The evolution of the ECMWF hybrid data assimilation system

By Massimo Bonavita, Elias V. Holm, Lars Isaksen, Michael Fisher. 2016

198 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/085c86433bf82d6f8db66ccba5836e0a4e191d38

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Citations in the corpus

A review of operational methods of variational and ensemble‐variational data assimilation (2017, methodology): “…window (25 ensemble members, twice per day, leading to 600 samples), or (ii) a combination of current members (25 members within an 8 h window – 200 samples) plus samples representing climatology (400 samples spread over the seasons, leading again to 600 samples in total) (Bonavita et al., 2016).”
The growing impact of satellite observations sensitive to humidity, cloud and precipitation (2017, methodology): “In both experiments, background errors are taken from the same operational ensemble of data assimilations (EDA; Bonavita et al., 2015) and the standard assumption is made that the change in quality of the short-range forecast is small enough that the same background errors are applicable in each exp”
The ERA5 global reanalysis (2020, methodology): “With respect to Cy31r2 on which ERA-Interim is based, this incorporates 10 years of R&D for all its components (atmosphere, land, ocean waves, observation operators and additional observations; Sections 4 and 5) and improvements in the DA methodology, which is now based on a hybrid incremental 4D-Va”
The ERA5 global reanalysis: Preliminary extension to 1950 (2021, methodology): “Also shown (bounded by blue vertical lines) is the period during which VTPR data were assimilated 63 km resolution) is used to estimate flow-dependent background error covariances (Bonavita et al., 2016) for the high-resolution component (HRES, at 31 km).”
Significance of changes in medium-range forecast scores (2016, methodology): “An EDA is used operationally at ECMWF to estimate flow-dependent background errors for the main analysis, and the additional ingredients are broadly just more members, plus perturbations to the observations and to a few model parameters (Isaksen et al., 2010; Bonavita et al., 2015).”