Evaluating global snow water equivalent products for testing land surface models

Abstract

(2013) 'Evaluating global snow water equivalent products for testing land surface models.', Remote sensing of environment., 128 . pp. 107-117. Further information on publisher's website:http://dx.doi.org/10.1016/j.rse. 2012.10.004 Publisher's copyright statement: NOTICE: this is the author's version of a work that was accepted for publication in Remote sensing of environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be re ected in this document. Changes may have been made to this work since it was submitted for publication. A de nitive version was subsequently published in Remote sensing of environment, 128, 2013, 10.1016/j.rse.2012.10.004 Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract 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 payed 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 correct or avoid these jumps if using Globsnow to validate land surface models or in an assimilation scheme.

Attribution of spring snow water equivalent (SWE) changes over the northern hemisphere to anthropogenic effects

“…This dataset is derived based on the Pulliainen assimilation methodology (Pulliainen 2006) and utilizes two different satellitebased passive radiometer data [i.e., Scanning Multichannel Microwave Radiometer (SMMR) and Special Sensor Microwave/Imager (SSM/I)] combined with ground-based meteorological station data, covering the September 1979 to the present period. The GlobSnow dataset is selected as the observed dataset in this study as it is a combination of earth observation and ground data and reproduces well maximum accumulation and seasonal cycle of SWE compared to the other earth observation derived products such as NASA/JAXA's AMSR-E/Aqua Daily L3 Global Snow Water Equivalent EASA-Grids (AE_DySno) and NSIDC's Global EASE-Grid 8-day Blended SSM/I and MODIS Snow Cover (NSIDC-0321) (Hancock et al 2013;Li et al 2014). The GlobSnow dataset provides monthly time series of SWE for the Northern Hemisphere land surface, excluding mountainous regions, glaciers, and Greenland, at a spatial resolution of 25 km (Fig.…”

“…Snow cover extent (SCE) modifies the surface albedo, thermal conductivity, heat capacity, and aerodynamic roughness (Gong et al 2004;Hancock et al 2013) and thus also influences the atmospheric circulation. The Northern Hemisphere has about 98 % of the global snow cover (Armstrong and Brodzik 2001), which has a strong seasonal cycle and ranges (on average for 1966-2004) from 44.2 million km 2 in January to 1.9 million km 2 in August (Lemke et al 2007).…”

Temperature and Snow-Mediated Moisture Controls of Summer Photosynthetic Activity in Northern Terrestrial Ecosystems between 1982 and 2011

“…GlobSnow combines SWE retrieved from multi-satellite microwave observations with forward snow emission model simulations and ground-based weather station data for non-mountainous regions of the Northern Hemisphere. Because of the improved accuracy achieved by assimilating independent sources of information, this is the best SWE product currently available for climate analysis [55]. The SWE data were remapped from their original 25-km spatial resolution to a coarser 0.5 • grid using bilinear interpolation.…”

Soil Moisture & Snow Properties Determination with GNSS in Alpine Environments: Challenges, Status, and Perspectives

“…Reference [83] discusses the calculated SWE and HS products available. As with active microwave measurements, the authors are not aware of any validated passive microwave products for the Alps.…”

“…As with active microwave measurements, the authors are not aware of any validated passive microwave products for the Alps. Indeed, the highest quality product (Globsnow) [83] actively masks mountainous areas.…”