Determination of the snow accumulation rate from satellite remote sensing by means of combining high resolution densitiy data and microwave images at low frequencies
Final Report Abstract
The over all topic of the project SAFIR is the retrieval of snow accumulation rates from polar ice sheets by means of passive microwave remote sensing, in order to improve mass balance estimations. The project investigated the potential of density layering as qualitative and quantitative link between accumulation rate and measured microwave emission. First, the spatial and temporal variability of density layering was studied by means of a 2-D trench analysis at Kohnen Station, Antarctica. In extension to previous studies, which analysed multi-firn cores as point measurements and reported un-correlated signals even at very nearby sites, the statistical evaluation of the 2D trench revealed not only, that there is a common signal in the density variability, which is superimposed by local noise. Further more it was shown, that this common density variability contains a seasonal signal – proving the concept of the project SAFIR, that the regional climate conditions influence the density layering of a site. Highlight: For the fist time it is possible to quantitatively explain, why spatially averaging methods such as remote sensing, can detect a common, seasonal signal in the firn within footprint scale, even though point measurements in the same area reveal uncorrelated, noisy density profiles. Second, the microstructure, obtained from microcomputer Tomography images, was analysed with respect to specific surface area (as a measure of grain size) and density. It was found, that locally, the variability in both proxies is linearly correlated. A parameterization for this linear relationship was derived. This parameterization was incorporated into a workflow to compute specific surface area from available high-resolution density measurements. The linear relationship and the derived workflow increase the performance of radiative transfer models, as a) the degree of freedom in the layer definition on the input side of the model is reduced, density and specific surface area do not vary independently from each other, b) a more realistic representation of firn layering is offered, as radiative transfer is sensitive to both – density contrasts and microstructure, c) density is commonly measured with increasing resolution, measuring microstructure is more elaborate. By applying the derived workflow it is possible to deduce specific surface area for sites, where only density is available, considerably increasing the possibilities of modelling. Highlight: The variability of density and specific surface area is linearly correlated in firn. This parameterization can be used to deduce specific surface area for sites, where only density measurements are available. Third, in order to investigate the effect of layering on all scales on microwave emission, high-resolution density profiles have to be used as input into models. On the other hand computing time is restricting the resolution input. Instead of simplifying density profiles by using equidistant increments, a procedure was established, to transfer high-resolution density profiles into layer-weighted profiles while keeping the original density variability over all length scales. This enabled a careful study of the influence of layering on all length scales on the microwave emission of snow. Fourth, based on the findings and improvements by 1-3, a sensitivity study was conducted, applying the DMRT-ML model by Picard et al. (2012). Three experiments were conducted, testing the effect of point measurements versus average input data, the effect of density variability at different amplitudes and the effect of layer sequences carrying the same density variability. This model experiment shows, that not the random small-scale density variability but the spatially integrated regional signal is influencing the microwave emission. This is a major step towards a direct connection between accumulation rate and microwave emission from the density layering of firn. Highlight: The experiment run indicate, that a climate signal in terms of a spatially common signal (such as accumulation rate) influences the layering and by that the microwave emission. This can be quantitatively assessed for the first time.
Publications
- (2013); Talking about grain size (in different languages); IACS Snow Grain Size Workshop, Measurements and Applications, Grenoble, France, 2013
Hörhold, M., and Linow, S.
- (2014); Accounting for the layering of snow and firn - on the link between density and grain size variability; Microstructure in Snow Microwave Radiative Transfer (MICROSNOW) Workshop, University of Reading, 2014
Hörhold, M., Linow, S., and Freitag, J.
- (2014); Understanding Snow Microstructure for Microwave Remote Sensing, Eos Trans.AGU, 95 (47), 432
Sandells, M., Hörhold, M., and N. Rutter
(See online at https://doi.org/10.1002/2014EO470005) - (2015); Microwave remote sensing of Antarctic firn properties. 36th International Symposium on Remote Sensing of Environment (ISRSE), Berlin, Germany
Linow, S., Dierking, W., Hörhold, M., and Rack, W.
- (2016); Layering of surface snow and firn at Kohnen Station, Antarctica - Noise or seasonal signal?; Journal of Geophysical Research: Earth Surface, 121, pp 1849-1860
Laepple, T., Hörhold, M., Münch, T., Freitag, J., Wegner, A. and Kipfstuhl, S.
(See online at https://doi.org/10.1002/2016JF003919) - (2016); Polar firn layering in radiative transfer models, EGU General Assembly 2016, Vienna, Austria
Linow, S. and Hörhold, M.
