Performance Evaluation of High-Resolution Numerical Weather Prediction Models Using MSG Brightness Temperatures

This study has been inspired by the activities developed within the IRIDE project in the service chain on “Hydro-meteorological mapping and monitoring atmospheric structure”. The work presents a preliminary evaluation of three numerical weather prediction models, WRF (Weather Research and Forecasting), ICON (ICOsahedral Non-hydrostatic), and COSMO (COnsortium for Small-scale MOdelling), by comparing synthetic and observed brightness temperatures (BTs) from the Meteosat Second Generation geostationary satellite. Synthetic satellite images were generated using the Radiative Transfer for TOVS (RTTOV) model, version 13.2. The analysis spans a verification period of over one month, with all models operating at a horizontal resolution of approximately 2 km and a temporal resolution of 1 hour.

A special focus of the study is the evaluation of the models' ability to reconstruct the intense weather events that struck some Italian regions during the recent years. This severe event caused widespread damage and highlighted the critical need for accurate and timely forecasting capabilities. By analyzing the models' performance during this extreme weather event, we aim to identify strengths and limitations in their ability to simulate localized and high-impact phenomena.

To assess the performance of the models, key verification metrics were calculated to provide a quantitative basis for understanding the accuracy and reliability of the models in predicting atmospheric conditions as represented by BTs.

The results of the verification are thoroughly discussed, with particular emphasis placed on their broader implications for both the development and refinement of numerical weather prediction models. This discussion delves into how these findings can inform improvements in various aspects of model design, from enhancing their ability to simulate complex physical processes to addressing persistent biases and inaccuracies. Differences in model performance are meticulously analyzed to identify potential sources of error, which may arise from a range of factors such as deficiencies in physical parameterizations, limitations in boundary condition specifications, or inaccuracies stemming from radiative transfer assumptions. 

These analyses aim to provide a deeper understanding of the underlying causes of discrepancies, paving the way for more targeted adjustments. This work represents a significant contribution to the ongoing evolution of high-resolution numerical weather prediction models, offering a wealth of valuable insights for both researchers striving to push the boundaries of modeling capabilities and operational forecasters seeking to improve real-time prediction accuracy. By shedding light on the intricate interplay between model dynamics and observational data, it underscores the importance of continuous innovation and refinement in the pursuit of more reliable and precise forecasting tools.