Assimilation of Remotely-Sensed Snow Observations in a Catchment-Based Land Surface Model
Chaojiao Sun, Jeffrey P. Walker and Paul R. Houser
Snow plays an important role in governing both the global energy and water budgets, as a result of its high albedo, thermal properties, and being a medium-term water store. However, the problem of accurately forecasting snow in regional and global atmospheric and hydrologic models is difficult, as a result of snow related features that display variability at scales below those resolved by the models and errors in model forcing data. Hence, any Land Surface Model (LSM) snow initialization based on model spin-up will be affected by these errors. By assimilating snow observation products into the LSM the effects of these errors may be offset, but special care must be taken to avoid erroneous systematic influences on the water budget as a result of the assimilation.
To prevent artificial moisture transfer as a result of direct data insertion in the assimilation of snow, a one-dimensional Kalman filter assimilation of snow observations has been added to the Catchment-based Land Surface Model (CLSM) of Koster et al. (2000) and Ducharne et al. (2000) used by NSIPP. The CLSM uses the three-layer snow model of Lynch-Stieglitz (1994). Our goal is to develop a snow assimilation scheme that optimally merges snow observations with the LSM forecast. This scheme takes into account the effects of snow melting as a result of bias in the LSM temperature. The snow assimilation scheme for updating of snow forecasts in the CLSM has been developed, using a version of the LSM that is uncoupled from the atmospheric and ocean models. Using an uncoupled LSM for an individual continent allows for development of a global snow assimilation scheme without the computational burden of a fully coupled global simulation. Moreover, this approach is consistent with the current soil moisture assimilation work being undertaken by NSIPP.
The snow assimilation algorithm is demonstrated in this paper through a series of identical-twin synthetic experiments, in which the same model used in the assimilation was used to generate the true states. A 20 year retrospective forcing data set for North America, suitable for the CLSM, is used (Berg et al., 2001). Special attention has been made to the updating of land surface and air temperature biases through the snow observation data, so that we may prevent erroneous snowmelt. This is a first step towards using SMMR and SSM/I satellite measurements of snow water equivalent and snow depth.
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Ducharne, A., R.D. Koster, M.J. Suarez, M. Stieglitz, and P. Kumar, 2000. A catchment-based approach to modeling land surface processes in a GCM. Part 2: Parameter estimation and model demonstration. Journal of Geophysical Research – Atmospheres, 105(D20): 24823-24838.
Koster, R.D., M.J. Suarez, A. Ducharne, M. Stieglitz, and P. Kumar, 2000. A catchment-based approach to modeling land surface processes in a GCM. Part 1: Model structure. Journal of Geophysical Research – Atmospheres, 105(D20): 24809-24822.
Lynch-Stieglitz, M., 1994. The development and validation of a simple snow model for the GISS GCM. Journal of Climate, 7: 1842-1855.