• Atmosphere
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• v.33 no.4
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• pp.423-440
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• 2023

The updated version of Global Ocean Data Assimilation and Prediction System (GODAPS) in the NIMS/KMA (National Institute of Meteorological Sciences/Korea Meteorological Administration), which has been in operation since December 2021, is being introduced. This technical note on GODAPS2 describes main progress and updates to the previous version of GODAPS, a software tool for the operating system, and its improvements. GODAPS2 is based on Forecasting Ocean Assimilation Model (FOAM) vn14.1, instead of previous version, FOAM vn13. The southern limit of the model domain has been extended from 77°S to 85°S, allowing the modelling of the circulation under ice shelves in Antarctica. The adoption of non-linear free surface and variable volume layers, the update of vertical mixing parameterization, and the adjustment of isopycnal diffusion coefficient for the ocean model decrease the model biases. For the sea-ice model, four vertical ice layers and an additional snow layer on top of the ice layers are being used instead of previous single ice and snow layers. The changes for data assimilation include the updated treatment for background error covariance, a newly added bias scheme combined with observation bias, the application of a new bias correction for sea level anomaly, an extension of the assimilation window from 1 day to 2 days, and separate assimilations for ocean and sea-ice. For comparison, we present the difference between GODAPS and GODAPS2. The verification results show that GODAPS2 yields an overall improved simulation compared to GODAPS.

• Atmosphere
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• v.28 no.2
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• pp.123-139
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• 2018

The prediction skills of stratospheric sudden warming (SSW) events and its impacts on the tropospheric prediction skills in global seasonal forecasting system version 5 (GloSea5), an operating subseasonal-to-seasonal (S2S) model in Korea Meteorological Administration, are examined. The model successfully predicted SSW events with the maximum lead time of 11.8 and 13.2 days in terms of anomaly correlation coefficient (ACC) and mean squared skill score (MSSS), respectively. The prediction skills are mainly determined by phase error of zonal wave-number 1 with a minor contribution of zonal wavenumber 2 error. It is also found that an enhanced prediction of SSW events tends to increase the tropospheric prediction skills. This result suggests that well-resolved stratospheric processes in GloSea5 can improve S2S prediction in the troposphere.

• Atmosphere
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• v.34 no.2
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• pp.83-95
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• 2024

Atmospheric rivers (ARs) are closely related to local precipitation which can be both beneficial and destructive. Although several studies have evaluated their predictability, there is a lack of studies on East Asian ARs. This study evaluates the prediction skill of East Asian ARs in the Korean Integrated Model (KIM) for 2020~2022 summer. The spatial distribution of AR frequency in KIM is qualitatively similar to the observation but overestimated. In particular, the model errors greatly increase along the boundary of the western North Pacific subtropical high as the forecast lead time increases. When the prediction skills are quantitatively verified by computing the Anomaly Correlation Coefficient and Mean Square Skill Score, the useful prediction skill of daily AR around the Korean Peninsula is found up to 5 days. Such prediction limit is primarily set by the wind field errors with a minor contribution of moisture distribution errors. This result suggests that the improved prediction of atmospheric circulation field can improve the prediction of East Asian summer ARs and the associated precipitation.

• Atmosphere
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• v.22 no.1
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• pp.117-128
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• 2012

Does snow depth initialization have a quantitative impact on sub-seasonal to seasonal prediction skill? To answer this question, a snow depth initialization technique for seasonal forecast system has been implemented and the impact of the initialization on the seasonal forecast of surface air temperature during the wintertime is examined. Since the snow depth observation can not be directly used in the model simulation due to the large systematic bias and much smaller model variability, an anomaly rescaling method to the snow depth initialization is applied. Snow depth in the model is initialized by adding a rescaled snow depth observation anomaly to the model snow depth climatology. A suite of seasonal forecast is performed for each year in recent 12 years (1999-2010) with and without the snow depth initialization to evaluate the performance of the developed technique. The results show that the seasonal forecast of surface air temperature over East Asian region sensitively depends on the initial snow depth anomaly over the region. However, the sensitivity shows large differences for different timing of the initialization and forecast lead time. Especially, the snow depth anomaly initialized in the late winter (Mar. 1) is the most effective in modulating the surface air temperature anomaly after one month. The real predictability gained by the snow depth initialization is also examined from the comparison with observation. The gain of the real predictability is generally small except for the forecasting experiment in the early winter (Nov. 1), which shows some skillful forecasts. Implications of these results and future directions for further development are discussed.

• International Union of Geodesy and Geophysics Korean Journal of Geophysical Research
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• v.21 no.1
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• pp.47-54
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• 1993

The major positive sea surface temperature(SST) anomalies not only occur in the region with the most moisture increase, but also in the flank of the area with sinking motion in the Subtropics. As the large amount of water vapor has been increased by the SST anomaly, the increased of the SST is expected to destabilize the air and leads under moist adiabatic unstable conditions, to an enhanced development of moisture cluster. The 4.0 K change of SST causes the positive difference of Brightness Temperature(TB) of about 10.0k for water vapor channels of TOVS over the north eastern and central tropical Pacific Ocean, but the negative difference of TB of about 7.5 K is shifted southward and southeastward to Southern Pacific Ocean along the equator correspondingly. The difference of the TBs for IR water vapor channel $11(7.3{\mu}m)$ and $12(6.7{\mu}m)$ of TOVS indicative of the moisture distribution during two time periods(January 1983 and 1984), leads us to infer significant changes in the entire tropospheric circulations and the dynamic processes over the Pacific Ocean.

• Atmosphere
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• v.30 no.4
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• pp.377-390
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• 2020

Not only emissions, but also atmospheric circulation is a key factor that affects local particulate matters (PM) concentrations in Korea through ventilation effects and transboundary transports. As part of the atmospheric circulation, air stagnation especially adversely affects local air quality due to weak ventilation. This study investigates the large-scale circulation related to air stagnation over Korea during winter and projects the climate change impacts on atmospheric patterns, using observed PM data, reanalysis and regional climate projections from HadGEM3-RA with Modified Korea Particulate matter Index. Results show that the stagnation affects the PM concentration, accompanied by pressure ridge at upper troposphere and weaken zonal pressure gradient at lower troposphere. Downscaling using HadGEM3-RA is found to yield Added-Value in the simulated low tropospheric winds. For projection of future stagnation, SSP5-8.5 and SSP1-2.6 (high and low emission) scenarios are used here. It has been found that the stagnation condition occurs more frequently by 11% under SSP5-8.5 and by 5% under SSP1-2.6 than in present-day climate and is most affected by changes in surface wind speed. The increase in the stagnation conditions is related to anticyclonic circulation anomaly at upper troposphere and weaken meridional pressure gradient at lower troposphere. Considering that the present East Asian winter monsoon is mainly affected by change in zonal pressure gradient, it is worth paying attention to this change in the meridional gradient. Our results suggest that future warming condition increase the frequency of air stagnation over Korea during winter with response of atmospheric circulation and its nonlinearity.

• Atmosphere
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• v.31 no.5
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• pp.511-523
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• 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 the Korean Society for Marine Environment & Energy
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• v.9 no.1
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• pp.55-62
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• 2006

Correlations between air-temperature variation and SST variation around Jeju Island have been studied with data JRMO($1924{\sim}2004$) and NFRDI($l971{\sim}2000$). Air-temperature has increased about $0.02^{circ}C/year$ for the period of $1924{\sim}2004$ but relatively high 0.035/year for the last 30 years. SST has increased about $0.024^{circ}C/year$ for the period of $1971{\sim}2000$ and relatively high $0.047^{circ}C/year$ in December. According to the analysis of time series of the two kind of variation, the SST and air-temperature are positively correlated. They are generally in phase, and SST anomaly is similar to air-temperature anomaly as well. Consequently, SST variation has high correlation with air-temperature variation around Jeju Island.

• Journal of Environmental Science International
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• v.4 no.2
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• pp.121-139
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• 1995

Based on the Results of Marine Meteorological and Oceanographical Observations (1966 -1987), the phenomenon of chimney is found as a candidate for the formation of the Japan Sea Proper Water (JSPW). The chimney phenomenon occurs twelve times Inuring 1966∼ 1987. The water types in the chimney denoting the deep convection are similar to those of the JSPW 0∼ 1℃ in potential temperature, 34.0∼34.1 ‰ in salinity and 68∼80 cl/t in potential thermosteric anomaly from the sea surface to the deep layer. The static stabilities in the chimney stations are unstable or neutral. This indicates that the winter time convection occurs. The JSPW sunken from the surface layer of chimney in winter spreads out under the Tsushima Warm Current area, following the isosteric surface of about 76 cl/t in Potential thermosteric anomaly. The formation of the deep water of the JSPW is mainly affected by the cooling of the sea surface than the evaporation of winds because the temperature and the salinity on the isoteric surface of about 76 cl/t in potential thermosteric anomaly ate cold and low The phenomenon of chimney occurred in here and there of the area in the north of 40" 30'N, west of 138" E. This suggests that the deep water of the JSPW is formed not in a limited area but probably in the overall region of the northern open ocean.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.55-58
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• 2024

In this paper, we propose a design and implementation of big data-based fine dust anomaly detection machine learning system. The proposed is system that classifies the fine dust air quality index through meteorological information composed of fine dust and big data. This system classifies fine dust through the design of an anomaly detection algorithm according to the outliers for each air quality index classification categories based on machine learning. Depth data of the image collected from the camera collects images according to the level of fine dust, and then creates a fine dust visibility mask. And, with a learning-based fingerprinting technique through a mono depth estimation algorithm, the fine dust level is derived by inferring the visibility distance of fine dust collected from the monoscope camera. For experimentation and analysis of this method, after creating learning data by matching the fine dust level data and CCTV image data by region and time, a model is created and tested in a real environment.