• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.2
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• pp.277-290
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• 2018

Wind data observed by wind profiler provide wind vectors with the altitudes using PCL1300, wind computation program. As a result of application with parameters set in program currently, it is difficult to compute wind vectors in the upper air over 3 km. This id because a very strict criterion for parameters removes large amounts of data. In this study, therefore, we improve the methods of application by resetting parameters to expand data collection area of wind vectors and reduce underestimation. Although the acquisition rate of the wind vector increased from 72.2% to 92.2%, the RMSE of the wind speed maintained 1.5 m/s - 3.1 m/s, which is less than 15% of the error rate at each altitude.

• Atmosphere
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• v.24 no.4
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• pp.523-532
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• 2014

In this study, thermal tropopause height defined from WMO (World Meteorological Organization) using temperature profile derived from Advance Microwave Sounding Unit-A (AMSU-A; hereafter named AMSU) onboard EOS (Earth Observing System) Aqua satellite is retrieved. The temperature profile of AMSU was validated by comparison with the radiosonde data observed at Osan weather station. The validation in the upper atmosphere from 500 to 100 hPa pressure level showed that correlation coefficients were in the range of 0.85~0.97 and the bias was less than 1 K with Root Mean Square Error (RMSE) of ~3 K. Thermal tropopause height was retrieved by using AMSU temperature profile. The bias and RMSE were found to be -5~ -37 hPa and 45~67 hPa, respectively. Correlation coefficients were in the range of 0.5 to 0.7. We also analyzed the change of tropopause height and temperature in middle troposphere in the extreme heavy rain event (23 October, 2003) associated with tropopause folding. As a result, the distinct descent of tropopause height and temperature decrease of ~8 K at 500 hPa altitude were observed at the hour that maximum precipitation and maximum wind speed occurred. These results were consistent with ERA (ECMWF Reanalysis)-Interim data (potential vorticity, temperature) in time and space.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.1
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• pp.93-102
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• 2019

In this study, in order to analyze the possibility of observing a typhoon track based on the Global Navigation Satellite System(GNSS), Typhoon NARI, the 11th typhoon of 2007, was analyzed in terms of the typhoon track as well as the local variation of perceptible water over time. The perceptible water was estimated using data obtained from observatories located on the typhoon track from Jeju to the southern coast of Korea for a total of 18 days from September 7(DOY 250) to September 24(DOY 267), 2007, including the period when the observatories were affected by the typhoon at full-scale, as well as one previous week and one following week. The results show that the trend of the variation of perceptible water was similar between the observatories near the typhoon track. Variation of perceptible water over time depending on the development and landing of the typhoon was distinctively observed. Several hours after the daily maximum of perceptible water was found at the JEJU Observatory, the first struck by the typhoon on the typhoon track, the maximum value was found at other observatories located on the southern coast. In the observation period, the time point at which the maximum perceptible water was recorded in each location was almost the same as the time point at which the typhoon landed at the location. To analyze the accuracy of the GNSS-based perceptible water measurement, the data were compared with radiosonde-based perceptible water data. The mean error was 0.0cm, and the root mean square error and the standard deviation were both 0.3cm, indicating that the GNSS-based perceptible water data were highly accurate and precise. The results of the this study show that the GNSS-based perceptible water data may be used as highly accurate information for the analysis of typhoon tracks over time.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1591-1604
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• 2023

In ocean color remote sensing, atmospheric correction is a vital process for ensuring the accuracy and reliability of ocean color products. Furthermore, in recent years, the remote sensing community has intensified its requirements for understanding errors in satellite data. Accordingly, research is currently addressing errors in remote sensing reflectance (R_rs) resulting from inaccuracies in meteorological variables (total ozone, pressure, wind field, and total precipitable water) used as auxiliary data for atmospheric correction. However, there has been no investigation into the error in R_rs caused by the variability of the water vapor profile, despite it being a recognized error source. In this study, we used the Second Simulation of a Satellite Signal Vector version 2.1 simulation to compute errors in water vapor transmittance arising from variations in the water vapor profile within the GOCI-II observation area. Subsequently, we conducted an analysis of the associated errors in ocean color products. The observed water vapor profile not only exhibited a complex shape but also showed significant variations near the surface, leading to differences of up to 0.007 compared to the US standard 62 water vapor profile used in the GOCI-II atmospheric correction. The resulting variation in water vapor transmittance led to a difference in aerosol reflectance estimation, consequently introducing errors in R_rs across all GOCI-II bands. However, the error of R_rs in the 412-555 nm due to the difference in the water vapor profile band was found to be below 2%, which is lower than the required accuracy. Also, similar errors were shown in other ocean color products such as chlorophyll-a concentration, colored dissolved organic matter, and total suspended matter concentration. The results of this study indicate that the variability in water vapor profiles has minimal impact on the accuracy of atmospheric correction and ocean color products. Therefore, improving the accuracy of the input data related to the water vapor column concentration is even more critical for enhancing the accuracy of ocean color products in terms of water vapor absorption correction.