• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.227-236
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• 2009

This study investigates the characteristics of cloud top brightness temperature (CTBT) of WV and IR1 from MTSAT-1R when lightning strikes in South Korea. For temporal and spatial collocations, lightnings, occurred only within ${\pm}5$ minutes from the six minutes added official satellite observation time (e.g., not 0600 UTC but 0606 UTC, considering the real scan time over South Korea), were selected. And the CTBTs corresponding to lightning spots were determined using the nearest pixel within 5 km. The brightness temperature difference (BTD, defined as WV - IR1) between two channels is negatively large when no lightning occurrs, whereas it increases up to positive values (sometimes, +5 K) and the largest frequency distributes around 225 K and 205 K in lightning cases. The probablistic approach for lightning frequency forecast, presented by Machado et al. (2008) in Southern America, was applied over South Korea and new exponential equations, with high coefficients of determination around 0.95 to 0.99, were developed using two channels' BTDs when lightning strikes. Moreover, a case study on 10th June, 2006, the largest number of lightning occurred between 2002 and 2006, was made. The major finding is that lightning activity is closely related to the dramatic decreases in BT and the increases in BTD (esp., equal to or larger than 0 K). Lightning frequency increases exponentially when BTD increases up to 0 K. Therefore, lightning forecast skill will be improved when the integrated strategy (synoptic background and satellite-based CTBT and BTD) is applied. It is believed that this study contributes to the application of the Korean first geostationary satellite (COMS), scheduled to launch at the end of this year, to severe weather detections.

• Journal of the Korean Regional Science Association
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• v.31 no.3
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• pp.99-112
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• 2015

Observation of land surface temperature in industrial areas is problematic, as it is not possible to construct a network of weather stations with sufficiently high density and continuous operation in such zones. Multiphase remote sensing data that cover a wide area and take a short time to process can enable the user to precisely and continuously measure the current and changing land surface temperatures in a certain region. Jeollanam-Do in South Korea is undergoing rapid industrialization, with the establishment of a number of industrial complexes, such as the Gwangyang Steelworks, Yeosu Industrial Complex, Yulchon Industrial complex, and Daebul Industrial Complex. To look into the properties of industrial complex's temperature, this study uses the thermal band of Landsat 7 ETM+ images acquired under thermal infrared wavelengths in order to calculate and compare the surface temperatures of the four above-named industrial complexes. From this, it is possible to obtain the basic information about industrial complex for environmental and natural resource management, which will aid industrial complex planners in developing methods of addressing environmental problems.

• Atmosphere
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• v.18 no.4
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• pp.441-458
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• 2008

A TP/WVP-3000A, ground-based microwave radiometer, that was first introduced to South Korea has been operated since August 22, 2007 at the National Center for Intensive Observation of Severe Weathers (NCIO). Using the dual-band, the radiometer provides temperature and humidity soundings from the surface up to 10 km height with the high-temporal resolution of a few minutes. In this study, the performance of the radiometer on the predictability of the high impact weathers was evaluated and various practical applications were investigated. To verify the retrieved profile data from the radiometer, temperature and relative humidity soundings are compared with those from the rawinsonde launched at the NCIO and Gwangju station. The root mean squared errors for temperature and relative humidity soundings were smaller under rainy weather conditions. The correlation coefficient between PWVs (Precipitable Water Vapors) obtained from the radiometer and Global Positioning System satellite at Mokpo station is 0.92 on average. In order to investigate the structure and characteristics of precipitation, stability indexes related to rainfall such as the Convective Available Potential Energy (CAPE), K-index, and Storm RElative Helicity (SREH) were calculated using windprofiler at the NCIO from 14 to 16 September, 2007. CAPE and K-index tended to be large when the thermodynamic unstability was strong. On the other hand, SREH index was dominantly large when the dynamic unstability was strong due to the passage of the typhoon 'Nari'.

• Ocean Systems Engineering
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• v.14 no.1
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• pp.85-99
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• 2024

Ocean surface currents have an essential role in the Earth's climate system and significantly impact the marine ecosystem, weather patterns, and human activities. However, predicting ocean surface currents remains challenging due to the complexity and variability of the oceanic processes involved. This review article provides an overview of the current research status, challenges, and opportunities in the prediction of ocean surface currents. We discuss the various observational and modelling approaches used to study ocean surface currents, including satellite remote sensing, in situ measurements, and numerical models. We also highlight the major challenges facing the prediction of ocean surface currents, such as data assimilation, model-observation integration, and the representation of sub-grid scale processes. In this article, we suggest that future research should focus on developing advanced modeling techniques, such as machine learning, and the integration of multiple observational platforms to improve the accuracy and skill of ocean surface current predictions. We also emphasize the need to address the limitations of observing instruments, such as delays in receiving data, versioning errors, missing data, and undocumented data processing techniques. Improving data availability and quality will be essential for enhancing the accuracy of predictions. The future research should focus on developing methods for effective bias correction, a series of data preprocessing procedures, and utilizing combined models and xAI models to incorporate data from various sources. Advancements in predicting ocean surface currents will benefit various applications such as maritime operations, climate studies, and ecosystem management.

• Atmosphere
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• v.26 no.4
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• pp.509-522
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• 2016

Heavy snowfall events have occurred frequently in the Yeongdong region but understanding of these events have trouble in lack of snowfall observation in this region because it is composed of complex topography like the "Taebaek mountains" and the "East sea". These problems can be solved by quantitative precipitation estimation technique using remote sensing such as radar, satellite, etc. Two radars which are able to cover over Yeondong region were installed at Gangneung (GNG) and Gwangdeoksan (GDK). This study uses radar and water equivalent of snow cover to investigate the characteristics of radar echoes and the $Z_e-R$ relations associated with the 10 Yeongdong heavy snowfall events during the last 5 years (2010~2014). It was found that the heights which the probability of detection (POD) of snow detection by GNG radar is more than 80% are 3,000 m and 1,500 m in convective cloud and stratiform cloud, respectively. The vertical gradient of radar reflectivity is less decreased in convective cloud than stratiform cloud. However, POD by GDK radar are lower than 80% at all layers because the majority of Yeondong observational stations are more than 100 km away from GDK radar site. Furthermore, we examined $Z_e-R$ relation from the 10 events using GNG radar and compared the "a" and "b" obtained from these examinations at Sokcho (SC) and Daegwallyeong (DG). These "a" and "b" are estimated from radar echo at 500 m (SC) and 1,500 m (DG). The values of "a" differ in their stations such as SC and DG are 30~116 and 6~39, respectively. But "b" is 0.4~1.7 irrespective of stations. Moreover, the value of "a" increased with surface air temperature. Therefore, quantitative precipitation estimation in heavy snowfall events by radar echo using fixed "a" and "b" is difficult because these values changed according to those precipitation characteristics.

• Korean Journal of Remote Sensing
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• v.36 no.6_1
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• pp.1449-1463
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• 2020

As recent global warming and climate changes become more serious, the importance of CO2 absorption by forests is increasing to cope with the greenhouse gas issues. According to the UN Framework Convention on Climate Change, it is required to calculate national CO2 absorptions at the local level in a more scientific and rigorous manner. This paper presents the gridded expansion of forest flux observations and mapping of daily CO2 absorption by the forests in Korea using numerical weather prediction data and satellite images. To consider the sensitive daily changes of plant photosynthesis, we built a machine learning model to retrieve the daily RACA (reference amount of CO2 absorption) by referring to the climax forest in Gwangneung and adopted the NIFoS (National Institute of Forest Science) lookup table for the CO2 absorption by forest type and age to produce the daily AACA (actual amount of CO2 absorption) raster data with the spatial variation of the forests in Korea. In the experiment for the 1,095 days between Jan 1, 2013 and Dec 31, 2015, our RACA retrieval model showed high accuracy with a correlation coefficient of 0.948. To achieve the tier 3 daily statistics for AACA, long-term and detailed forest surveying should be combined with the model in the future.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.321-335
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• 2021

Particulate matter (PM10 and PM2.5 with a diameter less than 10 and 2.5 ㎛, respectively) can be absorbed by the human body and adversely affect human health. Although most of the PM monitoring are based on ground-based observations, they are limited to point-based measurement sites, which leads to uncertainty in PM estimation for regions without observation sites. It is possible to overcome their spatial limitation by using satellite data. In this study, we developed machine learning-based retrieval algorithm for ground-level PM10 and PM2.5 concentrations using aerosol parameters from Geostationary Ocean Color Imager (GOCI) satellite and various meteorological parameters from a numerical weather prediction model during January to December of 2019. Gradient Boosted Regression Trees (GBRT) and Light Gradient Boosting Machine (LightGBM) were used to estimate PM concentrations. The model performances were examined for two types of feature sets-all input parameters (Feature set 1) and a subset of input parameters without meteorological and land-cover parameters (Feature set 2). Both models showed higher accuracy (about 10 % higher in R2) by using the Feature set 1 than the Feature set 2. The GBRT model using Feature set 1 was chosen as the final model for further analysis(PM10: R2 = 0.82, nRMSE = 34.9 %, PM2.5: R2 = 0.75, nRMSE = 35.6 %). The spatial distribution of the seasonal and annual-averaged PM concentrations was similar with in-situ observations, except for the northeastern part of China with bright surface reflectance. Their spatial distribution and seasonal changes were well matched with in-situ measurements.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.1
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• pp.85-97
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• 2017

In recent years, the damage scales of water disasters such as typhoons, tsunamis, and heavy snow have been increasing globally as a result of global warming and climate changes. In particular, the economic loss caused by typhoons has been increasing for overpopulated areas that have undergone economic development and urbanization since the 1960s. In this study, we investigated and analyzed satellite images captured before and after typhoons on the Korean peninsula, including Typhoon Chaba (2016), Typhoon Rusa ('02), and Typhoon Maemi ('03). There was a limitation in utilizing existing satellites. Domestic satellites have mostly been developed and operated for the observation of the weather, ocean, and topography, as well as for use in communication. There are therefore insufficient temporal and spatial observations for water management and disaster response. In this work, we expanded the scope to overseas satellites and collected data from GMS, TRMM, COMS, and GPM. In the future, it will be necessary to develop and launch water resources satellites that can provide sufficient temporal and spatial data analysis units to obtain rapid and accurate water hazard information for the Korean peninsula.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.1
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• pp.113-121
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• 2015

Remote sensing technology using a multi-spectral satellite imagery can be utilized for the analysis of urban heat island effect in large area. However, weather condition of Korea mostly has a lot of clouds and it makes periodical observation using time-series of satellite images difficult. For this reason, we proposed the analysis of urban heat island effect using time-series of Landsat TM images and ATC model. To analyze vegetation condition and urbanization, NDVI and NDBI were calculated from Landsat images. In addition, land surface temperature was calculated from thermal infrared images to estimate the parameters of ATC model. Furthermore, the parameters of ATC model were compared based on the land cover map created by Korean Ministry of Environment to analyze urban heat island effect relating to the pattern of land use and land cover. As a result of a correlation analysis between calculated spectral indices and parameters of ATC model, MAST had high correlation with NDVI and NDBI (-0.76 and 0.69, respectively) and YAST also had correlation with NDVI and NDBI (-0.53 and 0.42, respectively). By comparing the parameters of ATC model based on land cover map, urban area had higher MAST and YAST than agricultural land and grassland. In particular, residential areas, industrial areas, commercial areas and transportation facilities showed higher MAST than cultural facilities and public facilities. Moreover, residential areas, industrial areas and commercial areas had higher YAST than the other urban areas.

• Journal of the Korean earth science society
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• v.38 no.4
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• pp.283-292
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• 2017

In this study, the empirical models were established to estimate the concentrations of surface-level $PM_{2.5}$ over Seoul, Korea from 1 January 2012 to 31 December 2013. We used six different multiple linear regression models with aerosol optical thickness (AOT), ${\AA}ngstr{\ddot{o}}m$ exponents (AE) data from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua satellites, meteorological data, and planetary boundary layer depth (PBLD) data. The results showed that $M_6$ was the best empirical model and AOT, AE, relative humidity (RH), wind speed, wind direction, PBLD, and air temperature data were used as input data. Statistical analysis showed that the result between the observed $PM_{2.5}$ and the estimated $PM_{2.5}$ concentrations using $M_6$ model were correlations (R=0.62) and root square mean error ($RMSE=10.70{\mu}gm^{-3}$). In addition, our study show that the relation strongly depends on the seasons due to seasonal observation characteristics of AOT, with a relatively better correlation in spring (R=0.66) and autumntime (R=0.75) than summer and wintertime (R was about 0.38 and 0.56). These results were due to cloud contamination of summertime and the influence of snow/ice surface of wintertime, compared with those of other seasons. Therefore, the empirical multiple linear regression model used in this study showed that the AOT data retrieved from the satellite was important a dominant variable and we will need to use additional weather variables to improve the results of $PM_{2.5}$. Also, the result calculated for $PM_{2.5}$ using empirical multi linear regression model will be useful as a method to enable monitoring of atmospheric environment from satellite and ground meteorological data.