SOLA, 2020, Vol. 16, 109−114, doi:10.2151/sola.2020-019

109

The Diurnal Cycle of Clouds in Tropical Cyclones
over the Western North Pacific Basin
Kohei Fukuda1, Kazuaki Yasunaga1, Ryo Oyama2, 3, Akiyoshi Wada3,
Atsushi Hamada1, and Hironori Fudeyasu4
1
Department of Earth Science, Graduate School of Science and Engineering, University of Toyama, Toyama, Japan
2
Forecast Division, Forecast Department, Japan Meteorological Agency (JMA), Tokyo, Japan
3
Meteorological Research Institute (MRI), Tsukuba, Japan
4
Yokohama National University, Yokohama, Japan

Abstract
This study examined the diurnal cycles of brightness temperature (TB) and upper-level horizontal winds associated with tropical cyclones (TCs) over the western North Pacific basin, making
use of data retrieved from geostationary-satellite (Himawari-8)
observations that exhibited unprecedented temporal and spatial
resolutions. The results of a spectral analysis revealed that diurnal
cycles prevail in TB variations over the outer regions of TCs (300−500 km from the storm center). The dominance of the diurnal
cycle was also found in variations in the radial wind (Vr) in intensive TCs, although there was no dominant cycle in tangential windvariation. In addition, coherence spectra demonstrated that thediurnal cycles of TB and Vr are significantly coupled in intensive
TCs. The migration speed of TB and Vr anomalies exceeded thetime-mean Vr, and it was speculated that diurnal cycle signals
propagate (i.e., are not advected) toward the outer regions of TCs.
(Citation: Fukuda, K., K. Yasunaga, R. Oyama, A. Wada, A.
Hamada, and H. Fudeyasu, 2020: The diurnal cycle of clouds intropical cyclones over the western North Pacific basin. SOLA, 16,
109−114, doi:10.2151/sola.2020-019.)

1. Introduction
A tropical cyclone (TC) is a storm that develops over tropicalor subtropical oceans with sufficiently warm sea surface temperatures. It is characterized by a low-pressure center, intense winds,and heavy rainfall. It is also accompanied by horizontally-extended areas of cirrus cloud, which are referred to as a “cirrus canopy”(e.g., Merritt and Wexler 1967). Cirrus cloud generally forms
from the outflow of TC convection and its extension, therefore, isconsidered to be closely related to TC intensity.
Numerous previous studies have pointed out that cirrus cloud
coverage associated with TCs shows a diurnal cycle (e.g., Browner
et al. 1977; Muramatsu 1983; Lajoie and Butterworth 1984; Kossin
2002; Dunion et al. 2014; Wang et al. 2019). Using infrared imagery archive of TCs, Kossin (2002) found that diurnal oscillationof the cirrus canopy was evident over 300 km from the center ofTCs, although it was weak near the storm center. Examining theevolution of the diurnal cycle in major North Atlantic TCs from2001 to 2010, Dunion et al. (2014) documented that cyclicalpulses in the infrared cloud field regularly propagate radiallyoutward from the TC center, and that diurnal signals are especiallyprominent a few hundreds of kilometers (200−300 km) away fromthe circulation center.
Several issues relating to diurnal variations in cirrus cloud
extension in TCs remain unresolved. For example, it is recognizedthat not all TCs show a prominent diurnal cycle, but the reason
Corresponding author: Kazuaki Yasunaga, Department of Earth Science,
Graduate School of Science and Engineering, University of Toyama,
Gofuku 3190, Toyama-city, Toyama 930-8555, Japan. E-mail: yasunaga@
sus.u-toyama.ac.jp.

for this is not understood. In addition, previous studies by Kossin(2002) and Dunion et al. (2014) focused exclusively on Atlantic
storms or eastern Pacific storms. Our knowledge of the TC diurnal
cycle over the western North Pacific (WNP) basin is limited,even though the WNP is the area where TCs are most frequently
generated (e.g., Peduzzi et al. 2012). Furthermore, relationships
between cirrus cloud and dynamic fields have not been adequately
documented on the basis of observational data, while changes inmaximum wind velocity in TCs statistically depend on the local
time (e.g., Yaroshevich and Ingel 2013). From a technical point ofview, previous studies analyzed observations at a relatively lower
time interval of three hours, and it is likely that the results were
contaminated by higher-order harmonics due to aliasing.
To develop a more complete understanding of the TC diurnal
cycle, therefore, the current study employs datasets with higher
temporal and spatial resolutions from a new generation of Japa­
nese geostationary meteorological satellites (Himawari-8) andfocuses on TCs over the WNP basin. It should be also emphasizedthat atmospheric motion vectors (AMVs) derived from images
taken by Himawari-8 are included in the analysis, which will be
described in the next section.

2. Data and methodology
2.1 Data description
In order to examine the diurnal cycle of upper-level cirrus
clouds associated with TCs, the present study used observational
products from Himawari-8 and the Regional Specialized Meteorological Center (RSMC) Tokyo best track dataset for the WNPbasin.
Himawari-8 started routine operations on 7 July 2015, at
140.7°E. The Himawari-8 imager contains 16 spectral bands, andfull-disk images are obtained every 10 min. The spatial resolutionof the infrared band is about 2 km. A more complete descriptionof Himawari-8 can be found in Bessho et al. (2016). Regularlygridded brightness temperature (TB) datasets were provided by
the Center for Environmental Remote Sensing (CEReS) at Chiba
University. In order to determine cirrus cloud variations in TCs,
TBs estimated from 10.4-μm-wavelength-band (band 13) wereanalyzed.
AMVs are derived by tracking clouds and water vapor patterns
in successive (geostationary) satellite images. Retrieval methods
for AMVs were developed in the early 1960s (e.g., Menzel 2001),
and are being still updated through advanced sensor technologies
and/or the adoption of new algorithms (e.g., Velden et al. 2005).
AMVs provide unique information for TC analysis and monitoring, even though vertical distributions of AMV datasets are biased
in the upper level (e.g., Oyama et al. 2016; Oyama 2017; Oyama
et al. 2018). The present investigation used AMVs which wereretrieved every 10 min from Himawari-8 TC target observations
with a time interval of 5 min (Bessho et al. 2016; Oyama et al.
2018). The retrieval scheme was developed by the Meteorological Satellite Center of the Japan Meteorological Agency, and©The Author(s) 2020. This is an open access article published by the Meteorological Society of Japan under a Creative Commons
Attribution 4.0 International (CC BY 4.0) license (http://creativecommons.org/license/by/4.0).

Fukuda et al., Diurnal Cycle of Clouds in Tropical Cyclones

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27

1601
1605
1606
1607
1610
1612
1614
1616
1617
1618
1619
1620
1621
1626
1702
1703
1704
1705
1712
1713
1714
1715
1718
1721
1722
1723
1724

NepartakOmais
ConsonChanthu
Lionrock
Namtheun
MerantiMalakas
Megi
Chaba
Aere
SongdaSarikaNock-ten
MerbokNanmadol
Talas
Noru
BanyanHato
Pakhar
Sanvu
Talim
Lan
Saola
DamreyHaikuiLifetime Lifetime
Axisymmet- R50 R30
Min.
Max
ricity[km] [km]Pressure
MSW[hPa][knot]
110
60
45
55
90
70
120
95
85
115
60
100
95
105
55
55
50
95
80
75
55
80
95
100
60
70
40

900
975
985
980
940
955
890
930
945
905
975
925
935
915
985
985
985
935
955
965
985
955
935
915
975
970
998

1.00
0.65
0.43
0.67
0.60
0.67
0.63
0.80
0.74
0.80
0.67
0.80
1.00
0.50
0.80
0.60
1.00
1.00
1.00
0.50
0.67
1.00
1.00
1.00
0.78
1.00
0.50

111
333
0
222
222
56
130
111
259
111
56
111
111
74
74
37
0
111
56
93
56
167
204
389
167
130
0

444
695
648
389
648
167
444
444
602
444
222
333
389
333
185
222
389
389
222
333
556
926
556
833
556
500
222

a detailed description of the algorithm can be found in Shimoji(2014). AMVs in the range 100−200 hPa levels were selected foranalysis in this study in view of the quality and the amount of data
available at these levels. It is able to be assumed that the levels ofclouds and winds coincide with each other, since cloud top in the
TC usually reaches the tropopause layer and AMVs are retrieved
from sequence images of TB.
RSMC Tokyo best track data cover the period from 1951
to the present and contain 6-hourly measurements of different
dimensions of TCs, although the time interval becomes shorter
when a TC approaches or makes landfall on Japan: 3 hours or 1
hour. These measurements include the central pressure (Pc), thegeographical location of the center, size, grade, 10-min maximum
sustained wind (MSW), and longest radius of TCs. The time interval of the best track data is much longer than that of Himawari-8
retrievals (10 min), and variables associated with TCs are linearlyinterpolated to match the nominal time. The local standard time(LST) of TCs is determined by the location of the TC center withthe lowest Pc.
2.2 Analysis method
In the present study, 27 out of 53 TCs traced by Himawari-8
target observations in 2016 and 2017 were analyzed. Short-lived
and weak TCs were excluded (Table 1). We were interested in
the inherent axisymmetric structures of TCs and their variations
which are irrelevant to the environmental flows (e.g., Yasunaga
et al. 2016). First, therefore, gridded data of TB and (radial andtangential) winds were azimuthally averaged for annuli of 20 km
in radius from the TC center to a distance of 600 km. From thetime series of these axisymmetric components at each radius, a
2-day-long segment was sampled for each TC in order to matchthe resolution of the frequency, since the longevity of TCs isdifferent. The segment was detrended and tapered to zero overthe first and last 4.5 hours. Finally, power spectra were computedfor each sample, making use of fast Fourier transform algorithms,

1000
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03Jul 04Jul

03Jul 04Jul

05Jul

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120
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MSW[knot]Typhoon 2016 No.01

Pressure[hPa]

JMA
TC TC Name
No.

120
90
60
30
0

MSW[knot]Table 1. 2016−2017 major TC cases utilized in this study. In the table,
axisymmetricity is defined as a ratio of the longest and shortest radiusesof the wind speeds of 30 knots, when a TC reaches the lifetime minimumpressure. R50 and R30 indicate the longest radiuses of the wind speeds of
50 and 30 knots at the peak time of the TCs. All variables are derived from
RSMC Tokyo best track data.

Pressure[hPa]

110

1000
960
920
880

Fig. 1. Evolutions of the MSW (upper) and Pc (lower) of TC 1601 (redline). The period sampled for the calculation of power spectra is shadedin yellow. TB changes associated with the MSW and Pc evolutions areshown in Fig. 5.

and were averaged over all samples after normalization with thesum of the spectra over all frequencies (or variances). The numberof samples was limited, and normalization was required to avoid
emphasizing the characteristics of a specific TC. The statisticalsignificance of the spectral peak was evaluated, assuming that thepower spectrum is distributed according to the chi-square distribution, i.e., a peak was considered to be statistically significant if the
90% confidence interval of the peak exceeded the 90% confidence
range on both sides (or at adjacent frequencies).
The present study focused on the diurnal cycle of the developing and mature stage of a TC, and the 2-day-long segment wascentered on the time when a TC reached its maximum MSW or
minimum Pc (Fig. 1). The duration of the segment (2 days) is
short and might not represent the ideal duration for examining
the diurnal cycle. However, some TCs decayed within 3 days offormation so it was necessary to choose this duration to secure asufficient sample size. On the other hand, the results are not sensitive to the duration of the segment (2 days or 3 days).

3. Results
Figure 2 shows the power spectral densities of TB, radial(Vr), and tangential (Vt) winds averaged over the 27 TCs. It canbe seen that the power spectra of TB have distinct diurnal cycle
peaks over the outer areas of the TC (300−500 km from the storm
center), while the diurnal cycle peak is missing near the storm
center (Figs. 2a and 2d). In other words, the TB diurnal cycle isprominent in “distant rainbands,” which are located over the area
beyond 150−200 km from the storm center (e.g., Houze 2010).
The dominance of the diurnal cycle in the outer area of a TC canbe considered a universal feature, as it occurs over the WNP as
well as the North Atlantic (e.g., Kossin 2002; Dunion et al. 2014).
The power spectra of Vr also represent diurnal cycle peaks inthe corresponding regions (around 400 km from the TC center),although the signals are much weaker than those of TB (Figs. 2band 2e). Analyzing dropsonde observations in mature hurricanes,
Zhang et al. (2020) documented a clear diurnal signal of boundary
layer inflow around a radius of 250−500 km. Therefore, it is indicated that diurnal variations are not merely a passive process in
TC clouds, but that dynamic fields, such as horizontal divergencein the upper level, are significantly modulated by the diurnal cycle
as well. However, no dominant cycle is evident in the power
spectra of Vt (Figs. 2c and 2f), and upper-level circulations appear
to be unaffected by the diurnal cycle. It should be also noted that,the diurnal cycle peaks in TB and Vr barely exceed statisticallysignificant levels. This reflects the fact that not all TCs are affectedby the diurnal cycle, which provides motivation to investigate thediurnal cycle, further stratifying TCs appropriately.
In Fig. 3, the TCs are categorized into four classes with reference to MSW. The power spectra of the TB display diurnal cycle
peaks in almost all categories over those areas located further than
300 km from the storm center, and the peaks are especially sig-

SOLA, 2020, Vol. 16, 109−114, doi:10.2151/sola.2020-019(b) Vr(MSW)

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600

111

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2 3 4 5 6 7 8Frequency (cycles/48 h)

0

20
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2 3 4 5 6 7 8Frequency (cycles/48 h)0

Fig. 2. Power spectra of TB (a and d), Vr (b and e), and Vt (c and f) normalized by the sum of the power spectra over all frequencies (unit: %). For the
calculation, 2-day-long (or 48 hours) time series were sampled with reference to the maximum MSW (upper) and minimum Pc (lower) of the TC. The abscissa and ordinate values represent the frequency (unit: cycles per 2 days) and distance of the TC center, respectively (unit: km). A white-colored diamondindicates that the peak is statistically significant (see text).

nificant in TCs with MSWs of 60−80 knots and above 100 knots.
Variations in Vr are relatively weak (Figs. 3e−3h), as expectedfrom Fig. 2. However, the TCs with MSWs of 60−80 knots andabove 100 knots have prominent diurnal Vr cycles, corresponding
to TB variations. Moreover, the diurnal variations in TB and Vr
are significantly coherent in the strongest TCs (with MSWs ofover 100 knots), while such a significant coherence is absent inother categories (Figs. 3i−3l).
Similar features are also found when TCs are stratified withreference to minimum Pc levels (not shown). On the other hand,
one might claim that there are other physical parameters to moreclearly distinguish the TCs with and without the diurnal cycle.
Therefore, TCs are classified, according to the axisymmetricity at
the lifetime minimum pressure, lifetime maximum of the longest
radius (See Table 1) and latitude with the longest duration of the
TC stay. However, no consistent results are obtained in thesestratifications (not shown). As a result, it is suggested that the diurnal cycle is one of the essential features of strong TCs. Furthermore, it is possible that the diurnal cycle over the area of distantrainbands is related to dynamical processes that are responsible forthe intensification or maintenance of a TC, although convection inthis areas is considered to be relatively free from the constraints of
inner-core TC dynamics (Houze 2010).

4. Discussion
It is beyond the scope of the present study to clarify the mechanisms of the diurnal cycle. However, it is worth describing indetail the actual variations in TB and Vr in strong TCs. Figure 4
represents the evolution of the TB and Vr anomalies compositedfor the TCs with peak MSWs larger than 100 knots (or a minimum
Pc lower than 925 hPa), which are included in Figs. 3d, 3h and 3l.
The composited variations indicate that TB reaches a minimum ataround 15 LST at 300 km from the TC center, and the peak timeis delayed with the distance. In addition, it should be emphasized

here that most of the amplitudes of the TB diurnal cycle in the intensive TCs, which fall within about 10 K, are smaller than thoseestimated for several North Atlantic major hurricanes by previous
paper (e.g., Fig. 8 in Dunion et al. 2014), although the phases
agree with each other.
Vr variations reach a peak at around 12 LST at 300 km, andthe Vr signals gradually propagate to the outer regions as well.
It should be noted that the maximum Vr precedes the lowest TB
by 3−6 hours at 300−400 km, which is consistent with a phase
spectrum where the phase difference between diurnal cycles of TB
and Vr is about 60°−70°, or 4.0−4.7 hours (Fig. S1). Although it isdifficult to accurately estimate the migration speed of the anomalies from Fig. 4, the phase spectrum between the TB variations at
300 km and 400 km indicates that the diurnal cycle at 400 km lagsthat at 300 km by about 45°, or 3 hours (not shown). Similarly,
the phase difference between the Vr diurnal variations at 300 km
and 400 km is estimated to be 30°, or 2 hours (not shown).
These phase differences correspond to a migration speed of 9.3−
13.9 m s−1. On the other hand, the time-mean Vr at the corresponding radius composited for strong TCs is about 6−9 m s−1. Therefore, it is speculated that anomalies associated with the diurnal
cycle are not advected by the mean wind but propagate toward theouter region of the TC.
Snapshots of TB associated with the first TC of 2016, which
exhibits a clear diurnal cycle in its developing and mature stages,
are shown in Fig. 5. It is observed that, at 3 LST, deep convective
clouds (which are identified by bright white spots) appear around200 km from the TC center, while cloud cover is minimized at thattime. The deep convection reaches a mature stage at 9−12 LST.
At 15 LST, the edge of the convection becomes vague, and anvil
clouds cover more extended areas (The outer boundary of theanvils exceeds a radius of 300 km). The anvil clouds seem to thinout at 18 LST and decay over the outer region (the area around300−500 km from the storm center) at 21 LST. Cloud cover contracts at 24 LST and a cloud blob is evident around the TC center(within a radius of 300 km).

Fukuda et al., Diurnal Cycle of Clouds in Tropical Cyclones

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0.0

Fig. 3. Power spectra of the TB (upper), the Vr (middle), and the coherence-squared spectrum between TB and Vr (bottom). The power spectra are normalized by the sum of the power spectra over all frequencies (unit: %). TCs are stratified into four classes with reference to the MSW (left: 34 knots < MSW <60 knots, center left: 60 knots < MSW < 80 knots, center right: 80 knots < MSW < 100 knots, right: 100 knots < MSW), and the sample number is shownin the upper right corner of each panel. The abscissa and ordinate values represent the frequency (unit: cycles per 2 days) and distance of the TC center (unit:km), respectively. A white-colored diamond in the upper and middle panels indicates that the peak is statistically significant (see text), while a white-colored diamond in the bottom panels shows that the coherence exceeds 0.61 (which is statistically significant at the 99% level with 12 degrees of freedom).

10
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Fig. 4. Evolution of the (a) TB and (b) Vr anomalies composited for the TCs with a maximum MSW larger than 100 knots. The upper, middle, and bottom
panels represent the composites (thick solid line) at the distance of 300 km (upper), 350 km (middle), and 400 km (bottom) from the circulation center.
Anomalies are calculated by subtracting the time-mean. Thin solid lines in the panels indicate variations of each TC at a given distance.

SOLA, 2020, Vol. 16, 109−114, doi:10.2151/sola.2020-019
00LST 06 Jul. 2016

09LST 06 Jul. 2016

18LST 06 Jul. 2016

Center : (132.70 , 17.55)
Pressure : 932[hPa]MSW : 90[kt]Long Radius : 388[km]Short Radius : 277[km]
K
300
280
260
240
220
200
180

Center : (130.10 , 18.70)
Pressure : 915[hPa]MSW : 100[kt]Long Radius : 444[km]Short Radius : 277[km]
K
300
280
260
240
220
200
180

Center : (127.70 , 19.80)
Pressure : 900[hPa]MSW : 110[kt]Long Radius : 416[km]Short Radius : 333[km]
K
300
280
260
240
220
200
180

03LST 06 Jul. 2016

12LST 06 Jul. 2016

21LST 06 Jul. 2016

Center : (131.90 , 18.00)
Pressure : 925[hPa]MSW : 95[kt]Long Radius : 444[km]Short Radius : 277[km]
K
300
280
260
240
220
200
180

Center : (129.25 , 19.10)
Pressure : 907[hPa]MSW : 105[kt]Long Radius : 444[km]Short Radius : 277[km]
K
300
280
260
240
220
200
180

Center : (127.00 , 20.10)
Pressure : 900[hPa]MSW : 110[kt]Long Radius : 388[km]Short Radius : 388[km]
K
300
280
260
240
220
200
180

06LST 06 Jul. 2016

15LST 06 Jul. 2016

00LST 07 Jul. 2016

113

Center : (131.00 , 18.35)
Pressure : 920[hPa]MSW : 97[kt]Long Radius : 444[km]Short Radius : 277[km]
K
300
280
260
240
220
200
180

Center : (128.40 , 19.50)
Pressure : 900[hPa]MSW : 110[kt]Long Radius : 444[km]Short Radius : 277[km]
K
300
280
260
240
220
200
180

Center : (126.30 , 20.40)
Pressure : 900[hPa]MSW : 110[kt]Long Radius : 388[km]Short Radius : 388[km]
K
300
280
260
240
220
200
180

Fig. 5. TB Snapshots of TC 1601 (from 00 LST on July 6 to 00 LST on July 7). The time interval of the snapshots is 3 hours. Orange, green, and blue circles represent radii of 100 km, 300 km, and 500km from the TC center, respectively. The geographical location and pressure of the TC center, MSW, longest radius, and shortest radius of 30 knots are given in the upper right section of each panel, and the background color of the annotations indicates daytime(orange) or nighttime (purple). The locations, central pressure, MSW and radiuses in the annotations are linearly interpolated from the RSMC Tokyo best
track data (see text).

Although these snapshot sequences might include features ofthe spontaneous development, we speculate that the diurnal cycles
of convective activity and the associated anvil in the outer band of
a TC are the fundamental processes assisting in the intensificationor maintenance of a TC. However, further investigations are required to confirm this speculation.

5. Conclusion and summary
This study examined diurnal cycles of the TB and upper-levelwinds associated with TCs over the WNP basin, making use of
Himawari-8 images of unprecedented temporal and spatial resolution. The results of a spectral analysis revealed that the diurnal
cycle of the TB prevails in the areas beyond 300 km from theTC center, while diurnal cycle signals are absent near the storm
center. The dominance of the diurnal cycle was also found in Vrvariations in intensive TCs. In addition, the coherence spectra
between TB and Vr demonstrated that diurnal variation is not
merely a passive process in a TC, but that diurnal cloud cycles andhorizontal divergence in the upper level are significantly coupled
in intensive TCs. On the other hand, there is no dominant cycle
in Vt variations. Composites of TB and Vr variations in intensive
TCs illustrated that the lowest TB emerges around 15 LST, andthe TB minimum lags a Vr peak by 3−6 hours. The migration
speed estimated from the phase spectra significantly exceeds thetime-mean Vr. Therefore, it is speculated that diurnal cycle signals

propagate (i.e., are not advected).
The results obtained here are largely consistent with thosefrom previous studies. However, the present study is the firstto statistically examine the diurnal cycle of TCs over the WNPbasin. Another novel point is that AMV products derived from
Himawari-8 observations were included in the analysis in thisstudy. Various weather forecast centers in the world rely on the
Dvorak technique (Dvorak 1975) to estimate TC intensity. In
some cases, however, the Dvorak technique ignores rapid developments in TC intensity within several hours (e.g., Kishimoto et al.
2013). On the other hand, Oyama and Wada (2019) demonstrated,through numerical experiments using a non-hydrostatic model,that TC warm core intensification is associated with the diurnal
cycle and other short-timescale variations in TC convection.
Considering these facts, the results of the present study provide abasis for better estimation of TC intensity (and an improvementon the Dvorak technique). However, the physical mechanisms
responsible for the diurnal cycle and its role in the intensification
and/or maintenance of TCs were left unresolved and should beclarified in future studies. The following questions might be keys
to address those issues; why the tangential wind has no dominantcycles, and what kind of clouds are responsible for the diurnal
cycle (eyewall, rainbands, or local convection).
Storm sizes are different among the TCs, and normalization
by a certain length scale might be useful to highlight the criticaldistance where the diurnal cycle dominates. However, there areseveral options to determine the scale of a TC (e.g., R50 and R30

114

Fukuda et al., Diurnal Cycle of Clouds in Tropical Cyclones

in Table 1), and it is difficult to select the most adequate parameterfor the present purpose. Therefore, such normalization processing
is also left for the future study.

Acknowledgments
This work was supported by JSPS KAKENHI Grants
JP25400463, JP16KK0095, and JP19H05697, and Environment
Research and Technology Development fund (2-1904) of theEnvironmental Restoration and Conservation Agency of Japan.
Edited by: T.-Y. Koh

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