Microwave emissions from snow

The radiation emitted from dry and wet snowpack in the microwave region (1 to 100 GHz) is discussed and related to ground observations. Results from theoretical model calculations match the brightness temperatures obtained by truck mounted, airborne and spaceborne microwave sensor systems. Snow wetness and internal layer structure complicate the snow parameter retrieval algorithm. Further understanding of electromagnetic interaction with snowpack may eventually provide a technique to probe the internal snow propertie

Snow stratigraphic heterogeneity within ground-based passive microwave radiometer footprints: implications for emission modeling

Two-dimensional measurements of snowpack properties (stratigraphic layering, density, grain size and temperature) were used as inputs to the multi-layer Helsinki University of Technology (HUT) microwave emission model at a centimeter-scale horizontal resolution, across a 4.5 m transect of ground-based passive microwave radiometer footprints near Churchill, Manitoba, Canada. Snowpack stratigraphy was complex (between six and eight layers) with only three layers extending continuously throughout the length of the transect. Distributions of one-dimensional simulations, accurately representing complex stratigraphic layering, were evaluated using measured brightness temperatures. Large biases (36 to 68 K) between simulated and measured brightness temperatures were minimized (-0.5 to 0.6 K), within measurement accuracy, through application of grain scaling factors (2.6 to 5.3) at different combinations of frequencies, polarizations and model extinction coefficients. Grain scaling factors compensated for uncertainty relating optical SSA to HUT effective grain size inputs and quantified relative differences in scattering and absorption properties of various extinction coefficients. The HUT model required accurate representation of ice lenses, particularly at horizontal polarization, and large grain scaling factors highlighted the need to consider microstructure beyond the size of individual grains. As variability of extinction coefficients was strongly influenced by the proportion of large (hoar) grains in a vertical profile, it is important to consider simulations from distributions of one-dimensional profiles rather than single profiles, especially in sub-Arctic snowpacks where stratigraphic variability can be high. Model sensitivity experiments suggested the level of error in field measurements and the new methodological framework used to apply them in a snow emission model were satisfactory. Layer amalgamation showed a three-layer representation of snowpack stratigraphy reduced the bias of a one-layer representation by about 50%

Research relative to angular distribution of snow reflectance/snow cover characterization and microwave emission

Remote sensing has been applied in recent years to monitoring snow cover properties for applications in hydrologic and energy balance modeling. In addition, snow cover has been recently shown to exert a considerable local influence on weather variables. Of particular importance is the potential of sensors to provide data on the physical properties of snow with high spatial and temporal resolution. Visible and near-infrared measurements of upwelling radiance can be used to infer near-surface properties through the calculation of albedo. Microwave signals usually come from deeper within the snow pack and thus provide depth-integrated information, which can be measured through clouds and does not relay on solar illumination.Fundamental studies examining the influence of snow properties on signals from various parts of the electromagnetic spectrum continue in part because of the promise of new remote sensors with higher spectral and spatial accuracy. Information in the visible and near-infrared parts of the spectrum comprise nearly all available data with high spatial resolution. Current passive microwave sensors have poor spatial resolution and the data are problematic where the scenes consist of mixed landscape features, but they offer timely observations that are independent of cloud cover and solar illumination

Derivation and evaluation of a new extinction coefficient for use with the n-HUT snow emission model

In this study, snow slab data collected from the Arctic Snow Microstructure Experiment were used in conjunction with a six-directional flux coefficient model to calculate individual slab absorption and scattering coefficients. These coefficients formed the basis for a new semiempirical extinction coefficient model, using both frequency and optical diameter as input parameters, along with the complex dielectric constant of snow. Radiometric observations, at 18.7, 21.0, and 36.5 GHz at both horizontal polarization (H-Pol) and vertical polarization (V-Pol), and snowpit data collected as part of the Sodankylä Radiometer Experiment were used to compare and contrast the simulated brightness temperatures produced by the multi-layer Helsinki University of Technology snow emission model, utilizing both the original empirical model and the new semiempirical extinction coefficient model described here. The results show that the V-Pol RMSE and bias values decreased when using the semiempirical extinction coefficient; however, the H-Pol RMSE and bias values increased on two of the lower microwave bands tested. The unbiased RMSE was shown to decrease across all frequencies and polarizations when using the semiempirical extinction coefficient

The applicability of physical optics in the millimetre and sub-millimetre spectral region. Part II: Application to a three-component model of ice cloud and its evaluation against the bulk single-scattering properties of various other aggregate models

The bulk single-scattering properties of various randomly oriented aggregate ice crystal models are com- pared and contrasted at a number of frequencies between 89 and 874 GHz. The model ice particles consist of the ten-branched plate aggregate, five-branched plate aggregate, eight-branched hexagonal aggregate, Voronoi ice aggregate, six-branched hollow bullet rosette, hexagonal column of aspect ratio unity, and the ten-branched hexagonal aggregate. The bulk single-scattering properties of the latter two ice particle models have been calculated using the light scattering methods described in Part I, which represent the two most extreme members of an ensemble model of cirrus ice crystals. In Part I, it was shown that the method of physical optics could be combined with the T-matrix at a size parameter of about 18 to compute the bulk integral ice optical properties and the phase function in the microwave to sufficient ac- curacy to be of practical value. Here, the bulk single-scattering properties predicted by the two ensemble model members and the Voronoi model are shown to generally bound those of all other models at fre- quencies between 89 and 874 GHz, thus representing a three-component model of ice cloud that can be generally applied to the microwave, rather than using many differing ice particle models. Moreover, the Voronoi model and hollow bullet rosette scatter similarly to each other in the microwave. Furthermore, from the various comparisons, the importance of assumed shapes of the particle size distribution as well as cm-sized ice aggregates is demonstrated.Peer reviewedFinal Accepted Versio

A database of single scattering properties for hydrometeors at microwave and sub-millimetre frequencies

Remote sensing observations of hydrometeors (ice or liquid water particles) at microwave and sub-millimetre wavelengths provide important input to numerical weather forecasting through data assimilation and give insight to cloud processes that are relevant for climate prediction. The utilization of such measurements requires information on the single scattering properties (SSP), i.e., knowledge on how single hydrometeors scatter, absorb, and emit radiation. However, SSP are dependant on the particle orientation, shape, and size which in the case of ice hydrometeors are highly variable in nature. Furthermore, simulating the SSP of hydrometeors is challenging and computationally costly. These are the main challenges that this thesis aims to address. In the first study of this thesis, a new publicly available SSP database for randomly oriented ice hydrometeors was developed. In terms of covered frequencies, temperatures, sizes, and particle models it is the most extensive to date. Particle models include aggregates that were generated using a semi-realistic, stochastic aggregation simulator. The next study utilised the simulator for a more detailed investigation on the dependence of SSP upon aggregate characteristics. For instance, the size and aspect ratio of the constituent crystals were found to have a significant impact on the extinction and back-scattering cross-sections of the aggregates. The third study analysed the ability of the SSP database to reproduce a combination of real passive and active satellite observations, by the GPM (Global Precipitation Measurements) Microwave Imager (GMI) and the CloudSat Cloud Profiling Radar, in radiative transfer (RT) simulations. While the tested particle models could accurately reproduce the real observations, it was difficult to find a particle model that performed better than the others. However, complementary simulations show promise with respect to the upcoming Ice Cloud Imager. In the fourth study, SSP of ice particles that have a preference towards horizontal orientation were calculated and applied to passive RT simulations at 166 GHz. The characteristic polarization signals present in GMI observations of clouds were successfully reproduced by RT simulations. The final study provides SSP of non-spheroidal rain drops, accounting for the effect of aerodynamic pressure upon the drop shape. It was found that this effect can have a small, but non-negligible, impact on passive and active microwave observations

Characterization of snowfall using ground-based passive and active remote sensors.

Snowfall is a key quantity in the global hydrological cycle and has an impact on the global energy budget as well. In sub-polar and polar latitudes, snowfall is the predominant type of precipitation and rainfall is often initiated via the ice phase. Currently, the spatial distribution of snowfall is poorly captured by numerical weather prediction and climate models. In order to evaluate the models and to improve our understanding of snowfall microphysics, global observations of snowfall are needed. This can only be obtained by space-borne active and passive remote sensors. In order to be able to penetrate even thick snow clouds, sensors operating in the microwave frequency region are favoured. The challenge for snowfall retrieval development lies first in the complexity of snowfall microphysics and its interactions with liquid cloud water. Secondly, comprehensive knowledge is needed about the interaction of electromagnetic radiation with snowfall in order to finally relate the radiative signatures to physical quantities. A general advantage of ground-based observations is that simultaneous measurements of in-situ and remote sensing instruments can be obtained. Such a six-month dataset was collected within this thesis at an alpine site. The instrumentation included passive microwave radiometers that covered the frequency range from 22 up to \unit[150]{GHz} as well as two radar systems operating at 24.1 and 35.5 GHz. These data were complemented by optical disdrometer, ceilometer and various standard meteorological measurements. State-of-the-art single scattering databases for pristine ice crystals and complex snow aggregates were used within this thesis to investigate the sensitivity of ground--based passive and active remote sensors to various snowfall parameters such as vertical snow and liquid water distribution, snow particle habit, snow size distribution and ground surface properties. The comparison of simulations with measurements within a distinct case study revealed that snow particle scattering can be measured with ground--based passive microwave sensors at frequencies higher than 90 GHz. Sensitivity experiments further revealed that ground-based sensors have clear advantages over nadir measuring instruments due to a stronger snow scattering signal and lower sensitivity to variable ground surface emissivity. However, passive sensors were also found to be highly sensitive to liquid cloud water that was frequently observed during the entire campaign. The simulations indicate that the uncertainties of sizes distribution and snow particle habit are not distinguishable with a passive-only approach. In addition to passive microwave observations, data from a low-end radar system that is commonly used for rainfall were investigated for its capabilities to observe snowfall. For this, a snowfall specific data processing algorithm was developed and the re-processed data were compared to collocated measurements of a high-end cloud radar. If the focus can be narrowed down to medium and strong snowfall within the lowest 2-3 km height, the reflectivity and fall velocity measurements of the low-end system agree well with the cloud radar. The cloud radar dataset was used to estimate the uncertainty of retrieved snowfall rate and snow accumulation of the low-end system. Besides the intrinsic uncertainties of single-frequency radar retrievals the estimates of total snow accumulation by the low-end system lay within 7% compared to the cloud radar estimates. In a more general approach, the potential of multi-frequency radar systems for derivation of snow size distribution parameters and particle habit were investigated within a theoretical simulation study. Various single-scattering databases were combined to test the validity of dual-frequency approaches when applied to non-spheroid particle habits. It was found that the dual-frequency technique is dependent on particle habit. It could be shown that a rough distinction of snow particle habits can be achieved by a combination of three frequencies. The method was additionally tested with respect to signal attenuation and maximum particle size. The results obtained by observations and simulations within this thesis strongly suggest the further development of simultaneous ground-based in-situ and remote sensing observations of snowfall. Extending the sensitivity studies of this study will help to define the most suitable set of sensors for future studies. A combination of these measurements with a further development of single-scattering databases will potentially help to improve our understanding of snowfall microphysics

Comparison of passive microwave and modeled estimates of total watershed SWE in the continental United States

In the U.S., a dedicated system of snow measurement stations and snowpack modeling products is available to estimate the snow water equivalent (SWE) throughout the winter season. In other regions of the world that depend on snowmelt for water resources, snow data can be scarce, and these regions are vulnerable to drought or flood conditions. Even in the U.S., water resource management is hampered by limited snow data in certain regions, as evident by the 2011 Missouri Basin flooding due in large part to the significant Plains snowpack. Satellite data could potentially provide important information in under‐sampled areas. This study compared the daily AMSR‐E and SSM/I SWE products over nine winter seasons to spatially distributed, modeled output SNODAS summed over 2100 watersheds in the conterminous U.S. Results show large areas where the passive microwave retrievals are highly correlated to the SNODAS data, particularly in the northern Great Plains and southern Rocky Mountain regions. However, the passive microwave SWE is significantly lower than SNODAS in heavily forested areas, and regions that typically receive a deep snowpack. The best correlations are associated with basins in which maximum annual SWE is less than 200 mm, and forest fraction is less than 20%. Even in many watersheds with poor correlations between the passive microwave data and SNODAS maximum annual SWE values, the overall pattern of accumulation and ablation did show good agreement and therefore may provide useful hydrologic information on melt timing and season length

Impact of ice aggregate parameters on microwave and sub-millimetre scattering properties

Microwave scattering properties for 1101 aggregates were calculated using DDA (Discrete Dipole Approximation), at three typical radar bands (13.4, 35.6, 94.1 GHz) and three passive microwave frequencies (183.31, 325.15 and 664 GHz). The aggregates were generated in a semi-physical stochastic fashion and are composed of hexagonal crystals of varying axis ratio, ranging from 1/15 (plates) to 15 (columns). Horizontally aligned particles were assumed and scattering properties were assessed for zenith/nadir observations. Crystal axis ratio, number of crystals, effective density and aerodynamic area, were found to correlate with extinction and back-scattering efficiencies. However, the dependency between these variables and scattering properties vary between the frequencies. Interestingly, bulk extinction was found to have a relatively low sensitivity to particle shape at 664 GHz. Furthermore, extinction was found to be less shape sensitive than back-scattering. These results are promising for the sake of the upcoming Ice Cloud Imager (ICI) mission. In addition, for the considered set of aggregates, it is shown that both bulk extinction and back-scattering are more directly related to snow fall than ice water content. Triple frequency signatures were also calculated, which demonstrated clear dependence on constituent crystal axis ratio and conversely on aggregate effective density, in agreement with the literature

Challenges in measuring winter precipitation : Advances in combining microwave remote sensing and surface observations

Globally, snow influences Earth and its ecosystems in several ways by having a significant impact on, e.g., climate and weather, Earth radiation balance, hydrology, and societal infrastructures. In mountainous regions and at high latitudes snowfall is vital in providing freshwater resources by accumulating water within the snowpack and releasing the water during the warm summer season. Snowfall also has an impact on transportation services, both in aviation and road maintenance. Remote sensing instrumentation, such as radars and radiometers, provide the needed temporal and spatial coverage for monitoring precipitation globally and on regional scales. In microwave remote sensing, the quantitative precipitation estimation is based on the assumed relations between the electromagnetic and physical properties of hydrometeors. To determine these relations for solid winter precipitation is challenging. Snow particles have an irregular structure, and their properties evolve continuously due to microphysical processes that take place aloft. Hence also the scattering properties, which are dependent on the size, shape, and dielectric permittivity of the hydrometeors, are changing. In this thesis, the microphysical properties of snowfall are studied with ground-based measurements, and the changes in prevailing snow particle characteristics are linked to remote sensing observations. Detailed ground observations from heavily rimed snow particles to openstructured low-density snowflakes are shown to be connected to collocated triple-frequency signatures. As a part of this work, two methods are implemented to retrieve mass estimates for an ensemble of snow particles combining observations of a video-disdrometer and a precipitation gauge. The changes in the retrieved mass-dimensional relations are shown to correspond to microphysical growth processes. The dependence of the C-band weather radar observations on the microphysical properties of snow is investigated and parametrized. The results apply to improve the accuracy of the radar-based snowfall estimation, and the developed methodology also provides uncertainties of the estimates. Furthermore, the created data set is utilized to validate space-borne snowfall measurements. This work demonstrates that the C-band weather radar signal propagating through a low melting layer can significantly be attenuated by the melting snow particles. The expected modeled attenuation is parametrized according to microphysical properties of snow at the top of the melting layer