• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.3
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• pp.119-123
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• 2012

The duration of low temperature in winter season is one of the important agrometeorological characteristics in crop growing fields. This study was conducted to develop a method to estimate the duration of low-temperature with monthly meteorological data. Using daily meteorological data from 61 observation sites from 1981 to 2010, we analyzed the relationships between the averages of monthly temperature minima and the durations of low-temperature ranging from -15 to $5^{\circ}C$, The monthly mean of the January minimum air temperature was appropriate for theestimation of the durations of lowtemperature below $0^{\circ}C$. We tested a simple second order equation to predict durations of low-temperature. To apply the equation to various temperature ranges, we suggested two different equations for the estimation of coefficients a and b, which are dependent on the base temperatures from -15 to $0^{\circ}C$. Thevalidation of the equations using other daily meteorological datasets from 1971 to 2000 showed that they were appropriate for the range from -10 to $0^{\circ}C$, but underestimated at $-15^{\circ}C$.

• Journal of Environmental Science International
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• v.15 no.8
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• pp.701-718
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• 2006

The average ratio of the daily UV-B to total solar (75) irradiance at Busan (35.23$^{\circ}$N, 129.07$^{\circ}$E) in Korea is found as 0.11%. There is also a high exponential relationship between hourly UV-B and total solar irradiance: UV-B=exp (a$\times$(75-b))(R$^2$=0.93). The daily variation of total ozone is compared with the UV-B irradiance at Pohang (36.03$^{\circ}$N, 129.40$^{\circ}$E) in Korea using the Total Ozone Mapping Spectrometer (TOMS) data during the period of May to July in 2005. The total ozone (TO) has been maintained to a decreasing trend since 1979, which leading to a negative correlation with the ground-level UV-B irradiance doting the given period of cloudless day: UV-B=239.23-0.056 TO (R$^2$=0.52). The statistical predictions of daily total ozone are analyzed by using the data of the Brewer spectrophotometer and TOMS in East Asia including the Korean peninsula. The long-term monthly averages of total ozone using the multiplicative seasonal AutoRegressive Integrated Moving Average (ARIMA) model are used to predict the hourly mean UV-B irradiance by interpolating the daily mean total ozone far the predicting period. We also can predict the next day's total ozone by using regression models based on the present day's total ozone by TOMS and the next day's predicted maximum air temperature by the Meteorological Mesoscale Model 5 (MM5). These predicted and observed total ozone amounts are used to input data of the parameterization model (PM) of hourly UV-B irradiance. The PM of UV-B irradiance is based on the main parameters such as cloudiness, solar zenith angle, total ozone, opacity of aerosols, altitude, and surface albedo. The input data for the model requires daily total ozone, hourly amount and type of cloud, visibility and air pressure. To simplify cloud effects in the model, the constant cloud transmittance are used. For example, the correlation coefficient of the PM using these cloud transmissivities is shown high in more than 0.91 for cloudy days in Busan, and the relative mean bias error (RMBE) and the relative root mean square error (RRMSE) are less than 21% and 27%, respectively. In this study, the daily variations of calculated and predicted UV-B irradiance are presented in high correlation coefficients of more than 0.86 at each monitoring site of the Korean peninsula as well as East Asia. The RMBE is within 10% of the mean measured hourly irradiance, and the RRMSE is within 15% for hourly irradiance, respectively. Although errors are present in cloud amounts and total ozone, the results are still acceptable.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.1142-1146
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• 2004

The objective of this study is to the test applicability of SLURP on Soyanggang-dam watershed. The area of this watershed is $2,694km^2$ and mean elevation and slope is 650 m and $23^{\circ}$ respectively. Topographical parameters were derived from DEM using TOPAZ and SLURPAZ. NDVI was calculated from multi-temporal NOAA/AVHRR images. The daily meteorological data and hydrograph during $1999\~2001$ were selected for model calibration and performance tests. Weather elements (dew-point temperature, solar radiation, maximum and minimum temperature, relative humidity) were required from the S meteorological stations near the study area. The model parameters of each land cover class were optimized by sensitivity analysis and SCE-UA method. Runoff rate shows $49.33\%\~64.06\%$. Simulated results during 4 years were estimated by Nash-Sutcliffe efficiency and WMO volume error. Nash-Sutcliffe efficiency shows $0.61\~0.75$ and WMO volume error shows $6.1\%-18.8\%$.

• Water for future
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• v.29 no.2
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• pp.235-247
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• 1996

Meteorological and soil water content data measured from semiarid watersheds of Lucky Hills and Kendall during the summer rainy and winter periods were used to study the interrelationships between the controlling variables of the evapotranspiration, and to evaluate the effects of variables on daily actual evapotranspiration (ET) estimation. Simple and multiple linear regression (MLR) analyses were employed to evaluate the order of importance of the meteorological and soil water factors involved. The information gained was used for MLR model development. Theavailable energy and vapor pressure deficit were found to be the important variables to estimate actual ET (AET) for both periods and at both watersheds. Therefore, the important variables of evapotranspiration process in these semiarid watersheds appear to be simply the components of energy term in available energy and aerodynamic term in vapor pressure deficit of Penman potential evapotranspiration (PET) equation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.480-488
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• 2018

The development of information and communication technology has been carried out actively in the field of agriculture to generate valuable information from large amounts of data and apply big data technology to utilize it. Crops and their varieties are determined by the influence of the natural environment such as temperature, precipitation, and sunshine hours. This paper derives the climatic factors affecting the production of crops using the garlic growth process and daily meteorological variables. A prediction model was also developed for the production of garlic per unit area. A big data analysis technique considering the growth stage of garlic was used. In the exploratory data analysis process, various agricultural production data, such as the production volume, wholesale market load, and growth data were provided from the National Statistical Office, the Rural Development Administration, and Korea Rural Economic Institute. Various meteorological data, such as AWS, ASOS, and special status data, were collected and utilized from the Korea Meteorological Agency. The correlation analysis process was designed by comparing the prediction power of the models and fitness of models derived from the variable selection, candidate model derivation, model diagnosis, and scenario prediction. Numerous weather factor variables were selected as descriptive variables by factor analysis to reduce the dimensions. Using this method, it was possible to effectively control the multicollinearity and low degree of freedom that can occur in regression analysis and improve the fitness and predictive power of regression analysis.

• Journal of Environmental Science International
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• v.6 no.2
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• pp.95-106
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• 1997

The characteristics on the transtion probabilities and periodicity for the daily precipitation occurrence in Korean peninsula are investigated by applying the Markov chain properties to daily precipitation occurrence. In order to examine the responses of Markov Chain properties to the applied period and their magnitudes, three cases (Case A: 1956~ 1985 at 14 stations, Case B: 1965~ 1994 at 14 stations, and Case C: 1985~ 1994 at 63 stations) are considered In this study. The transition probabilities from wet day to wet day for all cases are about 0.50 and in summer, especially July, are higher. In addition, considering them in each station we can find that they are the highest at Ullung-do and lowest at Inchon for all cases. The annual equilibrium probabilities of a wet day appear 0.31 In Case A, 0.30 Case B, and 0. 29 Case C, respectively. This may explain that as the data-period used becomes shorter, the higher the equilibrium probability is. The seasonal distributions of equilibrium probabilities are appeared the lowest(0.23~0.28) in winter and the highest(more than 0.39) in spring and monthly in .truly and in October, repectively. The annual mean wet duration for all cases is 2.04 days in Case A, 1.99 Case B, and 1.89 Case C, repectively. The weather cycle obtained from the annual mean wet and dry duration is 6.54~6.59 days, which are closely associated with the movement of synoptic systems. And the statistical tests show that the transitions of daily precipitation occurrence for all cases may have two-state first Markov chain property, being the stationarity in time and heterogeneity in space.

• Journal of Environmental Science International
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• v.14 no.11
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• pp.1035-1047
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• 2005

By using hourly $NO_2$ concentration data$(1998\~2000)$ at the Busan Metropolitan City air qualify monitoring sites, characteristics of daily mean value of $NO_2$ concentration was discussed in space and time. The correlation between $NO_2$ concentration and other relating air pollutants was analyzed by using SAS program and meteorological parameters as well. After choosing representative 4 areas, this study used hourly concentration data$(1998\~2000)$ from air quality monitoring sites on $NO_2,\;NO,\;O_3,\;CO,\;SO_2\;and\;PM_{10}$. Typical metropolitan characteristics of two peaks in a day was shown in the variation of $NO_2$ concentration of Busan city.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.292-294
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• 2003

Evapotranspiration estimations are essential for monitoring drought, wild land fire risk etc. In this study, a surface energy balance method, which combines meteorological observations with spectral data derived from remote sensing measurements, was used to estimate the regional evapotranspiration in the Mongolia, a large arid and semi-arid region with heterogeneous surface conditions. The Surface Energy Balance method has been applied to Landsat+ETM and NOAA-AVHRR sensors for the estimation of evapotranspiration in the grassland of Mongolia. As a result, a daily evapotranspiration map of Mongolia was produced.

• Wind and Structures
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• v.26 no.3
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• pp.129-146
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• 2018

Extreme wind speed analysis has been carried out conventionally by assuming the extreme series data is stationary. However, time-varying trends of the extreme wind speed series could be detected at many surface meteorological stations in China. Two main reasons, exposure change and climate change, were provided to explain the temporal trends of daily maximum wind speed and annual maximum wind speed series data, recorded at Hangzhou (China) meteorological station. After making a correction on wind speed series for time varying exposure, it is necessary to perform non-stationary statistical modeling on the corrected extreme wind speed data series in addition to the classical extreme value analysis. The generalized extreme value (GEV) distribution with time-dependent location and scale parameters was selected as a non-stationary model to describe the corrected extreme wind speed series. The obtained non-stationary extreme value models were then used to estimate the non-stationary extreme wind speed quantiles with various mean recurrence intervals (MRIs) considering changing climate, and compared to the corresponding stationary ones with various MRIs for the Hangzhou area in China. The results indicate that the non-stationary property or dependence of extreme wind speed data should be carefully evaluated and reflected in the determination of design wind speeds.

• 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.