Improving land surface products from multiple EOS sensors by developing a prototype data assimilation system

Shunlin Liang, John Townshend, Department, Robert Dickinson (Georgia Institute of Technology)

After a decade of efforts by instrument science teams, many land products are now being produced systematically from data of NASA Earth Observing System (EOS). However, use of multiple EOS instruments to create land products has scarcely started yet, despite the existence of highly complementary instruments and the EOS program goal to support an integrated science program. There is an urgent need to develop more advanced new inversion methods and produce more accurate products. The products must also be optimized for specific applications such as climate models.

We are reformulating the analysis of EOS data in this project by developing a prototype data assimilation system to generate an improved suite of land products from multiple EOS data sets. The products include land surface broadband albedos, leaf area index, temperature, and spectral emissivities. It will also generate new products, such as new broadband albedos, incident radiation, broadband emissivities, and shortwave and longwave net radiation. The general idea is to use the surface and atmospheric radiation models with parameters that are adjusted to optimally reproduce the spectral radiance received by the EOS sensors. Such adjustments are usually made by identifying reasonably close “first guesses” for the model parameters and determining statistically optimum estimates of the parameters by giving appropriate weights to the first guesses versus addition to the error increments needed to get agreement with the observations. The first guesses are the 3-6 years land product climatologies of EOS products. The best estimate at present time is a climatological value corrected by some combinations of previous time’s departure from climatology weighted using temporal autocorrelation and what it takes to fit present observations.

The improved EOS products and the new products will be validated through an extensive validation plan. These products will be tested and assessed by the NCAR Climate Land Model in conjunction with Dr. Dickinson’s EOS IDS project for characterizing the impacts of land use change on surface hydrological processes in climate models. They will be generated and distributed through the University of Maryland Global Land Cover Facility (GLCF).