A neural network-based observation operator for weather radar data assimilation

Forecasting convective storms remains a significant challenge in Numerical Weather Prediction (NWP). Data Assimilation (DA) plays a crucial role by improving the initial conditions for forecasts through the integration of observational data with previous model outputs (background). Among observational platforms, weather radar is particularly valuable due to its high spatial and temporal resolution, offering detailed information for storm monitoring. Therefore, assimilating radar data into Numerical Weather Prediction models has the potential to substantially enhance the accuracy of storm forecasts. However, studies, such as Fabry and Meunier (2020), have shown that short-term precipitation forecasts produced through radar-based extrapolation methods (nowcasting) often outperform model-based forecasts with Data Assimilation. This is largely because radar primarily provides information on precipitation structures and intensities within the storm-affected region but lacks direct insights into broader environmental conditions like temperature, wind fields, and humidity. These atmospheric variables, both within and outside the storm system, are critical for accurate storm evolution forecasts. A promising approach to address this limitation is the application of machine learning (ML) to develop a more advanced observation operator for Data Assimilation. Specifically, an encoder-decoder neural network can be trained to relate Numerical Weather Prediction model variables (such as temperature, wind components, and relative humidity) to corresponding radar reflectivity observations. This ML-based observation operator captures complex non-linear relationships between model variables and radar data observations, while its Jacobian can propagate radar-derived information to other atmospheric variables in the Data Assimilation system, potentially leading to improved representation of storm dynamics and enhancing convective storm forecasting capabilities.