EMS Annual Meeting Abstracts
Vol. 18, EMS2021-161, 2021, updated on 18 Jun 2021
https://doi.org/10.5194/ems2021-161
EMS Annual Meeting 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

B-matrix estimation in the Copernicus European Regional Re-Analysis (CERRA)

Adam El-Said1, Pierre Brousseau1, Roger Randriamampianina3, and Martin Ridal2
Adam El-Said et al.
  • 1CNRM UMR3589 CNRS Météo-France, GMAP/ALGO, TOULOUSE, France (adam.el-said@meteo.fr)
  • 2Swedish Meteorological and Hydrological Institute, Norrkoping, Sweden (martin.ridal@smhi.se)
  • 3Norwegian Meteorological Institute, Oslo, Norway (rogerr@met.no)

A new augmented Ensemble of Data Assimilations (EDA) technique, which estimates background error covariances (B-matrix), has been developed for the new Copernicus European Regional Re-Analysis (CERRA-EDA). CERRA-EDA has 10 members with two main pools of forecast differences: seasonal and daily. The seasonal component is pre-prepared (`offline') at reanalysis-resolution (5.5km). The new augmentation governs the time-dependent mixture of winter and summer differences of this seasonal component with respect to the time of year. The daily component is (`online') and averaged in moving succession over 2.5 days with subsequent B-matrix computation every 2 days. This daily component runs at 11km and the forecasts are interpolated to 5.5km prior to use. The seasonal-daily split is set to a fixed value of 80-20\% for CERRA production. The EDA is cycled 6-hourly while CERRA has a 3-hour analysis cycle. The B-matrix is modelled on a bi-Fourier limited area weather model, where dependence of vertical correlations on horizontal scale (non-separability), horizontal homogeneity and isotropy are assumed. The mass-wind and specific humidity fields are related via vorticity and geopotential and the relationships are estimated via multiple linear regressions enforcing simplified analogues of flow-dependence. 

We demonstrate the potential of CERRA-EDA to estimate rapid changes in weather regime change over Europe by assessing B-matrix statistics and forecast skill scores in a case study. The case study assesses two like-periods bearing different weather regimes, Mar-03 (blocking regime) and Mar-18 (NAO- regime). The aptitude of the B-matrix to reflect weather regime change is shown to be mostly dependent on the observation network in a given year. We also illustrate the impact of: change in observation networks over time, and varying the seasonal-daily split. This is shown through analysing the spatio-temporal evolution of background standard deviations. Finally, analysis and forecast skill scores up to 24-hours are also shown to offer improvements worth considering.

How to cite: El-Said, A., Brousseau, P., Randriamampianina, R., and Ridal, M.: B-matrix estimation in the Copernicus European Regional Re-Analysis (CERRA), EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-161, https://doi.org/10.5194/ems2021-161, 2021.

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