• Korean Journal of Agricultural and Forest Meteorology
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• v.2 no.4
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• pp.175-182
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• 2000

Spatial interpolation of daily temperature forecasts and observations issued by public weather services is frequently required to make them applicable to agricultural activities and modeling tasks. In contrast to the long term averages like monthly normals, terrain effects are not considered in most spatial interpolations for short term temperatures. This may cause erroneous results in mountainous regions where the observation network hardly covers full features of the complicated terrain. We developed a spatial interpolation model for daily minimum temperature which combines inverse distance squared weighting and elevation difference correction. This model uses a time dependent function for 'mountain slope lapse rate', which can be derived from regression analyses of the station observations with respect to the geographical and topographical features of the surroundings including the station elevation. We applied this model to interpolation of daily minimum temperature over the mountainous Korean Peninsula using 63 standard weather station data. For the first step, a primitive temperature surface was interpolated by inverse distance squared weighting of the 63 point data. Next, a virtual elevation surface was reconstructed by spatially interpolating the 63 station elevation data and subtracted from the elevation surface of a digital elevation model with 1 km grid spacing to obtain the elevation difference at each grid cell. Final estimates of daily minimum temperature at all the grid cells were obtained by applying the calculated daily lapse rate to the elevation difference and adjusting the inverse distance weighted estimates. Independent, measured data sets from 267 automated weather station locations were used to calculate the estimation errors on 12 dates, randomly selected one for each month in 1999. Analysis of 3 terms of estimation errors (mean error, mean absolute error, and root mean squared error) indicates a substantial improvement over the inverse distance squared weighting.

• Journal of Korean Society on Water Environment
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• v.28 no.1
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• pp.137-147
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• 2012

Land surface temperature in ecohydrology is a variable that links surface structure to soil processes and yet its spatial prediction across landscapes with variable surface structure is poorly understood. And there are an insufficient number of soil temperature monitoring stations. In this study, a grid-based land surface temperature prediction model is proposed. Target sites are Andong and Namgang dam region. The proposed model is run in the following way. At first, geo-referenced site specific air temperatures are estimated using a kriging technique from data collected from 60 point weather stations. Then surface soil temperature is computed from the estimated geo-referenced site-specific air temperature and normalized difference vegetation index. After the model is calibrated with data collected from observed remote-sensed soil temperature, a soil temperature map is prepared based on the predictions of the model for each geo-referenced site. The daily and monthly simulated soil temperature shows that the proposed model is useful for reproducing observed soil temperature. Soil temperatures at 30 and 50 cm of soil depth are also well simulated.

• Journal of Environmental Health Sciences
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• v.42 no.1
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• pp.41-52
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• 2016

Objectives: Scrub typhus is one of the most prevalent vector-borne diseases. It is caused by Orientia tsutsugamushi, which is transmitted when people are bitten by infected chigger mites. This study aims at quantifying the association between the incidence of scrub typhus and meteorological factors in Jeollabuk-do Province over the period 2001-2015. Methods: Reported cases of scrub typhus were collected from the website of the Disease Web Statistical System supported by the Korea Centers for Disease Control and Prevention (KCDC). Simultaneous meteorological data, including temperature, rainfall, relative humidity, and sunshine duration were collected from the website of the National Climate Data Service System by the Korea Meteorological Administration. Correlation and regression analyses were applied to identify the association between the incidence of scrub typhus and meteorological factors. Results: The general epidemiological characteristics of scrub typhus in Jeollabuk-do Province were similar to those nationwide for sex, age, and geographical distribution. However, the annual incidence rate (i.e., cases per 100,000) of scrub typhus in Jeollabuk-do Province was approximately four times higher than all Korea's 0.9. The number of total cases was the highest proportion at 13.3% in Jeonbuk compared to other regions in Korea. The results of correlation analysis showed that there were significant correlations between annual cases of scrub typhus and monthly data for meteorological factors such as temperature and relative humidity in late spring and summer, especially in the case of temperature in May and June. The results of regression analysis showed that determining factors in the regression equation explaining the incidence of scrub typhus reached 46.2% and 43.5% in May and June. Using the regression equation, each 1oC rise in the monthly mean temperature in May or June may lead to an increase of 38 patients with scrub typhus compared to the annual mean of incidence cases in Jeollabuk-do Province. Conclusion: The result of our novel attempts provided rational evidence that meteorological factors are associated with the occurrence of scrub typhus in Jeollabuk-do. It should therefore be necessary to observe the trends and predict patterns of scrub typhus transmission in relation to global-scale climate change. Also, action is urgently needed in all areas, especially critical regions, toward taking steps to come up with preventive measures against scrub typhus transmission.

• New & Renewable Energy
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• v.8 no.2
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• pp.14-23
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• 2012

The TMY (Typical Meteorological Year) for the solar energy study is generated using observation data with 22 solar sites from KMA (Korea Meteorological Administration) during 11 years (2000-2010). The meteorological data for calculation the TMY are used solar radiation, temperature, dew point temperature, wind speed and humidity data. And the TMY is calculated to apply the FS (Finkelstein and Schafer) statistics and RMSE (Root Mean Squared Error) methods. FS statistics performed with each point and each variable and then selected top five candidate TMM months with statistical analysis and normalization. Finally TMY is generated to select the highest TMM score with evaluation the average errors for the 22 whole points. The TMY data is represented average state and long time variations with 22 sites and meteorological data. When TMY validated with the 11-year daily solar radiation data, the correlation coefficient was about 0.40 and the highest value is 0.57 in April and the lowest value is 0.23 in May. Mean monthly solar radiation of TMY is 411.72 MJ which is 4 MJ higher than original data. Average correlation coefficient is 0.71, the lowest correlation is 0.43 in May and the highest correlation is 0.90 in January. Accumulated annual solar radiation by TMY have higher value in south coast and southwestern region and have relatively low in middle regions. And also, differences between TMY and 11-year mean of is distributed lower 100 MJ in Kyeongbuk, higher 200 MJ in Jeju and higher 125 MJ in Jeonbuk and Jeonnam, respectively.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.21 no.2
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• pp.141-150
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• 2014

Purpose: This was a study on the expiration date of Ethylene Oxide (EO) gas sterilization and effects of the environmental factors of temperature, humidity and type of cabinet in sterile goods storage area on the expiration date. Methods: Sterile goods storage areas from 13 departments in one hospital were selected and 455 EO gas sterilization samples were prepared and kept in those areas over the 14 months of the study. Each sample was tested with a microbiological culture in the laboratory every week. If the result was positive, the sample was regarded as contaminated. The researcher visited once a month to check the temperature, humidity and type of cabinet. Results: With the exception of 1 sample which was positive at 56th week. 454 samples were confirmed as negative. The environment of the samples storage area was measured monthly. The annual average temperature was $24.2{\pm}1.6^{\circ}C$, and the mean relative humidity $34.7{\pm}15.2%$. The types of cabinet were 7 open and 6 closed. Conclusion: The results of the microbiological culture at 13 months showed that none of the samples were contaminated. Therefore the hospital's existing Expiration Date can be extended from 6 months to 13~14 months.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.147-149
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• 2003

The wetness, a function of precipitation and temperature etc, and the warmth, a function of temperature, are the dominant factor for global vegetation distribution. This paper employs the normalized difference vegetation index (NDVI), warmth index (WAI), and wetness index (WEI), and focuses on an essential climate-vegetation relationship at global scale. The NDVI was acquired from ‘Twenty-year global 4-minute AVHRR NDVI dataset.’ The WEI is defined as the fraction of the precipitation to the potential evaporation. The WAI was calculated by accumulating the monthly mean temperature of the portion exceeded 5$^{\circ}C$ throughout the year. Meteorological data for the WEI and WAI calculation were obtained from the ISLSCP CD-ROM. All analyses were conducted for 1 ${\times}$ 1 degree grid box on the terrestrial area of the Earth, and on annual value basis averaged in 1987 and 1988. The result of analyses demonstrated that there are two regimes in their relations, that is, a regime in which NDVIs vary depending on the WEI, and a regime in which NDVIs vary depending on the WAI. These two regimes appeared to correspond to the wetness dominant and warmth dominant vegetation, respectively. The geographical distributions of two regimes were mapped. Most of the world vegetation is categorized into wetness dominant, while warmth dominant vegetation is seen in the high-latitude area mainly to the north of 60$^{\circ}$N in the Northern Hemisphere and high-altitude areas.

• Journal of the Korean Wood Science and Technology
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• v.50 no.2
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• pp.81-92
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• 2022

To analyze the relationship between climatic factors (mean monthly temperature and total precipitation) and tree-ring growths of Pinus densiflora S. et Z. from National Parks (according to region) of the Korea, 20 trees were sampled from 13 National Parks. Only trees that were successfully cross-dated were used for dendrochronological analysis, and at least 11 trees were included. The tree-ring chronology of Mt. Bukhan (covering the shortest period of 1917 - 2016 [100 years]) was assessed, as well as that of Mt. Seorak (covering the longest period of 1687 - 2017 [331 years]). After cross-dating, each ring width series was double-standardized by first fitting a logarithmic curve and then a 50-year cubic spline. The relationships between climate and tree-ring growth were calculated with response function analysis. The results show a significant positive correlation between a given year's February-March temperature, May precipitation levels, and tree-ring growth. It indicates that a higher temperature in early spring and precipitation before cambium activity are important for radial growths of Pinus densiflora in the Korea.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.36 no.5
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• pp.535-540
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• 2003

Based on the monthly weather report of Korea Meteorological Administration (KMA) and daily sea surface temperature (SST) data from National Fisheries Research and Development Institute (NFRDl) (1991-2001), mean heat fluxes were estimated at the Gunsan harbor Net heat flux was transported from the air to the sea surface during March to early September, and it amounts to $125\;Wm^{-2}$ in average daily during May to June. During the middle of September to February, the transfer of net heat flux was conversed from the sea surface to the air with $-125\;Wm^{-2}$ in mininum value in October. Short wave radiation was ranged from 50 to $248\;Wm^{-2}$ showing maxima in April to June. Long wave radiation was ranged from 25 to $92\;Wm^{-2}$ with mininum value in June to July. Sensible heat flux denoting negative values in April to August was ranged from -30 to $72\;Wm^{-2}.$ Latent heat flux was ranged from 15 to $82\;Wm^{-2}$ with maxima in August to September. The phase of heat exchange was changed from cooling to heating in the end of February, and from heating to cooling In the beginning of September. The advective term of heat flux showed minima in April to June and maxima in November. The ratio of temperature variations was 1.37 in the sea surface process and the horizontal process by advection. This indicates that the main factor in variation of temperature at Gunsan harbor is the heat exchange process through the sea surface from the air.

• 한국해양학회지
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• v.24 no.1
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• pp.1-14
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• 1989

This study analyzes the interannual periodicity by using the statistical techniques of probability, spectral analysis, empirical orthogonal function analysis (EOF), and coherency analysis. The data base for this study is the time series of 1971-1985 temperature, salinity in the southern waters of the East Sea, 1960-1986 mean sea level at Pusan and Izuhara, and 1960-1986 sea level atmospheric pressure at Pusan. The appearances of anomalous temperatures higher and lower than 15-year mean monthly average with one standard deviation are about 30% of total data. The significant interannual period for temperature, salinity and sea level fluctuation is 36.6, and 23.3 months. The empirical orthogonal function analyses show that the 1st mode of the EOFs is responsible for more than 90% of total variance of the surface temperature variations, while in near-bottom waters, the relative importance of the higher EOF modes is much greater explaining more than 30% of total variance. The coherency between normalized temperatures and salinities is significant at the interannual period of 36.6 and 21.3 months.

• Ocean and Polar Research
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• v.34 no.1
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• pp.73-83
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• 2012

Antarctica is very sensitive to climate change but the number of stations is not sufficient to accurately analyze climate change in this regoin. Model reanalysis data supplements the lack of observation and can be used as long term data to verify climate change. In this study, the 20CR (Twentieth Century Reanalysis) Project data from NCEP/NCAR and monthly mean data (temperature, solar radiation and longwave radiation) from 1871 to 2008, was used to analyze the temperature trend and change in radiation. The 20CR data was used to validate the observation data from Antarctica since 1950 and the correlation coefficients between these data were determined to be over 0.95 at all stations. The temperature increased by approximately $0.23^{\circ}C$/decade during the study period and over $0.20^{\circ}C$/decade over all of the months. This increasing trend was observed throughout the Antarctica and a slight increase was observed in the Antarctic Peninsula. In addition, solar radiation (surface) and longwave radiation (surface and top of atmosphere) trends correlated with the increase in temperature. As a result, outgoing longwave radiation at the surface is attenuated by atmospheric water vapor or clouds and radiation at the top of the atmosphere was reduced. In addition, the absorbed energy in the atmosphere increases the temperature of the atmosphere and surface, and then the heated surface emits more longwave radiation. Eventually these processes are repeated in a positive feedback loop, which results in a continuous rise in temperature.