Numerical Study on the Precipitation Concentration over the Western Coast of Sumatra Island

Ryosuke Okugawa aDepartment of Earth Science, Graduate School of Science and Engineering, University of Toyama, Toyama, Japan

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Kazuaki Yasunaga aDepartment of Earth Science, Graduate School of Science and Engineering, University of Toyama, Toyama, Japan

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Atsushi Hamada aDepartment of Earth Science, Graduate School of Science and Engineering, University of Toyama, Toyama, Japan

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Satoru Yokoi bJapan Agency for Marine–Earth Science and Technology, Yokosuka, Japan

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Abstract

Large amounts of tropical precipitation have been observed as significantly concentrated over the western coast of Sumatra Island. In the present study, we used a cloud-resolving model to perform 14-day numerical simulations and reproduce the distinctive precipitation distributions over western Sumatra Island and adjacent areas. The control experiment, in which the warmer sea surface temperature (SST) near the coast was incorporated and the terminal velocity and effective radius of ice clouds were parameterized to be temperature dependent, adequately reproduced the precipitation concentration as well as the diurnal cycles of precipitation. We then used the column-integrated frozen moist static energy budget equation, which is virtually equivalent to the column-integrated moisture budget equation under the weak temperature gradient assumption, to formulate sensitivity experiments focusing on the effects of coastal SST and upper-level ice clouds. Analysis of the time-averaged fields revealed that the column-integrated moisture and precipitation in the coast were significantly reduced when a cooler coastal SST or larger ice cloud particle size was assumed. Based on the comparison of the sensitivity experiments and in situ observations, we speculate that ice clouds, which are exported from inland convection that is strictly regulated by solar radiation, promote the accumulation of moisture in the coastal region by mitigating radiative cooling. Together with the moisture and heat supplied by the warm ocean surface, they contribute to the large amounts of precipitation here.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

This article is included in the Years of the Maritime Continent Special Collection.

Corresponding author: Kazuaki Yasunaga, yasunaga@sus.u-toyama.ac.jp

Abstract

Large amounts of tropical precipitation have been observed as significantly concentrated over the western coast of Sumatra Island. In the present study, we used a cloud-resolving model to perform 14-day numerical simulations and reproduce the distinctive precipitation distributions over western Sumatra Island and adjacent areas. The control experiment, in which the warmer sea surface temperature (SST) near the coast was incorporated and the terminal velocity and effective radius of ice clouds were parameterized to be temperature dependent, adequately reproduced the precipitation concentration as well as the diurnal cycles of precipitation. We then used the column-integrated frozen moist static energy budget equation, which is virtually equivalent to the column-integrated moisture budget equation under the weak temperature gradient assumption, to formulate sensitivity experiments focusing on the effects of coastal SST and upper-level ice clouds. Analysis of the time-averaged fields revealed that the column-integrated moisture and precipitation in the coast were significantly reduced when a cooler coastal SST or larger ice cloud particle size was assumed. Based on the comparison of the sensitivity experiments and in situ observations, we speculate that ice clouds, which are exported from inland convection that is strictly regulated by solar radiation, promote the accumulation of moisture in the coastal region by mitigating radiative cooling. Together with the moisture and heat supplied by the warm ocean surface, they contribute to the large amounts of precipitation here.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

This article is included in the Years of the Maritime Continent Special Collection.

Corresponding author: Kazuaki Yasunaga, yasunaga@sus.u-toyama.ac.jp

1. Introduction

The Maritime Continent (MC) is a complex organization of islands of various sizes located in the tropical western Pacific and eastern Indian Oceans. Global satellite measurements have repeatedly shown that the moist and warm environment of the MC hosts one of the most active convection centers in the world (e.g., Heddinghaus and Krueger 1981; Liebmann and Hartmann 1982; Salby et al. 1991), which significantly influences the weather and climate on a global scale. For example, the released latent heat is one of the major sources driving the atmospheric general circulation as well as the circulation within the tropics (e.g., Ramage 1968; Chang and Lau 1982), and the Rossby wave trains that emanate from the convection distort atmospheric flow patterns at higher latitudes (e.g., Stan et al. 2017; Yang et al. 2019). Numerous field experiments have been conducted that have greatly improved understanding of the convective activity and associated circulations over the MC (Houze et al. 1981; Keenan et al. 1989; Yihui et al. 2004; Keenan et al. 2000; Fukao 2006; Yamanaka 2016; Yokoi et al. 2017; Mori et al. 2018; Yokoi 2020). However, several fundamental issues remain unresolved, such as climatological precipitation patterns.

Over the MC, precipitation is concentrated around coastal regions. For example, the heaviest precipitation occurs over the eastern coasts along the Bay of Bengal and Arabian Sea (e.g., Grossman and Durran 1984; Ogura and Yoshizaki 1988; Zuidema 2003; Xie et al. 2006; Shige et al. 2017). Similar features have also been observed in northwestern South America (e.g., Mapes et al. 2003b). Bergemann et al. (2015) quantitatively showed that, in regions where land–sea interactions play a crucial role in precipitation formation, 40%–60% of the total precipitation could be attributed to coastal effects. Ogino et al. (2016) showed that the tropical precipitation distribution could be simply expressed as a function of the distance from the coast and that it is predominantly concentrated on the coastline. However, the reality is more complex, even in the MC and adjacent regions. Pronounced precipitation peaks can be observed along the western coast of Sumatra Island and the Indochina Peninsula around Myanmar, but precipitation concentrations are barely visible or present along the northern, southern, and eastern coasts of the MC (e.g., Fig. 1a). Precipitation peaks are also observed around the elevated orography of Borneo and New Guinea Islands, which are surrounded by a warmer sea surface temperature (SST) (e.g., Fig. 1b). Therefore, although the warm SST of the MC likely contributes to the formation of coastal precipitation peaks, the SST distribution alone cannot explain the precipitation patterns.

Fig. 1.
Fig. 1.

(top) Geographical distributions of the climatological (i.e., 15-yr mean) (a) precipitation and (b) SST. (bottom) Magnified views of the areas enclosed by black rectangles in (a) and (b) and corresponding to the model domain for the numerical simulations. The precipitation and SST distributions are based on the TRMM 3A25 (e.g., Kummerow et al. 2000) and OI-SST (e.g., Reynolds et al. 2007) datasets, respectively.

Citation: Monthly Weather Review 152, 3; 10.1175/MWR-D-23-0037.1

The intricate topography of the MC forms elongated coastlines, where intensive radiative forcing by the Sun at lower latitudes and the land–sea contrast of the heat capacity dominantly drive the diurnal cycle of land–sea circulation. The pronounced daily pulse of the land–sea breeze regulates the development of deep convection over the land, and the land-originating convective systems generally migrate to the coast and further offshore. Previous observational studies have suggested that the precipitation around coastal regions is related to the seaward migration of such systems (e.g., Sakurai et al. 2005, 2009; Mori et al. 2011; Yanase et al. 2017). Several mechanisms have been proposed for the “migration” of convective systems, such as gravity waves (e.g., Mapes et al. 2003a; Hassim et al. 2016; Yokoi et al. 2017), the interaction between local winds and environmental monsoonal winds (e.g., Houze et al. 1981; Grossman and Durran 1984; Ogura and Yoshizaki 1988), the interaction between the cold outflow from convective downdrafts and environmental winds (e.g., Mori et al. 2004; Wu et al. 2008, 2009; Yokoi et al. 2019), and advection by environmental winds associated with large-scale disturbances (e.g., Ichikawa and Yasunari 2006; Shige et al. 2017; Sakaeda et al. 2020). However, the mechanism for the “concentrated precipitation” along the coasts is not well understood. Motivated by this incomplete understanding, we used a cloud-resolving model (CRM) in the present study to conduct numerical simulations and clarify the mechanism for the concentration of coastal precipitation. Our numerical experiments were focused on Sumatra Island and the adjacent ocean, where the coastal precipitation peak is most pronounced over a warm SST (Figs. 1a,b).

The remainder of the paper is organized as follows. Section 2 briefly describes the model settings and general design of the numerical experiments. Section 3 presents the control experiment and its results. Section 4 presents the design and results of the sensitivity experiments. Section 5 summarizes the results and concludes the paper.

2. Model description and experimental design

a. Model description

We performed numerical simulations by using ver. 5.2.5 of the Scalable Computing for Advanced Library Environment-Regional Model (SCALE-RM), which was developed at the RIKEN Center of Computational Science (R-CCS) in Japan as a nonhydrostatic meteorological model for large-eddy simulations or Reynolds-averaged numerical simulations. The basic equations and a description of the numerical model are given by Nishizawa et al. (2015) and Sato et al. (2015). SCALE-RM has been tested on several real events and ideal cases (e.g., Sueki et al. 2019; Yoshida et al. 2019; Tanji and Inatsu 2019).

b. Outlines of numerical experiments

The model domain of the present study covered the eastern Indian Ocean and Sumatra Island (bottom panels in Figs. 1a and 1b). The horizontal grid spacing was 3.5 km. In the vertical direction, the model had 80 layers up to a height of 24 km with a fine grid spacing (40 m) at the lowest level and a relatively coarse grid spacing (360 m) at the upper levels. The planetary boundary layer turbulence and radiation schemes were adapted from Nakanishi and Niino (2009) and Sekiguchi and Nakajima (2008), respectively. Moist processes were represented by a six-category (i.e., water vapor, water clouds, rain, ice clouds, snow, and graupel) single-moment bulk scheme (Tomita 2008). Although the horizontal grid spacing of 3.5 km might not be sufficient to fully resolve convective cores, no cumulus parameterization was employed in the simulations. The present investigation focuses on the better fidelity of the coastal precipitation concentration in a numerical model, and the omission of the cumulus parameterization is justified by the good agreement between the simulations and the in situ observations, as will be seen in section 3.

The integrations began on 22 November 2015 and ended on 7 December 2015. The initial 24-h simulation was assumed to be a spinup period, and 14-day results were compared. We selected this period because the mean precipitation showed a prominent peak along the western coast of Sumatra Island, which is similar to climatological precipitation distribution (Fig. 1a). In addition, we selected these dates for simulation because the pre–Years of the Maritime Continent (YMC) campaign was conducted in November–December 2015 along the western coast of Sumatra Island by the Japan Agency for Marine–Earth Science and Technology (JAMSTEC), the Indonesian Agency for the Assessment and Application of Technology (BPPT), and the Indonesian Agency for Meteorology, Climatology and Geophysics (BMKG). Intensive observations were collected by the R/V Mirai of JAMSTEC at approximately 4.078°S, 101.908°E and at the BMKG observatory in Bengkulu City at 3.868°S, 102.348°E. Among a variety of observations, we used volume scan data from a ship-bone C-band weather radar, intake seawater temperature, and 3-hourly upper-air soundings at the vessel and BMKG observatory to evaluate the model performance. A more detailed description of the pre-YMC campaign is given by Yokoi et al. (2017).

The initial and lateral boundary conditions were obtained by linear interpolation of the National Centers for Environmental Prediction Final Operational Global Analysis (NCEP-FNL) data, which have a horizontal grid spacing of 1° × 1° and are provided every 6 h. Data obtained in the pre-YMC campaign (e.g., radiosonde soundings and surface meteorological observations) were incorporated into the objective analysis through the Global Telecommunication System of the World Meteorological Organization (WMO). Accordingly, a better quality of the analyzed fields in the simulation period can be expected.

CRMs with a grid spacing of several kilometers have been widely used to study convective storms over the MC in conjunction with or independently of field experiments (e.g., Wu et al. 2008, 2009; Sato et al. 2009; Fujita et al. 2011; Hassim et al. 2016; Vincent and Lane 2016). Although most CRMs capture the general features of the diurnal precipitation cycle, not all CRMs realistically reproduce coastal precipitation peaks. For example, simulations by Vincent and Lane (2017), Argüeso et al. (2020) and Birch et al. (2016) significantly underestimated the precipitation, whereas the Met Office atmospheric model with a horizontal resolution of 4 km successfully reproduced the precipitation distribution (Love et al. 2011). However, no previous work has explained the reason for such different performances, although the fidelity of the diurnal cycle has been addressed. In fact, our model also failed to reproduce the coastal precipitation peak in a preliminary simulation prior to the control experiment using the NCEP-FNL and default parameterization settings (not shown). We systematically investigated various physical processes and found that the treatments of the SST and ice clouds are the most crucial to well reproduce the coastal precipitation concentration. Therefore, we set the simulation with the higher fidelity as a control experiment and examine the effects of the SST and ice clouds through the sensitivity tests.

In terms of the SST, we noticed that the analyzed SSTs are colder than the pre-YMC offshore observations by R/V Mirai in the first half of the period (Fig. 2d). Therefore, the NCEP-FNL SST datasets are replaced by the high-resolution SST analysis (OI-SST) provided by NOAA (Reynolds et al. 2007). The NOAA products show warmer SST distributions around the western coast of Sumatra Island (Figs. 2a–c), and the time series is closer to the observations (Fig. 2d).

Fig. 2.
Fig. 2.

Geographical distributions of (a) OI-SST, (b) NCEP-FNL SST, and (c) their differences averaged over the simulation period (23 Nov–7 Dec 2015). (d) Time series of the observed SST (black) and SST analyzed by NCEP-FNL (red) and NOAA (blue) at the stationary observation point of the R/V Mirai. In (a), locations of the BMKG observatory in Bengkulu City and the R/V Mirai are indicated by cross marks.

Citation: Monthly Weather Review 152, 3; 10.1175/MWR-D-23-0037.1

In the original code of the cloud microphysics parameterization, the terminal velocity of the ice clouds (υTi) was calculated solely from ice cloud mixing ratios following the approach of Heymsfield and Donner (1990), and the effective radius of ice clouds (Deff) was assumed constant (40 μm). However, in the control experiment, the parameterization of Heymsfield et al. (2007), which is based on in situ observations of low-latitude ice cloud layers, was used to determine υTi. In this scheme, υTi depends on both the ambient temperature and mixing ratio, so υTi becomes significantly smaller near the tropopause (close to 0 m s−1) where the temperature reaches a minimum. In addition, Deff was modified to be temperature dependent. The empirical relationship between Deff and temperature is based on a formula proposed by Thornberry et al. (2017), who derived it from observations of cirrus clouds in the tropical tropopause layer over the western Pacific.

c. Formulation of sensitivity experiments

Statistically, precipitation is known to be monotonically dependent on the CWV over the open ocean in the tropics (e.g., Bretherton et al. 2004; Neelin et al. 2009; Mapes et al. 2018; Rushley et al. 2018). This monotonic relationship between moisture and precipitation is weak or obscure over land (e.g., Schiro et al. 2016; Ahmed and Schumacher 2017). However, similar behavior has been observed over coasts (e.g., Bergemann and Jakob 2016), at least around the western coast of Sumatra Island (Ahmed and Schumacher 2017). Because the water vapor field is spatially homogeneous in contrast to more localized precipitation, it would be a better tactic to focus on the CWV budget equation rather than precipitation itself to formulate sensitivity experiments for inferring the mechanism of precipitation concentration. However, the CWV budget equation includes precipitation, so we opted to use the frozen moist static energy (FMSE) budget equation. The FMSE (m) is expressed as follows:
m=s+LυqυLfqi,
where s is the dry static energy, Lυ is the latent heat of vaporization, Lf is the latent heat of fusion, qυ is the water vapor mixing ratio, and qi is a mixing ratio of all ice phase condensates (ice cloud, snow, and graupel). The column-integrated FMSE (CFMSE) budget equation is given as follows:
mt=vm+QR+(H+LυE),
where v is the three-dimensional wind vector, QR is the net radiative heating rate, H is the sensible heat flux from the surface, E is the surface evaporation, and the angle brackets indicate mass-weighted column integration. Under the weak temperature gradient (WTG) approximation (Sobel et al. 2001), the evolution of CFMSE as determined by Eq. (2) is nearly equivalent to the CWV. This can be expressed as
LυCWVtvm+QR+(H+LυE).
Here, following the “precipitation budget” approach of Adames (2017), we focused on the source terms (i.e., radiative heating and surface heat flux) in the CFMSE equation in Eq. (3) and evaluated the role of ice cloud microphysics and the SST in shaping coastal precipitation peaks. In the control experiment, NOAA OI-SST products were employed instead of the NCEP-FNL SST datasets owing to the cool bias along the coast, as described before. According to Eq. (3), the warmer OI-SST may have increased the CWV by enhancing the surface heat flux in the corresponding region. Dipankar et al. (2019) pointed out that the coastal SST has a nonnegligible impact on the fidelity of numerical models representing the precipitation diurnal cycle. Therefore, to clarify the role of a warmer SST in the formation of coastal precipitation peaks, the first sensitivity experiment imposed the original NCEP-FNL SST on the model (i.e., N-SST experiment).

In the control experiment, the terminal velocity (υTi) and effective radius of ice clouds (Deff) were calculated from the mixing ratio and ambient temperature following the work of Heymsfield et al. (2007) and Thornberry et al. (2017). The temperature profiles had a minimum in the upper troposphere, and υTi and Deff became significantly smaller near the tropopause. These changes would increase the lifetime and optical thickness of ice clouds near the tropopause. Upper-level ice clouds generally tend to warm the surface and atmospheric column (e.g., Manabe and Strickler 1964; Stephens 2005). Accordingly, modifying the ice cloud parameterization in the control experiment would induce anomalous updrafts to maintain the temperature under the WTG assumption, and may have improved the fidelity of the coastal precipitation concentration by increasing the CWV through the enhancement of the vertical advection. To test our speculation, the second sensitivity experiment determined υTi solely from ice cloud mixing ratios following the approach of Heymsfield and Donner (1990), and Deff was assumed constant (40 μm) (i.e., ICEwoTD experiment).

3. Control experiment

As an overview of the cloud and precipitation fields during the integration period, Figs. 3a–c show the time-averaged precipitation derived from the Global Satellite Mapping of Precipitation (GSMaP) (Okamoto et al. 2005), the outgoing longwave radiation (OLR) retrieved from NOAA’s polar-orbiting satellites (Liebmann and Smith 1996), and the NCEP-FNL column-integrated water vapor (CWV), respectively. During the simulation period, clouds were organized over the eastern Indian Ocean in association with the active phase of the Madden–Julian oscillation (MJO) (Madden and Julian 1971, 1972), and the western coast of Sumatra Island experienced pronounced diurnal variation in the precipitation (e.g., Yokoi et al. 2017) that is typical when the MJO convective envelope is over the eastern Indian Ocean (e.g., Fujita et al. 2011; Kamimera et al. 2012). Reflecting the vigorous convective activity, large amounts of precipitation took place over western Sumatra Island, predominantly along the coast. This is similar to the climatological precipitation distribution (Fig. 1a). In accordance with the precipitation distribution, the minimum OLR was over the coastline, and a larger CWV was found over the coastal region.

Fig. 3.
Fig. 3.

Geographic distributions of the (a) satellite-retrieved precipitation, (b) OLR, and (c) analyzed CWV averaged over the simulation period (23 Nov–7 Dec 2015). Simulation results in the control experiment: (d) precipitation, (e) OLR, and (f) CWV.

Citation: Monthly Weather Review 152, 3; 10.1175/MWR-D-23-0037.1

Figures 3d–f show the simulated time-averaged precipitation, OLR, and CWV, respectively, in the control experiment. The model adequately reproduced the precipitation peak over the coastal region (Fig. 3d), although the simulated precipitation was more scattered over the domain than the retrieved precipitation (Fig. 3a). The OLR was slightly underestimated over the open ocean corresponding to the excess precipitation there (Figs. 3b,e). However, around the western coast of Sumatra Island where the precipitation peaks were located, the satellite-derived and simulated OLRs showed comparable values of about 200 W m−2, and the biases were small. While the model generally underestimated the CWV, the CWV distributions showed similar patterns, at least over the coast (Figs. 3c,f).

Figure 4 compares the simulation results with radiosonde observations during the pre-YMC campaign. The model accurately captured general features of the temporal variations in the horizontal winds and mixing ratio anomalies near the coast, such as the replacement of easterly and westerly winds at middle levels, prominent southerly wind anomalies in the early and late periods, and the occasional intrusion of dry air in the lower and middle levels.

Fig. 4.
Fig. 4.

Time–height cross sections of the (a),(b) zonal wind; (c),(d) meridional wind; and (e),(f) mixing ratio anomalies derived from (left) radiosonde soundings and (right) simulated in the control experiment during the simulation period (23 Nov–7 Dec 2015) at the stationary observation point of the R/V Mirai.

Citation: Monthly Weather Review 152, 3; 10.1175/MWR-D-23-0037.1

Convective systems over the land with a pronounced diurnal cycle have a significant impact on coastal precipitation. Accordingly, we evaluate the model performance for the diurnal variations separately here. Figure 5 compares the observed and simulated diurnal cycles of the precipitation. The observed diurnal cycle indicated that convection was initiated over the land at about 1400 LT on the western side of the elevated orography, about 30–50 km from the coast (Fig. 5a). After the development of convection, the main precipitation areas moved westward and redeveloped over the coast while weak signals moved eastward. The migration speed was estimated at about 9.3 m s−1 at more than 100 km from the coast. In the control experiment, the model also simulated the initial development of the inland convection and subsequent westward migration, although the precipitation systems predominantly moved eastward down the eastern side of the mountain ranges (Fig. 5b). In addition, the migration speed in the offshore region (10.4 m s−1) roughly agreed with the estimated migration speed from the satellite observations. Within 25 km of the coast, surface radar observations showed that precipitation migrated at a rate of about 4 m s−1 (Fig. 5c). In addition, slower migration signals (about 1.7 m s−1 or less) appeared in the transition region from the coast to offshore (25–100 km from the coast), and the precipitation here peaked twice at around 0000 and 0500 LT. Two propagation signals (with speeds of 4.6 and 2.3 m s−1) were also simulated in the control experiment. The strength of the faster and slower signals was almost comparable (Fig. 5d), although the former is more prominent in the radar observations.

Fig. 5.
Fig. 5.

Composite diurnal cycle of precipitation retrieved from (a) satellite and (c) in situ observations and (b),(d) simulated by the control experiment. Precipitation is averaged (top) over the rectangular areas in Fig. 3a and (bottom) around R/V Mirai. The ranges of the abscissa differ between the top and bottom panels because of the limited coverage of the ground observations.

Citation: Monthly Weather Review 152, 3; 10.1175/MWR-D-23-0037.1

According to radiosonde soundings, the sea breeze began to evolve near the surface at about 1000 LT (Fig. 6a). The layer of positive (i.e., inshore) horizontal winds gradually deepened and reached a height of 2 km at about 1600 LT. After 1600 LT, land breezes appeared near the surface, and the negative (i.e., offshore-ward) horizontal wind intensified in the evening. In combination with the landward surface wind, the vapor mixing ratios increased at the lower levels (below 3 km) up to 2200 LT with delayed moisture increase at the middle levels (Fig. 6b). If the diurnal circulations are assumed to not change in the direction parallel to the coastline, the horizontal divergence between the two observation sites (i.e., Bengkulu Observatory and R/V Mirai) can be estimated from the horizontal wind components perpendicular to the coastline (Fig. 6c). Corresponding to the diurnal variations in the wind and vapor, the convergence near the surface peaked at around 1900 LT. The divergence at the upper levels (above 12 km) slightly preceded the surface convergence, which is consistent with positive moisture anomalies.

Fig. 6.
Fig. 6.

Composite diurnal variations in the southwest–northeast component (perpendicular to the coastline): (a),(d) horizontal wind at the Bengkulu observatory; (b),(e) anomalies of the vapor mixing ratios derived at the stationary observation point of the R/V Mirai; and (c),(f) horizontal divergence between Bengkulu and R/V Mirai derived from (top) radiosonde soundings and (bottom) calculated in the control experiment. The abscissa and ordinate indicate the local time and height, respectively. The time-mean profiles are subtracted to emphasize the diurnal cycle in (b) and (e).

Citation: Monthly Weather Review 152, 3; 10.1175/MWR-D-23-0037.1

The diurnal cycles simulated in the control experiment showed some discrepancies with the in situ observations. For example, the depth of the sea breeze circulation exceeded 2 km in height (Fig. 6d), and the tilting structure of the positive vapor anomalies was shallower (Fig. 6e). In addition, the surface convergence peak lagged slightly behind that derived from the radiosonde (Fig. 6f). However, the observations and numerical simulations shared remarkably similar features: the offshore-ward wind anomalies ascended from 3 to 12 km in the morning, the moisture increase at the middle levels lagged behind the moisture increase at the surface, and the convergence and divergence exchanged above a height of 10 km. These similarities are robust even when spatial composites of the simulation over 100 km parallel to the coastline are compared with the point observations (not shown). It should be also noted that the upper-level divergence preceded the surface convergence. Since the positive anomalies of the vapor mixing ratios followed these variations, it is likely that the upper-level divergence is associated with the remnant of the inland convection.

These results indicate that the model adequately simulated the distinctive precipitation distribution over western Sumatra Island and its adjacent area, although the horizontal grid spacing of 3.5 km might not be sufficient to fully resolve convective cores. Furthermore, the high fidelity to the diurnal cycle (including the sea breeze) and time-averaged fields encouraged us to use the model in sensitivity experiments to investigate the precipitation concentration mechanism in detail.

4. Sensitivity experiments

a. Fidelity of the sensitivity experiments

In the N-SST experiment, the precipitation decreased over the ocean, particularly over the area where the NCEP-FNL SST had a negative bias (Fig. 7a). As a result, the coastal precipitation concentration was significantly attenuated, although a modest peak could still be recognized (Fig. 7c). In the ICEwoTD experiment, the negative precipitation anomalies were extended over almost the entire domain (Fig. 7d). As a result, the precipitation peak near the coast was barely visible (Fig. 7f), while the inland precipitation is enhanced despite the negative CWV anomalies. These results clearly indicate that the treatments of the coastal SST and ice clouds in the control experiment were relevant to the fidelity of the coastal precipitation concentration. The changes in precipitation generally followed those of the time-averaged CWV in both sensitivity experiments (Figs. 7b,e), which justified our focus on the CFMSE budget (The CFMSE is nearly equivalent to the CWV under the WTG assumption.) rather than the precipitation itself to deduce the mechanism for the coastal precipitation concentration.

Fig. 7.
Fig. 7.

(left) Precipitation and (center) CWV anomalies averaged over the simulation period in the (top) N-SST and (bottom) ICEwoTD experiments. The results in the control experiment were subtracted to obtain the anomalies. (right) Precipitation distribution expressed as a function of the distance from the western edge of Sumatra Island (blue solid line) over the rectangular area in (a) and (d) in each sensitivity experiment, as well as in the control experiment (red solid line) and satellite retrieval (GSMaP, black dashed line).

Citation: Monthly Weather Review 152, 3; 10.1175/MWR-D-23-0037.1

In terms of the diurnal cycle, both sensitivity experiments showed variations in the inland convection similar to those of the control experiment: development in the afternoon or early evening, the dominance of the eastward migration, and redevelopment of the westward migrating systems over the sea (Figs. 8a,b). However, beyond 100 km from the coast, the ICEwoTD experiment had a weaker offshore migration signal, and the N-SST experiment simulated propagation in the opposite (i.e., onshore) direction. Within 100 km of the coast, both sensitivity experiments significantly suppressed the amplitude of the diurnal variations compared to that in the control experiment (Figs. 8c,d). In the transition region (25–100 km from the coast), the ICEwoTD experiment only resulted in a single propagation signal while the N-SST experiment simulated two propagation signals as in the control experiment. It should be noted that the precipitation system crossed the coastline (from land to sea) earlier and more gradually in the N-SST experiment (about 1700 LT) than in the ICEwoTD experiment (about 2000 LT).

Fig. 8.
Fig. 8.

As in Fig. 5, but for the sensitivity experiments: (left) N-SST and (right) ICEwoTD.

Citation: Monthly Weather Review 152, 3; 10.1175/MWR-D-23-0037.1

The diurnal cycles of the horizontal wind and vapor mixing ratios in the sensitivity experiments also show similar features to those found in the control experiment: a thicker layer for the landward wind near the surface (Figs. 9a,d) and the tilted structure of the positive vapor anomalies near the surface with a delayed moisture increase at the middle levels (Figs. 9b,e). However, larger discrepancies with the control experiment were observed in the upper levels. The divergence was weaker and shifted downward in the N-SST and ICEwoTD experiments, respectively, despite the positive upper-level moisture anomalies. The degraded performance for the diurnal cycle and the less pronounced coastal precipitation peak in the sensitivity experiments indicate that the formation of the precipitation concentration is somewhat relevant to the diurnal cycle. Therefore, we needed to consider the role of the diurnal cycle when proposing a mechanism for the coastal precipitation concentration.

Fig. 9.
Fig. 9.

As in Fig. 6, but for the sensitivity experiments: (top) N-SST and (bottom) ICEwoTD.

Citation: Monthly Weather Review 152, 3; 10.1175/MWR-D-23-0037.1

b. Mechanism for the coastal precipitation concentration

Figure 10 shows the time-averaged advection and source terms in the CFMSE budget equation. The advection term in Eq. (2) was calculated as a residual of the other terms (i.e., radiative heating, surface heat flux, and local derivative of the CFMSE). The advection due to the resolved-scale motions could be evaluated directly from the simulation output. However, the temporal and spatial variations of the advection were much noisier than the variations of the radiative heating and surface heat flux. In addition, the effects of the unresolved-scale (eddy) motions (e.g., um¯, υm¯, wm¯) and numerical diffusion needed to be included to preserve the total energy. The contributions of these nonlinear terms are very difficult to evaluate accurately, especially near convection. Therefore, the calculation procedure for the advection in the present analysis likely mitigated numerical errors, but incompleteness was imposed on the advection.

Fig. 10.
Fig. 10.

Anomalies of the (left) column-integrated radiative heating, (center) surface heat flux, and (right) FMSE advection averaged over the simulation period in the (top) N-SST and(bottom) ICEwoTD experiments. The results in the control experiment are subtracted to obtain the anomaly.

Citation: Monthly Weather Review 152, 3; 10.1175/MWR-D-23-0037.1

In the N-SST experiment, the surface heat flux was enhanced and reduced in the offshore regions along the equator and in the coast, respectively (Fig. 10b). This reflects the substituted SST distributions in the N-SST experiment (NCEP-FNL SST versus NOAA OI-SST; Fig. 2). In addition, the radiative heating generally followed the changes in the surface flux (Fig. 10a), and the advection tended to cancel out the effects of the source terms (Fig. 10c), mainly due to the horizontal component (not shown). In the ICEwoTD experiment, negative anomalies of radiative heating and surface flux extended over the entire model domain and over the oceanic region, respectively (Figs. 10d,e). Interestingly, the changes in radiative heating and surface flux over the ocean are similar, as is seen in the N-SST experiment. This may indicate that a positive feedback process was occurring between the radiation and flux terms. In addition, advection counteracted the source terms (Fig. 10f), which is mainly contributed by the vertical component (not shown). Overall, the effects of the source terms overwhelmed those of the advection, and the time-averaged CWV (or CFMSE) decreased significantly (Figs. 7b,e). This resulted in the negative precipitation anomalies along the western coast (Figs. 7a,d).

The comparison between the control and N-SST experiments clearly demonstrated that a warmer coastal SST contributes to the formation of coastal precipitation peaks by enhancing the surface heat flux, which subsequently increases the moisture. While the role of the coastal SST is easy to understand, the precipitation peak near the coast in the control experiment was more modest than we expected. However, the observed SST variations showed a pronounced diurnal cycle, and the minimum–maximum range in the daytime sometimes exceeded 1 K during the pre-YMC period (Fig. 2d). The associated surface heat flux was enhanced and more moisture was supplied to the atmosphere before the precipitation peak (e.g., Yokoi 2020). However, the simulations ignored SST variations on a daily time scale. Therefore, the surface heat flux may have still been underestimated in the control experiment. In other words, it is likely that the pronounced increase in the SST and significant enhancement of the heat flux in the daytime combined with the climatologically warmer SST contributed to the precipitation peak along the western coast of Sumatra Island. These effects of the SST diurnal cycle should be clarified in future studies.

In the ICEwoTD experiment, the assumed large ice particles significantly reduced the amount of ice clouds, which enhanced the associated radiative cooling in the upper levels over the coast (Figs. 11a,b). In addition, consistent with the decreases in the CWV and precipitation (Figs. 7d–f), the vertical wind showed negative upper-level anomalies over the coast (Fig. 11c). However, the anomalous vertical winds included both the cause and effect of the suppressed convective activity. Under the WTG assumption, vertical wind anomalies can be computed using the diabatic heating (Q) and static stability (dS/dp) as Q/(dS/dp). The vertical winds diagnostically estimated from the temperature and radiative heating profiles largely accounted for the actual changes in the vertical wind around a height of 12 km in the ICEwoTD experiment (Fig. 12b). The upper-level anomalous downdrafts resulting from the enhanced radiative cooling in the ICEwoTD experiment contributed to the drastic decrease in moisture at the corresponding height by vertical advection (Fig. 11d). On the other hand, the discrepancies between the actual and diagnosed vertical wind anomalies were larger at slightly lower levels (about 9–10 km), which would represent different aspects of the weakened precipitation. The height of 9–10 km also corresponds to the peak of the negative vertical wind anomalies in the N-SST experiment (Fig. 12a). The diurnal cycles of precipitation are significantly suppressed in the drier environments of the sensitivity experiments, as shown in Fig. 8. Consequently, interactions between mean moisture fields and diurnal variations of precipitation might account for the vertical wind anomalies around the 9–10-km level in the both experiments, while the WTG assumption is valid for the time-averaged conditions. Further studies are required to clarify the detailed pathway by which the upper-level moisture affects the vertical wind at slightly lower levels and the convective activity around the surface.

Fig. 11.
Fig. 11.

Anomalies in the ICEwoTD experiment of the (a) ice cloud mixing ratio, (b) radiative heating, (c) vertical wind, and (d) vapor mixing ratios averaged over the simulation period in the rectangular areas in Fig. 7d. The mean profiles in the control experiment were subtracted to obtain the anomaly. In (d), the anomalies are normalized by the mean profiles in the control experiment. The abscissa and ordinate indicate the distance from the western coastline of Sumatra Island and the height, respectively.

Citation: Monthly Weather Review 152, 3; 10.1175/MWR-D-23-0037.1

Fig. 12.
Fig. 12.

Vertical wind anomalies averaged over the coastal regions (−20 and −200 km from the coastline in Figs. 11c,f): (left) N-SST and (right) ICEwoTD experiments. The solid lines represent the actual vertical wind anomalies, and the dashed lines indicate the vertical wind anomalies diagnosed by keeping the temperature profiles unchanged against radiative heating. The corresponding profiles in the control experiment were subtracted to obtain the anomalies.

Citation: Monthly Weather Review 152, 3; 10.1175/MWR-D-23-0037.1

Figure 13 shows the composite diurnal cycle of the column-integrated ice clouds over the coast. The control experiment reproduced the gradual migration of ice clouds offshore-ward within 100 km of the coast, as seen in the precipitation (Fig. 13a). However, a closer look reveals the ice cloud migration was slightly ahead of the precipitation (Fig. 5b). The snapshots of the composite diurnal cycle suggest that ice clouds formed by inland convection associated with the diurnal land–sea circulation were advected to the coast (not shown). This interpretation is supported by the fact that the upper-level positive moisture anomalies and divergence in both the radiosonde soundings and control experiment preceded a midlevel moisture increase and lower-level convergence near the coast (Figs. 6b,c,e,f). In addition, observations with the Mie-scattering lidar onboard R/V Mirai showed that upper-level clouds appeared around 1600 LT, which was before the arrival of the precipitation (e.g., Fig. 7 of Yokoi et al. 2017).

Fig. 13.
Fig. 13.

Composite diurnal cycle of the column-integrated cloud ice simulated by the (a) control, (b) N-SST, and (c) ICEwoTD experiments over the rectangular areas in Figs. 3d, 7a, and 7d.

Citation: Monthly Weather Review 152, 3; 10.1175/MWR-D-23-0037.1

Two propagation signals were found in the N-SST experiment (Fig. 13b), and the faster migration speed (about 9.3 m s−1) was comparable to that of the upper-level offshore wind (e.g., Fig. 9d). On the other hand, in the ICEwoTD experiment, the faster signal was significantly attenuated, and the slower signal dominated (Fig. 13c). However, the slight precedence of the upper-level positive moisture anomalies was also recognized in the ICEwoTD experiment, although the upper-level divergence shifted downward (e.g., Figs. 9e,f). Based on these results, we speculate that, in the ICEwoTD experiment, the ice particles originating from the inland convection promptly fell out because of their greater terminal velocity. This decreased the amount of ice clouds over the coast, which enhanced radiative cooling and resulted in an anomalous downdraft.

5. Summary and conclusions

Satellite observations have documented that significant amounts of precipitation are concentrated over coasts in the MC (e.g., Ogino et al. 2016), particularly along the western coast of Sumatra Island. However, the mechanism is still unclear. In the present study, we used a SCALE-RM developed at the R-CCS to conduct numerical experiments focusing on the precipitation patterns along the coast of Sumatra Island. The integration started on 22 November and ended on 7 December 2015, which corresponded to the intensive observation period of the pre-YMC campaign.

The control experiment adequately simulated the distinctive precipitation distributions over the western part of Sumatra Island and adjacent areas as well as the diurnal cycles of the land–sea circulation and precipitation. Statistically, the precipitation is monotonically dependent on the CWV along the coast as well as over the open ocean, and the CFMSE budget equation is virtually equivalent to the CWV budget equation under the WTG assumption. Therefore, we formulated sensitivity experiments to clarify the role of the source terms in the CFMSE budget equation (i.e., radiative heating and surface heat flux) in the formation of coastal precipitation peaks.

In the control experiment, the NOAA OI-SST product was used as the lower boundary condition instead of the NCEP-FNL SST dataset because of the cool bias of the latter along the coast, although the NCEP-FNL dataset was used as the initial and lateral boundary conditions. In addition, the terminal velocity and effective radius of ice clouds were calculated from the ambient temperature and mixing ratio based on in situ observations of low-latitude ice cloud layers. In the N-SST experiment, the original NCEP-FNL SST was imposed on the model as the lower boundary condition to highlight the effects of the coastal SST. In the ICEwoTD experiment, the ice cloud particles were assumed to be larger than in the control experiment, which would reduce their lifetime and optical thickness, since ice clouds have a significant impact on the radiation budget.

Analysis of the time-averaged fields revealed that the column-integrated moisture and precipitation along the coast were significantly reduced when a cooler coastal SST or larger ice cloud particle size were assumed. According to the CFMSE budget equation, the degraded simulation performance in the N-SST and ICEwoTD experiments resulted from a weakened surface heat flux and enhanced radiative cooling, respectively. Based on the comparison of the results of the sensitivity and control experiments, we propose the following mechanism for the precipitation concentration over the western coast of Sumatra Island. Large amounts of ice clouds are formed by vigorous convection inland, which is strictly regulated by the solar insolation and related land–sea circulation. The ice clouds are then horizontally advected offshore-ward across the coast by upper-level easterly winds, which alleviates the radiative cooling over the warm coastal SST. This in turn induces anomalous updrafts that transport more water vapor vertically, especially in the upper-level. Thus, the anomalous updraft and more abundant moisture in the upper troposphere would assist the development of deep convection and lead to more precipitation over the coast. It should be emphasized that the cirrus-induced moistening mechanism speculated in the present paper is not unique, as it has been previously proposed in the context of intraseasonal oscillations (Masunaga and Bony 2018).

The easterly wind is almost persistent in the upper troposphere over the MC, and inland convection is expected to supply ice clouds over the coast throughout the year. In fact, Yanase et al. (2017) pointed out that stratiform-dominated precipitation propagates exclusively westward, which corresponds to the easterly background wind in the mid- to upper troposphere. In contrast, convective-dominated precipitation migrates eastward or westward in association with the lower-level tropospheric wind. These observations also support our speculations. Moreover, our proposed mechanism seems to be relevant to the fact that the coastal precipitation peak is particularly pronounced along the western coast in other regions of the MC (e.g., Indochina Peninsula) while precipitation concentrations are weak along the northern, southern, and eastern coasts. More work is required to understand these different characteristics.

Acknowledgments.

Numerical simulations were conducted by using ver. 5.2.5 of the Scalable Computing for Advanced Library Environment-Regional Model (SCALE-RM), and the authors are grateful to all those who were engaged in the development of SCALE-RM. To evaluate the model performances, the following datasets were used: 1) in situ observations during the pre-YMC field campaign by the JAMSTEC, BPPT, and BMKG, 2) NCEP-FNL, 3) GSMaP, 4) NOAA Interpolated OLR, and 5) NOAA High-resolution (0.25 × 0.25) Blended Analysis of Daily SST and Ice, OISSTv2. We would like to express our sincere thanks to all concerned with these products. This work was supported by JSPS KAKENHI Grants JP17H04477, JP19H05697, JP20H05730, and JP20H02252, and Environment Research and Technology Development fund (2-1904) of the Environmental Restoration and Conservation Agency of Japan. This study was also supported by the Cooperative Research Activities of Collaborative Use of Computing Facility of the Atmosphere and Ocean Research Institute, The University of Tokyo.

Data availability statement.

The Scalable Computing for Advanced Library Environment-Regional Model (SCALE-RM) was used for numerical experiments, and it is distributed at the following address: https://scale.riken.jp/. The datasets used to conduct numerical simulations and to evaluate model performances can be accessed as follows: pre-YMC field campaign: https://www.jamstec.go.jp/ymc/obs/obs_preYMC.html; National Centers for Environmental Prediction Final Operational Global Analysis (NCEP-FNL): https://doi.org/10.5065/D6M043C6; Global Satellite Mapping of Precipitation by Japan Aerospace Exploration Agency (GSMap by JAXA): https://sharaku.eorc.jaxa.jp/GSMaP/; NOAA Interpolated OLR: https://psl.noaa.gov/data/gridded/data.olrcdr.interp.html; and NOAA High-resolution (0.25 × 0.25) Blended Analysis of Daily SST and Ice, OISSTv2: https://doi.org/10.5065/EM0T-1D34.

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