early online release · 1 33 abstract 34 this study compares the regional characteristics of heavy...
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The DOI for this manuscript is
DOI:10.2151/jmsj.2020-044
J-STAGE Advance published date: June 2nd 2020
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preliminary version at the above DOI once it is available.
1
Difference Between Cloud Top Height and Storm Height 2
for Heavy Rainfall using TRMM Measurements 3
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Hwan-Jin Song, Sunyoung Kim, Soonyoung Roh, and Hyesook Lee 6
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National Institute of Meteorological Sciences, 9
Korea Meteorological Administration, 10
Jeju-do, Republic of Korea 11
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January 9, 2020 (submitted) 17
May 8, 2020 (revised) 18
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Corresponding author: Hwan-Jin Song 30
Email: [email protected] 31
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Abstract 33
This study compares the regional characteristics of heavy rain clouds in terms of 34
Cloud Top Height (CTH) and Storm Height (SH) from long-term Tropical Rainfall 35
Measuring Mission (TRMM) observations. The SH is derived from Precipitation Radar 36
reflectivity and the CTH is estimated using Visible and InfraRed Scanner brightness 37
temperature (10.8 μm) and reanalysis temperature profiles. As the rain rate increases, 38
the average CTH and average SH increase, but by different degrees in different regions. 39
Heavy rainfall in continental rainfall regimes such as Central Africa and the United States 40
is characterized by high SH, in contrast to oceanic rainfall regions such as the 41
northwestern Pacific, Korea, and Japan; the increase of atmospheric instability in dry 42
environments is interpreted as a mechanism of continental floods. Conversely, heavy 43
rain events in Korea and Japan occur in a thermodynamically near-neutral environment 44
with large amounts of water vapor; these are characterized by the lowest CTH, SH, and 45
ice water content. The northwestern Pacific exhibits the lowest SH in humid 46
environments, similar to Korea and Japan; however, this region also characteristically 47
exhibits the highest convective instability condition as well as high CTH and CTH−SH 48
values, in contrast to Korea and Japan. The observed CTH and SH characteristics of 49
heavy rain clouds are expected to be useful for the evaluation and improvement of 50
satellite-based precipitation estimation and numerical model cloud parameterization. 51
Keywords Cloud Top Height, Storm Height, Heavy Rainfall, TRMM, Korea, Japan 52
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1. Introduction 54
Precipitation Radar (PR), TRMM Microwave Imager (TMI), Visible and InfraRed Scanner 55
(VIRS), and Lightning Imaging Sensor (LIS) observations have been made by the Tropical 56
Rainfall Measuring Mission (TRMM) satellite since 1997, significantly increasing our 57
scientific understanding of precipitating clouds for more than 20 years. For example, 58
continental precipitation regions typically experience maximum precipitation in the late 59
afternoon when radiative heating by the sun and the resulting atmospheric instability are at 60
their peak, whereas oceanic regions exhibit increased rainfall at night or dawn due to 61
radiative cooling in an already humid environment (Takayabu 2002; Nesbitt and Zipser 62
2003; Yang and Smith 2006; Liu and Zipser 2008). Continental precipitation clouds are 63
characterized by a higher storm height (SH) and larger radar reflectivity inside the clouds 64
compared to oceanic clouds (Liu et al. 2008; Xu and Zipser 2012). Furthermore, the TMI 65
Polarization Corrected Temperature (PCT) at 85 GHz is relatively low in continental 66
precipitation clouds because large scattering occurs due to ice crystals in the upper layer, 67
along with the frequent lightning flashes from LIS observation in continental regions 68
(Nesbitt et al. 2000). Zipser et al. (2006) reported that the central region of the African 69
continent, which is located in tropics, exhibits the clearest continental precipitation 70
characteristics, demonstrated by a lower TMI PCT, higher PR 40-dBZ height (an indicator of 71
vigorous deep convection), and frequent LIS lightning flashes. In a follow-up study, Liu et al. 72
(2007) analyzed the characteristics of deep convective clouds in the Congo region of Africa 73
3
and the northwestern (NW) Pacific Ocean. They found that the Cloud Top Height (CTH) 74
estimated by VIRS was similar between the two regions (due to a similar level of neutral 75
buoyancy); however, the altitude with PR 40-dBZ reflectivity values of over 20 dBZ or 40 76
dBZ was relatively low in the North Western (NW) Pacific region compared to that in Central 77
Africa (Here, this term refers to the middle part of the African continent, not the Economic 78
Community of Central African States). They inferred that the weaker vertical velocity in the 79
NW Pacific was the cause of this difference in reflectivity altitude. 80
Furthermore, in a study of middle latitude regions, Sohn et al. (2013) emphasized that 81
heavy rainfall in the Korean peninsula is accompanied by relatively low CTH, radar 82
reflectivity of the upper layer, and ice water content, in contrast to Oklahoma in the United 83
States, and argued that warm-type heavy rain (in contrast to cold-type) dominates the 84
precipitation system of Korean peninsula. Subsequently, Song and Sohn (2015) reported 85
that warm-type heavy rain classified from PR reflectivity profiles is not limited to the Korean 86
peninsula, but a common precipitation structure shown in the NW Pacific and East Asia 87
monsoon environments. This feature has led to significant problems for satellite-based 88
precipitation estimation (Ryu et al. 2012; Shige and Kummerow 2016) and microphysical 89
parameterization of numerical models (Song and Sohn 2018; Song et al. 2019) over these 90
monsoon areas. Active collision and coalescence of raindrops in a humid environment are 91
a major cause of warm-type heavy rain (Song et al. 2017). Hamada et al. (2015) also 92
reported a surprisingly low correlation between very high-altitude vertically developed 93
4
clouds and clouds with heavy precipitation, which highlights the importance of warm-rain 94
processes for producing heavy rainfall. Hamada and Takayabu (2018) further noted that 95
extreme rainfall events around Japan were produced by a huge supply of water vapor from 96
the southwest, similar to studies over South Korea (Sohn et al. 2013; Song and Sohn 97
2015). 98
In another approach, Song et al. (2019) and Kim et al. (2019) classified heavy rain types 99
in the Korean peninsula based on the frequency distribution of infrared-based CTH by 100
rainfall intensity. Here, as with SH, heavy rain systems with relatively low CTHs are also 101
considered warm-type heavy rain events. Moreover, Liu et al. (2007, 2008) investigated the 102
occurrence frequency of deep convective clouds, in which the VIRS brightness temperature 103
at 10.8 μm (TB11) is less than 235 K or 210 K. The results demonstrated that the majority of 104
such clouds are concentrated in tropical regions such as the western Pacific Ocean, 105
Central Africa, and the Amazon. Furthermore, Liu et al. (2007) investigated the differences 106
between the PR 20-dBZ height and the CTH estimated from TB11 and reanalysis data for 107
rain clouds with TB11 of 210 K or less, and discovered a very large difference between the 108
two heights, predominantly in tropical regions. Additionally, the Mesoscale Convective 109
System (MCS), which has an extremely cold cloud top temperature and a large horizontal 110
area, predominantly occurs in tropical regions (Yuan and Houze 2010). Based on the above 111
results, it is clear that there are large contrasts in CTH between tropical regions and middle 112
latitudes, including the Korean peninsula. This result from a CTH/SH point of view shows 113
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differences with the conclusion of Song and Sohn (2015), who stated that the vertical 114
structures of heavy rain around the NW Pacific Ocean and the Korean peninsula display a 115
similar structure in radar reflectivity profiles. This implies that fundamental differences 116
exists between the CTH obtained by TB11 and the SH derived from reflectivity profiles. 117
Based on this concept, many previous studies have analyzed the joint probability density 118
function (PDF) of TB11 and SH in order to understand cloud characteristics and evaluate 119
cloud parameterization in numerical weather prediction models (Masunaga and Kummerow 120
2006; Matsui et al. 2009; Roh and Satoh 2014, Matsui et al. 2016). However, because this 121
framework targets all clouds, there has been little emphasis on the characteristics of heavy 122
rain clouds, which occur with relatively low frequency. 123
Thus, this study aims to achieve a comprehensive understanding of warm-type heavy 124
rain in the middle latitude and continental/oceanic precipitation in tropics. Specifically, this 125
research aims to improve our understanding of the characteristics of heavy rain clouds in 126
Central Africa and the NW Pacific (Liu et al., 2007), Korea and the United States regions 127
(Sohn et al., 2013), and the NW Pacific and East Asia monsoon regions (Song and Sohn, 128
2015). The TB11 and SH characteristics of these four regions are interesting because it is 129
expected that SH is sensitive to the continental and oceanic precipitation system, and TB11 130
differs between tropical and middle latitude regions. Because it is expected that regional 131
characteristics will be more distinct for heavy rain clouds than light rain clouds, this study 132
focuses on the characteristics of heavy rain clouds. Instead of using the TB11 data, this 133
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study investigates the difference between CTH and SH in terms of the altitude scale by 134
converting TB11 data into CTH format along with reanalysis data. Furthermore, this study 135
discusses the mechanism behind regional differences in CTH and SH. 136
137
2. Data and methods 138
TRMM Multisatellite Precipitation Analysis (TMPA) precipitation data (version 7) for the 139
June–August period of 2002–2013 were used in this study. The final production of TMPA 140
precipitation scaled by surface rain-gauge observations (3B42) was compared with the 141
uncalibrated 3B42RT data (microwave and infrared merged product) in order to examine 142
the problem of satellite-based rain retrieval related with the regional characteristics of 143
precipitation (see Huffman et al. 2007 and 2010 more detail about precipitation products). 144
In addition, spatiotemporal matching data (2D-CLOUDSAT-TRMM) of the CloudSat Cloud 145
Profiling Radar (CPR) at 94 GHz with PR reflectivity profiles were used for the June–August 146
period of 2007–2010 (note that the product was limited within this period). Here, the 50-min 147
window was used for temporal matching between two satellites. The vertical resolution of 148
CPR and PR data was 240 m and 250 m, respectively. Unlike PR, CPR data is limited from 149
the spatial observation aspect because only along-track scanning is performed at 2-km 150
intervals. Additionally, Ice Water Content (IWC) profiles from the CPR 2B-CWC-RVOD 151
product (Austin et al. 2009; P1_R05 version) were used for the period of 2006–2016, which 152
were fully available throughout the period. 153
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This study predominantly used TRMM PR near-surface rain rate and echo-top height (or 154
SH), TMI PCT85, and VIRS TB11 instantaneous data (version 7) from June–August of 155
2002–2013. The TRMM data were available from 1997 but the analysis period was set to 156
12 years due to changes in the spatial resolution and coverage caused by an orbit boost in 157
August 2001. It should be noted that the TRMM ended on April 2015, and VIRS data were 158
available up to March 2014. The PR is a satellite-based radar operated at 13.8-GHz 159
frequency that performs observations for a 247-km swath width at a horizontal footprint of 160
5.0 km (at the nadir). The VIRS is a visible and infrared imager that makes observations in 161
a cross-track scanning format like the PR with a 833-km swath width at a horizontal 162
resolution of 2.4 km (at the nadir). TMI is a conical scanning microwave imager, which 163
performs observations at 10.7, 19.4, 21.3, 37.0, and 85.5 GHz channels with a 878-km 164
swath and 5-km horizontal resolution. The VIRS and TMI data were spatially matched with 165
nearest PR rain pixels within the PR foot print. Since parallax correction between two 166
sensors is not considered in here, it partly includes the potential possibility of spatial 167
mismatch. The PR-VIRS data were matched again with ERA-Interim data (Dee et al. 2011) 168
with 6-h temporal intervals and 1°×1° horizontal resolution by applying spatiotemporal 169
interpolation to the ERA-Interim data into PR instantaneous data (total 192,869,424 PR rain 170
pixels). The ERA-Interim data included temperature and geopotential height profiles, 171
Convective Available Potential Energy (CAPE), Total Precipitable Water (TPW), and 172
column-integrated moisture flux convergence (ConQ). We admit the limitations of current 173
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method for estimating CTH based on the ERA-Interim temperature profiles with coarse 174
temporal and horizontal resolutions. This issue can be more improved by using the recently 175
advanced ERA5 reanalysis products with more higher temporal and horizontal and 176
resolutions (1 hourly and 0.25°×0.25° grids) in the future study. 177
This study attempted to calculate the CTH of precipitation clouds from VIRS TB11 data 178
by referring to the temperature profiles of ERA-Interim data. In the case of opaque clouds 179
such as precipitation clouds, TB11 can be used to approximate cloud top temperature. 180
Additionally, if the vertical distribution of temperature is known, it can be converted to CTH 181
(Kwon et al. 2010). Under the assumption of a moist adiabatic lapse rate (6 K km-1), for 182
example, the 1-K error of cloud top temperature measurements can lead a CTH uncertainty 183
of approximately 0.16 km. However, latent heat cooling can occur due to sublimation in the 184
upper part of deep convective clouds; if its magnitude is large, it can lead to lower estimates 185
of cloud top temperature (higher CTH). Lastly, the vertical distribution of air temperature is 186
another factor that generates uncertainty; for example, temperature profiles inside 187
precipitation clouds can differ from those estimated from the reanalysis data. In this case, 188
the assumption the cloud top temperature equals the ambient environmental temperature 189
may no longer be valid, as shown in Luo et al. (2010) and Wang et al. (2014). The CTH of 190
this study was set to always be the same or higher than the SH and the same or lower than 191
the tropopause height. The SH is the altitude corresponding to a reflectivity of 192
approximately 15-dBZ and should be physically lower than the CTH because it refers to the 193
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height at which larger precipitation particles appear compared to the clouds. In addition, it is 194
assumed that there is no overshooting cloud above the troposphere, so that the CTH 195
cannot exceed the tropopause height. Although overshooting clouds are common in tropics, 196
(e.g., Luo et al. 2008; Liu and Zipser 2005), this study does not consider these here. The 197
tropopause height was determined from the vertical distribution of ERA-Interim 198
temperatures by referring to the WMO (1957) method, defined as the lowest level showing 199
a lapse rate of less than −2 K km-1. In the CTH calculation process, when TB11 was lower 200
than the temperature at the ERA-Interim tropopause height, the spatial distribution climate 201
values of the Atmospheric Infrared Sounder (AIRS) and Advanced Microwave Sounding 202
Unit (AMSU) tropopause height (monthly and 1°×1° grids; doi: 203
10.5067/Aqua/AIRS/DATA319) were substituted in the analysis. Tropopause heights of 204
14.5–16.5 km were typically used for the regions of major interest in this study. 205
3. Results 206
The regional differences between heavy rain clouds revealed in previous studies can be 207
related to problems of satellite precipitation estimation. For example, Sekaranom and 208
Masunaga (2019) showed that the regional difference of PR and TMI rain products 209
(focusing on oceanic and continental convection), resulting from characteristics that PR 210
tends to detect more organized heavy rain system under the humid environment, whereas 211
the TMI rainfall is sensitive to deep convective clouds under relatively dry and unstable 212
condition. Figure 1a shows the average value of TMPA summer precipitation. In general, 213
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substantial precipitation occurs along the Inter Tropical Convergence Zone (ITCZ). 214
Furthermore, among middle latitude regions, Korea and Japan regions show remarkably 215
high precipitation as a result of the East Asian summer monsoon, comparable with that of 216
tropical regions. In the TMPA products, microwave and infrared precipitation estimations 217
are calibrated by ground rain-gauge observations; when precipitation is compared before 218
and after performing this correction, an interesting result is found (Fig. 1b). A satellite-based 219
precipitation estimation algorithm over land is developed that depends on whether the 220
temperature in the upper part of the clouds is low (infrared method) or is a strong 221
depression occurs due to ice scattering (microwave method). However, if it is adjusted to a 222
certain region, a decrease in the retrieval accuracy for other regions is unavoidable; the 223
regions affected by this phenomenon are shown in red (positive deviation) and blue 224
(negative deviation) in Fig. 1b. As positive deviations are found in the United States, the 225
Eurasia continent, and Central Africa, it is determined that satellite precipitation over the 226
continental regime was overestimated compared to that obtained from ground observations. 227
On the other hand, negative deviations are clearly visible in monsoon regions such as India, 228
the Indochina Peninsula, islands of Southeast Asia, Taiwan, Southern China, Korea and 229
Japan, and northern South America, implying a relatively warm brightness temperature at 230
the satellite despite of heavy precipitation. In the southern hemisphere, negative deviations 231
are also observed in New Zealand and Chile. In general, the regions showing negative 232
deviations are adjacent to the sea and may be substantially influenced by the humid 233
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environment. Since the transition from the TMPA to the Global Precipitation Measurement 234
(GPM) Integrated Multi-satelliE Retrievals for GPM (IMERG) is underway (Huffman 2019), 235
regional bias in precipitation shown in this study may have been somewhat mitigated in the 236
IMERG data. 237
The regional cloud and precipitation characteristics should be more examined with 238
respect to the importance of satellite precipitation estimation. To accomplish this goal, a 239
suitable candidate is match-up data from the CloudSat CPR and TRMM PR, which are 240
active sensors. The CPR can estimate the CTH and cloud profiles relatively accurately 241
because it is sensitive to relatively small cloud particles. However, the PR is sensitive to 242
relatively large precipitation particles. The height corresponding to the echo top of radar 243
reflectivity can be regarded as the SH, and the vertical structure of precipitations can be 244
observed. Following analysis of the spatiotemporal matching data of CPR and PR, the CPR 245
and PR radar reflectivity for the PR precipitation intensity both smaller and larger than 10 246
mm h-1 are presented in Contoured Frequency by Altitude Diagrams (CFADs) in Figs. 2a 247
and b, respectively. As the rain rate increases, higher radar reflectivity is observed in the 248
upper layer, as well as an increase in CTH and SH. The cloud layer corresponding to the 249
zone between CTH and SH can be observed only at the CPR. Because the CPR is strongly 250
attenuated to the precipitation particles (Stephens et al. 2008), the radar reflectivity does 251
not increase continuously as the altitude decreases, but decreases below a certain altitude 252
(near melting layer), as in Luo et al. (2017). Therefore, the PR reflectivity below the SH 253
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shows more realistic conditions. When the precipitation intensity is larger than 10 mm h-1, 254
the difference between CPR and PR reflectivity increases further. However, because the 255
CloudSat observation width is close to a line and its trajectory differs from that of the TRMM 256
satellite, match-up between the two satellites occurs only approximately 27 times per day 257
(Fig. 2c). Even if four years of available data are gathered, this is far from sufficient to study 258
the regional cloud and precipitation characteristics (77,963 rain clouds and 6,511 heavy 259
rain clouds with near-surface rain rate larger than 10 mm h-1). Although the vertical 260
distribution inside the cloud layer could not be investigated as with CPR, this study 261
investigates the regional CTH and SH characteristics of heavy rain clouds by converting 262
VIRS infrared observation data, which are mounted along with PR on the TRMM satellite, 263
into CTH. 264
Figure 3 shows the summer spatial distribution of CTH and SH averaged for each rain 265
rate intensity. In Figs. 3a and b, the regions with higher CTH and SH values typically 266
correspond to regions with large precipitation. Note that regions with large precipitation in 267
Fig. 1a also exhibit large average rain intensity. In summer, the upward motion in the 268
southern hemisphere is much suppressed compared to that in the northern hemisphere; 269
consequently, CTH and SH are not high. Furthermore, CTH and SH are low in the 270
surrounding regions of subtropical high pressure because of the suppressed updraft. 271
Because CTH and SH generally have a proportional relationship with the precipitation 272
intensity (Song et al. 2019; Song and Sohn 2019), both CTH and SH increase clearly as the 273
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rain rate intensity increases (e.g., 10 > mm h-1, 40 > mm h-1). The SH of extreme heavy rain 274
shown in Fig. 3f is more than 9 km in continental precipitation regions (e.g., Central Africa, 275
China, the United States), and less than 9 km in oceanic precipitation regions (e.g., ocean 276
regions, Korea, Japan, Taiwan, Philippines, the Indochina peninsula, India, Amazon, and 277
parts of New Zealand and Chile). The majority of maritime continent regions correspond to 278
land regions with negative deviations in Fig. 1b. Specifically, even regions recording 279
average unconditional (≥ 0 mm h-1) precipitation of over 250 mm month-1 in Fig. 1a exhibit 280
an average SH of only 8–9 km. This is in contrast to the SH of 9–12 km observed in 281
continental precipitation regions, which have lower average precipitation than oceanic 282
precipitation regions (Fig. 1a). The CTH of extreme heavy rain clouds shows different 283
characteristics to the SH of those (Fig. 3e). In continental precipitation regions, maximum 284
CTH is approximately 15 km, which is higher than SH because the SH is already high. The 285
average CTH is high (13–14 km) in the ITCZ, similar to the average precipitation (Fig. 1a), 286
and the maximum value (14–15 km) is found in the NW Pacific Ocean. Conversely, the 287
average CTH of Korea and Japan is approximately 12–13 km, which is very different from 288
the NW Pacific Ocean. This is a very contrasting feature considering the similar SH 289
between these two regions. This implies that the CTH difference between tropical and 290
middle latitude regions was missed in the study of Song and Sohn (2015), which classified 291
the NW Pacific and East Asia monsoon regions as “warm-type heavy rain” based on the 292
similar vertical structure of radar reflectivity. Note that the mean tropopause heights are 293
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15.19 km in the United States, 15.84 km in Korea/Japan, and 16.23 km in tropics (Central 294
Africa and NW Pacific) in boreal summer. Although not all rain cases reach the tropopause, 295
the regional difference of in tropopause height can also affect the CTH results in this study, 296
especially for the difference between mid-latitude and tropics. It is also noted that Korea 297
and Japan are characterized by lower mean CTH despite of higher tropopause height than 298
the United States. 299
Figure 4a shows the spatial distribution of the difference between CTH and SH for 300
extreme heavy rain (> 40 mm h-1). That is, it refers to the depth of the cloud layer 301
corresponding to between approximately 10 km SH and 15 km CTH shown in Fig. 2b. The 302
regions of interest in previous studies (Li et al. 2007; Sohn et al. 2013; Song and Sohn 303
2015) such as the United States, Korea and Japan, Central Africa, and the NW Pacific are 304
shown by boxes in Fig. 4a. The statistical average values of the four regions are provided in 305
Table 1. Note that the visit frequency of middle latitude regions is higher than that of tropical 306
regions due to the characteristics of TRMM satellite orbit. The average precipitation for the 307
United States, Korea/Japan, Central Africa, and the NW Pacific is 94.9, 180.7, 171.3, and 308
268.1 mm month-1, respectively. Specifically, the average inland precipitation of 309
Korea/Japan is 220.9 mm month-1, which is 82% of that in the NW Pacific and the highest 310
level of all middle latitude regions. 311
First, the United States has an average CTH−SH (difference between CTH and SH) of 312
approximately 2.7 km, which is the lowest value among the four regions, and the CTH and 313
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SH are both high (13.2 km and 10.5 km, respectively). In Central Africa, the average SH is 314
10.4 km, similar to that of the United States; however, because the CTH is rather high at 315
14.3 km, the average CTH−SH is 3.9 km. In Korea/Japan, the CTH and SH are 12.6 km 316
and 8.3 km, respectively, which are the lowest values among the regions. The SH in the 317
NW Pacific is similar to that of Korea/Japan; however, because the CTH is high (14.2 km), 318
the average CTH−SH is 5.5 km, which is the largest among all four regions. The differences 319
in CTH and SH between these regions can be explained by the difference in the 320
thermodynamic environment. In fact, the CAPE is typically high in tropical regions (Fig. 4b) 321
and the TPW exhibits maximum values in South Asia, NW Pacific, and East Asia monsoon 322
regions (Fig. 4c). Specifically, because ConQ is high around Korea/Japan, the dynamic 323
conditions for producing heavy rainfall are satisfied over those regions (Fig. 4d). When the 324
average values are examined for the four regions (Table 1), the CAPE is highest (1,503.2 J 325
kg-1) in the NW Pacific and lowest (544.5 J kg-1) in Korea/Japan. The TPW is highest in the 326
NW Pacific (62.2 mm), followed by Korea/Japan, Central Africa, and the United States. 327
ConQ is highest in Korea/Japan, at 0.38 g m-2 s-1. In addition, the CAPE and CTH−SH of 328
extreme heavy rain clouds in the Eastern Pacific Ocean are quite lower than those in the 329
Western Pacific Ocean. These results are consistent with the relatively suppressed deep 330
convection in the Eastern Pacific in Sekaranom and Masunaga (2019), and can vary with 331
interannual changes of the El Niño Southern Oscillation (Henderson et al. 2018). However, 332
the mean CTH−SH of extreme heavy rain clouds in the Eastern Pacific is still higher than 333
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that in the Korea/Japan because of relatively high CTH in the EP (Fig. 3e). Therefore, the 334
small difference between CTH and SH of extreme heavy rain clouds found in Korea and 335
Japan seems to be very unique. 336
For a more detailed analysis, Fig. 5 shows the PDF distributions for CTH and SH, as well 337
as the thermodynamic environment variables of extreme heavy rain. First, in the CTH 338
distribution (Fig. 5a), double peaks appear in every region. Among them, the peak located 339
at the lower height corresponds to a location below the tropopause height during the 340
process of allocating the VIRS TB11 to the vertical distribution of ERA-Interim temperatures. 341
The peak located at a higher altitude corresponds to the case in which the peak is outside 342
of the ERA-Interim data; thus, the climatology of AIRS/AMSU tropopause height is 343
substituted. Note that different treatment of tropopause height will not much change the 344
regional difference tendency in CTH, although it can affect the statistics. It is confirmed that 345
there are many cases where the CTH is 15 km or higher in the NW Pacific and Central 346
Africa, with the NW Pacific exhibiting more cases where CTH values are close to 17 km. 347
Meanwhile, in Korea/Japan, the proportion of cases with a CTH of less than or equal to 13 348
km is 48.7%, which is the largest among the four regions. The CTH condition of less than or 349
equal to 13 km, required for classification of warm-type heavy rain in the Korean peninsula, 350
has also been discussed by Song et al. (2019) and Kim et al. (2019). For the PDFs of SH, 351
the results between NW Pacific and Korea/Japan are very similar (Fig. 5b), which is 352
consistent with the results from Song and Sohn (2015). Compared to these regions, the 353
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United States and Central Africa exhibit many cases where SH is greater than or equal to 354
10 km. In particular, in the United States, the SH is often in the 10–15 km range, and in 355
Central Africa, the SH is often greater than or equal to 15 km. The average SH is similar 356
between these two regions (Table 1). 357
The resulting CTH−SH distribution is the largest in the NW Pacific, followed by 358
Korea/Japan, Central Africa, and the United States (Fig. 5c). For example, the proportion of 359
CTH−SH cases with a thickness of greater than or equal to 4 km in the NW Pacific, 360
Korea/Japan, Central Africa, and the United States is 74%, 59%, 51%, and 24%, 361
respectively. The proportion of CAPE cases of greater than or equal to 1,000 K kg-1, which 362
can provide the thermodynamic energy for the growth of deep convective clouds in the NW 363
Pacific, Central Africa, the United States, and Korea/Japan is 75%, 52%, 34%, and 17%, 364
respectively (Fig. 5d). It is determined that the order of CAPE values is closely related to 365
the CTH results. The TPW distribution reveals a stark contrast between humid NW Pacific 366
and Korea/Japan regions and relatively dry Central Africa and United States regions (Fig. 367
5e). Such a difference of water vapor is likely the most fundamental cause of differences 368
between oceanic precipitation regions and continental precipitation regions, including the 369
SH difference. Furthermore, substantially lower TPW in the United States than that in 370
Central Africa is deemed to be the reason for it having the lowest average precipitation 371
among the four regions (Table 1). Cases where ConQ is higher than 0.5 g m-2 s-1 appear 372
most frequently in Korea/Japan among the four regions (Fig. 5f), indicating dynamically 373
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favorable conditions for heavy rainfall. 374
Figure 6 shows the joint frequency distribution of IWC observed from the CloudSat CPR. 375
The occurrence of heavy rain cannot be determined using CPR observations alone. 376
However, the IWC distribution above the SH for heavy rain clouds can be inferred from the 377
fact that the SH of heavy rain is typically higher than 9 km. The proportion of cases where 378
the IWC is greater than 2 g m-3 (above approximately 9 km) is 0.38%, 0.26%, 0.12%, and 379
0.03% in the United States, Central Africa, the NW Pacific, and Korea/Japan, respectively, 380
in descending order. The fact that the average rain rate is higher and the PCT85 is lower 381
(i.e., indicating abundant IWC) in the United States than in Central Africa for extreme heavy 382
rain clouds (Table 1) partially explains the proportion of high IWC in the United States. 383
However, considering that the occurrence of extreme heavy rain clouds between two 384
regions is similar despite of the relatively frequent visit number of TRMM satellite for 385
mid-latitudes more than twice as much as that for tropics, the relative occurrence frequency 386
of heavy rain clouds in the United States is less than half of that in Central Africa, implying 387
more suppressed convection over the United States. Rather, it can be interpreted that 388
clouds accompanied by abundant IWC produce localized heavy rain cases in the United 389
States. The small proportion of high IWC in the NW Pacific and Korea/Japan is consistent 390
with a relatively high PCT85 (Table 1). Because the NW Pacific has a deeper cloud layer 391
above the SH (i.e., CTH-SH) as well as smaller amount of IWC than Central Africa, this is 392
interpreted as lower volumetric ice density for the NW Pacific. Lastly, the CTH−SH of 393
19
Korea/Japan is the second largest, behind that of the NW Pacific, but this region contains 394
the smallest amount of ice crystals in the corresponding cloud layer (the proportion of IWC 395
values of 2 g m-3 or greater is only 21% in the NW Pacific and 7% in the United States). In 396
this case, as discussed in Fig. 1b (Ryu et al. 2012; Shige and Kummerow 2016), the 397
microwave-based precipitation algorithm based on a cold brightness temperature due to ice 398
scattering will fail in Korea/Japan, resulting in a severe precipitation underestimate. 399
A comprehensive explanation of the above results can be summarized as follows. The 400
heavy rain clouds of Central Africa and the United States, which are continental 401
precipitation regions, are characterized by higher SH compared to the NW Pacific and 402
Korea/Japan, which are oceanic regions. A strong updraft in a relatively dry environment is 403
thought to be the mechanism for continental precipitation. CTH is large in the NW Pacific 404
and Central Africa, which are located in tropical regions, and this characteristic is related to 405
high atmospheric instability (i.e., CAPE). Although the CAPE of Central Africa is lower than 406
that of the NW Pacific, the fat-type CAPE structure of continental precipitation regions can 407
provide stronger vertical velocity for clouds at the same CAPE than oceanic regions with 408
the skinny-type CAPE structure (Lucas et al., 1994). Heavy rain events in the United States 409
are generated at lower CAPE and TPW conditions than those in Central Africa, consistent 410
with lower occurrence frequency and precipitation over the United States. In addition, the 411
United States has the lowest CTH−SH among the four regions. Korea/Japan has high 412
precipitation, comparable to that of the NW Pacific in the thermodynamically near-neutral 413
20
environment (i.e., lower CAPE condition) with a large amount of total water vapor. As 414
discussed in the numerical analysis of Song et al. (2017), active collision and coalescence 415
processes of raindrops in a humid environment are a major cause of the “warm-type heavy 416
rain” found in Korea and Japan. The CTH−SH of Korea/Japan is the second highest among 417
the four regions, but the amount of ice crystals inside the corresponding layer is insufficient 418
due to the lowest CAPE environment. The NW Pacific has low SH in humid environments, 419
similar to Korea/Japan, but the highest CAPE environment, which is a large contrast from 420
Korea/Japan. Consequently, heavy rain clouds that develop in the NW Pacific region show 421
large average CTH and CTH−SH values. The most typical examples of similar SH and 422
contrasting CTH in Korea/Japan and the NW Pacific are probably the East Asia Summer 423
Monsoon front (called Changma in Korea and Baiu in Japan) and typhoon. Figure 7 424
provides typical examples of the Changma and typhoons. Despite similarities in the rain 425
rate and SH between the two cases, distinct differences are observed in the VIRS TB11 426
(i.e., a substantially lower cloud top temperature for typhoon case over the NW Pacific). 427
428
4. Summary and conclusions 429
The results of precipitation deviations in TMPA data before and after rain-gauge 430
adjustment typically showed positive deviations in continental precipitation regions and 431
negative deviations in humid monsoon regions. Therefore, it was confirmed that differences 432
in regional precipitation characteristics can cause severe problems in satellite-based 433
21
precipitation retrieval. Furthermore, this study analyzed the vertical structure of clouds and 434
precipitation, targeting precipitation clouds based on the spatiotemporal matching data of 435
TRMM PR and CloudSat CPR. When rain rate intensity was 10 mm h-1 or higher, the 436
average SH was approximately 10 km and the average CTH was approximately 15 km, 437
confirming that CPR reflectivity was observed for the layer between the SH and the CTH. 438
However, it was determined that the match-up data of TRMM and CloudSat satellites was 439
highly insufficient for studying the regional characteristics of precipitating clouds. 440
This study used long-term TRMM PR (near-surface rain rate and SH) and VIRS (TB11 or 441
CTH) data to investigate the regional CTH and SH characteristics of heavy rain clouds. For 442
the analysis, the CTH was calculated for precipitation clouds using VIRS TB11 data and 443
ERA-Interim temperature profiles. As the rain rate intensity increased to 0 mm h-1, 10 mm 444
h-1, and 40 mm h-1, the average CTH and SH increased notably; however, differences were 445
observed between regions. A detailed analysis was performed for the United States, 446
Central Africa, NW Pacific, and Korea/Japan, which were the regions of interest in previous 447
studies. First, the United States showed an average CTH−SH of 2.7 km, which was the 448
lowest among the four regions, whereas CTH and SH were both high (13.2 km and 10.5 km, 449
respectively). In Central Africa, the average SH was 10.4 km, but the average CTH was 450
14.3 km, which was relatively high compared to that of the United States, resulting in a 451
mean CTH−SH of 3.9 km. SH in the NW Pacific was similar to that in Korea/Japan (8.7 km); 452
however, because the CTH was high (14.2 km), the average CTH−SH was 5.5 km, which 453
22
was the largest among the four regions. The CTH and SH of Korea/Japan were 12.6 km 454
and 8.3 km, respectively, which were the lowest values among the four regions. Moreover, 455
heavy rain clouds in Korea and Japan regions were characterized by extremely lower ice 456
water content. 457
The heavy rain clouds found in Central Africa and the United States, which are 458
continental precipitation regions, were characterized by higher SH than those in the 459
oceanic precipitation regions of the NW Pacific and Korea/Japan. Strong updrafts in a dry 460
environment are a suggested mechanism for continental precipitation. CTH values were 461
large in the NW Pacific and Central Africa, which are located in tropical regions, and this 462
characteristic was related to the high CAPE and tropopause height conditions. 463
Korea/Japan exhibited a large amount of precipitation, comparable to that in the NW Pacific, 464
due to active raindrop collision and coalescence processes in humid environments, despite 465
a thermodynamically neutral condition (i.e., lower CAPE condition). The CTH−SH of 466
Korea/Japan was the second greatest among the four regions, but the ice water content 467
inside the corresponding layer was much lower with respect to the low CAPE environment. 468
The NW Pacific also had low SH in humid environments, similar to that in Korea/Japan. 469
However, the highest CAPE environment and high CTH/CTH−SH values caused by this 470
differed from those in Korea/Japan. In conclusion, this study has increased our 471
understanding of the regional cloud height characteristics of heavy rain clouds in 472
continental, oceanic, tropical, and middle latitude monsoon regions. Furthermore, it is 473
23
expected that the results of this study can be used to improve satellite-based precipitation 474
estimation or cloud parameterization for numerical weather prediction models. 475
476
Acknowledgments 477
The TRMM, CloudSat, AIRS, and ERA-Interim data were downloaded from 478
https://gpm.nasa.gov/data-access/downloads/trmm, http://www.cloudsat.cira.colostate.edu, 479
http://disc.sci.gsfc.nasa.gov, and http://apps.ecmwf.int/datasets/data/interim-full-daily, 480
respectively. This work was funded by the KMA Research and Development Program 481
“Development of the AI technique for the prediction of rainfall optimized over the Korean 482
peninsula” under Grant (KMA2018-00124). 483
484
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613
28
614
Fig. 1. (a) TMPA mean precipitation (version 7) and (b) the difference of TMPA precipitation 615
between without and with rain gauges (i.e., uncalibrated 3B42RT and calibrated 3B42 616
products) over a 12-yr summer (June–August) period (2002–2013). 617
29
618
Fig. 2. The reflectivity profiles of (a) light (< 10 mm h-1) and (b) heavy (≥ 10 mm h-1) rain 619
clouds from CloudSat CPR (grey colors) and TRMM PR (rainbow colors) measurements. 620
The PR-derived near-surface rain rate (RR) was used. The number of cases for light and 621
heavy rain during June–August 2007–2010 are given in parentheses. (c) The collocated 622
points between PR (red) and CPR (blue) visiting paths in a day. 623
624
625
30
626
Fig. 3. The spatial distributions of mean cloud top (left) and storm (right) heights for (a, b) 627
rain clouds with rain rate (RR) greater than 0 mm h-1, (c, d) heavy rain (RR > 10 mm h-1), 628
and (e, f) extreme heavy rain (RR > 40 mm h-1) during June–August 2002–2013. Regions 629
with mean precipitation less than 25 mm month-1 in Fig. 1 are expressed as gray color. 630
631
632
31
633
Fig. 4. The climatological mean of (a) difference between cloud top and storm heights, (b) 634
convective available potential energy (CAPE), (c) total precipitable water (TPW), and (d) 635
column-integrated water vapor flux convergence (ConQ) for extreme heavy rain (> 40 mm 636
h-1) during June–August 2002–2013. Regions with mean precipitation less than 25 mm 637
month-1 in Fig. 1 are expressed as gray color. The United States, Korea and Japan, Central 638
Africa, and western Pacific areas are represented by rectangles in (a). 639
640
641
32
642
Fig. 5. Probability Density Functions (PDFs) of (a) cloud top height, (b) storm height, (c) 643
difference between cloud top and storm heights, (d) CAPE, (e) TPW, and (f) ConQ for 644
extreme heavy rain (> 40 mm h-1) over the United States, Korea and Japan, Central Africa, 645
and western Pacific areas during June–August 2002–2013. 646
647
648
Cloud Top Height [km]6 8 10 12 14 16
(%)
0
3
6
9
12
15
18
Storm Height [km]6 8 10 12 14 16
0
1
2
3
4
5
6
Cloud Top Storm Height [km]0 3 6 9 12
0
1
2
3
4
5
6
United StatesKorea and JapanCentral AfricaNW Pacific
CAPE [J kg-1]0 1000 2000 3000
(%)
0
1
2
3
4
5
6
TPW [mm]20 30 40 50 60 70 80
0
1
2
3
4
5
6
Con Q [g m-2 s-1]0.0 0.3 0.6 0.9 1.2 1.5
0
1
2
3
4
5
6
_
(a) (b) (c)
(d) (e) (f)
33
649
Fig. 6. Mean frequency profiles of Ice Water Content (IWC) retrieved from CloudSat CPR 650
measurements over the United States, Korea and Japan, Central Africa, and western 651
Pacific areas during June–August 2006–2016. The numbers of cases are given in 652
parentheses. The percentages of IWC greater than 1 g m-3 are also displayed. 653
654
655
34
656
Fig. 7. The spatial distributions of (a) PR rain rate, (b) PR storm height, and (c) VIRS 657
brightness temperature at 10.8 μm (TB11) for Changma event (00UTC 22 June 2009) over 658
the Korean peninsula. (d), (e), and (f) Same as (a), (b), and (c), but for Typhoon Chaba 659
(08UTC 24 August 2004) over the northwestern Pacific ocean. 660
661
662
35
Table 1. The 12-yr (2002–2013) summer (June-August) mean precipitation, rain rate (RR), 663
polarization corrected temperature at 85 GHz (PCT85), brightness temperature at 11 μm 664
(TB11), cloud top height (CTH), storm height (SH), CTH-SH, convective available potential 665
energy (CAPE), total precipitable water (TPW), column-integrated water vapor flux 666
convergence (ConQ) for extreme heavy rain clouds (> 40 mm h-1) over four interested 667
areas. 668
United States Korea/Japan Central Africa NW Pacific
Precipitation (mm month-1)
94.9 180.7 220.9 (land)
171.3 268.1
# of pixels 14394 33761 14469 26507
RR (mm h-1) 54.9 56.2 53.6 55.7
PCT85 (K) 203.8 223.7 205.3 220.8
TB11 (K) 215.6 221.6 205.4 206.7
CTH (km) 13.2 12.6 14.3 14.2
SH (km) 10.5 8.3 10.4 8.7
CTH-SH (km) 2.7 4.3 3.9 5.5
CAPE (J kg-1) 853.0 544.5 1142.5 1503.2
TPW (mm) 40.7 59.2 47.6 62.2
ConQ (g m-2 s-1) 0.12 0.38 0.08 0.29 669
670