• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1292-1294
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• 2003

This paper presents the results of research and analysis with the satellite-derived snow data. It provides the main climatic characteristics of snow cover in China and shows the variation and distribution of snow in regions of Xinjiang, Inter Mongolia and Tibet plateau. The study reveals the vicissitude periods of winter snow cover in Tibetan Plateau by using wavelet analysis with the data from 1980 to 2001. It has about 10 years large period and 3-5 years small period. The analysis shows that the extension of snow increased in recent years in Xinjiang. The results of analysis proves the relationship between winter snow cover in Tibetan Plateau and next summer precipitation in the middle and lower reaches of the Yangtze River. They have good correlation.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1261-1263
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• 2003

Based on twelve observational stations low meteorological visibility (LMV) data during November 2002 to April 2003, the spatial distribution of LMV over Hongkong area (113.8$^{\circ}$ E-114.4$^{\circ}$ E, 22.1$^{\circ}$ N-22.4$^{\circ}$ N) is studied, using a PCA method. Optical spectrum of NOAA-16 associated with LMV shows that the significant effect factors correlated with LMV in the leading mode are the difference or rate between the visible and near-IR channels and single visible channel. A successful retrieval of LMV is done and a regression equation with a multiple correlation coefficient of 0.67 is obtained.

• Journal of Integrative Natural Science
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• v.11 no.1
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• pp.30-38
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• 2018

CALPUFF is one of the recommended air pollution models by EPA with AERMOD. It has been used to simulate the ambient concentration of critical air pollutants as well as non-critical pollutants such as persistent organic matters and the organic materials causing odor. In this model, the air pollutants go through dispersion, transportation, chemical reaction, and deposition process. These mechanisms are significantly influenced by meteorological condition. This study produces the meteorological field in three different methods for the simulation of $SO_2$ using CALPUFF: 1) CALMET model by using both ground-level and aerological observation, 2) CALMET model by using MM5 results with NCEP/NCAR reanalyzed data, 3) CALMET model by using MM5 results in which FDDA is applied with NCEP/NCAR reanalyzed data as well as the meteorological data of Korea Meteorological Administration. As a result of CALPUFF model, the resolved concentration of $SO_2$ showed different behaviors in three cases. For the first case, the fluctuation of SO2 concentration was frequently observed while the fluctuation is reduced in the second and third cases. In addition, the maximum concentration of $SO_2$ in the first case was about 2~3 times higher than the second case, and about 4~6 times higher than the third case. These results can be caused by the accuracy of the resolved meteorological field. It is inferred that the meteorological field of the first case could be less accurate than other two cases. These results show that the use of correct meteorological data can improve the result of dispersion model. Moreover, the contribution of various sources such as point, line, and area sources on the ambient concentration of air pollutant can be roughly estimated from the sensitivity analysis.

• Korean Journal of Remote Sensing
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• v.37 no.1
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• pp.169-176
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• 2021

The Global Space-based Inter-Calibration System (GSICS) is an international partnership sponsored by World Meteorological Organization (WMO) to continue and improve climate monitoring and to ensure consistent accuracy between observation data from meteorological satellites operating around the world. The objective for GSICS is to inter-calibration from pairs of satellites observations, which includes direct comparison of collocated Geostationary Earth Orbit (GEO)-Low Earth Orbit (LEO) observations. One of the GSICS inter-calibration methods, the Ray-matching technique, is a surrogate approach that uses matched, co-angled and co-located pixels to transfer the calibration from a well calibrated satellite sensor to another sensor. In Korea, the first GEO satellite, Communication Ocean and Meteorological Satellite (COMS), is used to participate in the GSICS program. The National Meteorological Satellite Center (NMSC), which operated COMS/MI, calculated the Radiative Transfer Model (RTM)-based GSICS coefficient coefficients. The L1P reproduced through GSICS correction coefficient showed lower RMSE and Bias than L1B without GSICS correction coefficient applied. The calculation cycles of the GSICS correction coefficients for COMS/MI visible channel are provided annual and diurnal (2, 5, 10, 14-day), but long-term evaluation according to these cycles was not performed. The purpose of this paper is to perform evaluation depending on the annual/diurnal cycles of COMS/MI GSICS correction coefficients based on the ray-matching technique using Suomi-NPP/Visible Infrared Imaging Radiometer Suite (VIIRS) data as reference data. As a result of evaluation, the diurnal cycle had a higher coincidence rate with the reference data than the annual cycle, and the 14-day diurnal cycle was the most suitable for use as the GSICS correction coefficient.

• Atmosphere
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• v.33 no.4
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• pp.423-440
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• 2023

The updated version of Global Ocean Data Assimilation and Prediction System (GODAPS) in the NIMS/KMA (National Institute of Meteorological Sciences/Korea Meteorological Administration), which has been in operation since December 2021, is being introduced. This technical note on GODAPS2 describes main progress and updates to the previous version of GODAPS, a software tool for the operating system, and its improvements. GODAPS2 is based on Forecasting Ocean Assimilation Model (FOAM) vn14.1, instead of previous version, FOAM vn13. The southern limit of the model domain has been extended from 77°S to 85°S, allowing the modelling of the circulation under ice shelves in Antarctica. The adoption of non-linear free surface and variable volume layers, the update of vertical mixing parameterization, and the adjustment of isopycnal diffusion coefficient for the ocean model decrease the model biases. For the sea-ice model, four vertical ice layers and an additional snow layer on top of the ice layers are being used instead of previous single ice and snow layers. The changes for data assimilation include the updated treatment for background error covariance, a newly added bias scheme combined with observation bias, the application of a new bias correction for sea level anomaly, an extension of the assimilation window from 1 day to 2 days, and separate assimilations for ocean and sea-ice. For comparison, we present the difference between GODAPS and GODAPS2. The verification results show that GODAPS2 yields an overall improved simulation compared to GODAPS.

• Atmosphere
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• v.28 no.2
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• pp.201-209
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• 2018

Benefits of the next generation geostationary meteorological satellite observation (e.g., GEO-KOMPSAT-2A) are qualitatively and comprehensively described and discussed. Main beneficial phenomena for application can be listed as tropical cyclones (typhoon), high impact weather (heavy rainfall, lightning, and hail), ocean, air pollution (particulate matter), forest fire, fog, aircraft icing, volcanic eruption, and space weather. The next generation satellites with highly enhanced spatial and temporal resolution images, expanding channels, and basic and additional products are expected to create the new valuable benefits, including the contribution to the reduction of socioeconomic losses due to weather-related disasters. In particular, the new satellite observations are readily applicable to early warning and very-short time forecast application of hazardous weather phenomena, global climate change monitoring and adaptation, improvement of numerical weather forecast skill, and technical improvement of space weather monitoring and forecast. Several policy plans for expanding the application of the next generation satellite data are suggested.

• Journal of Institute of Convergence Technology
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• v.4 no.2
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• pp.27-33
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• 2014

To well design the solar energy system using solar energy, the correlation to calculate solar irradiation is basically needed. So, this study was performed to reveal the relationships between the solar irradiation and four meteorological observation data(dry bulb temperature, relative humidity, sunshine duration, and cloud cover) which are different from previous other researches. And then, we finally proposed the first order non-linear correlation from the measured solar irradiation using four meteorological observation data with MINITAB. To show the deviation of the solar irradiation between measured and calculated, this study compared using the daily total solar irradiance and the maximum peak value. From those results, the calculation error was estimated about maximum 25.4% for the daily total solar irradiance. The error of the solar irradiation between measured and calculated was made from the curve fitting error. So, solar irradiation prediction correlation with higher accuracy can be obtained using 2nd or higher order terms with four meteorological observation data.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.282-285
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• 2007

Satellite observed brightness temperature simulation using a radiative transfer model (here after, RTM) is useful for various fields, for example sensor design and channel selection by using theoretically calculated radiance data, development of satellite data processing algorithm and algorithm parameter determination before launch. This study is focused on elaborating the simulation procedure, and analyzing of difference between observed and modelled clear sky brightness temperatures. For the CMDPS (COMS Meteorological Data Processing System) development, the simulated clear sky brightness temperatures are used to determine whether the corresponding pixels are cloud-contaminated in cloud mask algorithm as a reference data. Also it provides important information for calibrating satellite observed radiances. Meanwhile, simulated brightness temperatures of COMS channels plan to be used for assessing the CMDPS performance test. For these applications, the RTM requires fast calculation and high accuracy. The simulated clear sky brightness temperatures are compared with those of MTSAT-1R observation to assess the model performance and the quality of the observation. The results show that there is good agreement in the ocean mostly, while in the land disagreement is partially found due to surface characteristics such as land surface temperature, surface vegetation, terrain effect, and so on.

• The Korean Journal of Applied Statistics
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• v.29 no.5
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• pp.935-947
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• 2016

This paper considers the analysis of patterns in meteorological research and development using a text-mining algorithm as the method of analyzing unstructured data. To analyze text data, we define a list of terms related to meteorological research and development, construct times series of a term-document matrix through data preprocessing, and identify terms that have upward or downward patterns over time. The proposed methodology is applied to multi-year plans funded by Korea Meteorological Administration research and development programs from 2011 to 2015.

• Journal of Ocean Engineering and Technology
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• v.28 no.3
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• pp.254-259
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• 2014

The infrared signature of a ship is largely influenced by the ocean environment of the operating area, which has been known to cause large changes in the signature. As a result, the weather condition has to be clearly set for an analysis of the infrared signatures. It is necessary to analyze meteorological data for all the oceans where the ship is supposed to be operated. This is impossibly costly and time consuming because of the huge size of the data. Therefore, the creation of a standard environmental variable for an infrared signature research is necessary. In this study, we compared and analyzed sampling methods to represent ocean data close to the Korean peninsula. In order to perform this research, we collected ocean meteorological records from KMA (Korea Meteorological Administration), and sampled these in numerous ways considering five variables that are known to affect the infrared signature. Specifically, a simple random sampling method for all the data and 1-D, 2-D, and 3-D stratified sampling methods were compared and analyzed by considering the mean square errors for each method.