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Clouds and microphysics represent, probably, the most challenging issue for data assimilation and prediction. Correct assimilation and prediction of the related observations and variables requires development of new methodologies that can efficiently deal with nonlinearity and non-differentiable minimization. In practical applications one also needs to consider the high-performance computing issues, with consequences to weather and climate.
In this research we collaborate with the National Science Foundation Science and Technology Center at Colorado State University Center for Multi-scale Modeling, Assimilation and Prediction (CMMAP).
This research topic is a component of other research topics
and research projects
- Ensemble data assimilation system based on control theory
- Development of methods for data assimilation with advanced models and advanced data sources
- Mesoscale carbon data assimilation for the NACP
- Impact of fundamental assumptions of probabilistic data assimilation/ensemble forecasting: Conditional mode vs. conditional mean