• Atmosphere
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• v.34 no.2
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• pp.203-216
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• 2024

The National Institute of Meteorological Sciences in Korea has developed the Weather Modification Hybrid Rocket (WMHR), an advanced system that offers enhanced stability and cost-effectiveness over conventional solid-fuel rockets. Designed for precise operation, the WMHR enables accurate control over the ejection altitude of pyrotechnics by modulating the quantity of oxidizer, facilitating specific cloud seeding at various atmospheric layers. Furthermore, the rate of descent for pyrotechnic devices can be adjusted by modifying parachute sizes, allowing for controlled dispersion time and concentration of seeding agents. The rocket's configuration also supports adjustments in the pyrotechnic device's capacity, permitting tailored seeding agent deployment. This innovation reflects significant technical progression and collaborations with local manufacturers, in addition to efforts to secure testing sites and address hybrid rocket production challenges. Notable outcomes of this project include the creation of a national framework for weather modification technology utilizing hybrid rockets, enhanced cloud seeding methods, and the potential for broader meteorological application of hybrid rockets beyond precipitation augmentation. An illustrative case study confirmed the WMHR's operational effectiveness, although the impact on cloud seeding was limited by unfavorable weather conditions. This experience has provided valuable insights and affirmed the system's potential for varied uses, such as weather modification and deploying high-altitude meteorological sensors. Nevertheless, the expansion of civilian weather rocket experiments in Korea faces challenges due to inadequate infrastructure and regulatory limitations, underscoring the urgent need for advancements in these areas.

• Journal of Korea Water Resources Association
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• v.53 no.11
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• pp.999-1013
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• 2020

Weather modification research has been actively performed worldwide, but a technology that can more quantitatively prove the research effects are needed. In this study, the seeding effect, the efficiency of precipitation enhancement in weather modification experiment, was verified using the radar data. Also, the effects of seeding material on hydrometeor change was analyzed. For this, radar data, weather conditions, and numerical simulation data for diffusion were applied. First, a method to analyze the seeding effect in three steps was proposed: before seeding, during seeding, and after seeding. The proposed method was applied to three cases of weather modification experiments conducted in Gangwon-do and the West Sea regions. As a result, when there is no natural precipitation, the radar reflectivity detected in the area where precipitation change is expected was determined as the seeding effect. When natural precipitation occurs, the seeding effect was determined by excluding the effect of natural precipitation from the maximum reflectivity detected. For the application results, it was found that the precipitation intensity increased by 0.1 mm/h through the seeding effect. In addition, it was confirmed that ice crystals, supercooled water droplets, and mixed-phase precipitation were distributed in the seeding cloud. The results of these weather modification research can be used to secure water resources as well as for future study of cloud physics.

• Atmosphere
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• v.34 no.1
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• pp.35-53
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• 2024

Under the leadership of the National Institute of Meteorological Sciences (NIMS), the first domestic autonomous flight-type weather modification experimental drone for fog and lower-level cloud seeding was developed in 2021. This drone is designed based on a multi-copter configuration with a maximum takeoff weight of approximately 25 kg, enabling the installation of up to four burning flares for seeding materials and facilitating weather observations (temperature, pressure, humidity, and wind) as well as aerosol (PM₁₀, PM_2.5, and PM_1.0) particle measurements. This research aims to introduce the construction of the drone and its recent applications over the past two years, providing insights into the experimental procedures, effectiveness verification, and improvement directions of the weather modification drone-based rain enhancement. In particular, partial confirmation of the experimental effects was obtained through the fog dissipation experiment on December 10, 2021, and the lower-level cloud seeding case study on October 5, 2022. To enhance the scope and rainfall amount of weather modification experiments using drones, various technological approaches, including adjustments to experimental altitude, seeding lines, seeding amount, and verification methods are necessary. Through this research, we aim to propose the development direction for weather modification drone technology, which will serve as foundational technology for practical application of domestic rain enhancement technology.

• Journal of the Korean earth science society
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• v.43 no.4
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• pp.498-510
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• 2022

Cloud-aerosol interactions are one of the paramount but least understood forcing factors in climate systems. Generally, an increase in the concentration of aerosols increases the concentration of cloud droplet numbers, implying that clouds tend to persist for longer than usual, suppressing precipitation in the warm boundary layer. The cloud lifetime effect has been the center of discussion in the scientific community, partly because of the lack of cloud life cycle observations and partly because of cloud problems. In this study, the precipitation susceptibility (S_o) matrix was employed to estimate the aerosols' effect on precipitation, while the non-aerosol effect is minimized. The S_o was calculated for the typical coupled, well-mixed maritime stratocumulus decks and giant cloud condensation nucleus (GCCN) seeded clouds. The GCCN-artificially introduced to the marine stratocumulus cloud decks-is shown to initiate precipitation and reduces S_o to approximately zero, demonstrating the cloud lifetime hypothesis. The results suggest that the response of precipitation to changes in GCCN must be considered for accurate prediction of aerosol-cloud-precipitation interaction by model studies

• Korean Journal of Remote Sensing
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• v.14 no.2
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• pp.165-174
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• 1998

The observation of liquid water content(LWC) and the estimation of precipitation enhancement by cloud seeding were made over the Andong in Korea from March 1997 through Feb 1998. A dual-channel microwave radiometer was used to measure the liquid water content and water vapor. It was shown that the 90% of observational period had the amount of less than 0.1 mm in LWC, and that the amount of precipitation was proportionally increased to liquid water content. The amount of LWC has maximum in summer and minimum in winter. The content of liquid cloud water was showed higher value from the time of 12 to the time of 17 except for summer season in which it extremely fluctuated with a large precipitation. The majority of liquid water content over the area occurred with westerly and southwesterly wind which were flowed from the Sobaek mountain. The ratio of horizontal LWC flux and vertical precipitation flux, $P_{en}$ is almost ranked in the interval of 0.0~0.5 with maximum of 0.5 in spring, 0.2 in summer and fall, and 0.1 in winter. Accordingly, it is estimated that the potential enhancement of precipitation over Andong area by cloud seeding has high value in spring with westerly wind.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.65-73
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• 2010

To observe and analyze the characteristics of cloud and precipitation properties, the Cloud physics Observation System (CPOS) has been operated from December 2003 at Daegwallyeong ($37.4^{\circ}N$, $128.4^{\circ}E$, 842 m) in the Taebaek Mountains. The major instruments of CPOS are follows: Forward Scattering Spectrometer Probe (FSSP), Optical Particle Counter (OPC), Visibility Sensor (VS), PARSIVEL disdrometer, Microwave Radiometer (MWR), and Micro Rain Radar (MRR). The former four instruments (FSSP, OPC, visibility sensor, and PARSIVEL) are for the observation and analysis of characteristics of the ground cloud (fog) and precipitation, and the others are for the vertical cloud characteristics (http://weamod.metri.re.kr) in real time. For verification of CPOS products, the comparison between the instrumental products has been conducted: the qualitative size distributions of FSSP and OPC during the hygroscopic seeding experiments, the precipitable water vapors of MWR and radiosonde, and the rainfall rates of the PARSIVEL(or MRR) and rain gauge. Most of comparisons show a good agreement with the correlation coefficient more than 0.7. These reliable CPOS products will be useful for the cloud-related studies such as the cloud-aerosol indirect effect or cloud seeding. The visibility value is derived from the droplet size distribution of FSSP. The derived FSSP visibility shows the constant overestimation by 1.7 to 1.9 times compared with the values of two visibility sensors (SVS (Sentry Visibility Sensor) and PWD22 (Present Weather Detect 22)). We believe this bias is come from the limitation of the droplet size range ($2{\sim}47\;{\mu}m$) measured by FSSP. Further studies are needed after introducing new instruments with other ranges.

• Communications for Statistical Applications and Methods
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• v.25 no.1
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• pp.15-27
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• 2018

Parametric method of flood frequency analysis involves fitting of a probability distribution to observed flood data. When record length at a given site is relatively shorter and hard to apply the asymptotic theory, an alternative distribution to the generalized extreme value (GEV) distribution is often used. In this study, we consider the beta-P distribution (BPD) as an alternative to the GEV and other well-known distributions for modeling extreme events of small or moderate samples as well as highly skewed or heavy tailed data. The L-moments ratio diagram shows that special cases of the BPD include the generalized logistic, three-parameter log-normal, and GEV distributions. To estimate the parameters in the distribution, the method of moments, L-moments, and maximum likelihood estimation methods are considered. A Monte-Carlo study is then conducted to compare these three estimation methods. Our result suggests that the L-moments estimator works better than the other estimators for this model of small or moderate samples. Two applications to the annual maximum stream flow of Colorado and the rainfall data from cloud seeding experiments in Southern Florida are reported to show the usefulness of the BPD for modeling hydrologic events. In these examples, BPD turns out to work better than $beta-{\kappa}$, Gumbel, and GEV distributions.