The Regional Characteristics of Raindrop Size Distribution by Airflow Conditions on Mountain Halla, a Single Bell-Shaped Mountainous Area in South Korea

Sung-Ho Suh aFlight Safety Technology Division, NARO Space Center, Korea Aerospace Research Institute, Haban-ro, Bongrae-myeon, Goheung-gun, Jeollanam-do, South Korea

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Hyeon-Joon Kim bDepartment of Civil and Environmental Engineering, College of Engineering, Chung-Ang University, Heukseok-ro, Dongjak-gu, Seoul, South Korea

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Woonseon Jung cResearch Applications Department, National Institute of Meteorological Sciences, Seohobuk-ro, Seoqwipo-si, Jeju-do, South Korea

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Dong-In Lee dDepartment of Environmental Atmospheric Sciences, Pukyong National University, Yongso-ro, Nam-gu, Busan, South Korea

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Abstract

This study investigated the microphysical attributes of orographic precipitation based on the Froude number Fr. Orographic precipitation types were classified according to the background airflow movement condition, either around (Fr2 < 0.1, F1) or over (0.1 ≤ Fr2 < 1, F2) the mountain. Raindrop size distribution (DSD) was measured for 27 stratiform precipitation cases during a 2-yr changma (summer monsoon) season using two S-band single-pol weather radars and four Parsivel disdrometers installed along the major axis of Jeju Island—a single bell-shaped mountainous region (Mountain Halla) in South Korea. The present study revealed the orographic effect influenced by regional differences in precipitation characteristics during F1 condition, but these features were weakened during F2 condition. The leeward highlands exhibited the highest regional variation in precipitation characteristics: The smallest mass-weighted mean diameter Dm was 0.84 mm during F1, but it became similar to other regions during F2 (1.04 mm). The effects of orographic processes on precipitation and airflow conditions were apparent in the radar reflectivity Z–rainfall rate R relationship. Under F1 conditions, the radar-based estimated ground rainfall amount RA on the leeward highlands was significantly underestimated (−28.2%) by the ZR relationship optimized for the windward lowlands, where precipitation is negligibly affected by orographic effects. However, this regional bias in RA estimation caused by orographic effects was reduced (−4.2%) under F2 conditions.

Significance Statement

This study aimed to identify geographical differences in precipitation over a mountainous area through quantitative methods. While previous research has investigated quantitative precipitation estimation (QPE) for different precipitation types (stratiform and convective), this study focused on QPE for orographic precipitation. Statistical analyses were performed on the microphysics of precipitation at various locations and altitudes within a single bell-shaped mountainous area using Parsivel disdrometers and weather radars. The microphysical characteristics of precipitation are influenced by the geographical features of mountains, and these relationships affected QPE over the mountainous area, resulting in regional biases that need to be addressed. In particular, the estimated rainfall amounts optimized for areas without orographic effects were underestimated by up to 28.2% in the leeward highlands.

© 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).

Corresponding author: Sung-Ho Suh, suhsh@kari.re.kr

Abstract

This study investigated the microphysical attributes of orographic precipitation based on the Froude number Fr. Orographic precipitation types were classified according to the background airflow movement condition, either around (Fr2 < 0.1, F1) or over (0.1 ≤ Fr2 < 1, F2) the mountain. Raindrop size distribution (DSD) was measured for 27 stratiform precipitation cases during a 2-yr changma (summer monsoon) season using two S-band single-pol weather radars and four Parsivel disdrometers installed along the major axis of Jeju Island—a single bell-shaped mountainous region (Mountain Halla) in South Korea. The present study revealed the orographic effect influenced by regional differences in precipitation characteristics during F1 condition, but these features were weakened during F2 condition. The leeward highlands exhibited the highest regional variation in precipitation characteristics: The smallest mass-weighted mean diameter Dm was 0.84 mm during F1, but it became similar to other regions during F2 (1.04 mm). The effects of orographic processes on precipitation and airflow conditions were apparent in the radar reflectivity Z–rainfall rate R relationship. Under F1 conditions, the radar-based estimated ground rainfall amount RA on the leeward highlands was significantly underestimated (−28.2%) by the ZR relationship optimized for the windward lowlands, where precipitation is negligibly affected by orographic effects. However, this regional bias in RA estimation caused by orographic effects was reduced (−4.2%) under F2 conditions.

Significance Statement

This study aimed to identify geographical differences in precipitation over a mountainous area through quantitative methods. While previous research has investigated quantitative precipitation estimation (QPE) for different precipitation types (stratiform and convective), this study focused on QPE for orographic precipitation. Statistical analyses were performed on the microphysics of precipitation at various locations and altitudes within a single bell-shaped mountainous area using Parsivel disdrometers and weather radars. The microphysical characteristics of precipitation are influenced by the geographical features of mountains, and these relationships affected QPE over the mountainous area, resulting in regional biases that need to be addressed. In particular, the estimated rainfall amounts optimized for areas without orographic effects were underestimated by up to 28.2% in the leeward highlands.

© 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).

Corresponding author: Sung-Ho Suh, suhsh@kari.re.kr

1. Introduction

Raindrop size distribution (DSD) is a crucial factor in developing methods to accurately estimate the rainfall amount RA (mm) on the ground, known as quantitative precipitation estimation (QPE). DSD contains information about the microphysical processes of precipitation and includes the physical phenomena occurring in clouds due to the interaction between hydrometeors (Uijlenhoet et al. 2003; Jaffrain and Berne 2012). It also incorporates precipitation characteristics such as particle growth (e.g., Dolan et al. 2018), size sorting (e.g., Kumjian and Ryzhkov 2012), evaporation, and coalescence (Rutledge and Houze 1987; Houze 1997).

Most QPE schemes use the relationship between the ground rainfall rate R (mm h−1) and weather radar variables. Marshall et al. (1955) first proposed the radar reflectivity factor Z (mm6 m−3)–R relationship (i.e., Z = ARb) for stratiform precipitation. Various ZR relationships have been proposed because two parameters (A, b) depend on the DSD, which varies with the type of rainfall (Sekhon and Srivastava 1971; Rosenfeld et al. 1993; Tokay and Short 1996; Le Loh et al. 2019), climatological conditions (Bringi et al. 2003; Göke et al. 2007; Ulbrich and Atlas 2007; Suh et al. 2016), geographical conditions (Suh et al. 2021), seasonal conditions (Suh et al. 2016; Krishna et al. 2016; Seela et al. 2018), and diurnal variations (Kozu et al. 2006; Suh et al. 2016; Villalobos-Puma et al. 2019; Suh et al. 2021).

Raindrop size distribution can also be influenced by fluid dynamics, which are primarily determined by orographic effects (e.g., Blanchard 1953; Uijlenhoet 2001; Campos et al. 2006; Marzuki et al. 2013; Massmann et al. 2017; Murata et al. 2020). For instance, large amounts of water vapor entering mountain slopes can trigger mechanisms that result in the formation of small raindrops (Rosenfeld and Ulbrich 2003). Approximately 85% of the world’s countries (237) contain mountainous areas, which cover about 26% of the total continental surface. In South Korea, about 70% of the land area is mountainous (Lee et al. 2015). Therefore, understanding the factors affecting DSD in orographic precipitation is essential for improving QPE in remote sensing (Ryzhkov and Zrnić 1995) and refining microphysical schemes in numerical models (Saleeby and Cotton 2004; Abel and Boutle 2012; McFarquhar et al. 2015).

The Froude number (Fr) can be used to explain airflow properties especially in mountainous areas (e.g., Chen and Lin 2005a,b; Kim and Doyle 2005; Chen et al. 2008; Panziera and Germann 2010; Lin et al. 2021; Phadtare et al. 2022). Theoretically, if Fr is less than 1, a region of upstream-flow deceleration forms, which propagates upstream over time, and the windward side is characterized by flow stagnation with horizontal rerouting of the low-level flow. However, when Fr is higher than 1, the air flows freely over the mountains. Smolarkiewicz and Rotunno (1989) conducted a numerical simulation of prevailing winds passing through a single bell-shaped mountainous area finding that lee vortices developed at Fr2 ∼ 0.05 in the leeward highlands, creating banner clouds, while an updraft developed in the leeward lowlands caused by the low-level convergence. At Fr2 ∼ 0.43, these features disappeared, and orographic lifting occurred on the windward side, resulting in updraft development up to the upper layers in the leeward highlands. Lee et al. (2010) analyzed the characteristics of orographic precipitation over Mountain Halla, the same mountain investigated in this study, using a cloud-resolving storm simulator. They showed that orographic effects increased RA on the leeward side under Fr2 ∼ 0.30, and Lee et al. (2014) found that the precipitation system near the coastline on the leeward side developed due to updraft.

Jeju Island provides an ideal setting for studying orographic precipitation (e.g., Lee et al. 2012, 2014; Lee et al. 2018; Kim et al. 2022; You et al. 2022). Jeju Island, located in the southern part of the Republic of Korea (126.08°–127.02°E, 33.02°–33.32°N, approximately 75 km in a horizontal length), is a volcanic island with Mountain Halla (mountain top altitude HM of 1950 m). The Korea Meteorological Administration (KMA) has examined orographic precipitation on Jeju Island, revealing significant regional differences in annual average cumulative precipitation for the normal year (1981–2010) attributable to orographic effects. These differences reached up to 824 mm, with Gosan in the windward lowlands receiving 1142.8 mm and Sungsan in the leeward lowlands receiving 1966.8 mm. Notably, regional differences in cumulative precipitation recorded by KMA’s tipping-bucket rain gauges on Jeju Island over a decade (2011–20) are associated with Fr (Fig. 1). As Fr increased, wind direction over the entire region became more consistent, even when Fr2 < 1. The variation of normalized cumulative RA with Fr in regions A and B is relatively minor, approximately 0.1, but the greatest impact is observed in region C, where normalized cumulative RA is 0.88 for 0.1 < Fr2 < 0.3 dropping to 0.59 under conditions of 1 < Fr2 < 2.

Fig. 1.
Fig. 1.

Prevailing ground winds (red arrows) and normalized cumulated RA (vertical bars, colored according to their normalized amount) between 2011 and 2020 obtained by automatic weather systems for (a) 0.1 < Fr2 < 0.3, (b) 0.3 < Fr2 < 0.6, (c) 0.6 < Fr2 < 1, and (d) 1 < Fr2 < 2. The contour lines represent a 150-m elevation interval.

Citation: Journal of Hydrometeorology 25, 10; 10.1175/JHM-D-23-0087.1

Orographic effects influence not only regional differences in accumulated rainfall amounts but also QPEs due to local microphysical properties of precipitation (e.g., Rosenfeld and Ulbrich 2003). Previous research studies have primarily focused on case studies investigating the mechanism of severe weather phenomena causing heavy rainfall in mountainous areas. However, these studies face challenges in identifying the general microphysical features induced by orographic effects, which are essential for the development of QPE, due to variations in topography and airflow conditions. Consequently, statistical analyses of the microphysical properties of orographic precipitation under various airflow conditions, utilizing long-term ground-based disdrometer (i.e., DSD) data, are necessary.

Annual summer monsoon seasons, known as changma in Korea (e.g., Lee et al. 2017; Jung et al. 2022), mei-yu in China (e.g., Chen et al. 2013; Wen et al. 2016; Chang et al. 2022), and baiu in Japan (e.g., Karev et al. 2010; Oue et al. 2011; Chen et al. 2019), bring continuous frontal precipitation systems to East Asian regions. These systems contribute an average cumulative rainfall of 348.7 mm on Jeju Island. This study analyzed DSD characteristics during two changma periods (18 rainy days: 20 June–9 July 2013 and 28 June–13 July 2014) and their relationship with orographic effects via airflow conditions. Section 2 outlines the observation instruments used, the normalized gamma DSD model, data quality control (QC) measures, and the criteria for classifying cases and rainfall types. Section 3 presents the spatiotemporal features of regional variance in DSD and their relationships with radar variables influenced by orographic effects. In section 4, we discuss the mechanisms driving DSD characteristics due to orographic effects. Finally, section 5 summarizes the conclusions of the study.

2. Data and methods

a. Observation instruments

The study utilized four Parsivel disdrometers and two operational S-band single-polarization (hereafter “pol”) Doppler weather radars for case analysis (Fig. 2). The Parsivel disdrometer (OTT HydroMet) measures the diameter and fall velocity of hydrometeors using a laser-based optical signal, with a range from 0.31 mm and 0.2 m s−1 (third channel) to 25 mm and 20 m s−1 (32nd channel). It calculates the number concentration [N(D); mm−1 m−3] from raindrop data collected for each diameter channel. Both versions of the Parsivel disdrometers (Parsivel1 and Parsivel2) were employed. The four Parsivel disdrometers were strategically placed across Jeju Island in a zonal orientation to examine the orographic effects on precipitation on the windward lowland (WL) and leeward lowland (LL) and the windward highland (WH) and leeward highlands (LH), respectively (Table 1).

Fig. 2.
Fig. 2.

Locations of the weather radars (GSN and SSP; blue squares), radiosonde (R1 and R2; blue squares), and Parsivel disdrometers (WL, WH, LH, and LL; red bullets) operated on Jeju Island, South Korea. The contour lines represent a 150-m altitude H interval.

Citation: Journal of Hydrometeorology 25, 10; 10.1175/JHM-D-23-0087.1

Table 1.

Locations of the weather observation instruments.

Table 1.

The KMA operates 11 Doppler weather radars, among which two S-band single-pol Doppler weather radars, GSN (33.29°N, 126.16°E) and SSP (33.38°N, 126.88°E), were used to analyze the structural development of precipitation systems over Jeju Island. The radar scanning strategy consisted of 15 elevation angles with a 10-min volume scan time interval. Detailed specifications of the disdrometers and weather radars are summarized in Tables 2 and 3, respectively.

Table 2.

Specifications of Parsivel disdrometers.

Table 2.
Table 3.

Specification of GSN and SSP weather radars.

Table 3.

b. Normalized gamma DSD and radar variables

The combination of DSD parameters can describe the microphysical properties of precipitation. They can be calculated by N(D). It means that N(D) can be a key function of the DSD variable. Marshall and Parmer (1948) proposed the initial DSD model assuming the exponential distribution (i.e., shape parameter μ to be 0) of N(D) as follows:
N(D)=N0exp(ΛD),
where D corresponds to the volume-equivalent spherical raindrop diameter (mm), N0 is the intercept parameter (mm−1 m−3), and Λ is the slope parameter (mm−1).
Normalization of DSD parameters could be considered for statistical analysis, as it effectively addresses the issue of parameter interdependence across various rainfall rates (Testud et al. 2001; Ma et al. 2019; Mao et al. 2023). In this study, the normalized gamma DSD was adopted as it was deemed suitable for comparing DSDs between different regions in the statistical analysis. The gamma DSD model in normalized concept is as follows:
N(D)=Nwf(μ)(DDm)μexp[(4+μ)DDm].
DSD parameters are used to calculate the model N(D),and they can be derived from D and N(D) obtained from the Parsivel disdrometer. The mass-weighted mean diameter Dm (mm) is defined by the ratio of the fourth DSD moment to the third DSD moment:
Dm=M4M3.
The nth order of DSD moment Mn is calculated as follows:
Mn=DminDmaxDnN(D)dD.
The normalized N0 (Nw; mm−1 m−3) is calculated as follows:
Nw=44πρw(LWCDm4).
Furthermore, the liquid water content (LWC; g m−3) is calculated as follows:
LWC=π6ρwM3,
where π/6 is the volume of a spherical raindrop. The water density ρw was assumed to be 1 g cm−3 for a pure liquid phase. Rainfall rate considers the fall velocity of a raindrop V(D) (m s−1) and time conversion (3.6 × 10−3) in LWC, as follows:
R=3.6103π6DminDmaxV(D)D3N(D)dD.
Atlas et al. (1973) suggested that the ideal condition of V(D) is governed via the empirical relationship [see Eq. (8)]:
V(D)=9.6510.3exp(0.6D).
The shape parameter μ in the normalized concept is as follows (Bringi et al. 2003):
μ=(Dmσm)24,
where σm corresponds to the normalized standard deviation of mass spectrum with respect to Dm; it is defined as follows:
σm=[DminDmaxD3(DDm)2N(D)dDM3]1/2.
The f(μ) can be calculated using the gamma function Γ as follows:
f(μ)=644(μ+4)4+μΓ(4+μ).
The slope parameter Λ is given by
Λ=4+μDm.
The radar reflectivity ZH (dBZ) is calculated by Z; it can be estimated by N(D) under the Rayleigh scattering approximation. The equations are as follows:
Z=DminDmaxD6N(D)dD and ZH=10log10(Z).

c. QC of Parsivel data

Several factors, such as insects and rebounded drops, can adversely affect measurements from ground-based observation instruments. Therefore, it is necessary to remove nonmeteorological data to obtain reliable results. First, only DSD data with a temporal resolution of 1 min and a total number concentration NT of more than 10 were considered (Niu et al. 2010). Additionally, DSD data with R < 0.1 mm h−1 or R > 200 mm h−1 were excluded to minimize technical errors from very weak or strong rainfall events (Tokay and Short 1996; Jaffrain et al. 2011). The A ± 40% V(D) filter was applied to ensure reliable measurements of liquid-phase hydrometeors:
|V(D)MV(D)A|<0.4V(D)A,
where V(D)M and V(D)A denote the measured (Parsivel) and calculated [Eq. (8)] V(D), respectively (Thurai and Bringi 2005). The DSD data from the 3rd–23rd channels were considered, as natural rainfall droplet sizes are generally limited to ≤8 mm (Friedrich et al. 2013; Raupach and Berne 2015).

The Parsivel1 (i.e., LL in 2013) tends to underestimate and overestimate the N(D) for small (D < 1 mm) and large (D > 3 mm) drops, respectively, compared to the Parsivel2. To ensure consistent performance across all four disdrometers, this study applied the version correction proposed by Raupach and Berne (2015).

d. Rainfall event and type classifications

The daily accumulated rainfall amounts recorded by the four Parsivel disdrometers during the entire sampling period are shown in Fig. 3. The year 2014 experienced more precipitation than the year 2013 due to a heavy rainfall event (on 6 July) and Typhoon Neoguri (on 9 July), although typical precipitation patterns were observed continuously, except for these two extreme cases. These extreme cases were excluded from the analysis.

Fig. 3.
Fig. 3.

Daily rainfall amounts for four ground observation sites obtained by Parsivel disdrometers for the periods (a) 20 Jun–9 Jul 2013 and (b) 28 Jun–13 Jul 2014.

Citation: Journal of Hydrometeorology 25, 10; 10.1175/JHM-D-23-0087.1

The Froude number is considered in the present study as the criteria to classify the precipitation type, and it is defined as follows:
Fr=UNHM,
where U corresponds to the mean wind speed (m s−1) from the ground to over HM (m) and N represents the Brunt–Väisälä frequency (s−1).
N=gθdθdz,
where g is the local acceleration of gravity (m s−2), z is the geometric height (m), and θ is the potential temperature (K).
θ=T(P0/P)r/cp,
where T is the absolute temperature, P is the pressure for target altitude (i.e., HM), and P0 is a constant reference pressure (i.e., 1000 hPa). The term r/cp is assumed to be 0.286 for air, r is the gas constant, and cp is the specific heat capacity at a constant pressure. The Fr was calculated by radiosonde data (R1) regularly launched at 0000 and 1200 UTC (Fig. 4).
Fig. 4.
Fig. 4.

Time–height of the Fr (solid black line) and wind speed (blue contours) obtained by R1 from (a) 20 Jun to 9 Jul 2013 and from (b) 28 Jun to 13 Jul 2014.

Citation: Journal of Hydrometeorology 25, 10; 10.1175/JHM-D-23-0087.1

The selected data, spanning 18 days, were divided into precipitation analysis cases based on three conditions: 1) The analysis cases were classified into two groups using Fr2 as the criterion to investigate the characteristics of orographic precipitation based on airflow. The normalized fractions of Fr2 for the 10-yr radiosonde data (R1: January 2011–May 2016 and R2: June 2016–December 2020; see Fig. 2) were found to be more similar for the ranges of Fr2 < 0.1 (52%) and 0.1 ≤ Fr2 < 1 (46%) only during the precipitation period, compared to those of all days (Fig. 5).

Fig. 5.
Fig. 5.

Normalized fraction of the Fr2 for (a) all days and (b) rainy days during the 10 years from 2011 to 2020 obtained by R1 and R2.

Citation: Journal of Hydrometeorology 25, 10; 10.1175/JHM-D-23-0087.1

The orographic precipitation cases were divided into two categories based on the Fr2 value, namely, (i) those dominated by airflow moving primarily around the mountain (Fr2 < 0.1; labeled as F1) and (ii) those dominated by airflow moving over the mountains (0.1 ≤ Fr2 < 1; labeled as F2). This study did not consider severe weather phenomena that do not reflect common regional rainfall features, such as Typhoon Neoguri, which corresponds to Fr2 > 1. 2) Additionally, the cases were defined when precipitation persisted continuously for more than an hour at each site. These criteria were independently applied to each observation site, as the study focused on statistical analysis. 3) Finally, convective precipitation types such as heavy rainfall and typhoons were excluded to ensure that the cases had minimal spatiotemporal variability in precipitation and traversed all ground observation sites driven by westerlies. The stratiform precipitation types that met these conditions were identified using the criteria suggested by Bringi et al. (2003): Rainfall in which R > 0.5 mm h−1 and the standard deviation σ of R for 5 min < 1.5 mm h−1 was considered stratiform rainfall. Overall, the study identified 27 cases (11 cases for F1 and 16 cases for F2). Detailed results of the case classification are provided in Table 4.

Table 4.

The number of data samples and precipitation events observed from each Parsivel observation site.

Table 4.

3. Results

a. Spatial features of DSD over the mountainous area by orographic effects

The normalized N(D) demonstrates the relationship between orographic precipitation and Fr (Fig. 6). At all observation sites, the normalized N(D) was most consistent at D/Dm ∼ 0.8, irrespective of Fr. For F1 cases, the normalized N(D) showed differences at D/Dm > 1.2 or D/Dm < 0.7, indicating a dominance of smaller and larger particles at LH and LL, respectively. In contrast, under the F2 conditions, the normalized N(D) converged across all sites. The normalized N(D) for D/Dm < 0.5 was lower in F2 than in F1. At LH, the normalized N(D) was higher in F2 cases than in F1 cases for D/Dm > 1.4, suggesting that orographic effects on ground DSD diminish when airflow primarily moves over the mountain.

Fig. 6.
Fig. 6.

Average normalized N(D) for (a) F1 and (b) F2 cases at the four disdrometer stations. Vertical bars represent ±1σ for N(D)/Nw.

Citation: Journal of Hydrometeorology 25, 10; 10.1175/JHM-D-23-0087.1

In F1, the shape μ and slope Λ parameters for the lowlands were lower than those for the highlands (Fig. 7). Higher Λ compared to the southeastern China near Jeju Island (Chen et al. 2013; Huang et al. 2021) implies a more maritime-like climate with high N(D) in smaller raindrops. The LH site had the highest Λ, indicating a dominance of smaller particles (D < 1 mm), while the LL site had the lowest Λ. However, μ at LL was higher than at WL, despite both being coastal areas with similar geographical conditions. The average μ and Λ in F2 became consistent with those of WL in F1, where WL is expected to experience minimal orographic influence. The averages of μ and Λ in F2 were converged to approximately 10.5 and 18, respectively.

Fig. 7.
Fig. 7.

Average shape μ and slope Λ parameters of stratiform rainfall for the (a) F1 and (b) F2 cases at the four disdrometer stations. Vertical bars represent ±1σ for the slope values. The solid gray lines correspond to Λ = (4 + μ)/Dm. The gray and dark gray dashed curves represent the average curve for stratiform rainfall as defined by Chen et al. (2013) and Huang et al. (2021), respectively.

Citation: Journal of Hydrometeorology 25, 10; 10.1175/JHM-D-23-0087.1

The average logNw and Dm were lower than those found in midlatitudes (Bringi et al. 2003), but corresponded to those found in the southeastern coast of South Korea (Suh et al. 2021). Comparing the relationship between Dm and logNw in this region—adjacent to Jeju Island (∼315 km)—with the present study results helps investigate regional DSD features according to orographic effects and analyze regional DSD characteristics depending on the presence of mountainous areas (Fig. 8). High Dm values were found in the lowlands, with greater differences between F1 and F2 cases on the leeward sides. In F1, LL had the highest Dm and LH had the lowest Dm, but in F2, these values converged around those in the windward side, following the linear regression line suggested by Suh et al. (2021) (Fig. 8b). This suggests that the DSD range can be expected under similar climatological conditions regardless of topography and airflow condition. All σ values of logNw were lower in F2 than in F1. In F1, the σ of logNw was proportional to its mean value, but all σ values of logNw decreased and became consistent in F2. On the windward side, Dm variation was negligible (WL: 1.09–1.06 mm; WH: 0.99–0.98 mm) as Fr increased. However, Fr more significantly affected DSD on the leeward side. As Fr increased, Dm increased by 0.2 mm (from 0.84 to 1.04 mm) at LH but decreased by 0.17 mm (from 1.26 to 1.09 mm) at LL.

Fig. 8.
Fig. 8.

Average Dm vs logNw of stratiform rainfall for (a) F1 and (b) F2 cases at the four disdrometer stations—WL, WH, LH, and LL. Horizontal and vertical bars represent ±1σ for the Dm and logNw values, respectively. The solid black and broken gray lines correspond to the linear regression lines defined by Suh et al. (2021) at Busan and Bringi et al. (2003) at midlatitudes, respectively.

Citation: Journal of Hydrometeorology 25, 10; 10.1175/JHM-D-23-0087.1

One main reason for low Dm (and high Nw) under F1 can be found in the kernel density estimation (KDE) (Fig. 9; see the appendix). Second peaks at Dm < 0.7 mm and logNw > 4.5 (Figs. 9a,b) on the windward side are attributed to the coastal sea breeze (Suh et al. 2016, 2021). Consistent with expected orographic effects, the second peak did not appear in either Dm or logNw parameters at the LL site, despite its proximity to the coast. However, a primary peak corresponding to the ranges where the secondary peak is found at the other sites (Dm < 0.7 mm and logNw > 4.5) was identified in the KDE of LH.

Fig. 9.
Fig. 9.

Normalized probability density distribution with the function of KDE of (a) Dm for F1, (b) logNw for F1, (c) Dm for F2, and (d) logNw for F2 at the four disdrometer stations (WL, WH, LH, and LL). The shaded area represents the differences between KDE and the histogram, wherein the bandwidth and maximum frequency are synchronized with those of KDE. The values in parentheses, indicating each observation site, represent the mean and standard deviation of the frequency error for KDE, respectively.

Citation: Journal of Hydrometeorology 25, 10; 10.1175/JHM-D-23-0087.1

In F2, the KDEs showed similar shapes across all sites, and the second peak at all sites except WL disappeared (Fig. 9b). The second peak at WH where the variation in average Dm and Nw was negligible with respect to Fr disappeared as Fr increased. The Dm gradually increased (and Nw gradually decreased) to the distance from the coastline at similar elevations (Suh et al. 2021). However, a lower Dm was seen at WH, which is relatively inland compared to the coastal WL site, suggesting that DSD in WH was influenced by orographic effects. At LH, the primary peak with Dm < 0.7 mm vanished in F2. Additionally, the peak for LL at Dm ∼ 1.2 mm decreased. A common feature of DSD in probability density functions (PDFs) for all sites except for LH was that the second peak weakened or disappeared in the low Dm (high logNw) ranges, while the primary peak for LH disappeared and the second peak became the primary peak in F2.

b. Temporal features of DSD and radar variables over the mountainous area by orographic effects

A statistical analysis of the temporal variations in DSD was conducted to investigate the temporal characteristics of orographic precipitation that are not discernible from a spatial perspective (see section 3a). Figure 10 presents the averaged time series of DSD parameters for all precipitation events with the precipitation initiation local time for each event considered as the reference time (i.e., t = 0). Intervals containing data from fewer than three cases for the same reference time were excluded from this analysis.

Fig. 10.
Fig. 10.

Time series of the average of (a) Dm for F1, (b) logNw for F1, (c) Dm for F2, and (d) logNw for F2 at the four disdrometer stations (WL, WH, LH, and LL).

Citation: Journal of Hydrometeorology 25, 10; 10.1175/JHM-D-23-0087.1

In F1, Dm decreased and logNw increased continuously for the initial 2 h at all sites due to the size sorting phenomenon (e.g., Kumjian and Ryzhkov 2012). The precipitation periods at site LH were the longest, with the minimum Dm of approximately 0.6 mm and maximum logNw of approximately 5.0, occurring from the second hour to the sixth hour from the reference time. Site LL exhibited the highest Dm, although it continuously decreased over time. In F2, the temporal variations in DSDs observed in F1 cases disappeared at all sites (Figs. 10c,d), indicating that the averaged DSD parameters under F2 represent the entire precipitation period.

Understanding the vertical structure of a precipitation system is crucial for interpreting the characteristics of DSD at the ground level. Orographic effects can be validated by the composite column maximum Cmax (dBZ), which is the vertical maximum of ZH within a 1-km2 horizontal range. This measure is valuable for identifying areas of convective activity where vertical motion or airflow disturbance can lead to precipitation system formation. Figure 11 depicts a time series of the averaged Cmax and its altitude Hc (km). The method for averaging the Cmax time series for each site is identical to that presented in Fig. 10.

Fig. 11.
Fig. 11.

Time series of the average composited column maximum Cmax and altitude of Cmax Hc over the entire duration of stratiform rainfall events for the (a) F1 and (b) F2 cases at the four disdrometer stations (WL, WH, LH, and LL). The size and color of the symbols correspond to the value of Cmax.

Citation: Journal of Hydrometeorology 25, 10; 10.1175/JHM-D-23-0087.1

In F1, the Cmax over the precipitation system’s lifetime was proportional to Hc, ranging from 6 to 9 km in height (Fig. 11a). A common feature across all sites was the increase in Cmax to more than 28 dBZ from the fourth to sixth hours, with Hc increasing from 6 to 9 km, between the third and sixth hours, respectively. At site LH, the Cmax and Hc decreased from 30 to 27 dBZ and from 9 to 6 km, respectively, at site LH between the 5th and 5.5th hours, a pattern not observed at other sites. All sites except LH exhibited consistent temporal variations in Cmax and Hc, suggesting a weak correlation between altitude above mean sea level (MSL) and Hc. At LH, Cmax correlated negatively with Dm from the third to seventh hours. Although ZH and Dm are generally positively correlated because Z is proportional to D6, the minimum Dm was observed during the same period when the maximum Cmax suggested the presence of a disturbance highly related to particle breakup in these precipitation systems.

In F2, no correlation was found between Hc and Cmax in F2, but both were positively correlated with the MSL of the sites (Fig. 11b). Compared to F1, the Hc in F2 was relatively lower (4–7 km) at higher Cmax (>28 dBZ), with the peak stage occurring earlier, within the second–fifth hours. A decrease in the averaged Dm at WL was associated with a decrease in Hc from 9 to 5–6 km as Fr increased, reducing the raindrop’s falling distance, which can affect particle growth. The highland sites reached strong Cmax levels (WH: ∼33 dBZ at 2.5th hour; LH: ∼32 dBZ at 4.7th hour), while WH exhibited weak variation in Dm, even at its strongest Cmax at 2.5th hour. Furthermore, the variations in Hc observed in F1 at LH between the 5th and 5.5th hours were absent in F2. Additionally, the Cmax at LL decreased in F2, suggesting that this induced the decrease in Dm observed at that site (Figs. 810). No strong Cmax higher than 28 dBZ was detected until the fourth hour, and weak Cmax, below 25 dBZ, was observed after the fifth hour at LL.

c. Geographical uncertainty in radar-based precipitation estimations caused by orographic effects

The dependence of orographic precipitation on airflow conditions was verified in the previous section using ground-based DSD and radar measurements, indicating that these characteristics are also related to QPE (i.e., Z = ARb). Note that the ZR relationships were considered in the analysis owing to the use of single-pol weather radars and that these relationships were derived using a linear regression equation. The difference in Cmax with altitude became evident as Cmax increased at higher altitudes due to increasing Fr. Additionally, Cmax increased on the windward sides, while on the leeward side, it remained similar or decreased.

A trend of high A and low b in the lowlands was observed in F1 (Fig. 12 and Table 5). On the windward side, the altitude of the observation sites and the coefficients of the ZR relationship were negatively correlated, although this was not observed on the leeward side. A slight decrease in b and an increase in A were observed with altitude.

Fig. 12.
Fig. 12.

Mean coefficients A and b for the ZR relationship (Z = ARb) at the four observation sites (WL, WH, LH, and LL). The blue arrows represent the transitions from F1 to F2 conditions. The size and color of the symbols correspond to the elevation MSL of the observation site and the composited column maximum Cmax, respectively. The dark gray square symbols represent the means for WSR-88D and M-P data and the mean from Suh et al. (2021), which are Z = 300R1.4, Z = 200R1.6, and Z = 152.8R1.6, respectively. The gray square symbols represent means from Fujiwara and Yanase (1968).

Citation: Journal of Hydrometeorology 25, 10; 10.1175/JHM-D-23-0087.1

Table 5.

Summary of coefficients A and b in the ZR relationships and the correlation coefficient (CC) and root-mean-square error (RMSE) between measured and estimated R according to airflow conditions (F1 and F2).

Table 5.

The coefficients in F2 converged to around 155 < A < 201 and 1.50 < b < 1.54, which are similar to the range found in previous studies on stratiform precipitation (e.g., Marshall et al. 1955; Suh et al. 2021) and differ from results for convective precipitation data (Z = 300R1.6, WSR-88D). These findings indicate that orographic effects on DSD were more significant under F1 conditions. However, under F2, several notable characteristics were also observed: first, a tendency toward relatively lower coefficients A and b on the windward side of Mt Halla; second, coefficients A and b decreased with increasing elevation (e.g., Fujiwara and Yanase 1968), especially for the coefficient A. Coefficient A decreased and coefficient b increased at lowland sites (LL and WL) as Fr increased, with the most noticeable variation observed in LL. In the highlands, coefficient A consistently increased with increasing Fr.

In radar meteorology, RA is estimated by a unified ZR relationship within the detectable range of weather radar without considering the major factors influencing atmospheric phenomena (i.e., geographical, climatological, and airflow conditions). However, even in smaller regions such as Mountain Halla with approximately 70 km in zonal length, regional differences in estimated RA due to orographic effects were confirmed. The cumulative RA of all precipitation events measured by the Parsivel disdrometer was highly dependent on Fr (Fig. 13). The regional differences in RA in F1 were greater than those in F2. Regardless of Fr, higher cumulative RA was recorded in the highlands compared to the lowlands: 69.9 mm at highland sites under F1 and 46.3 mm under F2. In F1, site LH recorded the highest amount of cumulative RA, 42.1 mm, which was 4.7 times higher than that of WL. The cumulative RA in highland sites was 2.3 times higher (69.9 mm) than that in the lowland sites (30.5 mm). Furthermore, 1.7 times more cumulative RA was recorded on the leeward side (63.7 mm) than on the windward side (36.7 mm). Under F2 conditions, cumulative RA at the windward sites (46.4 mm) was approximately 1.2 times higher than at the leeward sites (38.6 mm).

Fig. 13.
Fig. 13.

Barplot of the cumulative rainfall amount RA for the entire precipitation period considered in the present study. Gray, blue, and red color bars represent the cumulative RA obtained by the Parsivel disdrometers Rtrue, estimated by the ZR relationship optimized for each site Rest, and estimated by the ZR relationship optimized for WL RWL, respectively. The values at the top of each bar are the cumulative RA, which, for estimates, is followed in parentheses by the ratio expressed as a percentage of differences between the estimate and Rtrue.

Citation: Journal of Hydrometeorology 25, 10; 10.1175/JHM-D-23-0087.1

The cumulative RA estimated by the ZR relationships calculated for each observation site based on its airflow conditions consistently underestimated the ground truths. The differences in cumulative RA were particularly noticeable in F1, with differences ranging from −3.6% at LL to −7.0% at LH. In F2, relatively better estimation performance was observed, with differences ranging from −3.9% at WL and LL to −4.5% at WH. To assess the impact of orographic effects on radar-based QPEs, the differences in RA between the ground truth measurements and the ZR relationship-based estimates for WL were analyzed. Among the observation sites, WL was considered to have no orographic effects on the precipitation, as confirmed by the smallest difference in its Z for the same R estimated using the ZR relationship (Table 5). When the parameters used to estimate cumulative RA for WL were applied to all sites, underestimations compared to the ground truth were observed. In LH, where a lee vortex would be expected in F1, the estimation error was significant (−28.2%). This discrepancy may occur when QPE estimation is not optimized for the geographical characteristics of a mountainous area.

4. Discussion

An understanding of the prevailing airflow around mountainous areas and cloud microphysical phenomena is required to interpret the key results of this study, as DSDs depend on various physical processes, such as drop breakup/coalescence, vertical air motion, and evaporation/condensation (Testik and Barros 2007). Several previous studies have analyzed the dominant features of airflow based on Fr in single bell-shaped mountainous areas using observational data and numerical models (e.g., Smolarkiewicz and Rotunno 1989; Jiang and Smith 2003; Kunz and Kottmeier 2006; Lee et al. 2010, 2014; Kim et al. 2019). Additionally, variations in the coefficients of the ZR relationship due to airflow conditions can help interpret the microphysics of raindrops (e.g., Rigby et al. 1954; Gunn and Marshall 1955; Atlas and Chmela 1957; Srivastava 1971; Rosenfeld and Ulbrich 2003). Based on previous finding, the DSD features observed in the present study can be interpreted as summarized in Fig. 14. The prevailing airflows and microphysics of precipitation at four observation sites are discussed except for at site WL where no orographic precipitation was identified.

Fig. 14.
Fig. 14.

Schematic representation of orographic precipitation according to the airflow conditions found in this study. (a) The mean ZH from site WL to site LL for R = 5 mm h−1. The red solid and blue dashed lines represent F1 and F2 conditions, respectively. Diagrams showing wind and precipitation over Mountain Halla during a theoretical precipitation event proceeding from (left) the windward side to (right) the leeward side under (b) F1 and (c) F2 conditions. The thick pink arrows in (b) and (c) above each observation site represent the expected dominant airflow condition; small, light-blue dots (small drops) and large, dark-blue dots (large drops) represent the DSD. The light and dark gray zones represent the composited column maximum Cmax for 20 < Cmax (dBZ) < 30 and Cmax > 30 dBZ, respectively. The abbreviations of references have the following meaning: S71 (Srivastava 1971), SR89 (Smolarkiewicz and Rotunno 1989), G92 (Geerts 1992), JS03 (Jiang and Smith 2003), RU03 (Rosenfeld and Ulbrich 2003), KK06 (Kunz and Kottmeier 2006), L10 (Lee et al. 2010), L14 (Lee et al. 2014), S16 (Suh et al. 2016), TP17 (Testik and Pei 2017), K19 (Kim et al. 2019), T20 (Tilg et al. 2020), and S21 (Suh et al. 2021).

Citation: Journal of Hydrometeorology 25, 10; 10.1175/JHM-D-23-0087.1

a. Windward highlands

As the elevation increased, the increase in Fr on the windward side negligibly affected ground DSD variation, but it did verify the development of the precipitation system: The term Cmax increased from 29.5 to 33 dBZ. The Froude number is proportional to low-level wind speed (see Fig. 4), which can induce upslope winds (i.e., increases in Cmax; Smolarkiewicz and Rotunno 1989; Lee et al. 2010) and particle breakup processes (i.e., decreases in Dm; Testik and Pei 2017; Tilg et al. 2020). The decrease in coefficient b in the ZR relationship with increasing Fr might be influenced by a combination of updraft (an increase in A and a decrease in b) and aerodynamic breakup processes (a decrease in A and b; Srivastava 1971; Rosenfeld and Ulbrich 2003).

b. Leeward sides

1) Highlands

Further particle breakup can be induced by airflow disturbances, such as lee vortices, which are also related to the development of banner clouds (Smolarkiewicz and Rotunno 1989; Geerts 1992). Kim et al. (2019) identified unstable atmospheric conditions that exhibit a continuous development of updrafts in the upper layer (H > 3 km) and a downward flow in the lower layer (H < 3 km) under Fr2 ∼ 0.04 on the leeward highlands of Mountain Halla. These phenomena are associated with the highest Cmax (31 dBZ) and the lowest Dm (0.84 mm) in F1. However, unstable airflows under F1 can be eliminated as Fr increases due to the formation of mountain waves in the leeward highlands (Jiang and Smith 2003). This can explain the leap of Dm to 1.04 mm and the drop of Λ from 33.5 to 9.5 in F2. The weakening of the particle breakup mechanism associated with these microphysical features matches the increases in coefficients A and b in the ZR relationship as Fr increased (Srivastava 1971).

2) Lowlands

The precipitation system on the leeward side lowlands can be attributed to updraft enhancement under conditions of Fr < 1 (Kunz and Kottmeier 2006; Lee et al. 2014; Kim et al. 2019). This updraft enhancement can increase particle growth, with the largest Dm reaching 1.26 mm at LL under F1 conditions. In F2, upper-layer Cmax decreased from approximately 30 to 28 dBZ, and Dm on the ground decreased to 1.09 mm with a slight decrease in μ from 11.5 to 10.5. These microphysical features correspond to the disappearance of the updrafts. The weakening of the updraft due to increasing Fr is consistent with the decrease in A and the increase in b (Rosenfeld and Ulbrich 2003).

5. Summary and conclusions

Analyzing the microphysics of precipitation is crucial for developing QPE, but it is heavily dependent on meteorological conditions. The DSD characteristics are determined by climatological, geographical, and orographic effects. However, few studies have examined the orographic effects on QPE. This study is the third in a series, following investigations into the effects of climatological (Suh et al. 2016) and geographical (Suh et al. 2021) conditions on DSD. Here, we present findings from a 2-yr field campaign on Mountain Halla—a single bell-shaped mountainous area—serving as an ideal case study to explain how orographic processes affect DSD characteristics. Our findings can be summarized as follows:

  1. Regional features in the mountainous area: In the F1 cases, Dm was higher on the leeward side than on the windward side. Specifically, the particle breakup effect due to lee vortices is assumed to have caused the lowest Dm at LH, while the enhanced updraft led to the highest Dm observed at LL. Variations in Dm on the windward side were negligible regardless of Fr. However, the weakening of the lee vortex in F2 corresponds to an increase in Dm at LH, while a decrease in Dm was found at LL. Consequently, variations in Dm on the leeward sides decreased remarkably from 0.42 to 0.05 mm as Fr increased.

  2. Features of orographic precipitation in QPE: The importance of DSD analysis for orographic precipitation is evident in the ZR relationship. In F1, cumulative RA was significantly underestimated by up to −28.2% at LH when estimated by using the ZR relationship for WL, where minimal orographic effects were observed. However, the underestimation error was reduced to −8.2% at WH in F2. Regional differences in the estimated ZH at R = 5 mm h−1, which is the maximum value in stratiform rainfall, were high under F1, particularly in the highlands. Estimated ZH at WL showed negligible differences (F1: 32.3 dBZ; F2: 32.4 dBZ), but the significant differences were apparent at LH (F1: 30.1 dBZ; F2: 32.7 dBZ) due to airflow conditions. An increase in Fr induces a particle breakup process due to enhanced wind speeds in the lower layer, which can decrease Dm, thereby decreasing the estimated ZH at WH (F1: 33.2 dBZ; F2: 31.8 dBZ). The highest Dm was found at LL, corresponding to the high estimated ZH (34.0 dBZ) in F1, when updraft enhancement was expected, but it was only slightly reduced to 33.6 dBZ in F2.

The outcomes of this study demonstrate an imperative need to develop QPE scheme that considers orographic effects with prevailing wind conditions. This study focused on the statistical analysis of DSDs measured by ground-based Parsivel disdrometers positioned evenly in a mountainous region. However, examining how airflow influences the microphysics of raindrops is also necessary. Additionally, this study did not consider severe weather phenomena that can cause spatially discontinuous ground rainfall amounts, such as heavy rainfall, mesoscale convective systems, and typhoons (i.e., Fr2 > 1). Therefore, a follow-up study will be conducted to address these limitations.

Acknowledgments.

We would like to express our gratitude to the members of the Group of Environmental Atmospheric Research (GEAR) working with Professor Dong-In Lee at Pukyong National University, Korea. They provided valuable observational data during the 2012–14 Jeju Island intensive observation periods (IOPs), which played a decisive role in the success of this study. This research study was supported by the NARO Space Center Advancement Project of Ministry of Science and ICT (MSIT).

Data availability statement.

The data obtained by the Parsivel disdrometers in this study are available on request from Professor Dong-In Lee.

APPENDIX

Kernel Density Estimation

KDE is a nonparametric method for estimating the PDF of a dataset without assuming a specific underlying distribution (Chen 2017; Węglarczyk 2018). The kernel technique produces smoothed PDF estimates, providing a better representation of the actual data distribution compared to traditional histograms. Therefore, the present study employs KDE to analyze the spectrum of DSD parameters, facilitating the interpretation of regional DSD features influenced by orographic effects. The basic form of kernel estimation is as follows:
fh^(x)=1nhi=1nKf(xxih),
where x is the data sample and xi represents the value of x at the ith point. In this study, xi is defined as the sum of initial value of x (x0) and the product of the bandwidth parameter h with i (i.e., xi = x0 + h × i). The term x0 for Dm and logNw was set to 0.5 and 2, respectively. Bandwidth parameter h optimized in the Gaussian kernel function is as follows:
h=(4σx53n)1/5,
where n is the number of data samples and σx is the standard deviation of x. The term Kf is the Gaussian kernel function, and it is expressed as follows:
Kf(u)=12πi=1nexp(12u2).

REFERENCES

  • Abel, S. J., and I. A. Boutle, 2012: An improved representation of the raindrop size distribution for single‐moment microphysics schemes. Quart. J. Roy. Meteor. Soc., 138, 21512162, https://doi.org/10.1002/qj.1949.

    • Search Google Scholar
    • Export Citation
  • Atlas, D., and A. C. Chmela, 1957: Physical-synoptic variations of raindrop-size parameters. Preprints, Proc. Sixth Weather Radar Conf., Boston, MA, Amer. Meteor. Soc., 21–29.

  • Atlas, D., R. C. Srivastava, and R. S. Sekhon, 1973: Doppler radar characteristics of precipitation at vertical incidence. Rev. Geophys., 11 (1), 135, https://doi.org/10.1029/RG011i001p00001.

    • Search Google Scholar
    • Export Citation
  • Blanchard, D. C., 1953: Raindrop size-distribution in Hawaiian rains. J. Meteor., 10, 457473, https://doi.org/10.1175/1520-0469(1953)010%3C0457:RSDIHR%3E2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bringi, V. N., V. Chandrasekar, J. Hubbert, E. Gorgucci, W. L. Randeu, and M. Schoenhuber, 2003: Raindrop size distribution in different climatic regimes from disdrometer and dual-polarized radar analysis. J. Atmos. Sci., 60, 354365, https://doi.org/10.1175/1520-0469(2003)060<0354:RSDIDC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Campos, E. F., I. Zawadzki, M. Petitdidier, and W. Fernandez, 2006: Measurement of raindrop size distributions in tropical rain at Costa Rica. J. Hydrol., 328, 98109, https://doi.org/10.1016/j.jhydrol.2005.11.047.

    • Search Google Scholar
    • Export Citation
  • Chang, Y., and Coauthors, 2022: Characteristics of raindrop size distributions during Meiyu season in Mount Lushan, eastern China. J. Meteor. Soc. Japan, 100, 5776, https://doi.org/10.2151/jmsj.2022-003.

    • Search Google Scholar
    • Export Citation
  • Chen, B., J. Yang, and J. Pu, 2013: Statistical characteristics of raindrop size distribution in the Meiyu season observed in eastern China. J. Meteor. Soc. Japan, 91, 215227, https://doi.org/10.2151/jmsj.2013-208.

    • Search Google Scholar
    • Export Citation
  • Chen, S.-H., and Y.-L. Lin, 2005a: Effects of moist Froude number and CAPE on a conditionally unstable flow over a mesoscale mountain ridge. J. Atmos. Sci., 62, 331350, https://doi.org/10.1175/JAS-3380.1.

    • Search Google Scholar
    • Export Citation
  • Chen, S.-H., and Y.-L. Lin, 2005b: Orographic effects on a conditionally unstable flow over an idealized three-dimensional mesoscale mountain. Meteor. Atmos. Phys., 88 (1–2), 121, https://doi.org/10.1007/s00703-003-0047-6.

    • Search Google Scholar
    • Export Citation
  • Chen, S.-H., Y.-L. Lin, and Z. Zhao, 2008: Effects of unsaturated moist Froude number and orographic aspect ratio on a conditionally unstable flow over a mesoscale mountain. J. Meteor. Soc. Japan, 86, 353367, https://doi.org/10.2151/jmsj.86.353.

    • Search Google Scholar
    • Export Citation
  • Chen, Y., J. Duan, J. An, and H. Liu, 2019: Raindrop size distribution characteristics for tropical cyclones and Meiyu-Baiu fronts impacting Tokyo, Japan. Atmosphere, 10, 391, https://doi.org/10.3390/atmos10070391.

    • Search Google Scholar
    • Export Citation
  • Chen, Y.-C., 2017: A tutorial on kernel density estimation and recent advances. Biostat. Epidemiol., 1, 161187, https://doi.org/10.1080/24709360.2017.1396742.

    • Search Google Scholar
    • Export Citation
  • Dolan, B., B. Fuchs, S. A. Rutledge, E. A. Barnes, and E. J. Thompson, 2018: Primary modes of global drop size distributions. J. Atmos. Sci., 75, 14531476, https://doi.org/10.1175/JAS-D-17-0242.1.

    • Search Google Scholar
    • Export Citation
  • Friedrich, K., S. Higgins, F. J. Masters, and C. R. Lopez, 2013: Articulating and stationary PARSIVEL disdrometer measurements in conditions with strong winds and heavy rainfall. J. Atmos. Oceanic Technol., 30, 20632080, https://doi.org/10.1175/JTECH-D-12-00254.1.

    • Search Google Scholar
    • Export Citation
  • Fujiwara, M., and T. Yanase, 1968: Raindrop Z-R relationships in different altitudes. Proc. 13th Radar Meteorology Conf., Montreal, Quebec, Canada, Amer. Meteor. Soc., 380–383.

  • Geerts, B., 1992: The origin of banner clouds: A potential vorticity perspective. Preprint volume, Sixth Conf. on Mountain Meteorology, Portland, OR, Amer. Meteor. Soc., 97–98.

  • Göke, S., H. T. Ochs III, and R. M. Rauber, 2007: Radar analysis of precipitation initiation in maritime versus continental clouds near the Florida Coast: Inferences concerning the role of CCN and giant nuclei. J. Atmos. Sci., 64, 36953707, https://doi.org/10.1175/JAS3961.1.

    • Search Google Scholar
    • Export Citation
  • Gunn, K. L. S., and J. S. Marshall, 1955: The effect of wind shear on falling precipitation. J. Meteor., 12, 339349, https://doi.org/10.1175/1520-0469(1955)012<0339:TEOWSO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Houze, R. A., Jr., 1997: Stratiform precipitation in regions of convection: A meteorological paradox? Bull. Amer. Meteor. Soc., 78, 21792196, https://doi.org/10.1175/1520-0477(1997)078<2179:SPIROC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Huang, C., S. Chen, A. Zhang, and Y. Pang, 2021: Statistical characteristics of raindrop size distribution in monsoon season over South China Sea. Remote Sens., 13, 2878, https://doi.org/10.3390/rs13152878.

    • Search Google Scholar
    • Export Citation
  • Jaffrain, J., and A. Berne, 2012: Quantification of the small-scale spatial structure of the raindrop size distribution from a network of disdrometers. J. Appl. Meteor. Climatol., 51, 941953, https://doi.org/10.1175/JAMC-D-11-0136.1.

    • Search Google Scholar
    • Export Citation
  • Jaffrain, J., A. Studzinski, and A. Berne, 2011: A network of disdrometers to quantify the small‐scale variability of the raindrop size distribution. Water Resour. Res., 47, W00H06, https://doi.org/10.1029/2010WR009872.

    • Search Google Scholar
    • Export Citation
  • Jiang, Q., and R. B. Smith, 2003: Cloud timescales and orographic precipitation. J. Atmos. Sci., 60, 15431559, https://doi.org/10.1175/2995.1.

    • Search Google Scholar
    • Export Citation
  • Jung, W., H. M. Sung, C.-H. You, H.-J. Kim, S.-H. Suh, D.-I. Lee, and K.-H. Chang, 2022: Relationships between aerosol and raindrop size distributions during rainfall period (Changma) in Jeju Island, Korea. Atmosphere, 13, 933, https://doi.org/10.3390/atmos13060933.

    • Search Google Scholar
    • Export Citation
  • Karev, A. R., D.-I. Lee, C.-H. You, and H. Uyeda, 2010: Variations in raindrop size distributions observed in mid-latitude ocean clouds during the East-Asian summer monsoon. Atmos. Res., 96, 6578, https://doi.org/10.1016/j.atmosres.2009.11.014.

    • Search Google Scholar
    • Export Citation
  • Kim, H.-J., K.-O. Lee, C.-H. You, H. Uyeda, and D.-I. Lee, 2019: Microphysical characteristics of a convective precipitation system observed on July 04, 2012, over Mt. Halla in South Korea. Atmos. Res., 222, 7487, https://doi.org/10.1016/j.atmosres.2019.02.011.

    • Search Google Scholar
    • Export Citation
  • Kim, H.-J., W. Jung, S.-H. Suh, D.-I. Lee, and C.-H. You, 2022: The characteristics of raindrop size distribution at windward and leeward side over mountain area. Remote Sens., 14, 2419, https://doi.org/10.3390/rs14102419.

    • Search Google Scholar
    • Export Citation
  • Kim, Y.-j., and J. D. Doyle, 2005: Extension of an orographic‐drag parametrization scheme to incorporate orographic anisotropy and flow blocking. Quart. J. Roy. Meteor. Soc., 131, 18931921, https://doi.org/10.1256/qj.04.160.

    • Search Google Scholar
    • Export Citation
  • Kozu, T., K. K. Reddy, S. Mori, M. Thurai, J. T. Ong, D. N. Rao, and T. Shimomai, 2006: Seasonal and diurnal variations of raindrop size distribution in Asian monsoon region. J. Meteor. Soc. Japan, 84A, 195209, https://doi.org/10.2151/jmsj.84A.195.

    • Search Google Scholar
    • Export Citation
  • Krishna, U. V. M., K. K. Reddy, B. K. Seela, R. Shirooka, P.-L. Lin, and C.-J. Pan, 2016: Raindrop size distribution of easterly and westerly monsoon precipitation observed over Palau islands in the western Pacific Ocean. Atmos. Res., 174–175, 4151, https://doi.org/10.1016/j.atmosres.2016.01.013.

    • Search Google Scholar
    • Export Citation
  • Kumjian, M. R., and A. V. Ryzhkov, 2012: The impact of size sorting on the polarimetric radar variables. J. Atmos. Sci., 69, 20422060, https://doi.org/10.1175/JAS-D-11-0125.1.

    • Search Google Scholar
    • Export Citation
  • Kunz, M., and C. Kottmeier, 2006: Orographic enhancement of precipitation over low mountain ranges. Part I: Model formulation and idealized simulations. J. Appl. Meteor. Climatol., 45, 10251040, https://doi.org/10.1175/JAM2389.1.

    • Search Google Scholar
    • Export Citation
  • Lee, J.-T., K.-Y. Ko, D.-I. Lee, C.-H. You, and Y.-C. Liou, 2018: Enhancement of orographic precipitation in Jeju Island during the passage of Typhoon Khanun (2012). Atmos. Res., 201, 5871, https://doi.org/10.1016/j.atmosres.2017.10.013.

    • Search Google Scholar
    • Export Citation
  • Lee, K.-O., S. Shimizu, M. Maki, C.-H. You, H. Uyeda, and D.-I. Lee, 2010: Enhancement mechanism of the 30 June 2006 precipitation system observed over the northwestern slope of Mt. Halla, Jeju Island, Korea. Atmos. Res., 97, 343358, https://doi.org/10.1016/j.atmosres.2010.04.008.

    • Search Google Scholar
    • Export Citation
  • Lee, K.-O., H. Uyeda, S. Shimizu, and D.-I. Lee, 2012: Dual-Doppler radar analysis of the enhancement of a precipitation system on the northern side of Mt. Halla, Jeju Island, Korea on 6 July 2007. Atmos. Res., 118, 133152, https://doi.org/10.1016/j.atmosres.2012.06.017.

    • Search Google Scholar
    • Export Citation
  • Lee, K.-O., H. Uyeda, and D.-I. Lee, 2014: Microphysical structures associated with enhancement of convective cells over Mt. Halla, Jeju Island, Korea on 6 July 2007. Atmos. Res., 135–136, 7690, https://doi.org/10.1016/j.atmosres.2013.08.012.

    • Search Google Scholar
    • Export Citation
  • Lee, M.-J., I. Park, and S. Lee, 2015: Forecasting and validation of landslide susceptibility using an integration of frequency ratio and neuro-fuzzy models: A case study of Seorak mountain area in Korea. Environ. Earth Sci., 74, 413429, https://doi.org/10.1007/s12665-015-4048-9.

    • Search Google Scholar
    • Export Citation
  • Lee, J. Y., and Coauthors, 2017: The long-term variability of Changma in the East Asian summer monsoon system: A review and revisit, Asia-Pac. J. Atmos. Sci., 53, 257272, https://doi.org/10.1007/s13143-017-0032-5.

    • Search Google Scholar
    • Export Citation
  • Le Loh, J., D.-I. Lee, and C.-H. You, 2019: Inter-comparison of DSDs between Jincheon and Miryang at South Korea. Atmos. Res., 227, 5265, https://doi.org/10.1016/j.atmosres.2019.04.031.

    • Search Google Scholar
    • Export Citation
  • Lin, Y.-L., W. Agyakwah, J. G. Riley, H.-H. Hsu, and L.-C. Jiang, 2021: Orographic effects on the propagation and rainfall modification associated with the 2007–08 Madden–Julian Oscillation (MJO) past the New Guinea Highlands. Meteor. Atmos. Phys., 133, 359378, https://doi.org/10.1007/s00703-020-00753-2.

    • Search Google Scholar
    • Export Citation
  • Ma, Y., G. Ni, C. V. Chandra, F. Tian, and H. Chen, 2019: Statistical characteristics of raindrop size distribution during rainy seasons in the Beijing urban area and implications for radar rainfall estimation. Hydrol. Earth Syst. Sci., 23, 41534170, https://doi.org/10.5194/hess-23-4153-2019.

    • Search Google Scholar
    • Export Citation
  • Mao, W., W. Zhang, and M. Kou, 2023: Statistical characteristics of raindrop size distribution during rainy seasons in complicated mountain terrain. Hydrol. Earth Syst. Sci., 27, 38953910, https://doi.org/10.5194/hess-27-3895-2023.

    • Search Google Scholar
    • Export Citation
  • Marshall, J. S., and W. M. K. Parmer, 1948: The distribution of raindrops with size. J. Meteor., 5, 165166, https://doi.org/10.1175/1520-0469(1948)005%3C0165:TDORWS%3E2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Marshall, J. S., W. Hitschfeld, and K. L. S. Gunn, 1955: Advances in radar weather. Advances in Geophysics, Vol. 2, Academic Press, 1–56, https://doi.org/10.1016/S0065-2687(08)60310-6.

  • Marzuki, M., H. Hashiguchi, M. K. Yamamoto, S. Mori, and M. D. Yamanaka, 2013: Regional variability of raindrop size distribution over Indonesia. Ann. Geophys., 31, 19411948, https://doi.org/10.5194/angeo-31-1941-2013.

    • Search Google Scholar
    • Export Citation
  • Massmann, A. K., and Coauthors, 2017: The Chilean Coastal Orographic Precipitation Experiment: Observing the influence of microphysical rain regimes on coastal orographic precipitation. J. Hydrometeor., 18, 27232743, https://doi.org/10.1175/JHM-D-17-0005.1.

    • Search Google Scholar
    • Export Citation
  • McFarquhar, G. M., T.-L. Hsieh, M. Freer, J. Mascio, and B. F. Jewett, 2015: The characterization of ice hydrometeor gamma size distributions as volumes in N0λμ phase space: Implications for microphysical process modeling. J. Atmos. Sci., 72, 892909, https://doi.org/10.1175/JAS-D-14-0011.1.

    • Search Google Scholar
    • Export Citation
  • Murata, F., T. Terao, K. Chakravarty, H. J. Syiemlieh, and L. Cajee, 2020: Characteristics of orographic rain drop-size distribution at Cherrapunji, northeast India. Atmosphere, 11, 777, https://doi.org/10.3390/atmos11080777.

    • Search Google Scholar
    • Export Citation
  • Niu, S., X. Jia, J. Sang, X. Liu, C. Lu, and Y. Liu, 2010: Distributions of raindrop sizes and fall velocities in a semiarid plateau climate: Convective versus stratiform rains. J. Appl. Meteor. Climatol., 49, 632645, https://doi.org/10.1175/2009JAMC2208.1.

    • Search Google Scholar
    • Export Citation
  • Oue, M., H. Uyeda, and D.-I. Lee, 2011: Raindrop size distribution parameters estimated from polarimetric radar variables in convective cells around Okinawa Island during the Baiu period. Asia-Pac. J. Atmos. Sci., 47, 3344, https://doi.org/10.1007/s13143-011-1003-x.

    • Search Google Scholar
    • Export Citation
  • Panziera, L., and U. Germann, 2010: The relation between airflow and orographic precipitation on the southern side of the Alps as revealed by weather radar. Quart. J. Roy. Meteor. Soc., 136, 222238, https://doi.org/10.1002/qj.544.

    • Search Google Scholar
    • Export Citation
  • Phadtare, J. A., J. K. Fletcher, A. N. Ross, A. G. Turner, and R. K. H. Schiemann, 2022: Froude‐number‐based rainfall regimes over the Western Ghats mountains of India. Quart. J. Roy. Meteor. Soc., 148, 33883405, https://doi.org/10.1002/qj.4367.

    • Search Google Scholar
    • Export Citation
  • Raupach, T. H., and A. Berne, 2015: Correction of raindrop size distributions measured by Parsivel disdrometers, using a two-dimensional video disdrometer as a reference. Atmos. Meas. Tech., 8, 343365, https://doi.org/10.5194/amt-8-343-2015.

    • Search Google Scholar
    • Export Citation
  • Rigby, E. C., J. S. Marshall, and W. Hitschfeld, 1954: The development of the size distribution of raindrops during their fall. J. Meteor., 11, 362372, https://doi.org/10.1175/1520-0469(1954)011<0362:TDOTSD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rosenfeld, D., and C. W. Ulbrich, 2003: Cloud microphysical properties, processes, and rainfall estimation opportunities. Radar and Atmospheric Science: A Collection of Essays in Honor of David Atlas, Meteor. Monogr., No. 30, 237–258, https://doi.org/10.1175/0065-9401(2003)030<0237:CMPPAR>2.0.CO;2.

  • Rosenfeld, D., D. B. Wolff, and D. Atlas, 1993: General probability-matched relations between radar reflectivity and rain rate. J. Appl. Meteor., 32, 5072, https://doi.org/10.1175/1520-0450(1993)032<0050:GPMRBR>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Rutledge, S. A., and R. A. Houze Jr., 1987: A diagnostic modelling study of the trailing stratiform region of a midlatitude squall line. J. Atmos. Sci., 44, 26402656, https://doi.org/10.1175/1520-0469(1987)044<2640:ADMSOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ryzhkov, A. V., and D. S. Zrnić, 1995: Comparison of dual-polarization radar estimators of rain. J. Atmos. Oceanic Technol., 12, 249256, https://doi.org/10.1175/1520-0426(1995)012<0249:CODPRE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Saleeby, S. M., and W. R. Cotton, 2004: A large-droplet mode and prognostic number concentration of cloud droplets in the Colorado State University Regional Atmospheric Modeling System (RAMS). Part I: Module descriptions and supercell test simulations. J. Appl. Meteor., 43, 182195, https://doi.org/10.1175/1520-0450(2004)043<0182:ALMAPN>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Seela, B. K., J. Janapati, P.-L. Lin, P. K. Wang, and M.-T. Lee, 2018: Raindrop size distribution characteristics of summer and winter season rainfall over North Taiwan. J. Geophys. Res. Atmos., 123, 11 60211 624, https://doi.org/10.1029/2018JD028307.

    • Search Google Scholar
    • Export Citation
  • Sekhon, R. S., and R. C. Srivastava, 1971: Doppler radar observations of drop-size distributions in a thunderstorm. J. Atmos. Sci., 28, 983994, https://doi.org/10.1175/1520-0469(1971)028<0983:DROODS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Smolarkiewicz, P. K., and R. Rotunno, 1989: Low Froude number flow past three-dimensional obstacles. Part I: Baroclinically generated lee vortices. J. Atmos. Sci., 46, 11541164, https://doi.org/10.1175/1520-0469(1989)046<1154:LFNFPT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Srivastava, R. C., 1971: Size distribution of raindrops generated by their breakup and coalescence. J. Atmos. Sci., 28, 410415, https://doi.org/10.1175/1520-0469(1971)028<0410:SDORGB>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Suh, S.-H., C.-H. You, and D.-I. Lee, 2016: Climatological characteristics of raindrop size distributions in Busan, Republic of Korea. Hydrol. Earth Syst. Sci., 20, 193207, https://doi.org/10.5194/hess-20-193-2016.

    • Search Google Scholar
    • Export Citation
  • Suh, S.-H., H.-J. Kim, D.-I. Lee, and T.-H. Kim, 2021: Geographical characteristics of raindrop size distribution in the southern parts of South Korea. J. Appl. Meteor. Climatol., 60, 157169, https://doi.org/10.1175/JAMC-D-20-0102.1.

    • Search Google Scholar
    • Export Citation
  • Testik, F. Y., and A. P. Barros, 2007: Toward elucidating the microstructure of warm rainfall: A survey. Rev. Geophys., 45, RG2003, https://doi.org/10.1029/2005RG000182.

    • Search Google Scholar
    • Export Citation
  • Testik, F. Y., and B. Pei, 2017: Wind effects on the shape of raindrop size distribution. J. Hydrometeor., 18, 12851303, https://doi.org/10.1175/JHM-D-16-0211.1.

    • Search Google Scholar
    • Export Citation
  • Testud, J., S. Oury, R. A. Black, P. Amayenc, and X. Dou, 2001: The concept of “normalized” distribution to describe raindrop spectra: A tool for cloud physics and cloud remote sensing. J. Appl. Meteor., 40, 11181140, https://doi.org/10.1175/1520-0450(2001)040<1118:TCONDT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Thurai, M., and V. N. Bringi, 2005: Drop axis ratios from a 2D video disdrometer. J. Atmos. Oceanic Technol., 22, 966978, https://doi.org/10.1175/JTECH1767.1.

    • Search Google Scholar
    • Export Citation
  • Tilg, A.-M., F. Vejen, C. B. Hasager, and M. Nielsen, 2020: Rainfall kinetic energy in Denmark: Relationship with drop size, wind speed, and rain rate. J. Hydrometeor., 21, 16211637, https://doi.org/10.1175/JHM-D-19-0251.1.

    • Search Google Scholar
    • Export Citation
  • Tokay, A., and D. A. Short, 1996: Evidence from tropical raindrop spectra of the origin of rain from stratiform versus convective clouds. J. Appl. Meteor., 35, 355371, https://doi.org/10.1175/1520-0450(1996)035<0355:EFTRSO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Uijlenhoet, R., 2001: Raindrop size distributions and radar reflectivity–rain rate relationships for radar hydrology. Hydrol. Earth Syst. Sci., 5, 615628, https://doi.org/10.5194/hess-5-615-2001.

    • Search Google Scholar
    • Export Citation
  • Uijlenhoet, R., M. Steiner, and J. A. Smith, 2003: Variability of raindrop size distributions in a squall line and implications for radar rainfall estimation. J. Hydrometeor., 4, 4361, https://doi.org/10.1175/1525-7541(2003)004<0043:VORSDI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ulbrich, C. W., and D. Atlas, 2007: Microphysics of raindrop size spectra: Tropical continental and maritime storms. J. Appl. Meteor. Climatol., 46, 17771791, https://doi.org/10.1175/2007JAMC1649.1.

    • Search Google Scholar
    • Export Citation
  • Villalobos-Puma, E., D. Martinez-Castro, J. L. Flores-Rojas, M. Saavedra-Huanca, and Y. Silva-Vidal, 2019: Diurnal cycle of raindrops size distribution in a valley of the peruvian central Andes. Atmosphere, 11, 38, https://doi.org/10.3390/atmos11010038.

    • Search Google Scholar
    • Export Citation
  • Węglarczyk, S., 2018: Kernel density estimation and its application. ITM Web Conf., 23, 00037, https://doi.org/10.1051/itmconf/20182300037.

    • Search Google Scholar
    • Export Citation
  • Wen, L., K. Zhao, G. Zhang, M. Xue, B. Zhou, S. Liu, and X. Chen, 2016: Statistical characteristics of raindrop size distributions observed in East China during the Asian summer monsoon season using 2-D video disdrometer and Micro Rain Radar data. J. Geophys. Res. Atmos., 121, 2265–2282, https://doi.org/10.1002/2015JD024160.

    • Search Google Scholar
    • Export Citation
  • You, C.-H., S.-H. Suh, W. Jung, H.-J. Kim, and D.-I. Lee, 2022: Dual-polarization radar-based quantitative precipitation estimation of mountain terrain using multi-disdrometer data. Remote Sens., 14, 2290, https://doi.org/10.3390/rs14102290.

    • Search Google Scholar
    • Export Citation
Save
  • Abel, S. J., and I. A. Boutle, 2012: An improved representation of the raindrop size distribution for single‐moment microphysics schemes. Quart. J. Roy. Meteor. Soc., 138, 21512162, https://doi.org/10.1002/qj.1949.

    • Search Google Scholar
    • Export Citation
  • Atlas, D., and A. C. Chmela, 1957: Physical-synoptic variations of raindrop-size parameters. Preprints, Proc. Sixth Weather Radar Conf., Boston, MA, Amer. Meteor. Soc., 21–29.

  • Atlas, D., R. C. Srivastava, and R. S. Sekhon, 1973: Doppler radar characteristics of precipitation at vertical incidence. Rev. Geophys., 11 (1), 135, https://doi.org/10.1029/RG011i001p00001.

    • Search Google Scholar
    • Export Citation
  • Blanchard, D. C., 1953: Raindrop size-distribution in Hawaiian rains. J. Meteor., 10, 457473, https://doi.org/10.1175/1520-0469(1953)010%3C0457:RSDIHR%3E2.0.CO;2.