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Continuous data assimilation for global numerical weather prediction
Quarterly Journal of the Royal Meteorological Society ( IF 8.9 ) Pub Date : 2020-09-27 , DOI: 10.1002/qj.3917
P. Lean 1 , E. V. Hólm 1 , M. Bonavita 1 , N. Bormann 1 , A. P. McNally 1 , H. Järvinen 2
Affiliation  

A new configuration of the European Centre for Medium‐Range Weather Forecasts (ECMWF) incremental 4D‐Var data assimilation (DA) system is introduced which builds upon the quasi‐continuous DA concept proposed in the mid‐1990s. Rather than working with a fixed set of observations, the new 4D‐Var configuration exploits the near‐continuous stream of incoming observations by introducing recently arrived observations at each outer loop iteration of the assimilation. This allows the analysis to benefit from more recent observations. Additionally, by decoupling the start time of the DA calculations from the observational data cut‐off time, real‐time forecasting applications can benefit from more expensive analysis configurations that previously could not have been considered. In this work we present results of a systematic comparison of the performance of a Continuous DA system against that of two more traditional baseline 4D‐Var configurations. We show that the quality of the analysis produced by the new, more continuous configuration is comparable to that of a conventional baseline that has access to all of the observations in each of the outer loops, which is a configuration not feasible in real‐time operational numerical weather prediction. For real‐time forecasting applications, the Continuous DA framework allows configurations which clearly outperform the best available affordable non‐continuous configuration. Continuous DA became operational at ECMWF in June 2019 and led to significant 2 to 3% reductions in medium‐range forecast root mean square errors, which is roughly equivalent to 2–3 hr of additional predictive skill.

中文翻译:

用于全球数值天气预报的连续数据同化

在1990年代中期提出的准连续DA概念的基础上,引入了欧洲中距离天气预报中心(ECMWF)增量4D-Var数据同化(DA)系统的新配置。新的4D-Var配置不是使用一组固定的观测值,而是通过在同化的每个外环迭代中引入最近到达的观测值,来利用近乎连续的输入观测值流。这使分析可以从最近的观察中受益。此外,通过将DA计算的开始时间与观测数据的截止时间脱钩,实时预测应用程序可以从以前无法考虑的更昂贵的分析配置中受益。在这项工作中,我们提出了将连续DA系统与两个更传统的基线4D-Var配置的性能进行系统比较的结果。我们表明,新的,更连续的配置所产生的分析质量与可以访问每个外部环路中所有观测值的常规基线相当,这在实时操作中是不可行的数值天气预报。对于实时预测应用,Continuous DA框架允许的配置明显优于最佳可用的负担得起的非连续配置。连续DA于2019年6月在ECMWF投入使用,并导致中期预测均方根误差显着降低了2-3%,
更新日期:2020-09-27
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