Data
Assimilation Algorithm Development:
•Explore advanced data assimilation algorithms ~ explicitly predict errors.
•Land models are
highly nonlinear -> push for model independent
assimilation algorithms.
•Computational Efficiency – Tradeoff between optimal information extraction
and data subsampling
•Radiance Assimilation – use forward models to assimilate brightness
temperatures directly.
Land
Observation Systems:
•Regular provision
of snow, soil moisture, and surface temperature.
•Improved
knowledge of observation errors in time and space.
•Observations have
much redundancy: Extract primary information content.
Land
Modeling:
•Better
correlation of land model states with observations
•Advanced
processes: River runoff/routing, vegetation and carbon dynamics, groundwater interaction
•Parallel
development of land model and their adjoints
•Improved
knowledge of prediction errors in time and
space
Assimilate
new types of data:
•Streamflow
•Vegetation/Carbon
dynamics
•Groundwater
(GRACE gravity measurements)
•Boundary layer
structures
Coupled
feedbacks:
•Understand the
impact of land assimilation feedbacks on coupled system predictions.