Continuous data assimilation for global numerical weather prediction. (14th October 2020)

• Record Type: Journal Article
• Authors: Lean, P.; Hólm, E. V.; Bonavita, M.; Bormann, N.; McNally, A. P.; Järvinen, H.
• Is Part Of: Quarterly journal of the Royal Meteorological Society. Volume 147:Number 734(2021)
• Page Start: 273
• Page End: 288
• Publication Date: 2020-10-14
• Subjects: 4D‐Var -- continuous data assimilation -- data assimilation -- operational numerical weather prediction; Meteorology -- Periodicals; 551.5
• DOI: 10.1002/qj.3917
• Languages: English
• ISSNs: 0035-9009

Abstract

Abstract: 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. Abstract : A new continuous configuration of ECMWF's 4D‐Var data assimilation system is introduced which exploits the near‐continuous stream of observations by inserting recently arrived observations at each outer loop iteration of the assimilation. This allows configurations which outperform the best affordable non‐continuous configuration. In the upgraded operational system, a fourth outer loop and an extra 85 min of observations (see Figure) led to 2–3 hrs of additional predictive skill.