Study on Multi-Scale Blending Initial Condition Perturbations for a Regional Ensemble Prediction System

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.

Evaluation of the NMC Regional Ensemble Prediction System During the Beijing 2008 Olympic Games

Based on the B08RDP（Beijing 2008 Olympic Games Mesoscale Ensemble Prediction Research and Development Project） that was launched by the World Weather Research Programme（WWRP） in 2004,a regional ensemble prediction system（REPS） at a 15-km horizontal resolution was developed at the National Meteorological Center（NMC） of the China Meteorological Administration（CMA）.Supplementing to the forecasters＇ subjective affirmation on the promising performance of the REPS during the 2008 Beijing Olympic Games（BOG）,this paper focuses on the objective verification of the REPS for precipitation forecasts during the BOG period.By use of a set of advanced probabilistic verification scores,the value of the REPS compared to the quasi-operational global ensemble prediction system（GEPS） is assessed for a 36-day period（21 July-24 August 2008）.The evaluation here involves different aspects of the REPS and GEPS,including their general forecast skills,specific attributes（reliability and resolution）,and related economic values.The results indicate that the REPS generally performs significantly better for the short-range precipitation forecasts than the GEPS,and for light to heavy rainfall events,the REPS provides more skillful forecasts for accumulated 6-and 24-h precipitation.By further identifying the performance of the REPS through the attribute-focused measures,it is found that the advantages of the REPS over the GEPS come from better reliability（smaller biases and better dispersion） and increased resolution.Also,evaluation of a decision-making score reveals that a much larger group of users benefits from using the REPS forecasts than using the single model（the control run） forecasts,especially for the heavy rainfall events.

Study of perturbing method in regional BGM ensemble prediction system

Based on an Ensemble Prediction System with the BGM method on the regional numerical prediction model AREM, Static State Perturbation （SSP, including Initial Random Perturbation and Perturbation Restriction） and Dynamic State Perturbation （DSP） are designed. The impacts of both perturbations on precipitation prediction are studied by analyzing a slrong precipitation process oc- curring during July 20-21, 2008. The results show that both SSP and DSP play a positive role in prediction of mesoscale precipita- tion, such as lowering the （missing） rate of precipitation prediction. SSP is mainly helpful for the 24-hour prediction, while DSP can improve both 24-hour and 48-hour prediction. DSP is better than the two SSPs in the hit rate of regional precipitation prediction. However, the former also has a little higher false alarm rate than the latter. DSP enlarges in some extent the dispersion of EPS, which is good for EPS.

Scale-dependent Regional Climate Predictability over North America Inferred from CMIP3 and CMIP5 Ensemble Simulations

Through the analysis of ensembles of coupled model simulations and projections collected from CMIP3 and CMIP5, we demonstrate that a fundamental spatial scale limit might exist below which useful additional refinement of climate model predictions and projections may not be possible. That limit varies among climate variables and from region to region. We show that the uncertainty（noise） in surface temperature predictions（represented by the spread among an ensemble of global climate model simulations） generally exceeds the ensemble mean（signal） at horizontal scales below 1000 km throughout North America, implying poor predictability at those scales. More limited skill is shown for the predictability of regional precipitation. The ensemble spread in this case tends to exceed or equal the ensemble mean for scales below 2000 km. These findings highlight the challenges in predicting regionally specific future climate anomalies, especially for hydroclimatic impacts such as drought and wetness.

Development and Testing of the GRAPES Regional Ensemble-3DVAR Hybrid Data Assimilation System

Based on the GRAPES（Global/Regional Assimilation and Prediction System） regional ensemble prediction system and 3DVAR（three-dimensional variational） data assimilation system,which are implemented operationally at the Numerical Weather Prediction Center of the China Meteorological Administration,an ensemble-based 3DVAR（En-3DVAR） hybrid data assimilation system for GRAPES-Meso（the regional mesoscale numerical prediction system of GRAPES） was developed by using the extended control variable technique to implement a hybrid background error covariance that combines the climatological covariance and ensemble-estimated covariance.Considering the problems of the ensemble-based data assimilation part of the system,including the reduction in the degree of geostrophic balance between variables,and the non-smooth analysis increment and its obviously smaller size compared with the 3DVAR data assimilation,corresponding measures were taken to optimize and ameliorate the system.Accordingly,a single pressure observation ensemble-based data assimilation experiment was conducted to ensure that the ensemble-based data assimilation part of the system is correct and reasonable.A number of localization-scale sensitivity tests of the ensemble-based data assimilation were also conducted to determine the most appropriate localization scale.Then,a number of hybrid data assimilation experiments were carried out.The results showed that it was most appropriate to set the weight factor of the ensemble-estimated covariance in the experiments to be 0.8.Compared with the 3DVAR data assimilation,the geopotential height forecast of the hybrid data assimilation experiments improved very little,but the wind forecast improved slightly at each forecast time,especially over 300 hPa.Overall,the hybrid data assimilation demonstrates some advantages over the3 DVAR data assimilation.