), where point 0 of the X-axis is the TC center. At different TC intensities, the shape of the warm core structure in the TC’s upper layer changed significantly. For TD, the shape of the warm core was approximately elliptical, with a long axis in the vertical direction, the warm core height extending up to 200 hPa, and the warm Atmosphere 2020, 11, 1181 8 of 16 core strength ranging from 1 to 1.5 K. For TS, the warm core extended up to 160 hPa and down to 900 hPa, while its strength increased to 3.5–4 K. For STS, the warm core strength continued to increase, reachingreaching 5–65–6 KK andand thenthen extendingextending upwardupward fromfrom 150150 toto 950 hPa. For TY, STY, andand SuperSuper TY,TY, thethe warmwarm corescores allall extendedextended upup toto 100100 hPahPa andand theirtheir strengthsstrengths increasedincreased toto 6–96–9 K,K, 9–129–12 K,K, and >>1515 K, respectively. respectively.

100 100 (a) 16.016.00 (b) 16.016.00 200 15.015.00 200 15.015.00 12.012.00 12.012.00 300 9.0009.0 300 9.0009.0 6.0006.0 6.0006.0 400 5.0005.0 400 5.0005.0

4.0004.0 ）） 4.0004.0

500 3.5003.5 500 3.5003.5 hpa

hpa 3.0003.0 3.0003.0 （

（ 2.5002.5 P P 600 2.5002.5 600 2.0002.0 2.0002.0 700 1.5001.5 700 1.5001.5 1.0001.0 1.0001.0 800 0.50000.5 800 0.50000.5 0.0000.0 0.0000.0 900 −-1.0001.0 900 −-1.0001.0

−5 −4 −3 −2 −1 0 1 2 3 4 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 Lon/° Lon/°

100 100 16.00 (c) 16.0 16.016.00 15.00 (d) 200 15.0 200 15.015.00 12.00 12.0 12.012.00 9.000 300 9.0 300 9.09.000 6.0006.0 6.06.000 400 5.0005.0 400 5.05.000

4.000 ） 4.0 ） 4.04.000

500 3.5003.5 500 3.53.500 hpa

3.0003.0 hpa 3.03.000 （ 2.500 （

P 600 2.5 P 600 2.52.500 2.0002.0 2.02.000 700 1.5001.5 700 1.51.500 1.0001.0 1.01.000 800 0.50000.5 800 0.50.5000 0.0000.0 0.00.000 900 −-1.0001.0 900 −-1.0001.0

−5 −4 −3 −2 −1 0 1 2 3 4 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 Lon/° Lon/°

100 100 (e) 16.016.00 (f) 16.016.00 200 15.015.00 200 15.015.00 12.012.00 12.012.00 300 9.09.000 300 9.09.000 6.06.000 6.06.000 400 5.05.000 400 5.05.000

4.04.000 4.000 ）） 4.0

500 3.53.500 500 3.53.500 hpa

hpa 3.03.000 3.03.000 （

（ 2.52.500 2.500 P P 600 600 2.5 2.02.000 2.02.000 700 1.51.500 700 1.51.500 1.01.000 1.01.000 800 0.50.5000 800 0.50.5000 0.00.000 0.000 900 900 0.0 −-1.0001.0 −-1.0001.0 −5 −4 −3 −2 −1 0 1 2 3 4 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 Lon/° Lon/° FigureFigure 5.5. Temperature anomaly (K) (K) vertical vertical profiles proﬁles by by TC TC intensity: intensity: (a) ( aTD,) TD, (b) ( bTS,) TS, (c) (STS,c) STS, (d) (TY,d) TY, (e) (STY,e) STY, and and (f) (Superf) Super TY TY..

When TC intensity reached TS, the shape of the warm core became uneven, with a relatively broad warm core in the upper layer, while the middle and lower layers concentrated in a relatively narrow range near the TC center. This was similar to the results from TC case studies published by Li and Luo [56]. The warm core height gradually increased with increasing TC intensity, different from conclusions in Zhang [57]. This is due to the synthetic analysis of large samples in our study, which provides a more comprehensive overview of the warm core structures under different environmental conditions and different intensity levels; this approach is bound to produce results different from individual diagnosis analyses using different data. Atmosphere 2020, 11, 1181 9 of 16

3.4. Analyses of Macro-/Micro- Physical Properties of TC Cloud Systems by Intensity Cloud type varies with differing TC intensity. Based on cloud-top temperature, clouds were divided into three categories: ice clouds (cloud-top temperature <260 K), water clouds (cloud-top temperature >273.15 K) and ice-water mixed clouds (cloud-top temperature 260–273.15 K) [58]. The proportions of different cloud types on the top of TC cloud system for all six TC intensity classes then were counted (Table4). For TD, TS, and STS, ice clouds accounted for ~95% of the total, and water clouds for ~1.6%. For TY, STY, and Super TY, ice clouds accounted for ~98%, ice-water mixed clouds for 1.02–1.49%, and water clouds for 0.15–0.34%. In general, on the top of TC cloud system, ice clouds accounted for >90% of the total, regardless of TC intensity, far more than the other types. The proportion of ice-water mixed clouds was slightly higher than that of water clouds, with the other two combined accounting for <6%. Therefore, we primarily focused on the macro-/micro- physical properties of ice clouds in TCs of different intensities.

Table 4. Cloud type on the top of TC cloud system by diﬀerent TC intensity.

Top of the TC Cloud System TD TS STS TY STY Super TY Ice cloud (%) 94.58 95.46 95.45 98.59 98.67 98.36 Water cloud (%) 1.81 1.49 1.71 0.34 0.31 0.15 Ice water mixing cloud (%) 3.61 3.05 2.84 1.07 1.02 1.49

Figure6 shows the spatial distribution of ice cloud fraction for all TC intensity classes. When TC intensity increased, the ice cloud fraction near the TC center increased while decreasing outward from the center. For TD, the high-value (0.6–0.7) area of ice cloud fraction was located northwest of the TC center with an irregular shape. For TS, the high-value increased to 0.7–0.8. For STS and TY, the high-value area of ice cloud fraction was located north of the TC center, with maximum values of 0.7–0.8 and 0.8–0.9, respectively. For STY and Super TY, the maximum of ice cloud fraction reached 0.9–1.0. Because TC spiral cloud-rain band was relatively complete and symmetrical, the high-value area of ice cloud fraction tended to be symmetrically distributed around the TC center and most TC would form a clear typhoon eye, where there was little or no cloud coverage. Stronger TCs generally had a clearer typhoon eye, especially for Super TY. The existences of clouds could decrease solar radiation reaching the earth surface and increase longwave radiation absorbed by the whole atmosphere in TC inner core [59], therefore, the contribution of the vertical heat transport by clouds to the warm core strength was significant. Figure7 shows the spatial distribution of cloud-top pressure for di fferent TC intensity classes. The distribution and numerical range varied, but all low-value areas appeared near the TC center, where the clouds reach their highest altitude [46]. Stronger TCs generally had larger regions of low cloud-top pressure near the center. For TD, TS, STS, and TY, the lowest value range of ice cloud-top pressure was 140–150 hPa, with an extremely irregular shape. The low-value zone in the TS cloud-top pressure formed a narrow strip, while those of STS and TY were mainly distributed near the TC center. For STY and Super TY, the cloud-top pressure reached the range of 120–150 hPa and the high clouds were much nearer to the TC center. In order to effectively and intuitively identify sample data characteristics and outliers as well as determinations of dispersion degree and bias, the characteristics of ice cloud-top pressure by different TC intensity were counted as shown in Figure8. Regardless of TC intensity, 75% were found at <200 hPa. The ice cloud-top pressure samples for different TC intensity classes were evenly distributed and the mean values for all classes were larger than the median values. With increasing TC intensity, the box length tended to be shorter, indicating that high clouds increased, especially for Super TY. Atmosphere 2020, 11, 1181 10 of 16 Atmosphere 2020, 11, x FOR PEER REVIEW 10 of 17

Atmosphere−5 2020, 11, x FOR PEER REVIEW 10 of 17 −5 (a) −4 (b) −5 100.0 −4 100.0 −5 −3 −4 (a) 90.00 −3 100.0 −4 (b) 90.00 −2 100.0 −3 80.00 −2 80.00 90.00 −3 90.00

−1 70.00 −1 )

−2 80.00 ) −2 70.00 °

° 80.00 ( 0 60.00 ( 0

−1 70.00 −1 60.00

) )

Lat 70.00

Lat

° °

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Lat 40.00 Lat 2 40.00 1 50.00 1 50.00 3 30.00 2 3 30.00 40.00 2 40.00 4 20.00 3 4 20.00 30.00 3 30.00 5 4 20.00 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 4−5 −4 −3 −2 −1 0 1 2 3 4 5 20.00 5 Lon(°) 5 Lon(°) −5 −4 −3 −2 −1 0 1 2 3 4 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 −5 Lon(°) −5 Lon(°) (c) (d) −4 100.0 −4 −5 −5 100.0 −3 (c) 90.00 −3 (d) 90.00 −4 100.0 −4 100.0 −2 80.00 −2 80.00 −3 90.00 −3 90.00

−1 70.00 ) −1 70.00 )

−2 ° −2 (

° 80.00 80.00 (

0 60.00 0 60.00 )

−1 70.00 Lat −1 70.00

)

Lat

( °

( 1 50.00 1 50.00

0 60.00 0 60.00 Lat

Lat 2 40.00 2 40.00 1 50.00 1 50.00 3 30.00 3 30.00 2 40.00 2 40.00 4 20.00 4 20.00 3 30.00 3 30.00 5 5 4−5 −4 −3 −2 −1 0 1 2 3 4 5 20.00 4−5 −4 −3 −2 −1 0 1 2 3 4 5 20.00 5 Lon(°) 5 Lon(°) −5 −4 −3 −2 −1 0 1 2 3 4 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 Lon(°) −5 Lon(°) −5 (e) −4 −4 100.0 −5 100.0 −5 (f) −3 −3 (e) 90.00 −4 90.00 −4 100.0 100.0 −2 −2 (f) 80.00 −3 80.00 −3 90.00 90.00 −1 70.00 −1 70.00

) −2

−2 80.00 ) 80.00

° ( 0 60.00 ( 0 60.00

−1 70.00 −1 70.00

)

Lat

) Lat

° 50.00 50.00 ° 1 ( 1 0 60.00 ( 0 60.00

2 40.00 2 40.00 Lat 1 50.00 Lat 1 50.00 3 30.00 3 30.00 2 40.00 2 40.00 4 20.00 4 20.00 3 30.00 3 30.00 5 5 20.00 4 20.00 4−5 −4 −3 −2 −1 0 1 2 3 4 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 5 Lon(°) 5 Lon(°) −5 −4 −3 −2 −1 0 1 2 3 4 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 FigureFigure 6. Spatial 6. Spatial distribution distributionLon(°) of of ice ice cloud cloud fractionfraction (%) (%) in in different diﬀerent TC TC intensity: intensity:Lon(°) (a) TD, (a) TD,(b) TS, (b )(c TS,) STS, (c) STS, (d) TY, (e) STY, and (f) Super TY. (d) TY,Figure (e) STY, 6. Spatial and (distributionf) Super TY. of ice cloud fraction (%) in different TC intensity: (a) TD, (b) TS, (c) STS, (d) TY, (e) STY, and (f) Super TY. −5 −5 (b) −4 (a) 250.0 −4 250.0 −5 −5 −3 240.0 (b) −4 (a) 250.0 −3 240.0 −4 250.0 −2 230.0 240.0 −2 230.0 −3 −3 240.0 220.0 220.0 ) −1 230.0 −1

° −2 230.0 ( 200.0 ) −2

° 200.0 0 220.0 ( 0 220.0

) −1 Lat 180.0 −1

° 180.0

( ) 200.0 Lat 1 ° 1 200.0 0 160.0 ( 0 160.0 Lat 2 180.0 2 180.0 1 150.0 Lat 1 150.0 3 160.0 160.0 2 140.0 3 140.0 150.0 2 4 150.0 3 120.0 4 120.0 140.0 3 140.0 5 5 4 −5 −4 −3 −2 −1 0 1 2 3 4 5 120.0 4 −5 −4 −3 −2 −1 0 1 2 3 4 5 120.0 5 5 Lon(°) Lon(°) −5 −4 −3 −2 −1 0 1 2 3 4 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 Lon(°) Lon(°)

Figure 7. Cont.

Atmosphere 2020, 11, 1181 11 of 16 Atmosphere 2020, 11, x FOR PEER REVIEW 11 of 17

−5 −5 (c) (d) −4 250.0 −4 250.0 −3 240.0 −3 240.0 −2 230.0 −2 230.0 220.0 220.0

) −1 −1

) (

° 200.0

200.0 ( 0 0

Lat 180.0 180.0 1 Lat 1 160.0 160.0 2 2 150.0 150.0 3 140.0 3 140.0 4 120.0 4 120.0 5 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 Lon(°) Lon(°)

−5 −5 −4 (e) 250.0 −4 (f) 250.0 −3 240.0 −3 240.0 −2 230.0 −2 230.0 −1 220.0 220.0

) −1

)

° (

200.0 ( 200.0 0 0

Lat 180.0 Lat 180.0 1 1 160.0 160.0 2 2 150.0 150.0 3 140.0 3 140.0 4 120.0 4 120.0 5 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 Lon(°) Lon(°)

FigureFigure 7. Spatial 7. Spatial distribution distribution of iceof ice cloud cloud top top pressure pressure by by di differentﬀerent TC TC intensity: intensity: (a) TD, ( (bb)) TS, TS, ( (cc)) STS, (d) TY,STS, (e )(d STY,) TY, and(e) STY, (f) Super and (f) TY. Super TY. Atmosphere 2020, 11, x FOR PEER REVIEW 12 of 17 In order to effectively and intuitively identify sample data characteristics and outliers as well as determinations of dispersion degree and bias, the characteristics of ice cloud-top pressure by different TC intensity were counted as shown in Figure 8. Regardless of TC intensity, 75% were found at <200 hPa. The ice cloud-top pressure samples for different TC intensity classes were evenly distributed and the mean values for all classes were larger than the median values. With increasing TC intensity, the box length tended to be shorter, indicating that high clouds increased, especially for Super TY. Figure 9 shows the spatial distribution of ice cloud-top temperature by TC intensity. Stronger TCs had larger regions of low cloud-top temperature near the center and high cloud-top temperature far from the center. For TD, STS, and TY, the cloud-top temperatures ranged from 205 to 220 K and the low-value areas were mainly located near the TC center. For TS, the ice cloud had larger cloud-top temperature areas with a lower value ranging from 200 to 205 K near the TC center. For STY, the cloud- top temperature near the TC center decreased further with increasing cloud-top height. For Super TY, the lower-temperature areas near the TC center were further increased but a small area with slightly

higher temperature (~5 K) occurred near the center, which might be affected by the warm core. FigureFigure 8. 8. StatisticsStatistics of of ice ice cloud cloud top top pres pressuresure by by different diﬀerent TC TC intensity. intensity.

Figure-5 9 shows the spatial distribution of ice cloud-top-5 temperature by TC intensity. Stronger TCs -4 (a) -4 (b) had larger regions of low cloud-top temperature250.0 near the center and high cloud-top temperature250.0 -3 far from the center. For TD, STS, and TY, the235.0 cloud-top-3 temperatures ranged from 205 to235.0 220 K -2 -2 and the low-value areas were mainly located230.0 near the TC center. For TS, the ice cloud had230.0 larger -1 -1 220.0

) 220.0 ) cloud-top° temperature areas with a lower value ranging from 200 to 205 K near the TC center. For STY, ( ° 0 ( 0 the cloud-top temperature near the TC center215.0 decreased further with increasing cloud-top215.0 height. Lat 1 210.0 Lat 1 For Super TY, the lower-temperature areas near the TC center were further increased but210.0 a small 2 205.0 2 205.0 area with slightly higher temperature (~5 K) occurred near the center, which might be aﬀected by the 3 200.0 3 200.0 warm core. 4 190.0 4 190.0 5 5 - 5 - 4 - 3 - 2 - 1 0 1 2 3 4 5 - 5 - 4 - 3 - 2 - 1 0 1 2 3 4 5 Lon(°) Lon(°)

-5 -5 (c) (d) -4 250.0 -4 250.0 -3 235.0 -3 235.0 -2 230.0 -2 230.0

) -1 220.0 -1 220.0 ° ) ( ° 0 215.0 ( 0 215.0 Lat

1 210.0 Lat 1 210.0 2 205.0 2 205.0 3 200.0 3 200.0 4 190.0 4 190.0 5 5 - 5 - 4 - 3 - 2 - 1 0 1 2 3 4 5 - 5 - 4 - 3 - 2 - 1 0 1 2 3 4 5 Lon(°) Lon(°)

-5 -5 (e) (f) -4 250.0 -4 250.0 -3 235.0 -3 235.0 -2 230.0 -2 230.0 -1 220.0 -1 220.0 ) ) ° ( ° 0 215.0 ( 0 215.0 Lat 1 210.0 Lat 1 210.0 2 205.0 2 205.0 3 200.0 3 200.0 4 190.0 4 190.0 5 5 - 5 - 4 - 3 - 2 - 1 0 1 2 3 4 5 - 5 - 4 - 3 - 2 - 1 0 1 2 3 4 5 Lon(°) Lon(°) Figure 9. Spatial distribution of ice cloud-top temperature (K) by TC intensity: (a) TD, (b) TS, (c) STS, (d) TY, (e) STY, and (f) Super TY.

Atmosphere 2020, 11, x FOR PEER REVIEW 12 of 17

Atmosphere 2020, 11, 1181 12 of 16 Figure 8. Statistics of ice cloud top pressure by different TC intensity.

−5 −5 −4 (a) (b) 250.0 −4 250.0 −3 235.0 −3 235.0 −2 230.0 −2 230.0

−1 220.0 −1 220.0

)

( °

0 215.0 ( 0 215.0 Lat 1 210.0 Lat 1 210.0

2 205.0 2 205.0

3 200.0 3 200.0

4 190.0 4 190.0 5 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 Lon(°) Lon(°)

−5 −5 (c) (d) −4 250.0 −4 250.0 −3 235.0 −3 235.0 −2 230.0 −2 230.0

) −1 220.0 −1 220.0

)

( °

0 215.0 ( 0 215.0 Lat

1 210.0 Lat 1 210.0 2 205.0 2 205.0 3 200.0 3 200.0 4 190.0 4 190.0 5 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 Lon(°) Lon(°)

−5 −5 (e) (f) −4 250.0 −4 250.0 −3 235.0 −3 235.0 −2 230.0 −2 230.0

−1 220.0 −1 220.0

)

( °

0 215.0 ( 0 215.0 Lat 1 210.0 Lat 1 210.0 2 205.0 2 205.0 3 200.0 3 200.0 4 190.0 4 190.0 5 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 Lon(°) Lon(°) FigureFigure 9. Spatial 9. Spatial distribution distribution of of ice ice cloud-top cloud-top temperaturetemperature (K) (K) by by TC TC intensity: intensity: (a) TD, (a) TD,(b) TS, (b) ( TS,c) STS, (c) STS, (d) TY,(d () eTY,) STY, (e) STY, and and (f) Super (f) Super TY. TY.

Figure 10 shows the spatial distribution of the cirrus effective diameter by TC intensity. Cirrus was mainly concentrated in the upper layers of the TC cloud system [60], so the cirrus effective diameter in this study only stood for the ice particles on the top of the TC cloud system. For TD, the high-value range was 50–52.5 µm, mainly distributed in a strip from southwest to northeast; this area also contained a small number of 52.5–57.5 µm. The spatial distribution for TS was consistent with that for TD. For STS and TY, a small number of ice particles with an effective diameter of 57.5–60 µm appeared dispersedly in the high-value areas and the areas with ice particles effective diameter ranging from 55 to 57.5 µm increased. For TY, the high-value areas tended to gather near the TC center. For STY, the diameters of cloud ice particles ranged from 52.5 to 55 µm and 55 to 60 µm around the TC center. For Super TY, the distribution was similar to that for STY but the low-value area near the TC center was smaller. The maximum diameter of ice particles near the TC center was greater than 60 µm and the high-value areas were much larger. Some large particles of cirrus could still be found far from the TC center, indicating there was an outflow of cirrus for Super TY [60]. Atmosphere 2020, 11, x FOR PEER REVIEW 13 of 17

Figure 10 shows the spatial distribution of the cirrus effective diameter by TC intensity. Cirrus was mainly concentrated in the upper layers of the TC cloud system [60], so the cirrus effective diameter in this study only stood for the ice particles on the top of the TC cloud system. For TD, the high-value range was 50–52.5 μm, mainly distributed in a strip from southwest to northeast; this area also contained a small number of 52.5–57.5 μm. The spatial distribution for TS was consistent with that for TD. For STS and TY, a small number of ice particles with an effective diameter of 57.5–60 μm appeared dispersedly in the high-value areas and the areas with ice particles effective diameter ranging from 55 to 57.5 μm increased. For TY, the high-value areas tended to gather near the TC center. For STY, the diameters of cloud ice particles ranged from 52.5 to 55 μm and 55 to 60 μm around the TC center. For Super TY, the distribution was similar to that for STY but the low-value area near the TC center was smaller. The maximum diameter of ice particles near the TC center was greater Atmospherethan 202060 μm, 11 ,and 1181 the high-value areas were much larger. Some large particles of cirrus could still be13 of 16 found far from the TC center, indicating there was an outflow of cirrus for Super TY [60].

−5 −5 (a) −4 (b) −4 62.5060.00 62.5060.00 −3 −3 60.00 60.00 −2 57.50 −2 57.50 −1 −1 55.00

) 55.00

)

° (

0 52.50 ( 0 52.50 Lat 1 50.00 Lat 1 50.00 2 45.00 2 45.00 3 40.00 3 40.00 4 35.00 4 35.00 5 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 Lon(°) Lon(°)

−5 −5 (c) (d) −4 62.5060.00 −4 60.0062.50 −3 60.00 −3 60.00 −2 57.50 −2 57.50 −1

−1 55.00 ) 55.00

)

° (

( 0 52.50 0 52.50 Lat Lat 1 50.00 1 50.00 2 45.00 2 45.00 3 40.00 3 40.00 4 35.00 4 35.00 5 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 Lon(°) Lon(°)

−5 −5 (e) −4 60.0062.50 −4 60.0062.50 (f) −3 60.00 −3 60.00 −2 57.50 −2 57.50

−1 55.00 −1 55.00

)

° (

0 52.50 ( 0 52.50 Lat 1 50.00 Lat 1 50.00 2 45.00 2 45.00 3 40.00 3 40.00 4 35.00 4 35.00 5 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 −5 −4 −3 −2 −1 0 1 2 3 4 5 Lon(°) Lon(°)

FigureFigure 10. Spatial 10. Spatial distribution distribution of of cirrus cirrus e effectiveﬀective diameter (μm) (µm) by by TC TC intensity: intensity: (a) TD, (a) TD,(b) TS, (b )(c TS,) STS, (c) STS, (d) TY, (e) STY, and (f) Super TY. (d) TY, (e) STY, and (f) Super TY.

4. Conclusions We used AIRS data to analyze the temperature profiles for different TC intensity classes as well as the macro-/micro-physical properties of clouds with the following conclusions: (1) There was a positive correlation between TC intensity and warm core strength (correlation coefficient is 0.8556). Correlation coefficients varied by intensity class, with the highest value (0.496) for TY. The linear fitting results also showed that TC intensity increased with increasing warm core strength. However, warm core strength and TC intensity were not correlated for TD because the convection around the TC center was not strong enough, weakening the nearby sinking warming and latent heat heating effects; the temperature anomalies between the TC center and the surrounding area were slightly different. In addition, inversion errors within the AIRS temperature data might also have erased the weak warm cores. (2) Vertical distributions of warm core height varied with TC intensity and warm core height slightly increased as TC intensity increased. For TD and TS, warm core heights were mainly distributed from 300 to 500 hPa, the range of STS was from 300 to 450 hPa, and that of TY and Super TY from Atmosphere 2020, 11, 1181 14 of 16

300 to 350 hPa. The vertical distribution of temperature structure showed that the warm core strength increased gradually from 1 K for TD to >15 K for Super TY. The vertical areas affected by warm core strength grew with increasing TC intensity. (3) The tops of TC cloud systems mainly consisted of ice clouds, which accounted for more than 90% of all samples regardless of the TC intensity. As TC intensity increased, cloud fraction and effective diameter of ice particles near the TC center gradually increased, while cloud-top pressure and temperature gradually decreased. With the increase in TC intensity, the vertical convection was stronger and the vertical heat transport by clouds was more significant, contributing to a warmer warm core.

Author Contributions: Conceptualization, Y.C. and B.Z.; methodology, H.W. and B.Z.; validation, Q.L. and X.L.; formal analysis, H.W.; investigation, W.J. and H.Z.; writing—original draft preparation, H.W. and W.J.; writing—review and editing, Q.L.; project administration, Y.C. and B.Z.; funding acquisition, Y.C. and Q.L. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Chinese Ministry of Science and Technology (grant number 2018YFC1506305) and the 2018 Open Research Program of the State Key Laboratory of Severe Weather(grant number 2018LASW-B11). Acknowledgments: The AIRS data were obtained from the NASA Goddard Earth Sciences Data Information and Services Center (GES DISC). We thank three anonymous reviewers and the editors for their helpful comments. Conﬂicts of Interest: The authors declare no conﬂict of interest.

References

1. Liu, Q.; Weng, F. Detecting the warm core of a hurricane from the Special Sensor Microwave Imager Sounder. Geophys. Res. Lett. 2006, 33, L06817. [CrossRef] 2. Zhang, H.; Chou, J.; Qiu, C. Assimilation analysis of Rammasun typhoon structure over Northwest Pacific using satellite data. Chin. Sci. Bull. 2004, 49, 389–395. [CrossRef] 3. Wei, Y.; Xu, J.; Zhou, X. Application of NOAA-AMSU microwave soundings to Typhoon Dujuan. J. Trop. Meteorol. 2005, 21, 359–367. (In Chinese) 4. Zhu, X.; Yu, H.; Yin, Q.; Mao, Z. Satellite-based analysis of concentric eyewall replacement cycles with super Typhoon Muifa (1109). J. Trop. Meteorol. 2014, 30, 34–44. (In Chinese) 5. Ritchie, E.A.; Wood, K.M.; Rodríguez-Herrera, O.G.; Piñeros, M.F.; Tyo, J.S. Satellite-Derived Tropical Cyclone Intensity in the North Pacific Ocean Using the Deviation-Angle Variance Technique. Wea. Forecast. 2014, 29, 505–516. [CrossRef] 6. Wang, X.; Ren, Y.; Li, X. The warm-core structure of Super Typhoon Rammasun derived by FY-3C microwave temperature sounder measurements. Atmos. Sci. Let. 2016, 17, 432–436. [CrossRef] 7. Yan, L.; Zhou, Y.; Liu, X. Dynamic and thermodynamic structure analysis of Typhoon Matmo (1410) and associated moisture characteristics before and after its landfall. Chin. J. Atmos. Sci. 2017, 41, 289–301. (In Chinese) 8. Yu, Z.; Wang, Y.; Xu, H.; Davidson, N.; Chen, Y.; Chen, Y.; Yu, H. On the Relationship between Intensity and Rainfall Distribution in Tropical Cyclones Making Landfall over China. J. Appl. Meteorol. Clim. 2017, 56, 2883–2901. [CrossRef] 9. Zhang, W.; Rutledge, S.A.; Xu, W.; Xu, W.; Zhang, Y. Inner-core lightning outbreaks and convective evolution in Super Typhoon Haiyan (2013). Atmos. Res. 2019, 219, 123–139. [CrossRef] 10. Chane Ming, F.; Jolivet, S.; Liou, Y.-A.; Jégou, F.; Mekies, D.; Hong, J.-S. Elliptical structures of gravity waves produced by Typhoon Soudelor in 2015 near Taiwan. Atmosphere 2019, 10, 260. [CrossRef] 11. Chen, B.-F.; Chen, B.; Lin, H.-T.; Elsberry, R.L. Estimating Tropical Cyclone Intensity by Satellite Imagery Utilizing Convolutional Neural Networks. Weather Forecast. 2019, 34, 447–465. [CrossRef] 12. Jiang, H.; Tao, C.; Pei, Y. Estimation of Tropical Cyclone Intensity in the North Atlantic and Northeastern Pacific Basins Using TRMM Satellite Passive Microwave Observations. J. Appl. Meteorol. Clim. 2019, 58, 185–197. [CrossRef] 13. Wang, J.; Zhang, L.; Guan, J.; Zhang, M. Comparison of assimilating all-sky and clear-sky satellite radiance for Typhoon Chan-Hom and Nangka forecasts. Atmosphere 2020, 11, 599. [CrossRef] Atmosphere 2020, 11, 1181 15 of 16

14. Velden, C.S.; Herndon, D. A Consensus Approach for Estimating Tropical Cyclone Intensity from Meteorological Satellites: SATCON. Weather Forecast. 2020, 35, 1645–1662. [CrossRef] 15. Chen, L.; Xu, X.; Luo, Z.; Wang, J. Introduction to Tropical Cyclone Dynamics; China Meteorological Press: Beijing, China, 2002; p. 317. (In Chinese) 16. Brand, S. Interaction of binary tropical cyclones of the western North Pacific Ocean. J. Appl. Meterol. 1970, 09, 433–441. [CrossRef] 17. Stern, D.P.; Zhang, F.The warm-core structure of Hurricane Earl (2010). J. Atmos. Sci. 2016, 73, 3305–3328. [CrossRef] 18. Kidder, S.Q.; Goldberg, M.D.; Zehr, R.M.; DeMaria, M.; Purdom, J.F.W.; Velden, C.S.; Grody, N.C.; Kusselson, S.J. Tropical Cyclone Analysis using AMSU Data. In Proceedings of the AMS 10th Conference on Satellite Meteorology and Oceanography, Long Beach, CA, USA, 14–18 January 2020; pp. 185–188. 19. Kidder, S.Q.; Goldberg, M.D.; Zehr, R.M.; DeMaria, M.; Purdom, J.F.W.; Velden, C.S.; Grody, N.C.; Kusselson, S.J. Satellite analysis of tropical cyclones using the Advanced Microwave Sounding Unit (AMSU). Bull. Amer. Meteorol. Soc. 2000, 81, 1241–1259. [CrossRef] 20. Liu, Z.; Li, W.; Han, Z.; Yao, Z.; Zhang, F.; Zhu, Y. Estimating the intensity of tropical cyclone in Western North Pacific basin with AMSU-A brightness temperature. Chin. J. Geophys. -Chin. Ed. 2008, 51, 51–57. [CrossRef] 21. Zhong, Y.; Xu, M.; Wang, Y. Thermal structure characteristics of the extratropical transition of Tropical Cyclone Chaba (0417). J. Appl. Meteorol. Sci. 2008, 19, 588–594. 22. Wang, Y.; Zhang, H.; Wang, B.; Han, Y.; Cheng, X. Reconstruct the Mesoscale Information of Typhoon with BDA Method Combined with AMSU-A Data Assimilation Method. Adv. Meteorol. 2010, 2010, 346516. [CrossRef] 23. Tian, X.; Zou, X. Capturing size and intensity changes of hurricanes Irma and Maria (2017) from Polar-Orbiting Satellite Microwave Radiometers. J. Atmos. Sci. 2018, 75, 2509–2522. [CrossRef] 24. Zhang, Z.; Wang, Y.; Zhang, W.; Xu, J. Coastal ocean response and its feedback to typhoon Hato (2017) over the South China Sea: A Numerical Study. J. Geophys. Res. -Atmos. 2019, 124, 13731–13749. [CrossRef] 25. Tang, X.; Ping, F.; Yang, S.; Li, M.; Peng, J. On the rapid intensification for Typhoon Meranti (2016): Convection, warm core, and heating budget. Front. Earth Sci. 2019, 13, 791–807. [CrossRef] 26. Shin, J.H. Vortex spinup process in the extratropical transition of Hurricane Sandy (2012). J. Atmos. Sci. 2019, 76, 3589–3610. [CrossRef] 27. Deng, L.; Gao, W.; Duan, Y.; Wang, Y. Microphysical properties of rainwater in Typhoon Usagi (2013): A numerical modeling study. Adv. Atmos. Sci. 2019, 36, 510–526. [CrossRef] 28. Oyama, R.; Wada, A. The relationship between convective bursts and warm-Core intensification in a nonhydrostatic simulation of Typhoon Lionrock (2016). Mon. Weather Rev. 2019, 147, 1557–1579. [CrossRef] 29. Stern, D.P.; Nolan, D.S. On the Height of the Warm Core in Tropical Cyclones. J. Atmos. Sci. 2012, 69, 1657–1680. [CrossRef] 30. Durden, S.L. Observed Tropical Cyclone Eye Thermal Anomaly Profiles Extending above 300hPa. Mon. Weather Rev. 2013, 141, 4256–4268. [CrossRef] 31. Zhang, D.; Chen, H. Importance of the upper-level warm core in the rapid intensification of a tropical cyclone. Geophys. Res. Lett. 2012, 39, L02806. [CrossRef] 32. Fei, J. Stability of off-stage wind disturbance under approximate actual wind field conditions. Acta Trop. Meteorol. Sin. 1996, 12, 18–24. 33. Velden, C.S.; Smith, W.L. Monitoring Tropical Cyclone Evolution with NOAA Satellite Microwave Observations. J. Clim. Appl. Meteorol. 1983, 22, 714–724. [CrossRef] 34. Wang, X.; Jiang, H. A 13-year global climatology of tropical cyclone warm-core structures from AIRS data. Mon. Weather Rev. 2019, 147, 773–790. [CrossRef] 35. Gao, S.; Chen, B.; Li, T.; Wu, N.; Deng, W. AIRS-observed warm core structures of tropical cyclones over the western North Pacific. Dyn. Atmos. Ocean. 2017, 77, 100–106. [CrossRef] 36. Zhu, T.; Weng, F. Hurricane Sandy warm-core structure observed from advanced technology microwave sounder. Geophys. Res. Lett. 2013, 40, 3325–3330. [CrossRef] 37. Knight, D.B.; Davis, R.E. Contribution of tropical cyclones to extreme rainfall events in the southeastern United States. J. Geophys. Res. 2009, 114, D23102. [CrossRef] 38. Jiang, H.; Zipser, E.J. Contribution of Tropical Cyclones to the Global Precipitation from Eight Seasons of TRMM Data: Regional, Seasonal, and Interannual Variations. J. Clim. 2010, 23, 1526–1543. [CrossRef] 39. Bagtasa, G. Contribution of Tropical Cyclones to Rainfall in the Philippines. J. Clim. 2017, 30, 3621–3633. [CrossRef] Atmosphere 2020, 11, 1181 16 of 16

40. Mei, W.; Primeau, F.; McWilliams, J.C.; Pasquero, C. Sea surface height evidence for long-term warming effects of tropical cyclones on the ocean. Proc. Natl. Acad. Sci. USA 2013, 110, 15207–15210. [CrossRef] 41. Fu, Y.; Xian, T.; Lu, D.; Liu, G.; Heng, Z.; Sun, L.; Liu, Q.; Wang, Y. Ozone vertical variations during a typhoon derived from the OMI observations and reanalysis data. Chin. Sci. Bull. 2013, 58, 3890–3894. [CrossRef] 42. Wu, D.L.; Austin, R.T.; Deng, M.; Durden, S.L.; Heymsfield, A.J.; Jiang, J.H.; Lambert, A.; Li, J.-L.; Livesey, N.J.; McFarquhar, G.M.; et al. Comparisons of global cloud ice from MLS, CloudSat, and correlative data sets. J. Geophys. Res. 2009, 114, D00A24. [CrossRef] 43. Black, R.A.; Hallett, J. Observations of the distribution of ice in hurricanes. J. Atmos. Sci. 1986, 43, 802–822. [CrossRef] 44. Black, R.A.; Hallett, J. Electrification of the hurricane. J. Atmos. Sci. 1999, 56, 2004–2028. [CrossRef] 45. Zhao, S.; Zhou, Y. Using various satellites to study typhoons Ewiniar macro and micro physical characteristics. Plateau Meteorol. 2010, 29, 1254–1260. (In Chinese) 46. Mitrescu, C.; Miller, S.; Hawkins, J. Near-Real-Times applications of CloudSat data. J. Appl. Phys. 2008, 47, 1982–1994. [CrossRef] 47. Zhou, Y.; Han, Y.; Wu, Y.; Wang, T.; Tang, X.; Wang, Y. Optical properties and spatial variation of tropical cyclone cloud systems from TRMM and MODIS in the East Asia region: 2010–2014. J. Geophys. Res. Atmos. 2018, 123, 9542–9558. [CrossRef] 48. Bu, Y.P.; Fovell, R.G.; Corbosiero, K.L. Influence of Cloud–Radiative Forcing on Tropical Cyclone Structure. J. Atmos. Sci. 2014, 71, 1644–1662. [CrossRef] 49. Fovell, R.G.; Bu, Y.P.; Corbosiero, K.L.; Tung, W.; Cao, Y.; Kuo, H.; Hsu, L.; Su, H. Influence of Cloud Microphysics and Radiation on Tropical Cyclone Structure and Motion. Meteorol. Monogr. 2016, 56, 11.1–11.27. [CrossRef] 50. Kieu, C.; Tallapragada, V.; Zhang, D.; Almon, Z. On the development of double warm-core structures in intense tropical cyclones. J. Atmos. Sci. 2016, 73, 4487–4506. [CrossRef] 51. Tobin, D.C.; Revercomb, H.E.; Knuteson, R.O.; Lesht, B.M.; Strow, L.L.; Hannon, S.E.; Feltz, W.F.; Moy, L.A.; Fetzer, E.J.; Cress, T.S. Atmospheric Radiation Measurement site atmospheric state best estimates for Atmospheric Infrared Sounder temperature and water vapor retrieval validation. J. Geophys. Res. 2006, 111, D09S14. [CrossRef] 52. Ying, M.; Zhang, W.; Yu, H.; Lu, X.; Feng, J.; Fan, Y.; Zhu, Y.; Chen, D. An overview of the China Meteorological Administration tropical cyclone database. J. Atmos. Ocean. Technol. 2014, 31, 287–301. [CrossRef] 53. Li, Y. A Study on the Sustaining Mechanism of Landfalling Tropical Cyclones; Chinese Academy of Meteorological Sciences: Beijing, China, 2004; pp. 34–35. (In Chinese) 54. Zhao, L. Application of NOAA/AMSU Data in the Study of Typhoon in the Western Pacific. Master’s Thesis, Ocean University of China, Qingdao, China, 2009. (In Chinese). 55. Niu, X. Tropical Cyclone Dynamics; China Meteorological Press: Beijing, China, 1992; p. 3. 56. Li, Y.; Luo, Z. A case study on the formation of warm core structure of typhoon in the South China Sea. China Sci. Technol. Inf. 2008, 17, 29–32. (In Chinese) 57. Zhang, Z. Application of NOAA/AMSU Temperature Data in the Study of Typhoon Structure and Path. Master’s Thesis, Ocean University of China, Qingdao, China, 2012. (In Chinese). 58. Chen, Y.; Huang, J.; Wang, T.; Jin, H.; Ge, J. The temporal and spatial distribution of different types of clouds and their relationship with precipitation in Northwest China. J. Appl. Meteorol. Sci. 2005, 16, 718–726. (In Chinese) 59. Yan, Y.; Tan, J.; Cui, L.; Yu, W.; Hu, Y. Asymmetry of Cloud Vertical Structures and Associated Radiative Effects in Typhoon over the Northwest Pacific Based on CloudSat Tropical Cyclone Dataset. Asia-Pac. J. Atmos. Sci. 2020, 56, 515–532. [CrossRef] 60. Gao, Y.; Fang, X. Analyses on vertical structure and microphysical features of typhoon cloud in the Western Pacific based on CloudSat satellite data. Meteorol. Mon. 2018, 44, 597–611. (In Chinese)

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