This Research Topic is Volume III of a series and will cover all aspects of numerical weather prediction issues. The previous volumes can be found here:
Advances in Numerical Model, Data Assimilation, and Observations for Hazardous Weather Prediction Advances in Numerical Model, Data Assimilation, and Observations for Hazardous Weather Prediction: Volume II Meteorology has seen significant advancements in numerical weather prediction (NWP) models, particularly in forecasting hazardous weather events such as storms, flash floods, damaging winds, and tropical cyclones. These improvements are largely due to the integration of multi-source observations, enhanced data assimilation algorithms, the development of convective-allowing models, and the utilization of high-performance computing and artificial intelligence (AI) techniques. Despite these advancements, challenges remain in accurately predicting high-impact weather events. Current gaps include the need for better data assimilation methods, more accurate observation error models, and the effective application of AI techniques. Addressing these gaps is crucial for improving the reliability and accuracy of weather forecasts, which can significantly mitigate the adverse effects of hazardous weather.
This research topic aims to explore and enhance the capabilities of meteorology numerical modeling by leveraging remote sensing observations and AI techniques. The primary objectives include refining regional NWP models, developing advanced data assimilation algorithms, and integrating new observational datasets. Additionally, the research will focus on testing hypotheses related to the effectiveness of AI in predicting hazardous weather events and improving verification methods for numerical products. By addressing these objectives, the research seeks to answer critical questions about the current limitations and potential improvements in weather prediction models.
To gather further insights into the boundaries of meteorology numerical modeling, we welcome articles addressing, but not limited to, the following themes:
- Regional numerical weather prediction development and application;
- Data assimilation algorithms and coupled data assimilation;
- Application of new observation datasets;
- Advances in observation operator and observation error models;
- Applications of machine learning and AI techniques for hazardous event prediction;
- Developments in verification methods for numerical products.