• Korean Management Science Review
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• v.32 no.4
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• pp.97-108
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• 2015

This study proposes a mathematical model to estimate the economic value of weather forecast service, among which the precipitation forecast service is focused. The value is calculated in terms of users' satisfaction or dissatisfaction resulted from the users' decisions made by using the precipitation probabilistic forecasts and thresholds. The satisfaction values can be quantified by the traditional value score model, which shows the scaled utility values relative to the perfect forecast information. This paper extends the value score concept to a collective value score model which is defined as a weighted sum of users' satisfaction based on threshold distribution in a group of the users. The proposed collective value score model is applied to the picnic scenario by using four hypothetical sets of probabilistic forecasts, i.e., under-confident, over-confident, under-forecast and over-forecast. The application results show that under-confident type of forecasts outperforms the others as a measure of the maximum collective value regardless of users' dissatisfaction patterns caused by two types of forecast errors, e.g., miss and false alarm.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.197-197
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• 2016

Forecasting future drought events in a region plays a major role in water management and risk assessment of drought occurrences. The creeping characteristics of drought make it possible to mitigate drought's effects with accurate forecasting models. Drought forecasts are inevitably plagued by uncertainties, making it necessary to derive forecasts in a probabilistic framework. In this study, a new probabilistic scheme is proposed to forecast droughts, in which a discrete-time finite state-space hidden Markov model (HMM) is used aggregated with the Representative Concentration Pathway 8.5 (RCP) precipitation projection (HMM-RCP). The 3-month standardized precipitation index (SPI) is employed to assess the drought severity over the selected five stations in South Kore. A reversible jump Markov chain Monte Carlo algorithm is used for inference on the model parameters which includes several hidden states and the state specific parameters. We perform an RCP precipitation projection transformed SPI (RCP-SPI) weight-corrected post-processing for the HMM-based drought forecasting to derive a probabilistic forecast that considers uncertainties. Results showed that the HMM-RCP forecast mean values, as measured by forecasting skill scores, are much more accurate than those from conventional models and a climatology reference model at various lead times over the study sites. In addition, the probabilistic forecast verification technique, which includes the ranked probability skill score and the relative operating characteristic, is performed on the proposed model to check the performance. It is found that the HMM-RCP provides a probabilistic forecast with satisfactory evaluation for different drought severity categories, even with a long lead time. The overall results indicate that the proposed HMM-RCP shows a powerful skill for probabilistic drought forecasting.

• Atmosphere
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• v.30 no.4
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• pp.405-420
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• 2020

In this study, we investigate the predictability of intraseasonal monthly temperature and precipitation variations using hindcast datasets from eight global circulation models participating in the operational multi-model ensemble (MME) seasonal prediction system of the Asia-Pacific Economic Cooperation Climate Center for the 1983~2010 period. These intraseasonal monthly variations are defined by categorical deterministic analysis. The monthly temperature and precipitation are categorized into above normal (AN), near normal (NN), and below normal (BN) based on the σ-value ± 0.43 after standardization. The nine patterns of intraseasonal monthly variation are defined by considering the changing pattern of the monthly categories for the three consecutive months. A deterministic and a probabilistic analysis are used to define intraseasonal monthly variation for the multi-model consisting of numerous ensemble members. The results show that a pattern (pattern 7), which has the same monthly categories in three consecutive months, is the most frequently occurring pattern in observation regardless of the seasons and variables. Meanwhile, the patterns (e.g., patterns 8 and 9) that have consistently increasing or decreasing trends in three consecutive months, such as BN-NN-AN or AN-NN-BN, occur rarely in observation. The MME and eight individual models generally capture pattern 7 well but rarely capture patterns 8 and 9.

• Atmosphere
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• v.30 no.2
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• pp.141-154
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• 2020

Seasonal forecast is growing in demand, as it provides valuable information for decision making and potential to reduce impact on weather events. This study examines how operational climate prediction systems can be reliable, producing the probability forecast in seasonal scale. A reliability diagram was used, which is a tool for the reliability by comparing probabilities with the corresponding observed frequency. It is proposed for a method grading scales of 1-5 based on the reliability diagram to quantify the reliability. Probabilities are derived from ensemble members using hindcast data. The analysis is focused on skill for 2 m temperature and precipitation from climate prediction systems in KMA, UKMO, and ECMWF, NCEP and JMA. Five categorizations are found depending on variables, seasons and regions. The probability forecast for 2 m temperature can be relied on while that for precipitation is reliable only in few regions. The probabilistic skill in KMA and UKMO is comparable with ECMWF, and the reliabilities tend to increase as the ensemble size and hindcast period increasing.

• The Korean Journal of Quaternary Research
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• v.19 no.2
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• pp.74-79
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• 2005

Recently, several attempts have been made to provide reasonable information on unusual severe weather phenomena such as tolerant heavy rains and very wild typhoons. Quantitative precipitation forecasts and probabilistic quantitative precipitation forecasts (QPFs and PQPFs, respectively) might be one of the most promising methodologies for early warning on the flesh floods because those diagnostic precipitation models require less computational resources than fine-mesh full-dynamics non-hydrostatic mesoscale model. The diagnostic rainfall model used in this study is the named QPM(Quantitative Precipitation Model), which calculates the rainfall by considering the effect of small-scale topography which is not treated in the mesoscale model. We examine the capability of probabilistic diagnostic rainfall model in terms of how well represented the observed several rainfall events and what is the most optimistic resolution of the mesoscale model in which diagnostic rainfall model is nested. Also, we examine the integration time to provide reasonable fine-mesh rainfall information. When we apply this QPM directly to 27 km mesh meso-scale model (called as M27-Q3), it takes about 15 min. while it takes about 87 min. to get the same resolution precipitation information with full dynamic downscaling method (called M27-9-3). The quality of precipitation forecast by M27-Q3 is quite comparable with the results of M27-9-3 with reasonable threshold value for precipitation. Based on a series of examination we may conclude that the proosed QPM has a capability to provide fine-mesh rainfall information in terms of time and accuracy compared to full dynamical fine-mesh meso-scale model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.2
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• pp.89-98
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• 2007

In this paper the economic value of weather forecasts is valuated for profit-oriented enterprise decision-making situations. Value is estimated in terms of monetary profits (or benefits) resulted from the forecast user's decision under the specific payoff structure, which is represented by a profit/loss ratio model combined with a decision function and a value score (VS). The forecast user determines a business-related decision based on the probabilistic forecast, the user's subjective reliability of the forecasts, and the payoff structure specific to the user's business environment. The VS curve for a meteorological forecast is specified by a function of the various profit/loss ratios, providing the scaled economic value relative to the value of a perfect forecast. The proposed valuation method based on the profit/loss ratio model and the VS is adapted for hypothetical sets of forecasts and verified for site-specific probability of precipitation forecast of 12 hour and 24 hour-lead time, which is generated from Korea meteorological administration (KMA). The application results show that forecast information with shorter lead time can provide the decision-makers with great benefits and there are ranges of profit/loss ratios in which high subjective reliability of the given forecast is preferred.

• Journal of Korea Water Resources Association
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• v.49 no.10
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• pp.835-849
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• 2016

Accurate drought outlook and drought monitoring have been preceded recently to mitigate drought damages that further deepen. This study improved the limitations of the previous MSWSI (Modified Surface Water Supply Index) used in Korea and carried out probabilistic drought forecasts based on ensemble technique with the improved MSWSI. This study investigated available hydrometeorological components in Geum river basin and supplemented appropriate components (dam water level, dam release discharge) in addition to the four components (streamflow, groundwater, precipitation, dam inflow) usedin the previous MSWSI to each sub-basin. Although normal distribution was fitted in the previous MSWSI, the most suitable probabilistic distributions to each meteorological component were estimated in this study, including Gumbel distribution for precipitation and streamflow data; 2-parameter log-normal distribution for dam inflow, water level, and release discharge data; 3-parameter log-normal distribution for groundwater. To verify the improved MSWSI results using historical precipitation and streamflow, simulated drought situations were used. Results revealed that the improved MSWSI results were closer to actual drought than previous MSWSI results. The probabilistic forecasts based on ensemble technique with improved MSWSI were performed and evaluated in 2006 and 2014. The accuracy of the improved MSWSI was better than the previous MSWSI. Moreover, the drought index of actual drought was included in ranges of drought forecasts using the improved MSWSI.

• The Journal of the Korea Contents Association
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• v.13 no.11
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• pp.382-395
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• 2013

The providers of meteorological information want to know the level of satisfaction of forecast users with their services. To provide better service, meteorological communities of each nation are administering a survey on satisfaction of forecast users. However, most researchers provided these users with simple questionnaires and the respondents had to choose one answer among different satisfaction levels. So, the results of this kind of survey have low explanation power and are difficult to use in developing strategy of forecast service. In this study, instead of cost-loss concept, we applied satisfaction-dissatisfaction concept to the $2{\times}2$ contingency table, which is a useful tool to evaluate value of forecast, and estimated satisfaction value of 24h precipitation forecasts in Shanghai, China and Seoul, Korea. Moreover, not only the individual satisfaction value of forecast but the user group's satisfaction value was evaluated. As for the result, it is effective to enhance forecast accuracy to improve the satisfaction value of deterministic forecast user group, but in the case of probabilistic forecast, it is important to know the level of dissatisfaction of user group and distribution of probability threshold of forecast users. These results can help meteorological communities to search for a solution which can provide better satisfaction value to forecast users.

• Journal of Korea Water Resources Association
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• v.50 no.8
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• pp.551-562
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• 2017

In order to utilize 6-month precipitation forecasts (6 months at maximum) of Global Seasonal Forecast System version 5 (GloSea5), which is being provided by KMA (Korea Meteorological Administration) since 2014, for water resources management as well as other applications, it is needed to correct the forecast model's quantitative bias against observations. This study evaluated applicability of bias-correction skill in GloSea5 and selected an optimal method among 11 techniques that include probabilistic distribution type based, parametric, and non-parametric bias-correction to fix GloSea5's bias in precipitation forecasts. Non-parametric bias-correction provided the most similar results with observed data compared to other techniques in hindcast for the past events, yet relatively generated some discrepancies in forecast. On the contrary, parametric bias-correction produced the most reliable results in both hindcast and forecast periods. The results of this study are expected to be applicable to various applications using seasonal forecast model such as water resources operation and management, hydropower, agriculture, etc.

• Journal of Korea Water Resources Association
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• v.49 no.9
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• pp.761-771
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• 2016

The GloSea5 (Global Seasonal forecasting system version 5) is provided and operated by the KMA (Korea Meteorological Administration). GloSea5 provides Forecast (FCST) and Hindcast (HCST) data and its horizontal resolution is about 60km ($0.83^{\circ}{\times}0.56^{\circ}$) in the mid-latitudes. In order to use this data in watershed-scale water management, GloSea5 needs spatial-temporal downscaling. As such, statistical downscaling was used to correct for systematic biases of variables and to improve data reliability. HCST data is provided in ensemble format, and the highest statistical correlation ($R^2=0.60$, RMSE = 88.92, NSE = 0.57) of ensemble precipitation was reported for the Yongdam Dam watershed on the #6 grid. Additionally, the original GloSea5 (600.1 mm) showed the greatest difference (-26.5%) compared to observations (816.1 mm) during the summer flood season. However, downscaled GloSea5 was shown to have only a -3.1% error rate. Most of the underestimated results corresponded to precipitation levels during the flood season and the downscaled GloSea5 showed important results of restoration in precipitation levels. Per the analysis results of spatial autocorrelation using seasonal Moran's I, the spatial distribution was shown to be statistically significant. These results can improve the uncertainty of original GloSea5 and substantiate its spatial-temporal accuracy and validity. The spatial-temporal reproducibility assessment will play a very important role as basic data for watershed-scale water management.