• Journal of Information Processing Systems
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• v.11 no.4
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• pp.556-572
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• 2015

This paper presents the applications of spatial interpolation and assimilation methods for satellite and ground meteorological data, including temperature, relative humidity, and precipitation in regions of Vietnam. In this work, Universal Kriging is used for spatially interpolating ground data and its interpolated results are assimilated with corresponding satellite data to anticipate better gridded data. The input meteorological data was collected from 98 ground weather stations located all over Vietnam; whereas, the satellite data consists of the MODIS Atmospheric Profiles product (MOD07), the ASTER Global Digital Elevation Map (ASTER DEM), and the Tropical Rainfall Measuring Mission (TRMM) in six years. The outputs are gridded fields of temperature, relative humidity, and precipitation. The empirical results were evaluated by using the Root mean square error (RMSE) and the mean percent error (MPE), which illustrate that Universal Kriging interpolation obtains higher accuracy than other forms of Kriging; whereas, the assimilation for precipitation gradually reduces RMSE and significantly MPE. It also reveals that the accuracy of temperature and humidity when employing assimilation that is not significantly improved because of low MODIS retrieval due to cloud contamination.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.962-964
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• 2003

In 2002 Thailand was faced with severe flooding in the North, Northeast and Central parts of the country caused by heavy rainfall of the monsoonal depression which brought about significant damages. According to the report by the Ministry of Interior and the Ministry of Agricultural and Co-operatives, the total damages were estimated to be about 6 billion bath. More than 850,000 farmers and 10 million livestock were effected. An area of 1,450,000 ha of farmland in 59 Provinces were put under water for a prolonged period. Satellite imageries were employed for mapping and monitoring the flood-inundated areas, flood damage assessment, flood hazard zoning and post-flood survey of river configuration and protection works. By integrating satellite data with other updated spatial and non-spatial data, likely flood zones can be predicted beforehand. Some examples of satellite data application to flood dis aster mitigation in Thailand during 2002 using mostly Radarsat-1 data and Landsat-7 data were illustrated and discussed in the paper. The results showed that satellite data can clearly identify and give information on the status, flooding period, boundary and damage of flooding. For comprehensive flood mitigation planning, other geo-informatic data, such as the elevation of topography, hydrological data need to be integrated. Ground truth data of the watershed area, including the water level, velocity, drainage pattern and direction were also useful for flood forecasting in the future.

• Journal of Korean Society of Disaster and Security
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• v.14 no.2
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• pp.13-23
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• 2021

This study intended to assess the reliability of topographic data using satellite imaging data. The topographical data using actual instrumentation data and satellite image data were established and applied to the rainfall-leak model, S-RAT, and the topographical data and outflow data were compared and analyzed. The actual measurement data were collected from the Water Resources Management Information System (WAMIS), and satellite image data were collected from MODIS observation sensors mounted on Terra satellites. The areas subject to analysis were selected for two rivers with more than 80% mountainous areas in the Han River basin and one river basin with more than 7% urban areas. According to the analysis, the difference between instrumentation data and satellite image data was up to 50% for peak floods and up to 17% for flood totals in rivers with high mountains, but up to 13% for peak floods and up to 4% for flood totals. The biggest difference in the video data is Landuse, which shows that MODIS satellite images tend to be recognized as cities up to 60% or more in urban streams compared to WAMIS instrumentation data, but MODIS satellite images are found to be less than 5% error in forest areas.

• 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.

• 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.

• Water for future
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• v.28 no.6
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• pp.229-236
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• 1995

The physically based heavy rainfall model developed by Ceon(1994) for storm events is modified in this study. The main parts of this paper are composed of modeling saturation vapor pressure, cloud thickness, cloud top pressure. In a different way from the previous model, cloud top temperature and albedo measured by satellite are used as input data to the model. In this paper, the defect of saturation vapor pressure equation in the previous model was improved. Furthermore, the parameters for temperature and pressure on cloud top are eliminated as well as the time of calculation in the model is decreased. Also, the results show that there are very small gab between the hourly calculated.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.3
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• pp.21-29
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• 2010

In this study, characteristics of natural disaster and damage in North Korea are examined by using CRED(Centre for Research on the Epidemiology of Disasters) data from 1980 to 2008. Result shows that most natural disasters are caused by summertime typhoon and floods with typical floods of 1995 and 2007. Also, synoptic weather condition associated with heavy rainfall in North Korea is analyzed by using satellite image and weather chart provided by JMA(Japan Meteorological Agency). The heavy rainfalls associated with flood in North Korea are mainly related to the effect of Changma front, abrupt development of southeastward moving low over Yellow Sea, convective instability at the edge of North Pacific high and passage of weakened tropical cyclone(typhoon).

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.3
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• pp.141-148
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• 2012

Food policy is considered as the most basic and central issue for all countries, while making efforts to keep each country's food sovereignty and enhance food self-sufficiency. In the case of Korea where the staple food is rice, the rice yield prediction is regarded as a very important task to cope with unstable food supply at a national level. In this study, Korean paddy Rice yield Prediction Model (KRPM) developed to predict the paddy rice yield using meteorological element and MODIS NDVI. A multiple linear regression analysis was carried out by using the NDVI extracted from satellite image. Six meteorological elements include average temperature; maximum temperature; minimum temperature; rainfall; accumulated rainfall and duration of sunshine. Concerning the evaluation for the applicability of the KRPM, the accuracy assessment was carried out through correlation analysis between predicted and provided data by the National Statistical Office of paddy rice yield in 2011. The 2011 predicted yield of paddy rice by KRPM was 505 kg/10a at whole country level and 487 kg/10a by agroclimatic zones using stepwise regression while the predicted value by KOrea Statistical Information Service was 532 kg/10a. The characteristics of changes in paddy rice yield according to NDVI and other meteorological elements were well reflected by the KRPM.

• Korean Journal of Remote Sensing
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• v.16 no.4
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• pp.293-303
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• 2000

The typhoon Jelawat, which was formed over the tropical Pacific ocean on August 1, 2000 and made a landfall over China on August 10, 2000, was observed by Korea Multi-purpose Satellite (KOMPSAT-1) Ocean Scanning Multispectral Imager (OSMI), Tropical Rainfall Measuring Mission (TRMM)/Precipitation Radar(PR) and Quick Scatterometer (QuikSCAT). In spite of discontinuous observation, important mesoscale features of typhoon depending on life cycle were detected prominently. It is possible to distinguish on the OSMI photograph between the eye-wall convection and the stratiform and other convective clouds near the center of typhoon Jelawat. The TRMM/PR observations show quite clearly the eye-wall convection, stratiform regions, and convective bands. Vertical cross section of rainfall in the genesis stage of typhoon Jelawat exhibits circular ring of intense convection surrounding the eye. The mature stage of typhoon Jelawat consists of a strong rotational circulation with clouds which are well organized about a center of low pressure. The OSMI, TRMM/PR and QuikSCAT measurements presented here agree qualitatively with each other and provide a wealth of information on the structure of typhoon Jelawat.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.1
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• pp.27-39
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• 2008

Landslides cause enormous economic losses and casualties. Korea has mountainous regions and heavy slopes in most parts of the land and has consistently built new roads and large-scale housing complexes according to its industrial and urban growth. As a result, the damage from landslides becomes greater every year. In summer, landslides frequently occur due to local torrential rains and storms. It is critical to predict the potential areas of landslides in advance and to take preventive measures to minimize consequences and to protect property and human life. The previous study on landslides mostly focused on identifying the causes of landslides in the areas where they occurred, and on analyzing landslide vulnerability around the areas without considering rainfall conditions. Thus there were not enough evaluations of the direct risk of landslides to human life. In this study, potentially risky areas for landslides were identified using the GIS data in order to evaluate direct risk on farmlands, roads, and artificial structures that were closely connected to human life. A map of landslide risk was made taking into account rainfall conditions, and a land use map was also drawn with satellite images and digital maps. Both maps were used to identify potentially risky areas for landslides.