## 2 Materials and Methods
### Forecasting Models  
#### 2.3.1 Meteorological Model Setup  
The meteorological model used in the MAGDA project is based on the Weather Research and Forecasting (WRF) system [42], configured to simulate atmospheric conditions over the selected study areas. The model was implemented with two domain configurations: one covering Italy and France, and the other focused on Romania. Each configuration consists of three nested domains with spatial resolutions of 22.5 km, 7.5 km, and 2.5 km (Figure 2A,B).  
The model employs physical parameterization schemes validated in prior studies on similar meteorological events [39; 43; 44; 45; 46]. The schemes adopted for the WRF-OL (Open Loop, without assimilation) and WRF-DA (Data Assimilation) simulations include the WSM6 microphysics scheme [47], the YSU planetary boundary layer scheme [48], the RRTMG longwave and shortwave radiation schemes [49], and the RUC land surface model [50]. Cumulus parameterization is explicitly applied in the innermost domain (D03). These parameterizations were selected for their ability to accurately reproduce the regional meteorological conditions relevant to the project. The YSU scheme ensures a realistic representation of vertical mixing, while the WSM6 scheme simulates mixed-phase cloud processes, thereby improving precipitation forecasts.  
Data assimilation was performed using a three-dimensional variational (3 DVAR) approach available in WRFDA [51], incorporating an outer-loop procedure [52]. This iterative process updates the analysis field, allowing previously rejected observations to be assimilated in subsequent cycles. The number of outer loops was determined via sensitivity tests on selected use cases balancing computational cost and the number of assimilated observations. Three outer loops were found to offer an optimal trade-off, significantly improving the analysis while maintaining reasonable computational demand. This setup is consistent with CIMA's operational meteorological forecasting chains, including those used for nowcasting [53; 54], and ensures progressive refinement of the background field through each assimilation cycle.  
The background error covariance matrix (B matrix) was computed using the NMC method [55], applied to July 2020 forecasts with 24-h and 12-h lead times at 00:00 UTC. Differences between t + 24 and t + 12 forecasts for the same initialization time were used to derive domain-specific error statistics, enabling seasonally adjusted background error estimates for each simulation period.  
Two forecasting strategies were implemented to support hydrological and convective storm applications. For hydrological and irrigation use cases, a 120-h (5-day) forecast is initialized daily at 00:00 UTC, assimilating observations at 18:00, 21:00, and 00:00 UTC (Figure 2C). The impact of assimilation is primarily evaluated within the first 24 h, while the full forecast provides input to the hydrological model. For convective storm nowcasting, a rapid-update cycle was adopted, featuring a 3-hourly 3 DVAR assimilation and a 12-h forecast horizon (Figure 2D).  
The main objective of the MAGDA project was to design a forecasting chain with a high degree of operational readiness by the end of the project. For this reason, the configuration of the assimilation cycles was not exploratory, but instead built upon a robust foundation of prior research, consolidated over time through multiple peer-reviewed publications, applications across various contexts, and several years of experience in operational forecasting environments [39; 45; 56; 57; 58; 59; 60; 54; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70].