Publication details for Professor B. HuntleyHancock, S., Baxter, R., Evans, J. & Huntley, B. (2013). Evaluating global snow water equivalent products for testing land surface models. Remote Sensing of Environment 128: 107-117.
- Publication type: Journal Article
- ISSN/ISBN: 0034-4257
- DOI: 10.1016/j.rse.2012.10.004
- Keywords: Snow water equivalent, Microwave, Remote sensing, Global.
- Further publication details on publisher web site
- Durham Research Online (DRO) - may include full text
Author(s) from Durham
This paper compares three global snow water equivalent (SWE) products, SSM/I (NSIDC), AMSR-E (NSIDC) and Globsnow (v1.0, ESA) to each other, snow covered area (SCA), ground measures of snow depth and meteorological data in an attempt to determine which might be most suitable for testing and developing land surface models. Particular attention is paid to which gives the most accurate peak accumulation, seasonal SWE changes and first and last dates of snow cover.
SSM/I and AMSR-E are pure earth observation (EO) derived products whilst Globsnow is a combination of EO and ground data. The results suggest that the pure EO products can saturate in deeper snow (SWE > 80–150 mm), can show spurious features during melt and can overestimate SWE due to strong thermal gradients and erroneous forest cover correction factors. Along with the comparison to ground data (only a single point) this suggests that Globsnow is the more accurate product for determining peak accumulation and seasonal SWE cycle.
The snow start and end dates of the three SWE products were compared to an optically derived SCA (MOD10C1, taken as truth) and found to give large errors of snow start date (root mean square error of 20 + days, though SSM/I was correct on average). The snow end dates had lower errors (a bias of 1–6 days) although the spread was still on the order of three weeks.
During the investigation, occasional abrupt changes in Globsnow were observed (in the v1.0 and v1.2 daily and v1.2 weekly products). These only occurred in around 1% of cases examined and seem to be spurious. Care should be taken to corrector avoid these jumps if using Globsnow to validate land surface models or in an assimilation scheme.