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Satellite observations are quickly becoming the main source of information for climate and weather systems. They include atmospheric, land, ocean, aerosol, and carbon measurements, in support of modeling and studying related processes. This research is concerned with assimilation of all types of satellite observations, including retrievals and direct radiances. One of the major challenges in satellite data assimilation is the enormous number of measurements, reducing their effective benefit. In order to deal with this issue, we are developing ensemble data assimilation methodologies that can efficiently handle the large amount of satellite observations, the nonlinearity of the forward model (observation) operator, and observation bias.
This research topic is a component of other research topics
and research projects
- 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