Nordic Snow Radar Experiment

Lemmetyinen, Juha; Kontu, Anna; Pulliainen, Jouni; Vehviläinen, Juho; Rautiainen, Kimmo; Wiesmann, Andreas; Mätzler, Christian; Werner, Charles; Rott, Helmut; Nägler, Thomas; Schneebeli, Martin; Proksch, Martin; Schüttemeyer, Dirk; Kern, Michael; Davidson, Malcolm

doi:10.5194/gi-5-403-2016

Cited by 52 publications

(74 citation statements)

References 32 publications

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“…It should be noted that the backscattering measurement we used for winter in 2009-2010 is the mean backscattering data over the whole azimuth range. Although the mean backscattering data we used has a poorer performance than that in the azimuth range of 0˝to 20˝ [44], the RMSE for the retrieved SWE is still less than 30 mm which is acceptable [48]. An obvious deviation between the inversion results and the measured values on 18 February 2011 is shown in Figure 9b and this is caused by the decreased soil background signal due to refreezing at snow surface [44].…”

Section: A Comparison Of Estimated and Measured Swementioning

confidence: 95%

“…The instrument readings were calibrated against SWE provided by manual snow pit observations. The average grain size measured from the in situ during 2009-2010 winter was smaller than that during 2010-2011 winter [44]. Using this information for the SWE estimation, the mean value of single scattering albedo in the cost function Equation (14) was set as 0.65 and 0.8 for the two seasons, respectively.…”

Section: Nosrex Experimental Datasetmentioning

confidence: 99%

“…In the NoSREx experiment, a time series active and passive microwave measurements of snow cover were carried out with a ground-based scatterometer and several radiometers [44]. The measurements were conducted at the Arctic Research Centre of the Finnish Meteorological Institute (26˝38.270 1 E; 67˝21.712 1 N) near the town of Sodankylä in Northern Finland between October 2009 and May 2013.…”

Section: Nosrex Experimental Datasetmentioning

confidence: 99%

“…In the NoSREx experiment, a time series active and passive microwave measurements of snow cover were carried out with a ground-based scatterometer and several radiometers [44]. Before SWE is calculated, the surface backscattering signals need to be removed from the total backscattering.…”

Section: Nosrex Experimental Datasetmentioning

confidence: 99%

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Estimating Snow Water Equivalent with Backscattering at X and Ku Band Based on Absorption Loss

et al. 2016

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Snow water equivalent (SWE) is a key parameter in the Earth's energy budget and water cycle. It has been demonstrated that SWE can be retrieved using active microwave remote sensing from space. This necessitates the development of forward models that are capable of simulating the interactions of microwaves and the snow medium. Several proposed models have described snow as a collection of sphere-or ellipsoid-shaped ice particles embedded in air, while the microstructure of snow is, in reality, more complex. Natural snow usually forms a sintered structure following mechanical and thermal metamorphism processes. In this research, the bi-continuous vector radiative transfer (bi-continuous-VRT) model, which firstly constructs snow microstructure more similar to real snow and then simulates the snow backscattering signal, is used as the forward model for SWE estimation. Based on this forward model, a parameterization scheme of snow volume backscattering is proposed. A relationship between snow optical thickness and single scattering albedo at X and Ku bands is established by analyzing the database generated from the bi-continuous-VRT model. A cost function with constraints is used to solve effective albedo and optical thickness, while the absorption part of optical thickness is obtained from these two parameters. SWE is estimated after a correction for physical temperature. The estimated SWE is correlated with the measured SWE with an acceptable accuracy. Validation against two-year measurements, using the SnowScat instrument from the Nordic Snow Radar Experiment (NoSREx), shows that the estimated SWE using the presented algorithm has a root mean square error (RMSE) of 16.59 mm for the winter of 2009

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Section: A Comparison Of Estimated and Measured Swementioning

confidence: 95%

Section: Nosrex Experimental Datasetmentioning

confidence: 99%

Section: Nosrex Experimental Datasetmentioning

confidence: 99%

“…In the NoSREx experiment, a time series active and passive microwave measurements of snow cover were carried out with a ground-based scatterometer and several radiometers [44]. Before SWE is calculated, the surface backscattering signals need to be removed from the total backscattering.…”

Section: Nosrex Experimental Datasetmentioning

confidence: 99%

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Estimating Snow Water Equivalent with Backscattering at X and Ku Band Based on Absorption Loss

et al. 2016

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“…Dual polarisation microwave radiometers, including at frequencies of 18.7 and 36.5 GHz, are situated on a 4 m tower pointing inwards on the edge of a large clearing surrounded by a mainly pine forest. Further details about the IOA site are given in Lemmetyinen et al (2016). Details on the manual snow profile observation programme are given by Leppänen et al (2016).…”

Section: Datamentioning

confidence: 99%

Microstructure representation of snow in coupled snowpack and microwave emission models

Sandells¹,

Essery

Rutter

et al. 2017

The Cryosphere

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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 SNTHERMempirical 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.

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