• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.31 no.3
• /
• pp.59-70
• /
• 2023

In order to predict low-level wind shear at Incheon International Airport (RKSI), a Low-Level Wind Shear prediction system (KMAP-LLWS) along the runway take-off and landing route at RKSI was established using Korea Meteorological Administration Post-Processing (KMAP). For the performance evaluation, the case of low-level wind shear cases calculated from Aircraft Meteorological Data Relay (AMDAR) from July 2021 to June 2022 was used. As a result of verification using the performance evaluation index, POD, FAR, CSI, and TSS were 0.5, 0.85, 0.13, and 0.34, respectively, and the prediction performance was improved by POD, CSI, and TSS compared to the Low-Level Wind Shear prediction system (LDPS-LLWS) calculated using the Korea Meteorological Administration's Local Data Assimilation and Prediction System (LDAPS). This means that the use of high-resolution numerical models improves the predictability of wind changes. In addition, to improve the high FAR of KMAP-LLWS, the threshold for low-level wind shear strength was adjusted. As a result, the most effective low-level wind shear threshold at 8.5 knot/100 ft was derived. This study suggests that it is possible to predict and respond to low-level wind shear at RKSI. In addition, it will be possible to predict low-level wind shear at other airports without wind shear observation equipment by applying the KMAP-LLWS.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.14 no.2
• /
• pp.53-62
• /
• 2012

Demand for high resolution weather data grows in the agriculture and forestry fields. Local Analysis and Prediction System (LAPS) can be used to analyze the local weather at high spatial and temporal resolution, utilizing the data from various sources including numerical weather prediction models, wind or temperature profilers, Automated Weather Station (AWS) networks, radars, and satellites. LAPS has been set to analyze weather elements such as air temperature, relative humidity, wind speed, and wind direction every hour at the spatial resolution of $100m{\times}100m$ for Gyeonggi-do on near real-time basis. The AWS data were revised by adding the agricultural field AWS data (33 stations) in addition to the KMA data. The analysis periods were from 1 to 31 August 2009 and from 15 to 21 February 2010. The comparison of the LAPS output showed the smaller errors when using the agricultural AWS observation data together with the KMA data as its input data than using only either the agricultural or KMA AWS data. The accuracy of the current system needs improvement by further optimization of analyzing options of the system. However, the system is highly applicable to various fields in agriculture and forestry because it can provide site specific data with reasonable time intervals.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.16 no.3
• /
• pp.157-162
• /
• 2016

In this paper, a yield prediction model based on relevance vector machine (RVM) and a granular computing model (quotient space theory) is presented. With a granular computing model, massive and complex meteorological data can be analyzed at different layers of different grain sizes, and new meteorological feature data sets can be formed in this way. In order to forecast the crop yield, a grey model is introduced to label the training sample data sets, which also can be used for computing the tendency yield. An RVM algorithm is introduced as the classification model for meteorological data mining. Experiments on data sets from the real world using this model show an advantage in terms of yield prediction compared with other models.

• Atmosphere
• /
• v.15 no.4
• /
• pp.229-239
• /
• 2005

The effect of inversion layer on the land and sea breeze near the Gangneung city was investigated. The land and sea breeze occurrence days were selected, and the height and the intensity of inversion layer were calculated with the upper air observational data of the Sokcho Station. The relationships between the temperature variation near the Gangneung and the inflow time, inland penetration and the inflow depth of the land and sea breeze were also analyzed. And the Gangwon Short-range prediction system was verified with the comparison of surface stream line by the Gangwon short-range prediction system with the AWS wind vector data. It was revealed that the inversion layer tended to block the sea breeze, shorten the inland penetration distance and lower the inflow depth, causing the temperature rise. The comparison and analysis of surface steam line by the Gangwon short-range prediction system and the AWS wind vector showed that the system quite well simulated the sea breeze, thus the system could be well utilized in the prediction of land and sea breeze.

• Atmosphere
• /
• v.31 no.5
• /
• pp.511-523
• /
• 2021

This study explores the optimal ensemble size to improve the prediction performance of the Korea Meteorological Administration's operational climate prediction system, global seasonal forecast system version 5 (GloSea5). The GloSea5 produces an ensemble of hindcast data using the stochastic kinetic energy backscattering version2 (SKEB2) and timelagged ensemble. An experiment to increase the hindcast ensemble from 3 to 14 members for four initial dates was performed and the improvement and effect of the prediction performance considering Root Mean Square Error (RMSE), Anomaly Correlation Coefficient (ACC), ensemble spread, and Ratio of Predictable Components (RPC) were evaluated. As the ensemble size increased, the RMSE and ACC prediction performance improved and more significantly in the high variability area. In spread and RPC analysis, the prediction accuracy of the system improved as the ensemble size increased. The closer the initial date, the better the predictive performance. Results show that increasing the ensemble to an appropriate number considering the combination of initial times is efficient.

• Journal of Ocean Engineering and Technology
• /
• v.35 no.4
• /
• pp.273-286
• /
• 2021

In recent years, as human casualties and property damage caused by hazardous waves have increased in the East Sea, precise wave prediction skills have become necessary. In this study, the Simulating WAves Nearshore (SWAN) third-generation numerical wave model was calibrated and optimized to enhance the accuracy of winter storm wave prediction in the East Sea. We used Source Term 6 (ST6) and physical observations from a large-scale experiment conducted in Australia and compared its results to Komen's formula, a default in SWAN. As input wind data, we used Korean Meteorological Agency's (KMA's) operational meteorological model called Regional Data Assimilation and Prediction System (RDAPS), the European Centre for Medium Range Weather Forecasts' newest 5th generation re-analysis data (ERA5), and Japanese Meteorological Agency's (JMA's) meso-scale forecasting data. We analyzed the accuracy of each model's results by comparing them to observation data. For quantitative analysis and assessment, the observed wave data for 6 locations from KMA and Korea Hydrographic and Oceanographic Agency (KHOA) were used, and statistical analysis was conducted to assess model accuracy. As a result, ST6 models had a smaller root mean square error and higher correlation coefficient than the default model in significant wave height prediction. However, for peak wave period simulation, the results were incoherent among each model and location. In simulations with different wind data, the simulation using ERA5 for input wind datashowed the most accurate results overall but underestimated the wave height in predicting high wave events compared to the simulation using RDAPS and JMA meso-scale model. In addition, it showed that the spatial resolution of wind plays a more significant role in predicting high wave events. Nevertheless, the numerical model optimized in this study highlighted some limitations in predicting high waves that rise rapidly in time caused by meteorological events. This suggests that further research is necessary to enhance the accuracy of wave prediction in various climate conditions, such as extreme weather.

• Atmosphere
• /
• v.15 no.1
• /
• pp.35-46
• /
• 2005

Today, the outreach of National Meteorological Service such as PC cluster based Numerical Weather Prediction (NWP) technique is vigorous in the world wide. In this regard, WMO (World Meteorological Organization) asked KMA (Korea Meteorological Administration) to formulate a regional project, which cover most of RA II members, using similar technical system with KMA's. In that sense, Meteorological Research Institute (METRI) in KMA developed Mongolian NWP System (MNWPS) based on PC cluster and transferred the technology to Weather Service Center in Mongolia. The hybrid parallel algorithm and channel bonding technique were adopted to cut cost and showed 41% faster performance than single MPI (Message Passing Interface) approach. The cluster technique of Beowulf type was also adopted for convenient management and saving resources. The Linux based free operating system provide very cost effective solution for operating multi-nodes. Additionally, the GNU software provide many tools, utilities and applications for construction and management of a cluster. A flash flood event happened in Mongolia (2 September 2003) was selected for test run, and MNWPS successfully simulated the event with initial and boundary condition from Global Data Assimilation and Prediction System (GDAPS) of KMA. Now, the cluster based NWP System in Mongolia has been operated for local prediction around the region and provided various auxiliary charts.

• Atmosphere
• /
• v.23 no.1
• /
• pp.113-121
• /
• 2013

The objective of this study is to develop and evaluate the system to produce the real-time ensemble drought prediction data. Ensemble drought prediction consists of 3 processes (meteorological outlook using the multi-initial conditions, hydrological analysis and drought index calculation) therefore, more processing time and data is required than that of single member. For ensemble drought prediction, data process time is optimized and hardware of existing system is upgraded. Ensemble drought data is estimated for year 2012 and to evaluate the accuracy of drought prediction data by using ROC (Relative Operating Characteristics) analysis. We obtained 5 ensembles as optimal number and predicted drought condition for every tenth day i.e. 5th, 15th and 25th of each month. The drought indices used are SPI (Standard Precipitation Index), SRI (Standard Runoff Index), SSI (Standard Soil moisture Index). Drought conditions were determined based on results obtained for each ensemble member. Overall the results showed higher accuracy using ensemble members as compared to single. The ROC score of SRI and SSI showed significant improvement in drought period however SPI was higher in the demise period. The proposed ensemble drought prediction system can be contributed to drought forecasting techniques in Korea.

• Atmosphere
• /
• v.26 no.4
• /
• pp.495-508
• /
• 2016

In order to determine the prediction possibility of heavy rainfall, a variety of analyses was conducted by using three-dimensional data obtained from Korea Local Analysis and Prediction System (KLAPS) re-analysis data. Strong moisture convergence occurring around the time of the heavy rainfall is consistent with the results of previous studies on such continuous production. Heavy rainfall occurred in the cloud system with a thick convective clouds. The moisture convergence, temperature and potential temperature advection showed increase into the heavy rainfall occurrence area. The distribution of integrated liquid water content tended to decrease as rainfall increased and was characterized by accelerated convective instability along with increased buoyant energy. In addition, changes were noted in the various characteristics of instability indices such as K-index (KI), Showalter Stability Index (SSI), and lifted index (LI). The meteorological variables used in the analysis showed clear increases or decreases according to the changes in rainfall amount. These rapid changes as well as the meteorological variables changes are attributed to the surrounding and meteorological conditions. Thus, we verified that heavy rainfall can be predicted according to such increase, decrease, or changes. This study focused on quantitative values and change characteristics of diagnostic variables calculated by using numerical models rather than by focusing on synoptic analysis at the time of the heavy rainfall occurrence, thereby utilizing them as prognostic variables in the study of the predictability of heavy rainfall. These results can contribute to the identification of production and development mechanisms of heavy rainfall and can be used in applied research for prediction of such precipitation. In the analysis of various case studies of heavy rainfall in the future, our study result can be utilized to show the development of the prediction of severe weather.

• Journal of the Korean earth science society
• /
• v.22 no.3
• /
• pp.186-194
• /
• 2001

The operational meso-scale short range NWP system was developed for Cheju Regional Meteorological Office located at Cheju island, Korea. The Central Meteorological Service Center, KMA has reported the information on numerical weather prediction every 12 hours. But this information is not enough to determine the detail forecast for the regional meteorological office because the terrain of the Korean peninsula is very complex and the resolution of the numerical model provided by KMA headquarter is too coarse to resolve the local severe weather system such as heavy rainfall. LAPS and MM5 models were chosen for three-dimentional data assimilation and numerical weather prediction tools respectively. LAPS was designed to provide the initial data to all regional numerical prediction models including MM5. Synoptic observational data from GTS, satellite brightness temperature data from GMS-5 and the composite reflectivity data from 5 radar sites were used in the LAPS data assimilation for producing the initial data. MM5 was performed on PC-cluster based on 16 pentium CPUs which was one of the cheapest distributed parallel computer in these days. We named this system as Halla Short Range Prediction System (HSRPS). HSRPS was verified by heavy rainfall case in July 9, 1999, it showed that HSRPS well resolved local severe weather which was not simulated by 30 km MM5/KMA. Especially, the structure of rainfall amount was very close to the corresponding observation. HSRPS will be operating every 6 hours in the Cheju Regional Meteorological Office from April 2000.