Microwave Measurements of Snowpack Properties

Prior microwave measurements of snow water equivalent and liquid water content and conceptualizations of emission and backscattering models are reviewed. The results of an experiment designed to collect simultaneous passive and active microwave data to be used in interpreting and analyzing the sensitivity of the microwave spectrum to changing snowpack properties are reported. Both the scattering coefficient, σ°, and the apparent radiometric temperature, Tap, were found to be sensitive to changes in snow water equivalent and liquid water content. The σ° data exhibit an exponential-like increase with increasing water equivalent, whereas, the Tap data exhibit an exponential-like decrease. For both the active and passive data, the snow water equivalent at which the microwave response begins to saturate decreases as the wavelength decreases. Increasing liquid water in the snowpack causes a decrease in σ° and an increase in the Tap. Diurnal data sets show the greatest σ° and Tap variation in response to snowmelt at 35 and 37 GHz with correspondingly less variation at the lower frequencies. Based on research results to date, immediate formulation of a comprehensive microwave and snow research program is recommended.

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Retrieval of Snow Water Equivalent, Liquid Water Content, and Snow Height of Dry and Wet Snow by Combining GPS Signal Attenuation and Time Delay

Water Resources Research ◽

10.1029/2018wr024431 ◽

2019 ◽

Vol 55 (5) ◽

pp. 4465-4487 ◽

Cited By ~ 9

Author(s):

Franziska Koch ◽

Patrick Henkel ◽

Florian Appel ◽

Lino Schmid ◽

Heike Bach ◽

...

Keyword(s):

Time Delay ◽

Water Content ◽

Liquid Water ◽

Snow Water Equivalent ◽

Liquid Water Content ◽

Signal Attenuation ◽

Wet Snow ◽

Snow Water

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Snow Wetness Influence on Impulse Radar Snow Surveys Theoretical and Laboratory Study

Hydrology Research ◽

10.2166/nh.2000.0007 ◽

2000 ◽

Vol 31 (2) ◽

pp. 89-106 ◽

Cited By ~ 23

Author(s):

A. Lundberg ◽

H. Thunehed

Keyword(s):

Water Content ◽

Liquid Water ◽

Snow Water Equivalent ◽

Liquid Water Content ◽

Late Winter ◽

Propagation Time ◽

Reservoir Water ◽

A Value ◽

Wet Snow ◽

Snow Water

The snow-water equivalent of late-winter snowpack is of utmost importance for hydropower production in areas where a large proportion of the reservoir water emanates from snowmelt. Impulse radar can be used to estimate the snow-water equivalent of the snowpack and thus the expected snowmelt discharge. Impulse radar is now in operational use in some Scandinavian basins. With radar technology the radar wave propagation time in the snowpack is converted into snow-water equivalent with help of a parameter usually termed the a-value. Use of radar technology during late winter brings about risk for measurements on wet snow. The a-value for dry snow cannot be used directly for wet snow. We have found that a liquid-water content of 5% (by volume) reduces the a-value by approximately 20%. In this paper an equation, based on snow density and snow liquid water content, for calculation of wet-snow a-value is presented.

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Field evaluation of a new method for estimation of liquid water content and snow water equivalent of wet snowpacks with GPR

Hydrology Research ◽

10.2166/nh.2012.182 ◽

2012 ◽

Vol 44 (4) ◽

pp. 600-613 ◽

Cited By ~ 8

Author(s):

Nils Sundström ◽

David Gustafsson ◽

Andrey Kruglyak ◽

Angela Lundberg

Keyword(s):

Water Content ◽

Liquid Water ◽

Ground Penetrating Radar ◽

Snow Water Equivalent ◽

Field Evaluation ◽

Liquid Water Content ◽

Snow Water ◽

The Mean ◽

Path Dependent ◽

Ground Penetrating

Estimates of snow water equivalent (SWE) with ground-penetrating radar can be used to calibrate and validate measurements of SWE over large areas conducted from satellites and aircrafts. However, such radar estimates typically suffer from low accuracy in wet snowpacks due to a built-in assumption of dry snow. To remedy the problem, we suggest determining liquid water content from path-dependent attenuation. We present the results of a field evaluation of this method which demonstrate that, in a wet snowpack between 0.9 and 3 m deep and with about 5 vol% of liquid water, liquid water content is underestimated by about 50% (on average). Nevertheless, the method decreases the mean error in SWE estimates to 16% compared to 34% when the presence of liquid water in snow is ignored and 31% when SWE is determined directly from two-way travel time and calibrated for manually measured snow density.

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Large scale snow water equivalent status monitoring: comparison of different snow water products in the upper Colorado Basin

Hydrology and Earth System Sciences ◽

10.5194/hess-17-5127-2013 ◽

2013 ◽

Vol 17 (12) ◽

pp. 5127-5139 ◽

Cited By ~ 6

Author(s):

G. A. Artan ◽

J. P. Verdin ◽

R. Lietzow

Keyword(s):

Energy Balance ◽

Water Content ◽

Snow Water Equivalent ◽

Weather Forecast ◽

Snow Accumulation ◽

Passive Microwave ◽

Data Sets ◽

Snow Water ◽

Trmm Precipitation ◽

Colorado Basin

Abstract. We illustrate the ability to monitor the status of snow water content over large areas by using a spatially distributed snow accumulation and ablation model that uses data from a weather forecast model in the upper Colorado Basin. The model was forced with precipitation fields from the National Weather Service (NWS) Multi-sensor Precipitation Estimator (MPE) and the Tropical Rainfall Measuring Mission (TRMM) data-sets; remaining meteorological model input data were from NOAA's Global Forecast System (GFS) model output fields. The simulated snow water equivalent (SWE) was compared to SWEs from the Snow Data Assimilation System (SNODAS) and SNOwpack TELemetry system (SNOTEL) over a region of the western US that covers parts of the upper Colorado Basin. We also compared the SWE product estimated from the special sensor microwave imager (SSM/I) and scanning multichannel microwave radiometer (SMMR) to the SNODAS and SNOTEL SWE data-sets. Agreement between the spatial distributions of the simulated SWE with MPE data was high with both SNODAS and SNOTEL. Model-simulated SWE with TRMM precipitation and SWE estimated from the passive microwave imagery were not significantly correlated spatially with either SNODAS or the SNOTEL SWE. Average basin-wide SWE simulated with the MPE and the TRMM data were highly correlated with both SNODAS (r = 0.94 and r = 0.64; d.f. = 14 – d.f. = degrees of freedom) and SNOTEL (r = 0.93 and r = 0.68; d.f. = 14). The SWE estimated from the passive microwave imagery was significantly correlated with the SNODAS SWE (r = 0.55, d.f. = 9, p = 0.05) but was not significantly correlated with the SNOTEL-reported SWE values (r = 0.45, d.f. = 9, p = 0.05).The results indicate the applicability of the snow energy balance model for monitoring snow water content at regional scales when coupled with meteorological data of acceptable quality. The two snow water contents from the microwave imagery (SMMR and SSM/I) and the Utah Energy Balance forced with the TRMM precipitation data were found to be unreliable sources for mapping SWE in the study area; both data sets lacked discernible variability of snow water content between sites as seen in the SNOTEL and SNODAS SWE data. This study will contribute to better understanding the adequacy of data from weather forecast models, TRMM, and microwave imagery for monitoring status of the snow water content.

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A portable calorimeter for measuring liquid-water content of wet snow

Annals of Glaciology ◽

10.3189/1998aog26-1-103-106 ◽

1998 ◽

Vol 26 ◽

pp. 103-106 ◽

Cited By ~ 7

Author(s):

Katsuhisa Kawashima ◽

Toru Endo ◽

Yukari Takeuchi

Keyword(s):

Water Content ◽

Liquid Water ◽

Liquid Water Content ◽

Total Weight ◽

Light Weight ◽

High Mountains ◽

Insulating Materials ◽

Wet Snow ◽

Snow Water

In order to facilitate the measurement of liquid-water content of snow in high mountains, a portable calorimeter named “Endo-type snow-water content meter” was developed. It is composed of a metal-coated container made of insulating materials and a lid of the container with a small-thermistor thermometer. Its strong points are its light weight, small size and easy fabrication with cheap materials. The total weight of the device is as light as 250 g, which is less than 10% of the snow-water content meter widely used in Japan (Akitaya-type snow-water content meter). The results of experiments have revealed that the device is capable of measuring the liquid-water content within 2 minutes with an accuracy of 2% by weight.

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Quantitative Investigation of Radiometric Interactions between Snowfall, Snow Cover, and Cloud Liquid Water over Land

Remote Sensing ◽

10.3390/rs13132641 ◽

2021 ◽

Vol 13 (13) ◽

pp. 2641

Author(s):

Zeinab Takbiri ◽

Lisa Milani ◽

Clement Guilloteau ◽

Efi Foufoula-Georgiou

Keyword(s):

Snow Cover ◽

Water Content ◽

Liquid Water ◽

Snow Water Equivalent ◽

A Priori ◽

Liquid Water Content ◽

Microwave Signal ◽

Liquid Water Path ◽

Cloud Liquid Water ◽

Microwave Signatures

Falling snow alters its own microwave signatures when it begins to accumulate on the ground, making retrieval of snowfall challenging. This paper investigates the effects of snow-cover depth and cloud liquid water content on microwave signatures of terrestrial snowfall using reanalysis data and multi-annual observations by the Global Precipitation Measurement (GPM) core satellite with particular emphasis on the 89 and 166 GHz channels. It is found that over shallow snow cover (snow water equivalent (SWE) ≤100kg m−2) and low values of cloud liquid water path (LWP 100–150 g m−2), the scattering of light snowfall (intensities ≤0.5mm h−1) is detectable only at frequency 166 GHz, while for higher snowfall rates, the signal can also be detected at 89 GHz. However, when SWE exceeds 200 kg m−2 and the LWP is greater than 100–150 g m−2, the emission from the increased liquid water content in snowing clouds becomes the only surrogate microwave signal of snowfall that is stronger at frequency 89 than 166 GHz. The results also reveal that over high latitudes above 60°N where the SWE is greater than 200 kg m−2 and LWP is lower than 100–150 g m−2, the snowfall microwave signal could not be detected with GPM without considering a priori data about SWE and LWP. Our findings provide quantitative insights for improving retrieval of snowfall in particular over snow-covered terrain.

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Spatiotemporal Variations in Liquid Water Content in a Seasonal Snowpack: Implications for Radar Remote Sensing

Remote Sensing ◽

10.3390/rs13214223 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4223

Author(s):

Randall Bonnell ◽

Daniel McGrath ◽

Keith Williams ◽

Ryan Webb ◽

Steven R. Fassnacht ◽

...

Keyword(s):

Water Content ◽

Liquid Water ◽

Electromagnetic Waves ◽

Snow Water Equivalent ◽

Canopy Cover ◽

Liquid Water Content ◽

Spatiotemporal Variability ◽

Snow Density ◽

Wet Snow ◽

Observation Method

Radar instruments have been widely used to measure snow water equivalent (SWE) and Interferometric Synthetic Aperture Radar is a promising approach for doing so from spaceborne platforms. Electromagnetic waves propagate through the snowpack at a velocity determined by its dielectric permittivity. Velocity estimates are a significant source of uncertainty in radar SWE retrievals, especially in wet snow. In dry snow, velocity can be calculated from relations between permittivity and snow density. However, wet snow velocity is a function of both snow density and liquid water content (LWC); the latter exhibits high spatiotemporal variability, there is no standard observation method, and it is not typically measured by automated stations. In this study, we used ground-penetrating radar (GPR), probed snow depths, and measured in situ vertically-averaged density to estimate SWE and bulk LWC for seven survey dates at Cameron Pass, Colorado (~3120 m) from April to June 2019. During this cooler than average season, median LWC for individual survey dates never exceeded 7 vol. %. However, in June, LWC values greater than 10 vol. % were observed in isolated areas where the ground and the base of the snowpack were saturated and therefore inhibited further meltwater output. LWC development was modulated by canopy cover and meltwater drainage was influenced by ground slope. We generated synthetic SWE retrievals that resemble the planned footprint of the NASA-ISRO L-band InSAR satellite (NISAR) from GPR using a dry snow density model. Synthetic SWE retrievals overestimated observed SWE by as much as 40% during the melt season due to the presence of LWC. Our findings emphasize the importance of considering LWC variability in order to fully realize the potential of future spaceborne radar missions for measuring SWE.

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