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The modular Snow Microwave Radiative Transfer (SMRT) model simulates microwave scattering behavior in snow via different selectable theories and snow microstructure representations, which is well suited to intercomparisons analyses. Here, five microstructure models were parameterized from X-ray tomography and thin-section images of snow samples and evaluated with SMRT. Three field experiments provided observations of scattering and absorption coefficients, brightness temperature, and/or backscatter with the increasing complexity of snowpack. These took place in Sodankylä, Finland, and Weissfluhjoch, Switzerland. Simulations of scattering and absorption coefficients agreed well with observations, with higher errors for snow with predominantly vertical structures. For simulation of brightness temperature, difficulty in retrieving stickiness with the Sticky Hard Sphere microstructure model resulted in relatively poor performance for two experiments, but good agreement for the third. Exponential microstructure gave generally good results, near to the best performing models for two field experiments. The Independent Sphere model gave intermediate results. New Teubner–Strey and Gaussian Random Field models demonstrated the advantages of SMRT over microwave models with restricted microstructural geometry. Relative model performance is assessed by the quality of the microstructure model fit to micro-computed tomography (CT) data and further improvements may be possible with different fitting techniques. Careful consideration of simulation stratigraphy is required in this new era of high-resolution microstructure measurement as layers thinner than the wavelength introduce artificial scattering boundaries not seen by the instrument. [ABSTRACT FROM AUTHOR]

Aboveground dry snow influences the L-band ground–snow system emissivity as the result of impedance matching and refraction effects. Against this background, a retrieval scheme was proposed to estimate dry-snow density and ground permittivity from passive L-band measurements. In this study, the sensitivity of the recently proposed retrieval scheme with respect to surface roughness and snowpack density profile heterogeneities is investigated using synthetic brightness temperatures TB^p . While the original retrieval algorithm proposed to use TB^p(\theta) at observation angles θ and both polarizations p = “H” and p = “V”, the present analysis involves three polarization retrieval modes: “HV”, “H”, and “V” to identify the most robust one. The analyses based on synthetic TB^p(\theta) suggest the use of exclusively vertical polarization (“V” retrieval mode) in case of low “instrumental noise” of \sigma TB\,< \,\text0.5\,\rmK, as it yields most accurate results in the presence of heterogeneities in profiles and imperfect parametrization of ground surface roughness. The applied retrieval scheme still yields accurate results even in the presence of “instrumental noise” ( \sigma TB\,\geq \,\text0.5 K) in retrieval mode “HV”. Furthermore, it is shown that ground-permittivity retrievals are less affected than snow density retrievals by ground roughness variations or heterogeneities in snow density profiles. Altogether, our sensitivity investigations indicate the robustness of the retrieval scheme applied thereto with respect to snow density profile heterogeneities, which is expedient for its use with spaceborne radiometry data from, for example, “Soil Moisture and Ocean Salinity” or “Soil Moisture Active Passive” satellites. [ABSTRACT FROM PUBLISHER]

European Space Agency's SnowScat instrument is a real aperture scatterometer which was developed by Gamma Remote Sensing AG. It operates in a continuous-wave mode, covers a frequency range of 9.15–17.9 GHz in a user-defined frequency-step and has a full polarimetric capability. The measurement campaigns were started first in February 2009 at Weissfluhjoch, in Davos, Switzerland, as an initial test of the instrument over a deep alpine snowpack. Physical characterizations of the snowpack and meteorological measurements were carried out, which formed a detailed in situ dataset. SnowScat was then moved to Sodankylä in Finland in early November 2009, a site of the Finnish Meteorological Institute in Lapland. In addition to the in situ snowpack characterizations and meteorological observations, continuous passive microwave observations were also performed. During the 2012–2013 winter period, a vertical time-domain snow profiling experiment was carried out in addition for resolving the scattering contributions from the snow layers of different physical properties. This paper summarizes the results of the SnowScat observations and initial comparisons against the in situ meteorological and snowpack data. The Sodankylä campaign data evidenced the high variability of the radar backscatter behavior of snowpack from year to year, which indicates its strong dependency on changing snow microstructure. Indeed, the snow microstructure is continuously driven by snow metamorphism, which are further affected by meteorological conditions and their interannual variability. The backscattering property of snowpack in the range X- to Ku-band for all polarizations appeared to be dominated by its microstructural morphology and underlying ground conditions, and to a lesser extent by the snow depth, or its snow-water-equivalent. [ABSTRACT FROM PUBLISHER]

A time series of the hemispheric distribution of integrated water vapor (IWV) measured with the All-Sky Multi Wavelength Radiometer is analyzed by means of empirical orthogonal functions (EOFs). The data were acquired in February 2007 at the Zimmerwald Observatory in Switzerland. It is shown that the analysis of temporally demeaned time series yields information about passing fronts. With EOF analysis of the spatially demeaned time series, information about the spatial distribution of IWV is obtained. Our results also indicate that water vapor behaves like a conservative and passive tracer within the examined sampling volume and with the given temporal sampling resolution of 12 min. [ABSTRACT FROM PUBLISHER]

The Scanning Polarimetric Imaging RAdiometer at 91 GHz with an angular resolution of 0.5 ^\circ was used to investigate the dynamics of the atmosphere. We introduced a new imaging method by continuously scanning the sky over a range of elevation angles in a fixed azimuth direction. The measurements were realized during three different situations: clear sky, sky with water clouds, and sky with cirrus clouds. In most situations, the scan direction was nearly parallel to the mean atmospheric flow. Particularly interesting structures were found in the images with water clouds. In contrast, cirrus clouds are highly transparent. Simulations of the applied imaging method helped to interpret the cloud images, particularly concerning the cloud movement. Characteristic shapes were identified as signatures of motions along the scan line, which were used to estimate the horizontal velocity of water clouds. It was also possible to estimate the integrated water vapor from the clear sky images. They allow a visualization of water vapor parcels. Some of these images contain similar signatures as the clouds, indicating advection of water vapor along the scan line. In the future, we plan to extend these measurements and to combine them with multifrequency observations. [ABSTRACT FROM AUTHOR]

In a combined experimental and model study, we investigated the thermal L-band signatures of a sandy soil with periodic topography (furrows) with dimensions close to the observation wavelength of 21 cm. Measurements were carried out with a radiometer mounted on a tower and aimed at a soil box with an artificially prepared furrowed soil surface. Corresponding reflectivities were derived from brightness temperature measurements performed under dry and moist conditions, with the furrow direction either along or perpendicular to the plane of incidence. Results showed that the furrows had a pronounced effect on the reflectivity, depending on the polarization of the observed radiance, the direction of the furrows, and the soil moisture. A physical reflectivity model for dielectric periodic surfaces was used to explain the soil reflectivities measured for the different furrow directions and soil-water contents. Using this model improved the agreement between the measured and modeled reflectivities considerably compared to the Fresnel reflectivities. The observed dependence of soil reflectivity on furrow orientation and soil moisture could be reproduced by the reflectivity model. The quantitative agreement with the observed reflectivities was further improved by using a simple empirical approach to consider the small-scale heterogeneity of the top soil layer. [ABSTRACT FROM PUBLISHER]

The detection of low-level clouds from ground-based infrared (IR) radiometry is usually based on the IR brightness temperature (IRBT) contrast between the warm clouds and the cold clear-sky background. This method works as long as the brightness temperature contrast subsists. It is not the case for cirrus clouds, which are usually optically thin and exhibit a low brightness temperature, comparable to that of clear sky in similar atmospheric conditions. In this paper, we propose a detection scheme to discriminate between clear sky and cirrus sky based on the fluctuations of the IRBT rather than on the absolute IRBT values. For this, we use the detrended fluctuation analysis (DFA) method on IRBT data when no liquid clouds were present. We compute the exponent \alpha for nine IRBT time series covering clear- and cirrus-sky situations. We find that, for the fluctuation range of 1–5 min, \alpha \simeq 0.1 for clear sky, \alpha \simeq 0.5 for stratiform cirrus layers, and \alpha \simeq 1 for broken cirrus layers. We suggest a threshold value of \alpha = 0.25 for the discrimination between clear sky and cirrus sky. We also examine the relationship between DFA and the classical spectral analysis and find that spectral analysis could be an alternative to the DFA method in detecting the presence of cirrus clouds. [ABSTRACT FROM AUTHOR]