Regional variability of raindrop size distribution over Indonesia

Marzuki, Marzuki; Hashiguchi, Hiroyuki; Yamamoto, Mamoru; Mori, Shuichi; Yamanaka, Manabu D.

doi:10.5194/angeo-31-1941-2013

Cited by 57 publications

(46 citation statements)

References 25 publications

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“…Even the three distinct neurons, which are isolated from the others, appear to be governed by the same relationship. Marzuki et al (2013) noted that during convective rain the increase in value of N * 0 with decreasing D m is nearly log linear, with a flatter slope. In the present case, the dependence is also log linear, with a slope that is slightly flatter for convective events than for stratiform events.…”

Section: Microphysical Point Of Viewmentioning

confidence: 98%

“…Many authors, including Atlas et al (1999), Bringi et al (2003), Marzuki et al (2013) and Suh et al (2016), have endeavored to associate specific microphysical properties with each type of precipitation (convective or stratiform). In view of the maps shown in Fig.…”

Section: Microphysical Point Of Viewmentioning

confidence: 99%

“…In convective rainfall, raindrops grow by the collision-coalescence mechanism, associated with relatively strong vertical wind speeds. Numerous studies have been published concerning the variability of N * 0 and D m : Bringi et al (2003) studied rain samples from diverse climates and analyzed their variability in stratiform and convective rainfall; Marzuki et al (2013) investigated the variability of the raindrop size distribution through a network of Parsivel disdrometers in Indonesia; and Suh et al (2016) investigated the raindrop size distribution in Korea using a POSS disdrometer. In the case of stratiform rain, all of these authors observe that N * 0 and D m are nearly log-linearly related, with a negative slope.…”

Section: Microphysical Point Of Viewmentioning

confidence: 99%

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Data-driven clustering of rain events: microphysics information derived from macro-scale observations

et al. 2017

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Abstract. Rain time series records are generally studied using rainfall rate or accumulation parameters, which are estimated for a fixed duration (typically 1 min, 1 h or 1 day). In this study we use the concept of "rain events". The aim of the first part of this paper is to establish a parsimonious characterization of rain events, using a minimal set of variables selected among those normally used for the characterization of these events. A methodology is proposed, based on the combined use of a genetic algorithm (GA) and self-organizing maps (SOMs). It can be advantageous to use an SOM, since it allows a high-dimensional data space to be mapped onto a two-dimensional space while preserving, in an unsupervised manner, most of the information contained in the initial space topology. The 2-D maps obtained in this way allow the relationships between variables to be determined and redundant variables to be removed, thus leading to a minimal subset of variables. We verify that such 2-D maps make it possible to determine the characteristics of all events, on the basis of only five features (the event duration, the peak rain rate, the rain event depth, the standard deviation of the rain rate event and the absolute rain rate variation of the order of 0.5). From this minimal subset of variables, hierarchical cluster analyses were carried out. We show that clustering into two classes allows the conventional convective and stratiform classes to be determined, whereas classification into five classes allows this convective-stratiform classification to be further refined. Finally, our study made it possible to reveal the presence of some specific relationships between these five classes and the microphysics of their associated rain events.

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Section: Microphysical Point Of Viewmentioning

confidence: 98%

Section: Microphysical Point Of Viewmentioning

confidence: 99%

Section: Microphysical Point Of Viewmentioning

confidence: 99%

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Data-driven clustering of rain events: microphysics information derived from macro-scale observations

et al. 2017

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“…The number of study particularly at Kototabang, west Sumatra increases after Equatorial Atmosphere Radar (EAR) facilities were completed in this area [1,9,[13][14][15][16][17][18]. Furthermore, regional variability of DSD along the equatorial Indonesia has been also observed through a network of Parsivel disdrometer observation at Kototabang, Pontianak, Manado and Biak [19]. The aforementioned studies mostly deal with the surface DSD.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Micro Rain Radar Over Sumatra Through Comparison With Disdrometer and Wind Profiler

Marzuki¹,

Hashiguchi²,

Shimomai³

et al. 2016

PIER M

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Abstract-Micro Rain Radar (MRR) is a vertical pointing microwave profiler to measure hydrometeors and related parameters in high resolution. However, it is known that the MRR suffers from certain limitations due to several factors. This paper evaluates the performance of the MRR installed at Kototabang, west Sumatra, Indonesia (0.20 • S, 100.32 • E, 864 m above sea level). The DSD and rainfall rate from the MRR standard processing method had been evaluated by using collocated measurements of MRR, Parsivel disdrometer and Optical Rain Gauge (ORG) during 2014. Furthermore, 1.3 GHz wind profiler (BLR) observation was used to examine the vertical profiles of radar reflectivity and Doppler velocity. It was found that there were noticeable differences between the MRR and Parsivel in the small and large size ends of the DSD. At small sized drop (< 1 mm), the DSD spectra of MRR was higher than that obtained by the Parsivel otherwise it was smaller for large sized drop (> 2 mm). Underestimation of large sized drops in the MRR could be responsible for the underestimation of surface rainfall rate and daily rainfall. The source of differences in the DSD seems the measurement shortcomings such as attenuation correction and vertical wind effects, particularly during heavy rain. The shortcomings were observed from the comparison of mean Doppler velocity profiles between the MRR and the BLR. While the melting layer height of the two instruments was the same, the mean Doppler velocities of MRR shown downward increasing (DI) pattern through all rainfall intensities. On the other hand, for the BLR, the DI was only observed for heavy rain (> 10 mm/h), while downward decreasing was observed for light rain (< 5 mm/h). Similar pattern was also observed for the vertical profile of radar reflectivity. Thus, some corrections are needed for heavy rain, nevertheless, the MRR installed at Kototabang can be utilized for light rain. Comparisons indicated that the mean Doppler velocity and the DSD for the light rain as well as Z-R relation were in reasonable agreement with the reference of BLR, Parsivel and previous studies using the MRR.

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“…Previous studies about the microphysical properties of the MCSs in Indonesia were conducted without considering the life stage of the MCSs (e.g., Cetrone and Houze 2009;Marzuki et al 2013). To obtain a complete understanding of MCSs over the region, it is important to examine the MCSs by also taking their evolution into consideration.…”

Section: Introductionmentioning

confidence: 99%

The Properties of Mesoscale Convective Systems in Indonesia Detected Using the Grab ‘Em Tag ‘Em Graph ‘Em (GTG) Algorithm

Putri

Hayasaka

Whitehall

2017

Journal of the Meteorological Society of Japan

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A mesoscale convective system (MCS) is organized thunderstorms with connected anvils, which has a significant impact on the global climate. By focusing on MCSs over the Maritime Continent of Indonesia, this study aims to gain a better understanding on the properties of the MCSs over the study area. The "Grab 'em Tag 'em Graph 'em" (GTG) tracking algorithm is applied to hourly Multi-functional Transport Satellite-1R data for two years to observe the distribution of MCSs and the evolution of MCSs along their lifetime. The results of MCS identification by using GTG are combined with CloudSat data products to study the vertical structure of the MCSs at various MCS life stages: developing, mature, and dissipating.The distribution of MCSs over Indonesia has a seasonal variation and distinct diurnal cycle. The life stages of the observed MCSs are characterized by distinct cloud microphysics at each stage. In the developing stage, the upper level of the MCS raining region shows the presence of precipitating ice particles. As the MCS progresses to the mature stage, the proportion of the raining area becomes small and the intensity of rain is reduced, accompanied by increasing occurrence of small-sized ice particles at the upper level. In the dissipating stage, large hydrometeors no longer exist at the upper part of the raining region. Within the MCS anvils, the dissipating stage shows a more uniform distribution of ice-particle effective radius compared to that shown by the developing and mature stages.MCS characteristics over the land and ocean differ on the basis of the minimum brightness temperature, the equivalent radius, the maximum rain rate, and the rain fraction that varies along the MCS evolution.

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Regional variability of raindrop size distribution over Indonesia

Cited by 57 publications

References 25 publications

Data-driven clustering of rain events: microphysics information derived from macro-scale observations

Data-driven clustering of rain events: microphysics information derived from macro-scale observations

Performance Evaluation of Micro Rain Radar Over Sumatra Through Comparison With Disdrometer and Wind Profiler

The Properties of Mesoscale Convective Systems in Indonesia Detected Using the Grab ‘Em Tag ‘Em Graph ‘Em (GTG) Algorithm

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