• Atmosphere
• /
• v.28 no.3
• /
• pp.233-245
• /
• 2018

This study explores the Subseasonal-to-Seasonal (S2S) prediction skills of the Northern Hemisphere mid-latitude geopotential height in the Global Seasonal forecasting model version 5 (GloSea5) hindcast experiment. The prediction skills are quantitatively verified for the period of 1991~2010 by computing the Anomaly Correlation Coefficient (ACC) and Mean Square Skill Score (MSSS). GloSea5 model shows a higher prediction skill in winter than in summer at most levels regardless of verification methods. Quantitatively, the prediction limit diagnosed with ACC skill of 500 hPa geopotential height, averaged over $30^{\circ}N{\sim}90^{\circ}N$, is 11.0 days in winter, but only 9.1 days in summer. These prediction limits are primarily set by the planetary-scale eddy phase errors. The stratospheric prediction skills are typically higher than the tropospheric skills except in the summer upper-stratosphere where prediction skills are substantially lower than upper-troposphere. The lack of the summer upper-stratospheric prediction skill is caused by zonal mean error, perhaps strongly related to model mean bias in the stratosphere.

• Atmosphere
• /
• v.28 no.2
• /
• pp.123-139
• /
• 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
• /
• v.33 no.5
• /
• pp.493-503
• /
• 2023

This study investigates the downward influences of sudden stratospheric warming (SSW) in February 2018 using a subseasonal-to-seasonal forecast model, Global Seasonal forecasting system version 6 (GloSea6). To quantify the influences of SSW on the tropospheric prediction skills, free-evolving (FREE) forecasts are compared to stratospheric nudging (NUDGED) forecasts where zonal-mean flows in the stratosphere are relaxed to the observation. When the models are initialized on 8 February 2018, both FREE and NUDGED forecasts successfully predicted the SSW and its downward influences. However, FREE forecasts initialized on 25 January 2018 failed to predict the SSW and downward propagation of negative Northern Annular Mode (NAM). NUDGED forecasts with SSW nudging qualitatively well predicted the downward propagation of negative NAM. In quantity, NUDGED forecasts exhibit a higher mean squared skill score of 500 hPa geopotential height than FREE forecasts in late February and early March. The surface air temperature and precipitation are also better predicted. Cold and dry anomalies over the Eurasia are particularly well predicted in NUDGED compared to FREE forecasts. These results suggest that a successful prediction of SSW could improve the surface prediction skills on subseasonal-to-seasonal time scale.

• Atmosphere
• /
• v.26 no.4
• /
• pp.711-716
• /
• 2016

The dynamical model forecasts using state-of-art general circulation models (GCMs) have some limitations to simulate the real climate system since they do not depend on the past history. One of the alternative methods to correct model errors is to use the canonical correlation analysis (CCA) correction method. CCA forecasts at the present time show better skill than dynamical model forecasts especially over the midlatitudes. Model outputs are adjusted based on the CCA modes between the model forecasts and the observations. This study builds a canonical correlation prediction model for subseasonal (June) precipitation. The predictors are circulation fields over western North Pacific from the Global Seasonal Forecasting System version 5 (GloSea5) and observed snow cover extent over Eurasia continent from Climate Data Record (CDR). The former is based on simultaneous teleconnection between the western North Pacific and the East Asia, and the latter on lagged teleconnection between the Eurasia continent and the East Asia. In addition, we suggest a technique for improving forecast skill by applying the ensemble canonical correlation (ECC) to individual canonical correlation predictions.

• Journal of the Korean earth science society
• /
• v.43 no.4
• /
• pp.511-519
• /
• 2022

Given the significant social and economic impact caused by heat waves, there is a pressing need to predict them with high accuracy and reliability. In this study, we analyzed the real-time forecast data from six models constituting the Subseasonal-to-Seasonal (S2S) prediction project, to elucidate the key mechanisms contributing to the prediction of the recent record-breaking Korean heat wave event in 2018. Weekly anomalies were first obtained by subtracting the 2017-2020 mean values for both S2S model simulations and observations. By comparing four Korean heat-wave-related indices from S2S models to the observed data, we aimed to identify key climate processes affecting prediction accuracy. The results showed that superior performance at predicting the 2018 Korean heat wave was achieved when the model showed better prediction performance for the anomalous anticyclonic activity in the upper troposphere of Eastern Europe and the cyclonic circulation over the Western North Pacific (WNP) region compared to the observed data. Furthermore, the development of upper-tropospheric anticyclones in Eastern Europe was closely related to global warming and the occurrence of La Niña events. The anomalous cyclonic flow in the WNP region coincided with enhancements in Madden-Julian oscillation phases 4-6. Our results indicate that, for the accurate prediction of heat waves, such as the 2018 Korean heat wave, it is imperative for the S2S models to realistically reproduce the variabilities over the Eastern Europe and WNP regions.

• Journal of Astronomy and Space Sciences
• /
• v.32 no.1
• /
• pp.73-80
• /
• 2015

The Yang-gyeong-gyu-il-ui (兩景揆日儀) is a kind of elevation sundial using three wooden plates. Sang-hyeok Lee (李尙爀, 1810~?) and Byeong-cheol Nam (南秉哲, 1817~1863) gave descriptions of this sundial and explained how to use it in their Gyu-il-go (揆日考) and Ui-gi-jip-seol (儀器輯說), respectively. According to Gyu-il-go (揆日考) there are two horizontal plates and two vertical plates that have lines of season and time. Subseasonal (節候) lines are engraved between seasonal (節氣) lines, subdividing the interval into three equal lines of Cho-hu (初候, early subseason), Jung-hu (中候, mid subseason) and Mal-hu (末候, late subseason); there are 13 seasonal lines for a year, thus resulting in 37 subseasonal lines; also, there are 12 double-hour (時辰) lines for a day engraved on these plates. The only remaining artifact of Yang-gyeong-gyu-il-ui was made in 1849 (the $15^{th}$ year of Heon-jong) and is kept at the Korea University Museum. We have compared and analyzed Yang-gyeong-gyu-il-ui and similar western sundials. Also, we have reviewed the scientific aspect of this artifact and built a replica. Yang-gyeong-gyu-il-ui is a new model enhanced from the miniaturization development in the early Joseon Dynasty and can be applied to the southern part of the tropic line through a structure change.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.268-268
• /
• 2023

전 지구적 급격한 기후변화로 인해 수문기상인자들의 비선형적 변동성이 발생함과 동시에 가뭄, 홍수와 같은 수재해의 발생빈도 및 강도가 증가하고 있는 추세이다. 이에 따라, 세계의 유수기관 (NASA, ESA 등)에서는 대기모형과 해양 모형의 결합 및 수치해석적 접근법을 활용하여 계절내-계절 (Subseasonal to seasonal; S2S) 예측치를 생산하여 제공하고 있다. 이에 따라, 본 연구에서는 European Centre for Medium-Range Weather Forecast (ECMWF)에서 산정되는 수문기상인자 (강수량, 증발산량 및 유출량)에 대한 정확도를 평가하고자 한다. 연구지역으로는 다양한 기후대 및 토지 피복으로 구성되어 있으며, El-Nino-Southern Oscillation (ENSO), Indian Ocean Diapole (IOD)와 같은 기후 현상이 빈번히 발생하는 호주지역을 대상으로 연구를 수행하였다. ECMWF S2S 자료에 대한 통계적 검증은 1) 지점 기반 관측치와 더불어 2) 물수지 모델 기반 수문 추정치 (The Australian Water Resources Assessment Landscape Model; AWRA-L)와 비교하였다. 연구 결과 S2S 강우 및 증발산량 산정치의 경우 비교적 짧은 예측기간(약 2주)에서 상대적으로 높은 상관관계 (R=0.5~0.6)와 낮은 편차 (강수량 = 0.10 mm/day, 증발산량 = 0.21 mm/day)를 나타내었다. 유출량의 경우, 강우 및 증발산량에 비해 상대적으로 낮은 정확도를 나타내었으며, 예측 기간이 길어짐에 따라 불확실성이 상당히 높아지는 것으로 확인되었다. 이는, S2S 계산과정에서 강우 및 증발산량 뿐만아니라 지표 유출로 도달하기 전까지의 수문기상인자들의 불확실성이 모두 모여 유출량의 불확실성이 높아진 것으로 확인할 수 있었다. 계절적 검증에서는, 강우 및 증발산량 모두 여름철에 높은 상관관계를 나타내었지만 불확실성은 상대적으로 큰 값을 나타내었다. 자세한 분석을 위해, 공간적인 불확실성을 분석해본 결과 ECMWF S2S가 매우 습윤하거나 건조한 지역에서 수문기상인자를 예측하는데 있어 한계성이 나타난 것을 확인하였다. 본 연구를 토대로, 추후 S2S 예측치에 대한 보정과 더불어 미래의 수재해 발생 위험도에 대한 정보를 획득하는데 적용될 수 있을 것으로 판단된다.

• Journal of the Korean earth science society
• /
• v.39 no.3
• /
• pp.211-227
• /
• 2018

This study evaluates the prediction skills of stratospheric polar vortex intensification events (VIEs) in Global Seasonal Forecasting System (GloSea5) model, an operational subseasonal-to-seasonal (S2S) prediction model of Korea Meteorological Administration (KMA). The results show that the prediction limits of VIEs, diagnosed with anomaly correlation coefficient (ACC) and mean squared skill score (MSSS), are 13.6 days and 18.5 days, respectively. These prediction limits are mainly determined by the eddy error, especially the large-scale eddy phase error from the eddies with the zonal wavenumber 1. This might imply that better prediction skills for VIEs can be obtained by improving the model performance in simulating the phase of planetary scale eddy. The stratospheric prediction skills, on the other hand, tend to not affect the tropospheric prediction skills in the analyzed cases. This result may indicate that stratosphere-troposphere dynamic coupling associated with VIEs might not be well predicted by GloSea5 model. However, it is possible that the coupling process, even if well predicted by the model, cannot be recognized by monotonic analyses, because intrinsic modes in the troposphere often have larger variability compared to the stratospheric impact.

• Journal of Korea Water Resources Association
• /
• v.52 no.7
• /
• pp.475-482
• /
• 2019

Subseasonal-to-Seasonal (S2S) prediction information which have 2 weeks to 2 months lead time are expected to be used through many parts of industry fields, but utilizability is not reached to expectation because of lower predictability than weather forecast and mid- /long-term forecast. In this study, we used multi-layer perceptron (MLP) which is one of machine learning technique that was built for regression training in order to improve predictability of S2S precipitation data at South Korea through post-processing. Hindcast information of ECMWF was used for MLP training and the original data were compared with trained outputs based on dichotomous forecast technique. As a result, Bias score, accuracy, and Critical Success Index (CSI) of trained output were improved on average by 59.7%, 124.3% and 88.5%, respectively. Probability of detection (POD) score was decreased on average by 9.5% and the reason was analyzed that ECMWF's model excessively predicted precipitation days. In this study, we confirmed that predictability of ECMWF's S2S information can be improved by post-processing using MLP even the predictability of original data was low. The results of this study can be used to increase the capability of S2S information in water resource and agricultural fields.

• Atmosphere
• /
• v.31 no.2
• /
• pp.215-227
• /
• 2021

The necessity of the prediction on the Seasonal-to-Subseasonal (S2S) timescale continues to rise. It led a series of studies on the S2S prediction models, including the Global Seasonal Forecasting System Version 5 (GloSea5) of the Korea Meteorological Administration. By extending previous studies, the present study documents sea surface temperature (SST) prediction skill around the Korean peninsula in the GloSea5 hindcast over the period of 1991~2010. The overall SST prediction skill is about a week except for the regions where SST is not well captured at the initialized date. This limited prediction skill is partly due to the model mean biases which vary substantially from season to season. When such biases are systematically removed on daily and seasonal time scales the SST prediction skill is improved to 15 days. This improvement is mostly due to the reduced error associated with internal SST variability during model integrations. This result suggests that SST around the Korean peninsula can be reliably predicted with appropriate post-processing.