CREST-Snow Field Experiment: analysis of snowpack properties using multi-frequency microwave remote sensing data

Abstract

Abstract. The CREST-Snow Analysis and Field Experiment (CREST-SAFE) was carried out during January-March 2011 at the research site of the National Weather Service office, Caribou, ME, USA. In this experiment dual-polarized microwave (37 and 89 GHz) observations were accompanied by detailed synchronous observations of meteorology and snowpack physical properties. The objective of this long-term field experiment was to improve understanding of the effect of changing snow characteristics (grain size, density, temperature) under various meteorological conditions on the microwave emission of snow and hence to improve retrievals of snow cover properties from satellite observations. In this paper we present an overview of the field experiment and comparative preliminary analysis of the continuous microwave and snowpack observations and simulations. The observations revealed a large difference between the brightness temperature of fresh and aged snowpack even when the snow depth was the same. This is indicative of a substantial impact of evolution of snowpack properties such as snow grain size, density and wetness on microwave observations. In the early spring we frequently observed a large diurnal variation in the 37 and 89 GHz brightness temperature with small depolarization corresponding to daytime snowmelt and nighttime refreeze events. SNTHERM (SNow THERmal Model) and the HUT (Helsinki University of Technology) snow emission model were used to simulate snowpack properties and microwave brightness temperatures, respectively. Simulated snow depth and snowpack temperature using SNTHERM were compared to in situ observations. Similarly, simulated microwave brightness temperatures using the HUT model were compared with the observed brightness temperatures under different snow conditions to identify different states of the snowpack that developed during the winter season.

Proof of Concept: Development of Snow Liquid Water Content Profiler Using CS650 Reflectometers at Caribou, ME, USA

“…Generally, SNTHERM appeared to simulate all snowpack properties closer to the snow surface better than those closer to the snow-ground interface. Additional studies by Lakhankar et al [73] and Koivusalo and Heikinheimo [74] have also shown good agreement between various SNTHERM simulated snowpack properties and in situ observations. Lakhankar et al demonstrated high agreement between bulk snow density (R = 0.97) and average grain size (R = 0.96) SNTHERM simulations and CREST-SAFE in situ observations.…”

“…Additional calibration parameters (i.e., irreducible water content for snow (0.017), density of new snow (73 kg/m 3 ), density limit for compaction of snow (96 kg/m 3 ), and the viscosity coefficient for overburden compaction (6.9 × 10 5 kg·s/m 2 )) needed by SNTHERM to simulate the deposition scheme were established based on previous studies [48,57,65,66,67,72]. CREST-SAFE provides all of its meteorological data in an hourly time step via an automated routine [73], whereas the NWS provides precipitation data in 15-min time steps. Naturally, the NWS precipitation data was aggregated to hourly time intervals.…”

A modified tank model including snowmelt and infiltration time lags for deep-seated landslides in alpine environments (Aggenalm, Germany)

“…It has to be pointed out that the snowmelt estimation is still not very precise, as the temperature index model is relatively simple (Garen and Marks, 2005;Herrero et al, 2009;Lakhankar et al, 2013). Also, we do not consider surface run-off due to the high permeability of surface deposits.…”

“…Thus, in our study the judgment of precipitation type still uses the widely used statistic model. For the snowmelt calculation we used empirical equations to make the process earlier, because sophisticated models which can calculate the snowmelt precisely are quite complex and require several physical parameters, including topography, precipitation, air temperature, wind speed and direction, humidity, downwelling short-wave and long-wave radiation, cloud cover and surface pressure (Garen and Marks, 2005;Herrero et al, 2009;Lakhankar et al, 2013). In addition, compared to the original tank model without considering the snowmelt, we emphasized the tank model coupling the function of snowmelt (we just choose the simple snowmelt module).…”

Snow accumulation/melting model (SAMM) for integrated use in regional scale landslide early warning systems

“…Such models are rather complex and require several physical parameters including (but not limited to) topography, precipitation, air temperature, wind speed and direction, humidity, downwelling shortwave and longwave radiation, cloud cover, surface pressure. The determination of accurate values of these parameters, and their variation in space and time, is only possible for very well-equipped experimental test sites (Lakhankar et al, 2013); therefore, simplified approaches as temperature-index methods are also widely used (Kustas et al, 1994;Rango and Martinec, 1995;Hock, 1999Hock, , 2003Jost et al, 2012). Those models use air temperature as an index to perform an empirical correlation with snowmelt and require only a few parameters (e.g.…”