Evaluating the performance of a numerical weather prediction model for precipitation and temperature in Luxembourg and the Greater Region: insights from WRF and WRFDA 3D-VAR

Rehman, Haseeb ur; Teferle, Felicia Norma Rebecca; Hunegnaw, Addisu; Thundathil, Rohith Muraleedharan; Wickert, Jens; Zus, Florian

doi:10.5194/nhess-26-5815-2026

Articles | Volume 26, issue 11

https://doi.org/10.5194/nhess-26-5815-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Early Warning Systems from Research to Operations: Status,...

https://doi.org/10.5194/nhess-26-5815-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 26, issue 11

Research article

|

13 Nov 2026

Research article |

| 13 Nov 2026

Evaluating the performance of a numerical weather prediction model for precipitation and temperature in Luxembourg and the Greater Region: insights from WRF and WRFDA 3D-VAR

Haseeb ur Rehman, Felicia Norma Rebecca Teferle, Addisu Hunegnaw, Rohith Muraleedharan Thundathil, Jens Wickert, and Florian Zus

Data sets

Observational rainfall data of the 2021 mid-July flood event in Belgium – Part 2. Radar product RADFLOOD21 E. Goudenhoofdt et al. https://doi.org/10.5281/zenodo.7740059

Short summary

We tested whether assimilating conventional weather observations and Global Navigation Satellite System (GNSS) satellite delay data improves Weather Research and Forecasting (WRF) forecasts of rain and temperature over Luxembourg and the Greater Region in the month around the July 2021 floods. How the model’s background errors are represented mattered more than which observations were used. With an error model derived from the study month, GNSS data improved forecasts of light and moderate rain, while no setup was clearly best for temperature.