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Joonlee Lee,Myong-In Lee 한국기상학회 2021 한국기상학회 학술대회 논문집 Vol.2021 No.10
Recent advances in science and computing power have significantly improved the prediction performance using numerical models. Nevertheless, the prediction skills of the state-of-art numerical models, coupled general circulation models (CGCMs), are still limited for certain regions such as Siberia and the eastern United States in winter. This study aims to improve the seasonal prediction skill of the boreal winter temperature in a CGCM using a dynamics-statistics chain. The predictors, statistically related to winter temperature over Siberia and the eastern United States, used here are the sea surface temperature (sea ice concentration, 2m air temperature) around the Barents-Kara Sea and snow cover in the Eurasian region, which are also highly associated with the Arctic oscillation for boreal winter. The temporal correlation coefficient (root mean square error) of winter temperature produced by the new hybrid (dynamics-statistics chain) system is 0.50 (0.99) globally, which improves prediction skill compared to the original model prediction (hereafter referred to as MME), which is 0.40 (1.04). Especially in the case of Siberia region (and eastern United States), in which MME shows inferior predictive performance, the temporal correlation coefficient increases significantly in the new prediction system from 0.25 (0.08) to 0.61 (0.41), and this value is statistically significant at the 99% confidence level. In addition, the corrected prediction results show improved performance in terms of root mean square error by 15.4% (32.1%) compared to the MME. It indicates that the limitation of prediction skills for original dynamical models can be overcome through a newly developed hybrid prediction system to produce more accurate temperature predictions. This study is expected to expanding the use of seasonal forecast data to various fields such as industry and economy in the future.
A new statistical correction strategy to improve long‐term dynamical prediction
Lee, Joonlee,Ahn, Joong‐,Bae John Wiley Sons, Ltd 2019 International journal of climatology Vol.39 No.4
<P>In this study, a new statistical strategy to improve the long‐term prediction skill of a numerical model was developed. This new strategy begins by finding the major principal time series (PTs) in the observations using the self‐organizing map (SOM) method. Next, values at the model grid points that are highly correlated with the observational PTs for each ensemble member (EM) are combined to yield a modelled PT. Finally, the model prediction is corrected using the model PTs from the previous step. As the predictors for correction are objectively selected from among the signals found in model prediction, automatically considering their statistical correlation with predictands, the correction strategy is relatively free from the problem of selecting the proper predictor compared to conventional statistical correction methods. In addition, SOM shows a better performance in classifying nonlinear complex patterns than conventional data analysis methods, while both SOM and conventional methods such as the empirical orthogonal function show a comparable performance when classifying linear patterns. The new strategy is applied to the 12‐month‐lead sea surface temperatures hindcasted by the Pusan National University coupled general circulation model. After correction using the new strategy, temporal correlation coefficients and the hit rate are increased while normalized root mean square errors and the false alarm rate are decreased for each season and each lead time. The correction becomes more effective as the lead time increases. In particular, this correction effect is large over the region where the prediction skill without correction is apparently low, which implies that the biases leading to poor prediction skills are effectively reduced by the new strategy. Additionally, the prediction skill is steadily improved for all lead times as the number of EMs is increased, whereas it reaches a plateau when the number of neurons in the output layer of the SOM method exceeds a certain threshold.</P>