Knowledge of snow's spatial distribution in terms of snow water equivalent (SWE) is important for hydrological forecasting, but current SWE products commonly disagree on regional scales. Assimilating passive microwave observations into a forecast from a physically-based snow model has been suggested to reduce or remove this disagreement, in which case the snow model must produce properties that are relevant to radiative transfer in snow. Here, the SNOWCAN model produces profiles of grain size for comparison with field measurements using contact probe spectroscopy and the impact of considering grain shape or conglomeration type (chain or cluster) is estimated. Prediction error in near-infrared optical grain size is estimated to be ±0.094 mm for all grains, or a possible best-case of ±0.083 mm if grain shape is included. The Helsinki University of Technology microwave radiative transfer model is used with the Cold Land Processes Experiment field data to make a preliminary estimate of the associated errors in simulated microwave brightness temperature difference, which is commonly used in SWE products such as Globsnow. Grain size error is associated with a ±5.1 K error and including grain shape, at best, reduces this error to ±4.5 K. Increasing stratigraphic detail by simulating more layers is an alternative method to reduce error, and layering error is found to increase linearly with snow depth. A single-layer simulation of 100 cm depth is associated with a ±8.7 K error relative to a pack described at the measurement resolution, whereas a 2-layer model is associated with a ±3.9 K error. Further work is required to determine the impact of grain: shape in the microwave regime, rather than the near infrared, but these results suggest that increased stratigraphic detail is a higher priority than including grain shape in order to improve the assimilation of passive microwave observations.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:625443 |
| Date | January 2014 |
| Creators | Richardson, Mark |
| Publisher | University of Reading |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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