Alexandre Langlois

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.

Improved Corrections of Forest Effects on Passive Microwave Satellite Remote Sensing of Snow Over Boreal and Subarctic Regions

Microwave radiometry has been extensively used in order to estimate snow water equivalent in northern regions. However, for boreal and taiga environments, the presence of forest causes important uncertainties in the estimates. Variations in snow cover and vegetation in northeastern Canada (north of the Québec province) were characterized in a transect from 50°N to 60 °N during the International Polar Year field campaign of February 2008. Forest properties show a strong latitudinal gradient in fraction and stem volume. A large database (>; 2000 points with a stem volume ranging between 0 and 700 m3 ·ha-1) showed that brightness temperatures (Tb) decrease as forest cover fraction decreases until a cover fraction of about 25% is reached. Furthermore, Tb values saturate at high stem volume, particularly at 37 GHz. We defined new relationships for the forest transmissivity as a function of stem volume and depending on the frequency/polarization. The proposed relationships give asymptotic transmissivity saturation levels of 0.51, 0.55, 0.53, and 0.53 for 19 GHz [vertical (V) polarization], 19 GHz [horizontal (H) polarization], 37 GHz (V polarization), and 37 GHz (H polarization), respectively. These relationships were used to estimate snow Tb from the Advanced Microwave Scanning Radiometer-Earth Observing System brightness temperatures at 18.7 and 36.5 GHz, and results show an estimated snow brightness temperature well correlated to the airborne snow brightness temperatures over vegetation-free areas.

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.

Evaluation of Spaceborne L-Band Radiometer Measurements for Terrestrial Freeze/Thaw Retrievals in Canada

The landscape freeze/thaw (F/T) state has an important impact on the surface energy balance, carbon fluxes, and hydrologic processes; the timing of spring melt is linked to active layer dynamics in permafrost areas. L-band (1.4 GHz) microwave emission could allow the monitoring of surface state dynamics due to its sensitivity to the pronounced permittivity difference between frozen and thawed soil. The aim of this paper is to evaluate the performance of both Aquarius and soil moisture and ocean salinity (SMOS) L-band passive microwave measurements using a polarization ratio (PR)-based algorithm for landscape F/T monitoring. Weekly L-band satellite observations are compared with a large set of reference data at 48 sites across Canada spanning three environments: 1) tundra; 2) boreal forest; and 3) prairies. The reference data include in situ measurements of soil temperature (Tsoil) and air temperature (Tair), and moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and snow cover area (SCA) products. Results show generally good agreement between L-band F/T detection and the surface state estimated from four reference datasets. The best apparent accuracies for all seasons are obtained using Tair as the reference. Aquarius radiometer 2 (incidence angle of 39.6°) data give the best accuracies (90.8%), while for SMOS, using the Aquarius temporal characteristics, the best results (87.8% of accuracy) are obtained at higher incidence angles (55°-60°). The F/T algorithm identifies both freeze onset and end with a delay of about 1 week in tundra and 2 weeks in forest and prairies, when compared to Tair. The analysis shows a stronger F/T signal at tundra sites due to the typically clean transitions between consistently frozen and thawed conditions (and vice versa) and the absence of surface vegetation. Results in the prairies were poorer because of the influence of vegetation growth in summer (which decreases the PR) and the high frequency of ephemeral thaw events during winter. Freeze onset and end maps created from the same algorithm applied to SMOS and Aquarius measurements characterize similar F/T patterns over Canada. This study shows the potential of using L-band spaceborne observations for F/T monitoring, but underlines some limitations due to ice crusts in the snowpack, liquid water content in snow cover during the spring freeze to thaw transition, and vegetation growth.

Simulation of Snow Water Equivalent (SWE) Using Thermodynamic Snow Models in Québec, Canada

Abstract Snow cover plays a key role in the climate system by influencing the transfer of energy and mass between the soil and the atmosphere. In particular, snow water equivalent (SWE) is of primary importance for climatological and hydrological processes and is a good indicator of climate variability and change. Efforts to quantify SWE over land from spaceborne passive microwave measurements have been conducted since the 1980s, but a more suitable method has yet to be developed for hemispheric-scale studies. Tools such as snow thermodynamic models allow for a better understanding of the snow cover and can potentially significantly improve existing snow products at the regional scale. In this study, the use of three snow models [SNOWPACK, CROCUS, and Snow Thermal Model (SNTHERM)] driven by local and reanalysis meteorological data for the simulation of SWE is investigated temporally through three winter seasons and spatially over intensively sampled sites across northern Quebec. Results show that the SWE ...

Analysis of simulated and spaceborne passive microwave brightness temperatures using in situ measurements of snow and vegetation properties.

Variations in snow cover and vegetation in the Province of Quebec, Canada, were characterized for a transect spanning from 50°N to 60°N during the International Polar Year field campaign of February 2008. The main objective of this study was to compare measured (AMSR-E) and modeled (MEMLS) brightness temperature (Tb) and to analyze differences in the in situ measurement of snow water equivalent (SWE) and vegetation. Sampling involved detailed snow measurements on the ground in four different ecological environments. Measured and modeled SWE were compared using a thermodynamic multilayered snow model (SNOWPACK) driven with North American Regional Reanalysis (NARR) data. The root mean square error (RMSE) of modeled data compared with measurements was 63 mm (30%). The simulated SWE was generally underestimated throughout the transect but stayed within the large standard deviation observed for measured SWE. In situ snow measurements were used as input to a microwave emission model (MEMLS) to simulate Tb. An innovative approach using calibrated near-infrared reflectance photographs was used to characterize the effective snow grain-size parameter needed for the radiative transfer model. Although some results provided Tb predictions similar to AMSR-E data for certain areas, large differences remained for the majority of sampling sites. The derived RMSE of 16 K and 32 K, respectively, for 18.7 and 36.5 GHz (vertical polarization) throughout the transect cannot be explained solely in terms of grain-size variations introduced into the simulations. Local variability in snow structure and thickness produced large variability (up to 60 K within one AMSR-E pixel) compared with AMSR-E Tb throughout the transect (15 K for 18.7 GHz and 35 K for 36.5 GHz).

Evaluation of CLASS 2.7 and 3.5 Simulations of Snow Properties from the Canadian Regional Climate Model (CRCM4) over Québec, Canada*

AbstractSnow cover simulations from versions 2.7 and 3.5 of the Canadian Land Surface Scheme (CLASS) coupled to the Canadian Regional Climate Model, version 4 (CRCM4), are evaluated over northern Quebec and the larger Quebec domain using in situ and remotely sensed datasets. Version 2.7 of CLASS has been used in the operational version of CRCM4 at Ouranos since 2006. Version 3.5 includes a number of improvements to the snow processes as well as a more realistic parameterization of snow thermal conductivity. The evaluation shows that version 3.5 provides improved simulations of snow water equivalent, density, depth, and snowpack temperature values. However, snowpack density still contains systematic biases during the snow season that need to be addressed. The snow albedo parameterization in CLASS was found to be very sensitive to an empirical snowfall rate threshold for albedo refreshment and does not keep track of the snow accumulation history in estimating the snow surface albedo. A modified albedo schem...

Passive microwave remote sensing of seasonal snow-covered sea ice

The Arctic is thought to be an area where we can expect to see the first and strongest signs of global-scale climate variability and change. We have already begun to see a reduction in: (1) the aerial extent of sea ice at about 3% per decade and (2) ice thickness at about 40%. At the current rate of reduction we can expect a seasonally ice-free Arctic by midway through this century given the current changes in thermodynamic processes controlling sea-ice freeze-up and decay. Many of the factors governing the thermodynamic processes of sea ice are strongly tied to the presence and geophysical state of snow on sea ice, yet snow on sea ice remains poorly studied. In this review, we provide a summary of the current state of knowledge pertaining to the geophysical, thermodynamic and dielectric properties of snow on sea ice. We first give a detailed description of snow thermophysical properties such as thermal conductivity, diffusivity and specific heat and how snow geophysical/electrical properties and the seasonal surface energy balance affect them. We also review the different microwave emission and scattering mechanisms associated with snow-covered first-year sea ice. Finally, we discuss the annual evolution of the Arctic system through snow thermodynamic (heat/mass transfer, metamorphism) and aeolian processes, with linkages to microwave remote sensing that have yet to be defined from an annual perspective in the Arctic.

C-Band Scatterometer Measurements of Multiyear Sea Ice Before Fall Freeze-Up in the Canadian Arctic

Backscatter signatures of multiyear sea ice (MYI) during the late summer and early fall season before the fall freeze-up in the Canadian Arctic archipelago (CAA) have been obtained through the use of a ship-based polarimetric scatterometer. The device operates in C-band, and measurements were conducted in swaths from incidence angles of 20 deg-60deg. Three characteristic sites on MYI floes were investigated in the high Arctic and the central Arctic regions. In situ snow and sea-ice thermophysical data were collected at each site in conjunction with local scatterometer measurements. The thermophysical data were subsequently analyzed using dielectric modeling techniques and coupled with the backscattering measurements (sigmadeg). Observed backscatter values and ratios were found to be in agreement with literature data, with volumetric scattering as the dominant scattering mechanism.