• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.1
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• pp.49-63
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• 2016

In this study, we analyzed the effects of regional hydrologic variability during warm season (June-September) in South Korea due to ENSO (El $Ni{\tilde{n}}o$-Southern Oscillation) pattern changes over the Tropical Pacific Ocean (TPO). We performed composite analysis (CA) and statistical significance test by Student's t-test using observed hydrologic data (such as, precipitation and streamflow) in the 113 sub-watershed areas over the 5-Major River basin, in South Korea. As a result of this study, during the warm-pool (WP) El $Ni{\tilde{n}}o$ year shows a significant increasing tendency than normal years. Particularly, during the cold-tongue (CT) El $Ni{\tilde{n}}o$ decaying years clearly decreasing tendency compared to the normal years was appeared. In addition, the La $Ni{\tilde{n}}a$ years tended to show a slightly increasing tendency and maintain the average year state. In addition, from the result of scatter plot of the percentage anomaly of hydrologic variables during warm season, it is possible to identify the linear increasing tendency. Also the center of the scatter plot shows during the WP El $Ni{\tilde{n}}o$ year (+17.93%, +26.99%), the CT El $Ni{\tilde{n}}a$ year (-8.20%, -15.73%), and the La $Ni{\tilde{n}}a$ year (+8.89%, +15.85%), respectively. This result shows a methodology of the tele-connection based long-range water resources prediction for reducing climate forecasting uncertainty, when occurs the abnormal SSTA (such as, El $Ni{\tilde{n}}o$ and La $Ni{\tilde{n}}a$) phenomenon in the TPO region. Furthermore, it can be a useful data for water managers and end-users to support long-range water-related policy making.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.3
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• pp.41-48
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• 1965

This study was aimed at knowing the magnitude of year variation in rice heading dates under the different seasonal cultures, and to estimate the heading date in advance. Using six rice varieties such as Kwansan, Suwon＃82, Suwon ＃144, Norin＃17, Yukoo＃132 and Paltal, the early, ordinary and late seasonal cultures had been carried out at Paddy Crop Division, Crop Experiment Station at Suwon for the six-year period 1959 to 1964. In addition the data of the standard rice cultures at the Provincial Offices of Rural Development for the 12-year period 1953 to 1954, were analyzed for the purpose of clarifying a relationship between variation of rice heading dates and some of meteorological data related to the locations and years. The results of this study are as follows: 1. Year variation of rice heading dates was as high as 14 to 21 days in the early seasonal culture and 7 to 14 days in the ordinary seasonal culture, while as low as one to seven days in the late seasonal culture which was the lowest among three cultures. The magnitude of variation depended greatly on variety, cultural season and location. 2. It was found out that there was a close negative correlation between the accumulated average air temperature for 40 days from 31 days after seeding and number of days to heading in the early seasonal culture. Accordingly, it was considered possible to predict the rice heading date through calculation of the accumulated average air temperature for the above period and then the linear regression(Y=a+bx). On the other hand, an estimation of the heading date in the late seasonal culture requires for the further studies. In the ordinary seasonal culture, no significant correlation between the accumulated average air temperature and number of days to heading was obtained in the six-year experiments conducted at Suwon. There was a varietal difference in relationship between the accumulated average air temperature for 70 days from seeding and number of days to heading in the standard cultures at the provincial offices of rural development. Some of varieties showed a significant correlation between two factors while the others didn't show any significant correlation. However, there was no regional difference in this relationship.

• Atmosphere
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• v.23 no.1
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• pp.85-92
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• 2013

We estimate atmospheric radiative heating effect of aerosols, based on AErosol RObotic NETwork (AERONET) and lidar observations and radiative transfer calculations. The column radiation model (CRM) is modified to ingest the AERONET measured variables (aerosol optical depth, single scattering albedo, and asymmetric parameter) and subsequently calculate the optical parameters at the 19 bands from the data obtained at four wavelengths. The aerosol radiative forcing at the surface and the top of the atmosphere, and atmospheric absorption on pollution (April 15, 2001) and dust (April 17~18, 2001) days are 3~4 times greater than those on clear-sky days (April 14 and 16, 2001). The atmospheric radiative heating rate (${\Delta}H$) and heating rate by aerosols (${\Delta}H_{aerosol}$) are estimated to be about $3\;K\;day^{-1}$ and $1{\sim}3\;K\;day^{-1}$ for pollution and dust aerosol layers. The sensitivity test showed that a 10% uncertainty in the single scattering albedo results in 30% uncertainties in aerosol radiative forcing at the surface and at the top of the atmosphere and 60% uncertainties in atmospheric forcing, thereby translated to about 35% uncertainties in ${\Delta}H$. This result suggests that atmospheric radiative heating is largely determined by the amount of light-absorbing aerosols.

• Journal of the Korean earth science society
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• v.40 no.3
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• pp.227-239
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• 2019

Heavy snowfall events frequently occur in the Gangwon province, and the snowfall amount significantly varies in space due to the complex terrain and topographical modulation of precipitation. Understanding the spatial characteristics of heavy snowfall and its prediction is particularly challenging during snowfall events in the easterly winds. The easterly wind produces a significantly different atmospheric condition. Hence, it brings different precipitation characteristics. In this study, we have investigated the microphysical characteristics of snowfall in the windward and leeward sides of the Taebaek mountain range in the easterly condition. The two snowfall events are selected in the easterly, and the snow particles size distributions (SSD) are observed in the four sites (two windward and two leeward sites) by the PARSIVEL distrometers. We compared the characteristic parameters of SSDs that come from leeward sites to that of windward sites. The results show that SSDs of windward sites have a relatively wide distribution with many small snow particles compared to those of leeward sites. This characteristic is clearly shown by the larger characteristic number concentration and characteristic diameter in the windward sites. Snowfall rate and ice water content of windward also are larger than those of leeward sites. The results indicate that a new generation of snowfall particles is dominant in the windward sites which is likely due to the orographic lifting. In addition, the windward sites show heavy aggregation particles by nearby zero ground temperature that is likely driven by the wet and warm condition near the ocean.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.29-41
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• 2019

Terrestrial Gross Primary Production (GPP) is the largest global carbon flux, and forest ecosystems are important because of the ability to store much more significant amounts of carbon than other terrestrial ecosystems. There have been several attempts to estimate GPP using mechanism-based models. However, mechanism-based models including biological, chemical, and physical processes are limited due to a lack of flexibility in predicting non-stationary ecological processes, which are caused by a local and global change. Instead mechanism-free methods are strongly recommended to estimate nonlinear dynamics that occur in nature like GPP. Therefore, we used the mechanism-free machine learning techniques to estimate the daily GPP. In this study, support vector machine (SVM), random forest (RF) and artificial neural network (ANN) were used and compared with the traditional multiple linear regression model (LM). MODIS products and meteorological parameters from eddy covariance data were employed to train the machine learning and LM models from 2006 to 2013. GPP prediction models were compared with daily GPP from eddy covariance measurement in a deciduous forest in South Korea in 2014 and 2015. Statistical analysis including correlation coefficient (R), root mean square error (RMSE) and mean squared error (MSE) were used to evaluate the performance of models. In general, the models from machine-learning algorithms (R = 0.85 - 0.93, MSE = 1.00 - 2.05, p < 0.001) showed better performance than linear regression model (R = 0.82 - 0.92, MSE = 1.24 - 2.45, p < 0.001). These results provide insight into high predictability and the possibility of expansion through the use of the mechanism-free machine-learning models and remote sensing for predicting non-stationary ecological processes such as seasonal GPP.