This Research Topic is Volume II of a series. The previous volume can be found here:
Advances in Numerical Model, Data Assimilation, and Observations for Hazardous Weather PredictionAccurate and timely forecasting of hazardous weather events induced by meso-scale convection systems (MCSs) is the key to safeguarding lives and property. Yet the MCS forecasting is challenging due to imperfect initial numerical conditions that lack meso-scale convective information and multi-scale dynamic and thermodynamic consistency. Remote sensing observations are the primary source of estimating weather conditions, such as moisture, wind velocity, and precipitation.
It is of fundamental pivotality to develop data assimilation technologies to enhance applications of multi-source observations. Performance assessments of new types of observations facilitate the network designment for regional- and storm-scale numerical models. This Research Topic seeks submissions underscoring the improvement of the accuracy of MCS predictions, warnings, and decision support for high-impact weather events as well as observation network designs.
Key themes include, but are not limited to:
• Advancements in radar and satellite data assimilation technologies
• Contributions to regional numerical weather prediction
• Development of convective-allowing models
• Development of probabilistic prediction methods
• Development of verification methods
• Application of artificial intelligence in numerical models
• Investigation on new types of observations in numerical models
This Research Topic is Volume II of a series. The previous volume can be found here:
Advances in Numerical Model, Data Assimilation, and Observations for Hazardous Weather PredictionAccurate and timely forecasting of hazardous weather events induced by meso-scale convection systems (MCSs) is the key to safeguarding lives and property. Yet the MCS forecasting is challenging due to imperfect initial numerical conditions that lack meso-scale convective information and multi-scale dynamic and thermodynamic consistency. Remote sensing observations are the primary source of estimating weather conditions, such as moisture, wind velocity, and precipitation.
It is of fundamental pivotality to develop data assimilation technologies to enhance applications of multi-source observations. Performance assessments of new types of observations facilitate the network designment for regional- and storm-scale numerical models. This Research Topic seeks submissions underscoring the improvement of the accuracy of MCS predictions, warnings, and decision support for high-impact weather events as well as observation network designs.
Key themes include, but are not limited to:
• Advancements in radar and satellite data assimilation technologies
• Contributions to regional numerical weather prediction
• Development of convective-allowing models
• Development of probabilistic prediction methods
• Development of verification methods
• Application of artificial intelligence in numerical models
• Investigation on new types of observations in numerical models