• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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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).

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2017년도 학술발표회
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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.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 춘계학술대회 논문집
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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.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume II
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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.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.153-153
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• 2022

Large-scale and accurate observations at fine spatial resolution through a means of remote sensing offer an effective tool for capturing rainfall variability over the traditional rain gauges and weather radars. Although satellite rainfall products (SRPs) derived using two major estimation approaches were evaluated worldwide, their practical applications suffered from limitations. In particular, the traditional top-down SRPs (e.g., IMERG), which are based on direct estimation of rain rate from microwave satellite observations, are mainly restricted with their coarse spatial resolution, while applications of the bottom-up approach, which allows backward estimation of rainfall from soil moisture signals, to novel high spatial resolution soil moisture satellite sensors over South Korea are not introduced. Thus, this study aims to evaluate the performances of a state-of-the-art bottom-up SRP (the self-calibrated SM2RAIN model) applied to the C-band SAR Sentinel-1, a statistically downscaled version of the conventional top-down IMERG SRP, and their integration for a targeted high spatial resolution of 0.01° (~ 1-km) over central South Korea, where the differences in climate zones (coastal region vs. mainland region) and vegetation covers (croplands vs. mixed forests) are highlighted. The results indicated that each single SRP can provide plus points in distinct climatic and vegetated conditions, while their drawbacks have existed. Superior performance was obtained by merging these individual SRPs, providing preliminary results on a complementary high spatial resolution SRP over central South Korea. This study results shed light on the further development of integration framework and a complementary high spatial resolution rainfall product from multi-satellite sensors as well as multi-observing systems (integrated gauge-radar-satellite) extending for entire South Korea, toward the demands for urban hydrology and microscale agriculture.

• 한국지리정보학회지
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• 제20권3호
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• pp.141-152
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• 2017

본 연구는 계측자료가 부족한 유역을 대상으로 위성강우 활용 및 위성강우의 보정방법을 통해 홍수량 추정의 기술적인 방법을 제시하는 것을 목적으로 하였다. 연구대상유역은 모로코 세부강 유역을 대상으로 하였다. 세부강 유역 홍수량 추정을 위한 모형은 IFAS(Integrated Flood Analysis System)와 GRM(Grid baed Rainfall-Runoff Model)을 이용하였다. 연구 유역에 대한 강우자료는 일일관측의 지상계측 자료와 시간계측 위성강우자료를 이용하였다. 위성강우를 이용한 홍수분석에서 일일 지상계측 강우량과 위성강우의 시간계측 자료를 합성하여 위성강우자료를 수정하였다. 지형자료는 90m 공간해상도의 Shuttle Radar Topographic Mission DEM(SRTM DEM)과, 1km 공간해상도의 Global map의 토지피복도와 US Food and Agriculture Organization(US FAO)의 Harmonized World Soil Database(HWSD) 토양도를 이용하였다. 과소추정되는 위성강우는 지상계측 자료를 활용하여 보정하였다. 수정된 위성강우를 이용한 유출분석에서는 첨두유출량이 IFAS는 $5,878{\sim}7,434m^3/s$, GRM은 $6,140{\sim}7,437m^3/s$의 유출이 발생하는 것으로 분석되었다. 그러므로 2009~2010년에 발생한 세부강 유역의 첨두홍수량은 $5,800m^3/s$에서 $7,500m^3/s$의 범위에서 발생한 것으로 추정되었다. 보정된 위성강우를 활용한 홍수량 추정결과는 두 모형 모두 유사한 홍수량을 나타내었다. 따라서 본 연구에서 제시한 위성강우의 보정기법은 계측자료가 부족한 지역의 적정 홍수량 추정에 적용될 수 있을 것으로 사료된다.

• 대한원격탐사학회지
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• 제35권5_2호
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• pp.851-859
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• 2019

천리안(COMS, Communication, Ocean, and Meteorological Satellite) 위성은 한반도를 15분마다 촬영하지만, 관측 채널의 한계로 강우 추정 시 과소 추정하는 경향이 있어 풍수해 발생시 활용하기 어려웠다. 따라서 본 연구에서는 천리안 위성과 GPM(Global Precipitation Measurement) 위성자료를 함께 이용하여 한반도 풍수해 발생시 활용할 수 있는 위성기반 강우추정 기술을 개발하였다. 천리안 위성과 GPM 위성의 시간 공간 해상도를 일치시키고 GPM 위성의 IMERG 자료를 통해 강우추정 정확도를 향상시킨 결과, 종관기상 관측값(ASOS)간의 상관계수가 0.7 이상으로 나타나 기존 천리안 위성 자료만을 이용한 강우추정 기술보다 정확한 결과를 도출하였다. 향후 천리안 위성의 후속 위성인 천리안 2A호(GK-2A)를 활용할 경우 보다 정확한 기상정보가 제공될 예정이므로, 미계측 지역에 대한 재난관리 활용성이 확대될 것으로 기대된다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2020년도 학술발표회
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• pp.120-120
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• 2020

Spatial precipitation data is one of the essential components in modeling hydrological problems. The estimation of these data has achieved significant achievements own to the recent advances in remote sensing technology. However, there are still gaps between the satellite-derived rainfall data and observed data due to the significant dependence of rainfall on spatial and temporal characteristics. An effective approach based on the Convolutional Neural Network (CNN) model to correct the satellite-derived rainfall data is proposed in this study. The Mekong River basin, one of the largest river system in the world, was selected as a case study. The two gridded precipitation data sets with a spatial resolution of 0.25 degrees used in the CNN model are APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks). In particular, PERSIANN-CDR data is exploited as satellite-based precipitation data and APHRODITE data is considered as observed rainfall data. In addition to developing a CNN model to correct the satellite-based rain data, another statistical method based on standard deviations for precipitation bias correction was also mentioned in this study. Estimated results indicate that the CNN model illustrates better performance both in spatial and temporal correlation when compared to the standard deviation method. The finding of this study indicated that the CNN model could produce reliable estimates for the gridded precipitation bias correction problem.

• 한국지구과학회지
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• 제31권5호
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• pp.465-474
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• 2010

Real-time rainfall monitoring is of great practical importance over the highly populated Indochina area, which is prone to natural disasters, in particular in association with rainfall. With the goal of d etermining near real-time half-hourlyrain estimates from satellite, the three-layer, artificial neural networks (ANN) approach was used to train the brightness temperatures at 6.7, 11, and $12-{\mu}m$ channels of the Japanese geostationary satellite MTSAT against passive microwavebased rain rates from Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and TRMM Precipitation Radar (PR) data for the June-September 2005 period. The developed model was applied to the MTSAT data for the June-September 2006 period. The results demonstrate that the developed algorithm is comparable to the PERSIANN (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) results and can be used for flood monitoring across the Indochina area on a half-hourly time scale.

• 대한원격탐사학회지
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• 제32권2호
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• pp.155-169
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• 2016

본 연구에서는 2014년 8월 부산 경남 집중호우 사례를 대상으로 레이더와 위성결합 Multi-sensor Blending 초단기 강우예측을 실시하였다. 레이더 최적 Z-R관계는 열대형 강수 Z-R관계식($Z=32R^{1.65}$)을 적용하였으며, 20 mm/h 이상의 강한 강우에서 강수량 추정 정확도가 향상됨을 확인하였다. 또한 60 mm/h 이상 강한 폭우사상에 대하여 천리안 위성자료와 레이더자료를 합성한 결과 정량강수 추정 성능이 향상됨을 확인하였다. 지속시간별 강우예측 정확도 검증을 위하여 AWS, MAPLE 자료와 비교결과, 강우예측 1시간까지 약 50%이상의 지점강우예측 정확도를 확보하였으며, 10분 단위 예측시간별 상관계수는 0.80~0.53, 평균제곱근오차는 3.99~6.43 mm/h로 분석되었다. 본 연구 결과 레이더와 위성정보를 이용한 보다 신뢰성 있는 강우예측 정보 활용이 가능할 것으로 판단되며, 향후 지속적인 사례연구와 레이더 위성 활용 정량강수량 추정 및 예측, 그리고 위성강수 추정 알고리즘 개선의 노력이 필요하다.