EMS Annual Meeting Abstracts
Vol. 18, EMS2021-135, 2021
https://doi.org/10.5194/ems2021-135
EMS Annual Meeting 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

A novel approach to surface reanalysis

Sabrina Wahl1,2, Clarissa Figura1,2, and Jan D. Keller2,3
Sabrina Wahl et al.
  • 1Institute for Geoscience, Meteorological Department, University of Bonn, Germany (wahl@uni-bonn.de)
  • 2Hans Ertel Center for Weather Research, Climate Monitoring Branch, Germany
  • 3Deutscher Wetterdienst, Offenbach, Germany

Reanalysis is a procedure to merge numerical model integrations and observations to obtain a synergetic representation of the past climatological state of a system, e.g., of the atmosphere. An alternative to running a full reanalysis scheme is a so-called surface reanalysis. Here, an existing reanalysis is used as prior information (for the near-surface state). This first guess is then corrected in a data assimilation step preferrably by applying observations not used in the original assimilation. In such a scheme, an additional downscaling is often performed to enhance the spatial representation of the surface reanalysis.

We present here the development of a new approach aiming to establish such a data set based on the COSMO-REA6 regional reanalysis of the Hans-Ertel-Centre and Deutscher Wetterdienst (DWD). The data assimilation step is based on the operational Local Ensemble Transform Kalman Filter (LETKF) of DWD. While the data assimilation is often performed univariately in such surface reanalysis schemes, here we apply it to various parameters at once thus conserving the covariances among the parameters and allowing for a consistent multivariate utilization of the data. Further, this reanalysis will not be restricted to the ground level and near-surface parameters. Instead, it will be extended to the lower part of the boundary layer aiming at an improved representation of wind speeds in wind turbine hub heights especially relevant for renewable energy applications. The envisaged resolution is approximately 1km and therefore enables an enhanced representation of spatial variability and heterogeneity on small scales. In addition, the LETKF is an ensemble-based data assimilation scheme which also provides uncertainty estimates through an ensemble of the re-analyzed parameters which can also be used as input for downstream applications.

How to cite: Wahl, S., Figura, C., and Keller, J. D.: A novel approach to surface reanalysis, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-135, https://doi.org/10.5194/ems2021-135, 2021.

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