• Journal of Astronomy and Space Sciences
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• 제31권4호
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• pp.325-333
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• 2014

We are motivated by both the accumulating evidence for the connection of solar variability to the chemistry of nitrogen oxide in the atmosphere and recent finding that the Galactic cosmic-ray (GCR) influx is associated with the solar north-south asymmetry. We have analyzed the measured pH in precipitation over the 109 stations distributed in the United States. We have found that data of pH in precipitation as a whole appear to be marginally anti-correlated with the solar asymmetry. That is, rain seems to become less acidic when the southern hemisphere of the Sun is more active. The acidity of rain is also found to be correlated with the atmospheric temperature, while not to be correlated with solar activity itself. We have carried on the analysis with two subsamples in which stations located in the east and in the west. We find that the pH data derived from the eastern stations which are possibly polluted by sulfur oxides and nitrogen oxides are not correlated with the solar asymmetry, but with the temperature. On the contrary, the pH data obtained from the western stations are found to be marginally anti-correlated with the solar asymmetry. In addition, the pH data obtained from the western stations are found to be correlated with the solar UV radiation. We conclude by briefly pointing out that a role of the solar asymmetry in the process of acidification of rain is to be further examined particularly when the level of pollution by sulfur oxides and nitrogen oxides is low.

• Journal of the Korean Data and Information Science Society
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• 제28권6호
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• pp.1415-1425
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• 2017

태풍은 강한 바람과 폭우를 동반하며 매년 한반도에 인명과 재산피해의 원인이 된다. 국내에서 발생한 자연재해 피해에서 태풍이 차지하는 비중이 높다. 태풍의 많은 피해는 폭우에 의해 발생하므로 태풍이 일 최대강수량에 미치는 영향을 정량적으로 살펴볼 필요가 있다. 일 최대강수량은 극치자료로 일반적으로 일반화극단치분포를 따른다. 연구자료로 1976년부터 2016년까지 한반도에 설치된 60개 종관기상관측장비에서 수집된 일강수량, 최대풍속, 평균풍속 자료가 사용되었다. 태풍이 온 기간을 제외한 일강우량 자료와 태풍이 온 기간을 포함한 일강우량 자료로 구분하여 일반화극단치모형에 적합시켰다. 모수추정방법으로 최우추정법과 L-적률추정법이 이용되었다. K-S검정과 $Cram{\acute{e}}r$ von Mises검정을 통해 모형의 적합도를 검정하였다. 추정된 모수를 기반으로 25년, 50년, 100년, 200년 재현수준을 계산하였다. 태풍기간 포함유무에 따른 재현수준을 비교한 결과 태풍은 강릉 인근의 동해안과 울산과 완도 인근의 남해안의 일 최대강수량에 영향을 미친다.

• 한국물환경학회지
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• 제31권3호
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• pp.241-252
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• 2015

Runoff behaviors by five bias correction methods were analyzed, which were Change Factor methods using past observed and estimated data by the estimation scenario with average annual calibration factor (CF_Y) or with average monthly calibration factor (CF_M), Quantile Mapping methods using past observed and estimated data considering cumulative distribution function for entire estimated data period (QM_E) or for dry and rainy season (QM_P), and Integrated method of CF_M+QM_E(CQ). The peak flow by CF_M and QM_P were twice as large as the measured peak flow, it was concluded that QM_P method has large uncertainty in monthly runoff estimation since the maximum precipitation by QM_P provided much difference to the other methods. The CQ method provided the precipitation amount, distribution, and frequency of the smallest differences to the observed data, compared to the other four methods. And the CQ method provided the rainfall-runoff behavior corresponding to the carbon dioxide emission scenario of SRES A1B. Climate change scenario with bias correction still contained uncertainty in accurate climate data generation. Therefore it is required to consider the trend of observed precipitation and the characteristics of bias correction methods so that the generated precipitation can be used properly in water resource management plan establishment.

• 한국지능시스템학회논문지
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• 제24권5호
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• pp.536-541
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• 2014

기상레이더를 통해 취득된 데이터에는 지형에코, 파랑에코, 이상에코, 그리고 청천에코등이 존재한다. 각 에코는 여러 종류의 비강수에코이고, 이 비강수에코를 제거하기 위해 각 에코들의 특성을 분석하였다. 기상레이더 데이터는 매우 방대한 양이기 때문에 전처리 절차를 통해 분석된다. 본 논문에서는 클러스터링 기반 방사형 기저함수 신경회로망(RBFNNs : Radial Basis Function Neural Networks)과 에코 판단 모듈을 이용하여 기상레이더 데이터에서 강수에코와 비강수에코들을 구별하기 위한 에코 패턴분류기를 설계하였다. HCM(Hard C-Mean) 클러스터링 기반 RBFNNs 와 FCM(Fuzzy C-Mean) 클러스터링 기반 RBFNNs를 이용하여 출력성능은 비교 및 분석된다.

• 한국지능시스템학회논문지
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• 제24권6호
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• pp.586-591
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• 2014

본 연구에서는 인공 벌 군집(ABC: Artificial Bee Colony) 알고리즘을 이용하여 주어진 레이더 데이터로부터 강수 사례와 비강수 사례를 분류하는 방사형 기저함수 신경회로망(RBFNNs: Radial Basis Function Neural Networks)분류기를 소개한다. 기상청에서 사용하고 있는 기상 레이더 데이터의 특성 분석을 통해 입력 데이터를 구성한다. 방사형 기저함수 신경회로망의 조건부에서는 Fuzzy C-Means 클러스터링 방법을 이용하여 적합도를 계산하고, 결론부에서는 최소자승법(LSE: Least Square Method)을 이용하여 다항식 계수를 추정한다. 추론부에서 최종출력 값은 퍼지 추론 방법을 이용하여 얻어진다. 제안된 분류기의 성능은 기상청에서 사용하는 QC와 CZ 데이터를 고려하여 비교 및 분석되어진다.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2012년도 학술발표회
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• pp.458-458
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• 2012

Climate extreme variability is a major cause of disaster such as flood and drought types occurred in Korea and its effects is also more severe damage in last decades which can be danger mature events in the future. The main aim of this study was to assess the effectives of climate change on drought for an agriculture as Nakdong basin in Korea using climate change data in the future from data of General Circulation Models (GCM) of ECHO-G, with the developing countries like Korea, the developed climate scenario of medium-high greenhouse gas emission was proposed of the SRES A2. The Standardized Precipitation Index (SPI) was applied for drought evaluation. The drought index (SPI) applied for sites in catchment and it is evaluated accordingly by current and future precipitation data, specific as determined for data from nine precipitation stations with data covering the period 1980-2009 for current and three periods 2010-2039, 2040-2069 and 2070-2099 for future; time scales of 3month were used for evaluating. The results determined drought duration, magnitude and spatial extent. The drought in catchment act intensively occurred in March, April, May and November and months of drought extreme often appeared annual in May and November; drought frequent is a non-uniform cyclic pattern in an irregular repetitive manner, but results showed drought intensity increasing in future periods. The results indicated also spatial point of view, the SPI analysis showed two of drought extents; local drought acting on one or more one of sites and entire drought as cover all of site in catchment. In addition, the meteorology drought simulation maps of spatial drought representation were carried out with GIS software to generate for some drought extreme years in study area. The method applied in this study are expected to be appropriately applicable to the evaluation of the effects of extreme hydrologic events, the results also provide useful for the drought warning and sustainable water resources management strategies and policy in agriculture basins.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2021년도 학술발표회
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• pp.136-136
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• 2021

In this study, a deep convolutional neural network (DCNN) model is proposed for short-term precipitation forecasting using weather radar-based images. The DCNN model is a combination of convolutional neural networks, autoencoder neural networks, and U-net architecture. The weather radar-based image data used here are retrieved from competition for rainfall forecasting in Korea (AI Contest for Rainfall Prediction of Hydroelectric Dam Using Public Data), organized by Dacon under the sponsorship of the Korean Water Resources Association in October 2020. This data is collected from rainy events during the rainy season (April - October) from 2010 to 2017. These images have undergone a preprocessing step to convert from weather radar data to grayscale image data before they are exploited for the competition. Accordingly, each of these gray images covers a spatial dimension of 120×120 pixels and has a corresponding temporal resolution of 10 minutes. Here, each pixel corresponds to a grid of size 4km×4km. The DCNN model is designed in this study to provide 10-minute predictive images in advance. Then, precipitation information can be obtained from these forecast images through empirical conversion formulas. Model performance is assessed by comparing the Score index, which is defined based on the ratio of MAE (mean absolute error) to CSI (critical success index) values. The competition results have demonstrated the impressive performance of the DCNN model, where the Score value is 0.530 compared to the best value from the competition of 0.500, ranking 16^th out of 463 participating teams. This study's findings exhibit the potential of applying the DCNN model to short-term rainfall prediction using weather radar-based images. As a result, this model can be applied to other areas with different spatiotemporal resolutions.

• 대기
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• 제25권4호
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• pp.623-637
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• 2015

The accurate analysis of water vapor in initial of numerical weather prediction (NWP) model is required as one of the necessary conditions for the improvement of heavy rainfall prediction and reduction of spin-up time on a very-short-range forecast. To study this effect, the impact of a ground-based Global Positioning System (GPS)-Precipitable Water Vapor (PWV) on very-short-range forecast are examined. Data assimilation experiments of GPS-PWV data from 19 sites over the Korean Peninsula were conducted with Advanced Storm-scale Analysis and Prediction System (ASAPS) based on the Korea Meteorological Administration's Korea Local Analysis and Prediction System (KLAPS) included "Hot Start" as very-short-range forecast system. The GPS total water vapor was used as constraint for integrated water vapor in a variational humidity analysis in KLAPS. Two simulations of heavy rainfall events show that the precipitation forecast have improved in terms of ETS score compared to the simulation without GPS-PWV data. In the first case, the ETS for 0.5 mm of rainfall accumulated during 3 hrs over the Seoul-Gyeonggi area shows an improvement of 0.059 for initial forecast time. In other cases, the ETS improved 0.082 for late forecast time. According to a qualitative analysis, the assimilation of GPS-PWV improved on the intensity of precipitation in the strong rain band, and reduced overestimated small amounts of precipitation on the out of rain band. In the case of heavy rainfall during the rainy season in Gyeonggi province, 8 mm accompanied by the typhoon in the case was shown to increase to 15 mm of precipitation in the southern metropolitan area. The GPS-PWV assimilation was extremely beneficial to improving the initial moisture analysis and heavy rainfall forecast within 3 hrs. The GPS-PWV data on variational data assimilation have provided more useful information to improve the predictability of precipitation for very short range forecasts.

• 한국지구과학회지
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• 제24권1호
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• pp.22-29
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• 2003

Temporal and spatial variability of precipitation (P), evaporation (E), and moisture balance (P-E; precipitation minus evaporation) has been investigated over the tropical ocean during the period from January 1998 to July 2001. Our data were analyzed by the EOF method using the satellite P and E observations made by the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and the Special Sensor Microwave/Imager (SSM/I). This analysis has been performed for two three-year periods as follow; The first period which includes the El Ni${\tilde{n}}$o in early 1998 ranges from January 1998 to December 2000, and the second period which includes the La Ni${\tilde{n}}$o events in the early 1999 and 2000 (without El Ni${\tilde{n}}$o) ranges from August 1998 to July 2001. The areas of maxima and high variability in the precipitation and in the P-E were displaced from the tropical western Pacific and the ITCZ during the La Ni${\tilde{n}}$o to the tropical middle Pacific during the El Ni${\tilde{n}}$o, consistent with those in previous P studies. Their variations near the Korean Peninsula seem to exhibit a weakly positive correlation with that in the tropical Pacific during the El Ni${\tilde{n}}$o. The evaporation, out of phase with the precipitation, was reduced in the tropical western Pacific due to humid condition in boreal summer, but intensified in the Kuroshio and Gulf currents due to windy condition in winter. The P-E variability was determined mainly by the precipitation of which the variability was more localized but higher by 2-3 times than that of evaporation. Except for the ITCZ (0-10$^{\circ}$N), evaporation was found to dominate precipitation by ${\sim}$2 mm/day over the tropical Pacific. Annual and seasonal variations of P, E, and P-E were discussed.

• 대기
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• 제28권1호
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• pp.85-97
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• 2018

This study evaluates the performance of the Weather Research and Forecasting (WRF) model in reproducing the present-day (1981~2005) precipitation over Far East Asia and South Korea. The WRF model is configured with 25-km horizontal resolution within the context of the COordinated Regional climate Downscaling Experiment (CORDEX) - East Asia Phase 2. The initial and lateral boundary forcing for the WRF simulation are derived from European Centre for Medium-Range Weather Forecast Interim reanalysis. According to our results, WRF model shows a reasonable performance to reproduce the features of precipitation, such as seasonal climatology, annual and inter-annual variabilities, seasonal march of monsoon rainfall and extreme precipitation. In spite of such model's ability to simulate major features of precipitation, systematic biases are found in the downscaled simulation in some sub-regions and seasons. In particular, the WRF model systematically tends to overestimate (underestimate) precipitation over Far East Asia (South Korea), and relatively large biases are evident during the summer season. In terms of inter-annual variability, WRF shows an overall smaller (larger) standard deviation in the Far East Asia (South Korea) compared to observation. In addition, WRF overestimates the frequency and amount of weak precipitation, but underestimates those of heavy precipitation. Also, the number of wet days, the precipitation intensity above the 95 percentile, and consecutive wet days (consecutive dry days) are overestimated (underestimated) over eastern (western) part of South Korea. The results of this study can be used as reference data when providing information about projections of fine-scale climate change over East Asia.