scholarly journals Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals

2022 ◽  
Vol 16 (1) ◽  
pp. 87-101
Author(s):  
Julien Meloche ◽  
Alexandre Langlois ◽  
Nick Rutter ◽  
Alain Royer ◽  
Josh King ◽  
...  
Keyword(s):  
Snow Depth ◽  
Climate Models ◽  
Lower Latitude ◽  
Mean Values ◽  
Brightness Temperatures ◽  
Snow Properties ◽  
Shrub Tundra ◽  
Aircraft System ◽  
Log Normal ◽  
Snowpack Properties

Abstract. Topography and vegetation play a major role in sub-pixel variability of Arctic snowpack properties but are not considered in current passive microwave (PMW) satellite SWE retrievals. Simulation of sub-pixel variability of snow properties is also problematic when downscaling snow and climate models. In this study, we simplified observed variability of snowpack properties (depth, density, microstructure) in a two-layer model with mean values and distributions of two multi-year tundra dataset so they could be incorporated in SWE retrieval schemes. Spatial variation of snow depth was parameterized by a log-normal distribution with mean (μsd) values and coefficients of variation (CVsd). Snow depth variability (CVsd) was found to increase as a function of the area measured by a remotely piloted aircraft system (RPAS). Distributions of snow specific surface area (SSA) and density were found for the wind slab (WS) and depth hoar (DH) layers. The mean depth hoar fraction (DHF) was found to be higher in Trail Valley Creek (TVC) than in Cambridge Bay (CB), where TVC is at a lower latitude with a subarctic shrub tundra compared to CB, which is a graminoid tundra. DHFs were fitted with a Gaussian process and predicted from snow depth. Simulations of brightness temperatures using the Snow Microwave Radiative Transfer (SMRT) model incorporating snow depth and DHF variation were evaluated with measurements from the Special Sensor Microwave/Imager and Sounder (SSMIS) sensor. Variation in snow depth (CVsd) is proposed as an effective parameter to account for sub-pixel variability in PMW emission, improving simulation by 8 K. SMRT simulations using a CVsd of 0.9 best matched CVsd observations from spatial datasets for areas > 3 km2, which is comparable to the 3.125 km pixel size of the Equal-Area Scalable Earth (EASE)-Grid 2.0 enhanced resolution at 37 GHz.

scholarly journals Characterizing Tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals

2021 ◽  
Author(s):  
Julien Meloche ◽  
Alexandre Langlois ◽  
Nick Rutter ◽  
Alain Royer ◽  
Josh King ◽  
...  
Keyword(s):  
Snow Depth ◽  
Climate Models ◽  
Specific Area ◽  
Lower Latitude ◽  
Mean Values ◽  
Brightness Temperatures ◽  
Snow Properties ◽  
Aircraft System ◽  
Log Normal ◽  
Snowpack Properties

Abstract. Topography and vegetation play a major role in sub-pixel variability of Arctic snowpack properties, but are not considered in current passive microwave (PMW) satellite SWE retrievals. Simulation of sub-pixel variability of snow properties is also problematic when downscaling snow and climate models. In this study, we simplified observed variability of snowpack properties (depth, density, microstructure) in a two-layer model with mean values and distributions of two multi-year tundra dataset so they could be incorporated in SWE retrieval schemes. Spatial variation of snow depth was parametrized by a log-normal distribution with mean (μsd) values and coefficients of variation (CVsd). Snow depth variability (CVsd) was found to increase as a function of the area measured by a Remotely Piloted Aircraft System (RPAS). Distributions of snow specific area (SSA) and density were found for the wind slab (WS) and depth hoar (DH) layers. The mean depth hoar fraction (DHF) was found to be higher in Trail Valley Creek (TVC) than Cambridge Bay (CB) where TVC is at a lower latitude with a sub-arctic shrub tundra compared to CB which is a graminoid tundra. DHF were fitted with a gaussian process and predicted from snow depth. Simulations of brightness temperatures using the Snow Microwave Radiative Transfer (SMRT) model incorporating snow depth and DHF variation were evaluated with measurements from the Special Sensor Microwave/Imager and Sounder (SSMIS) sensor. Variation in snow depth (CVsd) is proposed as an effective parameter to account for sub-pixel variability in PMW emission, improving simulation by 8 K. Snow depth simulations using a CVsd of 0.9 best matched CVsd observations from spatial datasets for areas > 3 km2, which is comparable to the 3.125 km pixel size of the Equal-Area Scalable Earth (EASE) grid 2.0 enhanced resolution at 37 GHz.

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

2013 ◽  
Vol 17 (2) ◽  
pp. 783-793 ◽  
Author(s):  
T. Y. Lakhankar ◽  
J. Muñoz ◽  
P. Romanov ◽  
A. M. Powell ◽  
N. Y. Krakauer ◽  
...  
Keyword(s):  
Grain Size ◽  
Field Experiment ◽  
Brightness Temperature ◽  
Snow Depth ◽  
Winter Season ◽  
Microwave Remote Sensing ◽  
Microwave Emission ◽  
Substantial Impact ◽  
Brightness Temperatures ◽  
Snowpack Properties

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.

The Utilization of Spaceborne Microwave Radiometers for Monitoring Snowpack Properties

1979 ◽  
Vol 10 (1) ◽  
pp. 25-40 ◽  
Author(s):  
A. Rango ◽  
A. T. C. Chang ◽  
J. L. Foster
Keyword(s):  
Brightness Temperature ◽  
Microwave Radiometer ◽  
Snow Accumulation ◽  
High Plains ◽  
Snow Hydrology ◽  
Microwave Brightness Temperature ◽  
Brightness Temperatures ◽  
Snow Properties ◽  
Snow Conditions ◽  
Snowpack Properties

Snow accumulation and depletion at specific locations can be monitored from space by observing related variations in microwave brightness temperatures. Using vertically and horizontally polarized brightness temperatures from the Nimbus 6 Electrically Scanning Microwave Radiometer, a discriminant function can be used to separate snow from no snow areas and map snowcovered area on a continental basis. For dry snow conditions on the Canadian high plains significant relationships between snow depth or water equivalent and microwave brightness temperature were developed which could permit remote determination of these snow properties after acquisition of a wider range of data. The presence of melt water in the snowpack causes a marked increase in brightness temperature which can be used to predict snowpack priming and timing of runoff. As the resolutions of satellite microwave sensors improve the application of these results to snow hydrology problems should increase.

Critical snow density threshold for Dall’s sheep (Ovis dalli dalli)

2018 ◽  
Vol 96 (10) ◽  
pp. 1170-1177 ◽  
Author(s):  
Kelly J. Sivy ◽  
Anne W. Nolin ◽  
Christopher L. Cosgrove ◽  
Laura R. Prugh
Keyword(s):  
Snow Depth ◽  
Animal Movement ◽  
Population Declines ◽  
Snow Density ◽  
Energetic Costs ◽  
Snow Properties ◽  
Ovis Dalli ◽  
Snow Conditions ◽  
Dall’S Sheep ◽  
Density Threshold

Snow cover can significantly impact animal movement and energetics, yet few studies have investigated the link between physical properties of snow and energetic costs. Quantification of thresholds in snow properties that influence animal movement are needed to help address this knowledge gap. Recent population declines of Dall’s sheep (Ovis dalli dalli Nelson, 1884) could be due in part to changing snow conditions. We examined the effect of snow density, snow depth, and snow hardness on sinking depths of Dall’s sheep tracks encountered in Wrangell–St. Elias National Park and Preserve, Alaska. Snow depth was a poor predictor of sinking depths of sheep tracks (R2 = 0.02, p = 0.38), as was mean weighted hardness (R2 = 0.09, p = 0.07). Across competing models, top layer snow density (0–10 cm) and sheep age class were the best predictors of track sink depths (R2 = 0.58). Track sink depth decreased with increasing snow density, and the snowpack supported the mass of a sheep above a density threshold of 329 ± 18 kg/m3 (mean ± SE). This threshold could aid interpretation of winter movement and energetic costs by animals, thus improving our ability to predict consequences of changing snowpack conditions on wildlife.

scholarly journals Theoretical framework for estimating snow distribution through point measurements

2015 ◽  
Vol 9 (1) ◽  
pp. 1-44
Author(s):  
E. Trujillo ◽  
M. Lehning
Keyword(s):  
Snow Depth ◽  
Survey Design ◽  
Spatial Location ◽  
Theoretical Framework ◽  
Statistical Properties ◽  
Spatial Covariance ◽  
Sample Mean ◽  
Snow Properties ◽  
Survey Designs

Abstract. In recent years, marked improvements in our knowledge of the statistical properties of the spatial distribution of snow properties have been achieved thanks to improvements in measuring technologies (e.g. LIDAR, TLS, and GPR). Despite of this, objective and quantitative frameworks for the evaluation of errors and extrapolations in snow measurements have been lacking. Here, we present a theoretical framework for quantitative evaluations of the uncertainty of point measurements of snow depth when used to represent the average depth over a profile section or an area. The error is defined as the expected value of the squared difference between the real mean of the profile/field and the sample mean from a limited number of measurements. The model is tested for one and two dimensional survey designs that range from a single measurement to an increasing number of regularly-spaced measurements. Using high-resolution (~1 m) LIDAR snow depths at two locations in Colorado, we show that the sample errors follow the theoretical behavior. Furthermore, we show how the determination of the spatial location of the measurements can be reduced to an optimization problem for the case of the predefined number of measurements, or to the designation of an acceptable uncertainty level to determine the total number of regularly-spaced measurements required to achieve such error. On this basis, a series of figures are presented that can be used to aid in the determination of the survey design under the conditions described, and under the assumption of prior knowledge of the spatial covariance/correlation properties. With this methodology, better objective survey designs can be accomplished, tailored to the specific applications for which the measurements are going to be used. The theoretical framework can be extended to other spatially distributed snow variables (e.g. SWE) whose statistical properties are comparable to those of snow depth.

scholarly journals Subgrid snow depth coefficient of variation spanning alpine to sub-alpine mountainous terrain

2021 ◽  
Author(s):  
Graham A. Sexstone ◽  
Steven R. Fassnacht ◽  
Juan I. López-Moreno ◽  
Christopher A. Hiemstra
Keyword(s):  
Coefficient Of Variation ◽  
Land Surface ◽  
Snow Depth ◽  
Mountainous Terrain ◽  
Mountainous Regions ◽  
Scale Modeling ◽  
Roughness Elements ◽  
Wide Range ◽  
Performance Statistics ◽  
Snowpack Properties

Given the substantial variability of snow in complex mountainous terrain, a considerable challenge of coarse scale modeling applications is accurately representing the subgrid variability of snowpack properties. The snow depth coefficient of variation (CVds) is a useful metric for characterizing subgrid snow distributions but has not been well defined by a parameterization for mountainous environments. This study utilizes lidar-derived snow depth datasets spanning alpine to sub-alpine mountainous terrain in Colorado, USA to evaluate the variability of subgrid snow distributions within a grid size comparable to a 1000 m resolution common for hydrologic and land surface models. The subgrid CVds exhibited a wide range of variability across the 321 km2 study area (0.15 to 2.74) and was significantly greater in alpine areas compared to subalpine areas. Mean snow depth was the dominant driver of CVds variability in both alpine and subalpine areas, as CVds decreased nonlinearly with increasing snow depths. This negative correlation is attributed to the static size of roughness elements (topography and canopy) that strongly influence seasonal snow variability. Subgrid CVds was also strongly related to topography and forest variables; important drivers of CVds included the subgrid variability of terrain exposure to wind in alpine areas and the mean and variability of forest metrics in subalpine areas. Two statistical models were developed (alpine and subalpine) for predicting subgrid CVds that show reasonable performance statistics. The methodology presented here can be used for characterizing the variability of CVds in snow-dominated mountainous regions, and highlights the utility of using lidar-derived snow datasets for improving model representations of snow processes.

scholarly journals Validation of modeled snow properties in Afghanistan, Pakistan, and Tajikistan

2019 ◽  
Author(s):  
Edward H. Bair ◽  
Karl Rittger ◽  
Jawairia A. Ahmad ◽  
Doug Chabot
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Median Number ◽  
In Situ Measurements ◽  
Snow Properties ◽  
Wide Range ◽  
Spatial Estimates ◽  
Snow Water ◽  
Critical Layers

Abstract. Ice and snowmelt feed the Indus and Amu Darya rivers, yet there are limited in situ measurements of these resources. Previous work in the region has shown promise using snow water equivalent (SWE) reconstruction, which requires no in situ measurements, but validation has been a problem until recently when we were provided with daily manual snow depth measurements from Afghanistan, Tajikistan, and Pakistan by the Aga Khan Agency for Habitat (AKAH). For each station, accumulated precipitation and SWE were derived from snow depth using the SNOWPACK model. High-resolution (500 m) reconstructed SWE estimates from the ParBal model were then compared to the modeled SWE at the stations. The Alpine3D model was then used to create spatial estimates at 25 km to compare with estimates from other snow models. Additionally, the coupled SNOWPACK and Alpine3D system has the advantage of simulating snow profiles, which provide stability information. Following previous work, the median number of critical layers and percentage of facets across all of the pixels containing the AKAH stations was computed. For SWE at the point scale, the reconstructed estimates showed a bias of −42 mm (−19 %) at the peak. For the coarser spatial SWE estimates, the various models showed a wide range, with reconstruction being on the lower end. For stratigraphy, a heavily faceted snowpack is observed in both years, but 2018, a dry year, according to most of the models, showed more critical layers that persisted for a longer period.

scholarly journals An 18-yr long (1993–2011) snow and meteorological dataset from a mid-altitude mountain site (Col de Porte, France, 1325 m alt.) for driving and evaluating snowpack models

2012 ◽  
Vol 4 (1) ◽  
pp. 13-21 ◽  
Author(s):  
S. Morin ◽  
Y. Lejeune ◽  
B. Lesaffre ◽  
J.-M. Panel ◽  
D. Poncet ◽  
...  
Keyword(s):  
Snow Depth ◽  
Snow Water Equivalent ◽  
Meteorological Data ◽  
Model Performance ◽  
Short Wave ◽  
Wave Radiation ◽  
Density Profiles ◽  
The Public ◽  
Snow Water ◽  
Snowpack Properties

Abstract. A quality-controlled snow and meteorological dataset spanning the period 1 August 1993–31 July 2011 is presented, originating from the experimental station Col de Porte (1325 m altitude, Chartreuse range, France). Emphasis is placed on meteorological data relevant to the observation and modelling of the seasonal snowpack. In-situ driving data, at the hourly resolution, consist of measurements of air temperature, relative humidity, windspeed, incoming short-wave and long-wave radiation, precipitation rate partitioned between snow- and rainfall, with a focus on the snow-dominated season. Meteorological data for the three summer months (generally from 10 June to 20 September), when the continuity of the field record is not warranted, are taken from a local meteorological reanalysis (SAFRAN), in order to provide a continuous and consistent gap-free record. Data relevant to snowpack properties are provided at the daily (snow depth, snow water equivalent, runoff and albedo) and hourly (snow depth, albedo, runoff, surface temperature, soil temperature) time resolution. Internal snowpack information is provided from weekly manual snowpit observations (mostly consisting in penetration resistance, snow type, snow temperature and density profiles) and from a hourly record of temperature and height of vertically free ''settling'' disks. This dataset has been partially used in the past to assist in developing snowpack models and is presented here comprehensively for the purpose of multi-year model performance assessment. The data is placed on the PANGAEA repository (http://dx.doi.org/10.1594/PANGAEA.774249) as well as on the public ftp server ftp://ftp-cnrm.meteo.fr/pub-cencdp/.

scholarly journals Spatial and temporal characteristics in streamflow-related hydroclimatic variables over western Canada. Part 2: future projections

2016 ◽  
Vol 48 (4) ◽  
pp. 932-944 ◽  
Author(s):  
H. C. L. O'Neil ◽  
T. D. Prowse ◽  
B. R. Bonsal ◽  
Y. B. Dibike
Keyword(s):  
Snow Depth ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Cold Season ◽  
Maximum Temperature ◽  
Western Canada ◽  
The North ◽  
Spring Freshet ◽  
Hydroclimatic Variables

Much of the freshwater in western Canada originates in the Rocky Mountains as snowpack. Temperature and precipitation patterns throughout the region control the amount of snow accumulated and stored throughout the winter, and the intensity and timing of melt during the spring freshet. Therefore, changes in temperature, precipitation, snow depth, and snowmelt over western Canada are examined through comparison of output from the current and future periods of a series of regional climate models for the time periods 1971–2000 and 2041–2070. Temporal and spatial analyses of these hydroclimatic variables indicate that minimum temperature is likely to increase more than maximum temperature, particularly during the cold season, possibly contributing to earlier spring melt. Precipitation is projected to increase, particularly in the north. In the coldest months of the year snow depth is expected to increase in northern areas and decrease across the rest of study area. Snowmelt results indicate increases in mid-winter melt events and an earlier onset of the spring freshet. This study provides a summary of potential future climate using key hydroclimatic variables across western Canada with regard to the effects these changes may have on streamflow and the spring freshet, and thus water resources, throughout the study area.

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