• Atmosphere
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• v.20 no.2
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• pp.73-89
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• 2010

An updateable model output statistics (UMOS) system for daily maximum and minimum temperature ($T_M$ and $T_m$) over South Korea based on the Canadian UMOS system were developed and validated. RDAPS (regional data assimilation and prediction system) and KWRF (Korea WRF) which have quite different physics and dynamics were used for the development of UMOS system. The 20 most frequently selected potential predictors for each season, station, and forecast projection time from the 68 potential predictors of the MOS system, were used as potential predictors of the UMOS system. The UMOS equations were developed through the weighted blending of the new and old model data, with weights chosen to emphasize the new model data while including enough old model data to ensure stable equations and a smooth transition of dependency from the old model to the new model. The UMOS equations are being updated by every 7 days. The validation results of $T_M$ and $T_m$ showed that seasonal mean bias, RMSE, and correlation coefficients for the total forecast projection times are -0.41-0.17 K, 1.80-2.46 K, and 0.80-0.97, respectively. The performance is slightly better in autumn and winter than in spring and summer. Also the performance of UMOS system are clearly dependent on location, better at the coastal region than inland area. As in the MOS system, the performance of UMOS system is degraded as the forecast day increases.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.413-420
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• 2013

Stochastic weather generator is a commonly used tool to simulate daily weather time series. Recently, a generalized linear model(GLM) has been proposed as a convenient approach to tting these weather generators. In the present paper, a stochastic weather generator is considered to model the time series of daily temperatures for Seoul South Korea. As a covariate, precipitation occurrence is introduced to a relate short-term predictor to short-term predictands. One of the limitations of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of monthly, seasonal, or annual total precipitation. To reduce this phenomenon, we incorporate a time series of seasonal mean temperatures in the GLM weather generator as a covariate.

• Korean Journal of Remote Sensing
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• v.20 no.2
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• pp.65-76
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• 2004

This study aims to estimate daily maximum air temperature estimated using satellite-derived surface temperature and Elevation Derivative Database (EDD). The analysis is focused on the establishment of a semi-empirical estimation technique of daily maximum air temperature through the multiple regression analysis. This tests the contribution of EDD in the air temperature estimation when it is added into regression model as an independent variable. The better correlation is shown with the EDD data as compared with the correlation without this data set. In order to provide a progressive estimation technique, we propose and compare three approaches: 1) seasonal estimation non-considering landcover, 2) seasonal estimation considering landcover, and 3) estimation according to landcover type and non-considering season. The last method shows the best fit with the root-mean-square error between 0.56$^{\circ}C$ and 3.14$^{\circ}C$. A cross-validation procedure is performed for third method to valid the estimated values for two major landcover types (cropland and forest). For both landcover types, the validation results show reasonable agreement with estimation results. Therefore it is considered that the estimation technique proposed may be applicable to most parts of South Korea.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.197-214
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• 2019

In this study, the correlation analysis was conducted between observed air temperature (maximum, minimum, and mean air temperature) and the daytime and nighttime data of Terra/Aqua MODIS LST(Moderate Resolution Imaging Spectroradiometer Land Surface Temperature) for 86 weather stations. All the data of the recent 11 years from 2008 to 2018 were prepared with daily base. In particular, the characteristics of the cold and heat waves incidence period in 2018 were analyzed. The correlation analysis was performed using the Pearson correlation coefficient(R) and root mean square error(RMSE). As a result of time series analysis, the trend between observed air temperature and MODIS LST were similar, showing the correlation above 0.9 in maximum temperature, above 0.8 in mean and minimum temperature. Especially, the maximum temperature was found to have the highest accuracy with Terra MODIS LST daytime, and the minimum temperature had the highest correlation with Terra MODIS LST nighttime. During the cold wave period, both Terra and Aqua MODIS LST showed higher correlations with nighttime data than daytime data. For the heat wave period, the Aqua MODIS LST daytime data was good, but the overall R was below 0.5. Additional analysis is necessary for further study considering such as land cover and elevation characteristics.

• Journal of Ecology and Environment
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• v.33 no.1
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• pp.47-57
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• 2010

To understand day-to-day fluctuations in soil moisture content in Seoul, I simulated daily soil moisture content from 1908 to 2009 using long-term climatic precipitation and temperature data collected at the Surface Synoptic Meteorological Station in Seoul for the last 98 years with a hydrological simulation model, BROOK. The output data set from the BROOK model allowed me to examine day-to-day fluctuations and the severity and duration of droughts in the Seoul area. Although the soil moisture content is highly dependent on the occurrence of precipitation, the pattern of changes in daily soil moisture content was clearly quite different from that of precipitation. Generally, there were several phases in the dynamics of daily soil moisture content. The period from mid-May to late June can be categorized as the initial period of decreasing soil moisture content. With the initiation of the monsoon season in late June, soil moisture content sharply increases until mid-July. From the termination of the rainy season in mid-July, daily soil moisture content decreases again. Highly stochastic events of typhoons from late June to October bring large amount of rain to the Korean peninsula, culminating in late August, and increase the soil moisture content again from late August to early September. From early September until early October, another sharp decrease in soil moisture content was observed. The period from early October to mid-May of the next year can be categorized as a recharging period when soil moisture content shows an increasing trend. It is interesting to note that no statistically significant increase in mean annual soil moisture content in Seoul, Korea was observed over the last 98 years. By simulating daily soil moisture content, I was also able to reconstruct drought phenomena to understand the severity and duration of droughts in Seoul area. During the period from 1908 to 2009, droughts in the years 1913, 1979, 1939, and 2006 were categorized as 'severe' and those in 1988 and 1982 were categorized as 'extreme'. This information provides ecologists with further potential to interpret natural phenomenon, including tree growth and the decline of tree species in Korea.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.135-135
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• 2021

In water resources management, rainfall prediction with high accuracy is still one of controversial issues particularly in countries facing heavy rainfall during wet seasons in the monsoon climate. The aim of this study is to develop an artificial neural network (ANN) for predicting future six months of rainfall data (from April to September 2020) from daily meteorological data (from 1971 to 2019) such as rainfall, temperature, wind speed, and humidity at Seoul, Korea. After normalizing these data, they were trained by using a multilayer perceptron (MLP) as a class of the feedforward ANN with 15,000 neurons. The results show that the proposed method can analyze the relation between meteorological datasets properly and predict rainfall data for future six months in 2020, with an overall accuracy over almost 70% and a root mean square error of 0.0098. This study demonstrates the possibility and potential of MLP's applications to predict future daily rainfall patterns, essential for managing flood risks and protecting water resources.

• Korean Journal of Agricultural and Forest Meteorology
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• v.7 no.2
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• pp.148-155
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• 2005

An accurate prediction of blooming date is crucial for many authorities to schedule and organize successful spring flower festivals in Korea. The Korea Meteorological Administration (KMA) has been using regression models combined with a subjective correction by forecasters to issue blooming date forecasts for major cities. Using mean monthly temperature data for February (observed) and March (predicted), they issue blooming date forecasts in late February to early March each year. The method has been proved accurate enough for the purpose of scheduling spring festivals in the relevant cities, but cannot be used in areas where no official climate and phenology data are available. We suggest a thermal time-based two-step phenological model for predicting the blooming dates of spring flowers, which can be applied to any geographic location regardless of data availability. The model consists of two sequential periods: the rest period described by chilling requirement and the forcing period described by heating requirement. It requires daily maximum and minimum temperature as an input and calculates daily chill units until a pre-determined chilling requirement for rest release. After the projected rest release date, it accumulates daily heat units (growing degree days) until a pre- determined heating requirement for flowering. Model parameters were derived from the observed bud-burst and flowering dates of cherry tree (Prunus serrulata var. spontanea) at KMA Seoul station along with daily temperature data for 1923-1950. The model was applied to the 1955-2004 daily temperature data to estimate the cherry blooming dates and the deviations from the observed dates were compared with those predicted by the KMA method. Our model performed better than the KMA method in predicting the cherry blooming dates during the last 50 years (MAE = 2.31 vs. 1.58, RMSE = 2.96 vs. 2.09), showing a strong feasibility of operational application.

• Journal of the Korean Association of Geographic Information Studies
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• v.16 no.4
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• pp.29-38
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• 2013

This study is to construe the spatio-temporal characteristics of temperature in cities and the changes of climatical regions by analyzing a climate change in Korea peninsular. We used daily mean air temperature data which were collected in South and North climate stations for the past 34 years from 1974 to 2007. We created temperature maps of 500m resolution with Inverse Distance Weight in application with adiabatic lapse rate per month in linear relation with height and temperature. In the urbanization area, the data analyzed population in comparison with temperature changes by the year. The south climate region in Korea by the Warmth index was expanded to the middle climate region by the latitude after 1990s. A rise of mean temperature was $0.5{\sim}1.2^{\circ}C$ in urban areas such as Seoul, metropolitan and cities which had a rapid urbanization and industrialization with the population increase between 1980s and 1990s. In case of North Korea, cities such as Pyeongyang, Anju, Gaecheon, and Hesan had the same pattern.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.46 no.3
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• pp.202-208
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• 2001

Under the constant daylength of 13 hours and growth temperatures of 15$^{\circ}C$ to 27$^{\circ}C$, the final number of loaves (FNL) on the main culm was constant as 15 regardless of temperature in rice variety 'Kwanganbyeo'. Leaf appearance rate (LAR) increased with rising temperature and decreased with phenological development. Threshold temperature (T$_{o}$) was not constant across growth stages, but increased with phenological development. Effective accumulated temperature (EAT), which is calculated by the summation of values subtracting T0 from daily mean temperature, is closely related with number of leaves appeared (LA). LA was fitted to bilinear, quadratic, power and logistic function of EAT. Among the functions, logistic function had the best fitness of which coefficient of determination was $R^2$=0.995. Therefore, LAR prediction model was established by differentiating this function in terms of time: (equation omitted). where dL/dt is LAR, T$_1$ is daily mean temperature, L is the number of leaves appeared, and a, b, and c are constants that were estimated as 41.8, 1098.38, and -0.9273, respectively. When predictions of LA were made by LAR prediction model using data independent of model establishment, the observed and predicted LA showed good agreement of $R^2$$\geq$0.99.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.6
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• pp.109-119
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• 2021

Thermal environment of city is getting worse due to severe urban heat island caused by climate change and urbanization. Green roof improves the urban thermal environment and save the cooling energy in buildings. This study presented a green roof combined with a storage system that stores rain-water and supplies water through a wick and evaluated the temperature reduction effect as surface temperature and amount of evapotranspiration. For about a week, the surface temperature using a infrared thermal imager and the evapotranspiration by recording change of module weight were measured at intervals of 30 minutes from sunrise to sunset. The results show that the mean surface temperature of the green roof was 15.4 degrees lower than that of the non-green roof from 12:00 P.M. to 14:00 P.M. There was no significant difference between mean surface temperature of green roof with and without storage system immediately after rain, but more than a week after rain, there was a difference with average of 2.49 degrees and maximum of 4.72 degrees. The difference in daily amount of evapotranspiration was measured to be 1.66 times on average. As drought stress increased over time, the difference in daily amount of evapotranspiration and surface temperature between with/without storage system increased simultaneously. The results of the study show a more excellent cooling effect of green roof combined with the rainwater storage system.