• Atmosphere
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• v.26 no.1
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• pp.203-214
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• 2016

This study explores the 6-month lead prediction skill of stratospheric temperature and circulations in the Global Seasonal forecasting model version 5 (GloSea5) hindcast experiment over the period of 1996~2009. Both the tropical and extratropical circulations are considered by analyzing the Quasi-Biennial Oscillation (QBO) and Northern Hemisphere Polar Vortex (NHPV). Their prediction skills are quantitatively evaluated by computing the Anomaly Correlation Coefficient (ACC) and Mean Squared Skill Score (MSSS), and compared with those of El Nino-Southern Oscillation (ENSO) and Arctic Oscillation (AO). Stratospheric temperature is generally better predicted than tropospheric temperature. Such improved prediction skill, however, rapidly disappears in a month, and a reliable prediction skill is observed only in the tropics, indicating a higher prediction skill in the tropics than in the extratropics. Consistent with this finding, QBO is well predicted more than 6 months in advance. Its prediction skill is significant in all seasons although a relatively low prediction skill appears in the spring when QBO phase transition often takes place. This seasonality is qualitatively similar to the spring barrier of ENSO prediction skill. In contrast, NHPV exhibits no prediction skill beyond one month as in AO prediction skill. In terms of MSSS, both QBO and NHPV are better predicted than their counterparts in the troposphere, i.e., ENSO and AO, indicating that the GloSea5 has a higher prediction skill in the stratosphere than in the troposphere.

• Atmosphere
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• v.25 no.2
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• pp.323-337
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• 2015

This study explores the 6-month lead prediction skill of several climate indices that influence on East Asian climate in the GloSea5 hindcast experiment. Such indices include Nino3.4, Indian Ocean Diploe (IOD), Arctic Oscillation (AO), various summer and winter Asian monsoon indices. The model's prediction skill of these indices is evaluated by computing the anomaly correlation coefficient (ACC) and mean squared skill score (MSSS) for ensemble mean values over the period of 1996~2009. In general, climate indices that have low seasonal variability are predicted well. For example, in terms of ACC, Nino3.4 index is predicted well at least 6 months in advance. The IOD index is also well predicted in late summer and autumn. This contrasts with the prediction skill of AO index which shows essentially no skill beyond a few months except in February and August. Both summer and winter Asian monsoon indices are also poorly predicted. An exception is the Western North Pacific Monsoon (WNPM) index that exhibits a prediction skill up to 4- to 6-month lead time. However, when MSSS is considered, most climate indices, except Nino3.4 index, show a negligible prediction skill, indicating that conditional bias is significant in the model. These results are only weakly sensitive to the number of ensemble members.

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

• Atmosphere
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• v.28 no.3
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• pp.233-245
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• 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
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• v.30 no.2
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• pp.115-124
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• 2020

The Northern Hemisphere extratropical prediction skill of the Korea Meteorological Administration (KMA) Global Data Assimilation and Prediction System (GDAPS) is examined for January 2019. The real-time prediction skill, evaluated with mean squared skill score (MSSS) of 30-90°N geopotential height field at 500 hPa (Z500), is ~8 days in the troposphere. The MSSS of Z500 considerably decreases after 3 days mainly due to the increasing eddy errors. The eddy errors are largely explained by the eddy-phased errors with minor contribution of amplitude errors. In particular, planetary-scale eddy errors are considered as a main reason of rapidly increasing errors. It turns out that such errors are associated with the blocking highs over North Pacific (NP) and Euro-Atlantic (EA) regions. The model overestimates the blocking highs over NP and EA regions in time, showing dependence of blocking predictability on blocking initializations. This result suggests that the extratropical prediction skill could be improved by better representing blocking in the model.

• Atmosphere
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• v.30 no.3
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• pp.293-309
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• 2020

There are lots of indices that define the intensity of East Asian summer monsoon (EASM) in climate systems. This paper assesses the prediction skill for EASM indices in a Global Seasonal Forecasting System (GloSea5) that is currently operating at KMA. Total 5 different types of EASM indices (WNPMI, EAMI, WYI, GUOI, and SAHI) are selected to investigate how well GloSea5 reproduces them using hindcasts with 12 ensemble members with 1~3 lead months. Each index from GloSea5 is compared to that from ERA-Interim. Hindcast results for the period 1991~2010 show the highest prediction skill for WNPMI which is defined as the difference between the zonal winds at 850 hPa over East China Sea and South China Sea. WYI, defined as the difference between the zonal winds of upper and lower level over the Indian Ocean far from East Asia, is comparatively well captured by GloSea5. Though the prediction skill for EAMI which is defined by using meridional winds over areas of East Asia and Korea directly affected by EASM is comparatively low, it seems that EAMI is useful for predicting the variability of precipitation by EASM over East Asia. The regressed atmospheric fields with EASM index and the correlation with precipitation also show that GloSea5 best predicts the synoptic environment of East Asia for WNPMI among 5 EASM indices. Note that the result in this study is limited to interpret only for GloSea5 since the prediction skill for EASM index depends greatly on climate forecast model systems.

• Atmosphere
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• v.32 no.4
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• pp.353-365
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• 2022

This paper evaluates precipitation forecast skill of Global/Regional Integrated Model system (GRIMs) over South Korea in a boreal winter from December 2013 to February 2014. Three types of precipitation are classified based on development mechanism: 1) convection type (C type), 2) low pressure type (L type), and 3) orographic type (O type), in which their frequencies are 44.4%, 25.0%, and 30.6%, respectively. It appears that the model significantly overestimates precipitation occurrence (0.1 mm d^-1) for all types of winter precipitation. Objective measured skill scores of GRIMs are comparably high for L type and O type. Except for precipitation occurrence, the model shows high predictability for L type precipitation with the most unbiased prediction. It is noted that Equitable Threat Score (ETS) is inappropriate for measuring rare events due to its high dependency on the sample size, as in the case of Critical Success Index as well. The Symmetric Extreme Dependency Score (SEDS) demonstrates less sensitivity on the number of samples. Thus, SEDS is used for the evaluation of prediction skill to supplement the limit of ETS. The evaluation via SEDS shows that the prediction skill score for L type is the highest in the range of 5.0, 10.0 mm d^-1 and the score for O type is the highest in the range of 1.0, 20.0 mm d^-1. C type has the lowest scores in overall range. The difference in precipitation forecast skill by precipitation type can be explained by the spatial distribution and intensity of precipitation in each representative case.

• Atmosphere
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• v.28 no.4
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• pp.479-490
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• 2018

This study assesses the prediction skill of regional scale model for the mean temperature anomaly over South Korea produced by Pusan National University Coupled General Circulation Model (PNU CGCM)-Weather Research and Forecasting (WRF) chain. The initial and boundary conditions of WRF are derived from PNU CGCM. The hindcast period is 11 years from 2007 to 2017. The model's prediction skill of mean temperature anomaly is evaluated in terms of the temporal correlation coefficient (TCC), root mean square error (RMSE) and skill scores which are Heidke skill score (HSS), hit rate (HR), false alarm rate (FAR). The predictions of WRF and PNU CGCM are overall similar to observation (OBS). However, TCC of WRF with OBS is higher than that of PNU CGCM and the variation of mean temperature is more comparable to OBS than that of PNU CGCM. The prediction skill of WRF is higher in March and April but lower in October to December. HSS is as high as above 0.25 and HR (FAR) is as high (low) as above (below) 0.35 in 2-month lead time. According to the spatial distribution of HSS, predictability is not concentrated in a specific region but homogeneously spread throughout the whole region of South Korea.

• Atmosphere
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• v.25 no.1
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• pp.67-83
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• 2015

Predicting the location and intensity of precipitation still remains a main issue in numerical weather prediction (NWP). Resolution is a very important component of precipitation forecasts in NWP. Compared with a lower resolution model, a higher resolution model can predict small scale (i.e., storm scale) precipitation and depict convection structures more precisely. In addition, an ensemble technique can be used to improve the precipitation forecast because it can estimate uncertainties associated with forecasts. Therefore, NWP using both a higher resolution model and ensemble technique is expected to represent inherent uncertainties of convective scale motion better and lead to improved forecasts. In this study, the limited area ensemble prediction system for the convective-scale (i.e., high resolution) operational Unified Model (UM) in Korea Meteorological Administration (KMA) was developed and evaluated for the ensemble forecasts during August 2012. The model domain covers the limited area over the Korean Peninsula. The high resolution limited area ensemble prediction system developed showed good skill in predicting precipitation, wind, and temperature at the surface as well as meteorological variables at 500 and 850 hPa. To investigate which combination of horizontal resolution and ensemble member is most skillful, the system was run with three different horizontal resolutions (1.5, 2, and 3 km) and ensemble members (8, 12, and 16), and the forecasts from the experiments were evaluated. To assess the quantitative precipitation forecast (QPF) skill of the system, the precipitation forecasts for two heavy rainfall cases during the study period were analyzed using the Fractions Skill Score (FSS) and Probability Matching (PM) method. The PM method was effective in representing the intensity of precipitation and the FSS was effective in verifying the precipitation forecast for the high resolution limited area ensemble prediction system in KMA.

• Atmosphere
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• v.17 no.4
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• pp.327-337
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• 2007

In this study, we examined the new ensemble training approach to reduce the systematic error and improve prediction skill of wind by using the Short-range Ensemble prediction system (SENSE), which is the mesoscale multi-model ensemble prediction system. The SENSE has 16 ensemble members based on the MM5, WRF ARW, and WRF NMM. We evaluated the skill of surface wind prediction compared with AWS (Automatic Weather Station) observation during the summer season (June - August, 2006). At first stage, the correction of initial state for each member was performed with respect to the observed values, and the corrected members get the training stage to find out an adaptive weight function, which is formulated by Root Mean Square Vector Error (RMSVE). It was found that the optimal training period was 1-day through the experiments of sensitivity to the training interval. We obtained the weighted ensemble average which reveals smaller errors of the spatial and temporal pattern of wind speed than those of the simple ensemble average.