An Effective Method for Correcting the Seasonal-Interannual Prediction of Summer Climate Anomaly

An effective method was proposed for correcting the seasonal—interannual prediction of the summer climate anomaly. The predictive skill can be substantially improved by applying the method to the seasonal—interannual prediction carried out by a coupled ocean—atmosphere model. Thus the method has the potential to improve the operational summer climate predictions. Key words Correction, Seasonal-interannual prediction - Quasi-biennial signal This research was supported by the National Key Programme for Developing Basic Sciences under Contract G1998040905-2 and the key project “ The Analytical Study on the Seasonal and Interannual Variability of the General Atmospheric Circulation (1998-2001)” of National Natural Science Foundation of China under Contract 49735160.

Seasonal Prediction of June Rainfall over South China:Model Assessment and Statistical Downscaling

The performances of various dynamical models from the Asia-Pacific Economic Cooperation（APEC） Climate Center（APCC） multi-model ensemble（MME） in predicting station-scale rainfall in South China（SC） in June were evaluated.It was found that the MME mean of model hindcasts can skillfully predict the June rainfall anomaly averaged over the SC domain.This could be related to the MME＇s ability in capturing the observed linkages between SC rainfall and atmospheric large-scale circulation anomalies in the Indo-Pacific region.Further assessment of station-scale June rainfall prediction based on direct model output（DMO） over 97 stations in SC revealed that the MME mean outperforms each individual model.However,poor prediction abilities in some in-land and southeastern SC stations are apparent in the MME mean and in a number of models.In order to improve the performance at those stations with poor DMO prediction skill,a station-based statistical downscaling scheme was constructed and applied to the individual and MME mean hindcast runs.For several models,this scheme can outperform DMO at more than 30 stations,because it can tap into the abilities of the models in capturing the anomalous Indo-Paciric circulation to which SC rainfall is considerably sensitive.Therefore,enhanced rainfall prediction abilities in these models should make them more useful for disaster preparedness and mitigation purposes.

QTc Interval Predicts Outcome of Catheter Ablation in Paroxysmal Atrial Fibrillation Patients with Type 2 Diabetes Mellitus

Catheter ablation has been recommended as a treatment option for paroxysmal atrial fibrillation（PAF） patients complicated with type 2 diabetes mellitus（T2DM）. PAF patients with T2 DM have a higher recurrence rate after catheter ablation. Prolongation of corrected QT（QTc） interval has been linked to poor outcomes in T2 DM patients. Whether the abnormal QTc interval is associated with the ablation outcome in the PAF patients with T2 DM remains unknown. In this study, 134 PAF patients with T2 DM undergoing primary catheter ablation were retrospectively enrolled. Pre-procedural QTc interval was corrected by using the Bazett＇s formula. Cox proportional hazards models were constructed to assess the relationship between QTc interval and the recurrence of AF. After a 29.1-month follow-up period, 61 patients experienced atrial tachyarrhythmia recurrence. Recurrent patients had a longer QTc interval than non-recurrent patients（425.2±21.5 ms vs. 414.1±13.4 ms, P=0.002）. Multivariate Cox regression analysis revealed that QTc interval [hazard ratio（HR）=1.026, 95% confidence interval（CI） 1.012–1.040, P=0.005] and left atrial diameter（LAD）（HR=1.125, 95% CI 1.062–1.192, P=0.003） were independent predictors of recurrent atrial tachyarrhythmia. Receiver operating characteristic analysis demonstrated that the cut-off value of QTc（418 ms） predicted arrhythmia recurrence with a sensitivity of 55.7% and a specificity of 69.9%. A combination of LAD and QTc was more effective than LAD alone（P〈0.001） in predicting arrhythmia recurrence after the procedure. QTc interval could be used as an independent predictor of arrhythmia recurrence in T2 DM patients undergoing AF ablation, thus providing a simple method to identify those patients who likely have a better outcome following the procedure.

Analogue Correction Method of Errors by Combining Statistical and Dynamical Methods

Based on the atmospheric analogy principle, the inverse problem that the information of historical analogue data is utilized to estimate model errors is put forward and a method of analogue correction of errors （ACE） of model is developed in this paper. The ACE can combine effectively statistical and dynamical methods, and need not change the current numerical prediction models. The new method not only adequately utilizes dynamical achievements but also can reasonably absorb the information of a great many analogues in historical data in order to reduce model errors and improve forecast skill. Purthermore, the ACE may identify specific historical data for the solution of the inverse problem in terms of the particularity of current forecast. The qualitative analyses show that the ACE is theoretically equivalent to the principle of the previous analogue-dynamical model, but need not rebuild the complicated analogue-deviation model, so has better feasibility and operational foreground. Moreover, under the ideal situations, when numerical models or historical analogues are perfect, the forecast of the ACE would transform into the forecast of dynamical or statistical method, respectively.

Multivariate Two-stage Adaptive-stacking Prediction of Regional Integrated Energy System

To reduce environmental pollution and improve the efficiency of cascaded energy utilization, regional integrated energy system(RIES) has received extensive attention. An accurate multi-energy load prediction is significant for RIES as it enables stakeholders to make effective decisions for carbon peaking and carbon neutrality goals. To this end, this paper proposes a multivariate two-stage adaptive-stacking prediction(M2ASP) framework. First, a preprocessing module based on ensemble learning is proposed. The input data are preprocessed to provide a reliable database for M2ASP, and highly correlated input variables of multi-energy load prediction are determined. Then, the load prediction results of four predictors are adaptively combined in the first stage of M2ASP to enhance generalization ability. Predictor hyper-parameters and intermediate data sets of M2ASP are trained with a metaheuristic method named collaborative atomic chaotic search(CACS) to achieve the adaptive staking of M2ASP. Finally, a prediction correction of the peak load consumption period is conducted in the second stage of M2ASP. The case studies indicate that the proposed framework has higher prediction accuracy, generalization ability, and stability than other benchmark prediction models.