• 한국농공학회논문집
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• 제62권2호
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• pp.39-52
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• 2020

A high quality, long-term, high-resolution precipitation dataset is an essential in climate analyses and global water cycles. Rainfall data from station observations are inadequate over many parts of the world, especially North Korea, due to non-existent observation networks, or limited reporting of gauge observations. As a result, satellite-based rainfall estimates have been used as an alternative as a supplement to station observations. The Climate Hazards Group Infrared Precipitation (CHIRP) and CHIRP combined with station observations (CHIRPS) are recently produced satellite-based rainfall products with relatively high spatial and temporal resolutions and global coverage. CHIRPS is a global precipitation product and is made available at daily to seasonal time scales with a spatial resolution of 0.05° and a 1981 to near real-time period of record. In this study, we analyze the applicability of CHIRPS data on the Korean Peninsula by supplementing the lack of precipitation data of North Korea. We compared the daily precipitation estimates from CHIRPS with 81 rain gauges across Korea using several statistical metrics in the long-term period of 1981-2017. To summarize the results, the CHIRPS product for the Korean Peninsula was shown an acceptable performance when it is used for hydrological applications based on monthly rainfall amounts. Overall, this study concludes that CHIRPS can be a valuable complement to gauge precipitation data for estimating precipitation and climate, hydrological application, for example, drought monitoring in this region.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2002년도 Proceedings of International Symposium on Remote Sensing
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• pp.499-499
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• 2002

The goal of this study is to apply and evaluate the precipitation outflow in river basin using satellite data and GIS for proposing the efficient watershed management method. Not only precipitation outflow data but also various spatial data such as digital map, soil map, geologic map and multi-temporal TM images were used. Using landcover classification result and soil map were applied to estimate the average CN. The CN value of 63.37 by SCS method was produced in AMC-2 condition otherwise the result of direct estimation with observation method was 63 CN value. The relative error of two results was 0.59%. It can be possible to apply the satellite data for precipitation outflow analysis. For more accurate and credible analysis of this, the more multi-temporal satellite and real observation data will be needed.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2022년도 학술발표회
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• pp.148-148
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• 2022

Spatiotemporal precipitation data is one of the primary quantities in hydrological as well as climatological studies. Despite the fact that the estimation of these data has made considerable progress owing to advances in remote sensing, the discrepancy between satellite-derived precipitation product (SPP) data and observed data is still remarkable. This study aims to propose an effective deep learning model (DLM) for bias correction of SPPs. In which TRMM (The Tropical Rainfall Measuring Mission), CMORPH (CPC Morphing technique), and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks) are three SPPs with a spatial resolution of 0.25o exploited for bias correction, and APHRODITE (Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation) data is used as a benchmark to evaluate the effectiveness of DLM. We selected the Mekong River Basin as a case study area because it is one of the largest watersheds in the world and spans many countries. The adjusted dataset has demonstrated an impressive performance of DLM in bias correction of SPPs in terms of both spatial and temporal evaluation. The findings of this study indicate that DLM can generate reliable estimates for the gridded satellite-based precipitation bias correction.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.180-180
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• 2023

To improve global risk management, understanding the characteristics and distribution of precipitation is crucial. However, obtaining spatially and temporally resolved climatic data remains challenging due to sparse gauge observations and limited data availability, despite the use of satellite and reanalysis products. To address this challenge, merging available precipitation products has been introduced to generate spatially and temporally reliable data by taking advantage of the strength of the individual products. However, most of the existing studies utilize all the available products without considering the varying performances of each dataset in different regions. Comprehensively considering the relative contributions of each parent dataset is necessary since their contributions may vary significantly and utilizing all the available datasets for data merging may lead to significant data redundancy issues. Hence, for this study, we introduce a site-specific precipitation merging method that utilizes the Quadruple Collocation (QC) approach, which acknowledges the existence of error-cross correlation between the parent datasets, to create a high-resolution global daily precipitation data from 2001-2020. The performance of multiple gridded precipitation products are first evaluated per region to determine the best combination of quadruplets to be utilized in estimating the error variances through the QC approach and computation of merging weights. The merged precipitation is then computed by adding the precipitation from each dataset in the quadruplet multiplied by each respective merging weight. Our results show that our approach holds promise for generating reliable global precipitation data for data-scarce regions lacking spatially and temporally resolved precipitation data.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.159-159
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• 2023

The Amazon River basin is one of the largest basins in the world, and its ecosystem is vital for biodiversity, hydrology, and climate regulation. Thus, understanding the hydrometeorological process is essential to the maintenance of the Amazon River basin. However, it is still tricky to monitor the Amazon River basin because of its size and the low density of the monitoring gauge network. To solve those issues, remote sensing products have been largely used. Yet, those products have some limitations. Therefore, this study aims to do bias corrections to improve the accuracy of Satellite Precipitation Products (SPPs) in the Amazon River basin. We use 331 rainfall stations for the observed data and two daily satellite precipitation gridded datasets (CHIRPS, TRMM). Due to the limitation of the observed data, the period of analysis was set from 1st January 1990 to 31st December 2010. The observed data were interpolated to have the same resolution as the SPPs data using the IDW method. For bias correction, we use convolution neural networks (CNN) combined with an autoencoder architecture (ConvAE). To evaluate the bias correction performance, we used some statistical indicators such as NSE, RMSE, and MAD. Hence, those results can increase the quality of precipitation data in the Amazon River basin, improving its monitoring and management.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.178-181
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• 2004

The Tropical Rainfall Measuring Mission (TRMM) is an US-Japan joint space mission to observe tropical and subtropical rainfall. This satellite is equipped with the world's first precipitation radar that operates at 13.8 GHz. We introduce the TRMM precipitation radar (PR) system, along with the PR data processing and analysis algorithms, and some observation results obtained by the TRMM PR. It is concluded that the TRMM PR can give quite useful rainfall data for the understanding of global climate changes, meteorology, climatology, atmospheric science, and also for the studies of satellite communication.

• 대한원격탐사학회지
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• 제26권5호
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• pp.511-525
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• 2010

MTSAT-1R의 적외 채널 밝기온도와 마이크로웨이브 강수량 자료를 이용하여 강수량을 추정하였다. 정지위성의 밝기온도와 다양한 마이크로웨이브(SSM/I, SSMIS, AMSU-B, AMSRE, TRMM) 강수량의 시공간일지 자료생성 및 관계성을 분석하여 MTSAT-1R 밝기온도와 마이크로웨이브 강수량의 조견표를 작성하였으며 밝기온도에 적용하여 강수량을 산출하였다. 산출 강수량은 지상 AWS 및 TRMM 위성자료를 이용하여 검증하였다. TRMM 2A12(TMI) 방법에 산출 강수량은 AWS 및 TRMM3B42 강수량 검증에서 상관계수는 0.38과 0.61, RMSE는 5.81과 2.44 mm/hr, PC는 0.79와 0.84 그리고 POD는 0.65와 0.87로 가장 높은 결과를 보였다. 전체적으로 위성을 이용한 강수량 산출에서 AWS 강수량과 비교하여 5 mm/hr 이상 그리고 TRMM3B42 강수량과 비교하여 2 mm/hr 이상 많은 강수를 추정하였다. 강수량의 검증 결과는 TRMM 2A12, AMSRE, SSM/I, AMSU-B 및 SSMIS 계열 방법순서로 상관성 등의 대부분 검증에서 높은 결과를 나타내었다.

• 한국지리정보학회지
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• 제25권4호
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• pp.1-18
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• 2022

현재 우리나라 기상청에서는 6개월 누적강수량 기준인 SPI6(standardized precipitation index 6)을 이용하여 기상가뭄을 지역별로 평가하고 있다. 하지만, SPI는 69개 기상관측소의 강수량만을 고려하여 산정되는 지수로 복합적인 이유로 나타나는 가뭄사상은 정확하게 판단하지 못하고 있는 실정이다. 따라서, 본 연구의 목적은 강수량만을 고려한 SPI와 강수량, 식생지수 및 기온을 복합적으로 고려하는 SDCI(Scaled Drought Condition Index)를 경기도 지역을 대상으로 산정 및 비교하고자 하였다. 또한, SPI와 SDCI의 비교를 통해 산정된 결과를 활용하여 지점자료기반 가뭄지수와 위성영상기반 가뭄지수의 장단점을 파악하고자 하였다. SDCI를 산정하기 위해 MODIS(MODerate resolution Imaging Spectroradiometer) 위성영상자료, 종관기상관측(ASOS) 자료 및 크리깅 기법을 사용하였다. 강수량의 지속기간은 2014년의 8개 시점에 대해 1개월, 3개월, 6개월을 각각 적용하여 SDCI1, SDCI3, SDCI6을 산정하였다. SDCI 산정 결과, SPI와 달리 약 두달 전부터 가뭄양상을 나타내기 시작하여 경기도 시군별 가뭄에 대해서 잘 드러냈다. 이를 통해, 위성영상자료와 지점자료의 결합이 가뭄지수 변화 양상에 있어서 효율성을 높였으며, 기존의 건조 지역과 더불어 습윤 지역에 대해 가뭄예측 가능성을 증대시켰음을 파악할 수 있었다.

• 한국농공학회논문집
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• 제66권3호
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• pp.25-37
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• 2024

Global warming-induced drought inflicts significant socio-economic and environmental damage. In Korea, the persistent drought in the southern region since 2022 has severely affected water supplies, agriculture, forests, and ecosystems due to uneven precipitation distribution. To effectively prepare for and mitigate such impacts, it is imperative to develop proactive measures supported by early monitoring systems. In this study, we analyzed the spatiotemporal changes of multiple evapotranspiration-based drought indices, focusing on the flash drought event in the southern region in 2022. The indices included the Evaporative Demand Drought Index (EDDI), Standardized Precipitation Evapotranspiration Index (SPEI) considering precipitation and temperature, and the Evaporative Stress Index (ESI) based on satellite images. The Standardized Precipitation Index (SPI) and SPEI indices utilized temperature and precipitation data from meteorological observation stations, while the ESI index was based on satellite image data provided by the MODIS sensor on the Terra satellite. Additionally, we utilized the Evaporative Demand Drought Index (EDDI) provided by the North Oceanic and Atmospheric Administration (NOAA) as a supplementary index to ESI, enabling us to perform more effective drought monitoring. We compared the degree and extent of drought in the southern region through four drought indices, and analyzed the causes and effects of drought from various perspectives. Findings indicate that the ESI is more sensitive in detecting the timing and scope of drought, aligning closely with observed drought trends.

• 대한원격탐사학회지
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• 제33권4호
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• pp.445-454
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• 2017

The performance of spatial downscaling models depends on the quality of input coarse scale products. Thus, the impact of intrinsic errors contained in coarse scale satellite products on predictive performance should be properly assessed in parallel with the development of advanced downscaling models. Such an assessment is the main objective of this paper. Based on a synthetic satellite precipitation product at a coarse scale generated from rain gauge data, two synthetic precipitation products with different amounts of error were generated and used as inputs for spatial downscaling. Geographically weighted regression, which typically has very high explanatory power, was selected as the trend component estimation model, and area-to-point kriging was applied for residual correction in the spatial downscaling experiment. When errors in the coarse scale product were greater, the trend component estimates were much more susceptible to errors. But residual correction could reduce the impact of the erroneous trend component estimates, which improved the predictive performance. However, residual correction could not improve predictive performance significantly when substantial errors were contained in the input coarse scale data. Therefore, the development of advanced spatial downscaling models should be focused on correction of intrinsic errors in the coarse scale satellite product if a priori error information could be available, rather than on the application of advanced regression models with high explanatory power.