Sub-Seasonal Predictability of the Northeast China Cold Vortex in BCC and ECMWF S2S Model Forecasts for 2006-2021

As an important atmospheric circulation system in the mid-high latitudes of East Asia,the Northeast China cold vortex(NCCV)substantially influences weather and climate in this region.So far,systematic assessment on the performance of numerical prediction of the NCCVs has not been carried out.Based on the Beijing Climate Centre(BCC)and the ECMWF model hindcast and forecast data that participated in the Sub-seasonal to Seasonal(S2S)Prediction Project,this study systematically examines the performance of both models in simulating and forecasting the NCCVs at the sub-seasonal timescale.The results demonstrate that the two models can effectively capture the seasonal variations in the intensity,active days,and spatial distribution of NCCVs;however,the duration of NCCVs is shorter and the intensity is weaker in the models than in the observations.Diagnostic analysis shows that the differences in the intensity and location of the East Asian subtropical westerly jet and the wave train pattern from North Atlantic to East Asia may be responsible for the deficient simulation of NCCV events in the S2S models.Nonetheless,in the deterministic forecasts,BCC and ECMWF provide skillful prediction on the anomalous numbers of NCCV days and intensity at a lead time of 4-5(5-6)pentads,and the skill limit of the ensemble mean is 1-2 pentads longer than that of individual members.In the probabilistic forecasts of daily NCCV activities,BCC and ECMWF exhibit a forecasting skill of approximately 7 and 11 days,respectively;both models show seasonal dependency in the simulation performance and forecast skills of NCCV events,with better performance in winter than in summer.The results from this study provide helpful references for further improvement of the S2S prediction of NCCVs.

An Assessment of the Predictability of the East Asian Subtropical Westerly Jet Based on TIGGE Data

The predictability of the position,spatial coverage and intensity of the East Asian subtropical westerly jet （EASWJ） in the summers of 2010 to 2012 was examined for ensemble prediction systems （EPSs） from four representative TIGGE centers,including the ECMWF,the NCEP,the CMA,and the JMA.Results showed that each EPS predicted all EASWJ properties well,while the levels of skill of all EPSs declined as the lead time extended.Overall,improvements from the control to the ensemble mean forecasts for predicting the EASWJ were apparent.For the deterministic forecasts of all EPSs,the prediction of the average axis was better than the prediction of the spatial coverage and intensity of the EASWJ.ECMWF performed best,with a lead of approximately 0.5-1 day in predictability over the second-best EPS for all EASWJ properties throughout the forecast range.For probabilistic forecasts,differences in skills among the different EPSs were more evident in the earlier part of the forecast for the EASWJ axis and spatial coverage,while they departed obviously throughout the forecast range for the intensity.ECMWF led JMA by about 0.5-1 day for the EASWJ axis,and by about 1-2 days for the spatial coverage and intensity at almost all lead times.The largest lead of ECMWF over the relatively worse EPSs,such as NCEP and CMA,was approximately 3-4 days for all EASWJ properties.In summary,ECMWF showed the highest level of skill for predicting the EASWJ,followed by JMA.

A hybrid ensemble deep reinforcement learning model for locomotive axle temperature using the deterministic and probabilistic strategy

This paper proposes a hybrid deep reinforcement learning framework for locomotive axle temperature by combining the wavelet packet decomposition(WPD),long short-term memory(LSTM),gated recurrent unit(GRU)reinforcement learning and generalized autoregressive conditional heteroskedasticity(GARCH)algorithms.The WPD is utilized to decompose the raw nonlinear series into subseries.Then the deep learning predictors LSTM and GRU are established to predict the future axle temperatures in each subseries.The Q-learning could generate optimal ensembleweights to integrate the predictors to finish the deterministic forecasting and GARCH is used to conduct the deterministic forecasting based on the deterministic forecasting residual.These parts of the hybrid ensemble structure contributed to optimal modelling accuracy and provided effective support in the real-time monitoring and fault diagnosis of transportation.