• Atmosphere
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• v.28 no.3
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• pp.273-289
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• 2018

This paper evaluates daily precipitation products from Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG), Tropical Rainfall Measuring Mission Multisatellite (TRMM) Precipitation Analysis (TMPA), and the Climate Prediction Center Morphing Method (CMORPH), validated against gauge observation over South Korea and gauge-based analysis data East Asia during one year from June 2014 to May 2015. It is found that the three products effectively capture the seasonal variation of mean precipitation with relatively good correlation from spring to fall. Among them, IMERG and TMPA show quite similar precipitation characteristics but overall underestimation is found from all precipitation products during winter compared with observation. IMERG shows reliably high performance in precipitation for all seasons, showing the most unbiased and accurate precipitation estimation. However, it is also noticed that IMERG reveals overestimated precipitation for heavier precipitation thresholds. This assessment work suggests the validity of the IMERG product for not only seasonal precipitation but also daily precipitation, which has the potential to be used as reference precipitation data.

• Atmosphere
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• v.26 no.1
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• pp.35-45
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• 2016

Initialization of the global seasonal forecast system is as much important as the quality of the embedded climate model for the climate prediction in sub-seasonal time scale. Recent studies have emphasized the important role of soil moisture initialization, suggesting a significant increase in the prediction skill particularly in the mid-latitude land area where the influence of sea surface temperature in the tropics is less crucial and the potential predictability is supplemented by land-atmosphere interaction. This study developed a new soil moisture initialization method applicable to the KMA operational seasonal forecasting system. The method includes first the long-term integration of the offline land surface model driven by observed atmospheric forcing and precipitation. This soil moisture reanalysis is given for the initial state in the ensemble seasonal forecasts through a simple anomaly initialization technique to avoid the simulation drift caused by the systematic model bias. To evaluate the impact of the soil moisture initialization, two sets of long-term, 10-member ensemble experiment runs have been conducted for 1996~2009. As a result, the soil moisture initialization improves the prediction skill of surface air temperature significantly at the zero to one month forecast lead (up to ~60 days forecast lead), although the skill increase in precipitation is less significant. This study suggests that improvements of the prediction in the sub-seasonal timescale require the improvement in the quality of initial data as well as the adequate treatment of the model systematic bias.

• Atmosphere
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• v.15 no.1
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• pp.1-16
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• 2005

Land-surface and soil processes significantly affect mesoscale local weather systems as well as global/regional climate. In this study, characteristics of land-surface models (LSMs) and soil models (SMs) that are frequently coupled into mesoscale meteorological models are investigated. In addition, detailed analyses on three LSMs, employed by the PSU/NCAR MM5, are provided. Some impacts of LSMs on heavy rainfall prediction are also discussed.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.5
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• pp.1-11
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• 2023

Due to climate change and aging of reservoirs, damage to embankment slopes is increasing. However, the safety diagnosis of the reservoir slope is mainly conducted by visual observation, and the time and economic cost are formidable to apply soil mechanical tests and slope stability analysis. Accordingly, this study presented a predicting method for the compaction and strength characteristics of the reservoir embankment soil using a portable static cone penetration test. The predicted items consisted of dry density, cohesion, and internal friction angle, which are the main factors of slope stability analysis. Portable static cone penetration tests were performed at 19 reservoir sites, and prediction equations were constructed from the correlation between penetration resistance data and test results of soil samples. The predicted dry density and strength parameters showed a correlation with test results between R² 0.40 and 0.93, and it was found to replace the test results well when used as input data for slope stability analysis (R² 0.8134 or more, RMSE 0.0320 or less). In addition, the prediction equations for the minimum safety factor of the slope were presented using the penetration resistance and gradient. As a result of comparing the predicted safety factor with the analysis results, R² 0.5125, RMSE 0.0382 in coarse-grained soil, R² 0.4182 and RMSE 0.0628 in fine-grained soil. The results of this study can be used as a way to improve the existing slope safety diagnosis method, and are expected to be used to predict the characteristics of various soils and inspect slopes.

• Journal of the Korean Geographical Society
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• v.44 no.1
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• pp.1-16
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• 2009

This study aims to classify climate zones using Empirical Orthogonal Function and clustering analyses considering both air temperature and rainfall features in South Korea. When examining climatic characteristics of air temperature and rainfall by seasons, the distribution of air temperature is affected by topography and latitude for all seasons in South Korea. The distribution of rainfall demonstrated that the Yeongdong area, the southern coastal area and Jeju island have higher rainfall while the central area in Gyeongsangbuk-do is the least rainfall area. Clustering analyses of average linkage method and Ward's method was carried out using input variables derived from principal component scores calculated through Empirical Orthogonal Function analysis for air temperature and rainfall. Ward's method showed the best result of classification of climate zones. It was well reflected effects of topography, latitude, sea, the movement of surface pressure systems, and an administrative district.

• Atmosphere
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• v.28 no.1
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• pp.15-24
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• 2018

Sea ice plays an important role in modulating surface conditions at high and mid-latitudes. It reacts rapidly to climate change, therefore, it is a good indicator for capturing these changes from the Arctic climate. While many models have been used to study the predictability of climate variables, their performance in predicting sea ice was not well assessed. This study examines the predictability of the Arctic sea ice extent from ensemble prediction systems. The analysis is focused on verification of predictability in each model compared to the observation and prediction in particular, on lead time in Sub-seasonal to Seasonal (S2S) scales. The S2S database now provides quasi-real time ensemble forecasts and hindcasts up to about 60 days from 11 centers: BoM, CMA, ECCC, ECMWF, HMCR, ISAC-CNR, JMA, KMA, Meteo France, NCEP and UKMO. For multi model comparison, only models coupled with sea ice model were selected. Predictability is quantified by the climatology, bias, trends and correlation skill score computed from hindcasts over the period 1999 to 2009. Most of models are able to reproduce characteristics of the sea ice, but they have bias with seasonal dependence and lead time. All models show decreasing sea ice extent trends with a maximum magnitude in warm season. The Arctic sea ice extent can be skillfully predicted up 6 weeks ahead in S2S scales. But trend-independent skill is small and statistically significant for lead time over 6 weeks only in summer.

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.425-433
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• 2009

Spatial models suitable for describing the evolving random fields in climate and environmental systems have been developed by many researchers. In general, rainfall in South Korea is highly variable in intensity and amount across space. This study characterizes the monthly and regional variation of rainfall fields using the spatial modeling. The main objective of this research is spatial prediction with the Bayesian hierarchical modeling (kriging) in order to further our understanding of water resources over space. We use the Bayesian approach in order to estimate the parameters and produce more reliable prediction. The Bayesian kriging also provides a promising solution for analyzing and predicting rainfall data.

• Journal of the Society of Disaster Information
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• v.15 no.2
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• pp.223-238
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• 2019

Purpose: This study aims at exploring implications of discourse and social practice produced by various stakeholders in politics, economy and society to provide useful material for effective disaster response in South Korea. Method: Applying the Critical Discourse Analysis model of Fairclough, this study analyzes the newspaper articles of three domestic press companies mainly about the November 2017 Pohang earthquake. Results: As a result, first, the three media companies point out the low effectiveness of disaster response manuals and evacuation training. Second, strengthening shelter services and expanding support for the victims are important for recovery from the earthquake. Third, to prevent the future damages, they suggest the implementation efforts to improve the seismic design and short message service based disaster alert system. Conclusion: Based on the findings, this study suggests to improve the practicality and effectiveness of disaster prevention measures, establish an organic and integrated disaster response system, emphasize the roles and participation of citizens, check the responsibility of experts, and make the media to form sound discourse on disaster response.

• Journal of Korean Society of societal Security
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• v.2 no.3
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• pp.61-66
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• 2009

Previous disaster management system in Korea was focusing on post-disaster, recovery phase. However, modern disaster management system should be focusing on the mitigation activities to minimize human and property damages based on the proper prediction and forecast of various natural disasters and timely dissemination of disaster information to the people. Recently, more frequent and larger scaled disasters have been reported around the globe, mainly due to the environment change not only by physical anomalies such as climate change but also by socio-economic transformations such as urbanization and industrialization. This study describes current practices in disaster management for climate change adaptation of advanced countries and a possible disaster management standard system that can be applied in Korea, based on the case studies on the European countries who are improving their disaster management systems against climate change.

• Atmosphere
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• v.26 no.2
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• pp.277-288
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• 2016

Polar lows are intense mesoscale cyclones that mainly occur over the sea in polar regions. Owing to their small spatial scale of a diameter less than 1000 km, simulating polar lows is a challenging task. At King Sejong station in West Antartica, polar lows are often observed. Despite the recent significant climatic changes observed over West Antarctica, adequate validation of regional simulations of extreme weather events such as polar lows are rare for this region. To address this gap, simulation results from a recent version of the Polar Weather Research and Forecasting model (Polar WRF) covering Antartic Peninsula at a high horizontal resolution of 3 km are validated against near-surface meteorological observations. We selected a case of high wind speed event on 7 January 2013 recorded at Automatic Meteorological Observation Station (AMOS) in King Sejong station, Antarctica. It is revealed by in situ observations, numerical weather prediction, and reanalysis fields that the synoptic and mesoscale environment of the strong wind event was due to the passage of a strong mesoscale polar low of center pressure 950 hPa. Verifying model results from 3 km grid resolution simulation against AMOS observation showed that high skill in simulating wind speed and surface pressure with a bias of $-1.1m\;s^{-1}$ and -1.2 hPa, respectively. Our evaluation suggests that the Polar WRF can be used as a useful dynamic downscaling tool for the simulation of Antartic weather systems and the near-surface meteorological instruments installed in King Sejong station can provide invaluable data for polar low studies over West Antartica.