• 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 the Korean Geographical Society
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• v.29 no.3
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• pp.266-280
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• 1994

Upper-air synoptic data and surface weather elements such as temperature, relative humidity, wind speed, cloud and precipitation were analyzed in some detail to determine the characteristics of Nopsae, a foehn-like surface wind over the Youngsuh region of Central Korea. NOAA AVHRR and GMS images are also referenced to identify the distribution of clouds and precipitation to classify the tpyes of foehn over the study area. The data period examined is from 1982 until 1993 of spring and summer months from March through August. Results of the anaylsis are as follows. Warm and dry air penetration over the Younesuh region has experienced on foehn days occured between March 21 and August 10 during study perion. The mean annual number of foehn the days were 28. Foehn phenomena were prominent during March 21-25, April 5-15, May 25-June 10, and June 26-30 pentads. The intensity of the phenomena can be evaluated as the difference of daily maximum temperature and relative humidity between windward sites and leeward sites. The intensity of daily maximum temperature reached 14.5$^{\circ}C$, but most values were in the range of 5.0-7.5$^{\circ}C$ (61%). Although strong intensity of foehns usually develop in June, it is common that farmers in the region experince more aridity during the foehnday of April and May due to the transplantation of rice seedlings. Long-run foehn are not common phenomena and 55% of foehn terminate in one day, but there is a record that Nopsae persisted up to 9 days continuously. The author identified using the cloud and precipitation data out of NOAA-11, AVHRR and GMS images is that one of them has no precipitation over windward side. The available data and the results of the analysis are somewhat inadequate. Since the results imply that wave phenomenon is potentially important in terms of local surface weather and vertical momentum transport, more detailed theoretical and observational studies are necessary to clarify the mechanism and the impacts of Nopsae.

• Journal of the Korean Society for Marine Environment & Energy
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• v.10 no.2
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• pp.67-85
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• 2007

A three-dimensional hydrodynamic model with the fine grid is applied to simulate the barotropic tides, tidal currents, residual currents and salinity dispersions in the Yellow Sea and the East China Sea. Data inputs include seasonal hydrography, mean wind and river input, and oceanic tides. Computed tidal distributions of four major tides($M_2,\;S_2,\;K_1$ and $O_1$) are presented and results are in good agreement with the observations in the domain. The model reproduces well the tidal charts. The tidal residual current is relatively strong around west coast of Korea including the Cheju Island and southern coast of China. The current by $M_2$ has a maximum speed of 10 cm/s in the vicinity of Cheju Island with a anti-clockwise circulation in the Yellow Sea. General tendency of the current, however, is to flow eastward in the South Sea. Surface residual current simulated with $M_2$ and with $M_2+S_2+K_1+O_1$ tidal forcing shows slightly different patterns in the East China Sea. The model shows that the southerly wind reduces the southward current created by freshwater discharge. In summer during high runoff(mean discharge about $50,000\;m^3/s$ of Yangtze), low salinity plume-like structure(with S < 30.0 psu) extending some 160 km toward the northeast and Changjiang Diluted Water(CDW), below salinity 26 psu, was found within about 95 km. The offshore dispersion of the Changjiang outflow water is enhanced by the prevailing southerly wind. It is estimated that the inertia of the river discharge cannot exclusively reach the around sea of Cheju Island. It is noted that spatial and temporal distribution of salinity and the other materials are controlled by mixture of Changjiang discharge, prevailing wind, advection by flowing warm current and tidal current.

• Journal of Korea Water Resources Association
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• v.51 no.10
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• pp.839-852
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• 2018

The purpose of this study is to predict monthly agricultural reservoir storage by developing weather data-based Multiple Linear Regression Model (MLRM) with precipitation, maximum temperature, minimum temperature, average temperature, and average wind speed. Using Naïve-Bayes classification, total 1,559 nationwide reservoirs were classified into 30 clusters based on geomorphological specification (effective storage volume, irrigation area, watershed area, latitude, longitude and frequency of drought). For each cluster, the monthly MLRM was derived using 13 years (2002~2014) meteorological data by KMA (Korea Meteorological Administration) and reservoir storage rate data by KRC (Korea Rural Community). The MLRM for reservoir storage rate showed the determination coefficient ($R^2$) of 0.76, Nash-Sutcliffe efficiency (NSE) of 0.73, and root mean square error (RMSE) of 8.33% respectively. The MLRM was evaluated for 2 years (2015~2016) using 3 months weather forecast data of GloSea5 (GS5) by KMA. The Reservoir Drought Index (RDI) that was represented by present and normal year reservoir storage rate showed that the ROC (Receiver Operating Characteristics) average hit rate was 0.80 using observed data and 0.73 using GS5 data in the MLRM. Using the results of this study, future reservoir storage rates can be predicted and used as decision-making data on stable future agricultural water supply.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.121-132
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• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.

• Journal of Korea Water Resources Association
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• v.50 no.1
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• pp.37-53
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• 2017

Pan evaporation has been used as an indirect method for the estimation of reservoir evaporation. Therefore, in this study, pan evaporation estimation equations using only temperature data were suggested in the case that available meteorological data is limited. A formula for estimating the pan evaporation were suggested by comparing estimated pan evaporation with measured pan evaporation in 12 study areas in Korea. The suggested pan evaporation equations were verified in 44 study areas by comparing not only with temperature-based equations but also with equations using other meteorological data (temperature, wind speed, relative humidity, and sunshine duration). The study results indicate that the suggested equations in this study provide much better pan evaporation estimates, compared with other temperature-based equations. Overall, the suggested equations provide appropriate pan evaporation estimates in most of 56 study areas. Therefore, the suggested equations using only temperature data in this study are considered appropriate for the estimation of pan evaporation in Korea especially in the case that available meteorological data is limited. In the future, using the air temperature and pan evaporation data measured at the reservoir, further research is needed to examine the applicability of suggested equations for the estimation of reservoir evaporation.

• Korean Journal of Agricultural and Forest Meteorology
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• v.18 no.4
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• pp.199-207
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• 2016

This study was conducted to develop a forest fire occurrence model using meteorological characteristics for practical forecasting of forest fire danger rate by reflecting the climate change for the time period of 2000yrs. Forest fire in South Korea is highly influenced by humidity, wind speed, temperature, and precipitation. To effectively forecast forest fire occurrence, we developed a forest fire danger rating model using weather factors associated with forest fire in 2000yrs. Forest fire occurrence patterns were investigated statistically to develop a forest fire danger rating index using times series weather data sets collected from 76 meteorological observation centers. The data sets were used for 11 years from 2000 to 2010. Development of the national forest fire occurrence probability model used a logistic regression analysis with forest fire occurrence data and meteorological variables. Nine probability models for individual nine provinces including Jeju Island have been developed. The results of the statistical analysis show that the logistic models (p<0.05) strongly depends on the effective and relative humidity, temperature, wind speed, and rainfall. The results of verification showed that the probability of randomly selected fires ranges from 0.687 to 0.981, which represent a relatively high accuracy of the developed model. These findings may be beneficial to the policy makers in South Korea for the prevention of forest fires.

• Journal of Bio-Environment Control
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• v.26 no.3
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• pp.181-187
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• 2017

The purpose of this study was to provide basic data for development of environmental design technology for greenhouses constructed in reclaimed lands. The climatic conditions around seven major reclaimed land areas with a plan to install advanced horticultural complexes in Korea were analyzed. The characteristics of natural ventilation and temperature rise through the thermal environment measurement of the greenhouse in Saemangeum were analyzed. The part to be applied to the environmental design of the greenhouses in reclaimed lands were reviewed. Results of comparing the ventilation rate of the greenhouse according to the presence or absence of plants showed the greenhouse with plants had the lower ventilation rate, but the smaller rise of indoor temperature due to the evapotranspiration of plants. In the greenhouse with plants, the number of air changes was in the range of 0.3 to 0.9 volumes/min and the average was 0.7 volumes/min. The rise of indoor temperature relative to outdoor temperature was in the range of 1 to $5^{\circ}C$ and the average $2.5^{\circ}C$. The natural ventilation performance of the experimental greenhouse constructed in the reclaimed land almost satisfied the recommended ventilation rate in summer and the rise of indoor temperature relative to outdoor temperature did not deviate considerably from the cultivation environment of plants. Therefore, it was determined that the greenhouse cultivation in Saemangeum reclaimed land is possible with only natural ventilation systems without cooling facilities. As the reclaimed land is located in the seaside, the wind is stronger than the inland area, and the fog is frequent. This strong wind speed increases the ventilation rate of greenhouses, which is considered to be a factor for reducing the cooling load. In addition, since the fog duration is remarkably longer than that of inland area, the seasonal cooling load is expected to decrease, which is considered to be advantageous in terms of the operation cost of cooling facilities.

• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.425-439
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• 2020

Through the operations of advanced geostationary meteorological satellite such as Himawari-8 and GK2A, higher resolution and frequency of AOD (Aerosol Optical Depth) data have become available. In this study, we analyzed the characteristics of Himawari-8/AHI (Advanced Himawari Imager) aerosol properties using the recent 4 years (2016~2019) of Sun photometer data observed at the five stations(Seoul National University, Yonsei University, Hankuk University of Foreign Studies, Gwangju Institute of Science and Technology, Anmyon island) which is a part of the AERONET (Aerosol Robotic Network). In addition, we analyzed the causes for the AOD differences between Himawari AOD and Sun photometer AOD. The results showed that the two AOD data are very similar regardless of geographic location, in particular, for the clear condition (cloud amount < 3). However, the quality of Himawari AOD data is heavily degraded compared to that of the clear condition, in terms of bias (0.05 : 0.21), correlation (0.74 : 0.64) and RMSE (Root Mean Square Error; 0.21 : 0.51), when cloud amount is increased. In general, the large differences between two AOD data are mainly related to the cloud amount and relative humidity. The Himawari strongly overestimates the AOD at all five stations when cloud amount and relative humidity are large. However, the wind speed, precipitable water, height of cloud base and Angstrom Exponent have been shown to have no effect on the AOD differences irrespective of geographic location and cloud amount. The results suggest that caution is required when using Himawari AOD data in cloudy conditions.

• Journal of Korea Water Resources Association
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• v.51 no.12
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• pp.1217-1227
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• 2018

This paper aims to evaluate the applicability of dam inflow prediction model using recurrent neural network theory. To achieve this goal, the Artificial Neural Network (ANN) model and the Elman Recurrent Neural Network(RNN) model were applied to hydro-meteorological data sets for the Soyanggang dam and the Chungju dam basin during dam operation period. For the model training, inflow, rainfall, temperature, sunshine duration, wind speed were used as input data and daily inflow of dam for 10 days were used for output data. The verification was carried out through dam inflow prediction between July, 2016 and June, 2018. The results showed that there was no significant difference in prediction performance between ANN model and the Elman RNN model in the Soyanggang dam basin but the prediction results of the Elman RNN model are comparatively superior to those of the ANN model in the Chungju dam basin. Consequently, the Elman RNN prediction performance is expected to be similar to or better than the ANN model. The prediction performance of Elman RNN was notable during the low dam inflow period. The performance of the multiple hidden layer structure of Elman RNN looks more effective in prediction than that of a single hidden layer structure.