• 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.

• 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.

• 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 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.

• 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 Korea Water Resources Association Conference
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• 2020.06a
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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.

• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.1
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• pp.107-118
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• 2014

The objective of this study is to suggest a method for estimating rainfall-runoff relationship using runoff analysis with satellite rainfall and global geographic data for the region due to lack of observed data. This study uses CMORPH and GSMaP_NRT as satellite rainfall data, and GTOPO30 and GLCC as global geographic data. IFAS is used for runoff modeling. In the evaluation of rainfall data, the correlation coefficients of CMORPH and GSMaP_NRT with observed data are 0.37 and 0.30 respectively. Calculated peak runoffs using IFAS show small relative errors with observed data in case of parameters are not calibrated with satellite rainfall data. Therefore, the methods suggested in this study could be applied to ungauged watershed. In the future, this study will analyze runoff for North Korea, a representative inaccessible region, using satellite rainfall and global geographic data.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.143-143
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• 2022

Precipitation plays an essential role in water resources management and disaster prevention. Therefore, the understanding related to spatiotemporal characteristics of rainfall is necessary. Nowadays, highly accurate precipitation is mainly obtained from gauge observation systems. However, the density of gauge stations is a sparse and uneven distribution in mountainous areas. With the proliferation of technology, satellite-based precipitation sources are becoming increasingly common and can provide rainfall information in regions with complex topography. Nevertheless, satellite-based data is that it still remains uncertain. To overcome the above limitation, this study aims to take the strengthens of machine learning to generate a new reanalysis of precipitation data by fusion of multiple satellite precipitation products (SPPs) with gauge observation data. Several machine learning algorithms (i.e., Random Forest, Support Vector Regression, and Artificial Neural Network) have been adopted. To investigate the robustness of the new reanalysis product, observed data were collected to evaluate the accuracy of the products through Kling-Gupta efficiency (KGE), probability of detection (POD), false alarm rate (FAR), and critical success index (CSI). As a result, the new precipitation generated through the machine learning model showed higher accuracy than original satellite rainfall products, and its spatiotemporal variability was better reflected than others. Thus, reanalysis of satellite precipitation product based on machine learning can be useful source input data for hydrological simulations in ungauged river basins.