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3

Multi-scale uncertainty of mesoscaleconvective systems in the Baiu frontal zone:
A case study from June 2022

4

Saori NAKASHITA

5

Graduate School of Science, Kyoto University, Kyoto, Japan

6

Takeshi ENOMOTO

1

2

Disaster Prevention Research Institute, Kyoto University, Uji,
8
Kyoto, Japan
9
Application Laboratory, Japan Agency for Marine-Earth10
Science and Technology, Yokohama, Kanagawa, Japan
7

11

Satoshi ISHII

12

Japan Meteorological Agency, Tokyo, Japan

13

July 19, 2024

Corresponding author: Saori NAKASHITA, Graduate School of Science, Kyoto
University, Kitashirakawa Oiwake-cho, Sakyo-ku, Kyoto, 606-8502, Japan.
E-mail: nakashita@dpac.dpri.kyoto-u.ac.jp

Abstract14

15

Mesoscale convective systems (MCSes) occasionally develop over the16

East China Sea (ECS) in the Baiu frontal zone under both the atmospheric17

and oceanic inﬂuence. The factors that determine their predictability have18

not been fully understood yet. This study investigates the uncertainties

19

aﬀecting two MCSes observed by research vessels on 19 June 2022 using

20

regional ensemble simulations. These MCSes have contrasting features:

21

the ﬁrst was triggered by an atmospheric mesoscale disturbance, while the22second was induced by the boundary layer destabilization over the warm

23

Kuroshio current.

24

The ﬁrst MCS shows high variability in the synoptic-scale uncertainties

25

detected by the breeding ensemble. The best-performing member success-26

fully represents the strong meso-β-scale cyclone and the frontal structure

27

with deep moist layers. The ensemble simulations are less skillful for the28

second MCS than the ﬁrst. The enhanced surface turbulent heat ﬂux in the29

SST frontal zone is found to be signiﬁcantly correlated to the precipitation30

due to the second MCS despite the cold bias of SST that is commonly im-

31

posed on all members. The dense upper-air information from the vessels

32

signiﬁcantly improves the representation of the sharp frontal structure asso-

33

ciated with the ﬁrst MCS, but has little impact on the second MCS proba-34

bly due to the underestimation of the boundary layer moistening. This case1

35

study indicates that the predictability of MCSes over the ECS depends on

36

their development mechanisms, and that the incorporation of uncertainties

37

in both the atmosphere and ocean are important for the ensemble forecast-38

ing of these MCSes.

2

39

Keywords mesoscale convective system, Baiu front, regional spectral model,

40

ensemble data assimilation

41

1.

Introduction

42

The Baiu (Meiyu) frontal zone (BFZ) has a characteristic hierarchi-

43

cal structure from planetary to meso- or convective scales (Ninomiya and44

Akiyama 1992; Ninomiya and Shibagaki 2007; Tsuji and Takayabu 2023).

45

From a planetary-scale view, the Baiu front is located between the sub-46

tropical westerly jet and the low-level jet, and these two jets contribute47

to maintain a convectively unstable low pressure zone, called the mon-48

soon trough, by the advection of warm air along the BFZ (Sampe and Xie

492010). Transient disturbances that propagate along the subtropical jet form

50

subsynoptic-scale cloud systems, and precondition an environment favor-

51

able for the development of mesoscale convective systems (MCSes). These52

MCSes can lead to disasters, including ﬂoods and landslides, due to heavy

53

rainfall; therefore, it is important to accurately predict their occurrence and54

development.

55

The studies on the predictability of MCSes during the summer monsoon

56

based on ensemble forecasts have been mainly limited to the cases developed

57

over the continent (Bei and Zhang 2007; Luo and Chen 2015; Zhuang et al.

58

2020; Ke et al. 2022, 2023). These studies agreed that the representation of3

59

MCSes is strongly sensitive to the initial conditions even if the large-scale

60

environment features are well represented. Ke et al. (2023) found that the61

initial perturbations that optimally reﬂected the ﬂow-dependent nature of62

the BFZ were important for representing the appropriate mesoscale error

63

growth. These initial perturbation structures aﬀected especially the error

64

growth of water vapor, suggesting the importance of optimal initial per-65

turbations for forecasting of moist convections. Luo and Chen (2015) also

66

showed that the representation of MCSes was the most sensitive to the ini-

67

tial moisture ﬁelds. They further demonstrated that the reproducibility of68

cold domes induced by the preexisting convective systems was the key fea-

69

ture of MCS predictability. The predictability of these continental MCSes70

was also found to be controlled by the orography due to regulation of the

71

moist convections (Zhuang et al. 2020). However, because MCSes over the72

East China Sea (ECS) develop under the moist environment aﬀected by73

both the atmosphere and ocean, the predictability of such MCSes should74

be diﬀerent from those over land. The key factors for the predictability of75

oceanic MCSes remain unclear.

76

The moist environment over the ECS with a large amount of free tropo-

77

spheric moisture is maintained by continental air from the southwest and78

oceanic air from the south. The southwesterly low-level jet that transports

79

the continental air intersects the southerly winds driven by the pressure gra-4

80

dient force that supplies oceanic moist air, and these two airstreams form a81large water vapor gradient over the ECS with respect to the dry and cold82

air to the north (Moteki et al. 2004a,b; Maeda et al. 2008). These moist

83

airstreams sometimes create a moist, absolutely unstable stratiﬁcation be-84

fore the initiation of precipitation (Tsuji et al. 2021), and trigger the heavy

85

rainfall in conjunction with upwelling induced by the lower inﬂow. This86

mechanism indicates that the development of individual MCSes is strongly

87

inﬂuenced by the representation of the large-scale wind distribution and the88

frontal zone.

89

The water vapor supply from the sea surface to the boundary layer

90

is another important factor for the development of MCSes. The ECS is91

characterized by a steep sea surface temperature (SST) gradient due to the92

warm Kuroshio current. Turbulent heat ﬂuxes intensify over the tongue of93

the Kuroshio, which enhances precipitation by destabilizing the boundary

94

layer (Sasaki et al. 2012; Kunoki et al. 2015). A warm SST also contributes

95

to the maintenance of instability near the surface by evaporation to the96

inﬂow across the Kuroshio (Kunoki et al. 2015; Sato et al. 2016). This97

suggests that the distributions of wind speed and temperature near the98

surface have a large inﬂuence on MCSes, as does the remote moisture supply

99

in the free troposphere.

100

Although the development mechanisms of MCSes over the ECS have

5

101

been widely investigated, it is still diﬃcult to accurately predict the loca-

102

tions and intensities of individual MCSes due to high uncertainties in moist

103

convections and lack of the observations of vertical proﬁles. Kato et al.

104(2003) found that underestimation of moisture amount in the lower tropo-

105

sphere caused the poor representation of meso-β-scale convective systems

106

in their numerical simulations. Kato and Aranami (2005) also emphasized107

the importance of lower moisture ﬁelds to the reproducibility of heavy rain-

108

fall in the BFZ and suggested that the suﬃcient vertical information would

109

reduce the forecast failure. Their results motivate us to quantify the fore-

110

cast uncertainty of MCSes using ensemble methods to compensate for the111

diﬃculty in deterministic forecasting and thereby contribute to prevention

112

and mitigation of disaster due to heavy rainfall associated with the MCSes.

113

In order to identify the factors aﬀecting the predictability of oceanic MC-

114

Ses, this study investigates the role of multi-scale uncertainties in the pre-115

diction of the initiation and development of MCSes through a case study for116

the MCSes developed during an intensive observation campaign deploying

117

three research vessels on 19 June 2022. Recently, Manda et al. (2024) ex-

118

amined the detailed environmental conditions related to the MCS observed

119

earlier in the intensive observations, and found that the near-saturation120

conditions in the free troposphere played an important role in supporting121

the MCS development. In this study, we examine the variability in the

6

122

prediction of this MCS and another one observed later through ensemble

123

simulations for this observation period. These simulations are conducted124

using a limited-area atmospheric model developed at the National Center

125

for Environmental Prediction (NCEP) with ﬂow-dependent initial perturba-

126

tions to estimate the inﬂuence of initial uncertainties from the synoptic-scale127

to the mesoscale. We also perform sensitivity experiments to the assimila-128

tion of the vessel observations to investigate the impact of the dense upper

129

information on the development of the MCSes.

130

The remainder of this paper is organized as follows. Section 2 describes

131

the experimental design and analysis methods. Section 3 overviews the132

observation campaign and the environmental features related to the MCSes

133

on 19 June 2022. Section 4 examines the sensitivity of MCSes to the initial

134

uncertainty through the ensemble simulations. The impact of the vessel

135

observations on the MCS representation is investigated by the assimilation

136

experiments as shown in Section 5. Finally, Section 6 provides a summary

137

and discussion.

7

138

2.

Methodology

139

2.1

Forecast model140

We use the NCEP regional spectral model (RSM, Juang and Kanamitsu

141

1994; Juang 2000) as the forecast model. The RSM retains large-scale

142

structures represented by a base atmospheric ﬁeld from a global or coarser

143

regional model (host model) using the perturbation method (Juang and

144

Kanamitsu 1994; Juang et al. 1997) and orographic blending at the lateral145boundaries (Hong and Juang 1998). The perturbation method calculates

146

the time evolution of the perturbations from the base ﬁeld. Juang and Hong147

(2001) showed that the prediction skill with the perturbation method did

148

not depend on the domain size or the discrepancy in resolution from the host149

model, unlike other conventional lateral boundary treatments. This method

150

represented reasonable monsoon rainfall over East Asia (Hong et al. 1999)

151

and the Indochina Peninsula (Nguyen et al. 2019), indicating an advantage

152

in simulating hierarchical phenomena such as MCSes in the BFZ. The RSM

153

was operationally used in Hawaii and Alaska for daily weather forecasts.

154

The RSM achieves a high eﬀective resolution using a double Fourier se-155

ries for horizontal discretization, and it oﬀers hydrostatic (RSM) and non-156

hydrostatic (MSM) options for the dynamical core (Juang 2000). The RSM

157

governing equations are primitive equations in sigma coordinates, whereas

8

158

those of the MSM are fully compressible equations with internally evolv-

159

ing hydrostatic sigma coordinates. Both the RSM and MSM use the same160

model physics, with some modiﬁcations used for the MSM (Juang et al.

1611997). The physics schemes and other numerical methods are shown in

162

Table 1.

Table 1163

We use the RSM for the outermost domain (D1) with a horizontal reso-

164

lution of 27 km, and the MSM for inner domains D2 and D3 with resolutions

165

of 9 and 3 km, respectively (Fig. 1a). All domains have 42 vertical layers

166with a model top of σ ∼ 0.005. The base ﬁelds of D1 are the three-hourly167

forecast data by the NCEP global forecast system (GFS) initialized at ev-

168

ery six hours in a 0.25◦ × 0.25◦ horizontal resolution and 33 vertical layers.

169

Boundary conditions, including SST and land surface variables, are also

170

obtained from the GFS.

171

2.2

Ensemble perturbations

172

We perform ensemble simulations from 1200 UTC 18 June to investigate

173

the inﬂuence of initial uncertainties on the MCSes that passed through the174

observed area. The breeding of growing modes (BGM, Toth and Kalnay

175

1993, 1997) is used to generate the initial ensemble perturbations. The

176

BGM method has been adopted for operational global ensemble forecast

177

systems at the NCEP (Toth and Kalnay 1993, 1997) and the Japan Meteo-

9Fig. 1

178

rological Agency (JMA, Kyouda 2002), and can extract the directions with179

the largest growing ratios, called bred vectors, from the diﬀerence between

180

unperturbed and perturbed forecasts. Bred vectors are nonlinear extensions

181of local Lyapunov vectors (Trevisan and Legnani 1995), and they represent

182

structures with large error saturation levels, such as baroclinic instability,

183

rather than structures with fast error growth, such as cumulus convection.

184

Therefore, an ensemble with the BGM perturbations could be expected to185

include the true state and appropriately represent the forecast error covari-186

ance for the features comparable to or larger than synoptic scales. However,187

because large-scale error growth tends to dominate that of small-scale and188

signiﬁcantly aﬀects the meso- to convective-scale forecast uncertainty (Bei189

and Zhang 2007; Ke et al. 2022), the BGM perturbations are also consid-

190

ered to be suitable for representing initial uncertainties related to mesoscale

191

phenomena like MCSes. For example, Saito et al. (2011) demonstrated that192

the BGM method for the JMA nonhydrostatic regional model oﬀered a bet-193

ter prediction of intense rainfall than did the downscaling method of global

194

ensemble forecasts.

195

The six-hourly breeding cycles in D1 proceed as follows. The diﬀerence

196

between unperturbed and perturbed runs for all atmospheric prognostic

197

variables is normalized every six hours. The magnitude of the bred vectors

198

is evaluated using the dry total energy (TE) norm (Ehrendorfer et al. 1999)

10

1
∥x∥ =
2D

∫ σt ∫ [σbD

cp
u′2 + v ′2 + T ′2 + Rd Tr
Tr( ′ )2 ]
ps
dDdσ
pr

(1)

199

where u′ , v ′ , T ′ , p′s are the zonal wind, meridional wind, temperature, and200

surface pressure perturbations, respectively. The constants cp and Rd are201

the speciﬁc heat at constant pressure and the gas constant for dry air. D

202

is the veriﬁcation region and indicates D2 in this study. We evaluate the203

TE norm between σt ∼ 0.5 and σb = 1 using the reference temperature204

Tr = 300 K and pressure pr = 800 hPa (Saito et al. 2011).

205

The normalization coeﬃcients are determined by the ratios of the TE

206

norms of the perturbations to the standard norm (=3.0 J kg −1 m−2 ), which207

is approximately 10% of the climatological variance. Supersaturation, neg-

208

ative speciﬁc humidity, and negative cloud water mixing ratio are removed

209

from each perturbed member at the initialization of each cycle. We generate

210

40 members using orthogonalization. The initial seeds of the ensemble per-211

turbations are the diﬀerences between two states that are arbitrarily chosen

212

from the GFS initial states from May 21 to June 30 in 2020 and 2021. The

213

breeding cycles are repeated seven times from 0000 UTC 17 June to 1200

214

UTC 18 June.

215

Lateral boundary perturbations are known to be important for the re-216

gional ensemble forecasts to retain the magnitude of the ensemble spread

11

217

near the lateral boundaries (Saito et al. 2012). Although we do not use the218

lateral boundary perturbations, we set the outermost domain to be much219

larger than the inner domains to prevent an underestimation of the inner220

ensemble spread. The SST or land surface are not perturbed either: all the221

ensemble members and the unperturbed run use the same SST and land222

surface conditions obtained from the GFS initial analysis at 1200 UTC 18

223

June.

224

2.3

Data assimilation

225

In the assimilation experiments, we use the same MSM as in the en-226

semble simulations but with smaller inner domains (D2b and D3b in Fig.

2271b) to focus on the impact of the dense observations. We use the same228

six-hourly breeding ensemble for D1 as was introduced in Section 2.2, and229

do not conduct the assimilation in D1 because no signiﬁcant improvement

230

could be expected from using a similar resolution (27 km vs 0.25◦ ) with

231

fewer observations in our experiments.

232

We use the maximum likelihood ensemble ﬁlter (MLEF, Zupanski 2005),

233

and we perform observation space localization using local gradients of the234

global cost function (Yokota et al. 2016). MLEF is an ensemble varia-

235

tional method that analyzes the unperturbed control run. We employ the236

Newton method to optimize the cost function because it has better conver-

12

237

gence properties than does the conjugate gradient method (Enomoto and238

Nakashita 2024). The ensemble size is 40 plus one unperturbed member.

239

The localization cut-oﬀ scale is 100 (D2b) or 50 (D3b) km in the horizontal

240

direction and 0.4 ln p in the vertical direction for both domains. The ana-241

lyzed ensemble perturbations are relaxed to the prior perturbations by 80%

242

as covariance inﬂation (Zhang et al. 2004).

243

The assimilated observation sets are extracted from the NCEP PREP-

244

BUFR: reports from surface stations (surface pressure), ships and buoys

245(surface pressure, zonal and meridional winds), and upper-air soundings

246(zonal and meridional winds, temperature and speciﬁc humidity) including

247

those from the intensive observation campaign. Because all the observation

248

types are set to be the same as the prognostic variables, the observation

249

operators are linear, and we limit the maximum number of iterations in the250

optimization to one.

Fig. 2

251

The control experiment (CNTL) that uses all the observations described252

above is compared against the data denial experiment that assimilates all

253

but the vessel observations (NOSHIP). The assimilation in D2b is initialized254

at 0000 UTC on the 18th by interpolation from D1, and the assimilation

255

in D3b is at 2100 UTC on the 18th by interpolation from D2b, and both256

assimilations end at 0300 UTC on the 20th (Fig. 2). The cycle interval is257

three hours until the start of the intensive observations at 0000 UTC on the

13

258

19th, and after that, the assimilation cycle forks into CNTL and NOSHIP

259

with shortening the cycle interval to an hour.

260

Our assimilation system does not analyze the SST and land surface261

conditions. All the ensemble members in the assimilation experiments are262

initialized with the SST and the land surface conditions obtained from the263

GFS analysis at 0000 UTC on the 19th. Hence, the diﬀerences between

264

CNTL and NOSHIP arise from the inﬂuence of the vessel observations on265

the atmospheric ﬁeld.

266

2.4

Data and analysis methods

267

The hourly-accumulated JMA nationwide radar composite rainfall (JMA

268

Observations Department 2004) and the three-hourly data of the JMA op-269

erational mesoscale analysis (JMA-MA, JMA 2019) are used as a reference

270

for the precipitation and atmospheric ﬁelds. The convective activity is eval-271

uated using the brightness temperature (BT) as measured by the Advanced

272

Himawari Imager (AHI) on the JMA Himawari-8 geostationary satellite.

273

The simulated radiances from the MSM ﬁelds are generated using the ra-274

diance simulator with the RTTOV fast radiative transfer model version 13

275(Saunders et al. 2018).

276

The representation of MCSes in each simulation is evaluated against the277

radar rainfall using two metrics related to the precipitation: the averaged

14

278

precipitation in the observed region (128.0◦ –129.3◦ E, 30.1◦ –31.1◦ N), and279

the fractions skill score (FSS, Roberts and Lean 2008) applied to a 95 per-280

centile of 6-h precipitation threshold with a neighborhood size of around 51

281

km in the veriﬁcation region including southern Kyushu (128.0◦ –131.5◦ E,

282

29.5◦ –32.5◦ N). While the former measures the accuracy in both amount and283

location of precipitation, the latter does the correspondence of precipitation

284

distribution with that of the JMA radar composite.

285

The development mechanisms of MCSes are analyzed from two perspec-286

tives: the formation of deep unstable layers due to moistening in the free287

troposphere and boundary layer destabilization due to the surface heat ﬂux

288

from the ocean. The former is measured by the existence of moist absolutely289

unstable layers (MAUL, Bryan and Fritsch 2000), which are deﬁned as∂θe< 0 & RH > 95%
∂z

(2)

290

where θe is the equivalent potential temperature, z is the geometric height291

and RH is the relative humidity. MAUL is a characteristic feature of MCSes

292

that develop in a relatively humid environment. The RH threshold in Eq.

293(2) is relaxed relative to that in the deﬁnition of Tsuji et al. (2021) so that294

the MAUL is visually consistent with the JMA-MA considering the bias in295

the RSM. The saturation can be alternatively evaluated by cloud or rain

15

296

water (Bryan and Fritsch 2000) for the RSM, but the location of MAUL

297

does not change signiﬁcantly.

298

The net surface heat ﬂux Fes (the sum of the sensible and latent heat299

ﬂux) is estimated from the wind speed in the boundary layer (|ub |) and the300

diﬀerence between the saturated θe at the sea surface (θess ) and θe in the301

boundary layer (θeb ) as

Fes = Cd Ue (θess − θeb ) = Cd Ue ∆θe

302

(3)

where Cd ∼ 10−3 is the drag coeﬃcient, Ue = (|ub |2 + W 2 )1/2 is the eﬀective

303

wind speed, i.e., the wind speed corrected for gustiness (W = 3 m s−1 in304

this study) in the domain (Raymond 1995). Equation (3) indicates that the305

sea surface ﬂux is approximately proportional to the product of |ub | and306∆θe . In the evaluation of Eq. (3), θess is calculated using the observed or307

prescribed SST, and θeb and |ub | are calculated from the values on the deck308

for the observations, and from those at 2 m altitude above the surface for309

the simulations, respectively, assuming well mixed boundary layers.

310

3.

Observed mesoscale convective systems

311

From June to July 2022, a ﬁeld campaign was conducted in the ECS

312

by the JMA and universities and research institutes in Japan. This cam-

16

313

paign was part of a coordinated eﬀort to elucidate the formation mechanism

314

of quasi-stationary line-shaped rain bands (Senjo-Kousuitai, Kato 2020) in315

the BFZ. During this campaign, three research vessels, Nagasaki-maru of316

Nagasaki University, Kagoshima-maru of Kagoshima University, and Seisui-317

maru of Mie University, conducted intensive synchronized atmospheric and318

oceanographic observations. Their observations were designed to inves-319

tigate the air-sea interaction between the BFZ and the warm Kuroshio

320

current in the planned study called “Two-way interactions between East321

Asian marginal seas and atmosphere and monsoon modulations” as a part322

of the project “Mid-latitude ocean-atmosphere interaction hotspots under323

the changing climate”. During this concentrated observation period, two324

MCSes passed through the observation area. These MCSes each had gen-325

eral characteristics of convective systems frequently observed in the BFZ on

326

the ECS. The environmental features related to these MCSes are described327

below.

328

3.1

Case overview

Fig. 3329

The Baiu front was located just above the observed area (at approxi-

330

mately 30◦ N) on June 19. The upper subtropical westerly jet over the Ti-

331

betan Plateau divided into two branches; the northern branch meandered332

largely until it reached 50◦ N, while the southern branch ran just south of

17

333

the Baiu front over the ECS (Fig. 3a). The southwesterly lower jet (Fig.

3343b) passed parallel to the southern branch in the upper troposphere (Fig.

3353a) and advected a large amount of moisture from the Philippine Sea. This336

southern jet tilted slightly northward with height (Fig. 3c), likely due to337

the diabatic heating over the Baiu-frontal rainband (Sampe and Xie 2010).

338

There were no obvious disturbances in the upper troposphere over the ECS

339

during the campaign period.

Fig. 4340

Figure 4 shows the environmental features during the ﬁrst (0300 UTC)341

and second (2100 UTC) halves of the intensive observation period. Con-342

vection was active along the front (Fig. 4a) at 0300 UTC. Two warm and343

moist airstreams (Fig. 4b, around 125◦ –129◦ E, 29◦ –30◦ N) that were due344

to southwesterlies in the lower troposphere and south-southwesterlies near345

the surface supplied a large amount of precipitable water, which made the346

environmental conditions favorable for the ﬁrst MCS development (Manda

347

et al. 2024), and generated distinct meridional water vapor gradients near348

31◦ N over the ECS. In the lower troposphere, a trough whose axis ran along349

the Baiu front extended eastward from the Yangtze River estuary. A meso-350

β-scale cyclone (hereafter meso-β cyclone) formed at the tip of the monsoon

351

trough and accompanied the ﬁrst MCS that passed through the observed352

area (Fig. 4a, c).

353

Although the Baiu front remained at almost the same latitude, convec-

18

354

tion was less active in the middle of the ECS during the latter half of the355

campaign period than the ﬁrst half (Fig. 4d). This suppression was due to356

the reduction of moisture supply by southwesterlies (Fig. 4e), and the free357

troposphere was relatively drier. The precipitable water was marked by two

358

maxima: near the Yangtze estuary (121◦ E, 30◦ N) and to the south of the359

Kyushu region (130◦ E, 30◦ N). The moist air from the Philippine Sea was360

transported to the south of Kyushu by a south-southwesterly ﬂow along the361

margin of the subtropical high and was further supplied moisture during the362

passage over the warm SST tongue of the Kuroshio. Some convective sys-363

tems successively occurred near this water vapor maxima and moved along364

the west-southwesterlies enhanced by the cyclonic circulation centered near365

33◦ N, 127◦ E (Fig. 4d, f). These convective systems merged to develop an

366

MCS.

367

3.2

Observed features by the vessels

368

The observed features by the vessels associated with the MCSes are369

examined. The tracks of three vessels formed a lattice network (Fig. 5).

370

All vessels started from their southeast corners at 0000 UTC 19 June and371

observed at almost the same latitude simultaneously at hourly intervals.

372

The observations continued until 0200 UTC 20 June and a total of 70 ra-

373

diosondes were launched from the vessels. Kagoshima-maru (center) and

19

Fig. 5

374

Seisui-maru (right) observed high SST (> 26.5 ◦ C) over the tongue of the375

Kuroshio from 0000 to 0500 UTC 19 June (Fig. 5). The observed SST in-

376

dicates steep SST gradients between Nagasaki-maru and Kagoshima-maru

377(128.3◦ E) and around 30.4◦ N. The initial SST analysis obtained from NCEP

378

GFS underestimates the warm tongue of the Kuroshio and fails to represent379

the steep gradients (Fig. 5). This cold bias is common to the GFS analysis

380

from June 18 to 20. As a result, all the simulations in this study signiﬁcantly

381

underestimates the warm SST eﬀect. The eﬀect of the SST underestimation382

on the MCS development will be discussed in detail in Section 4 and 5.

383

Figure 6 shows the time series of vertical proﬁles of horizontal winds

384

and θe . During the passage of the ﬁrst MCS (0200–0500 UTC), all the385

three vessels observe a signiﬁcantly moist and warm layer reaching up to

386

around 700 hPa. In particular, Nagasaki-maru (Fig. 6a) and Seisui-maru

387(Fig. 6c) detect deep MAUL from 925 to 500 hPa at 0300 UTC. In the388boundary layer, θe rises as the approach of the MCS, and abruptly drops

389

after the MCS passage with the change of wind direction. Surface winds

390

gradually return from northerly to southerly in about six hours. After 0600

391

UTC, the lower troposphere becomes less humid than before (Fig. 4e). At

392

the passage of the second MCS (1800–2000 UTC), Nagasaki-maru observes393

a relatively deep moist layer, but θe is lower than when the ﬁrst MCS devel-394

ops. Kagoshima-maru (Fig. 6b) and Seisui-maru (Fig. 6c) observe almost

20

Fig. 6

395

saturated boundary layers with θe close to 350 K, suggesting convectively

396

unstable stratiﬁcation.

Fig. 7397

Time evolutions of surface wind speed, θeb , θess and convective available

398

potential energy (CAPE) for parcels lifted from the deck level are shown399

in Fig. 7. Surface wind speed is larger than 5 m s−1 in the ﬁrst half of400

the observation period. During the ﬁrst MCS passage, θess observed by

401

Kagoshima-maru and Seisui-maru is remarkably high as shown in Fig. 5,402

and ∆θe becomes larger than 10 K. Although Nagasaki-maru is located over403

the cooler SST region than the other vessels, surface wind is stronger than404

the others during the ﬁrst two hours because of the approaching meso-β405

cyclone. Therefore, all the vessels indicate favorable conditions for release406

of the sea surface ﬂux. The resulting heating in the boundary layer yields

407

the largest CAPE during the observation period in Nagasaki-maru (839.5

408

J kg−1 ) and Kagoshima-maru (786.6 J kg −1 ) at 0200 UTC though smaller409

than those typically observed for MCSes over land. After that, all the vessels

410

observe a marked decrease in θeb and CAPE due to the convection and411

advection of cold dry air from the north as shown in Fig. 6. The decreasing412

θeb gradually recovers in almost the same time scale as the surface wind413

direction. At 1800 UTC when the second MCS was passing, although θeb414

of approximately 350 K is comparable to the values at 0200 UTC, CAPE

415

observed by Seisui-maru is almost half of the values at 0200 UTC.

21

416

4.

Sensitivity to initial uncertainties

417

In this section, sensitivity of the MCS representation to the initial un-

418

certainties is examined using the ensemble simulations. The key features419

of the MCS predictability are identiﬁed by the comparison between the420unperturbed run and the best-performing members.

421

4.1

Ensemble variability

Fig. 8422

First, we examine the ensemble variability in D2 to investigate the ﬂow-

423

dependent uncertainties. The ensemble spread over the ECS shows similar

424

distributions in D2 and D3 and becomes larger in the lower troposphere.

425

Figure 8 shows the kinetic energy and speciﬁc humidity spreads on the 850426

hPa surface in D2 from the initial time to forecast time 36 h (FT36). At

427

1200 UTC on the 18th, the kinetic energy spread becomes large in front of428

the trough extending eastward from the estuary of the Yangtze River (Fig.

4298a). This large spread grows further and moves eastward with the trough430

until FT24 (Fig. 8b, c), which could be interpreted as the uncertainty cor-

431

responding to the development of the meso-β cyclone. After that, the large

432

spread takes an elongated form along the BFZ without distinct maxima

433(Fig. 8d). The water vapor spread keeps large and shows narrow maxima

434

along the θe front over the ECS (Fig. 8e–h) during the simulation period.

435This large spread corresponds to the variability in the location of the largest
22

436

meridional gradient of speciﬁc humidity, i.e., this signal represents the un-437

certainty in the location of the western part of the BFZ characterized by the438water vapor front (Ninomiya and Akiyama 1992; Moteki et al. 2004b). The

439

water vapor has the largest variability at FT24, and after that the spread440

becomes wide in the north of the ECS (Fig. 8h). The distribution of these441

ensemble spreads indicates that the simulated MCSes are aﬀected by the442

upstream synoptic uncertainty.

Fig. 9443

Next, the 1-h precipitation averaged in the vessel observation area is444

veriﬁed against the JMA radar composite (Fig. 9). The precipitation peaks

445

associated with the two MCSes appear at 0400 and 1900 UTC in the JMA

446

radar composite (black bars in Fig. 9a). The unperturbed downscaling447

simulation (blue curve in Fig. 9a) generally follows the time evolution of448

the radar composite but underestimates the precipitation amounts. The 6-h449

precipitation amounts for the two peaks (38.1 mm and 14.9 mm) are smaller

450

than the observation (63.0 mm and 27.1 mm) by 30% and 45%, respectively.

451

The small FSSs of the unperturbed run (0.12 for the ﬁrst and 0.0 for the452

second) indicate the poor representation in precipitation patterns.

453

For the ﬁrst precipitation peak, the 40-member ensemble simulations

454

show signiﬁcant variation of predicted precipitation in both peak timings455

and amounts (Fig. 9a, b). Approximately one-third members (13/40) pre-456

dict precipitation that is closer to the radar composite than does the unper-

23

457

turbed run (Fig. 9b). In addition, more than half members represent the458

precipitation pattern better than the unperturbed run as indicated by the459

higher FSSs. The 6-h precipitation amounts in the observation area and460

FSS are strongly correlated because the location of the precipitation peak461

corresponds well to the observation area (as will be shown in Fig. 10a).

462

For the second precipitation peak, there is small variation among mem-463

bers in the precipitation amounts, and most members underestimate the464

precipitation maxima in the observation area (Fig. 9a). All members in-

465

cluding the unperturbed run show FSSs lower than 0.5 (Fig. 9c), indicating466

lower predictability of the second MCS than the ﬁrst. This small variation

467

may be partly because of the small spread in both kinetic energy and water468

vapor around the region where the second MCS develops (Fig. 8d, h).

469

4.2

Key features for the ﬁrst MCS

470

The ﬁrst MCS had a heavy rainfall area over 100 mm/3 h near the center471

of the meso-β cyclone having a minimum sea level pressure of 1006.1 hPa

472(Fig. 10a). Figure 11a and 11d show the latitude–height cross sections

473

of the thermodynamic stability and winds, respectively, through the center474

of the cyclone at 0300 UTC of the JMA-MA. The BFZ is identiﬁed by475

a meridional θe gradient at approximately 30◦ N. A deep MAUL rooted in476

the boundary layer reaches up to 700 hPa near the BFZ (Fig. 11a) as

24

Fig. 10
Fig. 11

477

observed by the vessels (Fig. 6). This deep MAUL developed due to the478abundant water vapor supply in the middle troposphere from the southwest

479

ocean (Fig. 4b) and the warm moist air in the lower troposphere that was480

advected by southerly winds toward the BFZ and then ascended along the481

frontal surface (Fig. 11a, d). The horizontal winds converge near 30 ◦ N with482

the southerly winds in the south and the easterly winds in the north, and483

the vertical shear of the horizontal winds is weaker below 850 hPa than484

above in the range of 29◦ –31◦ N due to vertical mixing by the disturbance485(Fig. 11d). Consequently, the enhanced convection in the BFZ intensiﬁes

486

the meso-β cyclone.

487

In the unperturbed run, although the migration speed of the meso-β488

cyclone corresponds well with the JMA-MA, as does the precipitation peak,489

the predicted cyclone is weaker by approximately 2 hPa (with a minimum490

sea level pressure of 1007.9 hPa) and biased northward (Fig. 10b). This

491

northward bias is caused by the northward migration of the predicted BFZ,

492

with both the MAUL and wind convergence located at approximately 31 ◦ N

493(Fig. 11b, e). Furthermore, the meridional θe gradient within the boundary494

layer in the north of the BFZ is weaker than that of the JMA-MA, and the495

MAUL does not reach the surface (Fig. 11b), which indicates less active

496

convection to intensify the meso-β cyclone. The vertical shear of zonal497

winds around the MAUL is consistently stronger than that of the JMA-MA

25

498

owing to less vertical mixing (Fig. 11e). The inﬂow of high θe air into the499

BFZ in the boundary layer is also weak due to the low θe in the boundary

500

layer and weak southerly winds in the south of the BFZ, which may be a

501

factor of the boundary layer being more stable than that in the JMA-MA.

502

Member 40, which predicts the realistic amounts and temporal variation503

of precipitation (yellow-green curve in Fig. 9a) with the highest FSS of 0.69,

504

signiﬁcantly mitigates the northward bias of the meso-β cyclone; its position505

corresponds well with the JMA-MA, although its intensity is overestimated506

by 1 hPa (with a minimum sea level pressure of 1004.9 hPa, Fig. 10c). In

507

the meridional cross section through the cyclone center, the horizontal θe508

gradient over the BFZ is much stronger than either the JMA-MA or the509unperturbed run up to the middle troposphere, and a deep MAUL develops

510

to the south of this strong gradient (Fig. 11c). There is a large horizontal

511

shear of zonal winds and a strong convergence below the MAUL (Fig. 11f).

512

The horizontal winds have a horizontal shear of up to 500 hPa and a weak513

vertical shear around the disturbance due to the strong mixing by the overly

514

intensiﬁed meso-β cyclone. The θe to the south of the BFZ (< 30◦ N) in the515boundary layer is still lower than that of the JMA-MA, and the MAUL

516

does not reach the surface, nor does the unperturbed run. However, the517

near-surface southerly winds are stronger than those in the unperturbed518

run and they supply warm and moist air to the BFZ, which contributes to

26

519

the formation of the MAUL in the south of the BFZ (29◦ –30◦ N, Fig. 11c,f).

520

4.3

Key features for the second MCS

521

The second MCS produced line-shaped precipitation bands elongated522

east-northeastward although the rainfall was moderate than that with the523

ﬁrst MCS (Fig. 10d). Unlike the ﬁrst MCS, no distinct mesoscale distur-524

bance was present at that time. The unperturbed run generates a false

525

meso-β cyclone to the north of the observation area (Fig. 10e). This false

526

cyclone induces a precipitation band which is too strong over the Kyushu

527

region. In the south of the observation area, wide-spread precipitation oc-

528

curs due to overestimated precipitable water in this region (not shown).

529

Member 10 predicts the precipitation due to the second MCS better than530

the other members (Fig. 9c), though the peak timing is later than observed

531(dark blue curve in Fig. 9a). This member represents well the surface

532

south-southwesterlies and a line-shaped rain band along the surface wind533

direction (Fig. 10f).

Fig. 12534

The facts that the direction of surface winds and the rainband corre-

535

sponds well and that the rising of θe is observed only in the boundary layer

536(Fig. 6) suggest that the destabilization of the boundary layer over the537

warm ocean is likely to play a dominant role in the convective initiation.

538

Figure 12 compares the distribution of surface heat ﬂux (Eq. 3) between

27

539

the unperturbed run and member 10 at 2100 UTC. The unperturbed run

540(Fig. 12a) shows two precipitation regions, one is in the north of the obser-

541

vation area due to the false cyclone (Fig. 10e) and the other is in the SST

542

frontal zone. The latter is accompanied by the local maximum of Fes . The

543

large release of surface heat ﬂux in the SST frontal zone is more evident in544

member 10 (Fig. 12b). This large release of the surface heat ﬂux is due to545

the local maximum of both ∆θe and surface wind speed induced by large546

pressure gradients in the SST frontal zone (Sasaki et al. 2012). The precip-

547

itation region in member 10 is located downstream of this local maximum548

of the surface heat ﬂux. There is a positive correlation (0.56) with a signif-549

icance level of 0.01 between the FSS of the second MCS precipitation (Fig.

5509c) and Fes averaged in the SST frontal zone (127◦ –128◦ E, 28.8◦ –29.8◦ N)

551

during the same period. This relationship suggests that the surface heat552

ﬂux in the SST frontal zone is an important factor for the predictability of553

the second MCS. However, the simulated ﬂux has some limitations because554

both simulations fail to reproduce a large ﬂux (> 500 K m h−1 ) observed by

555

Seisui-maru (dots at 129.3◦ E, 31◦ N in Fig. 12) due to the underestimation556

of SST (Fig. 5). To clarify the inﬂuence of the ocean uncertainty on the557

second MCS, sensitivity experiments to SST are required, which is out of558

scope of this study.

28

559

5.

Impact of intensive observations

560

This section investigates the eﬀect of dense upper observations by the561

three vessels on the representation of the MCSes through the assimilation

562

experiments.

563

5.1

Overall DA impact

Fig. 13564

Impacts of the upper observations are displayed by variables in the time–565

height cross section of the horizontally averaged ensemble spread diﬀerence

566(CNTL − NOSHIP) in D2b (Fig. 13). Blue layers indicate a decrease in567

spread, i.e., uncertainty reduction that is attributable to the assimilation of568

the intensive observations. The CNTL spreads in zonal winds (Fig. 13a)569

and temperature (Fig. 13c) in the bottom layers begin to decrease compared570

with NOSHIP from 0400 UTC on the 19th, which may correspond to the571

abrupt change in temperature and wind direction with the meso-β cyclone572

passage (Fig. 6). The diﬀerence between the two experiments becomes

573

larger for all variables after 1200 UTC. For the wind components (Fig. 13a,574b), a uniform spread reduction below 850 hPa is observed after 1500 UTC,575

whereas the reduction in the temperature spread (Fig. 13c) is relatively

576

small but signiﬁcant in the bottom layers. The spread of speciﬁc humidity

577(Fig. 13d) reduces more signiﬁcantly and more widely than that of the other578

variables. Overall, it is found that the impact of the vessel observations is
29

579

large in the lower troposphere and has a large impact on water vapor.

580

Figure 14a–d shows the ensemble spread of CNTL on the 850 hPa sur-581

face averaged over the assimilation period in D2b. The zonal (Fig. 14a) and582

meridional (Fig. 14b) wind spreads are large to the east (31 ◦ N, 125◦ –127◦ E)

583

and to the southwest (28◦ –30◦ N, 119◦ –121◦ E) of the trough extending from584

the continent (approximately 30◦ N, Fig. 8a), where the horizontal gradient585

of wind speed is large. The former is more pronounced for zonal winds

586

and the latter for meridional winds. The spread maximum to the east of587

the trough probably represents the uncertainty of the position of horizon-

588

tal wind shear due to the migration of the trough axis, and that to the589

southwest of the trough represent the uncertainty of the southerly winds

590

carrying warm and moist air across the continent. In contrast, the spreads591

of both temperature (Fig. 14c) and speciﬁc humidity (Fig. 14d) reach their592

maximum over the strong meridional θe gradient in the western ECS (Fig.

5938e–h) and represent the uncertainty in the position of the front.

594

The spread diﬀerence between CNTL and NOSHIP (Fig. 14e–h) is con-595

centrated around the observation area and its east (downstream) side for all

596

variables except for speciﬁc humidity (Fig. 14h). The spread reduction in597

zonal winds (Fig. 14e) is larger than the other variables and extends zonally598

along the trough axis. The spread reduction in meridional winds (Fig. 14f),

599

temperature (Fig. 14g), and speciﬁc humidity (Fig. 14h) commonly peaks

30

Fig. 14

600

to the southwest of the observation area, corresponding to the southwesterly

601

advection of warm and moist air. Changes in the speciﬁc humidity spread602

are also signiﬁcant in the frontal zone upstream of the observation area,603

where the spread in CNTL is smaller to the north and larger to the south of604

the spread maximum than that in NOSHIP (Fig. 14h). This dipole spread

605change indicates southward movement of the spread maximum in CNTL

606

relative to NOSHIP, and it could be interpreted to mean that the ensem-

607

ble mean position of the front is located more southward in CNTL than608

in NOSHIP. The fact that only the speciﬁc humidity spread changes along609

the front is consistent with the characteristics of the water vapor front on

610

the western part of the BFZ. These results indicate that the range of the611

inﬂuence of observation diﬀers depending on the variables. The localization

612

radius is common to all variables in this study, but the variable-dependent613

localization radius could have been alternatively used (Wang and Wang

6142023).

615

5.2

Impact on the predictability of MCSes

616

The MCS representations in the assimilation experiments are evaluated617

using precipitation (Fig. 15). All members in NOSHIP including the un-618

perturbed analysis underestimate the precipitation peak associated with619

the ﬁrst MCS around 0400 UTC (Fig. 15a). By contrast, CNTL shows

31

Fig. 15

620

an abrupt increase in precipitation at 0400 UTC, and more than 75% of621

the members in CNTL predict a larger amount of precipitation than the622radar composite at 0500 UTC, although the predicted precipitation peak is

623

delayed by an hour relative to the observed peak. Although the impact of624

observations accumulates (Fig. 13), the second precipitation peak around625

1900 UTC is not reproduced by either CNTL or NOSHIP. Note that the626

increase in precipitation from 2100 to 2300 UTC in NOSHIP is due to the627formation of a false meso-β cyclone near the observation area (not shown)628

and does not represent the second peak associated with line-shaped rain

629

bands (Fig. 10d). The diﬀerence in the impact of the vessel observations

630

between the ﬁrst and second MCSes is also clear in FSS (Fig. 15b, c). For631

the ﬁrst MCS (Fig. 15b), the vessel observations signiﬁcantly improve the632

representation of realistic precipitation in terms of both the amounts and633

patterns. For the second MCS (Fig. 15c), on the other hand, the vessel

634

observations make little diﬀerence between the two experiments though the635

assimilation of conventional observations helps to produce better predictions636

of precipitation than the ensemble forecasts (Fig. 9c).

637

We look into the diﬀerence of the ﬁrst MCS representation between

638

CNTL and NOSHIP. Figure 16 compares the 1-h accumulated precipita-

639

tion, sea level pressure, and surface winds from 0100 to 0500 UTC 19 June

640

between the radar composite with the JMA-MA, CNTL and NOSHIP. Both32

Fig. 16

641

CNTL and NOSHIP predict a small-scale cyclone developing around the642western edge of the observation area and heavy local rainfall at the cyclone643

at 0100 to 0300 UTC. However, compared with the widespread radar rain-

644

fall, both CNTL and NOSHIP underestimate the amount of precipitation

645

averaged over the observation area (Fig. 15). In addition, the predicted646

cyclone at 0300 UTC in CNTL and NOSHIP is smaller than that in the647

JMA-MA in diameter of an outer closed isobar (approximately 30 km vs

648

100 km) although the cyclone location corresponds well. The diﬀerence be-649

tween CNTL and NOSHIP is unclear up to this time, but NOSHIP shows

650

a slightly faster eastward migration of the cyclone than does the JMA-MA.

651

After the passage of the MCS through the observation area at 0300 UTC,

652

CNTL has an obvious advantage over NOSHIP consistent with Fig. 15,653

and reproduces convective cells elongated in the southwest-northeast direc-

654

tion and strong surface wind shear associated with the developed meso-β655

cyclone, whereas the cyclone in NOSHIP decays after 0400 UTC.

656

The diﬀerence between CNTL and NOSHIP in the ﬁrst MCS representa-

657

tion is clear in the distribution of convection. Figure 17 shows the observed658

or simulated Himawari-8 AHI channel 13 BT, which represents the cloud-659

top height. A convective system located upstream of the observation area660

at 0100 UTC develops and moves eastward while merging with the small661

convective system on the south side of the observation area to develop into a

33

Fig. 17

662

single zonally extended MCS (Fig. 17a). Although the MSM has an overall

663

shallow bias in cloud-top height (high BT), deep convective clouds with BT664

of approximately 210 K develop at 0400 UTC in CNTL (Fig. 17b), cor-665

responding well to the observed clouds (Fig. 17a), whereas NOSHIP fails

666

to represent deep clouds (Fig. 17c). The improvement in CNTL against

667

NOSHIP is concentrated on the downstream side of the vessel observations,668

which is consistent with the large spread reduction in Fig. 14.

669

The assimilation of the vessel observations improves not only the un-

670

perturbed analysis but also the ensemble members. Figure 18 shows the671

probability of deep convection. Here BT of 215 K (white contours in Fig.

67218) is chosen as a proxy for deep convection, and the ratio of the number673

of members that predict BT < 215 K to the ensemble size is shown in color

674

shades. Few members of either CNTL or NOSHIP represent the upstream

675

convective system in the earlier cycles, resulting in low reproducibility of676

the MCS until 0300 UTC. However, approximately one-third of the CNTL

677

members represent deep convection just over the observation area (near678129◦ E, 30.5◦ N) at 0300 UTC (Fig. 18a), whereas almost all members in

679

NOSHIP still do not predict deep convection as observed. After that, the680

number of members in CNTL simulating deep convection increases rapidly

681with the assimilation cycles, and more than 80% predict deep convection to682

the east of the observation area (129◦ –130◦ E, 30.5◦ –31.5◦ N) at 0500 UTC.

34

Fig. 18

683

This high probability within the observed deep convective area indicates an

684

increase in the number of successful ensemble members in the representation

685

of the MCS because of the assimilation of the vessel observations.

686

Next, we investigate the reason for the diﬀerence between the two exper-

687

iments in the representation of the ﬁrst MCS. Figure 19 shows the diﬀerence688

in the ﬁrst analysis between CNTL and NOSHIP, i.e., the increment due

689

to the vessel observations at 0000 UTC on the 19th. The wind increments

690

in the lower troposphere (Fig. 19a) yield southerly winds to the south691

and easterly winds over and to the north of the observation area. These

692

easterly winds enhance the horizontal wind shear in the frontal zone (Fig.

693

11d). The wind direction of the increments has a cyclonic shear and in-694

duces convergence to the west of the observation area, which is consistent695

with the initiation of upstream convection. The increments in tempera-

696

ture and speciﬁc humidity are also shown in the latitude–height (Fig. 19b)

697

and longitude–height (Fig. 19c) cross sections. The temperature and spe-698

ciﬁc humidity increments have a larger variation in the meridional rather

699

than the zonal direction. The temperature increment takes a dipole pattern

700

across the front (29◦ –30◦ N) with a positive increment in the south and a701

negative increment in the north, strengthening the frontal structure. The

702

speciﬁc humidity increment moistens below 850 hPa in the frontal zone and703

supports the formation of MAUL. Thus, the vessel observations contribute

35

Fig. 19

704

to a favorable environment for developing the ﬁrst MCS from the initial

705

cycle.

Fig. 20706

The increments by the vessel observations yield clear diﬀerences in the707

environmental features between CNTL and NOSHIP at 0300 UTC on the708

19th, just before the conspicuous improvement of the ﬁrst MCS in CNTL.

709

Figure 20 shows the distribution of precipitable water and the vertically710

integrated water vapor ﬂux of (a) CNTL and (b) NOSHIP. CNTL represents

711

a larger amount of precipitable water over the observation area and to its

712

west than does NOSHIP, which is favorable for the development of the713

MCS. Figure 21 shows the latitude–height cross section (the same location

714

as Fig. 11) of the (a, c) thermodynamic and (b, d) wind ﬁelds. NOSHIP

715

has MAUL with a northward bias (Fig. 21c), as does the downscaling

716

from 1200 UTC on the 18th (Fig. 11b) due to a less steep meridional θe717

gradient and a weak lower convergence (Fig. 21d). Figure 21c also shows

718

that the boundary layer is relatively cold and dry compared with that of the719

JMA-MA (Fig. 11a) because of the weak southerly ﬂow near the surface

720(Fig. 21d). CNTL (Fig. 21a) mitigates this northward bias of MAUL and721

has a steep frontal structure similar to that of the JMA-MA. This frontal

722

structural change is consistent with the increment in Fig. 19. CNTL also has

723

stronger near-surface southerly ﬂow into the BFZ and convergence below the724

MAUL (Fig. 21b) than those in NOSHIP. These wind structures enhance

36

Fig. 21725

the heating and moistening of the boundary layer and the upward motion

726

of this warm moist air, supporting the formation of the deep MAUL. These

727

results indicate that the assimilation of dense observations by the vessels

728

signiﬁcantly improves the representation of the MCS mainly by correcting729

the atmospheric frontal structure. Note that the vessel observations have730

little impact on the improvement in the upstream MCS at 0100–0300 UTC

731

on the 19th. Although the cumulative impact of the observations is certainly

732

important, this failure is partly due to the signiﬁcant underestimation of the733

upstream moisture content (Fig. 20a, b) compared to the JMA-MA (Fig.

7344b), which is largely determined by large-scale circulations. These large-

735

scale features are diﬃcult to modify only by the local observations from the736

vessels.

737

As shown in Fig. 15, the representation of the second MCS is relatively

738

insensitive to the vessel observations. Although the assimilation of the vessel

739

observations in CNTL helps to reproduce the realistic south-southwesterly

740

ﬂow near the surface and the convective initiation in the downstream region,741

the predicted convections decay faster than the observed (not shown). As

742

discussed above, the second MCS can be considered to be driven by the743

continuous moisture supply from the warm tongue of the Kuroshio (Fig.

74412). Although the vessel observations increase the moisture amounts in745

the south of the observation area (Fig. 20c, d), they cannot contribute to

37

746

represent the signiﬁcantly large amount of moisture where the second MCS

747

has matured (around 130◦ E, 30◦ N, Fig. 4d, e). The importance of the748boundary layer will be examined in the next section.

749

5.3

Comparison with the observations

750

The time series of vertical proﬁles of the analyzed ﬁeld of the two exper-

751

iments (Fig. 22) are compared against radiosondes from the three vessels

752(Fig. 6). CNTL (Fig. 22a–c) successfully represents the deep layer with

753

high θe and abrupt temperature change after the ﬁrst MCS passage, al-754

though the lower wind direction changes are not suﬃciently reproduced.

755

NOSHIP (Fig. 22d–f) also shows a slight increase in θe at the location of756

Nagasaki-maru (Fig. 22d), but θe remains lower at the other two ships’

757

locations and the moist layer is shallower than that of the observations.

758

Therefore, the ﬁrst MCS does not develop well and θe hardly decreases af-

759

ter the passage. At the time of the second MCS passage, CNTL fails to760

reproduce the rise in θe below 925 hPa observed by Kagoshima-maru and761

Seisui-maru (Fig. 6b, c), while NOSHIP maintains high θe at lower levels

762

close to the observations at the time of the second MCS passage (Fig. 22d–

763f) due to the poor development of the ﬁrst MCS, and this high θe probably

764

leads to generating the false meso-β cyclone.

765

Figure 23 shows the surface wind speed, θeb , θess , and CAPE in the38

Fig. 22

Fig. 23

766

two assimilation experiments equivalent to those of the vessel observations

767(Fig. 7). The surface wind speed of CNTL (Fig. 23a) corresponds well768

to that of the observations (Fig. 7) throughout the observation period and769

increases with the passage of the ﬁrst MCS. The wind speed also increases in

770

accordance with Kagoshima-maru and Seisui-maru when the second MCS

771

passes, despite the precipitation amounts being underestimated. NOSHIP

772(Fig. 23b) shows a ﬂat wind speed except at the end of the observation

773

period due to the development of the false cyclone. CNTL also reproduces774

the abrupt increase in CAPE just before the ﬁrst MCS due to the heating775

in the boundary layer (Fig. 23a), which are consumed by the development776

of intense convections. On the other hand, CAPE increases more slowly in

777

NOSHIP than in CNTL, and the moderate CAPE (∼ 400 J kg−1 ) is kept

778

until the false cyclone passage in NOSHIP (Fig. 23b).

779

In contrast to the wind speed and CAPE, there are clear diﬀerences be-

780

tween the observations and experiments in θeb and θess . The θess values in the781experiments are always cooler than the observed values and ﬂuctuate little

782

in contrast to the observed variation (Fig. 7) because the SST used in the783

experiments is cooler and smaller in its spatial variation than that observed

784(Fig. 5). Furthermore, the θess diﬀerences between the experiments are785

small because they only reﬂect the diﬀerence in sea level pressure. The θeb786

variations in CNTL are similar to those of the observations to some extent.

39

787

θeb increases before the passage of the ﬁrst MCS and then decreases from788

0500 to 0700 UTC in CNTL. ∆θe becomes positive with this drop in θeb ,789

which makes conditions more favorable for the sea surface ﬂux than those790

in NOSHIP. However, because θess is lower and its decrease is slower than791

that of the observations, the time when ∆θe reaches its maximum is later792

than the time of the ﬁrst MCS passage, and the sea surface ﬂux is down-

793

ward (∆θe < 0) at that time. Therefore, the contribution of sea surface794

ﬂux to the development of the predicted MCS is limited in CNTL. Never-795

theless, the presence of a deep moist layer contributes signiﬁcantly to the796

ﬁrst MCS development, as discussed above, and this creates an observable797

diﬀerence between CNTL and NOSHIP in the ﬁrst MCS representation. In

798

the later observation period, both θess and θeb ﬂuctuate little, which is un-799

favorable to the release of the sea surface ﬂux in either experiment. These

800

results suggest that the eﬀect of the surface heat ﬂux from the warm ocean

801

is underestimated for both MCSes in the assimilation experiments.

802

6.

Summary and discussion

803

In this study, we performed nested ensemble simulations and ensemble

804

data assimilation experiments for the MCSes in the BFZ using the NCEP re-805

gional spectral model. Two MCSes were captured by radiosondes launched

806

hourly by three research vessels from 0000 UTC 19 to 0200 UTC 20 June
40

807

2022 over the ECS. These MCSes have contrasting features: the ﬁrst one808

was accompanied by a meso-β-scale cyclone, and the other consisted of some809

convective systems developing over the warm tongue of the Kuroshio.

810

This case study indicates that the predictability of the MCSes on the811

ECS depends on their development mechanisms. The development of the812

ﬁrst MCS was mainly dominated by atmospheric features such as the meso-β813

cyclone that formed in front of the synoptic-scale trough and the formation814

of a deep moist unstable layer due to abundant moisture supply to the lower

815

and middle troposphere. Hence, the synoptic-scale ensemble perturbations

816

that reﬂect the uncertainties in the trough or water vapor front were able817

to represent the uncertainty of the MCS and showed signiﬁcant variations

818

in both the location and intensity of the MCS. A member with a more819

accurate representation of the MCS than the unperturbed run improved820

the representation of the strong meso-β cyclone and the frontal structure821with steep meridional θe gradient and deep MAUL.

822

In addition, dense upper soundings by the three research vessels signif-

823

icantly inﬂuenced the reproducibility of the ﬁrst MCS. The vessel observa-

824

tions had a signiﬁcant impact on the lower troposphere and the downstream

825

region. The inﬂuence of the observations on precipitation became clear just826

after the passage of the MCS. The unperturbed analysis of the CNTL rep-827

resented a strong meso-β cyclone with realistic deep convection elongated

41

828

in the southwest-northeast direction, whereas a meso-β cyclone in the NO-

829

SHIP decayed fast. The diﬀerence between CNTL and NOSHIP in the830

representation of the MCS was also clear among the ensemble members.

831

More than 80% of the CNTL ensemble members showed deep simulated

832

convective clouds that corresponded well to the satellite observations. The

833

increments due to the vessel observations steepened the front and moistened834

the frontal zone to increase the amount of precipitable water. These changes

835

contributed to the formation of deep moist unstable layers and to the devel-836

opment of the MCS as suggested in Manda et al. (2024). However, inten-

837

sive observations alone cannot improve the upstream MCS because of the838

signiﬁcantly underestimated upstream moisture determined by large-scale839

circulations. These large-scale circulations are usually represented better in840

a global analysis than in a regional analysis because of the global coverage of841

the observation network, so an appropriate treatment of the global analysis

842

in regional assimilations could improve the upstream representation in the843

BFZ, which will be addressed in future work.

844

In contrast to the ﬁrst MCS, the second MCS has low reproducibility in845

both ensemble simulations and assimilations. The best-performing member846

in the ensemble simulations represented the large amount of surface heat847

ﬂux in the SST frontal zone, and the heat ﬂux in this zone was positively

848

correlated to the precipitation patterns associated with the second MCS.

42

849

This suggests the importance of the sharp frontal structure in SST for the850

development of the second MCS. However, the comparison of simulations

851

with the observations revealed that the heat supply from the warm ocean852

to the boundary layer was underestimated throughout the observation pe-853

riod due to a cold SST bias in the warm Kuroshio current imposed on all854

simulations. This underestimation of the ocean inﬂuence may result in the855

unclear impact of the vessel observations on the second MCS. Therefore,856

improving the prediction of this MCS would require SST to be as accurate857

as possible. However, accurate SST in the BFZ is diﬃcult to obtain because

858

SST observations rely largely on the microwave sounders that cannot mea-859

sure SST under heavy rainfall conditions. As a result, there is signiﬁcant

860

variability in the representation of SST over the ECS in the Baiu season

861

between the diﬀerent products. To represent the uncertainty of the SST,

862

the ensemble of SST should be considered like the atmospheric ensemble.

863

Kunii and Miyoshi (2012) and Duc et al. (2015) showed that SST per-864

turbations had positive impacts on both the typhoon track and intensity

865

forecasts. The SST ensemble could also be useful for evaluating the inﬂu-

866

ence of the uncertainty in the SST on the MCS. Whereas the multi-center867

SST ensemble reﬂects the uncertainty of the observations, ocean dynamics

868

also has its own growing modes. Although a fully coupled atmospheric-869

ocean assimilation system may be able to introduce the inﬂuence of ocean

43

870

dynamical uncertainty into atmospheric variability, determining the impact871

of atmospheric observations on the ocean or vice versa is complicated (Ko-872mori et al. 2018). Ohishi et al. (2023) produced an ensemble analysis

873

product called local ensemble transform Kalman ﬁlter-based ocean research874

analysis (LORA) to incorporate oceanographic dynamic uncertainty into875

the estimation of analysis uncertainty. Such ensemble products would be876

useful for simply reﬂecting the impact of ocean uncertainty on atmospheric

877

disturbances. Furthermore, we should consider the uncertainties in the sur-878

face physics and planetary boundary layer schemes since the eﬀect of surface

879

heat ﬂux on the atmosphere is determined by surface conditions and verti-

880

cal diﬀusion. The sensitivity experiments that take into consideration the881

uncertainties in SST and physics schemes will be reported elsewhere.

882

Data availability statement883

All data from the ensemble experiments by the NCEP MSM will be

884

provided upon request. The NCEP GFS forecast data and PREPBUFR

885

were obtained from the National Oceanic and Atmospheric Administration

886(NOAA) Operational Model Archive Distributed System (NOMADS). The

887

data in June 2022 are currently available at the National Center for Atmo-

888

spheric Research (NCAR) Research Data Archive (RDA). The radar com-889

posite rainfalls and the mesoscale operational analysis of the JMA were ob44

890

tained from the database of Research Institute for Sustainable Humanosphere,

891

Kyoto University. Himawari-8 gridded data was obtained from the P-tree

892

System of the Japan Aerospace Exploration Agency. These data are avail-893

able at the following URLs:

894

Radar: http://database.rish.kyoto-u.ac.jp/arch/jmadata/data/jma-radar/895

synthetic/original/

896

JMA mesoscale analysis: http://database.rish.kyoto-u.ac.jp/arch/897

jmadata/data/gpv/original/

898

Himawari-8: https://www.eorc.jaxa.jp/ptree/index.html

899

Acknowledgements

900

We would like to thank all the researchers involved in the observation901

campaign in June 2022. The NCEP regional spectral model was provided902

by Dr. Hann-Ming Henry Juang. The NCEP GFS forecast data and

903

PREPBUFR were obtained from the National Oceanic and Atmospheric

904

Administration (NOAA) Operational Model Archive Distributed System

905(NOMADS). Research products of brightness temperature produced from906

Himawari data were supplied by the P-Tree System, Japan Aerospace Ex-

907

ploration Agency (JAXA). The radar composite rainfalls and the mesoscale

908

operational analysis data of the JMA were collected and distributed by909

Research Institute for Sustainable Humanosphere, Kyoto University.
45

910

911

This work is supported by JSPS KAKENHI Grant number 22KJ1966,
19H05698, 19H05605, and 21K03662.

912

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List of Figures

1120

1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23

Computational domains . . . . . . . . . . . . . . . . . . . .
DA design . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Large-scale environmental ﬁelds . . . . . . . . . . . . . . . .
Environmental features . . . . . . . . . . . . . . . . . . . . .
SST and ship tracks . . . . . . . . . . . . . . . . . . . . . .
Observed vertical proﬁles . . . . . . . . . . . . . . . . . . . .
Observed surface state . . . . . . . . . . . . . . . . . . . . .
Ensemble spread . . . . . . . . . . . . . . . . . . . . . . . .
Precipitation comparison . . . . . . . . . . . . . . . . . . . .
SLP and precipitation . . . . . . . . . . . . . . . . . . . . .
Meridional cross sections . . . . . . . . . . . . . . . . . . . .
Surface heat ﬂux . . . . . . . . . . . . . . . . . . . . . . . .
Analysis spread diﬀerence . . . . . . . . . . . . . . . . . . .
Time-averaged analysis ensemble spread . . . . . . . . . . .
Boxplots of precipitation . . . . . . . . . . . . . . . . . . . .
SLP and precipitation in DA . . . . . . . . . . . . . . . . . .
Comparison of cloud-top brightness temperature . . . . . . .
Ensemble probabilistic forecast of the brightness temperature
Incremental diﬀerence at 0000 UTC 19 June . . . . . . . . .
Precipitatble water in DA . . . . . . . . . . . . . . . . . . .
Meridional cross sections in DA . . . . . . . . . . . . . . . .
Vertical proﬁles in DA . . . . . . . . . . . . . . . . . . . . .
Surface state in DA . . . . . . . . . . . . . . . . . . . . . . .

57

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79
80

Fig. 1. Computational domains with model terrain heights (m) for (a)ensemble simulations (D1–3) and (b) assimilation experiments (D2b,
D3b). Red box in (b) indicates the observation area of the three vessels.

58

Fig. 2. Schematic diagram for assimilation cycles. The three-hourly cycles
in the two domains (D2b, D3b) begin at 0000 UTC on the 18th and2100 UTC on the 18th, respectively. The hourly CNTL and NOSHIP
cycles in D2b and D3b start at 0000 UTC on the 19th and end at 0300
UTC on the 20th.

59

Fig. 3. Large-scale environmental ﬁelds of the NCEP GSM initial analysis
averaged from 0000 UTC 18 to 1200 UTC 20 June 2022. Horizontal
wind speed (m s−1 , color) and geopotential height (gpm, contour) onthe (a) 500 hPa and (b) 850 hPa surfaces; (c) meridional cross sectionof zonal winds (m s−1 ) averaged from 125◦ E to 130◦ E.

60

Fig. 4. Environmental features at (a–c) 0300 UTC and (d–f) 2100 UTC 19
June 2022. (a, d) Brightness temperature (K) at the cloud-top (channel
13) of Himawari-8. (b, e) Vertically integrated water vapor ﬂux (scale
located upper left, vector) and precipitable water (kg m −2 , color), and(c, f) relative vorticity (10−4 s−1 , color) and geopotential height (gpm,
contour) at the 850 hPa surface from the JMA-MA. A Box in each plot
indicates the vessel observation area.
61

Fig. 5. Sea surface temperature (◦ C) prescribed in assimilation experiments
and observed by the three vessels (circles). Gray lines show the ship
tracks.

62

Fig. 6. Time–height cross sections of θe (K, color) and horizontal wind(m s−1 , arrows) at the observation locations of (a) Nagasaki-maru (between 127.9◦ E and 128.3◦ E), (b) Kagoshima-maru (between 128.4◦ E
and 128.8◦ E) and (c) Seisui-maru (between 128.9◦ E and 129.3◦ E). The
arrow scale is shown in the upper right corner. White dots indicate thelayers in which the relative humidity is over 95%.
63

Fig. 7. Surface wind speed (m s−1 , top panel), θess (solid) and θe on the deck(dashed, middle panel), and CAPE (J kg −1 , bottom panel) observed by
the vessels.

64Fig. 8. Ensemble spread in D2 every 12 hours from the initial time to the
36-h forecast. (a) Kinetic energy spread (m2 s−2 , color) and the ensemble mean geopotential height (gpm, contour) and (b) speciﬁc humidity
spread (g kg−1 , color) and the ensemble mean θe (K, contour) at the
850 hPa surface. Thicker contours indicate (a) 1500 gpm and (b) 336
K.
65

Fig. 9. (a) Precipitation accumulated for the preceding 1-h (mm) averaged
in the observation area from 2100 UTC on the 18th to 0300 UTC on the20th June. The horizontal axis indicates the valid date in UTC. Blackbars indicate the precipitation from the JMA radar composite. Blue,dark blue, and yellow-green curves show the unperturbed run, member
10 and 40 in D3, respectively. Gray curves show the other ensemble members. (b, c) Fractions Skill Score (ordinate) and accumulated
precipitation in the observation area (mm, abscissa). The mark ”c”
represents the unperturbed run. Red vertical lines show 6-h precipitation in the observation area obtained from the JMA radar composite.
(b) 0000 to 0600 UTC on the 19th and (c) 1800 UTC on the 19th to0000 UTC on the 20th.
66Fig. 10. Mean sea level pressure (contour, per 1 hPa), surface winds (windbarbs) and accumulated precipitation during preceding 3-h (color, mm)
at (a–c) 0300 UTC and (d–f) 2100 UTC 19 June. (a, d) The JMA-MA(sea level pressure and winds) and the JMA radar composite, (b, e)
the unperturbed run, (c) member 40 and (f) member 10 in D3. Blackboxes indicate the observation area.
67Fig. 11. Meridional cross sections at dashed lines in Fig. 10a–c at 0300
UTC 19 June. (a–c) θe (color, K), virtual θ (gray contour, per 1 K),
95% relative humidity (blue contour), MAUL (gray shades), and (d–f)
zonal winds (contours in 4 m s−1 interval) and meridional winds (m s−1 ,color shades). For zonal winds, negative contours are dashed, and zero
contours are thicker than the others. (a, d) The JMA-MA, (b, e) theunperturbed run and (c, f) member 40 in D3.
68

Fig. 12. Fes (K m h−1 , color) and θess (white contours, interval 3 K) at 2100
UTC 19 June for (a) the unperturbed run and (b) member 10 in D3.
The values calculated from the observations by two vessels (Seisui-maru
and Kagoshima-maru, Nagasaki-maru is missing) are marked by circles.
Green contours indicate the preceding 1-h precipitation amount (8, 16,
and 32 mm).

69

Fig. 13. Time–height cross sections of the diﬀerence of analysis ensemble
spread (%) of CNTL from NOSHIP averaged in D2b for the (a) zonal
winds, (b) meridional winds, (c) temperature, and (d) speciﬁc humidity.

70Fig. 14. Time-averaged analysis ensemble spread at the 850 hPa surface of
CNTL in D2b for (a) zonal wind, (b) meridional wind, (c) temperature,and (d) speciﬁc humidity. White contours show the ensemble meanstate of each variable. (e–h) As for (a–d) but showing the diﬀerence (%)
of CNTL from NOSHIP. Gray contours indicate the ensemble spread of
CNTL. Thick white (a–d) or black (e–h) boxes in each panel indicate
the observed area.
71Fig. 15. (a) Boxplots of 1-h accumulated precipitation (mm) averaged in
the observation area. The horizontal axis indicates the valid date inUTC. Red (Blue) markers and boxes represent the CNTL (NOSHIP)
unperturbed analysis and members in D3b. Black rectangles show theradar composite. (b, c) As in Fig. 9b, c, but for six consecutive cyclesof CNTL (red) and NOSHIP (blue).

72

Fig. 16. As in Fig. 10 but for precipitation accumulated for the preceding
1-h from 0100 to 0500 UTC 19 June. (a) The JMA radar composite
and the JMA-MA. 1-h forecast from the unperturbed analysis in D3bof (b) CNTL and (c) NOSHIP.

73

Fig. 17. Comparison of cloud-top brightness temperature (BT, K) from0100 to 0500 UTC 19 June. (a) Channel 13 of Himawari-8. Simulated
BT for 1-h forecast from the unperturbed analysis in D3b of (b) CNTL
and (c) NOSHIP.

74

Fig. 18. Ensemble probabilistic forecast of the brightness temperature (BT)from 0100 to 0500 UTC 19 June of (a) CNTL and (b) NOSHIP in D3b.
The color of each grid indicates the ratio of the number of members
whose BT < 215 K to the ensemble size. White contours show theobserved BT of 215 K.

75

Fig. 19. Incremental diﬀerence at 0000 UTC 19 June (the ﬁrst cycle) between CNTL and NOSHIP in D2b. (a) Horizontal wind (scale locatedin upper right, vector) and divergence (10−4 s−1 , color) at the 950 hPa
surface. (b) Meridional and (c) zonal cross sections shown in (a) for
temperature (K, contour, dashed curves are negative) and speciﬁc humidity (g kg−1 , color). Gray contours show the analysis of virtual θ
(K).

76

Fig. 20. As in Fig. 4b, e but for the ensemble mean state at (a, b) 0300
UTC (the fourth cycle) and (c, d) 2100 UTC (the 22th cycle) 19 Junein D2b of (a, c) CNTL and (b, d) NOSHIP.

77

Fig. 21. As in Fig. 11 but for the ensemble mean state at 0300 UTC 19
June (the fourth cycle) in D2b. (a, b) CNTL and (c, d) NOSHIP.

78

Fig. 22. As in Fig. 6 but for virtual samplings from (a–c) CNTL and (d–f)
NOSHIP in D3b.

79

Fig. 23. As in Fig. 7 but for virtual samplings from (a) CNTL and (b)NOSHIP in D3b.

80

List of Tables

1144

1145

1

Model physics schemes and numerical methods . . . . . . . .

81

82

Physics scheme
Shortwave radiation
Longwave radiation
Cumulus convection
Shallow convection
Microphysics
Planetary boundary layer
Orographic gravity wave drags
Land surface model
Numerical method
Time ﬁlter
Semi-implicit adjustment
Implicit lateral boundary relaxation

Speciﬁcation
Chou and Suarez (1999)
Mlawer et al. (1997)
Pan and Wu (1995), Hong and Pan (1998)
Tiedtke (1983)
Ferrier et al. (2002)
Hong and Pan (1996)
Kim and Arakawa (1995)
Ek et al. (2003)
Asselin (1972)
Robert et al. (1972), Ikawa (1988)
Tatsumi (1986), Juang and Kanamitsu (1994)

Table 1. Model physics schemes and numerical methods used in NCEP
RSM and MSM.

82