• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.141-152
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• 2017

This paper presents a technical method for flood estimation based on satellite rainfall and satellite rainfall correction method for watersheds lacking measurement data. The study area was the Sebou Watershed, Morocco. The Integrated Flood Analysis System(IFAS) and Grid-based Rainfall-Runoff Model(GRM) were applied to estimate watershed runoff. Daily rainfall from ground gauges and satellite-derived hourly data were used. In the runoff simulation using satellite rainfall data, the composites of the daily gauge rainfall and the hourly satellite data were applied. The Shuttle Radar Topographic Mission Digital Elevation Model(SRTM DEM) with a 90m spatial resolution and 1km resolution data from Global map land cover and United States Food and Agriculture Organization(US FAO) Harmonized World Soil Database(HWSD) were used. Underestimated satellite rainfall data were calibrated using ground gauge data. The simulation results using the revised satellite rainfall data were $5,878{\sim}7,434m^3/s$ and $6,140{\sim}7,437m^3/s$ based on the IFAS and GRM, respectively. The peak discharge during flooding of Sebou River Watershed in 2009~2010 was estimated to range from $5,800m^3/s$ to $7,500m^3/s$. The flood estimations from the two hydrologic models using satellite-derived rainfall data were similar. Therefore, the calibration method using satellite rainfall suggested in this study can be applied to estimate the flood discharge of watersheds lacking observational data.

• Atmosphere
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• v.20 no.2
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• pp.131-151
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• 2010

This study establishes a conceptual model to analyze heavy rainfall events in Korea using multi-functional transport satellite-1R satellite images. Three heavy rainfall episodes in two major synoptic types, such as synoptic low (SL) type and synoptic flow convergence (SC) type, are analyzed through a conceptual model procedure which proceeds on two steps: 1) conveyer belt model analysis to detect convective area, and 2) cloud top temperature analysis from black body temperature (TBB) data to distinguish convective cloud from stratiform cloud, and eventually estimate heavy rainfall area and intensity. Major synoptic patterns causing heavy rainfall are Changma, synoptic low approach, upper level low in the SL type, and upper level low, indirect effect of typhoon, convergence of tropical air in the SC type. The relationship between rainfall and TBBs in overall well resolved areas of heavy rainfall. The SC type tended to underestimate the intensity of heavy rainfall, but the analysis with the use of water vapor channel has improved the performance. The conceptual model improved a concrete utilization of images and data of satellite, as summarizing characteristics of major synoptic type causing heavy rainfall and composing an algorism to assess the area and intensity of heavy rainfall. The further assessment with various cases is required for the operational use.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.188-188
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• 2017

Rainfall is an important input to hydrological models. The accuracy of hydrological studies for water resources and floods management depend primarily on the estimation of rainfall. Thailand is among the countries that have regularly affected by floods. Flood forecasting and warning are necessary to prevent or mitigate loss and damage. Merging near real time satellite-based precipitation estimation with relatively high spatial and temporal resolutions to ground gauged precipitation data could contribute to reducing uncertainty and increasing efficiency for flood forecasting application. This study tested the applicability of satellite-based rainfall for water resources management and flood forecasting. The objectives of the study are to assess uncertainty associated with satellite-based rainfall estimation, to perform bias correction for satellite-based rainfall products, and to evaluate the performance of the bias-corrected rainfall data for the prediction of flood events. This study was conducted using a case study of Thai catchments including the Chao Phraya, northeastern (Chi and Mun catchments), and the eastern catchments for the period of 2006-2015. Data used in the study included daily rainfall from ground gauges, telegauges, and near real time satellite-based rainfall products from TRMM, GSMaP and PERSIANN CCS. Uncertainty in satellite-based precipitation estimation was assessed using a set of indicators describing the capability to detect rainfall event and efficiency to capture rainfall pattern and amount. The results suggested that TRMM, GSMaP and PERSIANN CCS are potentially able to improve flood forecast especially after the process of bias correction. Recommendations for further study include extending the scope of the study from regional to national level, testing the model at finer spatial and temporal resolutions and assessing other bias correction methods.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.117-120
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• 2006

Heavy rainfall events are occurred exceedingly various forms by a complex interaction between synoptic, dynamic and atmospheric stability. As the results, quantitative precipitation forecast is extraordinary difficult because it happens locally in a short time and has a strong spatial and temporal variations. GOES-9 imagery data provides continuous observations of the clouds in time and space at the right resolution. In this study, an power-law type algorithm(KAE: Korea auto estimator) for estimating rainfall based on the rainfall type was developed using geostationary meteorological satellite data. GOES-9 imagery and automatic weather station(AWS) measurements data were used for the classification of rainfall types and the development of estimation algorithm. Subjective and objective classification of rainfall types using GOES-9 imagery data and AWS measurements data showed that most of heavy rainfalls are occurred by the convective and mired type. Statistical analysis between AWS rainfall and GOES-IR data according to the rainfall types showed that estimation of rainfall amount using satellite data could be possible only for the convective and mixed type rainfall. The quality of KAE in estimating the rainfall amount and rainfall area is similar or slightly superior to the National Environmental Satellite Data and Information Service's auto-estimator(NESDIS AE), especially for the multi cell convective and mixed type heavy rainfalls. Also the high estimated level is denoted on the mature stage as well as decaying stages of rainfall system.

• Proceedings of the KSRS Conference
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• v.2
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• pp.660-663
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• 2006

Rainfall estimation is important to weather forecast, flood control, hydrological plan. The empirical and statistical methods by measured data(surface rain gauge, rainfall radar, Satellite) is commonly used for rainfall estimation. In this study, the rainfall intensity for East Asia region was estimated using the empirical relationship between SSM/I data of DMSP satellite and brightness temperature of GEOS-9(10.7${\mu}m$) with cloud types(ISCCP and MSG classification). And the empirical formula for rainfall estimation was produced by PMM (Probability Matching Method).

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.11a
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• pp.46-47
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• 2013

In the satellite system design, the processes from the initial design to launch take about 5 years and the broadcasting satellite lifetime goes over 15 years. Furthermore, global warming phenomenon causes rainfall rate increasing more and more in some regions on the earth. Consequently, at the stage of the satellite link design, we need to consider the future rain attenuation over 20 years. In this paper, we investigated two time-series system models for forecasting to consider the future rainfall rate for the satellite broadcasting service. We found that rainfall rate of the future 30 years is increasing continuously.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.28-35
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• 2006

Global Flood Alert System (GFAS) is an attempt to make the best use of satellite rainfall data in flood forecasting. The project of GFAS is promoted both by Ministry of Land, Infrastructure and Transport-Japan (MLIT) and Japan Aerospace Exploration Agency (JAXA), under which Infrastructure Development Institute-Japan (IDI) has been working on the development of Internet-based information system and just launched trial run of GFAS in April 2006 on International Flood Network (IFNet) website. The function of GFAS is to connect space agencies and hydrological services/river authorities in charge of flood forecasting and warning by providing global rainfall information in maps, text data e-mails and so on which is produced from binary global rainfall data downloaded from National Aeronautics and Space Administration (NASA) website. Although the effectiveness of satellite rainfall data in flood forecasting and warning has yet to be verified, satellite rainfall is expected to play an important role to strengthen existing flood forecasting systems by diversifying hydrological data source.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.53-56
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• 2014

In the satellite system design, the design processes from the initial design to launch take about 5 years and the broadcasting satellite lifetime goes over 15 years. Furthermore, global warming phenomenon causes rainfall rate to increase more and more in some regions on the earth. Consequently, at the stage of the satellite link design, we need to consider the future rain attenuation over 20 years. In this paper, we investigated a time-series system model for forecasting to consider the future rainfall rate for the satellite broadcasting service. We found that rainfall rate of the future 20 years is increasing continuously.

• Journal of Satellite, Information and Communications
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• v.11 no.3
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• pp.110-113
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• 2016

In this paper of ka band satellite communication using geostationary satellite is very weak to rainfall. So the rain attenuation reflect the values calculated using the satellite communication links vulnerable when designing a more reliable rainfall area distribution of rain attenuation and accurate predictive models must analyze the link budget. In this paper, by utilizing domestic distribution analysis in the recent local rainfall Crane and regional rainfall in the model and compared with the country of the regional distribution of rainfall in your area to fit the rain attenuation in Ka band frequency characteristics Crane rain attenuation prediction models were analyzed to between geostationary satellites and ground station position, distance and year time percentage(%).

• Proceedings of the KSRS Conference
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• v.2
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• pp.930-933
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• 2006

A new algorithm for satellite microwave rainfall retrievals over the land of Taiwan using TMI (TRMM Microwave Imager) data on board TRMM (Tropical Rainfall Measuring Mission) satellite is described in this study. The scattering index method (Grody, 1991) was accepted to develop a rainfall estimation algorithm and the measurements from Automatic Rainfall and Meteorological Telemetry System (ARMTS) were employed to evaluate the satellite rainfall retrievals. Based on the standard products of 2A25 derived from TRMM/PR data, the rainfall areas over Taiwan were divided into convective rainfall area and stratiform rainfall areas with/without bright band. The results of rainfall estimation from the division of rain type are compared with those without the division of rain type. It is shown that the mean rainfall difference for the convective rain type is reduced from -6.2mm/hr to 1.7mm/hr and for the stratiform rain type with bright band is decreased from 10.7 mm/hr to 2.1mm/hr. But it seems not significant improvement for the stratiform rain type without bright band.