87th AMS Annual Meeting

Wednesday, 17 January 2007
Assimilating MODIS Snow Cover Data for Global Snow Water Equivalent Estimation Using Ensemble Kalman Filter and An Improved Observational Operator
Exhibit Hall C (Henry B. Gonzalez Convention Center)
Hua Su, Univ. of Texas, Austin, TX; and Z. L. Yang and G. Y. Niu
Accurately estimating spatial and temporal distribution of snow water equivalent is crucial for a wide range of climate and water resources studies. The storage of moisture in the snowpack and its spatial coverage can significantly influence the energy and water budgets across the atmospheric boundary layer, because snow has distinct hydrological and thermodynamic properties. In addition, the winter snowpack conditions have substantial memory in terms of moisture and heat storage, which control the subsequent status of water resources in related regions, and have profound implications for seasonal and interannual predictions of land surface-atmospheric interaction.

Of all the large-scale surface features, snow water equivalent and the spatial cover exhibit the largest fluctuations in space and time, making the numerical simulation of their variability challenging. At continental scale the comprehensive and high quality observations of winter and spring snow conditions are markedly insufficient, increasing the difficulty in model calibration and validation. In the previous studies, land surface models are used to estimate large scale snow water equivalent, Although these studies produce important results, their accuracy is limited by uncertainty in (1) meteorological data used to drive land surface model, and (2) the parameterization of the relationship between gridcell based snow cover fraction and gird mean snow water equivalent (i.e., the depletion curve). This work focuses on utilizing MODIS satellite observed snow cover fraction data and a data assimilation methodology to estimate global snow water equivalent, by addressing the above two challenges. The assimilation will be implemented using Ensemble Kalman Filter (EnKF), which can effectively resolve uncertainty in meteorological forcing data. We will use Community Land Model (CLM) in the assimilation scheme. Meanwhile in this research some improved observational operator for MODIS SCF data, namely the SCF parameterization scheme linking grid averaged snow water equivalent and areal snow cover fraction, will be coupled into EnKF experiments for achieving an unbiased observation function. And the incremental value to assimilated snow water equivalent results will be assessed. The overall performance of the simulation will also be evaluated through comparison with other independent regional and global data sources.

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