• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.102-114
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• 2017

The purpose of this study was the assessment of the accuracy of Global Precipitation Measurement (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG), a rainfall data source derived from satellite images, for evaluation of its applicability to use in ungauged or inaccessible areas. The study area was the overall area of the Korean peninsula divided into six regions. Automated Surface Observing System (ASOS) rainfall data from the Korean Meteorological Administration and IMERG satellite rainfall were used. Their average correlation coefficient was 0.46 for a 1-h temporal resolution, and it increased to 0.69 for a 24-h temporal resolution. The IMERG data quantitatively estimated less than the rainfall totals from ground gauges, and the bias decreased as the temporal resolution was decreased. The correlation coefficients of the two rainfall events, which had relatively greater rainfall amounts, were 0.68 and 0.69 for a 1-h temporal resolution. Additionally, the spatial distributions of the ASOS and IMERG data were similar to each other. The study results showed that the IMERG data were very useful in the assessment of the hydro-meteorological characteristics of ungauged or inaccessible areas. In a future study, verification of the accuracy of satellite-derived rainfall data will be performed by expanding the analysis periods and applying various statistical techniques.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.166-166
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• 2020

Recently, As the frequency of localized heavy rains increases, the use of high-resolution radar data is increasing. The produced radar rainfall has still gaps of spatial and temporal compared to gauge observation rainfall, and in many studies, various statistical techniques are performed for correct rainfall. In this study, the precipitation correction of the S-band Dual Polarization radar in use in the flood forecast was performed using the ConvAE algorithm, one of the Convolutional Neural Network. The ConvAE model was trained based on radar data sets having a 10-min temporal resolution: radar rainfall data, gauge rainfall data for 790minutes(July 2017 in Cheongju flood event). As a result of the validation of corrected radar rainfall were reduced gaps compared to gauge rainfall and the spatial correction was also performed. Therefore, it is judged that the corrected radar rainfall using ConvAE will increase the reliability of the gridded rainfall data used in various physically-based distributed hydrodynamic models.

• Journal of The Korean Society of Agricultural Engineers
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• v.58 no.5
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• pp.91-99
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• 2016

High resolution rainfall data at 1-hour or a finer scale are essential for reliable flood analysis and forecasting; nevertheless, many observations, forecasts, and climate projections are still given at coarse temporal resolutions. This study aims to evaluate a chaotic method for disaggregation of 6-hour rainfall data sets so as to apply operational 6-hour rainfall forecasts of the Korean Meteorological Association to flood models. We computed parameters of a state-of-the-art multiplicative random cascade model with two combinations of cascades, namely uniform splitting and diversion, using rainfall observations at Seoul station, and compared statistical performance. We additionally disaggregated 6-hour rainfall time series at 58 stations with the uniform splitting and evaluated temporal transferability of the parameters and changes in multifractal properties. Results showed that the uniform splitting outperformed the diversion in reproduction of observed statistics, and hence is better to be used for disaggregation of 6-hour rainfall forecasts. We also found that multifractal properties of rainfall observations has adequate temporal consistency with an indication of gradually increasing rainfall intensity across South Korea.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.87-97
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• 2018

Rainfall depth is an important meteorological information. Generally, high spatial resolution rainfall data such as road-level rainfall data are more beneficial. However, it is expensive to set up sufficient Automatic Weather Systems to get the road-level rainfall data. In this paper, we proposed to use deep learning to recognize rainfall depth from road surveillance videos. To achieve this goal, we collected two new video datasets, and proposed a new deep learning architecture named Temporal and Spatial Segment Networks (TSSN) for rainfall depth recognition. Under TSSN, the experimental results show that the combination of the video frame and the differential frame is a superior solution for the rainfall depth recognition. Also, the proposed TSSN architecture outperforms other architectures implemented in this paper.

• Journal of Korean Society for Atmospheric Environment
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• v.34 no.5
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• pp.735-747
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• 2018

Rainfall depth is an important meteorological information. Generally, high spatial resolution rainfall data such as road-level rainfall data are more beneficial. However, it is expensive to set up sufficient Automatic Weather Systems to get the road-level rainfall data. In this paper, we propose to use deep learning to recognize rainfall depth from road surveillance videos. To achieve this goal, we collect a new video dataset and propose a procedure to calculate refined rainfall depth from the original meteorological data. We also propose to utilize the differential frame as well as the optical flow image for better recognition of rainfall depth. Under the Temporal Segment Networks framework, the experimental results show that the combination of the video frame and the differential frame is a superior solution for the rainfall depth recognition. The final model is able to achieve high performance in the single-location low sensitivity classification task and reasonable accuracy in the higher sensitivity classification task for both the single-location and the multi-location case.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.1
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• pp.111-117
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• 2015

We investigate the influence of spatial rainfall distribution on hillslope soil erosion through laboratory experiments. Two distinct spatial distributions are examined in this study, i.e., rainfall concentrated on central area versus upper area of hillslope. During the entire period of 8 hours for each experiment, direct runoff, subsurface flow, and sediment yield are measured at high temporal resolution (10 minutes). Compared to the case that rainfalll concentrated on central area, upstream concentrated rainfall results in lower peak of the sediment yield curve while greater cumulative sediment yield. Cumulative sediment yield increases over time linearly but its growth rate shows a sudden decrease at around 2 hours. This should be taken into consideration when temporal variability of sediment yield is estimated from observed total amount, and demonstrates the necessity of measuring sediment yield at high temporal resolution.

• Journal of Korea Water Resources Association
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• v.50 no.12
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• pp.849-862
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• 2017

The increased frequency of meteorological disasters has been observed due to increased extreme events such as heavy rainfalls and flash floods. Numerous studies using high-resolution weather radar rainfall data have been carried out on the hydrological effects. In this study, a conditional merging technique is employed, which makes use of geostatistical methods to extract the optimal information from the observed data. In this context, three different techniques such as kriging, inverse distance weighting and spline interpolation methods are applied to conditionally merge radar and ground rainfall data. The results show that the estimated rainfall not only reproduce the spatial pattern of sub-hourly rainfall with a relatively small error, but also provide reliable temporal estimates of radar rainfall. The proposed modeling framework provides feasibility of using conditionally merged rainfall estimation at high spatio-temporal resolution in ungauged areas.

• Journal of Korea Water Resources Association
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• v.51 no.7
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• pp.599-606
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• 2018

Most hydrological analysis such as probability rainfall and rainfall time distributions have typically carried out based on hourly rainfall and rainfall - runoff analysis have carried out by applying different periods of rainfall time distribution and probability rainfall. In this study, to quantify the change of design flood due to the data type (hourly and minutely rainfall data) and the probability rainfall and application of different data period to the rainfall time distribution, probability rainfall is calculated by point frequency analysis according to data type and period and rainfall time distribution was calculated by Huff's quartile distributions. In addition, the change analysis of design flood was carried out by rainfall - runoff analysis applying different data periods of design rainfall time distribution. and probability rainfall. As a result, rainfall analysis using minute rainfall data was more accurate and effective than using hourly rainfall data. And the design flood calculated by applying different data period of rainfall time distribution and probability rainfall made a large difference than by applying different data type. It is expected that this will contribute to the hydrological analysis using minutely rainfall.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.1
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• pp.1-13
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• 2023

Various techniques for bias correction and statistical downscaling have been developed to overcome the limitations related to the spatial and temporal resolution and error of climate change scenario data required in various applied research fields including agriculture and water resources. In this study, the characteristics of three different statistical dowscaling methods (i.e., SQM, SDQDM, and BCSA) provided by AIMS were summarized, and climate change scenarios produced by applying each method were comparatively evaluated. In order to compare the average rainfall characteristics of the past period, an index representing the average rainfall characteristics was used, and the reproducibility of extreme weather conditions was evaluated through the abnormal climate-related index. The reproducibility comparison of spatial distribution and variability was compared through variogram and pattern identification of spatial distribution using the average value of the index of the past period. For temporal reproducibility comparison, the raw data and each detailing technique were compared using the transition probability. The results of the study are presented by quantitatively evaluating the strengths and weaknesses of each method. Through comparison of statistical techniques, we expect that the strengths and weaknesses of each detailing technique can be represented, and the most appropriate statistical detailing technique can be advised for the relevant research.

• 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.