Tuesday, January 12, 2010
Diagnostics and enhancements to the Noah LSM snow model
Land surface models play an important role in the performance of atmospheric simulations, representing the lower boundary, which partitions radiative inputs into heat fluxes. Correctly predicting snow cover is particularly important, since it greatly alters the response of the land surface (largely because of the strong contrast in albedo of snow covered and snow free areas), through altered heat fluxes and surface temperature. A downward snow water equivalent (SWE) bias in the snow model of the Noah land surface scheme used in the NCEP suite of numerical weather and climate prediction models (as well as most versions of the WRF model) has been noted by several investigators. This bias motivated a series of offline tests of model extensions and improvements intended to reduce or eliminate the bias. These improvements include changes to the model’s albedo formulation to include a parameterization for aging of snowpacks, changes to how pack temperature is computed, and inclusion of a provision for refreeze of liquid water in the pack. Less extensive testing was done on the performance of model extensions with alternate areal depletion parameterizations. Model improvements were evaluated through comparisons of point simulations with NRCS Snowpack Telemetry (SNOTEL) SWE data for deep mountain snowpacks at selected stations in the western U.S., as well as simulations of snow areal extent over the CONUS domain, compared with observations from the NOAA Interactive Multisensor Snow and Ice Mapping System (IMS). The changes noted generally improved greatly on the model’s ability to simulate snow water equivalent in deep snowpacks, and the areal extent of snow cover over the CONUS.