EARLY ONLINE RELEASE
This is a PDF of a manuscript that has been peer-reviewed
and accepted for publication. As the article has not yet beenformatted, copy edited or proofread, the final publishedversion may be different from the early online release.
This pre-publication manuscript may be downloaded,
distributed and used under the provisions of the Creative
Commons Attribution 4.0 International (CC BY 4.0) license.
It may be cited using the DOI below.
The DOI for this manuscript is
DOI:10.2151/jmsj.2024-035
J-STAGE Advance published date: September 3, 2024
The final manuscript after publication will replace thepreliminary version at the above DOI once it is available.

Page 1 of 37

For Peer Review

Investigation of characteristics of warm clouds inThailand under different climate patterns with
contoured frequency by optical depth diagrams andground-based meteorological data

Panuwong WONGNIM1
Graduate School, Course of Science and Technology,
Tokai University, Japanand

Minrui WANG and Takashi Y. NAKAJIMA
Research and Information Center,
Tokai University, Japan

June 12, 2024-----------------------------------1) Corresponding author: Panuwong WONGNIM, Graduate School, Course of Scienceand Technology, Tokai University, Japan, Tokai University, 4-1-1, Kitakaname, Hiratsukashi, Kanagawa 259-1292, Japan JAPAN
Email: Panuwong.won@gmail.com
Tel: 080-9689-2778

For Peer Review

Page 2 of 37

Abstract

The study investigates the relationship between climate pattern, rainfall, and internalwarm cloud structure in Thailand using ground-based meteorological and satellite data.
Situated in the tropical climate zone, Thailand faces drought conditions exacerbated bychanges in warm cloud patterns due to climate change. Results indicate a negative
correlation between rainfall and Oceanic Nino Index (ONI) and Pacific Decadal Oscillation(PDO), with rainfall decreasing as ONI and PDO values increase, especially in April andOctober. In contrast, Indian Ocean Dipole (IOD) shows a weaker correlation with the rainfall
during the period from 1991 to 2022.
During the period from 2006 to 2014, the internal warm cloud structure over Thailand wasanalyzed using Contoured Frequency by Optical Depth Diagrams (CFODDs) derived fromCloudSat Cloud Profiling Radar (CPR) and Aqua Moderate Resolution Imaging
Spectroradiometer (MODIS). The results show the warm cloud process, indicated by thetransfer of cloud droplets to drizzle and rain as cloud particle size increases near the cloud
top. The cloud droplet mode occurs from 4 to 15 μm, drizzle mode occurs from 15 to 21 μm
and dominance of rain mode occurs above 21 μm.
Differences in cloud structures are observed under seasonal and El Niño–Southern
Oscillation (ENSO) phases, with the warm clouds contain larger cloud droplet effective
radius (Re) during the wet season compared to the dry season. Meanwhile, in cloud structure1Page 3 of 37

For Peer Review

exhibit thicker cloud and faster transfer drizzle mode to rain mode with Re increasing during
La Niña phase compared to El Niño phase. These differences in cloud structures are
attributed to variations in aerosols, differences in humidity, and temperature influenced bygeographical characteristics.

Keywords

CFODD, Thailand, climate, rainfall, warm cloud, remote sensing

2For Peer Review

Page 4 of 37

1. Introduction
Climate change characterized by soaring temperatures, accelerated snow and glaciermelt, and elevated sea levels, exacerbates widespread drought conditions globally, with
Southeast Asia bearing a significant impact (Eckstein and Schäfer, 2021). These climate
dynamics are closely intertwined with cloud behavior, precipitation patterns, and thefrequency of drought events.
Thailand, situated between 5°N to 21°N and 97°E to 106°E, grapples with a tropicalclimate characterized by distinct wet and dry seasons driven by the southwest and northeast
monsoons (Chabangborn, 2012). The monsoons bring about cycles of rain and dry spells,with droughts worsened by phenomena such as ENSO. Between 2007 and 2014, droughts
intensified, affecting an average agricultural area of 2,486,382,400 square meters per yearwith an average damage value of more than 18 million US dollars annually, indicative of an
upward trend (Institute of Ombudsman Studies, 2016). Monitoring ENSO events involves
assessing ocean indices including the ONI, PDO, and IOD (Lam et al., 2019; Gale andSaunders, 2013).
Thailand can experience rain from both warm and ice clouds, typically with predominantly
warm clouds, followed by mixed and cold clouds, with cloud top temperatures ranging from
282.09 K to 185.51 K depending on the season and analysis of MTSAT-TIR imagery from2006-2007 revealed that liquid clouds dominate over Thailand throughout the year. Even
during the wet season, many large cold clouds persist for several hours; however, the most3

For Peer Review

Page 5 of 37

clouds are observed still liquid clouds, with temperatures ranging from 10°C to -10°C(Bumrungklang et al., 2009).
Satellites play a pivotal role in climate monitoring, providing comprehensive, continuous,and representative global data. This data is improving numerical simulations of clouds andenhancing our understanding of cloud microphysical properties. Nakajima et al. (2010a,
2010b) introduced contoured frequency by optical depth diagrams (CFODDs) to visualize
the evolution of warm clouds, utilizing data from instruments of Aqua’s Moderate Resolution
Imaging Spectroradiometer (MODIS) and CloudSat’s Cloud Profiling Radar (CPR).
CFODDs depict cloud evolution based on in-cloud optical depth (COD) and cloud droplet
effective radius (Re), as cloud particle radius near the cloud top closely relates to the cloud
evolution state (Nakajima, 2010b). Similar structures have been observed in CFODDs
obtained through statistical methods and time series analysis (Sato et al., 2014). CFODDs
effectively evaluate low-level cloud evolution, including condensation, growth, and extinction,assessing cloud microphysical processes validity in global-scale models using CFODDs
derived from low-level cloud signals worldwide (Suzuki et al., 2013a; Jing et al., 2017; Jing
and Suzuki, 2018). Additionally, Suzuki et al. (2013b) examined aerosol effects on low-levelcloud processes based on CFODDs.
Although CFODDs have been employed globally to study cloud evolution (Nakajima,
2010b), including diverse regions like East Asia, California, Peru. (Matsumoto et al., 2023),
and the Eastern Asian, Tropical Warm Pool, Australian, North Atlantic, and Equatorial Cold
4

For Peer Review

Page 6 of 37

Tongue regions (Michibata et al., 2019), there's been limited focus on warm clouds over thetropical zone associated with seasonal and climate variations. This study aims to fill this gap
by investigating the cloud evolution process, with a focus on Thailand areas. The goal is tounderstand the impact of cloud processes during climate change, particularly theirconnection to the ENSO climate pattern, which directly influences rainfall over land in thisregion, this study marks the first attempt to elucidate the internal structure of warm clouds
over Thailand using CFODDs.

2. Data and Method
2.1 Data
The study’s location covered land in Thailand region, from 3° to 23°N and 95° to 108°E(Fig. 1), the analysis region using the QGIS, version 3.30 (QGIS, 2021). In this region, rainfall
data were obtained from rain gauges, sourced from the National Hydroinformatics Data
Center, a government agency of Thailand. Climate patterns are related to ocean indices,which track changing sea surface temperatures in specific ocean areas. These indicesinclude ONI from a 3-month running mean of ERSST.v5 sea surface temperature anomalies
in the Niño 3.4 region, PDO, and IOD that are obtained from the National Oceanic andAtmospheric Administration (NOAA) and the Japan Meteorological Agency (JMA).
This study used satellite data from Aqua/MODIS and CloudSat/CPR, both of which arepart of the A-train constellation, provides an estimate of cloud droplet collection efficiency in5For Peer Review

Page 7 of 37

single-layer warm liquid clouds (SLWCs). Aqua/MODIS sensor provides data including cloudproperty products, aerosols, and surface temperatures using its 36 channels (Baum and
Platnick, 2006). CloudSat/CPR sensor emit millimeter waves and observe backscatter fromcloud particles (Stephens et al., 2008). The CPR sensor is valuable in collecting verticalprofiles of cloud microphysical properties, offering direct information on cloud microphysicalprocesses by reflectivity (Witkowski and Livermore, 2018).

2.2 Method
The rainfall in Thailand was analyzed and correlated with the ENSO climate pattern using
the ONI, PDO, and IOD index values from 1991 to 2022. The internal warm cloud structure
was investigated by CFODDs between 2006 and 2014. The CFODDs using cloud properties
products from the Aqua/MODIS satellite that are obtained from the Comprehensive Analysis
Program for Cloud Optical Measurements (CAPCOM) algorithm (Nakajima and Nakajima,
1995; Kawamoto et al., 2001; Nakajima et al., 2009), the products are cloud optical thickness(COT), Cloud droplet effective radius (CDR), and cloud top temperature, which are observed
at specific wavelengths (0.68 μm, 2.1 μm, and 10.8 μm) respectively. In additional study of
Nakajima et al. (2010a), CDR from the 2.1 μm channel of MODIS can distinguish cloud
droplets and drizzle particles and offers advantages over the 3.7 μm channel in terms of its
sensitivity to drizzle particles. The behavior of CDR retrieved from the 1.6 μm channel was
similar to that of CDR retrieved from the 2.1 μm channel. Moreover, warm cloud phase6

For Peer Review

Page 8 of 37

identification was performed using the Cloud and Aerosol Unbiased Decision Intellectual
Algorithm (CLAUDIA) algorithm developed by Ishida and Nakajima (2009), the CLAUDIA
algorithm was employed to address partial cloud cover along the edges of clouds within apixel. The COT and CDR values obtained from CAPCOM were used. In this study, thethreshold for cloud top temperature was set at 260 K, aligning with ISCCP guidelines. COT,
measured from solar radiation reflected by clouds, is examined for both liquid water and ice
clouds. A temperature threshold of 260 K is used to characterize warm clouds, with specificdifferentiation based on temperature (Raschke et al., 2005).
The CFODDs are created by combining cloud properties such as COT, selected CDR(Re), and radar reflectivity. COT, serving as the vertical axis, is determined using theadiabatic condensation growth model, which is represented by the in-cloud optical depth(COD). The 2B-TAU product is used only for identifying cloud top bin. The COT slicing in
our CFODDs had been given using the adiabatic growth assumption. The COT from thecloud bottom to a given height (h), denoted as the total optical thickness (Tc), with thegeometric thickness of the cloud (H), is represented by the internal cloud optical depth(COD) at each height (h) (Suzuki et al., 2010) followed equation as.
ℎ5 3
COD(ℎ) = 𝑇𝑐 1 ―𝐻While, CloudSat CPR provide radar reflectivity data (Ze), obtained from the 2BGEOPROF product that represent in horizontal axis. The CFODD represents cloudproperties in terms of COD and Ze. The Ze ranges correspond to different particle modes:
7

For Peer Review

Page 9 of 37

less than -15 dBZe indicates cloud droplet mode, -15 to 0 dBZe indicates drizzle mode, and0 to 15 dBZe indicates rain mode. These ranges help identify cloud condensation, collision,and precipitation processes, which involve the internal cloud structure. With the changes instructure associated with increasing Re, CFODDs can provide insights into the growth ofclouds from cloud droplets to rain (Nakajima et al., 2010b).

3. Results
3.1 Analysis of rainfall in Thailand with climate patterns
Over 32 years (1991–2022), rainfall data were transformed to normal percentage rainfall(NPR) for a year to study trends in rainfall with ocean indices (Fig. 2). The NPR of rainfall
data showed fluctuations, the highest annual rainfall was 1848 mm, which is 23% above the
NPR in 2022. In contrast, the lowest recorded rainfall was 1218 mm, which is 19% below
the NPR in 2019. When comparing normal percentage rainfall (NPR) with ocean indicessuch as ONI, PDO, and IOD, a strong correlation is observed between NPR and ONI and
PDO, but a weaker correlation is noted with IOD. Typically, when NPR values are above 0,
these indices tend to be lower than normal, while NPR tends to exceed the normal level
when the indices are below 0 (Fig 2). The association between Thailand's rainfall and thechanging sea surface temperatures of the Pacific and Indian oceans is highlighted by ONI,PDO, and IOD, respectively.
The correlation coefficients between annual rainfall and these indices were explained,8For Peer Review

Page 10 of 37

revealing a strong negative correlation between ONI and annual rainfall (-0.73), and anegative correlation between PDO and annual rainfall (-0.61). In contrast, IOD shows a weak
positive correlation, as illustrated in Table 1
Analyzing the average annual rainfall in Thailand by season (Fig. 3), which consists ofthe dry season (November to April) and the wet season (May to October), each spanning 6
months over the period from 1991 to 2022. This analysis is based on months influenced by
El Niño phase (ONI values over 0) and La Niña phase (ONI values below 0). The difference
in rainfall between the dry and wet seasons amounted to 905 mm and 909 mm during the
El Niño and La Niña phases, respectively. Focusing on the comparison between El Niño
and La Niña phases in each season, rainfall during El Niño is lower than during La Niña.
Rainfall errors vary between 40-70 mm in the dry season and between 90-96 mm in the wet
season. On average, rainfall during both seasons was approximately 100 mm lower during
El Niño phase compared to La Niña phase. In table 1, the ONI index exhibits a very strong
negative correlation of -0.79 with dry season rainfall and a correlation value of -0.57 in thewet season. Meanwhile, PDO demonstrates a moderate to strong negative correlation with
rainfall in the dry and wet seasons, respectively, while IOD exhibits a weak correlation with
rainfall in both seasons. The monthly accumulated rainfall data revealed a very strong
negative correlation with the ONI at -0.81 in April, moderate negative correlations of -0.46 in
March and -0.49 in May, and a strong negative correlation of -0.58 in October. PDO also
shows moderate negative correlations with the monthly rainfall of -0.48 in April and -0.39 in
9For Peer Review

Page 11 of 37

October, as well as weak correlations and moderate negative correlations of the IOD withthe monthly rainfall of 0.28 in March and -0.41 in November, respectively (Fig. 4). These
results show ENSO climate pattern, indicated by ocean indices that affect Thailand's rainfall
depending on different conditions between the wet and dry seasons, including air stability
and varying aerosols. These factors will be discussed in further study.

3.2 Using CFODD to study cloud evolution processes
CFODDs were used to reveal the overall warm cloud characteristics in Thailand,considering both seasonal variations and the ENSO climate pattern. The analysis used 9
years from 2006 to 2014. Six threshold values of cloud droplet effective radius (Re) wereselected to examine the following ranges: 4 μm < Re < 12 μm, 12 μm < Re < 15 μm, 15 μm< Re < 18 μm, 18 μm < Re < 21 μm, 21 μm < Re < 27 μm, and 27 μm < Re < 36 μm.

a. Cloud characteristics in Thailand
As Re expands from 4 μm to 12 μm (Fig. 5), the cloud droplet mode becomes dominant,leading to a condensation process that results in COD of approximately 40. With the Re
range extending to 15 μm, the cloud droplet mode continues to dominate, and COD
increases to above 40. A transition from the cloud droplet mode to the drizzle and rain modes
occurs at the cloud base when Re reaches 15-18 μm, with COD remaining above 40. Thistransition is characterized by collision and coalescence processes, along with condensation
10

For Peer Review

Page 12 of 37

from the cloud top. Between Re of 18 μm and 21 μm, the drizzle and rain modes are more
observed from the middle to the base of the cloud, resulting in a decrease in COD to 40.
Condensation processes are observed at the cloud top. Finally, when Re exceeds 21 μm,
the rain modes dominate, with COD at 40. COD decreases to 30 when Re extends to 27 μm.

b. Cloud characteristics during seasonal events
During the wet season (Fig. 6), a high frequency of condensation processes occurs when
Re ranges between 4 μm and 12 μm from the top to the base of the cloud, with a COD of
40, and from the top to the middle of the cloud when Re ranges between 12 μm and 15 μm.
When Re reaches 15-18 μm, the COD is approximately 40. A transition from the cloud droplet
mode to the drizzle mode occurs in the middle of the cloud, and a transition from the drizzle
mode to the rain mode occurs at the cloud base. Between Re of 18 μm and 21 μm, thedrizzle and rain modes are observed from the middle to the base of the cloud, with a COD
below 40. Between Re of 21 μm and 27 μm, the rain mode dominates from the middle to thebase, with a COD below 40. Finally, when Re exceeds 27 μm, the COD decreases to 30,
with rain modes dominating. In contrast, during the dry season (Fig. 7), Re between 4 μm
and 12 μm exhibit a COD of 40, which increases to 50 when Re ranges from 12 μm to 15
μm. Between Re of 15 μm and 18 μm, the COD is approximately 50, a transition from thecloud droplet mode to the drizzle and rain modes occurs near the base of the cloud.
Eventually, with Re extending from 18 to 21 μm, a transition from the drizzle mode to the11

Page 13 of 37

For Peer Review

rain mode is most observed from the middle to the base of the cloud, with COD rapidly
decreasing to below 30 and dissipating after that. For the CFODD used in the analysis, it
indicates that warm clouds during the dry season had smaller Re, approximately 4–21 μm,
compared to wet season clouds, which had Re ranging from 4–36 μm.

c. Cloud characteristics during ENSO phases
Fig. 8 and 9 illustrate warm cloud structure during La Niña and El Niño phase. During the
La Niña phase, a high frequency of condensation processes occurs when Re ranges
between 4 μm and 15 μm, with a COD above 40. When Re reaches 15-18 μm, the COD isapproximately 40, a transition from the cloud droplet mode to the drizzle and rain modes
occurs in the middle and at the base of the cloud, respectively, along with condensation fromthe cloud top. Between Re of 18 μm and 21 μm, the drizzle and rain modes appear from the
middle to the base of the cloud, with COD decreasing below 40. Between Re of 21 μm and27 μm, the rain mode dominates from the middle to the base, with COD below 40. Finally,
when Re exceeds 27 μm, the COD decreases to 30, with rain modes dominating. However,during El Niño phase, Re between 4 μm and 12 μm exhibit a COD below 40, which increases
to approximately 40 when Re ranges from 12 μm to 15 μm. Between Re of 15 μm and 18
μm, a transition from the cloud droplet mode to drizzle is most observed from the middle tothe base cloud, with COD decreasing to above 30. Between Re of 18 μm and 27 μm, atransition from the drizzle mode to the rain mode is most observed from the middle to the12For Peer Review

Page 14 of 37

base cloud, with COD decreasing to below 30. Eventually, Re exceeds 27 μm, the COD
decreases to approximately 20 with the domination of rain modes. For the CFODD analysis,
warm clouds under the El Niño phase show a slower transition from the drizzle mode to therain mode and exhibit thinner clouds compared to warm clouds under the La Niña phase.

4. Discussion
In Section 3.1, the results reveal a correlation between the ENSO climate pattern and theamount of rainfall in Thailand, which is located in Southeast Asia. A 2015 Met Office reportillustrates reduced rainfall in Southeast Asia during El Niño phase, the historical analysis
shows that ENSO is associated with anomalous surface temperatures in both the Pacificand Indian oceans. During El Niño events, weakened surface winds in the tropical Pacificresult in warmer ocean temperatures in the central and eastern regions. This induces
increased sinking air motion over Southeast Asia, leading to elevated surface pressure anddry conditions. Consequently, below-average rainfall occurs, contrasting with conditions
during La Niña phases (Bogale and Temesgen, 2021). As the result, the ONI and PDO
indices demonstrate a strong negative correlation with heavy rainfall in Thailand, whereas
the IOD index exhibits a weaker correlation. According to studies by Ueangsawat et al.
(2015), the ENSO phase impacts the variability of temperature and rainfall. During the
El Niño phase, there is an increase in maximum surface temperature in Thailand, particularly
significant in May and June, and a tendency for decreased rainfall during these months.
13

For Peer Review

Page 15 of 37

Consequently, studies by Kirtphaiboon et al. (2014) observed that ENSO phases leadrainfall anomalies in Thailand, with a lag of 4 months, based on historical data spanningfrom 1971 to 2010.
Thailand's seasons are categorized into dry and wet seasons based on warm
temperatures and variations in humidity levels. The dry season is characterized by warmer
temperatures and lower humidity, influenced by factors such as northeast monsoon, cold air
masses, upper westerly winds, thermal low pressure, local convection, and the sun's
declination. In contrast, the wet season is dominated by southwest monsoon, monsoon
trough, tropical cyclones, and low-pressure areas from the ocean, which introduce marine
aerosols and bring lower temperatures and higher humidity (Laonamsai et al., 2021). The
ONI exhibited a stronger influence on dry season rainfall more than wet season, showing
very strong and strong negative correlations, respectively. The ONI had the most significant
impact in April and October, preceding and following the wet season, respectively.
Meanwhile, the influence of the PDO remained consistent across both seasons. Positive
ONI index values are strongly associated with dry conditions, promoting biomass burningand resulting in increased aerosol optical depth (AOD) over the maritime continent. This
induces a decrease in relative humidity and reduced precipitation over Southeast Asia
beyond normal levels for the season (Ng et al., 2017). Bridhikitti (2013) found that ENSO
significantly affects Thai rainfall in the pre-monsoon and post-monsoon months of thefollowing year, significantly decreased rainfall with increasing AOD during the post-summer
14

For Peer Review

Page 16 of 37

monsoon months in inland regions.

The CFODDs in Section 3.2 provide internal warm cloud structure of Thailand that
represent clouds grow with increases in Re. The studies conducted in Eastern Asia indicatethat the macrophysical structures of clouds (such as liquid water path and cloud thickness)and their dynamical regimes (including lower-tropospheric stability and updraft velocity)
exhibit considerable seasonal variability. This variability leads to significant differences in
how clouds convert to rain depending on land and ocean (Takahashi et al., 2017; Michibata
et al., 2014). The warm clouds over Thailand progress from cloud droplet to drizzle andeventually to rain, and that COD depends on the size of the cloud particles. The behavior of
the COD during cloud growth in Thailand changes, which can be explained by theprecipitation process. Re increases from 4 to 15 μm during the condensation phase,increasing the COD to higher than those of the other phases. When Re reaches 15 to
21 μm, the collision process becomes more prevalent, leading to the appearance of drizzle
droplets. Eventually, the warm cloud undergoes a dissipation stage, leading to clouddisintegration that information is based on a study conducted by Nakajima et al. in 2010b.
According to studies by Michibata et al. (2019), warm rain is observed from a small Re range(5 < Re < 12 µm) in the Thailand region. Regarding a study by Matsumoto et al. (2023),
it demonstrates that the characteristics of clouds over Asia include a high frequency ofvarious cloud formations with Re of 6 – 27 μm, as indicated by the broad frequency of15

Page 17 of 37

For Peer Review

CFODD results. This is consistent with the warm cloud patterns observed over Thailand,
which also exhibit a high frequency of various cloud formations.

The difference of warm cloud structure depends on the season (see Section 3.2.2.) canbe explained by a transition behavior, COD and Re. During the wet season, clouds undergo
collision and coalescence processes when Re reaches 12-15 μm, while during the dry
season, clouds remain in condensation processes, as indicated by the cloud frequency.
Between Re of 15-18 μm, dry season clouds experience rainfall at the base of the cloud,after which the cloud dissipates and loses COD. In contrast, warm clouds during the wet
season can maintain COD and continue cloud processes as Re increases. Thesephenomena can be explained by the differences in temperature, humidity, and aerosol
conditions between the two seasons. Typically, during dry season, there is a combination ofhigher surface temperature and lower surface humidity, with temperatures reaching 33.1°C
and humidity at 73%, which can increase to 44.6°C. In contrast, during wet season,
temperatures average around 32.5°C with humidity at 80.4% (Long term climate changemonitoring and warning working group, 2021). Additionally, dry season is characterized byincreased aerosols from biomass burning, competing with available moisture and leading to
the formation of clouds with smaller droplet sizes (Lamb and Verlinde, 2011). As a result,warm clouds are harder to form during the dry season due to the inhibition of cloud
processes. This leads to clouds dissipating as Re increases. This result aligns with a study

16For Peer Review

Page 18 of 37

by Sapphaphab and Ruangrungrote (2019) in Chiang Mai, Thailand, which observed thatdry season conditions inhibit cloud formation compared to the wet season. This is indicatedby the lower cloud base height and more complicated cloud patterns observed during thewet season compared to dry season. Moreover, when comparing the results with
Precipitable Water (PW) during the period (Fig. 10), which indicates the precipitation
potential over Thailand, it becomes evident that there is a difference between the wet anddry seasons in 2011.
For the warm cloud under ENSO phases (see Section 3.2.3), as a result, the warm cloud
during the La Niña phase is thicker compared to the El Niño warm cloud as Re increases.
Additionally, the La Niña clouds rapidly transform from cloud droplets to rain when Re ranges
between 15 and 18 μm. The effect of ENSO phases on warm clouds is significant. This
climate phenomenon can impact warm clouds through factors such as air speed, the Pacificcold tongue process, and atmospheric moisture conditions, which vary depending on thephase of ENSO (Watanabe et al., 2011). In East Asia, El Niño phase shows negative effects
on warm clouds, with opposing effects during the La Niña phase. During the El Niño phase,
there is a decrease in precipitation, water content, and water vapor (Zhang and Sumi, 2002).
Additionally, during the El Niño phase, there is an increase in fire aerosols in equatorial Asia.
These fire aerosols can reduce precipitation by cooling the surface, thereby reducing
humidity and surface convergence. This contributes to increased static stability and heatingof the troposphere, which inhibits convection wind and cloud processes (Tosca et al., 2010),
17

For Peer Review

Page 19 of 37

resulting in slower cloud-to-rain transition due to aerosol burdens. Moreover, whencomparing the results with PW during the period under ENSO phases (Fig. 10), it becomes
evident that there is a difference between the La Niña phase and the El Niño phase.
Therefore, this study suggests that warm cloud process over Thailand expands fromdroplets to rain, with diverse cloud types that increase Re from 4 to 36 μm depending on theseason and ENSO phase. Higher temperatures and fire aerosol levels, along with lower
humidity during the dry season, inhibit cloud processes, as evidenced by the smaller Re
observed in warm clouds compared to the wet season. El Niño phases in Thailand induce
drier atmospheric conditions, higher fire aerosol levels, and sinking air conditions,contrasting with La Niña phases. El Niño phases inhibit warm cloud growth processes,
resulting in thinner COD and slower rain cloud processes as Re increases.

Data Availability Statement
All rain gauge data used in this study were obtained from the National Hydroinformatics Data
Center, which can be accessed online at (https://www.tmd.go.th/en/ClimateChart). The
ocean index data in this study can be found online from NOAA (https://psl.noaa.gov) andJMA (https://ds.data.jma.go.jp/tcc/tcc/products/elnino/index.html). Eventually, the CloudSat
and Aqua data can be requested by contacting Takashi Y. Nakajima (nkjm@yoyogi.ycc.utokai.ac.jp)

18For Peer Review

Page 20 of 37

Acknowledgments
The authors are grateful to Yu Matsumoto (who graduated from Tokai University in March
2023) for her data analysis of the CloudSat and Aqua data. This research was supported by
a Grant-in-Aid for Scientific Research (B) 21H01159 from the Japan Society for the
Promotion of Science (JSPS), and the EarthCARE project (JX-PSPC-538977) and GCOMC project (ER3GCF201) of the Japan Aerospace Exploration Agency (JAXA).

References
Baum, B. A. and S. Platnick, 2006: Introduction to MODIS cloud products. Earth Science
Satellite Remote Sensing: Vol. 1: Science and Instruments, Berlin, Heidelberg: Springer
Berlin Heidelberg, 74-91, doi.org/10.1007/978-3-540-37293-6_5.
Bogale, G. A. and T. Temesgen, 2021: Impacts and challenges of seasonal variabilities of
El Niño and La Niña on crop and livestock production in the central rift valley ofEthiopia: a review. Environment Pollution and Climate Change, 5(199), 2, DOI:
10.4172/2573-458X.1000199.
Bumrungklang, P., S. Dasananda and S. Dusadee, 2009: An analysis of seasonal
thunderstorm cloud distribution and its relation to rainfall occurrence in Thailand using
remotely sensed data, Suranaree J. Sci. Technol. 17(1), 71-86.
Bridhikitti, A., 2013: Connections of ENSO/IOD and aerosols with Thai rainfall anomalies
and associated implications for local rainfall forecasts. International journal of19For Peer Review

Page 21 of 37

climatology, 33, 2836-2845, doi.org/10.1002/joc.3630.
Chabangborn, A., 2012: The Asian monsoon - 50-7 ka BP, Stockholm University,1-121,[Available at https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-75974.].
Eckstein, D., V. Künzel and L. Schäfer, 2021: The global climate risk index 2021, Bonn:
Germanwatch, 1-52.
Gale, E. L. and M. A. Saunders, 2013: The 2011 Thailand flood: climate causes and return
periods, Weather, 68, 233-237, doi.org/10.1002/wea.2133.
Institute of Ombudsman Studies, 2016: Final report: The drought management in Thailand.
Office of the Ombudsman Thailand ,2-3, ISBN 9786169114659, [Available at
http://ombstudies.ombudsman.go.th/download/resources/Drought%20management%20
Thailand.pdf].
Ishida, H., and T. Y. Nakajima, 2009: Development of an unbiased cloud detection algorithm
foraspaceborne

multispectral

imager,J.

Geophys.

Res.

Atmos,

114,

doi.org/10.1029/2008JD010710.
Jing, X., K. Suzuki, H. Guo, D. Goto, T. Koshiro, and J. Mülmenstädt, 2017: A multimodel
study on warm precipitation biases in global models compared to satellite observations,J. Geophys. Res. Atmos, 122, 11806–11824, doi.org/10.1002/2017JD027310.
Jing, X., and K. Suzuki, 2018: The impact of process-based warm rain Constraints on theaerosolindirecteffect,J.

Geophys.

doi.org/10.1029/2018GL079956.
20

Res.

Atmos,

45,

10729–10737,

For Peer Review

Page 22 of 37

Kawamoto, K., T. Nakajima, and T. Y. Nakajima, 2001: A global determination of cloud
microphysics

with

AVHRR

remotesensing,J.

Climate,14,2054–2068,

doi.org/10.1175/1520-0442(2001)014<2054:AGDOCM>2.0.CO;2.
Kirtphaiboon, S., P. Wongwises, A. Limsakul, S. Sooktawee and U. Humphries, 2014:
Rainfall Variability over Thailand Related to the El Niño-Southern Oscillation (ENSO), J
Sustain Energy Environ, 5, 37-42.
Lam, H. C. Y., A. Haines, G. McGregor, E. Y. Y. Chan and S. Hajat, 2019: Time-series studyof associations between rates of people affected by disasters and the El Niño Southern
Oscillation (ENSO) cycle, international journal of environmental research and publichealth, 16, 3146 pp., doi.org/10.3390/ijerph16173146.
Lamb, D. and J., Verlinde, 2011: Physics and chemistry of clouds. Cambridge University
Press, 562-567, doi.org/10.1017/CBO9780511976377.019.
Laonamsai, J., K. Ichiyanagi, K. Kamdee, A. Putthividhya and M. Tanoue, 2021: Spatial
and temporal distributions of stable isotopes in precipitation over Thailand. Hydrologicalprocesses, 35(1), 1-15, doi.org/10.1002/hyp.13995.
Long term climate change monitoring and warning working group, 2021: Change intemperature and rainfall trends of Thailand, Technical Document No. 551.557.3 – 10 –
2021,

Thai

Meteorological

Department,

6.

1-74,

[Availableat

http://climate.tmd.go.th/content/file/2497].
Matsumoto, Y., M. Wang, Y. Sato and T. Y. Nakajima, 2023: Regional dependency of the21

For Peer Review

Page 23 of 37

cloud droplet growth process in combined analysis of Aqua MODIS and CloudSat CPR,
SOLA, 19, 63-69, doi.org/10.2151/sola.2023-009.
Michibata, T., K. Kawamoto and T. Takemura, 2014: The effects of aerosols on water cloud
microphysics and macrophysics based on satellite-retrieved data over East Asia and theNorth Pacific. Atmospheric Chemistry and Physics, 14(21), 11935-11948, doi.org/
10.5194/acp-14-11935-2014, 2014.
Michibata, T., K. Suzuki, T. Ogura and X. Jing, 2019: Incorporation of inline warm rain
diagnostics

into

the

COSP2

satellite

simulator

forprocess-oriented

model

evaluation. Geoscientific Model Development, 12(10), 4297-4307, doi.org/10.5194/gmd12-4297-2019.
Nakajima, T. Y., H. Masunaga, and T. Nakajima, 2009: Near-global scale retrieval of the
optical and microphysical properties of clouds from Midori-II GLI and AMSR data, J.
Remote Sens. Soc. of Japan, 29, 29–39.
Nakajima, T. Y., and T. Nakajima, 1995: Wide-area determination of cloud microphysicalproperties from NOAA AVHRR measurements for FIRE and ASTEX regions, J. Atmos.
Sci, 52, 4043-4059, doi.org/10.1175/1520-0469(1995)052<4043:WADOCM>2.0.CO;2.
Nakajima, T. Y., K. Suzuki, and G. L. Stephens, 2010a: Droplet growth in warm water clouds
observed by the A-Train. Part I: Sensitivity analysis of the MODIS-derived cloud droplet
size, J. Atmos. Sci., 67, 1884–1896, doi.org/10.2151/sola.2023-009.
Nakajima, T. Y., K. Suzuki, and G. L. Stephens, 2010b: Droplet growth in warm water clouds
22For Peer Review

Page 24 of 37

observed by the A-Train. Part II: A Multi-sensor view, J. Atmos. Sci., 67, 1897–1907,
doi.org/10.1175/2010JAS3276.1.
Ng, D. H. L., R. Li, S. V. Raghavan and S. Y. Liong, 2017: Investigating the relationshipbetween aerosol optical depth and precipitation over Southeast Asia with relative humidity
as an influencing factor. Scientific Reports, 7(1), 1-14, doi:10.1038/s41598-017-10858-1.
Raschke, E., A. Ohmura, W. B. Rossow, B. E. Carlson, Y. C. Zhang, C. Stubenrauch and
M. Wild, 2005: Cloud effects on the radiation budget based on ISCCP data (1991 to1995). International Journal of Climatology: A Journal of the Royal MeteorologicalSociety, 25(8), 1103-1125, doi.org/10.1002/joc.1157.
Sapphaphab, P., and S. Ruangrungrote, 2019: Cloud base height determination bymeteorological model simulation: the case study of Chiang Mai, Doctoral dissertation,
Srinakharinwirot University. 1-83, [Available at http://ir-ithesis.swu.ac.th/dspace/handle/
123456789/231].
Scaife, A., R., Allan, S. E, Belcher, A., Brookshaw, M., Davey, R., Eade, C., Folland, M.,
Gordon, L., Hermanson, S., Ineson, and J., Kennedy, J., Knight, J., Slingo and D., Smith,
2015: Big Changes Underway in the Climate System? Met Office Research News article.
1-21, [Available at https://www.researchgate.net/publication/282354112_Big_Changes_
Underway_in_the_Climate_System].
Sato, Y., T. Y. Nakajima, and T. Nakajima, 2012: Investigation of the vertical structure ofwarm-cloud microphysical properties using the cloud evolution diagram, CFODD,
23

For Peer Review

Page 25 of 37

simulated by a three-dimensional spectral bin microphysical model, J. Atmos. Sci., 69,
2012–2030, doi.org/10.1175/JAS-D-11-0244.1.
Stephens, G. L., D. G. Vane, S. Tanelli, E. Im, S. Durden, M. Rokey, D. Reinke, P. Partain,
G. G. Mace, R. Austin, T. L’Ecuyer, J. Haynes, M. Lebsock, K. Suzuki, D. Waliser, D. Wu,
J. Kay, A. Gettelman, Z. Wang, and R. Marchand, 2008: CloudSat mission: Performanceand early science after the first year of operation, J. Geophys. Res. Atmos., 113, 1-18,
doi.org/10.1029/2008JD009982.
Suzuki, K., J.-C. Golaz, and G. L. Stephens, 2013a: Evaluating cloud tuning in a climate
model

with

satellite

observations,GeophysRes.

Lett.,

40,

4464–4468.

doi.org/10.1002/grl.50874.
Suzuki, K., G. L. Stephens, and M. D. Lebsock, 2013b: Aerosol effect on the warm rain
formation process: Satellite observations and modeling, J. Geophys. Res. Atmos., 118,
170–184, doi.org/10.1002/jgrd.50043.
Suzuki, K., T. Y. Nakajima and G. L. Stephens, 2010: Particle growth and drop collection
efficiency

of

warm

observations. Journal

clouds

as

of

the

inferredfrom

Atmosphericjoint

CloudSat

Sciences, 67(9),

and

MODIS

3019-3032,

doi.org/10.1175/2010JAS3463.1.
Takahashi, H., K. Suzuki and G. Stephens, 2017: Land–ocean differences in the warm‐rainformation

processinsatellite

andground‐based24

observations

and

model

For Peer Review

Page 26 of 37

simulations, Quarterly Journal of the Royal Meteorological Society, 143(705), 1804-1815,
doi.org/10.1002/qj.3042
Tosca, M. G., J. T. Randerson, C. S. Zender, M. G. Flanner and P. J. Rasch, 2010: Do
biomassburningaerosols

intensify

droughtinequatorial

Asia

duringEl

Niño?. Atmospheric Chemistry and Physics, 10(8), 3515-3528, doi.org/10.5194/acp-103515-2010.
Ueangsawat, K., S. Nilsamranchit and A. Jintrawet, 2015: Fate of ENSO phase on upper
northern Thailand, a case study in Chiang Mai, Agriculture and Agricultural Science
Procedia, 5, 2-8, doi:10.1016/j.aaspro.2015.08.001.
Watanabe, M., M. Chikira, Y. Imada and M. Kimoto, 2011: Convective control of ENSO
simulated in MIROC, Journal of Climate, 24, 543-562, doi.org/10.1175/2010JCLI3878.1.
Witkowski, M. M., D. Vane, and T. Livermore, 2018: CloudSat-Life in Daylight Only
Operations(DO-Op),In

2018

SpaceOps

Conference,

2562,[Available

atdoi.org/10.2514/6.2018-2562].
Zhang, R. and A. Sumi, 2002: Moisture circulation over East Asia during El Niño episode innorthern winter, spring and autumn. J. Meteor. Soc. Japan. Ser. II, 80(2), 213-227,
doi.org/10.2151/jmsj.80.213.

25

Page 27 of 37

For Peer Review

Fig. 1 Map of the analysis region, the white box indicates the land area in Thailand analyzed in this work
(3°N–23°N, 95°E–108°E).
241x166mm (330 x 330 DPI)For Peer Review

Fig. 2 Comparison of annual NPR with ocean indices.
153x73mm (330 x 330 DPI)

Page 28 of 37

Page 29 of 37

For Peer Review

Fig. 3 Average rainfall divided into two distinct periods, dry season and wet season, each spanning 6
months. The error bars represent the variation in monthly rainfall within each period, influenced by ENSO
phases from 1991 to 2022.
114x81mm (330 x 330 DPI)For Peer Review

Fig. 4 Correlation coefficients between monthly ocean indices and rainfall in Thailand for period of 1991–
2022.
153x62mm (330 x 330 DPI)

Page 30 of 37

Page 31 of 37

For Peer Review

Fig. 5 CFODDs classified by Re with threshold values between 4 and 36 μm for Thailand.
263x176mm (330 x 330 DPI)For Peer Review

Fig. 6 CFODDs classified by Re with threshold values between 4 and 36 μm for Thailand during wet season.
264x175mm (330 x 330 DPI)

Page 32 of 37

Page 33 of 37

For Peer Review

Fig. 7 CFODDs classified by Re with threshold values between 4 and 36 μm for Thailand during dry season.
264x175mm (330 x 330 DPI)For Peer Review

Fig. 8 CFODDs classified by Re with threshold values between 4 and 36 μm for Thailand under La Niña
phase.
263x174mm (330 x 330 DPI)

Page 34 of 37

Page 35 of 37

For Peer Review

Fig. 9 CFODDs classified by Re with threshold values between 4 and 36 μm for Thailand under El Niño
phase.
264x175mm (330 x 330 DPI)For Peer Review

Fig. 10 An overview of precipitable water over Thailand using MYD08_M3 MODIS data from (a) April 2011 interm of dry season and La Niña phase, (b) April 2015 in term of dry season and El Niño phase, (c) October
2011 in term of wet season and La Niña phase, and (d) October 2015 in term of wet season and El Niño
phase.
314x111mm (330 x 330 DPI)

Page 36 of 37

Page 37 of 37

For Peer Review

Table 1 Correlation coefficients between annual average ocean index and seasonal rainfall in Thailand for
period of 1991–2022.
633x119mm (38 x 38 DPI)