• Journal of Climate Change Research
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• v.7 no.4
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• pp.507-512
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• 2016

This study was conducted to classify agroclimatic zones in South Korea. To classify the agroclimatic zones, such climatic factors as amount of rainfall from April to May, amount of rainfall in October, monthly average air temperature in January, monthly average air temperature from April to May, monthly average air temperature from April to September, monthly average air temperature from December to March, monthly minimum air temperature in January, monthly minimum air temperature from April to May, Warmth Index were considered as major influencing factors on the crop growth. Climatic factors were computed from monthly air temperature and precipitation of climatological normal year (1981~2010) at 1 km grid cell estimated from a geospatial climate interpolation method. The agroclimatic zones using k-means cluster analysis method were classified into 6 zones.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.927-929
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• 2003

Using received northeast region in China of Terra/MODIS data at Tokyo University of information Sciences. Make monthly division Land Surface Temperature maximum composite image. Using monthly division Land Surface Temperature maximum composite image, considered characteristic of monthly variation of Land surface temperature and relation with land covering and NDVI at the northeast region in China.

• Atmosphere
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• v.30 no.4
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• pp.405-420
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• 2020

In this study, we investigate the predictability of intraseasonal monthly temperature and precipitation variations using hindcast datasets from eight global circulation models participating in the operational multi-model ensemble (MME) seasonal prediction system of the Asia-Pacific Economic Cooperation Climate Center for the 1983~2010 period. These intraseasonal monthly variations are defined by categorical deterministic analysis. The monthly temperature and precipitation are categorized into above normal (AN), near normal (NN), and below normal (BN) based on the σ-value ± 0.43 after standardization. The nine patterns of intraseasonal monthly variation are defined by considering the changing pattern of the monthly categories for the three consecutive months. A deterministic and a probabilistic analysis are used to define intraseasonal monthly variation for the multi-model consisting of numerous ensemble members. The results show that a pattern (pattern 7), which has the same monthly categories in three consecutive months, is the most frequently occurring pattern in observation regardless of the seasons and variables. Meanwhile, the patterns (e.g., patterns 8 and 9) that have consistently increasing or decreasing trends in three consecutive months, such as BN-NN-AN or AN-NN-BN, occur rarely in observation. The MME and eight individual models generally capture pattern 7 well but rarely capture patterns 8 and 9.

• Journal of Korea Water Resources Association
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• v.43 no.5
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• pp.471-482
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• 2010

Rainfall events in the hydrologic circulation are closely related with various meteorological factors. Therefore, in this research, correlation relationship was analyzed between sea surface temperature of typical meteorological factor and monthly rainfall on Korean peninsula. The cluster analysis was performed monthly average rainfall data, longitude and latitude observed by rainfall observatory in Korea. Results from cluster analysis using monthly rainfall data in South Korea were divided into 4 regions. The principal components of monthly rainfall data were extracted from rainfall stations separated cluster regions. A correlation analysis was performed with extracted principal components and sea surface temperatures. At the results of correlation analysis, positive correlation coefficients were larger than negative correlation coefficients. In addition, The 3 month of principal components on monthly rainfall predicted by locally weighted polynomial regression using observed data of sea surface temperature where biggest correlation coefficients have. The result of forecasting through the locally weighted polynomial regression was revealed differences in accuracy. But, this methods in the research can be analyzed for forecasting about monthly rainfall data. Therefore, continuous research need through hydrological meteorological factors like a sea surface temperature about forecasting of the rainfall events.

• Journal of Forest and Environmental Science
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• v.34 no.3
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• pp.249-252
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• 2018

Climate is one of most important environmental factors on the forest ecosystem. This study was conducted to analyze the characteristics of meteorological factors in the forest area and weather stations from July 2015 to June 2016 in Cheuncheon and Hongcheon of Kangwon Province in Korea. The HOBO data logger was installed for meteorological analysis in forests area (site 1 and site 2). The meteorological data from the HOBO data logger compared with meteorological data of the weather station. The meteorological data used for the analysis was monthly mean temperature ($^{\circ}C$), monthly mean minimum temperature ($^{\circ}C$), monthly mean maximum average temperature ($^{\circ}C$), and monthly mean relative humidity (%). As a result of this study, the mean temperature ($^{\circ}C$) of forest area was relatively lower than weather station which is the outside the forest area, and the mean maximum temperature ($^{\circ}C$) of weather station was relatively higher than that of forest area. The mean relative humidity (%) was higher in forest area than weather station.

• Atmosphere
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• v.19 no.2
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• pp.107-125
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• 2009

Statistical downscaled surface temperature datasets by employing the cyclostationary empirical orthogonal function (CSEOF) analysis and multiple linear regression method are examined. For evaluating the efficiency of this statistical downscaling method, monthly surface temperature of the ECMWF has been downscaled into monthly temperature having a fine spatial scale of ~20km over the Korean peninsula for the 1973-2000 period. Monthly surface temperature of the ECHOG has also been downscaled into the same spatial scale data for the same period. Comparisons of temperatures between two datasets over the Korean peninsula show that annual mean temperature of the ECMWF is about $2^{\circ}C$ higher than that of the ECHOG. After applying to the statistical downscaling method, the difference of two annual mean temperatures reduces less than $1^{\circ}C$ and their spatial patterns become even close to each other. Future downscaled data shows that annual temperatures in the A1B scenario will increase by $3.5^{\circ}C$ by the late 21st century. The downscaled data are influenced by the ECHOG as well as observation data which includes effects of complicated topography and the heat island.

• Korean Journal of Agricultural and Forest Meteorology
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• v.7 no.1
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• pp.35-44
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• 2005

Microclimate of Gwangneung forest watershed is characterized by analyzing wind, radiation, profiles of air temperature and humidity, soil and bole temperature, precipitation and soil water content measured at and around the flux tower from April 2000 to September 2003. Mountain-valley wind was prevalent due to the topographic effect with dominant wind from east during daytime and relatively weak wind from west during nighttime. Air temperature reaches its peak in July-August whereas monthly-averaged incoming shortwave radiation shows its peak in May due to summer monsoon. Albedo ranges from 0.12 to 0.16 during the growing season. Monthly-averaged bole temperature is in phase with monthly- averaged air temperature which is consistently higher. Monthly-averaged soil temperature lags behind air temperature and becomes higher with leaf fall. With the emergence of leafage in April, maximum temperature level during midday shifts from the ground surface to the crown level of 15-20m in May. Profiles of water vapor pressure show a similar shift in May but the ground surface remains as the major source of water. Vapor pressure deficit is highest in spring and lowest in winter. Monthly averaged surface soil temperatures range from 0 to 20℃ with a maximum in August. Monthly averaged trunk temperatures of the dominant tree species range from -5.8 to 21.6℃ with their seasonal variation and the magnitudes similar to those of air temperature. Annual precipitation amount varies significantly from year to year, of which >60% is from July and August. Vertical profiles of soil moisture show different characteristics that may suggest an important role of lateral movement of soil water associated with rainfall events.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.2B
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• pp.237-248
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• 2008

Precipitation variability in Korea is mainly influenced by climate circulation such as sea surface temperature, not a local convection. Therefore, this study investigates relationship between monthly precipitation of 61 station observed by Korea Meteorological Administration and global sea surface temperatures (SSTs). The main components of monthly precipitation in Korea are extracted by a method which consists of the principal analysis combined with the cluster analysis, to examine the correlation between monthly rainfalls and SSTs. The relationships between main components of monthly precipitation and SSTs exists in Pacific Ocean. At the result of Wavelet Transform analysis, The 2-4 year band have a strong wavelet power spectrum and the low frequency. the correlation coefficient between low frequency components of monthly rainfalls and SSTs calculated bigger then correlation coefficient between main components and SSTs. Hence, these results propose a prediction possibility of monthly precipitations using the varition of SSTs.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.491-495
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• 1999

We compared the satellite observed temperature with the radiosonde observed temperature in the Korean Peninsula. The radiosonde observed data were obtained from four upper air observation stations in the Korean Peninsula from 1981 to 1998, and that was compared with the satellite observed data of the channel-2 and channel-4 of microwave sounding unit(MSU) on board NOAA series of polar-orbiting satellites. The radiosonde data were reconstructed into monthly radiosonde T$_{b}$ using MSU weighting function. The monthly climatology shows radiosonde T$_{b2}$ is higher than MSU T$_{b2}$ in summer. The correlation between MSU T$_{b2}$ and radiosonde T$_{b2}$ is 0.72-0.76 and 0.73-0.81 between MSU T$_{b4}$ and radiosonde T$_{b4}$.

• Journal of Climate Change Research
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• v.5 no.4
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• pp.323-329
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• 2014

Since the terrain of Korea is complex, micro- as well as meso-climate variability is extreme by locations in Korea. In particular, air temperature of agricultural fields is influenced by topographic features of the surroundings making accurate interpolation of regional meteorological data from point-measured data. This study was carried out to compare spatial interpolation methods to estimate air temperature in agricultural fields surrounded by rugged terrains in South Korea. Four spatial interpolation methods including Inverse Distance Weighting (IDW), Spline, Ordinary Kriging (with the temperature lapse rate) and Cokriging were tested to estimate monthly air temperature of unobserved stations. Monthly measured data sets (minimum and maximum air temperature) from 588 automatic weather system(AWS) locations in South Korea were used to generate the gridded air temperature surface. As the result, temperature lapse rate improved accuracy of all of interpolation methods, especially, spline showed the lowest RMSE of spatial interpolation methods in both maximum and minimum air temperature estimation.