| Session Details | |
| Section | AS - Atmospheric Sciences |
| Session Title | Data Assimilation for Earth System Applications |
| Main Convener | Dr. Soyoung Ha (National Center for Atmospheric Research, United States) |
| Co-convener(s) | Prof. Lili Lei (Nanjing University, China) |
| Session Description | Observations are the key foundation for understanding and improving the state-of-the-art numerical prediction system for air pollution, extreme weather events, and their climatological variability and changes. How can existing observations best contribute to evaluate and improve numerical simulations in their forecast reliability and skill across scales? Data assimilation is a process that produces the best estimate of model states given observations. It is indispensable for numerical prediction systems since it provides best possible initial conditions for the numerical integration as well as an objective evaluation of the forecast at the end of the integration. However, data assimilation is extremely challenging in real applications due to numerous factors. Included are the lack of understanding of physical processes in the modeling system, large uncertainties in data retrieval process and the numerical discretization, significant systematic error (e.g., “bias”) in data retrievals and numerical prediction systems, multiscale interactions among different physical processes (radiation, planetary boundary layer, microphysics, etc.) or various geophysical components (such as ocean, land, ice, and atmosphere), nonlinear interactions between biology-chemistry-atmosphere or between troposphere and stratosphere, suboptimal characterization of observation error statistics considering its spatial/temporal correlation and a large gap between the scales that point-based observations and the numerical modeling system might represent or resolve. And various analysis techniques such as variational, ensemble, or hybrid methods have their own approximation and limitations to make it even more difficult. In this session, observations are discussed within the data assimilation framework through theoretical advancement in the analysis algorithm and real applications such as practical case studies. Topics include (but are not limited to): Observation network design, Data retrieval algorithms, Data Quality Control, Data assimilation techniques, Data impact on numerical weather prediction on a wide range of scales, Data assimilation on coupled systems. |