• 대기
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• 제28권3호
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• pp.273-289
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• 2018

This paper evaluates daily precipitation products from Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG), Tropical Rainfall Measuring Mission Multisatellite (TRMM) Precipitation Analysis (TMPA), and the Climate Prediction Center Morphing Method (CMORPH), validated against gauge observation over South Korea and gauge-based analysis data East Asia during one year from June 2014 to May 2015. It is found that the three products effectively capture the seasonal variation of mean precipitation with relatively good correlation from spring to fall. Among them, IMERG and TMPA show quite similar precipitation characteristics but overall underestimation is found from all precipitation products during winter compared with observation. IMERG shows reliably high performance in precipitation for all seasons, showing the most unbiased and accurate precipitation estimation. However, it is also noticed that IMERG reveals overestimated precipitation for heavier precipitation thresholds. This assessment work suggests the validity of the IMERG product for not only seasonal precipitation but also daily precipitation, which has the potential to be used as reference precipitation data.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.158-158
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• 2023

Characterizing the performance of precipitation (hereafter PRE) products in estimating the uncertainties in daily PRE in the era of global warming is of great value to the ecosystem's sustainability and human survival. This study intercompares the performance of different PRE products (gauge-based, satellite and reanalysis) sourced from the Frequent Rainfall Observations on GridS (FROGS) database over diverse climate zones in Africa and identifies regions where they depict minimal uncertainties in order to build optimal maps as a guide for different climate users. This is achieved by utilizing various techniques, including the triple collection (TC) approach, to assess the capabilities and limitations of different PRE products over nine climatic zones over the continent. For daily scale analysis, the uncertainties in light PRE (0.1 5mm/day) are prevalent over most regions in Africa during the study duration (2001-2016). Estimating the occurrence of extreme PRE events based on daily PRE 90th percentile suggests that extreme PRE is mainly detected over central Africa (CAF) region and some coastal regions of west Africa (WAF) where the majority of uncorrected satellite products show good agreement. The detection of PRE days and non-PRE days based on categorical statistics suggests that a perfect POD/FAR score is unattainable irrespective of the product type. Daily PRE uncertainties determined based on quantitative metrics show that consistent, satisfactory performance is demonstrated by the IMERG products (uncorrected), ARCv2, CHIRPSv2, 3B42v7.0 and PERSIANN_CDRv1r1 (corrected), and GPCC, CPC_v1.0, and REGEN_ALL (gauge) during the study period. The optimal maps that show the classification of products in regions where they depict reliable performance can be recommended for various usage for different stakeholders.

• 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 2021년도 학술발표회
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• pp.134-134
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• 2021

Precipitation is a crucial component of water cycle and play a key role in hydrological processes. Traditionally, gauge-based precipitation is the main method to achieve high accuracy of rainfall estimation, but its distribution is sparsely in mountainous areas. Recently, satellite-based precipitation products (SPPs) provide grid-based precipitation with spatio-temporal variability, but SPPs contain a lot of uncertainty in estimated precipitation, and the spatial resolution quite coarse. To overcome these limitations, this study aims to generate new grid-based daily precipitation using Automatic weather system (AWS) in Korea and multiple SPPs(i.e. CHIRPSv2, CMORPH, GSMaP, TRMMv7) during the period of 2003-2017. And this study used a machine learning based Random Forest (RF) model for generating new merging precipitation. In addition, several statistical linear merging methods are used to compare with the results of the RF model. In order to investigate the efficiency of RF, observed data from 64 observed Automated Synoptic Observation System (ASOS) 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 random forest model showed higher accuracy than each satellite rainfall product and spatio-temporal variability was better reflected than other statistical merging methods. Therefore, a random forest-based ensemble satellite precipitation product can be efficiently used for hydrological simulations in ungauged basins such as the Mekong River.

• 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.

• 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.

• 대한원격탐사학회지
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• 제29권2호
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• pp.263-274
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• 2013

본 연구에서는 다중위성 강수자료의 수문학적 적용성을 평가하기 위하여 Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), Global Satellite Mapping of Precipitation (GSMaP), Climate Prediction Center (CPC) Morphing technique(CMORPH) 등 전 지구 규모의 고해상도 다중위성 강수자료와 분포형 수문모형을 이용하여 유출모의를 수행하였다. 충주댐 유역에 대하여 2002년 1월 1일부터 2009년 12월 31일까지의 기간에 대하여 Coupled Routing and Excess Storage (CREST) 모형을 적용하였다. 분석기간은 준비기간(2002-2003년, 2006-2007년), 보정기간(2004-2005년), 그리고 검증기간(2008-2009년)으로 구분하여 모의를 수행하였다. 각 다중위성 강수자료를 지상관측자료와 비교결과, 강수의 계절적 변동특성은 잘 반영하고 있으나 연강수량합계 및 월평균강수량에서 TMPA는 과대추정을, GSMaP과 CMORPH는 과소추정하는 경향을 보여주었다. 또한 유출분석결과, TMPA를 제외한 GSMaP과 CMORPH의 충주댐 유역에 대한 수문학적 적용성이 매우 낮은 것을 알 수 있었으며, 향후 다중위성 강수자료의 활용에 앞서 통계적 보정이나 강수알고리즘에 대한 개선이 필요한 것으로 판단된다.

• 한국농림기상학회지
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• 제14권4호
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• pp.143-154
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• 2012

수자원의 수요 증가와 ENSO (El Ni$\tilde{n}$o/La Ni$\tilde{n}$a Southern Oscillation) 등의 기후변화 현상으로 인한 수자원 공급의 불안정 요소가 제기됨에 따라, 수자원 관리 계획 수립 시 장/단기강우 모의의 중요성이 강조되고 있다. 본 연구에서는 미국 플로리다 템파 지역의 두 개 유역을 대상으로 1986년부터 2008년까지의 MM5 지역기후모델을 이용한 강우모의 결과를 시험지역의 33개 관측자료와 CDF-mapping 기법을 이용하여 통계적으로 보정하였으며 그 결과를 바탕으로 ENSO 패턴에 따른 모델의 성능을 평가하였다. 보정된 MM5일 강우 모의결과는 대체적으로 각 관측소의 월 평균 강우량 (ME: 1.0mm)을 잘 모의하는 것으로 나타났다. 블락-크리깅 기법을 이용하여 추정된 유역 평균 일/월 강우량 또한 관측치를 잘 재현하였다(일 강우 ME: 0.8mm, 월 강우 ME: 7.1mm). 한편, ONI (Oceanic Ni$\tilde{n}$o index)를 이용하여 구분한 ENSO 패턴에 따른 강우 모의치를 분석한 결과, 월별 엘리뇨/라니냐 해에 대한 유역 단위의 강우량 모의 성능이 상이한 것으로 나타났다. 이 원인으로 한정된 모수화 적용 및 모델 경계자료 오차 등을 제시하고 이에 대한 보정 방법개선 등의 추가 연구의 필요성을 지적하였다. 본 연구는 ENSO 패턴을 고려한 월별 기후모델 결과를 활용함에 있어 유의점을 제시하였기에, 우기와 건기에 대한 수자원 관리를 위한 적용 등에 유용하게 활용될 것으로 기대된다.

• 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.

• 한국지구과학회지
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• 제38권7호
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• pp.481-495
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• 2017

본 연구의 목적은 2016년 5월에서 8월 기간 동안 단양지역의 아로니아 표본 재배 농가들을 대상으로 기상 기후학적 인자와 아로니아 과실 특성과의 관계를 조사 분석하는 것이다. 이를 위해 아로니아 표본 재배 농가들로부터 기상 요소, 과실과 토양의 물리 화학적 특성과 조건, 비가림 및 해가림 설치에 따른 과실의 특성 변화 등을 조사한다. 그 결과로서, 첫째, 단양지역의 최근 최고기온, 누적강수량, 상대습도, 일조시간 등의 기상 기후 인자가 아로니아 재배 적지뿐만 아니라 일정한 품질 유지 및 생산량에 긍정적인 영향을 준다. 하지만 4월과 5월의 강풍은 아로니아의 개화기 및 만개기의 낙화현상에 큰 영향을 준다. 둘째, 아로니아 품질과 생산량은 일최고기온, 일토양온도, 일토양 pH, 누적강수량, 일토양습도 등의 농업 기상 기후 인자와 0.9 이상의 높은 상관을 나타낸다. 그리고 이들 인자들을 이용한 회귀식을 통해 그 품질과 생산량을 예측할 수 있다. 셋째, 강수량이 많은 경우에 아로니아의 당도와 안토시아닌 성분을 감소시키기 때문에 과일 변색기 이후에는 비가림을 유지하는 것이 필요하다. 그리고 아로니아 표본 농가의 노지 및 비가림 재배 시에 당도와 안토시아닌 성분 간 회귀분석 결과는 모두 높은 상관을 나타낸다.