• Korean Journal of Remote Sensing
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• v.14 no.1
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• pp.53-68
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• 1998

Observation of hydrometeors' behavior in the atmosphere is important to understand weather and climate. By conventional observations, we can get the distribution of water vapor at limited number of points on the earth. In this study, the precipitable water has been estimated from the split window channel data on GMS-5 based upon the technique developed by Chesters et al.(1983). To retrieve the precipitable water, water vapor absorption parameter depending on filter function of sensor has been derived using the regression analysis between the split window channel data and the radiosonde data observed at Osan, Pohang, Kwangiu and Cheju staions for 4 months. The air temperature of 700 hPa from the Global Spectral Model of Korea Meteorological Administration (GSM/KMA) has been used as mean air temperature for single layer radiation model. The retrieved precipitable water for the period from August 1996 through December 1996 are compared to radiosonde data. It is shown that the root mean square differences between radiosonde observations and the GMS-5 retrievals range from 0.65 g/$cm^2$ to 1.09 g/$cm^2$ with correlation coefficient of 0.46 on hourly basis. The monthly distribution of precipitable water from GMS-5 shows almost good representation in large scale. Precipitable water is produced 4 times a day at Korea Meteorological Administration in the form of grid point data with 0.5 degree lat./lon. resolution. The data can be used in the objective analysis for numerical weather prediction and to increase the accuracy of humidity analysis especially under clear sky condition. And also, the data is a useful complement to existing data set for climatological research. But it is necessary to get higher correlation between radiosonde observations and the GMS-5 retrievals for operational applications.

• Korean Journal of Mineralogy and Petrology
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• v.34 no.3
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• pp.169-176
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• 2021

This study aimed to fundamentally understand structural changes of zeolite under pressure and in the presence of different pressure-transmitting media (PTM) for application studies such as immobilization of heavy metal cation or CO₂ storage using pressure. High-pressure X-ray powder diffraction study was conducted on the zeolite-W (K_6.4Al_6.5Si_25.8O₆₄× 15.3H₂O, K-MER) to understand linear compressibility and the bulk moduli in different PTM conditions. Zeolite-w is a synthetic material having the same framework as natural zeolite merlinoite ((K, Ca_0.5, Ba_0.5, Na)₁₀ Al₁₀Si₂₂O₆₄× 22H₂O). The space group of the sample was identified as I4/mmm belonging to the tetragonal crystal system. Water, carbon dioxide, and silicone-oil were used as pressure-transmitting media. The mixture of sample and each PTM was mounted in a diamond anvil cell (DAC) and then pressurized up to 3 GPa with an increment of ca. 0.5 GPa. Pressure-induced changes of powder diffraction patterns were measured using a synchrotron X-ray light source. Lattice constants, and bulk moduli were calculated using the Le-Bail method and the Birch-Murnaghan equation. In all PTM conditions, linear compressibility of c-axis (𝛽^c) was 0.006(1) GPa^-1 or 0.007(1) GPa^-1. On the other hand, the linear compressibility of a(b)-axis (𝛽^a) was 0.013(1) GPa^-1 in silicone-oil run, which is twice more compressible than the a(b)-axis in water and carbon dioxide runs, 𝛽^a = 0.006(1) GPa^-1. The bulk moduli were measured as 50(3) GPa, 52(3) GPa, and 29(2) GPa in water, carbon dioxide, and silicone-oil run, respectively. The orthorhombicities of ac-plane in the water, and carbon dioxide runs were comparatively constant, near 0.350~0.353, whereas the value decreased abruptly in the silicone-oil run following formula, y = -0.005(1)x + 0.351(1) by non-penetrating pressure fluid condition.

• Korean Journal of Remote Sensing
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• v.38 no.2
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• pp.179-188
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• 2022

Global warming causes climate change and increases extreme weather events worldwide, and the occurrence of heatwaves and droughts is also increasing in Korea. For the monitoring of extreme weather, various satellite data such as LST (Land Surface Temperature), TCI (Temperature Condition Index), NDVI (Normalized Difference Vegetation Index), VCI (Vegetation Condition Index), and VHI (Vegetation Health Index) have been used. VHI, the combination of TCI and VCI, represents the vegetation stress affected by meteorological factors like precipitation and temperature and is frequently used to assess droughts under climate change. TCI and VCI require historical reference values for the LST and NDVI for each date and location. So, it is complicated to produce the VHI from the recent satellite GK2A (Geostationary Korea Multi-Purpose Satellite-2A). This study examined the retrieval of VHI using GK2A AMI (Advanced Meteorological Imager) by referencing the historical data from VIIRS (Visible Infrared Imaging Radiometer Suite) NDVI and LST as a proxy data. We found a close relationship between GK2A and VIIRS data needed for the retrieval of VHI. We produced the TCI, VCI, and VHI for GK2A during 2020-2021 at intervals of 8 days and carried out the interpretations of recent extreme weather events in Korea. GK2A VHI could express the changes in vegetation stress in 2020 due to various extreme weather events such as heatwaves (in March and June) and low temperatures (in April and July), and heavy rainfall (in August), while NOAA (National Oceanic and Atmospheric Administration) VHI could not well represent such characteristics. The GK2A VHI presented in this study can be utilized to monitor the vegetation stress due to heatwaves and droughts if the historical reference values of LST and NDVI can be adjusted in a more statistically significant way in the future work.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.3
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• pp.164-178
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• 2022

Uncertainties in weather forecasts would affect the reliability of yield prediction using crop models. The objective of this study was to compare uncertainty in crop yield prediction caused by the use of the weather forecast data. Daily weather data were produced at 10 km spatial resolution using W eather Research and Forecasting (W RF) model. The nearest neighbor method was used to downscale these data at the resolution of 5 km (W RF5K). Parameter-elevation Regressions on Independent Slopes Model (PRISM) was also applied to the WRF data to produce the weather data at the same resolution. W RF5K and PRISM data were used as inputs to the CROPGRO-SOYBEAN model to predict crop yield. The uncertainties of the gridded data were analyzed using cumulative growing degree days (CGDD) and cumulative solar radiation (CSRAD) during the soybean growing seasons for the crop of interest. The degree of agreement (DOA) statistics including structural similarity index were determined for the crop model outputs. Our results indicated that the DOA statistics for CGDD were correlated with that for the maturity dates predicted using WRF5K and PRISM data. Yield forecasts had small values of the DOA statistics when large spatial disagreement occured between maturity dates predicted using WRF5K and PRISM. These results suggest that the spatial uncertainties in temperature data would affect the reliability of the phenology and, as a result, yield predictions at a greater degree than those in solar radiation data. This merits further studies to assess the uncertainties of crop yield forecasts using a wide range of crop calendars.