• Proceedings of the Korea Water Resources Association Conference
• /
• 2021.06a
• /
• pp.205-205
• /
• 2021

Surface soil moisture, which governs the partitioning of precipitation into infiltration and runoff, plays an important role in the hydrological cycle. The assimilation of satellite soil moisture retrievals into a land surface model or hydrological model has been shown to improve the predictive skill of hydrological variables. This study aims to improve streamflow prediction with Weather Research and Forecasting model-Hydrological modeling system (WRF-Hydro) by assimilating Soil Moisture Active and Passive (SMAP) data at 3 km and analyze its impacts on hydrological components. We applied Cumulative Distribution Function (CDF) technique to remove the bias of SMAP data and assimilate SMAP data (April to July 2015-2019) into WRF-Hydro by using an Ensemble Kalman Filter (EnKF) with a total 12 ensembles. Daily inflow and soil moisture estimates of major dams (Soyanggang, Chungju, Sumjin dam) of South Korea were evaluated. We investigated how hydrologic variables such as runoff, evaporation and soil moisture were better simulated with the data assimilation than without the data assimilation. The result shows that the correlation coefficient of topsoil moisture can be improved, however a change of dam inflow was not outstanding. It may attribute to the fact that soil moisture memory and the respective memory of runoff play on different time scales. These findings demonstrate that the assimilation of satellite soil moisture retrievals can improve the predictive skill of hydrological variables for a better understanding of the water cycle.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1191-1205
• /
• 2022

Particulate matter(PM) among air pollutants with complex and widespread causes is classified according to particle size. Among them, PM_2.5 is very small in size and can cause diseases in the human respiratory tract or cardiovascular system if inhaled by humans. In order to prepare for these risks, state-centered management and preventable monitoring and forecasting are important. This study tried to predict PM_2.5 in Seoul, where high concentrations of fine dust occur frequently, using two ensemble models, random forest (RF) and extreme gradient boosting (XGB) using 15 local data assimilation and prediction system (LDAPS) weather-related factors, aerosol optical depth (AOD) and 4 chemical factors as independent variables. Performance evaluation and factor importance evaluation of the two models used for prediction were performed, and seasonal model analysis was also performed. As a result of prediction accuracy, RF showed high prediction accuracy of R² = 0.85 and XGB R² = 0.91, and it was confirmed that XGB was a more suitable model for PM_2.5 prediction than RF. As a result of the seasonal model analysis, it can be said that the prediction performance was good compared to the observed values with high concentrations in spring. In this study, PM_2.5 of Seoul was predicted using various factors, and an ensemble-based PM_2.5 prediction model showing good performance was constructed.

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

• Advances in Energy Research
• /
• v.6 no.2
• /
• pp.91-101
• /
• 2019

This paper proposes an efficient data-driven approach to build models for predicting energy consumption in buildings. Data used in this research is collected by installing humidity and temperature sensors at different locations in a building. In addition to this, weather data from nearby weather station is also included in the dataset to study the impact of weather conditions on energy consumption. One of the main emphasize of this research is to make feature selection independent of domain knowledge. Therefore, to extract useful features from data, two different approaches are tested: one is feature selection through principal component analysis and second is relative importance-based feature selection in original domain. The regression model used in this research is gradient boosting regression and its optimal parameters are chosen through a two staged coarse-fine search approach. In order to evaluate the performance of model, different performance evaluation metrics like r2-score and root mean squared error are used. Results have shown that best performance is achieved, when relative importance-based feature selection is used with gradient boosting regressor. Results of proposed technique has also outperformed the results of support vector machines and neural network-based approaches tested on the same dataset.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
• /
• 2011.11a
• /
• pp.20-27
• /
• 2011

A system for fine-resolution long-range weather forecast is introduced in this study. The system is basically consisted of a global-scale coupled general circulation model (CGCM) and Weather Research and Forecast (WRF) regional model. The system makes use of a data assimilation method in order to reduce the initial shock or drift that occurs at the beginning of coupling due to imbalance between model dynamics and observed initial condition. The long-range predictions are produced in the system based on a non-linear ensemble method. At the same time, the model bias are eliminated by estimating the difference between hindcast model climate and observation. In this research, the predictability of the forecast system is studied, and it is illustrated that the system can be effectively used for the high resolution long-term weather prediction. Also, using the system, fine-resolution climatological data has been produced with high degree of accuracy. It is proved that the production of agrometeorological variables that are not intensively observed are also possible.

• Atmosphere
• /
• v.29 no.3
• /
• pp.257-267
• /
• 2019

This study is to assess the applicability of the Extreme Forecast Index (EFI) algorithm of the ECMWF seasonal forecast system to the Global Seasonal Forecasting System version 5 (GloSea5), operational seasonal forecast system of the Korea Meteorological Administration (KMA). The EFI is based on the difference between Cumulative Distribution Function (CDF) curves of the model's climate data and the current ensemble forecast distribution, which is essential to diagnose the predictability in the extreme cases. To investigate its applicability, the experiment was conducted during the heat-wave cases (the year of 1994 and 2003) and compared GloSea5 hindcast data based EFI with anomaly data of ERA-Interim. The data also used to determine quantitative estimates of Probability Of Detection (POD), False Alarm Ratio (FAR), and spatial pattern correlation. The results showed that the area of ERA-Interim indicating above 4-degree temperature corresponded to the area of EFI 0.8 and above. POD showed high ratio (0.7 and 0.9, respectively), when ERA-Interim anomaly data were the highest (on Jul. 11, 1994 (> $5^{\circ}C$) and Aug. 8, 2003 (> $7^{\circ}C$), respectively). The spatial pattern showed a high correlation in the range of 0.5~0.9. However, the correlation decreased as the lead time increased. Furthermore, the case of Korea heat wave in 2018 was conducted using GloSea5 forecast data to validate EFI showed successful prediction for two to three weeks lead time. As a result, the EFI forecasts can be used to predict the probability that an extreme weather event of interest might occur. Overall, we expected these results to be available for extreme weather forecasting.

• Journal of the Korean Dietetic Association
• /
• v.25 no.1
• /
• pp.44-58
• /
• 2019

Predicting the number of meals in a foodservice organization is an important decision-making process that is essential for successful food production, such as reducing the amount of residue, preventing menu quality deterioration, and preventing rising costs. Compared to other demand forecasts, the menu of dietary personnel includes diverse menus, and various dietary supplements include a range of side dishes. In addition to the menus, diverse subjects for prediction are very difficult problems. Therefore, the purpose of this study was to establish a method for predicting the number of meals including predictive modeling and considering various factors in addition to menus which are actually used in the field. For this purpose, 63 variables in eight categories such as the daily available number of people for the meals, the number of people in the time series, daily menu details, weekdays or seasons, days before or after holidays, weather and temperature, holidays or year-end, and events were identified as decision variables. An ensemble model using six prediction models was then constructed to predict the number of meals. As a result, the prediction error rate was reduced from 10%~11% to approximately 6~7%, which was expected to reduce the residual amount by approximately 40%.

• Journal of Korea Water Resources Association
• /
• v.39 no.3 s.164
• /
• pp.275-288
• /
• 2006

This study develops ESP (Ensemble Streamflow Prediction) system by using medium-term numerical weather prediction model which is GDAPS(T213) of KMA. The developed system forecasts medium- and long-range exceedance Probability for streamflow and RPSS evaluation scheme is used to analyze the accuracy of probability forecasts. It can be seen that the daily probability forecast results contain high uncertainties. A sensitivity analysis with respect to forecast time resolution shows that uncertainties decrease and accuracy generally improves as the forecast time step increase. Weekly ESP results by using the GDAPS output with a lead time of up to 28 days are more accurately predicted than traditional ESP results because conditional probabilities are stably distributed and uncertainties can be reduced. Therefore, it can be concluded that the developed system will be useful tool for medium- and long-term reservoir inflow forecasts in order to manage water resources.

• Atmosphere
• /
• v.21 no.4
• /
• pp.391-404
• /
• 2011

This study investigates the impact of cumulus parameterization scheme (CPS) with different horizontal grid sizes on the simulation of the local heavy rainfall case over the Korean Peninsula. The Weather Research and Forecasting (WRF)-based real-time forecast system of the Joint Center for High-impact Weather and Climate Research (JHWC) is used. Three CPSs are used for sensitivity experiments: the BMJ (Betts-Miller-Janjic), GD (Grell-Devenyi ensemble), and KF (Kain-Fritsch) CPSs. The heavy rainfall case selected in this study is characterized by low-level jet and low-level transport of warm and moist air. In 27-km simulations (DM1), simulated precipitation is overestimated in the experiment with BMJ scheme, and it is underestimated with GD scheme. The experiment with KF scheme shows well-developed precipitation cells in the southern and the central region of the Korean Peninsula, which are similar to the observations. All schemes show wet bias and cold bias in the lower troposphere. The simulated rainfall in 27-km horizontal resolution has influence on rainfall forecast in 9-km horizontal resolution, so the statements on 27-km horizontal resolution can be applied to 9-km horizontal resolution. In the sensitivity experiments of CPS for DM3 (3-km resolution), the experiment with BMJ scheme shows better heavy rainfall forecast than the other experiments. The experiments with CPS in 3-km horizontal resolution improve rainfall forecasts compared to the experiments without CPS, especially in rainfall distribution. The experiments with CPS show lower LCL(Lifted Condensation Level) than those without CPS at the maximum rainfall point, and weaker vertical velocity is simulated in the experiments with CPS compared to the experiments without CPS. It means that CPS suppresses convective instability and influences mainly convective rainfall. Consequently, heavy rainfall simulation with BMJ CPS is better than the other CPSs, and even in 3-km horizontal resolution, CPS should be applied to control convective instability. This conclusion can be generalized by conducting more experiments for a variety of cases over the Korean Peninsula.

• Atmosphere
• /
• v.30 no.2
• /
• pp.195-207
• /
• 2020

This study aims to introduce the structure of the impact-based heat health warning system on 165 counties in South Korea developed by the National Institute of Meteorological Sciences. This system was developed using the daily maximum perceived temperature (PTmax), which is a human physiology-based thermal comfort index, and the Local ENSemble prediction system for the probability forecasts. Also, A risk matrix proposed by the World Meteorological Organization was employed for the impact-based forecasts of this system. The threshold value of the risk matrix was separately set depending on regions. In this system, the risk level was issued as four levels (GREEN, YELLOW, ORANGE, RED) for first, second, and third forecast lead-day (LD1, LD2, and LD3). The daily risk level issued by the system was evaluated using emergency heat-related patients obtained at six cities, including Seoul, Incheon, Daejeon, Gwangju, Daegu, and Busan, for LD1 to LD3. The high-risks level occurred more consistently in the shorter lead time (LD3 → LD1) and the performance (r_s) was increased from 0.42 (LD3) to 0.45 (LD1) in all cities. Especially, it showed good performance (r_s = 0.51) in July and August, when heat stress is highest in South Korea. From an impact-based forecasting perspective, PTmax is one of the most suitable temperature indicators for issuing the health risk warnings by heat in South Korea.