• Journal of the Korean Association of Geographic Information Studies
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• v.21 no.1
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• pp.46-56
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• 2018

This paper aimed to identify main factors of community characters, which have an effect on the land surface temperature(LST) change and estimate the impacting coefficient(ratio) of factors in a significant level of statistics. Chungcheongbuk-do province was selected and then partitioned into city and county areas for the sake of convenience of modeling. LST time series data and the community character data were developed based on Terra Satellite MODIS data and collected from the National Statistical Office, respectively. By the cause and effect relationship between community characters and LST, regression coefficients were estimated using a penal model. In a panel modeling, LST and community characters were used as a dependent variable and explanatory variables, respectively. Panel modeling analysis was carried out using statistical package STATA14 and one-way fixed effect model was selected as the most suitable model to evaluate the regression coefficients in the study area. The impacting ratio of LST change by any explanatory variable derived from the regression coefficients of the panel model fixed. Impacting ratios for industrial areas, elevation ${\times}$ building, energy usage, average window speed, non-urban management area, agricultural, nature and environmental conservation, average precipitation were 3.746, 2.856, 2.742, 0.553, 0.102, 0.071 and 0.003, respectively.

• Journal of the Korean Geographical Society
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• v.45 no.5
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• pp.573-591
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• 2010

In this study, trends of heat waves in the populous Seoul metropolitan area over the last 100 years (1908-2007) and spatio-temporal patterns of extreme heat waves and excessive human mortality are examined. In spite of recent global warming, there is no observable increasing or decreasing pattern in the frequency and intensity of heat waves in Seoul due to increases of summer precipitation. Among numerous episodes over the last 100 years in Seoul, 1994 summer is recognized as the unprecedented, most extreme hot episode with long-lasting, intense heat waves Meteorological data observed at the Automatic Weather Stations (AWS) and land surface temperature data derived from Landsat TM satellite imagery in July 1994 reveal that extreme heat waves cause more abnormal increase of elderly mortality in the urbanized areas than in the surroundings covered with more vegetation. This study provides bioclimatological evidences of why urban thermal environments should be seriously considered in the future urban revitalization planning.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.104-104
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• 2020

강수량은 수문 순환의 결정적인 연결 고리이며 공간적, 시간적 변화는 매우 크며, 또한 전 세계적인 범위의 강수량 자료는 지구상의 수문 순환에 대한 이해와 날씨 및 기후 예측을 위해 필요하다. 그리고 지역적 강수량에 대한 지식은 사회 복지에 필수적이다. 지상에 있는 강우관소에서 관측된 강우는 본질적으로 강우의 공간적 불균일성을 반영하기 어려우며, 관측 주기가 하루 이상으로 긴 경우에는 홍수와 연계한 생태-수문학 연구에 적용하는데 한계가 있다. 또한, 지상계측 방법은 해양, 극지방 및 산악지역의 강수량을 관찰하는데 어려움이 있다. 이에 반하여 원격탐사 기술은 지구 강수를 관찰하는데 많은 도움을 주는 기술로 인식되고 있다. 위성자료를 이용한 강우 추정은 지상 강우관측소 및 기상레이더와 비교하여 광역적 공간범위를 대상으로 하며, 지속적이고 균일한 강우를 생산한다는 장점을 갖고 있다(Hong et al. 2016). 위성강우 자료는 일반적으로 전 세계 강수량에 대한 지식과 글로벌 수문순환에 대한 연구를 촉진하고 있으며, 특히, 동아시아, 동남아시아, 아프리카 등지에는 수문학적 미계측 지역이 많기 때문에 위성강우 자료를 이용한 강수량 평가에 대한 연구가 다수 진행되고 있다(Hoscilo et al., 2015; Dembélé et al., 2016; Dandridge et al., 2019; Kim et al., 2019; Yuan et al., 2019). 본 연구는 위성으로부터 유도된 강수자료 중 NASA의 IMERG, NOAA의 CMORPH, 그리고 일본 JAXA의 GSMaP의 위성강우자료와 국내의 ASOS 시간강우자료의 비교를 통해 위성강우의 정확도를 평가하는 것을 목적으로 하고 있다. 분석 결과 총강우에 대한 편이는 그림 1에서 보는바와 같이 CMORPH가 가장 작고 가장 최근에 제공되기 시작한 IMERG 강수자료가 가장 큰 것으로 분석되었다. 지상계측강우와의 상관계수는 1시간 및 3시간의 시간해상도에서 2019년 18호 태풍 미탁(Mitak)의 경우 IMERG 및 GSMaP 각각 0.63 및 0.60와 0.73 및 0.70으로 분석되었다.

• Journal of Korea Water Resources Association
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• v.30 no.5
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• pp.441-447
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• 1997

Rainfall observation using rain gage network or satellites includes the sampling error depending on the observation methods or plans. For example, the sampling using rain gages is continuous in time but discontinuous in space, which is nothing but the source of the sampling error. The sampling using satellites is the reverse case that continuous in space and discontinuous in time. The sampling error may be quantified by use of the temporal-spatial characteristics of rainfall and the sampling design. One of recent works on this problem was done by North and Nakamoto (1989), who derived a formulation for estimating the sampling error based on the temporal-spatial rainfall spectrum and the design scheme. The formula enables us to design an optimal rain gage network or a satellite operation plan providing the statistical characteristics of rainfall. In this paper the formula is reviewed and applied for the sampling error problems using several multi-dimensional precipitation models. The results show the limitation of the formulation, which cannot distinguish the model difference in case the model parameters can reproduce similar second order statistics of rainfall. The limitation can be improved by developing a new way to consider the higher order statistics, and eventually the probability density function (PDF) of rainfall.

• Journal of the Korean earth science society
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• v.33 no.2
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• pp.113-125
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• 2012

The estimations of the surface rain intensity and rain-related physical variables derived from two independent Tropical Rainfall Measuring Mission (TRMM) satellite sensors, TRMM Microwave Imager (TMI) and Precipitation Radar (PR), were compared over four different oceans. The precipitating clouds developed most frequently in the warmest sea surface temperature (SST) region of the west Pacific, which is 1.5 times more frequent than in the east Pacific and the tropical Atlantic oceans. However, the east Pacific exhibited the most intense rain intensity for the convective and mixed rain types while the tropical Atlantic showed the most intense rain intensity for all TMI rainy pixels. It was found that the deviation of TMI-derived rain rate yielded a big difference in region-to-region and rain type-to-type if the PR rain intensity value is assumed to be closer to the truth. Furthermore, the deviation by rain types showed opposite signs between convective and non-convective rain types. It was found that the region-to-region deviation differences reached more than 200% even though the selected tropical oceans have relatively similar geophysical environments. Therefore, the validation for the microwave rain estimation needs to be performed according to both rain types and climate regimes, and it also requires more sophisticated TMI algorithm which reflects the locality of rainfall characteristics.

• Journal of Korea Water Resources Association
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• v.54 no.8
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• pp.577-587
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• 2021

Drought is generally considered to be a natural disaster caused by accumulated water shortages over a long period of time, taking months or years and slowly occurring. However, climate change has led to rapid changes in weather and environmental factors that directly affect agriculture, and extreme weather conditions have led to an increase in the frequency of rapidly developing droughts within weeks to months. This phenomenon is defined as 'Flash Drought', which is caused by an increase in surface temperature over a relatively short period of time and abnormally low and rapidly decreasing soil moisture. The detection and analysis of flash drought is essential because it has a significant impact on agriculture and natural ecosystems, and its impacts are associated with agricultural drought impacts. In South Korea, there is no clear definition of flash drought, so the purpose of this study is to identify and analyze its characteristics. In this study, flash drought detection condition was presented based on the satellite-derived drought index Evaporative Stress Index (ESI) from 2014 to 2018. ESI is used as an early warning indicator for rapidly-occurring flash drought a short period of time due to its similar relationship with reduced soil moisture content, lack of precipitation, increased evaporative demand due to low humidity, high temperature, and strong winds. The flash droughts were analyzed using hydrometeorological characteristics by comparing Standardized Precipitation Index (SPI), soil moisture, maximum temperature, relative humidity, wind speed, and precipitation. The correlation was analyzed based on the 8 weeks prior to the occurrence of the flash drought, and in most cases, a high correlation of 0.8(-0.8) or higher(lower) was expressed for ESI and SPI, soil moisture, and maximum temperature.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05a
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• pp.43-50
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• 2002

Accurate quantitative forecasting of rainfall for basins with a short response time is essential to predict streamflow and flash floods. Previously, neural networks were used to develop a Quantitative Precipitation Forecasting (QPF) model that highly improved forecasting skill at specific locations in Pennsylvania, using both Numerical Weather Prediction (NWP) output and rainfall and radiosonde data. The objective of this study was to improve an existing artificial neural network model and incorporate the evolving structure and frequency of intense weather systems in the mid-Atlantic region of the United States for improved flood forecasting. Besides using radiosonde and rainfall data, the model also used the satellite-derived characteristics of storm systems such as tropical cyclones, mesoscale convective complex systems and convective cloud clusters as input. The convective classification and tracking system (CCATS) was used to identify and quantify storm properties such as life time, area, eccentricity, and track. As in standard expert prediction systems, the fundamental structure of the neural network model was learned from the hydroclimatology of the relationships between weather system, rainfall production and streamflow response in the study area. The new Quantitative Flood Forecasting (QFF) model was applied to predict streamflow peaks with lead-times of 18 and 24 hours over a five year period in 4 watersheds on the leeward side of the Appalachian mountains in the mid-Atlantic region. Threat scores consistently above .6 and close to 0.8 ∼ 0.9 were obtained fur 18 hour lead-time forecasts, and skill scores of at least 4％ and up to 6％ were attained for the 24 hour lead-time forecasts. This work demonstrates that multisensor data cast into an expert information system such as neural networks, if built upon scientific understanding of regional hydrometeorology, can lead to significant gains in the forecast skill of extreme rainfall and associated floods. In particular, this study validates our hypothesis that accurate and extended flood forecast lead-times can be attained by taking into consideration the synoptic evolution of atmospheric conditions extracted from the analysis of large-area remotely sensed imagery While physically-based numerical weather prediction and river routing models cannot accurately depict complex natural non-linear processes, and thus have difficulty in simulating extreme events such as heavy rainfall and floods, data-driven approaches should be viewed as a strong alternative in operational hydrology. This is especially more pertinent at a time when the diversity of sensors in satellites and ground-based operational weather monitoring systems provide large volumes of data on a real-time basis.

• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.235-258
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• 2011

The purpose of this study is to investigate climatological variations from the temporal and spatial surface particulate organic carbon (POC) estimates based on SeaWiFS spectral radiance, and to determine the physical mechanisms that affect the distribution of pac in the Gulf of Mexico. 7-year monthly mean values of surface pac concentration (Sept. 1997 - Dec. 2004) were estimated from Maximum Normalized Difference Carbon Index (MNDCI) algorithm using SeaWiFS data. Synchronous 7-year monthly mean values of remote sensing data (sea surface temperature (SST), sea surface wind (SSW), sea surface height anomaly (SSHA), precipitation rate (PR)) and recorded river discharge data were used to determine physical forcing factors. The spatial pattern of POC was related to one or more factors such as river runoff, wind-derived current, and stratification of the water column, the energetic Loop Current/Eddies, and buoyancy forcing. The observed seasonal change in the POC plume's response to wind speed in the western delta region resulted from seasonal changes in the upper ocean stratification. During late spring and summer, the low-density river water is heated rapidly at the surface by incoming solar radiation. This lowers the density of the fresh-water plume and increases the near-surface stratification of the water column. In the absence of significant wind forcing, the plume undergoes buoyant spreading and the sediment is maintained at the surface by the shallow pycnocline. However, when the wind speed increases substantially, wind-wave action increases vertical motion, reducing stratification, and the sediment were mixed downward rather than spreading laterally. Maximum particle concentrations over the outer shelf and the upper slope during lower runoff seasons were related to the Loop Current/eddies and buoyancy forcing. Inter-annual differences of POC concentration were related to ENSO cycles. During the El Nino events (1997-1998 and 2002-2004), the higher pac concentrations existed and were related to high runoffs in the eastern Gulf of Mexico, but the opposite conditions in the western Gulf of Mexico. During La Nina conditions (1999-2001), low Poe concentration was related to normal or low river discharge, and low PM/nutrient waters in the eastern Gulf of Mexico, but the opposite conditions in the western Gulf of Mexico.

• Korean Journal of Remote Sensing
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• v.11 no.3
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• pp.1-21
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• 1995

The characteristics of the Asian summer monsoon have been investigated for the periods of 1993/1994, the contrasting years in a view of the summer monsoon precipitation. In order to investigate the monsoon features over the eastern Asian monsoon region, the cloudiness(using the extensive data derived by the geostationary meteorological satellite), the condition of underlying surface including sea-surface temperature, and the summer rainfall are analyzed and some comparisons with 1993 and 1994 are also made and the characteristic differences are discussed. An analysis of the 2-degree latitude-longitude gridded 5-day mean high cloud amount data shows the detailed movement and persistence of the convective activities. In order to describe the spatial and temporal structures of the intraseasonal oscillation for the movement and evolution of the monsoon cloud, the extended empirical orthogonal fnction analysis with the twenty-day window size is used for the each year. Also, in order to find out the periodicity of the equatorial convective cluster, Fourier harmonic analysis is applied to the each year. The most prevailing intraseasonal oscillations of high cloud amount are 61 day mode and 15day mode in the equatorial and the subtropical oceans. However it was found that the most prevailing modes over the equatorial western Pacific and Indian Ocean were different for each year, hence raising the possibillity that the contrasting monsoon presipitation may be more fundamentally related to the interaction of intraseasonal oscillations and seasonal variation of convective activities over the lower latitude ocean.

• Korean Journal of Agricultural and Forest Meteorology
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• v.4 no.4
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• pp.224-236
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• 2002

A long-term growth simulation was performed at 496 land units in the western coastal plains (WCP) of North Korea to test the potential adaptability of each land unit for growing South Korean rice cultivars. The land units for rice cultivation (CZU), each of them represented by a geographically referenced 5 by 5 km grid tell, were identified by analyzing satellite remote sensing data. Surfaces of monthly climatic normals for daily maximum and minimum temperature, precipitation number of rain days and solar radiation were generated at a 1 by 1 km interval by spatial statistical methods using observed data at 51 synoptic weather stations in North and South Korea during 1981-2000. Grid cells felling within a same CZU and, at the same time, corresponding to the satellite data- identified rice growing pixels were extracted and aggregated to make a spatially explicit climatic normals relevant to the rice growing area of the CZU. Daily weather dataset for 30 years was randomly generated from the monthly climatic normals of each CZU. Growth and development parameters of CERES-rice model suitable for 11 major South Korean cultivars were derived from long-term field observations. Eight treatments comprised of 2 transplanting dates $\times$ 2 cropping systems $\times$ 2 irrigation methods were assigned to each cultivar. Each treatment was simulated with the randomly generated 30 years' daily weather data (from planting to physiological maturity) for 496 land units in WCP to simulate the growth and yield responses to the interannual climate variation. The same model was run with the input data from the 3 major crop experiment stations in South Korea to obtain a 30 year normal performance of each cultivar, which was used as a "reference" for comparison. Results were analyzed with respect to spatial and temporal variation in yield and maturity, and used to evaluate the suitability of each land unit for growing a specific South Korean cultivar. The results may be utilized as decision aids for agrotechnology transfer to North Korea, for example, germplasm evaluation, resource allocation and crop calendar preparation.