The SMAP mission is one of the first satellites being developed by NASA in response to the National Research Council’s Decadal Survey. SMAP will make global measurements of surface soil moisture its freeze/thaw state. The measurements should enable science and applications users to: 1) Understand processes that link the terrestrial water, energy and carbon cycles, 2) Estimate global water and energy fluxes at the land surface, 3) Quantify net carbon flux in boreal landscapes, 4) Enhance weather and climate forecast skill, 5) Develop improved flood prediction and drought monitoring capability. The SMAP mission concept would utilize L-band radar and radiometry. The SMAP project also includes model value-added data assimilation products on deeper profile soil moisture (rootzone) and net ecosystem exchange of carbon. In this presentation a description of the data assimilation product and a few representative soil moisture data- denial and control experiments with atmospheric models will be presented.

The difference between observations and a model simulation can be decomposed into instrument error, model forecast error, and representation error. A major challenge for data assimilation is accurate characterization of these errors. We have developed a technique which identifies the information content of a model. We use a long simulation to formulate a basis for a reduced state space of the model as determined by our metric for the information content. The projection of a sequence of model-data misfits into the reduced model state space can be used to estimate the model forecast errors. The estimate, so obtained, is analogous to the estimate obtained by ensemble methods. The remainder of the misfits, which have no projection on the model state space, can be assigned to the model representation error and instrument error. Work has begun on construction of a system to test the consequences of incorporating our error estimates into the operational climate forecast system.