EMS Annual Meeting Abstracts
Vol. 23, EMS2026-767, 2026, updated on 22 Jun 2026
https://doi.org/10.5194/ems2026-767
EMS Annual Meeting 2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Oral | Tuesday, 08 Sep, 15:30–15:45 (CEST)| Room Expedition
A high resolution regional reanalysis dataset for Austria (ARA)
Nauman Khurshid Awan and Phillip Scheffknecht
Nauman Khurshid Awan and Phillip Scheffknecht
  • GeoSphere Austria, Numerical Weather Prediction, Vienna, Austria (nauman.awan@geosphere.at)

In this study, we present a comprehensive evaluation of the Austrian Reanalysis ensemble dataset (ARA). ARA is an 11-member high resolution  (2.5 km) regional reanalysis ensemble system developed to provide spatially and temporally consistent 2D & 3D essential climate variables from 2012-2022 with an hourly temporal resolution for greater Alpine region with particular focus over Austria.

ARA ensemble is based on dynamical downscaling of the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis ensemble (31 km resolution) using the non-hydrostatic limited-area model AROME (Application of Research to Operations at Mesoscale). A key component of ARA is the integration of a three-dimensional variational (3DVAR) data assimilation framework within AROME, enabling the incorporation of diverse observational datasets, including satellite, radiosonde, aircraft, and wind profiler measurements. This approach allows for the generation of a dynamically consistent ensemble, capturing both large-scale forcing and small-scale atmospheric variability.

The performance of the ARA system is evaluated across a comprehensive range of synoptic conditions, including extreme precipitation, fog, freezing events, storm situations, and snowstorms. Model outputs are rigorously validated against in-situ station observations and gridded datasets, with operational model outputs used as a reference. The evaluation results show that ARA consistently matches or outperforms current operational models, demonstrating clear added value from high-resolution dynamical downscaling and data assimilation. Results indicate a significant improvement in the representation of mesoscale processes and spatial variability. Quantitative assessment using bias, root mean square error (RMSE), and spatial correlation, combined with detailed two-dimensional spatial analyses at daily timescales, confirms the system’s capability to deliver accurate and physically consistent fields. These findings establish ARA as a state-of-the-art high-resolution reanalysis ensemble, providing significant benefits for climate monitoring and impact-oriented applications in Austria.

How to cite: Awan, N. K. and Scheffknecht, P.: A high resolution regional reanalysis dataset for Austria (ARA), EMS Annual Meeting 2026, Utrecht, Netherlands, 6–11 Sep 2026, EMS2026-767, https://doi.org/10.5194/ems2026-767, 2026.