• The Korean Journal of Applied Statistics
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• v.23 no.4
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• pp.759-765
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• 2010

In this paper, two new inverse regression methods are introduced. One is a distance based method, and the other is a likelihood based method. While a model is fitted by minimizing the sum of squared prediction errors of y's and x's in the classical and inverse methods, respectively. In the new distance based method, we simultaneously minimize the sum of both squared prediction errors. In the likelihood based method, we propose an inverse regression with Arnold-Beaver Skew Normal(ABSN) error distribution. Using the cross validation method with an asymmetric real data set, two new and two existing methods are studied based on the relative prediction bias(RBP) criteria.

• Journal of Wetlands Research
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• v.12 no.1
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• pp.63-73
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• 2010

Rainfall is one of the most important input data of hydrologic models. Rain gage is used to estimate areal rainfall for hydrologic models using several interpolation method such as Thiessen polygon, Inverse Distance Squared(IDS) and Kriging. However, it is still difficult to derive actual spatial distribution of the rainfall using the aforementioned approaches. On the other hand, radar can offer a significant analytic improvement for rainfall analysis by providing directly more representative of the true spatial distribution of rainfall. In this study, In this study, spatial distributions of rainfall derived form rain gages using IDS and Kriging and rainfall from radar are compared. As results, it is found that using radar can provide actual spatial distribution than rain gages.

• Atmosphere
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• v.31 no.2
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• pp.185-198
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• 2021

In this study, we examined a spatial downscaling method based on Gradient and Inverse Distance Squared (GIDS) weighting to produce high-resolution grid data from a numerical weather prediction model over Korean Peninsula with complex terrain. The GIDS is a simple and effective geostatistical downscaling method using horizontal distance gradients and an elevation. The predicted meteorological variables (e.g., temperature and 3-hr accumulated rainfall amount) from the Limited-area ENsemble prediction System (LENS; horizontal grid spacing of 3 km) are used for the GIDS to produce a higher horizontal resolution (1.5 km) data set. The obtained results were compared to those from the bilinear interpolation. The GIDS effectively produced high-resolution gridded data for temperature with the continuous spatial distribution and high dependence on topography. The results showed a better agreement with the observation by increasing a searching radius from 10 to 30 km. However, the GIDS showed relatively lower performance for the precipitation variable. Although the GIDS has a significant efficiency in producing a higher resolution gridded temperature data, it requires further study to be applied for rainfall events.

• Korean Journal of Agricultural and Forest Meteorology
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• v.2 no.4
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• pp.175-182
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• 2000

Spatial interpolation of daily temperature forecasts and observations issued by public weather services is frequently required to make them applicable to agricultural activities and modeling tasks. In contrast to the long term averages like monthly normals, terrain effects are not considered in most spatial interpolations for short term temperatures. This may cause erroneous results in mountainous regions where the observation network hardly covers full features of the complicated terrain. We developed a spatial interpolation model for daily minimum temperature which combines inverse distance squared weighting and elevation difference correction. This model uses a time dependent function for 'mountain slope lapse rate', which can be derived from regression analyses of the station observations with respect to the geographical and topographical features of the surroundings including the station elevation. We applied this model to interpolation of daily minimum temperature over the mountainous Korean Peninsula using 63 standard weather station data. For the first step, a primitive temperature surface was interpolated by inverse distance squared weighting of the 63 point data. Next, a virtual elevation surface was reconstructed by spatially interpolating the 63 station elevation data and subtracted from the elevation surface of a digital elevation model with 1 km grid spacing to obtain the elevation difference at each grid cell. Final estimates of daily minimum temperature at all the grid cells were obtained by applying the calculated daily lapse rate to the elevation difference and adjusting the inverse distance weighted estimates. Independent, measured data sets from 267 automated weather station locations were used to calculate the estimation errors on 12 dates, randomly selected one for each month in 1999. Analysis of 3 terms of estimation errors (mean error, mean absolute error, and root mean squared error) indicates a substantial improvement over the inverse distance squared weighting.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.4
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• pp.53-63
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• 2019

The paper examines the influence of profitability on distance to default (DD) in Vietnam securities market. The investigated sample consists of 211 companies listed on HOSE during 18 years from 2010 to 2017. We apply KMV model to calculate distance to default and use both macroeconomics factors and firm specific factors as independent variables. Using General Least Squared (GLS) method, we find evidence to confirm the positive relationship between profitability and distance to default. This result showed that, although profitability did not directly reflect the cash flow generated, a good profitable enterprise would be an important factor to help facilitate and generate cash flow and at the same time debt was guaranteed when it was due. Besides, the test results revealed that the financial structure and sales on assets have the inverse effect on the distance to default at the significance level of 5%. The results also revealed that a group of macro factors had an influence on the distance to default of businesses, including spread, GDP and trade balance (via exchange rates). Gross domestic income had certain impacts on the distance to default of businesses. This was also a basic indicator measuring the national economic cycle.

• Journal of Wetlands Research
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• v.21 no.spc
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• pp.51-60
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• 2019

In this study, we performed At-site Frequency Analysis(AFA) and Regional Frequency Analysis(RFA) using the observed and climate change scenario data, and the relative root mean squared error(RMMSE) was compared and analyzed for both approaches through Monte Carlo simulation. To evaluate the rainfall quantile, the daily rainfall data were extracted for 615 points in Korea from HadGEM3-RA(12.5km) climate model data, one of the RCM(Regional Climate Model) data provided by the Korea Meteorological Administration(KMA). Quantile mapping(QM) and inverse distance squared methods(IDSM) were applied for bias correction and spatial disaggregation. As a result, it is shown that the RFA estimates more accurate rainfall quantile than AFA, and it is expected that the RFA could be reasonable when estimating the rainfall quantile based on climate change scenarios.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2008.06a
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• pp.455-460
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• 2008

본 연구에서는 1997년 1월부터 2006년 12월까지의 기상청에서 제공하는 31개 자동기상관측망(AWS: Automatic Weather Stations)에 의한 지표 근처 기온($^{\circ}C$) 및 강수(mm) 자료를 이용하여 서울 지역 기상인자의 시 공간 구조 분석 및 변화경향과 변이성을 도출하였다. 미관측지점의 값을 추정하기 위하여 주변 관측지점들을 고려하여, 그 영향은 거리에 반비례함을 반영하는 공간통계학적 방법 중 IDSW(Inverse Distance Squared Weighing:거리자승역산가중)를 적용하여 보관하였다. 그 결과 서울시의 기온과 강수량 모두 1997년에 비해 2006년의 기온이 약 $1.03^{\circ}C$, 강수량이 약 483mm 증가한 것으로 나타났다. 기후변이성의 특성은 과거 10년 동안 기온의 경우 산림지역에서는 변화의 폭이 높게 나타났으며, 시간이 지나면서 감소하는 경향을 보였다. 주거 지역의 경우 변화이 폭이 낮게 나타났으며, 시간이 지나면서 증가하는 경향을 보였다. 그러나 강수량의 경우 산림지역과 주거지역의 변이성의 차이가 나타나지 않았다.

• Spatial Information Research
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• v.16 no.4
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• pp.467-478
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• 2008

This study analyzes the spatial and temporal variability of temperature ($^{\circ}C$) and precipitation (mm) in Seoul, Korea. The temperature and precipitation data were measured at 31 automatic weather stations (AWSs) in Seoul for 10 years from 1997 to 2006. In this study, inverse distance squared weighting (IDSW) was applied to interpolate the non-measured spaces. To estimate the temperature and precipitation variability, the mean values and frequencies of hot and cold days were examined. The maximum and minimum temperatures were $32.80^{\circ}C$ in 1999 and $-19.94^{\circ}C$ in 2001, respectively. The year 2006 showed the highest frequency of hot temperatures with 79 hot days, closely followed by 2004 and 2005. The coldest year was in 2001 with 105 cold days. The annual mean temperature and precipitation increased by about $1^{\circ}C$ and 483mm during the 10-year period, respectively. The temperature variability differed between high-elevation forested areas and low-elevation residential areas. However, the precipitation variability showed little relation with the topography and land use patterns.

• Spatial Information Research
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• v.15 no.3
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• pp.267-278
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• 2007

Internationally many models are developed and applied to predict the impact of the climate change, as occurring a lot of symptoms by climate change. Also, in Korea, according to increasing the application of the climate effect model in many research fields, it is required to study the method for preparing climate data and the characteristics of the climate. In this study IDSW (Inverse Distance Squared Weighting), one of the spatial statistic methods, is applied to interpolate. This method estimates a point of interest by assigning more weight to closer points, which are limited to be select by 3 in 100 km radius. As a result, annual average temperature and precipitation had increased by $0.4^{\circ}C$ and 412 mm during 1977 to 2006. They are also predicted to increase by $3.96^{\circ}C$, 319 mm in the 2100 compared to 2007. High variability of temperature and precipitation for last 30 years shows in some part of the Gangwon-do and in the southern part of Korea. Besides in the study of the variable trend, the variability of temperature and precipitation is inclined to increase in Gangwon-do and southern east part, respectively. However, during 2071 to 2100 variability of temperature is predicted to be high in midwest of Korea and variability of precipitation in the east. In the trend of variability, variability of temperature is apt to increase into west south, and variability of precipitation increase in midwest and a part of south.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.135-135
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• 2020

우리나라의 기후 자료는 일반적으로 기상청에서 발표하는 종관기상관측(ASOS)과 방재기상관측(AWS), 그리고 북한이 세계기상기구(WMO, World Meteorogical Organization)의 기상통신망(GTS)을 통해 보낸 북한기상관측(NKO)을 사용 할 수 있다. 그러나 이 중 40년 이상의 완전한 관측 자료를 얻을 수 있는 건 ASOS가 유일하지만 공간적인 표현에 한계를 갖고 있다. AWS는 관측소가 많다는 장점이 있지만 관측 기간이 길지 않고 이용 가능한 기간에도 관측이 연속적이지 못한 경우가 많다. NKO는 비록 27개의 관측소가 있지만 많은 데이터가 누락되어 일별 기후자료의 사용에 한계를 갖고 있다. 이러한 미관측 기간이나 관측 자료의 누락은 연속적인 시계열 자료분석을 기반으로 하는 수자원 모델링에 있어서 문제를 야기한다. 본 연구는 1973년부터 2019년까지 47년의 신뢰도 높은 한반도 일일 기후 자료를 구축하기 위해 다양한 방법론을 비교하였다. 추정에 사용한 방법은 총 7개로 EM algorithm for probabilistic principal components (PPCA-EM), Inverse distance weight method (IDWM), Nearest neighbor method (NNM), Multivariate normal copulas (Copula), Elastic net model (Elastic), Ordinary kriging (OK), Regularized principal components with EM algorithm (RPCA-EM)를 살펴보았다. 다양한 형태의 결측치를 가정하여 그 결과값을 비교하였고 이는 Root mean squared error(RMSE), Kling-Gupta efficiency(KGE), Nash-Sutcliffe efficiency(NSE)를 통해 평가하였다. 최종 선택된 방법론을 통하여 한반도 전역을 그리드 기반의 강수 및 최저온도/최고온도의 일별자료로 생성하였다.