Three-dimensional Analysis of Heavy Rainfall Using KLAPS Re-analysis Data

Jang, Myoung-Uoon; You, Cheol‐Hwan; Jee, Joon-Bum; Park, Sung-Hwa; Kim, Sang Il; Choi, Young-Jean

doi:10.14191/atmos.2016.26.1.097

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…The KLAPS is a numerical forecasting system used to predict weather on the Korean Peninsula and it is mainly used for 3D analysis and the prediction of local-scale phenomena [31,32]. Choi et al [33] and Jang et al [34] stated that using KLAPS data is advantageous in improving the accuracy of meteorological phenomena.…”

Section: Datamentioning

confidence: 99%

AIDER: Aircraft Icing Potential Area DEtection in Real-Time Using 3-Dimensional Radar and Atmospheric Variables

Kim,

Ye,

Suk

2024

Remote Sensing

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Aircraft icing refers to the accumulation of ice on the surface and components of an aircraft when supercooled water droplets collide with the aircraft above freezing levels (at altitudes at which the temperature is below 0 °C), which requires vigilant monitoring to avert aviation accidents attributable to icing. In response to this imperative, the Weather Radar Center (WRC) of the Korea Meteorological Administration (KMA) has developed a real-time icing detection algorithm. We utilized 3D dual-polarimetric radar variables, 3D atmospheric variables, and aircraft icing data and statistically analyzed these variables within the icing areas determined by aircraft icing data from 2018–2022. An algorithm capable of detecting icing potential areas (icing potential) was formulated by applying these characteristics. Employing this detection algorithm enabled the classification of icing potential into three stages: precipitation, icing caution, and icing warning. The algorithm was validated, demonstrating a notable performance with a probability of detection value of 0.88. The algorithm was applied to three distinct icing cases under varying environmental conditions—frontal, stratiform, and cumuliform clouds—thereby offering real-time observable icing potential across the entire Korean Peninsula.

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Section: Datamentioning

confidence: 99%

AIDER: Aircraft Icing Potential Area DEtection in Real-Time Using 3-Dimensional Radar and Atmospheric Variables

Kim,

Ye,

Suk

2024

Remote Sensing

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Analysis of Determinants for an Enhanced and Long-lasting Coastal Convective System by Means of a Case Study (26 July 2011)

Lee

Shimizu

et al. 2019

Adv. Atmos. Sci.

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Studies on the Predictability of Heavy Rainfall Using Prognostic Variables in Numerical Model

Jang¹,

Jee²,

Min³

et al. 2016

Atmosphere

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In order to determine the prediction possibility of heavy rainfall, a variety of analyses was conducted by using three-dimensional data obtained from Korea Local Analysis and Prediction System (KLAPS) re-analysis data. Strong moisture convergence occurring around the time of the heavy rainfall is consistent with the results of previous studies on such continuous production. Heavy rainfall occurred in the cloud system with a thick convective clouds. The moisture convergence, temperature and potential temperature advection showed increase into the heavy rainfall occurrence area. The distribution of integrated liquid water content tended to decrease as rainfall increased and was characterized by accelerated convective instability along with increased buoyant energy. In addition, changes were noted in the various characteristics of instability indices such as K-index (KI), Showalter Stability Index (SSI), and lifted index (LI). The meteorological variables used in the analysis showed clear increases or decreases according to the changes in rainfall amount. These rapid changes as well as the meteorological variables changes are attributed to the surrounding and meteorological conditions. Thus, we verified that heavy rainfall can be predicted according to such increase, decrease, or changes. This study focused on quantitative values and change characteristics of diagnostic variables calculated by using numerical models rather than by focusing on synoptic analysis at the time of the heavy rainfall occurrence, thereby utilizing them as prognostic variables in the study of the predictability of heavy rainfall. These results can contribute to the identification of production and development mechanisms of heavy rainfall and can be used in applied research for prediction of such precipitation. In the analysis of various case studies of heavy rainfall in the future, our study result can be utilized to show the development of the prediction of severe weather.

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Three-dimensional Analysis of Heavy Rainfall Using KLAPS Re-analysis Data

Cited by 3 publications

References 25 publications

AIDER: Aircraft Icing Potential Area DEtection in Real-Time Using 3-Dimensional Radar and Atmospheric Variables

AIDER: Aircraft Icing Potential Area DEtection in Real-Time Using 3-Dimensional Radar and Atmospheric Variables

Analysis of Determinants for an Enhanced and Long-lasting Coastal Convective System by Means of a Case Study (26 July 2011)

Studies on the Predictability of Heavy Rainfall Using Prognostic Variables in Numerical Model

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