• Atmosphere
• /
• v.34 no.1
• /
• pp.23-34
• /
• 2024

The Korea Meteorological Administration has improved medium-range weather forecasts by implementing post-processing methods to minimize numerical model errors. In this study, we employ a statistical correction technique known as the minimum continuous ranked probability score (CRPS) to refine medium-range forecast guidance. This technique quantifies the similarity between the predicted values and the observed cumulative distribution function of the Unified Model Ensemble Prediction System for Global (UM EPSG). We evaluated the performance of the medium-range forecast guidance for surface air temperature and relative humidity, noting significant enhancements in seasonal bias and root mean squared error compared to observations. Notably, compared to the existing the medium-range forecast guidance, temperature forecasts exhibit 17.5% improvement in summer and 21.5% improvement in winter. Humidity forecasts also show 12% improvement in summer and 23% improvement in winter. The results indicate that utilizing the minimum CRPS for medium-range forecast guidance provide more reliable and improved performance than UM EPSG.

• Atmosphere
• /
• v.29 no.2
• /
• pp.219-226
• /
• 2019

Generally, the weather forecast system has been run using prescribed ocean condition. As it is widely known that coupling between atmosphere and ocean process produces consistent initial condition at all-time scales to improve forecast skill, there are many trials on the application of data assimilation of coupled model. In this study, we implemented a weakly coupled data assimilation (short for WCDA) system in global NWP model with low horizontal resolution for coupled forecast with uncoupled initialization, following WCDA system at the Met Office. The experiment is carried out for a typhoon evolution forecast in 2017. Air-sea exchange process provides SST cooling and gives a substantial impact on tendency of central pressure changes in the decaying phase of the typhoon, except the underestimated central pressure. Coupled data assimilation is a challenging new area, requiring further work, but it would offer the potential for improving air-sea feedback process on NWP timescales and finally contributing forecast accuracy.

• International Journal of Advanced Culture Technology
• /
• v.7 no.4
• /
• pp.327-333
• /
• 2019

Wind speed data constitute important weather information for aircrafts flying at low altitudes, such as drones. Currently, the accuracy of low altitude wind predictions is much lower than that of high-altitude wind predictions. Deep neural networks are proposed in this study as a method to improve wind speed forecast information. Deep neural networks mimic the learning process of the interactions among neurons in the brain, and it is used in various fields, such as recognition of image, sound, and texts, image and natural language processing, and pattern recognition in time-series. In this study, the deep neural network model is constructed using the wind prediction values generated by the numerical model as an input to improve the wind speed forecasts. Using the ground wind speed forecast data collected at the Boseong Meteorological Observation Tower, wind speed forecast values obtained by the numerical model are compared with those obtained by the model proposed in this study for the verification of the validity and compatibility of the proposed model.

• Journal of the Korean Solar Energy Society
• /
• v.40 no.5
• /
• pp.13-22
• /
• 2020

Day ahead forecast is necessary for the electricity market to stabilize the electricity penetration. Numerical weather prediction is usually employed to produce the solar irradiance as well as electric power forecast for longer than 12 hours forecast horizon. Korea Meteorological Administration operates the UM-LDAPS model to produce the 36 hours forecast of hourly total irradiance 4 times a day. This study interpolates the hourly total irradiance into 15 minute instantaneous irradiance and then compare them with observed solar irradiance at four ground stations at 1 minute resolution. Numerical weather prediction model employed here was produced at 00 UTC or 18 UTC from January to December, 2018. To compare the statistical model for the forecast horizon less than 3 hours, smart persistent model is used as a reference model. Relative root mean square error of 15 minute instantaneous irradiance are averaged over all ground stations as being 18.4% and 19.6% initialized at 18 and 00 UTC, respectively. Numerical weather prediction is better than smart persistent model at 1 hour after simulation began.

• Atmosphere
• /
• v.24 no.4
• /
• pp.503-514
• /
• 2014

Evaluation of predictability of numerical models for tropical cyclone track was performed using along-and cross-track component. The along-and cross-track bias were useful indicators that show the numerical models predictability associated with cause of errors. Since forecast errors, standard deviation and consistency index of along-track component were greater than those of cross-track component, there was some rooms for improvement in alongtrack component. There was an overall slow bias. The most accurate model was JGSM for 24-hour forecast and ECMWF for 48~96-hour forecast in direct position error, along-track error and cross-track error. ECMWF and GFS had a high variability for 24-hour forecast. The results of predictability by track type showed that most significant errors of tropical cyclone track forecast were caused by the failure to estimate the recurvature phenomenon.

• Atmosphere
• /
• v.25 no.1
• /
• pp.1-18
• /
• 2015

The most objective way to overcome the limitation of numerical weather prediction model is to represent the uncertainty of prediction by introducing probabilistic forecast. The uncertainty of the numerical weather prediction system developed due to the parameterization of unresolved scale motions and the energy losses from the sub-scale physical processes. In this study, we focused on the growth of model errors. We performed ensemble forecast to represent model uncertainty. By employing the multi-physics scheme (PHYS) and the stochastic kinetic energy backscatter scheme (SKEBS) in simulating typhoon Rusa (2002), we assessed the performance level of the two schemes. The both schemes produced better results than the control run did in the ensemble mean forecast of the track. The results using PHYS improved by 28% and those based on SKEBS did by 7%. Both of the ensemble mean errors of the both schemes increased rapidly at the forecast time 84 hrs. The both ensemble spreads increased gradually during integration. The results based on SKEBS represented model errors very well during the forecast time of 96 hrs. After the period, it produced an under-dispersive pattern. The simulation based on PHYS overestimated the ensemble mean error during integration and represented the real situation well at the forecast time of 120 hrs. The displacement speed of the typhoon based on PHYS was closest to the best track, especially after landfall. In the sensitivity tests of the model uncertainty of SKEBS, ensemble mean forecast was sensitive to the physics parameterization. By adjusting the forcing parameter of SKEBS, the default experiment improved in the ensemble spread, ensemble mean errors, and moving speed.

• Journal of the Korean earth science society
• /
• v.36 no.2
• /
• pp.158-170
• /
• 2015

This study evaluates the wind speed forecast near the surface layer using the Weather Research Forecasting with Large Eddy Simulation (WRF-LES) model in order to compare the planetary boundary layer (PBL) parameterization with the LES model in terms of different spatial resolution. A numerical simulation is conducted with 1-km and 333-m horizontal resolution over the Gangwon Province including complex mountains and coastal region. The numerical experiments with 1-km and 333-m horizontal resolution employ PBL parameterization and LES, respectively. The wind speed forecast in mountainous region shows a better forecast performance in 333-m experiment than in 1-km, while wind speed in coastal region is similar to the observation in 1-km spatial resolution experiment. Therefore, LES experiment, which directly simulates the turbulence process near the surface layer, contributes to more accurate forecast of surface wind speed in mountainous regions.

• Atmosphere
• /
• v.25 no.2
• /
• pp.375-387
• /
• 2015

A Selected Multi-model CONsensus (SMCON) technique was developed and verified for the tropical cyclone track forecast in the western North Pacific. The SMCON forecasts were produced by averaging numerical model forecasts showing low 70% latest 6 h prediction errors among 21 models. In the homogeneous comparison for 54 tropical cyclones in 2013 and 2014, the SMCON improvement rate was higher than the other forecasts such as the Non-Selected Multi-model CONsensus (NSMCON) and other numerical models (i.e., GDAPS, GEPS, GFS, HWRF, ECMWF, ECMWF_H, ECMWF_EPS, JGSM, TEPS). However, the SMCON showed lower or similar improvement rate than a few forecasts including ECMWF_EPS forecasts at 96 h in 2013 and at 72 h in 2014 and the TEPS forecast at 120 h in 2013. Mean track errors of the SMCON for two year were smaller than the NSMCON and these differences were 0.4, 1.2, 5.9, 12.9, 8.2 km at 24-, 48-, 72-, 96-, 120-h respectively. The SMCON error distributions showed smaller central tendency than the NSMCON's except 72-, 96-h forecasts in 2013. Similarly, the density for smaller track errors of the SMCON was higher than the NSMCON's except at 72-, 96-h forecast in 2013 in the kernel density estimation analysis. In addition, the NSMCON has lager range of errors above the third quantile and larger standard deviation than the SMCON's at 72-, 96-h forecasts in 2013. Also, the SMCON showed smaller bias than ECMWF_H for the cross track bias. Thus, we concluded that the SMCON could provide more reliable information on the tropical cyclone track forecast by reflecting the real-time performance of the numerical models.

• Communications for Statistical Applications and Methods
• /
• v.12 no.2
• /
• pp.295-304
• /
• 2005

This paper concentrates on the prediction of typhoon tracks using the dynamic linear model (DLM) for the statistical correction of the numerical model guidance used in the JMA. The DLM with proposed forecast strategy is applied to reduce their systematic errors using the latest observation. All parameters of the DLM are updated dynamically and backward forecasting is performed to remove the effect of initial values.

• Atmosphere
• /
• v.30 no.1
• /
• pp.17-30
• /
• 2020

KMA performed the special observation program to provide information about severe weather and to monitor typhoon PRAPIROON using the ship which called the Gisang 1 from 29 June 2018 to 4 July 2018 (UTC). For this period, upper-air was observed 21 times with 6 hour intervals using rawinsonde in the Gisang 1. We investigated the impact of upper-air observation data from the Gisang 1 on the performance of the operational convective scale model (we called LDAPS). We conducted two experiments that used all observation data including upper-air observation data from the Gisang 1 (OPER) and without it (EXPR). For a typhoon PRAPIROON case, track forecast error of OPER was lower than EXPR until forecast 24 hours. The intensity forecast error of OPER for minimum sea level pressure was lower than EXPR until forecast 12 hours. The intensity forecast error of OPER for maximum wind speed was mostly lower than EXPR until forecast 30 hours. OPER showed good performance for typhoon forecast compared with EXPR at the early lead time. Two precipitation cases occurred in the south of the Korean peninsula due to the impact of Changma on 1 July and typhoon on 3 July. The location of main precipitation band predicted from OPER was closer to observations. As assimilating upper-air data observed in the Gisang 1 to model, it showed positive results in typhoon and precipitation cases.