• Atmosphere
• /
• v.32 no.3
• /
• pp.179-189
• /
• 2022

The purpose of this study is to build and evaluate a high-resolution (50 m) KMAPP (Korea Meteorological Administration Post Processing) reflecting building data. KMAPP uses LDAPS (Local Data Assimilation and Prediction System) data to detail ground wind speed through surface roughness and elevation corrections. During the detailing process, we improved the vegetation roughness data to reflect the impact of city buildings. AWS (Automatic Weather Station) data from a total of 48 locations in the metropolitan area including Seoul in 2019 were used as the observation data used for verification. Sensitivity analysis was conducted by dividing the experiment according to the method of improving the vegetation roughness length. KMAPP has been shown to improve the tendency of LDAPS to over simulate surface wind speeds. Compared to LDAPS, Root Mean Square Error (RMSE) is improved by approximately 23% and Mean Bias Error (MBE) by about 47%. However, there is an error in the roughness length around the Han River or the coastline. Accordingly, the surface roughness length was improved in KMAPP and the building information was reflected. In the sensitivity experiment of improved KMAPP, RMSE was further improved to 6% and MBE to 3%. This study shows that high-resolution KMAPP reflecting building information can improve wind speed accuracy in urban areas.

• Atmosphere
• /
• v.22 no.4
• /
• pp.455-464
• /
• 2012

This study investigates a 12 month-lead predictability of PNU Coupled General Circulation Model (CGCM) V1.1 hindcast, for which an oceanic data assimilated initialization is used to generate ocean initial condition. The CGCM, a participant model of APEC Climate Center (APCC) long-lead multi-model ensemble system, has been initialized at each and every month and performed 12-month-lead hindcast for each month during 1980 to 2011. The 12-month-lead hindcast consisted of 2-5 ensembles and this study verified the ensemble averaged hindcast. As for the sea-surface temperature concerns, it remained high level of confidence especially over the tropical Pacific and the mid-latitude central Pacific with slight declining of temporal correlation coefficients (TCC) as lead month increased. The CGCM revealed trustworthy ENSO prediction skills in most of hindcasts, in particular. For atmospheric variables, like air temperature, precipitation, and geopotential height at 500hPa, reliable prediction results have been shown during entire lead time in most of domain, particularly over the equatorial region. Though the TCCs of hindcasted precipitation are lower than other variables, a skillful precipitation forecasts is also shown over highly variable regions such as ITCZ. This study also revealed that there are seasonal and regional dependencies on predictability for each variable and lead.

• Journal of the Korean earth science society
• /
• v.43 no.2
• /
• pp.239-252
• /
• 2022

Quality control methods for the first G-band vapor radiometer (GVR) mounted on a weather aircraft in Korea were developed using the GVR Precipitable Water Vapor (PWV). The aircraft attitude information (degree of pitch and roll) was applied to quality control to select the shortest vertical path of the GVR beam. In addition, quality control was applied to remove a GVR PWV ≥20 mm. It was found that the difference between the warm load average power and sky load average power converged to near 0 when the GVR PWV increased to 20 mm or higher. This could be due to the high brightness temperature of the substratus and mesoclouds, which was confirmed by the Communication, Ocean and Meteorological Satellite (COMS) data (cloud type, cloud top height, and cloud amount), cloud combination probe (CCP), and precipitation imaging probe (PIP). The GVR PWV before and after the application of quality control on a cloudy day was quantitatively compared with that of a local data assimilation and prediction system (LDAPS). The Root Mean Square Difference (RMSD) decreased from 2.9 to 1.8 mm and the RMSD with Korea Local Analysis and Precipitation System (KLAPS) decreased from 5.4 to 4.3 mm, showing improved accuracy. In addition, the quality control effectiveness of GVR PWV suggested in this study was verified through comparison with the COMS PWV by using the GVR PWV applied with quality control and the dropsonde PWV.

• Atmosphere
• /
• v.14 no.4
• /
• pp.19-23
• /
• 2004

Progresses in numerical prediction of weather and climate have been in parallel with those of computing resources, especially the development of supercomputers. Advanced techniques in numerical modeling, computational schemes, and data assimilation cloud not have been practically achieved without the aid of supercomputers. With such techniques and computing powers, the accuracy of numerical forecasts has been tremendously improved. Supercomputers are also indispensible in constructing and executing the synthetic Earth system models. In this study, a brief overview on numerical weather / climate prediction, Earth system modeling, and the values of supercomputing is provided.

• Atmosphere
• /
• v.28 no.4
• /
• pp.369-382
• /
• 2018

Planetary boundary layer height (PBLH), produced by the Local Data Assimilation and Prediction System (LDAPS), was verified using RawinSonde (RS) data obtained from observation at Daegwallyeong (DGW) and Sokcho (SCW) during the International Collaborative Experiments for Pyeongchang 2018 Olympic and Paralympic winter games (ICE-POP 2018). The PBLH was calculated using RS data by applying the bulk Richardson number and the parcel method. This calculated PBLH was then compared to the values produced by LDAPS. The PBLH simulations for DGW and SCW were generally underestimation. However, the PBLH was an overestimation from surface to 200 m and 450 m at DGW and SCW, respectively; this result of model's failure to correctly simulate the Surface Boundary Layer (SBL) and the Mixing Layer (ML) as the PBLH. When the accuracy of the PBLH simulation is low, large errors are seen in the mid- and low-level humidity. The highest frequencies of Planetary boundary layer (PBL) types, calculated by the LDAPS at DGW and SCW, were presented as types Ι and II, respectively. Analysis of meteorological factors according to the PBL types indicate that the PBLH of the existing stratocumulus were overestimated when the mid- and low-level humidity errors were large. If the instabilities of the surface and vertical mixing into clouds are considered important factors affecting the estimation of PBLH into model, then mid- and low-level humidity should also be considered important factors influencing PBLH simulation performance.

• Atmosphere
• /
• v.32 no.1
• /
• pp.27-37
• /
• 2022

The Korean Integrated Model (KIM) forecast system was extended to assimilate Horizontal Line-Of-Sight (HLOS) wind observations from the Atmospheric Laser Doppler Instrument (ALADIN) on board the Atmospheric Dynamic Mission (ADM)-Aeolus satellite. Quality control procedures were developed to assess the HLOS wind data quality, and observation operators added to the KIM three-dimensional variational data assimilation system to support the new observed variables. In a global cycling experiment, assimilation of ALADIN observations led to reductions in average root-mean-square error of 2.1% and 1.3% for the zonal and meridional wind analyses when compared against European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) analyses. Even though the observable variable is wind, the assimilation of ALADIN observation had an overall positive impact on the analyses of other variables, such as temperature and specific humidity. As a result, the KIM 72-hour wind forecast fields were improved in the Southern Hemisphere poleward of 30 degrees.

• Atmosphere
• /
• v.21 no.3
• /
• pp.319-336
• /
• 2011

The objective of this study was to assess the meteorological capability of Korea by comparing with that of the U.S. and Japan as of 2010. The research was conducted based on various indices and surveys, and quantified the results using the Gordon's scoring model. The index assessment used 11 items derived from 9 segments - surface observation, advanced observation and observations quality in the observation field; data assimilation, numerical model and infrastructure in the data processing field; forecast accuracy in the forecast field; climate prediction and climate change in the climate field - in this research, we classified the meteorological technology into four fields. In the survey assessment, another 10 items in addition to the above 11 ones (total 21 items) were used. In the field of climate, Korea was found to lag far behind the U.S. (96.5p) and Japan (90.5p) with 77.6 points out of 100, which is 18.9 and 12.9 points lower than them respectively. On the other hand, Korea showed the narrowest gap with Japan (95.3p) and the U.S. (94.2) in the forecasting field, recording 90.3 points. Particularly, in surface observation, infrastructure and forecast accuracy segment, Korea was on a par with the U.S. and Japan, boasting 100.5 percent compared to their counterparts. However, in advanced observation, data quality and climate change segment, Korea was only at the level of 81.5 percent compared to that of the U.S. and Japan. All in all, the technological prowess of Korea, scoring 84.6 points, stood at 89.7 percent of that of the U.S. (94.3p) and 91.9 percent of Japan (92.1p).

• The Korean Journal of Applied Statistics
• /
• v.22 no.6
• /
• pp.1143-1152
• /
• 2009

A canonical correlation analysis(CCA)-based method is proposed for prediction of future climate change which combines information from ensembles of atmosphere-ocean general circulation models(AOGCMs) and observed climate values. This paper focuses on predictions of future climate on a regional scale which are of potential economic values. The proposed method is obtained by coupling the classical CCA with empirical orthogonal functions(EOF) for dimension reduction. Furthermore, we generate a distribution of climate responses, so that extreme events as well as a general feature such as long tails and unimodality can be revealed through the distribution. Results from real data examples demonstrate the promising empirical properties of the proposed approaches.

• Journal of the Korean earth science society
• /
• v.31 no.5
• /
• pp.454-464
• /
• 2010

A probability prediction model for tropical cyclone (TC) genesis in the Northwestern Pacific area was developed using the logistic regression method. Total five predictors were used in this model: the lower-level relative vorticity, vertical wind shear, mid-level relative humidity, upper-level equivalent potential temperature, and sea surface temperature (SST). The values for four predictors except for SST were obtained from difference of spatial-averaged value between May and January, and the time average of Ni$\tilde{n}$o-3.4 index from February to April was used to see the SST effect. As a result of prediction for the TC genesis frequency from June to December during 1951 to 2007, the model was capable of predicting that 21 (22) years had higher (lower) frequency than the normal year. The analysis of real data indicated that the number of year with the higher (lower) frequency of TC genesis was 28 (29). The overall predictability was about 75%, and the model reliability was also verified statistically through the cross validation analysis method.

• Atmosphere
• /
• v.31 no.1
• /
• pp.73-83
• /
• 2021

Numerical weather prediction (NWP) models play an essential role in predicting weather factors, but using them is challenging due to various factors. To overcome the difficulties of NWP models, deep learning models have been deployed in weather forecasting by several recent studies. This study adapts long short-term memory (LSTM), which demonstrates remarkable performance in time-series prediction. The combination of LSTM model input of meteorological features and activation functions have a significant impact on the performance therefore, the results from 5 combinations of input features and 4 activation functions are analyzed in 9 Automated Surface Observing System (ASOS) stations corresponding to cities/islands/mountains. The optimized LSTM model produces better performance within eight forecast hours than Local Data Assimilation and Prediction System (LDAPS) operated by Korean meteorological administration. Therefore, this study illustrates that this LSTM model can be usefully applied to very short-term weather forecasting, and further studies about CNN-LSTM model with 2-D spatial convolution neural network (CNN) coupled in LSTM are required for improvement.