Observed and modelled effects of ice lens formation on passive microwave brightness temperatures over snow covered tundra

Rees, Andrew; Lemmetyinen, Juha; Derksen, Chris; Pulliainen, Jouni; English, Michael

doi:10.1016/j.rse.2009.08.013

Cited by 79 publications

(54 citation statements)

References 32 publications

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“…The errors at H polarization are notably larger for both frequencies, with a positive bias (overestimation) up to over 20 K. This plausibly results from the higher sensitivity of horizontal polarization to snow layering effects (see e.g. Rees et al, 2010), which are omitted in the one-layer model configuration. Bias errors from −2.4 to 1.1 dB were obtained for the simulated backscatter values, using the scaled p ex values; the magnitude of the bias varied from season to season for each channel with no notable pattern.…”

Section: Comparison To Observationsmentioning

confidence: 88%

Nordic Snow Radar Experiment

Lemmetyinen

Kontu

Pulliainen

et al. 2016

Geosci. Instrum. Method. Data Syst.

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Abstract. The objective of the Nordic Snow Radar Experiment (NoSREx) campaign was to provide a continuous time series of active and passive microwave observations of snow cover at a representative location of the Arctic boreal forest area, covering a whole winter season. The activity was a part of Phase A studies for the ESA Earth Explorer 7 candidate mission CoReH2O (Cold Regions Hydrology Highresolution Observatory).The NoSREx campaign, conducted at the Finnish Meteorological Institute Arctic Research Centre (FMI-ARC) in Sodankylä, Finland, hosted a frequency scanning scatterometer operating at frequencies from X-to Ku-band. The radar observations were complemented by a microwave dualpolarization radiometer system operating from X-to Wbands. In situ measurements consisted of manual snow pit measurements at the main test site as well as extensive automated measurements on snow, ground and meteorological parameters.This study provides a summary of the obtained data, detailing measurement protocols for each microwave instrument and in situ reference data. A first analysis of the microwave signatures against snow parameters is given, also comparing observed radar backscattering and microwave emission to predictions of an active/passive forward model.All data, including the raw data observations, are available for research purposes through the European Space Agency and the Finnish Meteorological Institute. A consolidated dataset of observations, comprising the key microwave and in situ observations, is provided through the ESA campaign data portal to enable easy access to the data.

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Section: Comparison To Observationsmentioning

confidence: 88%

Nordic Snow Radar Experiment

Lemmetyinen

Kontu

Pulliainen

et al. 2016

Geosci. Instrum. Method. Data Syst.

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“…These winter melt-refreeze events modify the physical properties of snow (albedo, density, grain size, thermal conductivity), generate winter runoff (Bulygina et al, 2010; and can result in potentially significant impacts on the surface energy budget, hydrology and soil thermal regime (Boon et al, 2003;Hay and McCabe, 2010;Rennert et al, 2009). The refreezing of melt water can also create ice layers that adversely impact the ability of ungulate travel and foraging (Hansen et al, 2011;Grenfell and Putkonen, 2008), and exert uncertainties in snow mass retrieval from passive microwave satellite data (Derksen et al, 2014;Rees et al, 2010). Winter warming and melt events may also damage shrub species and tree roots, affecting plant phenology and reproduction in the Arctic (AMAP, 2011;Bokhorst et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Frequency and distribution of winter melt events from passive microwave satellite data in the pan-Arctic, 1988–2013

et al. 2016

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Abstract. This study presents an algorithm for detecting winter melt events in seasonal snow cover based on temporal variations in the brightness temperature difference between 19 and 37 GHz from satellite passive microwave measurements. An advantage of the passive microwave approach is that it is based on the physical presence of liquid water in the snowpack, which may not be the case with melt events inferred from surface air temperature data. The algorithm is validated using in situ observations from weather stations, snow pit measurements, and a surface-based passive microwave radiometer. The validation results indicate the algorithm has a high success rate for melt durations lasting multiple hours/days and where the melt event is preceded by warm air temperatures. The algorithm does not reliably identify short-duration events or events that occur immediately after or before periods with extremely cold air temperatures due to the thermal inertia of the snowpack and/or overpass and resolution limitations of the satellite data. The results of running the algorithm over the pan-Arctic region (north of 50 • N) for the 1988-2013 period show that winter melt events are relatively rare, totaling less than 1 week per winter over most areas, with higher numbers of melt days (around two weeks per winter) occurring in more temperate regions of the Arctic (e.g., central Québec and Labrador, southern Alaska and Scandinavia). The observed spatial pattern is similar to winter melt events inferred with surface air temperatures from the ERA-Interim (ERA-I) and Modern Era-Retrospective Analysis for Research and Applications (MERRA) reanalysis datasets. There was little evidence of trends in winter melt event frequency over 1988-2013 with the exception of negative trends over northern Europe attributed to a shortening of the duration of the winter period. The frequency of winter melt events is shown to be strongly correlated to the duration of winter period. This must be taken into account when analyzing trends to avoid generating false positive trends from shifts in the timing of the snow cover season.

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“…Even though many applications still rely on empirical approaches to relate snowpack properties (e.g., snow water equivalent, SWE) and measured signals, it is generally accepted that a physical understanding of the interaction between snow and electromagnetic waves is necessary to improve the accuracy and overcome inherent difficulties of the retrieval as an underdetermined problem. The retrieval of snow properties is therefore often preceded by forward modeling and data as-similation (Durand and Margulis, 2007;Picard et al, 2009;Takala et al, 2011;Toure et al, 2011;Huang et al, 2012) to predict the satellite signal from prescribed snowpack properties that can be either obtained from measurements (e.g., Rosenfeld and Grody, 2000;Brucker et al, 2011a;Rees et al, 2010;Derksen et al, 2012Derksen et al, , 2014Kontu et al, 2014) or snow models (e.g., Flach et al, 2005;Brucker et al, 2011b;Andreadis and Lettenmaier, 2012;Kang and Barros, 2012;Wójcik et al, 2008;Kontu et al, 2017). The actual modeling challenge lies in the snowpack and the underlying surface (soil, ice, or water) where the coupling of various ingredients needs to be understood with sufficient accuracy to build efficient forward models.…”

Section: Introductionmentioning

confidence: 99%

“…As a remedy, more and more studies include predictions from different models (e.g., Wójcik et al, 2008;Rees et al, 2010;Roy et al, 2013;Kwon et al, 2015;Sandells et al, 2017) to draw more general conclusions. Other studies directly focused on the intercomparison of different models (Tedesco and Kim, 2006;Tse et al, 2007;Tian et al, 2010;Xiong and Shi, 2013;Pan et al, 2016;Löwe and Picard, 2015;Sandells et al, 2017;Royer et al, 2017) to quantify the differences.…”

Section: Introductionmentioning

confidence: 99%

SMRT: an active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1.0)

Picard

Sandells²,

Löwe³

2018

Geosci. Model Dev.

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Abstract. The Snow Microwave Radiative Transfer (SMRT) thermal emission and backscatter model was developed to determine uncertainties in forward modeling through intercomparison of different model ingredients. The model differs from established models by the high degree of flexibility in switching between different electromagnetic theories, representations of snow microstructure, and other modules involved in various calculation steps. SMRT v1.0 includes the dense media radiative transfer theory (DMRT), the improved Born approximation (IBA), and independent Rayleigh scatterers to compute the intrinsic electromagnetic properties of a snow layer. In the case of IBA, five different formulations of the autocorrelation function to describe the snow microstructure characteristics are available, including the sticky hard sphere model, for which close equivalence between the IBA and DMRT theories has been shown here. Validation is demonstrated against established theories and models. SMRT was used to identify that several former studies conducting simulations with in situ measured snow properties are now comparable and moreover appear to be quantitatively nearly equivalent. This study also proves that a third parameter is needed in addition to density and specific surface area to characterize the microstructure. The paper provides a comprehensive description of the mathematical basis of SMRT and its numerical implementation in Python. Modularity supports model extensions foreseen in future versions comprising other media (e.g., sea ice, frozen lakes), different scattering theories, rough surface models, or new microstructure models.

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Observed and modelled effects of ice lens formation on passive microwave brightness temperatures over snow covered tundra

Cited by 79 publications

References 32 publications

Nordic Snow Radar Experiment

Nordic Snow Radar Experiment

Frequency and distribution of winter melt events from passive microwave satellite data in the pan-Arctic, 1988–2013

SMRT: an active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1.0)

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