• Atmosphere
• /
• v.26 no.1
• /
• pp.97-109
• /
• 2016

Heavy rainfall (over $80mm\;hr^{-1}$) system associated with unstable atmospheric conditions occurred over the Seoul metropolitan area on 27 July 2011. To investigate the heavy rainfall system, we used three-dimensional data from Korea Local Analysis and Prediction System (KLAPS) reanalysis data and analysed the structure of the precipitation system, kinematic characteristics, thermodynamic properties, and Meteorological condition. The existence of Upper-Level Jet (ULJ) and Low-Level Jet (LLJ) are accelerated the heavy rainfall. Convective cloud developed when a strong southwesterly LLJ and strong moisture convergence occurring around the time of the heavy rainfall is consistent with the results of previous studies on such continuous production. Environmental conditions included high equivalent potential temperature of over 355 K at low levels, and low equivalent potential temperature of under 330 K at middle levels, causing vertical instability. The tip of the band shaped precipitation system was made up of line-shaped convective systems (LSCSs) that caused flooding and landslides, and the LSCSs were continuously enhanced by merging between new cells and the pre-existing cell. Difference of wind direction between low and middle levels has also been considered an important factor favouring the occurrence of precipitation systems similar to LSCSs. Development of LSCs from the wind direction difference at heights of the severe precipitation occurrence area was also identified. This study can contribute to the identification of production and development mechanisms of heavy rainfall and can be used in applied research for prediction of severe weather.

• Journal of the Korean earth science society
• /
• v.44 no.1
• /
• pp.1-12
• /
• 2023

Oceanic wind waves have been recognized as one of the important indicators of global warming and climate change. It is necessary to study the spatial and temporal variability of significant wave height (SWH) and wave direction in the Yellow Sea and a part of the East China Sea, which is directly affected by the East Asian monsoon and climate change. In this study, the spatial and temporal variability including seasonal and interannual variability of SWH and wave direction in the Yellow Sea and East China Sea were analyzed using European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5) data. Prior to analyzing the variability of SWH and wave direction using the model reanalysis, the accuracy was verified through comparison with SWH and wave direction measurements from Ieodo Ocean Science Station (I-ORS). The mean SWH ranged from 0.3 to 1.6 m, and was higher in the south than in the north and higher in the center of the Yellow Sea than in the coast. The standard deviation of the SWH also showed a pattern similar to the mean. In the Yellow Sea, SWH and wave direction showed clear seasonal variability. SWH was generally highest in winter and lowest in late spring or early summer. Due to the influence of the monsoon, the wave direction propagated mainly to the south in winter and to the north in summer. The seasonal variability of SWH showed predominant interannual variability with strong variability of annual amplitudes due to the influence of typhoons in summer.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.23 no.4
• /
• pp.391-404
• /
• 2021

This study developed a long-term prediction model for the potential yield of garlic based on a genetic algorithm (GA) by utilizing global climate reanalysis data. The GA is used for digging the inherent signals from global climate reanalysis data which are both directly and indirectly connected with the garlic yield potential. Our results indicate that both deterministic and probabilistic forecasts reasonably capture the inter-annual variability of crop yields with temporal correlation coefficients significant at 99% confidence level and superior categorical forecast skill with a hit rate of 93.3% for 2 × 2 and 73.3% for 3 × 3 contingency tables. Furthermore, the GA method, which considers linear and non-linear relationships between predictors and predictands, shows superiority of forecast skill in terms of both stability and skill scores compared with linear method. Since our result can predict the potential yield before the start of farming, it is expected to help establish a long-term plan to stabilize the demand and price of agricultural products and prepare countermeasures for possible problems in advance.

• Journal of the Korean earth science society
• /
• v.45 no.1
• /
• pp.19-36
• /
• 2024

The global seasonal forecasting system version 6 (GloSea6) operated by the Korea Meteorological Administration for 1- and 3-month prediction products does not include complex atmospheric chemistry-aerosol physical processes (UKCA). In this study, low-resolution GloSea6 and GloSea6 coupled with UKCA (GloSea6-UKCA) were installed in a CentOS-based Linux cluster system, and preliminary prediction results for the spring of 2000 were examined. Low-resolution versions of GloSea6 and GloSea6-UKCA are highly needed to examine the effects of atmospheric chemistry-aerosol owing to the huge computational demand of the current high resolution GloSea6. The spatial distributions of the surface temperature and daily precipitation for April 2000 (obtained from the two model runs for the next 75 days, starting from March 1, 2000, 00Z) were compared with the ERA5 reanalysis data. The GloSea6-UKCA results were more similar to the ERA5 reanalysis data than the GloSea6 results. The surface air temperature and daily precipitation prediction results of GloSea6-UKCA for spring, particularly over East Asia, were improved by the inclusion of UKCA. Furthermore, compared with GloSea6, GloSea6-UKCA simulated improved temporal variations in the temperature and precipitation intensity during the model integration period that were more similar to the reanalysis data. This indicates that the coupling of atmospheric chemistry-aerosol processes in GloSea6 is crucial for improving the spring predictions over East Asia.

• The Korean Journal of Applied Statistics
• /
• v.24 no.3
• /
• pp.535-546
• /
• 2011

This study develops statistical models for the binary forecast of Asian dust days over South Korea in the winter season. For this study, we used three kinds of data; the rst one is the observed Asian dust days for a period of 31 years (1980 to 2010) as target values, the second one is four meteorological factors(near surface temperature, precipitation, snowfall, ground wind speed) in the source regions of Asian dust based on the NCEP reanalysis data and the third one is the large-scale climate indices. Four kinds of statistical models(multiple regression models, logistic regression models, decision trees, and support vector machines) are applied and compared based on skill scores(hit rate, probability of detection and false alarm rate).

• Ocean and Polar Research
• /
• v.27 no.1
• /
• pp.87-95
• /
• 2005

The vertical profiles of radiative flux and heating rate at King Sejong Station in West Antarctica were calculated with radiative transfe model by Chou and Suarez (1999) and Chou et al (2001). To run this model, the profiles of temperature, mixing ratios of water vapor and ozone at King Sejng Station were derived from ECMWF Reanalysis data. The surface temperature and albedo were also derived from NCEP/NCAR Reanalysis and CERES data. The radiative flux strongly depends on the cloud optical path length that was calculated using the measured W-h data and model by Chou and Lee(1996). Durins the period of $2000{\sim}2001$ (12 and 18 UTC), the correlation coefficient between calculated and measured downward solar fluxes at surface was 0.90 and the coefficient for downward longwave flux was 0.61. The calculated net heating rates of surface layer decreased during the same period, the trend of which was in accordance with the decrease of measured temperature.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.22 no.6
• /
• pp.950-956
• /
• 2013

This paper describes a sensitivity-coefficient-based iterative method for detecting cracks in a structure. The sensitivity coefficients of a cracked structure are obtained by changing its eigenvectors. The proposed method is applied to a cracked cantilever. The crack is modeled as a rotational stiffness. The predicted cracks are in good agreement with those from a structural reanalysis of the cracked structure.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.12 no.6
• /
• pp.50-55
• /
• 2003

This study proposed the analysis of damage detection due to the change of the stiffness of structure by using the original and modified dynamic characteristics. The present approach allows the use of composite data which consist of eigenvalues and eigenvectors. The suggested method is applied to examples of a cantilever and 3 degree of freedom system by modifying the stiffness. The predicted damage detections are in good agreement with these from the structural reanalysis using the modified stiffness.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
• /
• 2005.09a
• /
• pp.212-215
• /
• 2005

• Journal of Korea Water Resources Association
• /
• v.52 no.1
• /
• pp.11-19
• /
• 2019

In this study, the actual evapotranspiration products of Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model (GLEAM) and MOD16, which are satellite- and reanalysis-based dataset, were validated at the flux tower sites (i.e., CFK and SMK) managed by Korea Institute of Hydrological Survey, and the uncertainty and correlation analysis were conducted using Triple Collocation (TC) method. The result of validation with the flux tower showed better agreement in the order of GLEAM> GLDAS>MOD16. At the result of three combinations (S1: flux tower vs. GLDAS vs. MOD16, S2: flux tower vs. GLDAS vs. GLEAM, S3: flux tower vs. GLEAM vs. MOD16), the order of best to worst is GLEAM, GLDAS, MOD16, and flux tower for CFK (GLDAS> GLEAM>MOD16>flux tower for SMK). Since the error variance and correlation coefficients of the flux tower show relatively worse performance in TC analysis than the other products, By applying TC method to three products (GLDAS vs. GLEAM vs. MOD16), the uncertainty of each dataset were evaluated at the Korean Peninsula, As a results, the GLDAS and GLEAM performed reasonable performance (low error variance and high correlation coefficient), whereas results of MOD16 showed high error variance and low correlation coefficient at the cropland.