• Journal of The Korean Society of Agricultural Engineers
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• v.58 no.3
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• pp.57-69
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• 2016

In this study, we analyzed the extreme rainfall distribution scenarios based on probable rainfall calculation and applying various time distribution models over the landslide high risk zones in urban areas. We used observed rainfall data form total 71 ASOS (Automated Synoptic Observing System) station and AWS (Automatic Weather Station) in KMA (Korea Meteorological Administration), and we analyzed the linear trends for 1-hr and 24-hr annual maximum rainfall series using simple linear regression method, which are identified their increasing trends with slopes of 0.035 and 0.660 during 1961-2014, respectively. The Gumbel distribution was applied to obtain the return period and probability precipitation for each duration. The IDF (Intensity-Duration-Frequency) curves for landslide high risk zones were derived by applying integrated probability precipitation intensity equation. Results from IDF analysis indicate that the probability precipitation varies from 31.4~38.3 % for 1 hr duration, and 33.0~47.9 % for 24 hr duration. It also showed different results for each area. The $Huff-4^{th}$ Quartile method as well as Mononobe distribution were selected as the rainfall distribution scenarios of landslide high risk zones. The results of this study can be used to provide boundary conditions for slope collapse analysis, to analyze sediment disaster risk, and to use as input data for risk prediction of debris flow.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.208-219
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• 2020

This study aims to determine the applicability of satellite precipitation to the ungauged or inaccessible areas by comparing the accuracy of satellite precipitation. The accuracy assessment showed that the overall spatial distributions of ground-based rainfall and satellite precipitation were similar in all three events. For one-month precipitation with one-hour temporal resolution, the correlations between ground-based precipitation (ASOS) and satellite precipitation were analyzed to be between 0.42 and 0.46. In the evaluation during the period in which precipitation was concentrated, the correlation coefficients for one-hour temporal resolution data were analyzed as 0.55 to 0.66 for IMERG and 0.56 to 0.67 for GSMAP. According to the total rainfall analysis of each rainfall station for the three events, the correlation coefficients of IMERG and GSMaP were relatively better than CMORPH, and the bias of CMORPH data was relatively better than IMERG and GSMaP. However, all the three satellite precipitation were underestimated compared to the ground-based precipitation. In the future, a study will be carried out to estimate precipitation across the Korean Peninsula, including North Korea, reflecting the results from this study.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1179-1194
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• 2020

This study investigates the feasibility of three algorithms, K-Nearest Neighbors (K-NN), Random Forest (RF) and Neural Network (NN), for estimating the air temperature of an unobserved area where the weather station is not installed. The satellite image were obtained from Landsat-8 and MODIS Aqua/Terra acquired in 2019, and the meteorological ground weather data were from AWS/ASOS data of Korea Meteorological Administration and Korea Forest Service. In addition, in order to improve the estimation accuracy, a digital surface model, solar radiation, aspect and slope were used. The accuracy assessment of machine learning methods was performed by calculating the statistics of R² (determination coefficient) and Root Mean Square Error (RMSE) through 10-fold cross-validation and the estimated values were compared for each target area. As a result, the neural network algorithm showed the most stable result among the three algorithms with R² = 0.805 and RMSE = 0.508. The neural network algorithm was applied to each data set on Landsat imagery scene. It was possible to generate an mean air temperature map from June to September 2019 and confirmed that detailed air temperature information could be estimated. The result is expected to be utilized for national disaster safety management such as heat wave response policies and heat island mitigation research.

• Journal of Digital Convergence
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• v.14 no.8
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• pp.245-252
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• 2016

The purpose of this study is to make up for missing of weather informations from ASOS and AWS using artificial neural networks. We collected temperature, relative humidity and wind velocity for August during 5-yr (2011-2015) and sample designed artificial neural networks, assuming the Seoul weather station was missing. The result of sensitivity study on number of epoch shows that early stopping appeared at 2,000 epochs. Correlation between observation and prediction was higher than 0.6, especially temperature and humidity was higher than 0.9, 0.8 respectively. RMSE decreased gradually and training time increased exponentially with respect to increase of number of epochs. The predictability at 40 epoch was more than 80% effect on of improved results by the time the early stopping. It is expected to make it possible to use more detailed weather information via the rapid missing complemented by quick learning time within 2 seconds.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1071-1081
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• 2021

In addition to the current quality control procedures for the weather observation performed by the Korea Meteorological Administration (KMA), this study proposes quality inspection standards for Internet of Things (IoT) urban weather observed data based on machine learning that can be used in smart cities of the future. To this end, in order to confirm whether the standards currently set based on ASOS (Automated Synoptic Observing System) and AWS (Automatic Weather System) are suitable for urban weather, usability was verified based on SKT AWS data installed in Seoul, and a machine learning-based quality control algorithm was finally proposed in consideration of the IoT's own data's features. As for the quality control algorithm, missing value test, value pattern test, sufficient data test, statistical range abnormality test, time value abnormality test, spatial value abnormality test were performed first. After that, physical limit test, stage test, climate range test, and internal consistency test, which are QC for suggested by the KMA, were performed. To verify the proposed algorithm, it was applied to the actual IoT urban weather observed data to the weather station located in Songdo, Incheon. Through this, it is possible to identify defects that IoT devices can have that could not be identified by the existing KMA's QC and a quality control algorithm for IoT weather observation devices to be installed in smart cities of future is proposed.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.434-439
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• 2019

Recently, interest in urban temperature change and ground surface temperature change has been increasing due to weather phenomenon due to global warming, heat island phenomenon caused by urbanization in urban areas. In Korea, weather data such as temperature and precipitation have been collected since 1904. In recent years, there are 96 ASOS stations and 494 AWS weather observation stations. However, in the case of terrestrial networks, terrestrial meteorological data except measurement points are predicted through interpolation because they provide point data for each installation point. In this study, to improve the resolution of ground surface temperature measurement, the surface temperature using satellite image was calculated and its applicability was analyzed. For this purpose, the satellite images of Landsat 8 OLI TIRS were obtained for Seoul Metropolitan City by seasons and transformed to surface temperature by applying NASA equation to the thermal bands. The ground measurement data was based on the temperature data measured by AWS. Since the AWS temperature data is station based point data, interpolation is performed by Kriging interpolation method for comparison with Landsat image. As a result of comparing the satellite image base surface temperature with the AWS temperature data, the temperature difference according to the season was calculated as fall, winter, summer, based on the RMSE value, Spring, in order of applicability of Landsat satellite image. The use of that attribute and AWS support starts at $2.11^{\circ}C$ and RMSE ${\pm}3.84^{\circ}C$, which reflects information from the extended NASA.

• Journal of Korea Water Resources Association
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• v.55 no.6
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• pp.447-459
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• 2022

This study estimated the grid-type precipitation quantile for the Korean Peninsula using PERSIANN-CCS-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record), a satellite based re-analysis precipitation data. The period considered is a total of 38 years from 1983 to 2020. The spatial resolution of the data is 0.04° and the temporal resolution is 3 hours. For the probability distribution, the Gumbel distribution which is generally used for frequency analysis was used, and the probability weighted moment method was applied to estimate parameters. The duration ranged from 3 hours to 144 hours, and the return period from 2 years to 500 years was considered. The results were compared and reviewed with the estimated precipitation quantile using precipitation data from the Automated Synoptic Observing System (ASOS) weather station. As a result, the parameter estimates of the Gumbel distribution from the PERSIANN-CCS-CDR showed a similar pattern to the results of the ASOS as the duration increased, and the estimates of precipitation quantiles showed a rather large difference when the duration was short. However, when the duration was 18 h or longer, the difference decreased to less than about 20%. In addition, the difference between results of the South and North Korea was examined, it was confirmed that the location parameters among parameters of the Gumbel distribution was markedly different. As the duration increased, the precipitation quantile in North Korea was relatively smaller than those in South Korea, and it was 84% of that of South Korea for a duration of 3 h, and 70-75% of that of South Korea for a duration of 144 h.

• Atmosphere
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• v.24 no.2
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• pp.159-171
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• 2014

This study investigated the temporal and spatial characteristics of summertime (June-August) precipitation over Korean peninsula, using Korea Meteorological Administration (KMA)is Automated Synoptic Observing System (ASOS) data for the period of 1973-2010 and Automatic Weather System (AWS) data for the period of 1998-2010.The authors looked through climatological features of the summertime precipitation, then examined the degree of locality of the precipitation, and probable precipitation amount and its return period of 100 years (i.e., an extreme precipitation event). The amount of monthly total precipitation showed increasing trends for all the summer months during the investigated 38-year period. In particular, the increasing trends were more significant for the months of July and August. The increasing trend of July was seen to be more attributable to the increase of precipitation intensity than that of frequency, while the increasing trend of August was seen to be played more importantly by the increase of the precipitation frequency. The e-folding distance, which is calculated using the correlation of the precipitation at the reference station with those at all other stations, revealed that it is August that has the highest locality of hourly precipitation, indicating higher potential of localized heavy rainfall in August compared to other summer months. More localized precipitation was observed over the western parts of the Korean peninsula where terrain is relatively smooth. Using the 38-years long series of maximum daily and hourly precipitation as input for FARD2006 (Frequency Analysis of Rainfall Data Program 2006), it was revealed that precipitation events with either 360 mm $day^{-1}$ or 80 mm $h^{-1}$ can occur with the return period of 100 years over the Korean Peninsula.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.3
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• pp.69-82
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• 2023

Representative meteorological data of the rural water district, which is the spatial unit of the study, was produced using the grid-based national standard RCP scenario rainfall data provided by the Korea Meteorological Administration. The retrospective reproducibility of the climate model scenario data was analyzed, and the change in climate characteristics in the water district unit for the future period was presented. Finally the data characteristics and differences of each meteorological element according to various spatial resolution conversion and post-processing methods were examined. As a main result, overall, the distribution of average precipitation and R95p of the grid data, has reasonable reproducibility compared to the ASOS observation, but the maximum daily rainfall tends to be distributed low nationwide. The number of rainfall days tends to be higher than the station-based observation, and this is because the grid data is generally calculated using the area average concept of representative rainfall data for each grid. In addition, in the case of coastal regions, there is a problem that administrative districts of islands and rural water districts do not match. and In the case of water districts that include mountainous areas, such as Jeju, there was a large difference in the results depending on whether or not high rainfall in the mountainous areas was reflected. The results of this study are expected to be used as foundation for selecting data processing methods when constructing future meteorological data for rural water districts for future agricutural water management plans and climate change vulnerability assessments.

• Journal of the Korean association of regional geographers
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• v.20 no.4
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• pp.393-408
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• 2014

In this study, the spatio-temporal characteristics of summer extreme precipitation events in the Republic of Korea are examined based on the daily precipitation data observed at approximately 360 sites of both Automatic Weather Station (AWS) and Automated Synoptic Observation System (ASOS) networks by the Korea Meteorological Administration for the recent decade(2002~2011). During the summer Changma period(late June~mid July), both the frequency of extreme precipitation events exceeding 80mm of daily precipitation and their decadal maximum values are greatest at most of weather stations. In contrast, during the Changma pause period (late July~early August), these patterns are observed only in the northern regions of Geyeonggi province and western Kangwon province as such patterns are detected around Mt. Sobaek and Mt. Halla as well as in the southern regions of Geyeonggi province and western Kangwon province during the late Changma period (mid August~early September) due to north-south oscillation of the Changma front. Investigation of their regional patterns confirms that not only migration of the Changma front but also topological components in response to the advection of moistures such as elevation and aspect of major mountain ridges are detrimental to spatio-temporal patterns of extreme precipitation events. These results indicate that each local administration needs differentiated strategies to mitigate the potential damages by extreme precipitation events due to the spatiotemporal heterogeneity of their frequency and intensity during each Changma period.