• Journal of Environmental Science International
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• v.22 no.8
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• pp.965-977
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• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. It is necessary to get probabilistic forecasts to establish risk-based reservoir operation policies. Probabilistic forecasts may be useful for the users who assess and manage risks according to decision-making responding forecasting results. Probabilistic forecasting of seasonal inflow to Andong dam is performed and assessed using selected predictors from sea surface temperature and 500 hPa geopotential height data. Categorical probability forecast by Piechota's method and logistic regression analysis, and probability forecast by conditional probability density function are used to forecast seasonal inflow. Kernel density function is used in categorical probability forecast by Piechota's method and probability forecast by conditional probability density function. The results of categorical probability forecasts are assessed by Brier skill score. The assessment reveals that the categorical probability forecasts are better than the reference forecasts. The results of forecasts using conditional probability density function are assessed by qualitative approach and transformed categorical probability forecasts. The assessment of the forecasts which are transformed to categorical probability forecasts shows that the results of the forecasts by conditional probability density function are much better than those of the forecasts by Piechota's method and logistic regression analysis except for winter season data.

• Proceedings of the Korean Statistical Society Conference
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• 2002.11a
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• pp.265-272
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• 2002

An Integer-valued autoregressive integrated (INARI) model is introduced to eliminate stochastic trend and seasonality from time series of count data. This INARI extends the previous integer-valued ARMA model. We show that it is stationary and ergodic to establish asymptotic normality for conditional least squares estimator. Optimal estimating equations are used to reflect categorical and serial correlations arising from panel count data and variations arising from three random processes for obtaining observation into estimation. Under regularity conditions for martingale sequence, we show asymptotic normality for estimators from the estimating equations. Using cancer mortality data provided by the U.S. National Center for Health Statistics (NCHS), we apply our results to estimate the probability of cells classified by 4 causes of death and 6 age groups and to forecast death count of each cell. We also investigate impact of three random processes on estimation.

• The Korean Journal of Applied Statistics
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• v.24 no.3
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• pp.535-546
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• 2011

This study develops statistical models for the binary forecast of Asian dust days over South Korea in the winter season. For this study, we used three kinds of data; the rst one is the observed Asian dust days for a period of 31 years (1980 to 2010) as target values, the second one is four meteorological factors(near surface temperature, precipitation, snowfall, ground wind speed) in the source regions of Asian dust based on the NCEP reanalysis data and the third one is the large-scale climate indices. Four kinds of statistical models(multiple regression models, logistic regression models, decision trees, and support vector machines) are applied and compared based on skill scores(hit rate, probability of detection and false alarm rate).

• Atmosphere
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• v.16 no.2
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• pp.125-139
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• 2006

Precipitation forecasts from MM5 have been verified for the period 1989-2001 over Yeongdong region to show a tendency of model forecast. We select 57 events which are related with the heavy snowfall in Yeongdong region. They are classified into three precipitation types; mountain type, cold-coastal type, and warm type. The threat score (TS), the probability of detection (POD), and the false-alarm rate (FAR) are computed for categorical verification and the mean squared error (MSE) is also computed for scalar accuracy measures. In the case of POD, warm, mountain, and cold-coastal precipitation type are 0.71, 0.69, and 0.55 in turn, respectively. In aspect of quantitative verification, mountain and cold-coastal type are relatively well matched between forecasts and observations, while for warm type MM5 tends to overestimate precipitation. There are 12 events for the POD below 0.2, mountain, cold-coastal, warm type are 2, 7, 3 events, respectively. Most of their precipitation are distributed over the East Sea nearby Yeongdong region. These events are also shown when there are no or very weak easterlies in the lower troposphere. Even in the case that we use high resolution sea surface temperature (about 18 km) for the boundary condition, there are not much changes in the wind direction to compare that with low resolution sea surface temperature (about 100 km).