Evaluating the Performance of Numerical Weather Prediction Models for Precipitation and Temperature in Luxembourg and Greater Region: Insights from WRF and WRFDA 3D-Var

Compared to alluvial floods, flash or pluvial floods are difficult to predict because they result from intense and brief periods of extreme precipitation. Luxembourg has a history of being impacted by floods, with notable occurrences in January 2011, May 2016, December 2017, January 2018, February 2019, and February 2020. However, July 2021 stands out as the most severe flood year on record in the region. Study evaluates the Weather Research and Forecasting (WRF) model, with and without WRFDA 3D-VAR data assimilation, for precipitation and temperature forecasts in Luxembourg and the Greater Region during June–July 2021, a period marked by severe flooding. Conventional meteorological observations and GNSS Zenith Total Delay (ZTD) data were assimilated into WRF, using Global Forecast System (GFS) data for initial conditions. Precipitation forecasts were validated against NASA’s GPM IMERG data and against regional station measurements. Results demonstrate that data assimilation enhances the WRF model’s ability to replicate the spatial distribution and intensity of precipitation, with visual comparisons (e.g., July 14, 2021) showing improved alignment with satellite observations post-assimilation. Quantitatively, data assimilation reduces bias in precipitation and temperature forecasts at most stations, with mean absolute error (MAE) and symmetric mean absolute percentage error (SMAPE) often improving, though root mean square error (RMSE) exhibits mixed outcomes. These findings underscore the potential of integrating GNSS‑ZTD and conventional observations into high‑resolution numerical weather prediction to enhance flash flood forecasts in greater region.

Keywords: NWP; WRF; Flash flood; Weather forecast; High‑Resolution; GNSS; ZTD; Data assimilation; 3D‑VAR; GPM IMERG; Precipitation forecasting; Temperature forecasting; Convective processes; Flood modeling; Regional NWP; Global Forecast System; Luxembourg; Greater Region