Quantifying the uncertainty in passive microwave snow water equivalent observations

Foster, James L.; Sun, Chengbo; Walker, Jeffrey P.; Kelly, Richard; Chang, A. T. C.; Dong, Jing; Powell, Hugh

doi:10.1016/j.rse.2004.09.012

Cited by 312 publications

(247 citation statements)

References 39 publications

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“…For 205 the snow properties shown in Figure 1, saturation could be determined as the point at which the SWE 206 inverted from the brightness temperature difference using a linear fit diverges by more than 10% away 207 from the true value. In this case, this occurs at a SWE of 143 mm, equivalent to a depth of 79.4 cm 208 which matches well with the value provided by Foster et al (2005). 209…”

Section: Imagers (Ssm/i) (Eg Tedesco Et Al 2004a) And/or the Advansupporting

confidence: 86%

Global Snow Mass Measurements and the Effect of Stratigraphic Detail on Inversion of Microwave Brightness Temperatures

Richardson¹,

Davenport²,

Gurney³

2013

The Earth's Hydrological Cycle

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Snow provides large seasonal storage of freshwater, and information about the distribution 6 of snow mass as Snow Water Equivalent (SWE) is important for hydrological planning and detecting 7 climate change impacts. Large regional disagreements remain between estimates from reanalyses, 8 remote sensing and modelling. Assimilating passive microwave information improves SWE estimates 9 in many regions but the assimilation must account for how microwave scattering depends on snow 10 stratigraphy. Physical snow models can estimate snow stratigraphy, but users must consider the 11 computational expense of model complexity versus acceptable errors. Using data from the National 12 Aeronautics and Space Administration Cold Land Processes Experiment (NASA CLPX) and the 13Helsinki University of Technology (HUT) microwave emission model of layered snowpacks, it is 14shown that simulations of the brightness temperature difference between 19 GHz and 37 GHz 15 vertically polarised microwaves are consistent with Advanced Microwave Scanning Radiometer-Earth 16Observing System (AMSR-E) and Special Sensor Microwave Imager (SSM/I) retrievals once known 17 stratigraphic information is used. Simulated brightness temperature differences for an individual snow 18 profile depend on the provided stratigraphic detail. Relative to a profile defined at the 10 cm 19 resolution of density and temperature measurements, the error introduced by simplification to a single 20 layer of average properties increases approximately linearly with snow mass. If this brightness 21 temperature error is converted into SWE using a traditional retrieval method then it is equivalent to 22 ±13 mm SWE (7% of total) at a depth of 100 cm. This error is reduced to ±5.6 mm SWE (3 % of 23 total) for a two-layer model. 24 25

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Section: Imagers (Ssm/i) (Eg Tedesco Et Al 2004a) And/or the Advansupporting

confidence: 86%

Global Snow Mass Measurements and the Effect of Stratigraphic Detail on Inversion of Microwave Brightness Temperatures

Richardson¹,

Davenport²,

Gurney³

2013

The Earth's Hydrological Cycle

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“…Passive microwave (PM) satellite sensors can retrieve SWE based on brightness temperature at a resolution of around 25 km. However, PM SWE retrievals have problems over forested areas and complex topography, as well as for wet and deep snowpacks (Foster et al, 2005). Both gravimetric and PM sensors are able to retrieve SWE independent of cloud coverage, resulting in gap-free time series.…”

Section: Introductionmentioning

confidence: 99%

Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites

et al. 2018

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Abstract. With its high albedo, low thermal conductivity and large water storing capacity, snow strongly modulates the surface energy and water balance, which makes it a critical factor in mid-to high-latitude and mountain environments. However, estimating the snow water equivalent (SWE) is challenging in remote-sensing applications already at medium spatial resolutions of 1 km. We present an ensemble-based data assimilation framework that estimates the peak subgrid SWE distribution (SSD) at the 1 km scale by assimilating fractional snow-covered area (fSCA) satellite retrievals in a simple snow model forced by downscaled reanalysis data. The basic idea is to relate the timing of the snow cover depletion (accessible from satellite products) to the peak SSD. Peak subgrid SWE is assumed to be lognormally distributed, which can be translated to a modeled time series of fSCA through the snow model. Assimilation of satellite-derived fSCA facilitates the estimation of the peak SSD, while taking into account uncertainties in both the model and the assimilated data sets. As an extension to previous studies, our method makes use of the novel (to snow data assimilation) ensemble smoother with multiple data assimilation (ES-MDA) scheme combined with analytical Gaussian anamorphosis to assimilate time series of Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 fSCA retrievals. The scheme is applied to Arctic sites near Ny-Ålesund (79 • N, Svalbard, Norway) where field measurements of fSCA and SWE distributions are available. The method is able to successfully recover accurate estimates of peak SSD on most of the occasions considered. Through the ES-MDA assimilation, the root-mean-square error (RMSE) for the fSCA, peak mean SWE and peak subgrid coefficient of variation is improved by around 75, 60 and 20 %, respectively, when compared to the prior, yielding RMSEs of 0.01, 0.09 m water equivalent (w.e.) and 0.13, respectively. The ES-MDA either outperforms or at least nearly matches the performance of other ensemble-based batch smoother schemes with regards to various evaluation metrics. Given the modularity of the method, it could prove valuable for a range of satellite-era hydrometeorological reanalyses.

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“…GlobSnow is a hybrid product which assimilates passive microwave satellite 212 measurements, as well as records from climate stations to derive daily SWE 213 maps with a spatial resolution of 25 km (Takala et al, 2011 lakes and rivers (Foster et al, 2005;Derksen et al, 2005Derksen et al, , 2011 …”

mentioning

confidence: 99%

Satellite-based modeling of permafrost temperatures in a tundra lowland landscape

Langer

Westermann

Heikenfeld

et al. 2013

Remote Sensing of Environment

105

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Remote sensing offers great potential for detecting changes of the thermal state of permafrost and active layer dynamics in the context of Arctic warming. This study presents a comprehensive feasibility analysis of satellite-based permafrost modeling for a typical lowland tundra landscape in the Lena River Delta, Siberia. We assessed the performance of a transient permafrost model which is forced by time series of land surface temperatures (LSTs) and snow water equivalents (SWEs) obtained from MODIS and GlobSnow products. Both the satellite products and the model output were evaluated on the basis of long-term field measurements from the Samoylov permafrost observatory. The model was found to successfully reproduce the evolution of the permafrost temperature and freeze-thaw dynamics when calibrated with ground measurements. Monte-Carlo simulations were performed in order to evaluate the impact of inaccuracies and in model forcing and uncertainties in the parameterization. The sensitivity analysis showed that a correct SWE forcing and parameterization of the snow's thermal properties are essential for reliable permafrost modeling. In the worst case, the bias in the modeled permafrost temperatures can amount to 5 • C. For the thaw depth, a maximum uncertainty of about ±15 cm is found due to possible uncertainties in the soil composition.

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Quantifying the uncertainty in passive microwave snow water equivalent observations

Cited by 312 publications

References 39 publications

Global Snow Mass Measurements and the Effect of Stratigraphic Detail on Inversion of Microwave Brightness Temperatures

Global Snow Mass Measurements and the Effect of Stratigraphic Detail on Inversion of Microwave Brightness Temperatures

Ensemble-based assimilation of fractional snow-covered area satellite retrievals to estimate the snow distribution at Arctic sites

Satellite-based modeling of permafrost temperatures in a tundra lowland landscape

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