Improving Seasonal Forecasts: A Sensitivity Analysis of the WRF Model Configurations

Seasonal forecasting refers to the prediction of weather conditions over one to several months, offering insights into temperature and precipitation anomalies relative to climatological means. Positioned between short-term weather forecasting and long-term climate projections, it supports critical decision-making in sectors such as agriculture and water resource management. Despite operational advances, forecast skill remains limited in many regions, particularly for precipitation, due to challenges in model resolution, data quality and the representation of complex climate dynamics. Improving seasonal forecast systems is essential to mitigate the impacts of climate variability and support sustainable development.

The primary objective of this study is to identify the optimal combination of input datasets and cumulus convection parameterization schemes for conducting seasonal hindcast and forecast simulations using the Weather Research and Forecasting (WRF) model version 4.5. Sensitivity tests were conducted in the European domain at a horizontal resolution of 0.5° × 0.5°. A total of four simulations were carried out with changes in both the driving datasets that are used for the initial and boundary conditions, and the cumulus parameterization schemes, for the evaluation period 1998-2000. The simulations were forced by two different reanalysis products: the ECMWF Reanalysis version 5 (ERA5) and the NCEP Climate Forecast System Reanalysis (CFSR). Additionally, two cumulus parameterizations were utilized: the Grell-Freitas and the Kain-Fritsch scheme. The different simulated data were assessed to ensure the model’s robust performance. The evaluation of the simulations was performed for both temperature and total precipitation using the Climatic Research Unit (CRU) high-resolution gridded dataset.

The results showed that WRF simulates temperature quite satisfactorily in the ERA5-driven simulations, and especially in the one with Kain-Fritsch scheme, while the CFSR-driven simulations highly deviate from the gridded data. Regarding total precipitation – a rather challenging parameter that is difficult to predict in numerical weather models as it depends on many different atmospheric conditions – WRF appear to overestimate this parameter in all simulations during winter, with the overestimation being more enhanced in mountainous areas. On the contrary, during summer total precipitation is mainly underestimated in the CFSR-driven simulations, while an enhanced overestimation is observed in mountainous areas (and especially the Alps) in all four simulations. These findings suggest that using ERA5 as forcing data in conjunction with the Kain-Fritsch cumulus scheme provides a more reliable performance of WRF model for seasonal simulations in the area of study.

Acknowledgments
The work was supported by PREVENT project. This project has received funding from Horizon Europe programme under Grant Agreement No: 101081276.