Comparison of Microphysical Characteristics between the Southern Korean Peninsula and Oklahoma Using Two-Dimensional Video Disdrometer Data

Wonbae Bang; GyuWon Lee; Alexander Ryzhkov; Terry Schuur; Kyo-Sun Sunny Lim

doi:10.1175/JHM-D-20-0087.1

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Journal of Hydrometeorology

Abstract
1. Introduction
2. Two-dimensional video disdrometer data
3. Analysis method
- a. Derivation of rain microphysical characteristics
- b. Derivation of rainfall estimation relationships
4. Analysis result
- a. Difference in rain microphysical characteristics
- b. Difference in rainfall estimation relationships
5. Summary and conclusion

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Diagram of 2DVD measurement (Joanneum Research 2016). The illumination unit directs light toward each camera, causing a raindrop shadow that is captured by the camera. The height difference between the two cameras is 6.2 mm.

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(a) Map of East Asia showing Daegu and Boseong (red circles). (b) Map of North America showing Oklahoma (blue circle; National Severe Storms Laboratory site, NSSL). (c) Photograph of 2DVD installed at Daegu and Boseong. (d) Photograph of 2DVD (old version) installed at the NSSL site (Schuur et al. 2001).

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Comparison of the RSD shape for KOR and OKL. (a) N(D)–D graph. Solid (dashed) lines are KOR (OKL). (b) ΔN(D) as a function of Z. The Δ means N(D) of OKL subtracted by N(D) of KOR.

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Comparison of normalized RSD (NRSD) for KOR and OKL. (a) h(x)–x graph for KOR. (b) h(x)–x graph for OKL. The solid line is the average h(x) [⟨h(x)⟩] with Δx = 0.2 and the dot–dash line is least squares fit (h_gg). The vertical bar is the standard deviation at each Δx. (c) Average h(x) [⟨h(x)⟩] and (d) fitted h(x) (h_gg). Red (blue) is KOR (OKL).

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Normalized frequency distributions (NFDs) of RSD variables. (a) NFD of R (KOR). (b) NFD of R (OKL). The N is the data number and ⟨⟩ indicates the average value. (c) NFD of log $N_{0}^{'}$ . MP indicates the $N_{0}^{'}$ of the Marshall and Palmer (1948) distribution. Red (blue) is KOR (OKL) and the dashed line is the average value. (d) NFD of $D_{m}^{'}$ .

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Comparison of generalized parameters for KOR and OKL as a function of Z (ΔZ = 2 dBZ). The $N_{0}^{'} - Z$ scatterplot for (a) KOR and (b) OKL. (c) The $D_{m}^{'} - Z$ scatterplot for (c) KOR and (d) OKL. Solid (dashed) lines indicate the median values of KOR (OKL). Note high $N_{0}^{'}$ values (oval) at Z ~20 dBZ in (a) and high $D_{m}^{'}$ values (circle) at Z ~ 50 dBZ in (d).

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Average RSDs at different Z intervals with $D_{m}^{'} > 2.0$ mm for KOR (solid line) and OKL (dashed line).

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Comparison of generalized parameters between KOR, OKL, the maritime convective cluster (MT), continental convective cluster (CT), and Nanjing, East China (ECN). (a) The $〈 N_{0}^{'} 〉 - 〈 D_{m}^{'} 〉$ scatterplot with ΔZ = 2 dBZ. The diamond (square) symbol is KOR (OKL). (b) The $N_{0}^{'} - D_{m}^{'}$ scatterplot with statistical values in convective rainfall. The vertical bar is $SD \log (N_{0}^{'})$ , the horizontal bar is $SD \log (D_{m}^{'})$ , and the cross point of the bars is the average value.

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R–Z_H scatterplots. (a) KOR, S band. (b) OKL, S band. (c) KOR, C band. (d) OKL, C band. (e) KOR, X band. (f) OKL, X band. The black solid line is R–Z_H relationship of the MP distribution. The dash–dot line is the derived relationship.

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Direct comparison of the R–Z_H relationships between KOR (red) and OKL (blue). The solid, dash–dot, and dashed lines are the relationships at the S-, C-, and X-band wavelengths, respectively.

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R–K_DP scatterplots. (a) KOR, S band. (b) OKL, S band. (c) KOR, C band. (d) OKL, C band. (e) KOR, X band. (f) OKL, X band. The dash–dot line is the derived relationship.

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R–K_DP/f graph for direct comparison of the R–K_DP relationships in KOR (red) and OKL (blue). The solid, dash–dot, and dashed lines are the relationships at the S-, C-, and X-band wavelengths, respectively. The f indicates the radar frequency (GHz).

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R–A_H scatterplots. (a) KOR, S band. (b) OKL, S band. (c) KOR, C band. (d) OKL, C band. (e) KOR, X band. (f) OKL, X band. The dash–dot line is the derived relationship.

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R–A_H graph for direct comparison of R–A_H relationships in KOR (red) and OKL (blue). The solid, dash–dot, and dashed lines are the relationships at the S-, C-, and X-band wavelengths, respectively.

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GENERAL CONDITIONS

Author: A. J. HENRY

No. III

BAROMETRIC PRESSURE

VERIFICATIONS

MISCELLANEOUS PHENOMENA

Editorial Type:: Article

Article Type:: Research Article

Comparison of Microphysical Characteristics between the Southern Korean Peninsula and Oklahoma Using Two-Dimensional Video Disdrometer Data

Wonbae Bang¹, GyuWon Lee¹, Alexander Ryzhkov^2,3, Terry Schuur^2,3, and Kyo-Sun Sunny Lim¹

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¹ Center for Atmospheric Remote Sensing, Department of Astronomy and Atmospheric Sciences, Kyungpook National University, South Korea
| ² Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, Oklahoma
| ³ NOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

Published-online:: 09 Nov 2020

Print Publication:: 01 Nov 2020

DOI:: https://doi.org/10.1175/JHM-D-20-0087.1

Page(s):: 2675–2690

Received:: 11 Apr 2020

Final Form:: 10 Aug 2020

Published Online:: 09 Nov 2020

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Abstract

Differences in atmospheric environments can have a significant impact on microphysical processes of precipitation. Dominant warm (cold) rain processes in East Asia (southern Great Plains of the United States) are implied by a large (small or constant) gradient of reflectivity at low levels in vertical reflectivity profiles. Long-term ground observations using two-dimensional video disdrometers were conducted in the southern Korean Peninsula (KOR) and Norman, Oklahoma, United States (OKL). Raindrop size distributions (RSD) and their moments in the two regions were analyzed in the framework of scaling normalized RSDs. Results show that the concentrations of small (big) raindrops were higher (smaller) in KOR than in OKL. KOR RSDs were also found to be characterized by relatively high characteristic number concentrations $N_{0}^{'}$ and small characteristic diameters $D_{m}^{'}$ when compared to OKL RSDs. The $N_{0}^{'}$ increases with increasing $D_{m}^{'}$ in both KOR and OKL at lower Z with the opposite trend at higher Z. In addition, OKL RSDs with $D_{m}^{'} > 2.5 mm$ indicate the existence of an equilibrium between coalescence and breakup processes. Rainfall estimation relationships between the rain rate R and radar variables were retrieved from scattering simulations at S-, C-, and X-band wavelengths. KOR RSDs showed relatively small horizontal reflectivity and specific differential phase shift at the same R and same wavelength when compared to OKL RSDs. The regional dependency was significant due to the different microphysical process in KOR and OKL. The specific attenuation of KOR was, however, similar to that of OKL only at S band, indicating an advantage of using specific attenuation in S band in rainfall estimation.

Corresponding author: GyuWon Lee, gyuwon@knu.ac.kr

Abstract

Corresponding author: GyuWon Lee, gyuwon@knu.ac.kr

Keywords: Asia; North America; Cloud microphysics; Drop size distribution; Surface observations

1. Introduction

Different climatology and regional differences of the atmospheric environment can significantly affect the microphysical characteristics of precipitation (Bringi et al. 2003). When compared with Oklahoma (OKL), United States, the atmospheric environment of the southern Korean Peninsula (KOR) is characterized by abundant lower-level moisture caused by an ample supply of water vapor from the sea during the summer, which leads to deficient ice crystals at upper levels (Sohn et al. 2013). The relatively abundant ice crystals in OKL are attributed to strong updrafts and relative dry air at the lower layers in the region. The strong updrafts produce graupel by mostly efficient riming of snow within convection. In particular, many of the larger raindrops comprising the OKL raindrop size distributions (RSDs) also comes from melted hail with a relatively small number concentration (Ryzhkov and Zrnić 2019). The abundant moisture in KOR is likely to cause the growth of raindrops at lower levels and thus efficient warm rain process (Sohn et al. 2013). Other studies have also suggested that the growth of lower-level raindrops in East Asian coastal regions can be attributed to water vapor supplied from the ocean (Fu and Liu 2003; Fu et al. 2003; Cao and Qi 2014).

Several studies have analyzed the characteristics of RSD in different regions by using generalized characteristic number concentrations $N_{0}^{'}$ (m⁻³ mm⁻¹) and generalized characteristic diameters $D_{m}^{'}$ (mm) that is derived from the scaling normalization of RSD without any assumption on the functional form of RSD (Lee et al. 2004). Bringi et al. (2003) used two-dimensional video disdrometer (2DVD) data to show that convective RSDs of Colorado, United States, in June 2000 were characterized by $\log N_{0}^{'} \approx 1.5 - 2.5$ , $〈 D_{m}^{'} 〉 \approx 2.4 mm$ (the symbol ⟨⟩ indicates a simple average value, and the values are hereafter converted from the original definition into $N_{0}^{'}$ and $D_{m}^{'}$ ). Chen et al. (2013) showed that convective RSDs of Nanjing in East China (ECN) were characterized by $\log N_{0}^{'} \approx 1.95 - 2.39$ , $D_{m}^{'} \approx 1.47 - 1.95 mm$ using Particle Size and Velocity (PARSIVEL) data collected in July from 2009 to 2011. Furthermore, Wen et al. (2016) showed that convective RSDs of ECN were characterized by $\log N_{0}^{'} \approx 2.36 - 3 .12$ , $D_{m}^{'} \approx 1.17 - 1.65 mm$ with using 2DVD data collected from June to August in 2014 and 2015. These studies illustrated that the convective RSDs in the continental air mass tended to have larger raindrops and a smaller total number concentration and vice versa for those in the tropical/maritime air mass. When compared with Bringi et al. (2003), RSDs of OKL during April to June 2011 in the Midlatitude Continental Convective Clouds Experiment (MC3E) showed slightly higher $N_{0}^{'}$ and smaller $D_{m}^{'}$ [ $\log 〈 N_{0}^{'} 〉 \approx 1.95$ , $\log SD (N_{0}^{'}) \approx 2.250$ , and $D_{m}^{'} \approx 1.05 - 1.93 mm$ ] for both stratiform and convective rain (Tokay et al. 2017). However, RSDs for stratiform rain in Wallops Island in Virginia (WIV) during December 2013–March 2014 had relatively larger $N_{0}^{'}$ and smaller $D_{m}^{'}$ [ $\log 〈 N_{0}^{'} 〉 \approx 2.184$ , $\log SD (N_{0}^{'}) \approx 2.338$ , and $D_{m}^{'} \approx 0.81 - 1.41 mm$ ] than those of MC3E (Tokay et al. 2016). Here, the SD stands for the standard deviation.

RSD variability, raindrop shape–size relation, type of disdrometers, drop axis ratio, measurement uncertainty, sampling volume, and way of the fitting can influence rainfall estimation relationships (Chandrasekar et al. 1990; Smith et al. 1993; Lee and Zawadzki 2005a,b; Ryzhkov et al. 2005; Lee 2006; Lee and Zawadzki 2006; Gorgucci and Baldini 2009; Adirosi et al. 2018). Many studies have compared rainfall estimation relationships of two distinctive RSDs: one with a large $N_{0}^{'}$ and small $D_{m}^{'}$ , and the other with a small $N_{0}^{'}$ and large $D_{m}^{'}$ (Ulbrich and Atlas 1978; Tokay and Short 1996; Ulbrich and Atlas 1998; Illingworth and Blackman 2002; Martner et al. 2008; Schönhuber et al. 2015). The radar reflectivity factor at horizontal polarization Z_H (dBZ or mm⁶ m⁻³) of the former RSD is smaller when compared to the Z_H of the latter at the same rain rate R (mm h⁻¹; Ulbrich and Atlas 1978; Tokay and Short 1996; Ulbrich and Atlas 1998; Martner et al. 2008). Furthermore, the specific differential phase K_DP (° km⁻¹) of the former is smaller when compared to the K_DP of the latter at the same R (Illingworth and Blackman 2002). The specific attenuation at horizontal polarization A_H (dB km⁻¹) of the former is also smaller at shorter wavelength when compared to the A_H of the latter at the same R (Schönhuber et al. 2015). In addition, the three parameters (Z_H, K_DP, A_H) have different dependency on the diameter and thus on raindrop concentration. The K_DP is more sensitive to the raindrop concentration than Z_H due to the dependency of Z_H on the 6th power of the particle diameter (Zrnić and Ryzhkov 1996; Straka et al. 2000; Lee 2006; Kumjian and Ryzhkov 2009). Similarly, the A_H has the highest sensitivity to raindrop concentration among these three parameters. The R depends on the 3.67th power of the raindrop diameter. These dependency suggest that R(Z_H), R(K_DP), and R(A_H) reflect different microphysical processes and subsequent different characteristics of RSDs.

This study focuses on two objectives. First, we compare RSD characteristics between KOR and OKL. Second, we examine the impact of RSD differences through a comparison of rainfall estimation relationships between the two regions. To achieve this, we use long-term 2DVD observations from KOR and OKL. RSDs, their moments, characteristic parameters derived from scaling RSD, and dual-polarization variables obtained from each dataset are also analyzed.

2. Two-dimensional video disdrometer data

The 2DVD is an optical disdrometer that observes the shadow of precipitation particles using two orthogonal cameras and illumination units (Fig. 1). The nominal measurement area is 100 cm² and the height difference between the two cameras is 6.2 mm. Equivalent diameter D (mm), measured fall velocity V_f (m s⁻¹), and the axis ratio of raindrop can be obtained from the observation data (Kruger and Krajewski 2002).

The 2DVD observed data are used only for the warm season (May–September) from 1998 to 2006 in Norman of OKL and from 2011 to 2015 in the southern part [combined dataset from two regions; Daegu (2011–12) and Boseong (2013–15)] of KOR. The solid and mixed precipitation were excluded by selecting data during the warm season and by checking the hail events. Measurement locations and the instruments used are shown in Fig. 2. Figure 2a shows a map of East Asia with the KOR observation sites (red circles), Fig. 2b shows a map of North America with the OKL observation site (blue circle), and Figs. 2c and 2d show photographs of the 2DVD instruments used in KOR and OKL, respectively. The monthly average relative humidity in Daegu (Norman) was in the range of 60%–68% (50%–60%).

The 2DVD manufactured three versions from 1991 to now: tall unit (1991–2001; first generation), low-profile unit (2002–08; second generation), and compact unit (2009–present; third generation). Although different versions of 2DVD instruments were used in OKL (tall unit; first generation) and KOR (compact unit; third generation), the main processing software was identical. The hardware was similar, with the exception of the scan frequency and number of pixels of line scan camera. The number of pixels and scan frequencies were 500–512 and 34.1 kHz for the tall unit and 632 and 55.3 kHz for the compact unit, respectively (Schuur et al. 2001; Kruger and Krajewski 2002; Schönhuber et al. 2008). This change should improve the size resolution and the accuracy in fall velocity. The internal temperature of the tall unit is not stabilized. Thus, it requires frequent recalibration due to temperature change and can create the disturbance of airflow around the measuring area. In addition, the first generation had a design issue that tiny droplets can hang or land on the mirrors or slits (Larsen and Schönhuber 2018).

There are few studies about the intercomparison of RSD measurement between different 2DVDs. Brandes et al. (2005) showed that the difference of 1-min drop size distribution and its characteristics parameters (total number concentration, rainfall rate, median volume diameter, and drop maximum diameter) between tall and low-profile units was quite small regardless of different wind fences. A similar comparison of the low-profile and compact units showed that the measured fall velocity, rainfall intensity, mass-weighted mean diameter, and width of the mass spectrum were in agreement and, furthermore, the fall velocity, shape, and axis ratio were close to the expected or reference values (Thurai et al. 2010).

The KOR dataset were from the two regions (Daegu and Boseong). These two regions were about 180 km apart. Boseong is close to the ocean and is directly affected by the abundant moisture and land/sea breeze. However, Daegu is located in inland and downstream location of mountains during the dominant westerly, and is significantly affected by the dry air and summertime heating. This discrepancy may cause some difference in microphysics. Detailed analysis of $N_{0}^{'}$ and $D_{m}^{'}$ indicated the difference of their average is within about 7%–9%, which is much less than that between KOR and OKL (not shown). That is, Daegu is slightly close to OKL with slightly smaller (larger) $N_{0}^{'}$ ( $D_{m}^{'}$ ). Hereafter, the comparison of KOR and OKL is only shown.

The RSD N(D) (m⁻³ mm⁻¹) is defined as follows:

N (D_{i}) = \sum_{j = 1}^{n} \frac{1}{A V_{f} (D_{j}) Δ t Δ D},

(1)

where i is the diameter bin number. A total of 41 bins were used, ranging from 0 to 10.25 mm; ∆D is 0.25 mm, ∆t is 1 min, n indicates the total number of raindrops at the ith bin during 1 min, and A is the measurement area of the 2DVD.

Processing of the 2DVD data is done in two steps. First, outliers were eliminated using fall velocity according to Kruger and Krajewski (2002):

| V_{a} (D) - V_{f} (D) | < 0.4 V_{a} (D),

(2)

where V_a(D) = 9.65–10.3 exp(−0.6D) (Atlas et al. 1973). Data that satisfied Eq. (2) were retained. Second, the data were inspected and RSDs that had one or more contiguous zero-concentration bins between nonzero concentration bins were eliminated by assuming RSD as a continuous function (Marshall and Palmer 1948; Ulbrich 1983; Auf der Maur 2001). This second data processing is to use continuous RSDs with no missing data between diameter channels. This is quite reasonable since the RSD should be considered as a continuous distribution. A total of 34 780 (22 066) RSDs for KOR (OKL) were acquired from 116 (120) rainfall events. In this study, we assume that one day of rainfall represents a single rainfall event.

3. Analysis method

a. Derivation of rain microphysical characteristics

RSD diversity can be attributed to cloud and precipitation microphysical processes such as coalescence, breakup, evaporation, accretion, aggregation, and so on (Rosenfeld and Ulbrich 2003; Lee et al. 2004; Tapiador et al. 2014; Testik and Pei 2017). These microphysical processes can lead to variety of different RSDs. We compared the RSD shape of the two regions as a first step in our analysis to reveal the difference in dominant microphysical processes. To reveal these differences, the RSDs were averaged for 5-dB intervals from 0 to 55 dBZ. The relative difference ΔN(D) [N_OKL(D) − N_KOR(D)] of averaged RSD between two regions was then compared at each 5-dB interval.

RSDs can be normalized using generalized characteristic parameters (generalized characteristic number concentration

N_{0}^{'}

and generalized characteristic diameter

D_{m}^{'}

) with an assumption of the scaling law (Lee et al. 2004):

N (D) = N_{0}^{'} h (D / D_{m}^{'}) = N_{0}^{'} h (x),

(3)

where h is the nondimensional normalized generic function and x is the nondimensional normalized diameter. Here, we defined

N_{0}^{'} = M_{i}^{(j + 1) / (j - 1)} M_{j}^{(i + 1) / (i - j)}

and

D_{m}^{'} = {(M_{j} / M_{i})}^{1 / (j - i)}

, and the index i and j are the order of the moment. We examined the normalized function and characteristic parameters of the KOR and OKL RSD data. In this study, RSDs are assumed to be a generalized gamma distribution, as suggested by Auf der Maur (2001). The normalized form of the generalized gamma distribution h_gg is expressed using the following equation:

h_{gg} (x, c, μ) = c Γ_{i}^{(i + c μ) / (i - j)} Γ_{j}^{(- i - c μ) / (i - j)} x^{c μ - 1} \exp [{(\frac{Γ_{j}}{Γ_{i}})}^{c / (i - j)} x^{c}],

(4)

where Γ_i = Γ(μ + i/c), Γ_j = Γ(μ + j/c) (Lee et al. 2004). The normalization of Testud et al. (2001) is a special case of (3) when i = 3, and j = 4. The nondimensional parameters (c and μ) are shape parameters of the generalized gamma distribution and were estimated using least squares method. The 1-min RSDs are used in the scaling normalization and the negative μ is allowed in the fitting. These shape parameters were compared between the two regions.

The characteristic variables of RSD can be helpful to explain characteristics of RSD quantitatively. Therefore, we calculated R, radar reflectivity Z (mm⁶ m⁻³ or dBZ),

N_{0}^{'}

, and

D_{m}^{'}

used in many previous studies. The

N_{0}^{'}

and

D_{m}^{'}

are derived by using i = 3 and j = 4:

N_{0}^{'} (M_{3}, M_{4}) = M_{3}^{5} M_{4}^{- 4},

(5)

D_{m}^{'} (M_{3}, M_{4}) = M_{4} M_{3}^{- 1},

(6)

where M_n (m⁻³ mmⁿ) means nth moment of RSD. The magnitude of

N_{0}^{'}

(

D_{m}^{'}

) depends on the intercept parameter of RSD (mean diameter). We compared normalized frequency distributions (NFDs) of R,

N_{0}^{'}

, and

D_{m}^{'}

in the two regions and analyzed the relationship of

N_{0}^{'} - Z

and

D_{m}^{'} - Z

RSD characteristics between the convective and stratiform rainfall significantly differ (Tokay and Short 1996; Thurai et al. 2016). Herein, convective (stratiform) rainfall is classified as a precipitation system without (with) a bright band in cold rain. Methodologies to classify between convective and stratiform rainfall using disdrometer data have been suggested in previous studies, for example, Bringi et al. (2003), Chen et al. (2013), and Wen et al. (2016). In this study, we classify precipitation using the Chen et al. (2013) method to in order to compare our datasets with the ECN data. Chen et al. (2013) classified the convective rainfall with the thresholds of R_min ≥ 5 mm h⁻¹ and SD(R) ≥ 1.5 mm h⁻¹. Here, the R_min and SD(R) are the minimum R value and standard deviation within a window of 10 min. An $N_{0}^{'} - D_{m}^{'}$ scatterplot was analyzed to compare our results with previous studies such as the maritime convective cluster, continental convective cluster (Bringi et al. 2003), and ECN (Chen et al. 2013; Wen et al. 2016).

b. Derivation of rainfall estimation relationships

Several studies have shown that R(Z_H), R(K_DP), and R(A_H) can be influenced by RSD characteristics (Ulbrich and Atlas 1978; Tokay and Short 1996; Ulbrich and Atlas 1998; Illingworth and Blackman 2002; Schönhuber et al. 2015). For example, using the fall velocity data of Gunn and Kinzer (1949), R can be approximated to D^3.67. Parameter Z_H is proportional to D⁶. Furthermore, K_DP depends on concentration and shape of the raindrop (Kumjian and Ryzhkov 2008), and A_H is proportional to D³ (D³–D⁶) when Rayleigh (Mie) approximation can be adopted (Sauvageot 1992). Consequently, we compared the rainfall estimation relationships between KOR and OKL to examine the impacts of microphysical differences. Radar variables were derived using a T matrix and rainfall relationships were assumed to have a power law (Y = aX^b). The multiplicative factor a and exponent b were estimated using the weighted total least squares (WTLS) method following Amemiya (1997). The R threshold (>0.5 mm h⁻¹) was adopted to improve the fitness of the relationships. After adopting the threshold, the RSD sample number for KOR was 17 849 (51.3% of data) and for OKL was 13 070 (59.2%).

The T-matrix method can be performed under control conditions such as temperature, radar wavelength, radar elevation angle, and raindrop shape model (Mishchenko et al. 1996). The control conditions and the values used in this study are shown in Table 1. The elevation angle of the radar was set at 0°. Three radar wavelengths were considered: 11.01 cm (S band), 5.61 cm (C band), and 3.23 cm (X band). The raindrop shape model of Thurai et al. (2007) was used. The environment temperature was fixed to be 23°C based on Table 2, which shows the monthly mean temperatures of KOR and OKL during the summer. Temperature data for Daegu and Jangheung (adjacent to Boseong) were obtained from the automatic weather station (AWS) during 1981–2010 (KMA 2011). Temperature for OKL was obtained from climate data during 1982–2012 (Climate-data.org 2016). Climatological temperatures are important because A_H can be influenced by temperature variation (Ryzhkov et al. 2014). The canting angle of raindrops was assumed to be a Gaussian distribution with a width of 10°.

Table 1.

Control conditions for the T-matrix method. The μ indicates mean, and σ indicates standard deviation.

Table 2.

Monthly mean temperatures for KOR and OKL during summer.

Parameters Z_H, K_DP, and A_H were calculated as follows:

Z_{H, V} = \frac{4 λ^{4}}{π^{4} | K^{2} |} \int_{D_{\min}}^{D_{\max}} {| s_{H, V} (π, D) |}^{2} N (D) d D,

(7)

K_{DP} = \frac{180 λ}{π} \int_{D_{\min}}^{D_{\max}} Re [f_{H} (0, D) - f_{V} (0, D)] N (D) d D,

(8)

A_{H} = 8.686 λ \int_{D_{\min}}^{D_{\max}} Im [f (0, D)] N (D) d D,

(9)

where λ is the radar wavelength (cm), |K²| is the dielectric term of water, and f(π, D) is the complex forward scattering amplitude and s(π, D) is complex backscattering amplitude at horizontal H or vertical V polarization (Mishchenko et al. 1996).

Statistical values, such as SD [Eq. (10)], average fractional error [AFE, Eq. (11)], and standard deviation of the fractional error [SDFE, Eq. (12)] were calculated as follows:

SD (mm h^{- 1}) = {[\frac{1}{k - 1} \sum {(R_{obs} - R_{est})}^{2}]}^{0.5},

(10)

AFE (%) = \frac{1}{k - 1} \sum \frac{| R_{obs} - R_{est} |}{R_{obs}},

(11)

SDFE (%) = {[\frac{1}{k - 1} \sum {(\frac{R_{obs} - R_{est}}{R_{obs}})}^{2}]}^{0.5},

(12)

where k is the data number, obs is the observed value, and est is the value estimated from the empirical rainfall estimation relationship.

4. Analysis result

a. Difference in rain microphysical characteristics

A comparison of the average RSD for each ΔZ = 5-dB interval (⟨N(D)⟩) between the two regions is shown in Fig. 3 (KOR in solid line and OKL in dashed line). The different colors represent the intervals of Z in which RSDs are averaged. It is apparent that RSDs become broad (high number concentration at larger sizes) as Z increases. In addition, the number concentration in the size of 0.5–1 mm increases with higher Z. The RSDs in the two regions differ in that N_KOR(D) has a relatively high (low) concentration at small (large) D. This difference [∆N(D) = N_OKL(D) − N_KOR(D)] is highlighted in Fig. 3b. Warm colors [higher N(D) in OKL than in KOR] dominates the larger sizes and cold colors [lower N(D) in OKL than in KOR] are shown in smaller sizes. Therefore, N_KOR(D) is characterized by higher number concentration of small raindrops and lower concentration of large raindrops when compared with N_OKL(D).

Figure 4 shows the results of the normalized RSD for KOR and OKL. All RSDs are normalized and average h(x) (⟨h(x)⟩) with Δx = 0.2 is shown as the black solid line in Fig. 4a for KOR and 4b for OKL. The vertical bar is the standard deviation at each Δx. The dash–dot line represents the best-fit line [h_gg(x, c, μ)] estimated from ⟨h(x)⟩ using the least squares method. The normalized RSD collapses into a single line at the size range of 0.5–1.5. The large scatter in the smaller and larger sizes may be attributed to the disdrometer measurement uncertainty (Lee et al. 2004). The average h(x) and fitted h(x) are nearly identical for the two regions [h_gg,KOR(x, 2.46, 0.39) and h_gg,OKL(x, 2.79, 0.12)] in Figs. 4c and 4d. These results indicate that the different microphysical processes in the two regions do not significantly affect the change of average h(x) and most of the microphysical variation is contained in the two normalization parameters, characteristic number concentration and characteristic diameter.

The NFDs of RSD variables are shown in Fig. 5. The OKL NFD shows a wide R distribution and large ⟨R⟩ as compared to KOR. These differences indicate that light rainfall (<2.5 mm h⁻¹) is more frequent in KOR, whereas extreme heavy rainfall (>100 mm h⁻¹) is more frequent in OKL with a long tail. The NFD of $\log N_{0}^{'}$ of KOR (Fig. 5c) shows a large average value (2.47) and shifts to larger values as compared to $\log N_{0}^{'}$ of OKL (2.10). We attribute this result to frequent light rainfall from a moisture rich environment in KOR (Fu and Liu 2003; Fu et al. 2003; Sohn et al. 2013; Cao and Qi 2014). A second peak with $\log N_{0}^{'} \geq 2.85$ is evident in both regions, with higher and distinctive frequency in KOR. This larger values of $N_{0}^{'}$ suggest frequent development of shallow systems and drizzle-like precipitation (Joss and Waldvogel 1969; Wen et al. 2016) whereas evaporation in the relatively dry environment in OKL causes depletion of smaller drops, leading to lower $N_{0}^{'}$ values. In particular, the low R (<2.5 mm h⁻¹ with low $N_{0}^{'}$ ) in OKL is dominated from trailing stratiform of mesoscale convective systems. The NFD of $D_{m}^{'}$ of OKL (Fig. 5d) shows a large average value (1.28 mm) as compared to the $D_{m}^{'}$ of KOR (0.99 mm). We attribute this result to OKL experiencing frequent extreme heavy rainfall from deep convective systems, such as a supercell storm (Schuur et al. 2005). Deep convective systems allow more time for the growth of large particles through ice microphysical processes and, in particular, heavy riming to produce graupel by strong updraft, resulting in a large $D_{m}^{'}$ . In addition, RSDs from melted hail in deep convective systems tend to produce larger raindrops but a relatively small number concentration (Ryzhkov and Zrnić 2019).

Relationships between $N_{0}^{'} - Z$ and $D_{m}^{'} - Z$ in KOR (right panels) and OKL (left panels) are analyzed to examine microphysical differences in detail (Fig. 6). The black solid (dashed) line indicates the median value in KOR (OKL) at each interval of Z (ΔZ = 2 dBZ). The $N_{0}^{'}$ ( $D_{m}^{'}$ ) of KOR shows a relatively large (smaller) median value for given Z values. This indicates that the concentration of small raindrops in KOR is generally higher as compared to OKL regardless of Z. In addition, this trend persists over the entire range of Z with the difference between the two median values becoming larger in the range Z = 30–45 dBZ, implying the precipitation growth is controlled more by the number concentration in KOR. Furthermore, the $N_{0}^{'}$ of KOR shows higher frequency at 15 dBZ < Z < 25 dBZ and $N_{0}^{'} > 10^{2.85} m^{- 3} {mm}^{- 1}$ as compared to OKL (see the circle in Fig. 6a). This second peak of high $N_{0}^{'}$ is less distinctive in OKL. As explained earlier, this indicates the frequent development of shallow precipitation systems at KOR, which provide heavier R for a given Z.

Compared with OKL, $D_{m}^{'}$ of KOR shows a relatively small median value for the entire range of Z, indicating the smaller raindrop sizes for given Z. The difference of $D_{m}^{'}$ is rather small in the range of 10 dBZ < Z < 25 dBZ and becomes more distinctive for Z > 30 dBZ. The $D_{m}^{'}$ is typically derived from the differential reflectivity and/or reflectivity. The noisy differential reflectivity in Z < 25 dBZ alludes to use the reflectivity only. However, the smaller slope of $D_{m}^{'} - Z$ suggests large retrieval uncertainty. Thus, other parameters such as attenuation may require to improve the accuracy.

The growth of drops is more prominent with increasing Z in OKL. The frequency of $D_{m}^{'} > 3 mm$ in OKL is higher as compared to KOR at Z > 40 dBZ (see the circle on Fig. 6d), indicating that the microphysical characteristics of heavy precipitation systems differ in the two regions. Studies indicate that heavy precipitation systems in KOR are dominantly driven by cloud clusters (Lee and Kim 2007) whereas supercell storms are the source of heavy precipitation in OKL (Hocker and Basara 2008). The leading edge of a supercell storm produces an RSD with D₀ > 3 mm (Schuur et al. 2005). Here, D₀ is the median volume diameter (mm) and the value is very similar to $D_{m}^{'}$ (Ulbrich 1983).

Figure 7 shows the average RSDs for four different intervals of Z when $D_{m}^{'} > 2.0 mm$ in KOR (solid line) and OKL (dashed line). As Z increases, the number concentration systematically increases, that is, a parallel shift of RSDs toward increasing number concentration, in particular, for OKL. This trend indicates the constant $D_{m}^{'}$ and continuous increase of $N_{0}^{'}$ with increasing Z and, furthermore, is evidence of the equilibrium RSD as noted by Zawadzki and Antonio (1988), Uijlenhoet et al. (2003), and Lee et al. (2004). In addition, the shape of RSDs, the so-called “S” shape, shows high number concentrations for small (D < 1.0 mm) and big (D = 2.0–4.0 mm) raindrops and a relative small number concentration for medium-size (D = 1.0–2.0 mm) drops. The average N(D) for KOR shows higher number concentration than OKL at D < 2 mm and significant increase of the number concentration at D = 2.0–2.5 mm. It is noted that the sudden drop of N(D) at D < 0.4 mm, in general, significant underestimation of small drop is due to the instrumental limitation (Thurai et al. 2017; Chang et al. 2020). In particular, the first channel of the observed RSD by 2DVD is significantly low (Thurai et al. 2017). This shape is similar to the simulated shape from the equilibrium of coalescence and breakup processes in the quasi-stochastic growth equation (Valdez and Young 1985; List 1988; Hu and Srivastava 1995; Straub et al. 2010) and the N(D) with cμ − 1 = −2 in Fig. 9 of Lee et al. (2004). The simulated shape of the equilibrium RSD changes with breakup parameterization (Straub et al. 2010).

Figure 8 shows a comparison of $N_{0}^{'} - D_{m}^{'}$ in the two regions. Both $N_{0}^{'}$ and $D_{m}^{'}$ are derived from the average RSDs shown in Fig. 3a. The diamond and square symbols represent KOR and OKL, respectively. The colors indicated different reflectivity intervals and the typical values of the maritime convective cluster (MT) and continental convective cluster (CT) from Bringi et al. (2003) are shown as the boxes in Fig. 8a. It is apparent that the value $N_{0}^{'}$ of KOR is close to that of MT while that of OKL is close to CT. For further comparison, we classified the convective rainfall by the method of Chen et al. (2013) and calculated typical $N_{0}^{'}$ and $D_{m}^{'}$ values (Fig. 8b). The ranges of R (or Z) and standard deviation of log $N_{0}^{'}$ and $D_{m}^{'}$ are shown in Table 3 for the different regions. The minimum rainfall intensity is 5 mm h⁻¹ and the maximum one varies from 109.3 to 185 mm h⁻¹ with the maximum in the maritime convective cluster (MT) of Bringi et al. (2003). The values from ECN (Wen et al. 2016) are shown as an additional box in Fig. 8b. The values of KOR overlaps with MT and ECN while those of OKL is in between MT and CT with $N_{0}^{'}$ close to MP value and $D_{m}^{'}$ smaller than CT.

Table 3.

Ranges of R (or Z) and standard deviation of $\log N_{0}^{'}$ and $D_{m}^{'}$ for the four different regions used in Fig. 8b.

The $N_{0}^{'} - D_{m}^{'}$ shows different dependence on reflectivity values (Fig. 8a). The $N_{0}^{'}$ value increases as $D_{m}^{'}$ increases for Z < 16 dBZ in KOR and OKL, indicating the growth and new generation of drops are significant. However, it decreases with increasing $D_{m}^{'}$ for Z > 16 dBZ in KOR and for 16 dBZ < Z < 32 dBZ in OKL. This indicates that the precipitation processes are dominated by drop growth by coalescence as well as possible aggregation in light/moderate stratiform system. The $N_{0}^{'}$ value increases with increasing $D_{m}^{'}$ for Z > 32 dBZ in OKL. This may be attributed to further generation of smaller drops due to strong convection. This is somehow linked with the traditional classification of convection with Z > 35 dBZ. Furthermore, the collision–coalescence process increases with further increasing Z and there finally exists an equilibrium between coalescence and breakup processes with extreme Z values (see Fig. 7). In addition, the strong convection produces graupel and possible hail as Z further increases, resulting into large $D_{m}^{'}$ .

b. Difference in rainfall estimation relationships

Figure 9 shows a comparison of R–Z_H relationships at different radar wavelengths for the two regions, KOR and OKL, to examine the impacts of the differences in RSD characteristics. The black solid line is the Marshall and Palmer (1948) relationship, R_MP(Z) (Z = 296R^1.47), and the dashed line is the best fit relationship, R(Z_H), derived using the WTLS method. The Marshall and Palmer RSDs were obtained from Ottawa, Canada, and were affected by the continental environment. The left (right) panels are for KOR (OKL). The R_KOR is larger than R_MP at the same Z for each radar wavelength whereas R_OKL is similar to R_MP. This is an outcome of higher number concentration of smaller drops in KOR and of larger drops in OKL. It is worthwhile to recall the R_MP(Z) is derived for stratiform rain in summer from Ottawa, Canada. In addition, the OKL RSDs at 35 dBZ are dominant as shown in the shade in Fig. 9b or Fig. 9d. Thus, the R_OKL is close to R_MP. Furthermore, R_OKL is well fit data at Z > 35 dBZ. In addition, the exponent of R(Z) ranges from 1.42 to 1.52 [similar to that of R_MP(Z)] except for X band in OKL. This similar exponent is valid only when the $N_{0}^{'}$ is independent to R.

The error (AFE and SDFE) in R estimation with reflectivity only is smaller in KOR and is the largest at X band, in particular, in OKL. Larger the error, higher the variability of RSDs that cannot explain by a single parameter, Z. This is thus partially attributed to the significant variation of RSD at high Z as shown in the large scatter at Z > 45 dBZ in OKL (Fig. 9f). The best fit relationships are overlapped in Fig. 10 and the coefficient and exponent are summarized in Table 4. In general, no significant variation is shown in the different wavelengths and the RSD difference is the dominant factor to deviate the relationships from the two regions.

Table 4.

The R–Z_H relationships derived from the RSD for KOR and OKL.

Figure 11 shows the R–K_DP scatterplots for KOR and OKL at each radar wavelength. The dashed line is the best fit relationship, R(K_DP), derived using the WTLS method for R > 0.5 mm h⁻¹and K_DP > 0.1° km⁻¹. The K_DP is normalized with the radar frequency (in GHz). The error in R estimation did not show the significant dependency to the radar wavelengths and regions when the best fit relationships are used. Similar to R(Z_H), the best fit relationships, R(K_DP), show the slight dependency to different RSDs in the two regions. For a given K_DP, R in KOR is higher than in OKL (shown in Fig. 11 and Table 5). The dependency to the radar wavelengths is eliminated by normalizing K_DP with the radar frequency (Fig. 12). The exponents (0.79–0.86) of R(K_DP) slightly vary with wavelengths and regions and the coefficients vary with the wavelengths and regions. The exponent of 0.83 is corresponding to the moment order of K_DP as 4.6. When the lower threshold of K_DP (>0.001° km⁻¹) is used (i.e., light rain is included), the exponent (0.75–0.76) is nearly constant, showing no dependency on wavelengths and regions. The exponent of 0.75 implies that the moment order of K_DP is close to 5.2, that is, to radar reflectivity (Illingworth 2003). This value is related to the mean axis ratio versus diameter relation for small sized drops (Thurai et al. 2007).

Table 5.

The R–K_DP relationships derived from RSD for KOR and OKL. RSDs with R > 0.5 mm h⁻¹ and K_DP > 0.1° km⁻¹ are used.

The R–A_H scatterplots for KOR and OKL are shown in Fig. 13. It is apparent that the SD, AFE, and SDFE of R_KOR(A_H) vary significantly with the wavelengths and regions. In particular, the SD at C band is the largest. The best fit R–A_H relationships show different characteristics compared with R–Z_H and R–K_DP (Fig. 14 and Table 6). Significant variation is shown in different wavelengths due to the dependency of attenuation to the wavelength. However, the regional variation of R(A_H) is small at S band, indicating that this relationship is immune to different RSDs and is less sensitive to microphysical variation. In addition, the exponent is close to unity at S band and decreases with shorter wavelengths. The exponent of 1 implies the moment order of A_H is close to 3.67. As the radar wavelength decreases, the regional difference becomes significant, particularly at higher values of A_H. The exponent in the same wavelength is smaller in OKL due to higher number concentration in larger size drops in OKL. The smaller exponent at shorter wavelength suggests that the moment order of A_H is higher (4.4–5.1), likely due to strong Mie effect and higher dependency of attenuation into diameter.

Table 6.

The R–A_H relationships derived from RSD for KOR and OKL.

5. Summary and conclusion

Rainfall characteristics in OKL and KOR were compared using a 2DVD dataset. First, we compared microphysical characteristics through the scaling normalization of RSDs. Second, we examined the impact of different RSD characteristics on the two regions through the comparison of rainfall estimation relationships.

Comparison of RSD characteristics highlighted the different systems driving precipitation in the two regions. Rainfall in the southern Korean peninsula tended to be light and derived from shallow systems. The NFD of $N_{0}^{'}$ of KOR showed a relatively large NF for large $N_{0}^{'}$ (≥10^2.85 m⁻³ mm⁻¹) as compared to the NFD of $N_{0}^{'}$ of OKL. The $N_{0}^{'} - Z$ scatterplots showed that RSDs with large $N_{0}^{'}$ have a small Z (15 dBZ < Z < 25 dBZ). Hence, N_KOR(D) has a relatively narrow distribution when rainfall is light. Rainfall in OKL is typically heavy and from supercell systems; producing large raindrops relative to KOR. The NFD of $D_{m}^{'}$ of OKL displayed a relatively large NF for large $D_{m}^{'}$ (≥2 mm) as compared to the NFD of $D_{m}^{'}$ of KOR. The $D_{m}^{'} - Z$ scatterplots showed that the RSDs with large $D_{m}^{'}$ also had a large Z (>40 dBZ). Hence, N_OKL(D) has a relatively wide distribution when rainfall is heavy. Our results were compared with previous studies (Marshall and Palmer 1948; Bringi et al. 2003; Chen et al. 2013; Wen et al. 2016). Typical values for KOR generalized parameters were found to be similar to ECN and MT groups. Conversely, typical values for OKL show relatively large $D_{m}^{'}$ and small $N_{0}^{'}$ as compared to the MT group.

In summary, the KOR RSD showed large $N_{0}^{'}$ and small $D_{m}^{'}$ and vice versa in OKL. This was persistent throughout different range of Z. Thus, we can conclude the KOR RSD is controlled by the new generation of drops and less cold microphysics, in particular, riming process, while the OKL RSD is controlled by the drop growth by the collision–coalescence process and by significant production of graupel and heavily rimed particles due to strong updraft in the deep layer. In particular, the OKL RSD with larger characteristic diameter shows the typical behavior of the equilibrium between coalescence and breakup processes. The shape of DSD was close to the so-called “S” shape that is predicted by the quasi-stochastic growth equation in this equilibrium (Valdez and Young 1985; List 1988; Hu and Srivastava 1995). The RSD moved toward higher number concentration with increasing Z while maintaining the shape and the characteristic diameter. The RSD was dominantly controlled by the number concentration. In addition, the larger $N_{0}^{'}$ in lower Z (so-called second peak in this work) was prominent in the KOR RSD. This was an indication of drizzle-like precipitation driven by the shallow systems.

The two regions showed similar dominant microphysical processes up to moderate rain (<32 dBZ). The drop growth and new generation of drops were dominant in light rain (<16 dBZ), indicated by increasing trend in both $N_{0}^{'}$ and $D_{m}^{'}$ with increasing Z. However, the drop growth becomes dominant in the moderate rain (<32 dBZ) since the $N_{0}^{'}$ value decreases with increasing $D_{m}^{'}$ . The $N_{0}^{'}$ value becomes nearly constant with higher Z in KOR RSD but again increases with increasing Z over 32 dBZ in OKL RSD. Finally, the OKL RSD showed the clear signature of the equilibrium RSD in extreme Z values by the balance between collision–coalescence and breakup processes.

Rainfall estimation relationships such as R(Z_H), R(K_DP), and R(A_H) were compared at S, C, and X bands. Dual-polarization radar variables were retrieved from the RSD dataset using the T-matrix method. KOR displayed smaller Z_H and K_DP as compared to OKL at the same R and radar wavelength. The result was attributed to the relatively wide distribution of N_OKL(D). The R–A_H relationships with S-band wavelength showed a negligible dependence on microphysical processes because R(A_H,KOR) was similar to R(A_H,OKL). Conversely, C- and X-band A_H,KOR showed relatively large differences as compared to A_H,OKL.

This study compared the different RSD characteristics of KOR and OKL. The impacts of these differences were determined by comparing the rainfall estimation relationships of the two regions. These results will be useful for improving the results of global precipitation estimation projects, such as the Global Precipitation Measurement (GPM) or TRMM. We also anticipate that the results of this study will aid in the understanding of cloud and precipitation microphysics during the East Asian monsoon.

Acknowledgments

This study was funded by the Korea Environmental Industry & Technology Institute (KEITI) of the Korea Ministry of Environment (MOE) as “Advanced Water Management Research Program” (79615) and by the Korea Meteorological Administration Research and Development Program “Enhancement of Convergence Technology of Analysis and Forecast on Severe Weather” under Grant (KMA2018-00121). Funding for T. Schuur and A. Ryzhkov was provided by NOAA/Office of Oceanic and Atmospheric Research under NOAA–University of Oklahoma Cooperative Agreement NA16OAR4320115, U.S. Department of Commerce.

REFERENCES

Adirosi, E., N. Roberto, M. Montopoli, E. Gorgucci, and L. Baldini, 2018: Influence of disdrometer type on weather radar algorithms from measured DSD: Application to Italian climatology. Atmosphere, 9, 360, https://doi.org/10.3390/atmos9090360.
- Crossref
- Search Google Scholar
- Export Citation
Amemiya, Y., 1997: Generalization of the TLS approach in the errors-in-variables problem. Recent Advances in Total Least Squares Techniques and Errors-in-Variables Modeling, S. Van Huffel, Ed., SIAM, 77–86, https://dl.acm.org/doi/abs/10.5555/267248.267258.
Atlas, D., R. C. Srivastava, and R. S. Sekhon, 1973: Doppler radar characteristics of precipitation at vertical incidence. Rev. Geophys., 11, 1–35, https://doi.org/10.1029/RG011i001p00001.
- Crossref
- Search Google Scholar
- Export Citation
Auf der Maur, A. N., 2001: Statistical tools for drop size distributions: Moments and generalized gamma. J. Atmos. Sci., 58, 407–418, https://doi.org/10.1175/1520-0469(2001)058<0407:STFDSD>2.0.CO;2.
- Crossref
- Search Google Scholar
- Export Citation
Brandes, E. A., T. J. Schuur, J. Vivekanandan, G. Zhang, and K. Ikeda, 2005: Rain microphysics retrieval with a polarimetric WSR-88D. 32nd Conf. on Radar Meteorology, Albuquerque, NM, Amer. Meteor. Soc., 9R.2, https://ams.confex.com/ams/32Rad11Meso/techprogram/paper_97017.htm.
Bringi, V. N., J. Hubbert, E. Gorgucci, W. Randeu, and M. Schoenhuber, 2003: Raindrop size distribution in different climatic regions from disdrometer and dual-polarized radar analysis. J. Atmos. Sci., 60, 354–365, https://doi.org/10.1175/1520-0469(2003)060<0354:RSDIDC>2.0.CO;2.
- Crossref
- Search Google Scholar
- Export Citation
Cao, Q., and Y. Qi, 2014: The variability of vertical structure of precipitation in Huaihe River Basin of China: Implications from long-term spaceborne observations with TRMM precipitation radar. Water Resour. Res., 50, 3690–3705, https://doi.org/10.1002/2013WR014555.
- Crossref
- Search Google Scholar
- Export Citation
Chandrasekar, V., V. N. Bringi, N. Balakrishnan, and D. S. Zrnić, 1990: Error structure of multiparameter radar and surface measurements of rainfall. Part III: Specific differential phase. J. Atmos. Oceanic Technol., 7, 621–629, https://doi.org/10.1175/1520-0426(1990)007<0621:ESOMRA>2.0.CO;2.
- Crossref
- Search Google Scholar
- Export Citation
Chang, W. Y., G. Lee, B. J. D. Jou, W. C. Lee, P. L. Lin, and C. K. Yu, 2020: Uncertainty in measured raindrop size distributions from four types of collocated instruments. Remote Sens., 12, 1167, https://doi.org/10.3390/rs12071167.
- Crossref
- Search Google Scholar
- Export Citation
Chen, B., J. Yang, and J. Pu, 2013: Statistical characteristics of raindrop size distribution in Meiyu season observed in eastern China. J. Meteor. Soc. Japan, 91, 215–227, https://doi.org/10.2151/jmsj.2013-208.
- Crossref
- Search Google Scholar
- Export Citation
Climate-data.org, 2016: Norman climate. Accessed 1 April 2020, https://en.climate-data.org/location/17142/.
Fu, Y., and G. Liu, 2003: Precipitation characteristics in mid-latitude East Asia as observed by TRMM PR and TMI. J. Meteor. Soc. Japan, 81, 1353–1369, https://doi.org/10.2151/jmsj.81.1353.
- Crossref
- Search Google Scholar
- Export Citation
Fu, Y., Y. Lin, G. Liu, and Q. Wang, 2003: Seasonal characteristics of precipitation in 1998 over East Asia as derived from TRMM PR. Adv. Atmos. Sci., 20, 511–529, https://doi.org/10.1007/BF02915495.
- Crossref
- Search Google Scholar
- Export Citation
Gorgucci, E., and L. Baldini, 2009: An examination of the validity of the mean raindrop-shape model for dual-polarization radar rainfall retrievals. IEEE Trans. Geosci. Remote Sens., 47, 2752–2761, https://doi.org/10.1109/TGRS.2009.2017936.
- Crossref
- Search Google Scholar
- Export Citation
Gunn, R., and G. D. Kinzer, 1949: The terminal velocity of fall for water droplets in stagnant air. J. Meteor., 6, 243–248, https://doi.org/10.1175/1520-0469(1949)006<0243:TTVOFF>2.0.CO;2.
- Crossref
- Search Google Scholar
- Export Citation
Hocker, J. E., and J. B. Basara, 2008: A 10-year spatial climatology of squall line storms across Oklahoma. Int. J. Climatol., 28, 765–775, https://doi.org/10.1002/joc.1579.
- Crossref
- Search Google Scholar
- Export Citation
Hu, Z., and R. C. Srivastava, 1995: Evolution of raindrop size distribution by coalescence, breakup, and evaporation: Theory and observations. J. Atmos. Sci., 52, 1761–1783, https://doi.org/10.1175/1520-0469(1995)052<1761:EORSDB>2.0.CO;2.
- Crossref
- Search Google Scholar
- Export Citation
Illingworth, A., 2003: Reply. J. Appl. Meteor., 42, 1190–1195, https://doi.org/10.1175/1520-0450(2003)042<1190:R>2.0.CO;2.
- Crossref
- Search Google Scholar
- Export Citation
Illingworth, A., and T. M. Blackman, 2002: The need to represent raindrop size spectra as normalized gamma distributions for the interpretation of polarization radar observations. J. Appl. Meteor., 41, 286–297, https://doi.org/10.1175/1520-0450(2002)041<0286:TNTRRS>2.0.CO;2.
- Crossref
- Search Google Scholar
- Export Citation
Joanneum Research, 2016: 2D Video Disdrometer Beyond State-of-the-Art Precipitation Measurement. Joanneum Research, 4 pp.
Joss, J., and A. Waldvogel, 1969: Raindrop size distribution and sampling size errors. J. Atmos. Sci., 26, 566–569, https://doi.org/10.1175/1520-0469(1969)026<0566:RSDASS>2.0.CO;2.
- Crossref
- Search Google Scholar
- Export Citation
KMA, 2011: Climatological Normals of Korea. Korea Meteorological Administration, 688 pp.
- Search Google Scholar
- Export Citation
Kruger, A., and W. F. Krajewski, 2002: Two-dimensional video disdrometer: A description. J. Atmos. Oceanic Technol., 19, 602–617, https://doi.org/10.1175/1520-0426(2002)019<0602:TDVDAD>2.0.CO;2.
- Crossref
- Search Google Scholar
- Export Citation
Kumjian, M. R., and A. V. Ryzhkov, 2008: Polarimetric signatures in supercell thunderstorms. J. Appl. Meteor. Climatol., 47, 1940–1961, https://doi.org/10.1175/2007JAMC1874.1.
- Crossref
- Search Google Scholar
- Export Citation
Kumjian, M. R., and A. V. Ryzhkov, 2009: Storm-relative helicity revealed from polarimetric radar measurements. J. Atmos. Sci., 66, 667–685, https://doi.org/10.1175/2008JAS2815.1.
- Crossref
- Search Google Scholar
- Export Citation
Larsen, M. L., and M. Schönhuber, 2018: Identification and characterization of an anomaly in two-dimensional video disdrometer data. Atmosphere, 9, 315, https://doi.org/10.3390/atmos9080315.
- Crossref
- Search Google Scholar
- Export Citation
Lee, G. W., 2006: Sources of errors in rainfall measurements by polarimetric radar: Variability of drop size distributions, observational noise, and variation of relationships between R and polarimetric parameters. J. Atmos. Oceanic Technol., 23, 1005–1028, https://doi.org/10.1175/JTECH1899.1.
- Crossref
- Search Google Scholar
- Export Citation
Lee, G. W., and I. Zawadzki, 2005a: Variability of drop size distributions: Noise and noise filtering in disdrometric data. J. Appl. Meteor., 44, 634–652, https://doi.org/10.1175/JAM2222.1.
- Crossref
- Search Google Scholar
- Export Citation
Lee, G. W., and I. Zawadzki, 2005b: Variability of drop size distributions: Time-scale dependence of the variability and its effects on rain estimation. J. Appl. Meteor., 44, 241–255, https://doi.org/10.1175/JAM2183.1.
- Crossref
- Search Google Scholar
- Export Citation
Lee, G. W., and I. Zawadzki, 2006: Radar calibration by gage, disdrometer, and polarimetry: Theoretical limit caused by the variability of drop size distribution and application to fast scanning operational radar data. J. Hydrol., 328, 83–97, https://doi.org/10.1016/j.jhydrol.2005.11.046.
- Crossref
- Search Google Scholar
- Export Citation
Lee, G. W., I. Zawadzki, W. Szyrmer, D. Sempere-Torres, and R. Uijlenhoet, 2004: A general approach to double-moment normalization of drop size distributions. J. Appl. Meteor., 43, 264–281, https://doi.org/10.1175/1520-0450(2004)043<0264:AGATDN>2.0.CO;2.
- Crossref
- Search Google Scholar
- Export Citation
Lee, T. Y., and Y. H. Kim, 2007: Heavy precipitation systems over the Korean peninsula and their classification. J. Korean Meteor. Soc., 43, 367–396.
- Search Google Scholar
- Export Citation
List, R., 1988: A linear radar reflectivity–rainrate relationship for steady tropical rain. J. Atmos. Sci., 45, 3564–3572, https://doi.org/10.1175/1520-0469(1988)045<3564:ALRRRF>2.0.CO;2.
- Crossref
- Search Google Scholar
- Export Citation
Marshall, J. S., and W. M. K. Palmer, 1948: The distribution of raindrops with size. J. Meteor., 5, 165–166, https://doi.org/10.1175/1520-0469(1948)005<0165:TDORWS>2.0.CO;2.
- Crossref
- Search Google Scholar
- Export Citation
Martner, B. E., S. E. Yuter, A. B. White, S. Y. Matrosov, D. E. Kingsmill, and F. M. Ralph, 2008: Raindrop size distributions and rain characteristics in California coastal rainfall for periods with and without a radar bright band. J. Hydrometeor., 9, 408–425, https://doi.org/10.1175/2007JHM924.1.
- Crossref
- Search Google Scholar
- Export Citation
Mishchenko, M. I., L. D. Travis, and D. W. Mackowski, 1996: T-matrix computations of light scattering by nonspherical particles: A review. J. Quant. Spectrosc. Radiat. Transfer, 55, 535–575, https://doi.org/10.1016/0022-4073(96)00002-7.
- Crossref
- 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. 52, Amer. Meteor. Soc., 237–258, https://doi.org/10.1175/0065-9401(2003)030<0237:CMPPAR>2.0.CO;2.
- Crossref
- Export Citation
Ryzhkov, A., and D. Zrnić, 2019: Radar Polarimetry for Weather Observations. Springer, 486 pp.
- Crossref
- Search Google Scholar
- Export Citation
Ryzhkov, A., S. E. Giangrande, V. M. Melnikov, and T. J. Schuur, 2005: Calibration issues of dual-polarization radar measurements. J. Atmos. Oceanic Technol., 22, 1138–1155, https://doi.org/10.1175/JTECH1772.1.
- Crossref
- Search Google Scholar
- Export Citation
Ryzhkov, A., M. Diederich, P. Zhang, and C. Simmer, 2014: Potential utilization of specific attenuation for rainfall estimation, mitigation of partial beam blockage, and radar networking. J. Atmos. Oceanic Technol., 31, 599–619, https://doi.org/10.1175/JTECH-D-13-00038.1.
- Crossref
- Search Google Scholar
- Export Citation
Sauvageot, H., 1992: Radar Meteorology. Artec House, 366 pp.
- Search Google Scholar
- Export Citation
Schönhuber, M., G. Lammer, and W. L. Randeu, 2008: The 2D-video-distrometer. Precipitation: Advances in Measurement, Estimation and Prediction, S. Michaelides, Ed., Springer, 3–31.
- Crossref
- Export Citation
Schönhuber, M., K. Plimon, and M. Thurai, 2015: Statistical significance of specific rain attenuation dependence on geographic and climatic conditions. Ninth European Conf. on Antennas and Propagation, Lisbon, Portugal, University of Lisbon, 1–5, https://ieeexplore.ieee.org/document/7228484.
Schuur, T. J., A. V. Ryzhkov, D. S. Zrnić, and M. Schönhuber, 2001: Drop size distributions measured by a 2D video disdrometer: Comparison with dual-polarization radar data. J. Appl. Meteor., 40, 1019–1034, https://doi.org/10.1175/1520-0450(2001)040<1019:DSDMBA>2.0.CO;2.
- Crossref
- Search Google Scholar
- Export Citation
Schuur, T. J., A. V. Ryzhkov, and D. R. Clabo, 2005: Climatological analysis of DSDs in Oklahoma as revealed by 2D-video disdrometer and polarimetric WSR-88D. 32nd Conf. on Radar Meteorology, Albuquerque, NM, Amer. Meteor. Soc., 15R.4, https://ams.confex.com/ams/32Rad11Meso/techprogram/paper_95995.htm.
Smith, P. L., Z. Liu, and J. Joss, 1993: A study of sampling-variability effects in raindrop size observations. J. Appl. Meteor., 32, 1259–1269, https://doi.org/10.1175/1520-0450(1993)032<1259:ASOSVE>2.0.CO;2.
- Crossref
- Search Google Scholar
- Export Citation
Sohn, B. J., G. H. Ryu, H. J. Song, and M. L. Ou, 2013: Characteristic features of warm-type rain producing heavy rainfall over the Korean Peninsula inferred from TRMM measurements. Mon. Wea. Rev., 141, 3873–3888, https://doi.org/10.1175/MWR-D-13-00075.1.
- Crossref
- Search Google Scholar
- Export Citation
Straka, J. M., D. S. Zrnić, and A. V. Ryzhkov, 2000: Bulk hydrometeor classification and quantification using polarimetric radar data: Synthesis of relations. J. Appl. Meteor., 39, 1341–1372, https://doi.org/10.1175/1520-0450(2000)039<1341:BHCAQU>2.0.CO;2.
- Crossref
- Search Google Scholar
- Export Citation
Straub, W., K. D. Beheng, A. Seifert, J. Schlottke, and B. Weigand, 2010: Numerical investigation of collision-induced breakup of raindrops. Part II: Parameterizations of coalescence efficiencies and fragment size distributions. J. Atmos. Sci., 67, 576–588, https://doi.org/10.1175/2009JAS3175.1.
- Crossref
- Search Google Scholar
- Export Citation
Tapiador, F. J., Z. S. Haddad, and J. Turk, 2014: A probabilistic view on raindrop size distribution modeling: A physical interpretation of rain microphysics. J. Hydrometeor., 15, 427–443, https://doi.org/10.1175/JHM-D-13-033.1.
- Crossref
- Search Google Scholar
- Export Citation
Testik, F. Y., and B. Pei, 2017: Wind effects on the shape of raindrop size distribution. J. Hydrometeor., 18, 1285–1303, https://doi.org/10.1175/JHM-D-16-0211.1.
- Crossref
- 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, 1118–1140, https://doi.org/10.1175/1520-0450(2001)040<1118:TCONDT>2.0.CO;2.
- Crossref
- Search Google Scholar
- Export Citation
Thurai, M., G. J. Huang, V. N. Bringi, W. L. Randeu, and M. Schönhuber, 2007: Drop shapes, model comparisons, and calculations of polarimetric radar parameters in rain. J. Atmos. Oceanic Technol., 24, 1019–1032, https://doi.org/10.1175/JTECH2051.1.
- Crossref
- Search Google Scholar
- Export Citation
Thurai, M., W. A. Petersen, and L. D. Carey, 2010: DSD characteristics of a cool-season tornadic storm using C-band polarimetric radar and two 2D-video disdrometers. Sixth European Conf. on Radar in Meteorology and Hydrology, Sibiu, Romania, NMA of Romania, 6 pp., https://www.erad2010.com/pdf/oral/tuesday/radpol1/5_ERAD2010_0101.pdf.
Thurai, M., P. N. Gatlin, and V. N. Bringi, 2016: Separating stratiform and convective rain types based on the drop size distribution characteristics using 2D video disdrometer data. Atmos. Res., 169, 416–423, https://doi.org/10.1016/j.atmosres.2015.04.011.
- Crossref
- Search Google Scholar
- Export Citation
Thurai, M., P. N. Gatlin, V. N. Bringi, W. Petersen, P. Kennedy, B. Notaroš, and L. Carey, 2017: Toward completing the raindrop size spectrum: Case studies involving 2D-video disdrometer, droplet spectrometer, and polarimetric radar measurements. J. Appl. Meteor. Climatol., 56, 877–896, https://doi.org/10.1175/JAMC-D-16-0304.1.
- Crossref
- 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, 355–371, https://doi.org/10.1175/1520-0450(1996)035<0355:EFTRSO>2.0.CO;2.
- Crossref
- Search Google Scholar
- Export Citation
Tokay, A., L. P. D’Adderio, D. B. Wolff, and W. A. Petersen, 2016: A field study of pixel-scale variability of raindrop size distribution in the mid-Atlantic region. J. Hydrometeor., 17, 1855–1868, https://doi.org/10.1175/JHM-D-15-0159.1.
- Crossref
- Search Google Scholar
- Export Citation
Tokay, A., L. P. D’Adderio, F. Porcù, D. B. Wolff, and W. A. Petersen, 2017: A field study of footprint-scale variability of raindrop size distribution. J. Hydrometeor., 18, 3165–3179, https://doi.org/10.1175/JHM-D-17-0003.1.
- Crossref
- Search Google Scholar
- Export Citation
Uijlenhoet, R., J. A. Smith, and M. Steiner, 2003: The microphysical structure of extreme precipitation as inferred from ground-based raindrop spectra. J. Atmos. Sci., 60, 1220–1238, https://doi.org/10.1175/1520-0469(2003)60<1220:TMSOEP>2.0.CO;2.
- Crossref
- Search Google Scholar
- Export Citation
Ulbrich, C. W., 1983: Natural variations in the analytical form of the raindrop size distribution. J. Climate Appl. Meteor., 22, 1764–1775, https://doi.org/10.1175/1520-0450(1983)022<1764:NVITAF>2.0.CO;2.
- Crossref
- Search Google Scholar
- Export Citation
Ulbrich, C. W., and D. Atlas, 1978: The rain parameter diagram: Methods and applications. J. Geophys. Res., 83, 1319–1325, https://doi.org/10.1029/JC083iC03p01319.
- Crossref
- Search Google Scholar
- Export Citation
Ulbrich, C. W., and D. Atlas, 1998: Rainfall microphysics and radar properties: Analysis methods for drop size spectra. J. Appl. Meteor., 37, 912–923, https://doi.org/10.1175/1520-0450(1998)037<0912:RMARPA>2.0.CO;2.
- Crossref
- Search Google Scholar
- Export Citation
Valdez, M. P., and K. C. Young, 1985: Number fluxes in equilibrium raindrop populations: A Markov chain analysis. J. Atmos. Sci., 42, 1024–1036, https://doi.org/10.1175/1520-0469(1985)042<1024:NFIERP>2.0.CO;2.
- Crossref
- Search Google Scholar
- Export Citation
Wen, L., K. Zhao, G. Zhang, B. Zhou, S. Liu, X. Chen, and M. Xue, 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.
- Crossref
- Search Google Scholar
- Export Citation
Zawadzki, I., and M. A. Antonio, 1988: Equilibrium raindrop size distributions in tropical rain. J. Atmos. Sci., 45, 3452–3459, https://doi.org/10.1175/1520-0469(1988)045<3452:ERSDIT>2.0.CO;2.
- Crossref
- Search Google Scholar
- Export Citation
Zrnić, D. S., and A. Ryzhkov, 1996: Advantages of rain measurements using specific differential phase. J. Atmos. Oceanic Technol., 13, 454–464, https://doi.org/10.1175/1520-0426(1996)013<0454:AORMUS>2.0.CO;2.
- Crossref
- Search Google Scholar
- Export Citation

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Comparison of Microphysical Characteristics between the Southern Korean Peninsula and Oklahoma Using Two-Dimensional Video Disdrometer Data

Abstract

Abstract

1. Introduction

2. Two-dimensional video disdrometer data

3. Analysis method

a. Derivation of rain microphysical characteristics

b. Derivation of rainfall estimation relationships

4. Analysis result

a. Difference in rain microphysical characteristics

b. Difference in rainfall estimation relationships

5. Summary and conclusion

Acknowledgments

REFERENCES

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GENERAL CONDITIONS

No. III

BAROMETRIC PRESSURE

VERIFICATIONS

MISCELLANEOUS PHENOMENA

Comparison of Microphysical Characteristics between the Southern Korean Peninsula and Oklahoma Using Two-Dimensional Video Disdrometer Data

Abstract

Abstract

1. Introduction

2. Two-dimensional video disdrometer data

3. Analysis method

a. Derivation of rain microphysical characteristics

b. Derivation of rainfall estimation relationships

4. Analysis result

a. Difference in rain microphysical characteristics

b. Difference in rainfall estimation relationships

5. Summary and conclusion

Acknowledgments

REFERENCES

Share Link

AMS Publications

Get Involved with AMS

Affiliate Sites

Follow Us

Contact Us