• Atmosphere
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• v.25 no.1
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• pp.149-154
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• 2015

As an attempt to improve fog predictability at Incheon International Airport (IIA) we couple the 3D weather forecasting model currently operational in Korea Meteorological Administration (regional Unified Model, UM_RE) with a 1D turbulence model (PAFOG). The coupling is done by extracting the meteorological data from the 3D model and properly inserting them in the PAFOG model as initial conditions and external forcing. The initial conditions include surface temperature, 2 m temperature and dew point temperature, geostrophic wind at 850 hPa and vertical profiles of temperature and dew point temperature. Moisture and temperature advections are included as external forcing and updated every hr. To validate the performance of the coupled system, simulation results of the coupled system are compared to those of the 3D model alone for the 22 sea fog cases observed over the Yellow Sea. Three statistical indices, i.e., Root Mean Square Error (RMSE), linear correlation coefficient (R) and Critical Success Index (CSI), are examined, and they all indicate that the coupled system performs better than the 3D model alone. These are certainly promising results but more improvement is required before the coupled system can actually be used as an operational fog forecasting model. For the RMSE, R, and CSI values for the coupled system are still not good enough for operational fog forecast.

• Journal of Korean Society for Atmospheric Environment
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• v.20 no.E1
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• pp.1-14
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• 2004

Meteorological mechanisms in association with long-range transport of Asian dust in April 2001 have been investigated using weather maps, satellite images, TOMS and surface $PM_{10}$ data, backward trajectories, plus modeling output results (geopotential heights, horizontal wind vectors, potential temperatures, and streamlines). The results indicated that long -range transport of Asian dust to the west coast of North America was associated with strong westerlies between the Aleutian low and the Pacific high acting as a conveyor belt. Accelerating westerly flows due to cyclogenesis at the source regions over East Asia transported pollution from the continent to the central Pacific. When the system reached the Aleutian Islands, the intensity of troughs and the westerlies were amplified in the North Pacific. Thereafter the winds between the Aleutian Islands and the Pacific Ocean were more intensified from the air flow transport of the conveyor belt. Consequently, the strong wind in the conveyor belt enhanced the dust transport from the Pacific Ocean to the west coast of North America. This was evidenced by $PM_{10}$ concentration (maximum of about $100{\mu}g\;m^{-3}$) observed In California. Further evidence of the dust transport was found through the observation of satellite images, the distribution of TOMS aerosol index, and the analyses of streamlines and backward trajectories.

• Journal of the Korean earth science society
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• v.31 no.5
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• pp.454-464
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• 2010

A probability prediction model for tropical cyclone (TC) genesis in the Northwestern Pacific area was developed using the logistic regression method. Total five predictors were used in this model: the lower-level relative vorticity, vertical wind shear, mid-level relative humidity, upper-level equivalent potential temperature, and sea surface temperature (SST). The values for four predictors except for SST were obtained from difference of spatial-averaged value between May and January, and the time average of Ni$\tilde{n}$o-3.4 index from February to April was used to see the SST effect. As a result of prediction for the TC genesis frequency from June to December during 1951 to 2007, the model was capable of predicting that 21 (22) years had higher (lower) frequency than the normal year. The analysis of real data indicated that the number of year with the higher (lower) frequency of TC genesis was 28 (29). The overall predictability was about 75%, and the model reliability was also verified statistically through the cross validation analysis method.

• Atmosphere
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• v.24 no.2
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• pp.235-243
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• 2014

Sources of aviation turbulence vary through the seasons, especially in the East Asia including Korean peninsula, associated primarily with the changes in the jet/front system and convective activities. For this reason, a seasonal Korean aviation Turbulence Guidance (KTG) system (seasonal-KTG) is developed in the present study by using pilot reports (PIREPs) and analysis data of the operational Unified Model (UM) of the Korea Meteorological Administration (KMA) for two years between June 2011 and May 2013. Twenty best diagnostics of aviation turbulence in each season are selected by the method of probability of detection (POD) using the PIREPs and UM data. After calculating a weighting value of each selected diagnostics using their area under curve (AUC), the 20 best diagnostics are combined with the weighting scores into a single ensemble-averaged index by season. Compared with the current operational-KTG system that is based on the diagnostics applying all seasons, the performances of the seasonal-KTG system are better in all seasons, except in fall.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.527-544
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• 2018

This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the $3.7{\mu}m$ and $10.8{\mu}m$ channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation-maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.

• Spatial Information Research
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• v.17 no.1
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• pp.117-129
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• 2009

South Korea has been undergoing spring drought periodically and diverse researches using vegetation index have been carried out to monitor spring droughts. The strength of the vegetation index-based drought monitoring is that the monitoring method enables efficient spatio-temporal grasp of changes in drought events. According to the development of low resolution satellite images such as MODIS, which are characterized by outstanding temporal resolution, the use of the method is expected to increase. Drought analysis using vegetation index considered only meteorological factor as a cause that affects vitality of vegetation. But many indirect and direct factors affect vegetation stress, So many uncertainties are involved in such method of analysis. To secure objectivity of drought analysis that uses vegetation index it is therefore necessary to compare the method with most representative drought analysis tools that are used for drought management. In this study, PDSI and SPI which a meteorological drought index that quantifies drought and that is used as a basic index for drought monitoring and MODIS NDVI are compared to propose correlation among them and to show usefulness of drought assessment that uses vegetation index. This study shows changing patterns of NDVI and SPI 6-month are similar and correlation between NDVI and SPI was highest in inland vegetation cover.

• Journal of Korea Water Resources Association
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• v.35 no.4 s.129
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• pp.397-410
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• 2002

In order to quantitatively identify historical drought conditions and to evaluate their variability, drought indices commonly used. The calculation method for the drought index based on the principal hydrological factors, such as precipitation and reservoir storage, can estimate the duration and intensity of a drought. In this study the Palmer-type formula for drought index is derived for the Nakdong River basin by analyzing the monthly rainfall and meteorological data at 21 stations. The Palmer Drought Severity Index(PDSI) is used for dry land sectors to evaluate the meteorological anomaly in terms of an index which permits time and space comparisons of drought severity. The Surface Water Supply Index(SWSI) is devised for the use in conjunction with the Palmer index to provide an objective indicator of water supply conditions in Nakdong River basin. The SWSI was designed to quantify surface water supply capability of a watershed which depends on river and reservoir water The Standardized Precipitation Index(SPI) is evaluated for various time periods of 1 to 12 months in Nakdong River basin. For the purpose of comparison between drought indices correlation coefficient was calculated between indices and appropriate SPI time period was selected as 10 months for Nakdong River basin. A comparative study is made to evaluate the relative severity of the significant droughts occurred in Nakdong River basin since 1976. It turned out that $'94{\sim}'97$ drought was the worst drought in it's severity. It is found that drought indices are very useful tools in quantitatively evaluating the severity of a drought over a river basin.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.3
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• pp.309-319
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• 2023

Recently, the frequency and intensity of meteorological disasters have increased due to climate change. In South Korea, there are regional differences in vulnerability and response capability to cope with climate change because of regional climate characteristics. In particular, drought results from various factors and is linked to extensive meteorological, hydrological, and agricultural impacts. Therefore, in order to effectively cope with drought, it is necessary to use a composite drought index that can take into account various factors, and to evaluate future droughts comprehensively considering climate change. This study evaluated hydrologic risk(${\bar{R}}$) of future drought in the Nakdong River basin based on the Dynamic Naive Bayesian Classification (DNBC)-based composite drought index, which was calculated by applying Standardized Precipitation Index (SPI), Streamflow Drought Index (SDI), Evaporate Stress Index (ESI) and Water Supply Capacity Index (WSCI) to the DNBC. The indices used in the DNBC were calculated using observation data and climate scenario data. A bivariate frequency analysis was performed for the severity and duration of the composite drought. Then using the estimated bivariate return periods, hydrologic risks of drought were calculated for observation and future periods. The overall results indicated that there were the highest risks during the future period (2021-2040) (${\bar{R}}$=0.572), and Miryang River (#2021) had the highest risk (${\bar{R}}$=0.940) on average. The hydrologic risk of the Nakdong River basin will increase highly in the near future (2021-2040). During the far future (2041-2099), the hydrologic risk decreased in the northern basins, and increased in the southern basins.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.7-9
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• 2004

The purpose of this study was to analyze the climatic water balance of the Korean peninsula using meteorological data and the evapotranspiration (ET) derived from NOAA/AVHRR. Quantifying water balance components is important to understand the basic hydrology. In this study, a simple method to estimate the ET was proposed based on a regression approach between NDVI and Morton's actual ET using NOAA/AVHRR data. The Morton's actual ET for land surface conditions was evaluated using a daily meteorological data from 77 weather stations, and the monthly averaged Morton's ETs for each land cover was compared with the monthly NDVIs during the year 2001. According to the climatic water balance analysis, water deficit and surplus distributed maps were created from spatial rainfall, soil moisture, and actual and potential ETs map. The results clearly showed that the temporal and spatial characteristics of dryness and wetness may be detected and mapped based on the wetness index.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.124-133
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• 2004

Global climate variations are expected to affect local hydro-meteorological variables like precipitation and temperature. The Southern Oscillation (SO) is one of the major driving forces that give impact on regional and local climatic variation. The relationships between SO and local climate variation are, however, characterized by strong nonlinear variation patterns. In this paper, the nonlinear dynamic relationship between the Southern Oscillation Index (SOI), precipitation, and temperature in Fukuoka, Japan, is investigated using by a nonlinear multivariable approach. This approach is based on the joint variation of these variables in the phase space. The joint phase-space variation of SOI, precipitation, and temperature is studied with the primary objective to obtain a better understanding of the dynamical evolution of local hydro-meteorological variables affected by global atmospheric-oceanic phenomena.