• Journal of Trauma and Injury
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• v.34 no.1
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• pp.21-30
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• 2021

Purpose: The purpose of this study was to determine the influence of seasons and weather on the volume of trauma patients in central Korea. Methods: The records of 4,665 patients treated at Chungbuk National Hospital Regional Trauma Center from January 2016 to December 2019 were retrospectively reviewed. Meteorological data including hourly temperature (℃), precipitation (mm), humidity (%), and wind speed (m/s) for each district were collected retrospectively. Statistical analysis was done using the independent t-test, one-way analysis of variance (ANOVA), and linear regression analysis. Results: Patients' average age was 53.66 years, with a significant difference between men (49.92 years) and women (60.48 years) (p<0.001). Rolling/slipping down was a prominent cause of injury in winter (28.4%, n=283), with statistical significance (p<0.001). Trauma occurred least frequently in winter (p=0.005). Linear regression analysis revealed an increasing number of patients as the temperature increased (p<0.05), the humidity increased (p<0.001), and the wind speed decreased (p<0.001). Precipitation did not affect patient volume (p=0.562). One-way ANOVA revealed a decreased incidence of trauma when the temperature exceeded 30℃ (p<0.001), and when the humidity was more than 75%, compared to 25-50% and 50-75%. Conclusions: At the regional trauma center of Chungbuk National University Hospital, in central Korea, the number of trauma patients was lowest in winter, and patient volume was affected by temperature, humidity, and wind speed.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.5
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• pp.819-826
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• 2020

The UHF band wind profiler radars of the Korea Meteorological Administration (KMA), which produces the vertical profile of the wind, need to be calibrated for better performance. The capabilities of the radar in detecting even light precipitation were used for the calibration of which reference takes the hourly series of ground rainfall rate measured by a rain gauge at the radar site. This calibration must be renewed regularly according to the methodology implemented in this work since errors occur on the wind vectors in the clear sky without reflectivity calibration. Comparing the wind by wind profiler with that by radiosonde, the optimal radar constant contributed to the improvement of wind accuracy.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.5
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• pp.515-528
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• 2003

The characteristics of one year observation aerosol data in Seoul, 200 I was studied using an OPC (Optical Particle Counter). The size resolved aerosol number concentrations of 0.3 ∼ 25 11m were measured. The results were compared with PM$_{10}$ mass concentration data under various meteorological conditions including dust and precipitation events. For fine particles whose diameter is less than 2.23 ${\mu}{\textrm}{m}$, the number concentration increases in the early morning which is considered due to transportation. while the coarse mode particles increase during daytime. This increase can be explained as local sources and human activities near sampling site. Hourly averaged data show that there exists diurnal variation. Generally, PM$_{10}$ data showed a similar tendency with OPC data. The size resolved OPC data showed that the particles of 0.5 ∼ 3.67 ${\mu}{\textrm}{m}$ are positively correlated with PM$_{10}$ data. The accumulated volume fraction of size resolved aerosol concentration in 0.5 ∼ 10 ${\mu}{\textrm}{m}$ showed that 0.5 ∼ 2.23 ${\mu}{\textrm}{m}$ particles occupied 59.2% of total aerosol volume of 0.5 ∼ 10 ${\mu}{\textrm}{m}$./TEX>.

• Journal of the korean society of oceanography
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• v.38 no.2
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• pp.60-67
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• 2003

Typhoon Rusa passed over the East China Sea and crossed over the Korea Peninsula on August 31, 2002. The core of the typhoon passed directly over a data buoy mooring site at ($127^{\circ}45'E,\;34^{\circ}25'\;N$) and several ARGOS-tracked drifters capable of measuring salinity. Peak hourly mean wind speed reached 28 m/s at the mooring site and wind pattern in the East China Sea changed from southerly wind to northwesterly wind after the typhoon passage. Two or three days before the typhoon tile drifter displacement changed significantly and the region-wide circulation pattern changed from a northeastward current to a westward current one week after the typhoon had passed. The surface water in the East China Sea was cooled to about $4^{\circ}C$ under the typhoon core and a general cooling occurred in most of the East China Sea with the exception of the Chinese coast. The salinity as observed by the drifters in the East China Sea increased about 2 psu but the near-shore water along the Korean coast observed by the mooring was freshened about 3 psu. The freshening of near-shore water was caused by an intrusion of off-shore water rather than local freshening by typhoon precipitation.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.537-537
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• 2015

In recent decades, the independence and identical distribution (iid) assumption for extreme events has been shown to be invalid in many cases because long-term climate variability resulting from phenomena such as the Pacific decadal variability and El Nino-Southern Oscillation may induce varying meteorological systems such as persistent wet years and dry years. Therefore, in the current study we propose a new parameter estimation method for probability distribution models to more accurately predict the magnitude of future extreme events when the iid assumption of probability distributions for large-scale climate variability is not adequate. The proposed parameter estimation is based on a metaheuristic approach and is derived from the objective function of the rth power probability-weighted sum of observations in increasing order. The combination of two distributions, gamma and generalized extreme value (GEV), was fitted to the GEV distribution in a simulation study. In addition, a case study examining the annual hourly maximum precipitation of all stations in South Korea was performed to evaluate the performance of the proposed approach. The results of the simulation study and case study indicate that the proposed metaheuristic parameter estimation method is an effective alternative for accurately selecting the rth power when the iid assumption of extreme hydrometeorological events is not valid for large-scale climate variability. The maximum likelihood estimate is more accurate with a low mixing probability, and the probability-weighted moment method is a moderately effective option.

• Journal of the Korean earth science society
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• v.35 no.4
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• pp.215-224
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• 2014

Data assimilation effect of mobile rawinsonde observation was evaluated using Unified Model (UM) with a Three-Dimensional Variational (3DVAR) data assimilation system during the intensive observation program of 2013 summer season (rainy season: 20 June-7 July 2013, heavy rain period: 8 July-30 July 2013). The analysis was performed by two sets of simulation experiments: (1) ConTroL experiment (CTL) with observation data provided by Korea Meteorological Administration (KMA) and (2) Observing System Experiment (OSE) including both KMA and mobile rawinsonde observation data. In the model verification during the rainy season, there were no distinctive differences for 500 hPa geopotential height, 850 hPa air temperature, and 300 hPa wind speed between CTL and OSE simulation due to data limitation (0000 and 1200 UTC only) at stationary rawinsonde stations. In contrast, precipitation verification using the hourly accumulated precipitation data of Automatic Synoptic Observation System (ASOS) showed that Equivalent Threat Score (ETS) of the OSE was improved by about 2% compared with that of the CTL. For cases having a positive effect of the OSE simulation, ETS of the OSE showed a significantly higher improvement (up to 41%) than that of the CTL. This estimation thus suggests that the use of mobile rawinsonde observation data using UM 3DVAR could be reasonable enough to assess the improvement of prediction accuracy.

• Journal of Environmental Science International
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• v.27 no.11
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• pp.1129-1140
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• 2018

Temporal and spatial characteristics of the frequency of several weather types and the change in air pollutant concentrations according to these weather types were analyzed over a decade (2007-2016) in seven major cities and a remote area in Korea. This analysis was performed using hourly (or daily) observed data of weather types (e.g., mist, haze, fog, precipitation, dust, and thunder and lighting) and air pollutant criteria ($PM_{10}$, $PM_{2.5}$, $O_3$, $NO_2$, CO, and $SO_2$). Overall, the most frequent weather type across all areas during the study period was found to be mist (39%), followed by precipitation (35%), haze (17%), and the other types (${\leq}4%$). In terms of regional frequency distributions, the highest frequency of haze (26%) was in Seoul (especially during winter and May-June), possibly due to the high population and air pollutant emission sources, while that of precipitation (47%) was in Jeju (summer and winter), due to its geographic location with the sea on four sides and a very high mountain. $PM_{10}$ concentrations for dust and haze were significantly higher in three cities (up to $250{\mu}g/m^3$ for dust in Incheon), whereas those for the other four types were relatively lower. The concentrations of $PM_{2.5}$ and its major precursor gases ($NO_2$ and $SO_2$) were higher (up to $69{\mu}g/m^3$, 48 ppb, and 16 ppb, respectively, for haze in Incheon) for haze and/or dust than for the other weather types. On the other hand, there were no distinct differences in the concentrations of $O_3$ and CO for the weather types. The overall results of this study confirm that the frequency of weather types and the related air quality depend on the geographic and environmental characteristics of the target areas.

• Journal of Korea Water Resources Association
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• v.53 no.2
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• pp.97-105
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• 2020

Recently, Artificial Neural Network receives attention as a data prediction method. Among these, a Long Shot-term Memory (LSTM) model specialized for time-series data prediction was utilized as a prediction method of hydrological time series data. In this study, the LSTM model was constructed utilizing deep running open source library TensorFlow which provided by Google, to predict inflows of multipurpose dams. We predicted the inflow of the Yongdam Multipurpose Dam which is located in the upper stream of the Geumgang. The hourly flow data of Yongdam Dam from 2006 to 2018 provided by WAMIS was used as the analysis data. Predictive analysis was performed under various of variable condition in order to compare and analyze the prediction accuracy according to four learning parameters of the LSTM model. Root mean square error (RMSE), Mean absolute error (MAE) and Volume error (VE) were calculated and evaluated its accuracy through comparing the predicted and observed inflows. We found that all the models had lower accuracy at high inflow rate and hourly precipitation data (2006~2018) of Yongdam Dam utilized as additional input variables to solve this problem. When the data of rainfall and inflow were utilized together, it was found that the accuracy of the prediction for the high flow rate is improved.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.415-423
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• 2021

Recently, an R-based point time series data validation system has been established for the statistical post processing and improvement of the National Center for AgroMeteorology-Land Atmosphere Modeling Package (NCAM-LAMP) medium-range prediction data. The time series verification system was used to compare the NCAM-LAMP with the AWS observations and GDAPS medium-range prediction model data operated by Korea Meteorological Administration. For this comparison, the model latitude and longitude data closest to the observation station were extracted and a total of nine points were selected. For each point, the characteristics of the model prediction error were obtained by comparing the daily average of the previous prediction data of air temperature, wind speed, and hourly precipitation, and then we tried to improve the next prediction data using Support Vector Machine( SVM) method. For three months from August to October 2017, the SVM method was used to calibrate the predicted time series data for each run. It was found that The SVM-based correction was promising and encouraging for wind speed and precipitation variables than for temperature variable. The correction effect was small in August but considerably increased in September and October. These results indicate that the SVM method can contribute to mitigate the gradual degradation of medium-range predictability as the model boundary data flows into the model interior.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.21 no.1
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• pp.9-17
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• 2003

Typhoon RUSA, which caused serious damage was passed over in Korea peninsula during 30 August to 1 September, 2002. We estimated tropospheric wet delay using GPS data and meteorological data during this period. Integrated Water Vapor(IWV) gives the total amount of water vapor from tropospheric wet delay and Precipitable Water Vapor(PWV) is calculated the IWV scaled by the density of water. We obtained GPS PWV at 13th GPS permanent stations(Seoul, Wonju. Seosan, Sangju, Junju, Cheongju, Taegu, Wuljin, Jinju, Daejeon, Mokpo, Sokcho, Jeju). We retrieve GPS data hourly and use Gipsy-Oasis II software and we compare PWV and precipitation. GPS observed PWV time series demonstrate that PWV is, in general, high before and during the occurrence of the typhoon RUSA, and low after the typhoon RUSA. GPS PWV peak time at each station is related to the progress of a typhoon RUSA. We got very near result as we compare GMS Satellite image with tomograph using GPS PWV and we could present practical use possibility by numerical model for weather forecast.