B. Montpetit

Coupling the snow thermodynamic model SNOWPACK with the microwave emission model of layered snowpacks for subarctic and arctic snow water equivalent retrievals

[1] Satellite-passive microwave remote sensing has been extensively used to estimate snow water equivalent (SWE) in northern regions. Although passive microwave sensors operate independent of solar illumination and the lower frequencies are independent of atmospheric conditions, the coarse spatial resolution introduces uncertainties to SWE retrievals due to the surface heterogeneity within individual pixels. In this article, we investigate the coupling of a thermodynamic multilayered snow model with a passive microwave emission model. Results show that the snow model itself provides poor SWE simulations when compared to field measurements from two major field campaigns. Coupling the snow and microwave emission models with successive iterations to correct the influence of snow grain size and density significantly improves SWE simulations. This method was further validated using an additional independent data set, which also showed significant improvement using the two-step iteration method compared to standalone simulations with the snow model.

Snow Microwave Emission Modeling of Ice Lenses Within a Snowpack Using the Microwave Emission Model for Layered Snowpacks

Ice lens formation, which follows rain on snow events or melt-refreeze cycles in winter and spring, is likely to become more frequent as a result of increasing mean winter temperatures at high latitudes. These ice lenses significantly affect the microwave scattering and emission properties, and hence snow brightness temperatures that are widely used to monitor snow cover properties from space. To understand and interpret the spaceborne microwave signal, the modeling of these phenomena needs improvement. This paper shows the effects and sensitivity of ice lenses on simulated brightness temperatures using the microwave emission model of layered snowpacks coupled to a soil emission model at 19 and 37 GHz in both horizontal and vertical polarizations. Results when considering pure ice lenses show an improvement of 20.5 K of the root mean square error between the simulated and measured brightness temperature (Tb) using several in situ data sets acquired during field campaigns across Canada. The modeled Tbs are found to be highly sensitive to the vertical location of ice lenses within the snowpack.

Brightness Temperature Simulations of the Canadian Seasonal Snowpack Driven by Measurements of the Snow Specific Surface Area

Snow grain size is the snowpack parameter that most affects the microwave snow emission. The specific surface area (SSA) of snow is a metric that allows rapid and reproducible field measurements and that well represents the grain size. However, this metric cannot be used directly in microwave snow emission models (MSEMs). The aim of this paper is to evaluate the suitability and the adaptations required for using the SSA in two MSEMs, i.e., the Dense Media Radiative Theory-Multilayer model (DMRT-ML) and the Helsinki University of Technology model (HUT n-layer), based on in situ radiometric measurements. Measurements of the SSA, using snow reflectance in the short-wave infrared, were taken at 20 snowpits in various environments (e.g., grass, tundra, and dry fen). The results show that both models required a scaling factor for the SSA values to minimize the root-mean-square error between the measured and simulated brightness temperatures. For DMRT-ML, the need for a scaling factor is likely due to the oversimplified representation of snow as spheres of ice with a uniform radius. We hypothesize that the need for a scaling factor is related to the grain size distribution of snow and the stickiness between grains. For HUT n-layer, using the SSA underestimates the attenuation by snow, particularly for snowpacks with a significant amount of depth hoar. This paper provides a reliable description of the grain size for DMRT-ML, which is of particular interest for the assimilation of satellite passive microwave data in snow models.

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 multilayer 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 specific surface area 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 that 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 that a three-layer representation of snowpack stratigraphy reduced the bias of a one-layer representation by about 50%.

Microwave snow emission modeling of boreal forest environments

This study focuses on the coupling of the Canadian land surface scheme (CLASS) and a radiative transfer model (RTM) to simulate brightness temperature (TB) in boreal forest environments. A parameterization of winter vegetation emission and transmission is proposed to improve the winter TB simulations. When compared to AMSR-E observations, RMSE values of 8K at 37 GHz, 5 K at 19 GHz and 3 K at 10 GHz were obtained. It is also shown that ice crusts have an important impact on the TB in H-pol at the three frequencies. Despite the low sensitivity of microwaves in dense forest, the study provides an interesting tool for the assimilation of TB for SWE retrieval in CLASS.

Creation of a Lambertian Microwave Surface for Retrieving the Downwelling Contribution in Ground-Based Radiometric Measurements

Downwelling radiative fluxes from inhomogeneous environments are not easy to quantify from the total measured signal contribution. We propose the use of a Lambertian reflector to assess this issue. Using ray tracing to extrapolate the bidirectional reflectance distribution function of a surface with high reflectivity in the microwave spectrum, we characterized a surface that acts as a near-Lambertian reflector at 37 GHz. After implementing such a plate using molded aluminum and getting its emissivity, we validated its Lambertian reflectance properties based on microwave surface-based radiometer measurements. This analysis also gives guidelines on how to make a Lambertian surface for different wavelengths in the microwave spectrum. Field experiments show the usefulness of such a Lambertian plate to estimate the downwelling contribution in ground-based radiometric measurements.

Meteorological inventory of rain-on-snow events in the Canadian Arctic Archipelago and satellite detection assessment using passive microwave data

ABSTRACT The spatial and temporal distributions of rain-on-snow (ROS) events across the Canadian Arctic Archipelago (CAA) remain poorly understood owing to their sporadic nature in time and space. This situation motivated the development of remote sensing detection algorithms. This paper uses a large meteorological dataset across the CAA to adapt an existing ROS-detection algorithm developed in a previous study by our group. Results highlight the spatial distribution and evolution of ROS occurrences reported since 1985 at 14 weather stations across the CAA. Results show that >600 ROS events were inventoried since 1985, for which >70% were classified as pure rain (liquid form) and 30% as mixed precipitation (solid/liquid). Of the pure rain events, 75% occurred during spring, 14% during fall, 8% during summer and <1% during winter. Such events can have significant impacts on ungulate grazing conditions through the creation of ice layers, causing serious problems for caribou calf survival, especially during the migration period. This paper introduces an adaptation for larger scale Arctic application of a detection algorithm (sensitivity analysis on the detection threshold) with an error of ~5%. The validation, however, remains limited due to a short study period and limited number of sites.

Assessment of the Barren Ground Caribou Die‐off During Winter 2015–2016 Using Passive Microwave Observations

In summer 2016, more than 50 Arctic Barren Ground caribous were found dead on Prince Charles Island (Nunavut, Canada), a species recently classified as threatened. Neither predator nor sign of diseases was observed and reported. The main hypothesis is that caribous were not able to access food due to a very dense snow surface, created by a strong storm system in spring. Using satellite microwave data, a significant increase in brightness temperature polarization ratio at 19 and 37 GHz was observed in spring 2016 (60% higher than previous two winter seasons). Based on microwave radiative transfer simulations, such anomaly can be explained with a very dense snow surface. This is consistent with the succession of storms and strong winds highlighted in ERA-Interim over Prince Charles Island in spring 2016. Using several sources of data, this study shows that changes in snow conditions explain the caribou die-off due to restricted foraging. Plain Language Summary In this paper, it is discussed that the snow conditions could be caused by the massive die-off events of the caribou herd on Price Charles Island, Nunavut. Using ERA-Interim reanalysis data, it is possible to find the reason of surface snow condition changes. This change creates an anomaly in signal, in particularly using different parameters derived from passive microwave data (brightness temperature) from SSM/I and SSMI/S sensors. Moreover, modeling of brightness temperature using radiative transfer model in passive microwaves domain, allowed to determine new thresholds for high density layer detection, may have an ecological consequence (food do not accessible for several ungulates).

Meteorological inventory of rain-on-snow events and detection assessment in the Canadian arctic archipelago using passive microwave radiometry

The spatial and temporal distribution of ROS across the Canadian Arctic Archipelago (CAA) remains poorly understood owing to their sporadic nature in time and space. In this study, we highlight the distribution and evolution of ROS occurrences inventoried since 1984 at 14 Environment and Climate Change Canada (ECCC) weather stations in the CAA. We introduce an adaptation of the detection algorithm proposed by Dolant et al., (2016), to investigate spatio-temporal patterns in occurrence. Across the 14 weather stations, more than 600 ROS events were identified since 1984, 80% of which occurred during the Spring season.