Microstructure representation of snow in coupled snowpack and microwave emission models

Melody Sandells,Richard Essery,Leanne Wake,Juha Lemmetyinen,Nick Rutter,Leena Leppänen

doi:10.5194/tc-11-229-2017

Melody Sandells, Richard Essery + Show 4 more

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https://doi.org/10.5194/tc-11-229-2017

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Abstract

Abstract. This is the first study to encompass a wide range of coupled snow evolution and microwave emission models in a common modelling framework in order to generalise the link between snowpack microstructure predicted by the snow evolution models and microstructure required to reproduce observations of brightness temperature as simulated by snow emission models. Brightness temperatures at 18.7 and 36.5 GHz were simulated by 1323 ensemble members, formed from 63 Jules Investigation Model snowpack simulations, three microstructure evolution functions, and seven microwave emission model configurations. Two years of meteorological data from the Sodankylä Arctic Research Centre, Finland, were used to drive the model over the 2011–2012 and 2012–2013 winter periods. Comparisons between simulated snow grain diameters and field measurements with an IceCube instrument showed that the evolution functions from SNTHERM simulated snow grain diameters that were too large (mean error 0.12 to 0.16 mm), whereas MOSES and SNICAR microstructure evolution functions simulated grain diameters that were too small (mean error −0.16 to −0.24 mm for MOSES and −0.14 to −0.18 mm for SNICAR). No model (HUT, MEMLS, or DMRT-ML) provided a consistently good fit across all frequencies and polarisations. The smallest absolute values of mean bias in brightness temperature over a season for a particular frequency and polarisation ranged from 0.7 to 6.9 K. Optimal scaling factors for the snow microstructure were presented to compare compatibility between snowpack model microstructure and emission model microstructure. Scale factors ranged between 0.3 for the SNTHERM–empirical MEMLS model combination (2011–2012) and 3.3 for DMRT-ML in conjunction with MOSES microstructure (2012–2013). Differences in scale factors between microstructure models were generally greater than the differences between microwave emission models, suggesting that more accurate simulations in coupled snowpack–microwave model systems will be achieved primarily through improvements in the snowpack microstructure representation, followed by improvements in the emission models. Other snowpack parameterisations in the snowpack model, mainly densification, led to a mean brightness temperature difference of 11 K at 36.5 GHz H-pol and 18 K at V-pol when the Jules Investigation Model ensemble was applied to the MOSES microstructure and empirical MEMLS emission model for the 2011–2012 season. The impact of snowpack parameterisation increases as the microwave scattering increases. Consistency between snowpack microstructure and microwave emission models, and the choice of snowpack densification algorithms should be considered in the design of snow mass retrieval systems and microwave data assimilation systems.

Highlights

Global observations of the snow cover extent from optical and microwave satellite observations combined with in situ data have shown a reduction in the spring snow cover (Brown et al, 2010; Brown and Robinson, 2011)
The purpose of this study is to inform future design of retrieval and assimilation systems where snowpack evolution models may be used to provide microstructural parameters for microwave emission models, by examining how particular snowpack and emission model choices lead to a variation in simulated brightness temperatures throughout the winter period, and evaluate how the simulated values compare to observations
Simulated snow water equivalent (SWE) is relatively insensitive to the snow parameterisation in the accumulation period, but three distinct model groups emerge in the melt period, which are due to the three different representations of the liquid-water flow

Summary

Introduction

Global observations of the snow cover extent from optical and microwave satellite observations combined with in situ data have shown a reduction in the spring snow cover (Brown et al, 2010; Brown and Robinson, 2011). Altimetry, or coarser-scale gravity satellite sensors offer the only feasible way to measure snow mass or depth on a global scale, with microwave observations spanning the longest timescale of these. Microwave algorithms such as those developed by Chang et al (1987) and Kelly (2009) can result in large errors because of the high sensitivity of applied forward models to parameterization of the snow microstructure (Davenport et al, 2012). Development of the assimilation-based technique in GlobSnow allows changes in the snow microstructure to be taken into account through inversion of groundbased observations of snow depth and coinciding microwave brightness temperatures (Takala et al, 2011). The intermediate retrieval of the snow “grain size” in GlobSnow is a parameter that incorporates other land surface features, so is not a true representation of the snow effective diameter (Lemmetyinen et al, 2015)

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