| 1 | Study on Multi-Scale Blending Initial Condition Perturbations for a Regional Ensemble Prediction System显示文摘An initial conditions(ICs) perturbation method was developed with the aim to improve an operational regional ensemble prediction system(REPS). Three issues were identified and investigated:(1) the impacts of perturbation scale on the ensemble spread and forecast skill of the REPS;(2) the scale characteristic of the IC perturbations of the REPS; and(3) whether the REPS's skill could be improved by adding large-scale information to the IC perturbations. Numerical experiments were conducted to reveal the impact of perturbation scale on the ensemble spread and forecast skill. The scales of IC perturbations from the REPS and an operational global ensemble prediction system(GEPS) were analyzed. A 'multi-scale blending'(MSB) IC perturbation scheme was developed, and the main findings can be summarized as follows: The growth rates of the ensemble spread of the REPS are sensitive to the scale of the IC perturbations; the ensemble forecast skills can benefit from large-scale perturbations; the global ensemble IC perturbations exhibit more power at larger scales, while the regional ensemble IC perturbations contain more power at smaller scales; the MSB method can generate IC perturbations by combining the small-scale component from the REPS and the large-scale component from the GEPS; the energy norm growth of the MSB-generated perturbations can be appropriate at all forecast lead times; and the MSB-based REPS shows higher skill than the original system, as determined by ensemble forecast verification. | ZHANG Hanbin CHEN Jing ZHI Xiefei WANG Yi WANG Yanan | 2015 | Advances in Atmospheric Sciences2015，32，8： | 29 |
| 2 | Using CMIP5 model outputs to investigate the initial errors that cause the “spring predictability barrier” for El Nio events显示文摘Most ocean-atmosphere coupled models have difficulty in predicting the El Nio-Southern Oscillation(ENSO) when starting from the boreal spring season. However, the cause of this spring predictability barrier(SPB) phenomenon remains elusive. We investigated the spatial characteristics of optimal initial errors that cause a significant SPB for El Nio events by using the monthly mean data of the pre-industrial(PI) control runs from several models in CMIP5 experiments. The results indicated that the SPB-related optimal initial errors often present an SST pattern with positive errors in the central-eastern equatorial Pacific, and a subsurface temperature pattern with positive errors in the upper layers of the eastern equatorial Pacific, and negative errors in the lower layers of the western equatorial Pacific. The SPB-related optimal initial errors exhibit a typical La Ni-a-like evolving mode, ultimately causing a large but negative prediction error of the Nio-3.4 SST anomalies for El Nio events. The negative prediction errors were found to originate from the lower layers of the western equatorial Pacific and then grow to be large in the eastern equatorial Pacific. It is therefore reasonable to suggest that the El Nio predictions may be most sensitive to the initial errors of temperature in the subsurface layers of the western equatorial Pacific and the Nio-3.4 region, thus possibly representing sensitive areas for adaptive observation. That is, if additional observations were to be preferentially deployed in these two regions, it might be possible to avoid large prediction errors for El Nio and generate a better forecast than one based on additional observations targeted elsewhere. Moreover, we also confirmed that the SPB-related optimal initial errors bear a strong resemblance to the optimal precursory disturbance for El Nio and La Nia events. This indicated that improvement of the observation network by additional observations in the identified sensitive areas would also be helpful in detecting the signals provided by the precursory disturbance, which may greatly improve the ENSO prediction skill. | ZHANG Jing DUAN WanSuo ZHI XieFei | 2015 | Science China Earth Sciences2015，58，5： | 6 |
| 3 | Impact of Model Bias Correction on a Hybrid Data Assimilation System显示文摘Hybrid data assimilation combines a conventional 3-D or 4-D variational system with background error covariance(BEC)generated from ensemble forecast systems.In order to achieve better BEC,three perturbation schemes,namely,the random combination of multiple physical paramterization schemes(referred to as MP),the MP plus stochastical perturbation on physical process tendencies(MP-SPPT),and the unified perturbation of stochastic physics with bias correction(UPSB,proposed by the authors of this paper in a previous work),were first used in a regional ensemble model,i.e.,the Global and Regional Assimilation and Prediction System-Regional Ensemble Prediction System(GRAPES-REPS),and the BECs thus obtained were compared for 7-day ensemble forecasts.The results show that UPSB,which is in fact an MP-SPPT but with the systematic model bias removed,has a better consistency,i.e.,the ratio between root-mean-square error(RMSE)and ensemble spread is much closer to 1,especially at low model levels,compared to the other two schemes.Moreover,the BEC derived from UPSB captured more reasonable distributions of forecast errors.Second,performance of a hybrid data assimilation system(the GRAPES-MESO hybrid En-3DVar)was evaluated by using the BECs from the three perturbation schemes for 7-day hybrid data assimilation forecasts,and thus disclosing the effect of the model bias correction(assuming that the random stocastical features are in general offset in the three perturbation schemes)on the hybrid system forecasts.A covariance weight of 0.8 was prescribed,and this value was determined through sensitivity experiments.The forecast results from the hybrid data assimilation system show that UPSB reduced the false correlation between distant points.The quality of analysis fields of the UPSB scheme shows visible improvement,i.e.,the analysis fields produced by UPSB have much smaller RMSEs than those of the other two schemes,at all vertical model levels.The quality of the hybrid data assimilation forecast fields was also improved by this scheme.Furthermore,the improvement was much greater in the early stage of the assimilation cycle than in the late stage.Generally,the quality of the hybrid data assimilation of GRAPES-MESO hybrid En-3DVar could be efficiently improved by the model bias correction in the UPSB scheme. | Yu XIA Jing CHEN Xiefei ZHI Lianglyu CHEN Yang ZHAO Xueqing LIU | 2020 | Journal of Meteorological Research2020，34，2： | 0 |
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