Book chapter
Dynamic Data Assimilation for Atmospheric Composition: Advances and Perspectives
Handbook of Dynamic Data Driven Applications Systems volume 3, pp.93-112
Springer Nature Switzerland
2026
DOI: 10.1007/978-3-031-88574-7_3
Abstract
The importance of chemical atmospheric air quality prediction remains high. The Dynamic Data Driven Applications Systems (DDDAS) paradigm has proved essential to enhance the current understanding of atmospheric composition. Specifically, data assimilation enables the fusion of information from complex model runs and environmental measurements, resulting in a more accurate representation of the distribution of pollutants, their sources, and their sinks. This chapter discusses new DDDAS-based algorithmic and software advancements in chemical data assimilation, including variational and ensemble-based methodologies in the context of coupled models that support simultaneous weather, climate, and environment predictions. Ongoing efforts and future research needs and directions, such as coupled data assimilation techniques, are also discussed.
Details
- Title: Subtitle
- Dynamic Data Assimilation for Atmospheric Composition: Advances and Perspectives
- Creators
- Adrian Sandu - Virginia TechDaven K. Henze - University of Colorado BoulderGregory R. Carmichael - University of Iowa
- Contributors
- Erik Blasch (Editor)Frederica Darema (Editor)Alex Aved (Editor)
- Resource Type
- Book chapter
- Publication Details
- Handbook of Dynamic Data Driven Applications Systems volume 3, pp.93-112
- DOI
- 10.1007/978-3-031-88574-7_3
- Publisher
- Springer Nature Switzerland; Cham
- Language
- English
- Date published
- 2026
- Academic Unit
- Civil and Environmental Engineering; Nursing; Chemical and Biochemical Engineering
- Record Identifier
- 9985180788702771
Metrics
1 Record Views