Snow Depth and Snow Density Measurements From Spacebased Lidar Measurements
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Yongxiang Hu↗
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Snow Depth and Snow Density Measurements From Spacebased LIDAR Measurements
Explore the source record for details and available documents.
Yongxiang Hu↗
Antarctic Sea Ice Thickness and Snow-to-Ice Conversion from Atmospheric Reanalysis and Passive Microwave Snow Depth
Passive microwave snow depth, ice concentration, and ice motion estimates are combined with snowfall from the European Centre for Medium Range Weather Forecasting (ECMWF) reanalysis (ERA-40) from 1979-200 1 to estimate the prevalence of snow-to-ice conversion (snow-ice formation) on level sea ice in the Antarctic for April-October. Snow ice is ubiquitous in all regions throughout the growth season. Calculated snow- ice thicknesses fall within the range of estimates from ice core analysis for most regions. However, uncertainties in both this analysis and in situ data limit the usefulness of snow depth and snow-ice production to evaluate the accuracy of ERA-40 snowfall. The East Antarctic is an exception, where calculated snow-ice production exceeds observed ice thickness over wide areas, suggesting that ERA-40 precipitation is too high there. Snow-ice thickness variability is strongly controlled not just by snow accumulation rates, but also by ice divergence. Surprisingly, snow-ice production is largely independent of snow depth, indicating that the latter may be a poor indicator of total snow accumulation. Using the presence of snow-ice formation as a proxy indicator for near-zero freeboard, we examine the possibility of estimating level ice thickness from satellite snow depths. A best estimate for the mean level ice thickness in September is 53 cm, comparing well with 51 cm from ship-based observations. The error is estimated to be 10-20 cm, which is similar to the observed interannual and regional variability. Nevertheless, this is comparable to expected errors for ice thickness determined by satellite altimeters. Improvement in satellite snow depth retrievals would benefit both of these methods.
Markus, Thorsten↗
Sensitivity of Passive Microwave Snow Depth Retrievals to Weather Effects and Snow Evolution
Snow fall and snow accumulation are key climate parameters due to the snow's high albedo, its thermal insulation, and its importance to the global water cycle. Satellite passive microwave radiometers currently provide the only means for the retrieval of snow depth and/or snow water equivalent (SWE) over land as well as over sea ice from space. All algorithms make use of the frequency-dependent amount of scattering of snow over a high-emissivity surface. Specifically, the difference between 37- and 19-GHz brightness temperatures is used to determine the depth of the snow or the SWE. With the availability of the Advanced Microwave Scanning Radiometer (AMSR-E) on the National Aeronautics and Space Administration's Earth Observing System Aqua satellite (launched in May 2002), a wider range of frequencies can be utilized. In this study we investigate, using model simulations, how snow depth retrievals are affected by the evolution of the physical properties of the snow (mainly grain size growth and densification), how they are affected by variations in atmospheric conditions and, finally, how the additional channels may help to reduce errors in passive microwave snow retrievals. The sensitivity of snow depth retrievals to atmospheric water vapor is confirmed through the comparison with precipitable water retrievals from the National Oceanic and Atmospheric Administration's Advanced Microwave Sounding Unit (AMSU-B). The results suggest that a combination of the 10-, 19-, 37-, and 89-GHz channels may significantly improve retrieval accuracy. Additionally, the development of a multisensor algorithm utilizing AMSR-E and AMSU-B data may help to obtain weather-corrected snow retrievals.
Markus, Thorsten↗
Airborne Surveys of Snow Depth over Arctic Sea Ice
During the spring of 2009, an ultrawideband microwave radar was deployed as part of Operation IceBridge to provide the first cross-basin surveys of snow thickness over Arctic sea ice. In this paper, we analyze data from three approx 2000 km transects to examine detection issues, the limitations of the current instrument, and the regional variability of the retrieved snow depth. Snow depth is the vertical distance between the air \snow and snow-ice interfaces detected in the radar echograms. Under ideal conditions, the per echogram uncertainty in snow depth retrieval is approx 4 - 5 cm. The finite range resolution of the radar (approx 5 cm) and the relative amplitude of backscatter from the two interfaces limit the direct retrieval of snow depths much below approx 8 cm. Well-defined interfaces are observed over only relatively smooth surfaces within the radar footprint of approx 6.5 m. Sampling is thus restricted to undeformed, level ice. In early April, mean snow depths are 28.5 +/- 16.6 cm and 41.0 +/- 22.2 cm over first-year and multiyear sea ice (MYI), respectively. Regionally, snow thickness is thinner and quite uniform over the large expanse of seasonal ice in the Beaufort Sea, and gets progressively thicker toward the MYI cover north of Ellesmere Island, Greenland, and the Fram Strait. Snow depth over MYI is comparable to that reported in the climatology by Warren et al. Ongoing improvements to the radar system and the utility of these snow depth measurements are discussed.
Kwok, R.↗
Analysis of Gauge-Measured and Passive Microwave Derived Snow Depth Variations of Snowfields
Remote sensing of snow depth has been used to infer snow depth for many years. Passive microwave remote sensing of snow depth is compared with the snow gauge data.
Chang, A. T. C.↗
Snowex 2017 Community Snow Depth Measurements: A Quality-Controlled, Georeferenced Product
Snow depth was one of the core ground measurements required to validate remotely-sensed data collected during SnowEx Year 1, which occurred in Colorado. The use of a single, common protocol was fundamental to produce a community reference dataset of high quality. Most of the nearly 100 Grand Mesa and Senator Beck Basin SnowEx ground crew participants contributed to this crucial dataset during 6-25 February 2017. Snow depths were measured along ~300 m transects, whose locations were determined according to a random-stratified approach using snowfall and tree-density gradients. Two-person teams used snowmobiles, skis, or snowshoes to travel to staked transect locations and to conduct measurements. Depths were measured with a 1-cm incremented probe every 3 meters along transects. In shallow areas of Grand Mesa, depth measurements were also collected with GPS snow-depth probes (a.k.a. MagnaProbes) at ~1-m intervals. During summer 2017, all reference stake positions were surveyed with <10 cm accuracy to improve overall snow depth location accuracy. During the campaign, 193 transects were measured over three weeks at Grand Mesa and 40 were collected over two weeks in Senator Beck Basin, representing more than 27,000 depth values. Each day of the campaign depth measurements were written in waterproof field books and photographed by National Snow and Ice Data Center (NSIDC) participants. The data were later transcribed and prepared for extensive quality assessment and control. Common issues such as protocol errors (e.g., survey in reverse direction), notebook image issues (e.g., halo in the center of digitized picture), and data-entry errors (sloppy writing and transcription errors) were identified and fixed on a point-by-point basis. In addition, we strove to produce a georeferenced product of fine quality, so we calculated and interpolated coordinates for every depth measurement based on surveyed stakes and the number of measurements made per transect. The product has been submitted to NSIDC in csv format. To educate data users, we present the study design and processing steps that have improved the quality and usability of this product. Also, we will address measurement and design uncertainties, which are different in open vs. forest areas.
Brucker, L.↗
Estimation of Sea Ice Thickness Distributions through the Combination of Snow Depth and Satellite Laser Altimetry Data
Combinations of sea ice freeboard and snow depth measurements from satellite data have the potential to provide a means to derive global sea ice thickness values. However, large differences in spatial coverage and resolution between the measurements lead to uncertainties when combining the data. High resolution airborne laser altimeter retrievals of snow-ice freeboard and passive microwave retrievals of snow depth taken in March 2006 provide insight into the spatial variability of these quantities as well as optimal methods for combining high resolution satellite altimeter measurements with low resolution snow depth data. The aircraft measurements show a relationship between freeboard and snow depth for thin ice allowing the development of a method for estimating sea ice thickness from satellite laser altimetry data at their full spatial resolution. This method is used to estimate snow and ice thicknesses for the Arctic basin through the combination of freeboard data from ICESat, snow depth data over first-year ice from AMSR-E, and snow depth over multiyear ice from climatological data. Due to the non-linear dependence of heat flux on ice thickness, the impact on heat flux calculations when maintaining the full resolution of the ICESat data for ice thickness estimates is explored for typical winter conditions. Calculations of the basin-wide mean heat flux and ice growth rate using snow and ice thickness values at the 70 m spatial resolution of ICESat are found to be approximately one-third higher than those calculated from 25 km mean ice thickness values.
Kurtz, Nathan T.↗
A Comparison of Snow Depth on Sea Ice Retrievals Using Airborne Altimeters and an AMSR-E Simulator
A comparison of snow depths on sea ice was made using airborne altimeters and an Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) simulator. The data were collected during the March 2006 National Aeronautics and Space Administration (NASA) Arctic field campaign utilizing the NASA P-3B aircraft. The campaign consisted of an initial series of coordinated surface and aircraft measurements over Elson Lagoon, Alaska and adjacent seas followed by a series of large-scale (100 km ? 50 km) coordinated aircraft and AMSR-E snow depth measurements over portions of the Chukchi and Beaufort seas. This paper focuses on the latter part of the campaign. The P-3B aircraft carried the University of Colorado Polarimetric Scanning Radiometer (PSR-A), the NASA Wallops Airborne Topographic Mapper (ATM) lidar altimeter, and the University of Kansas Delay-Doppler (D2P) radar altimeter. The PSR-A was used as an AMSR-E simulator, whereas the ATM and D2P altimeters were used in combination to provide an independent estimate of snow depth. Results of a comparison between the altimeter-derived snow depths and the equivalent AMSR-E snow depths using PSR-A brightness temperatures calibrated relative to AMSR-E are presented. Data collected over a frozen coastal polynya were used to intercalibrate the ATM and D2P altimeters before estimating an altimeter snow depth. Results show that the mean difference between the PSR and altimeter snow depths is -2.4 cm (PSR minus altimeter) with a standard deviation of 7.7 cm. The RMS difference is 8.0 cm. The overall correlation between the two snow depth data sets is 0.59.
Cavalieri, D. J.↗
Snow Depth from AMSR-2 Using Multispectral Satellite Data in an Artificial Neural Network
By using diffusion theory and Monte Carlo lidar radiative transfer simulations, Hu et al. (2022b) has derived snow depth from the first-, second- and third-order moments of the lidar backscattering pathlength distribution. Lu et al. (2022) calculated the snow depth by applying the methods to the satellite ICESat-2 lidar measurements over the Arctic sea ice, as well as land surfaces of Northern Hemisphere. In this paper, an artificial neural network (ANN) algorithm, employing several channels from Advanced Microwave Scanning Radiometer 2 (AMSR-2) and the humidity vertical profiles from Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System for Instrument Teams (GEOS-IT) product, is trained to determine snow depth identified by time and geolocation matched 2019 ICESat-2 snow-depth data during winter months over the Arctic sea ice. The trained ANN snow-depth was applied to 2018 AMSR-2 clear pixel data, although the algorithms perform reasonably well in thinner clouds. The validation data (different from the training set) of ANN snow depth from AMSR-2 showed a good agreement with time matched and co-located snow-depth values from ICESat-2. The bias was near zero, with mean absolute error (MAE) 0.05 cm and a root-mean-square-error (RMSE) 0.08 cm. Prior applying the trained ANN snow depth to AMSR-2 data, a cloud screening algorithm was developed with a similar approach. A separate ANN cloud mask was trained to determine an AMSR-2 pixel is clear or cloudy with time and geolocation matched 2015 CALIOP Vertical Feature Mask (VFM) over Arctic sea ice. The ANN cloud mask from AMSR-2 under-estimated cloud fraction by 3-6% compared to CALIOP . The additional research is needed to conclusively evaluate the ANN cloud mask accuracy. Finally, this paper will lay the foundation for a sustained long-term snowfall and snow-storm monitoring system. The future Cloud Aerosol LIdar for Global scale Observations of the ocean-Land Atmosphere system (CALIGOLA) mission will provide a means to calculate snow depth from the lidar backscattering pathlength distribution, benefiting from the UV, visible and infrared pulses. With the calculated snow depth as the truth one could develop a machine learning algorithm, as it was done in this paper, using a passive microwave instrument available at that time to generate a wide range of snow depth data, covering extensive spatial areas in the cross-orbit direction.
Neural Network↗
Feasibility of Estimating Snow Depth in Complex Terrain Using Satellite Lidar Altimetry
Satellite retrievals of snow depth and water equivalent (SWE) are critical for monitoring watershed scale processes around the world. However, the problem is especially challenging in mountainous regions where complex heterogeneities limit the utility of low resolution satellite sensors. The Geoscience Laser Altimeter Sensor (GLAS) aboard the Ice, Cloud, and land Elevation Satellite (ICESat) collected surface elevation data along near-repeat reference transects over land areas from 2003-2009. Although intended for monitoring ice caps and sea ice, the seven year global GLAS data base has provided unprecedented opportunity to test the capability of satellite lidar technology for estimating snow depth over land. GLAS single track and low repeat frequency does not provide data sufficient for operational estimates. However, its comparatively small footprint size of -65 m and its database of seasonal repeat observations during both snow and no-snow conditions have been sufficient to evaluate the potential of spacebased lidar altimetry for estimating snow depth. Recent analysis of ICESat elevations in the Uinta Mountains in NE Utah provide encouraging results for watershed scale estimates of snow depth. Research reported here focuses on the sensitivity of several versions of an ICESat snow depth algorithm to a range of landscape types defined by vegetation cover, slope and roughness. Results are compared to available SNOTEL data.
Jasinski, Michael F.↗
Inter-comparison of snow depth over Arctic sea ice from reanalysis reconstructions and satellite retrieval
In this study, we compare eight recently developed snow depth products over Arctic sea ice, which use satellite observations, modeling, or a combination of satellite and modeling approaches. These products are further compared against various ground-truth observations, including those from ice mass balance observations and airborne measurements. Large mean snow depth discrepancies are observed over the Atlantic and Canadian Arctic sectors. The differences between climatology and the snow products early in winter could be in part a result of the delaying in Arctic ice formation that reduces early snow accumulation, leading to shallower snowpacks at the start of the freeze-up season. These differences persist through spring despite overall more winter snow accumulation in the reanalysis-based products than in the climatologies. Among the products evaluated, the University of Washington (UW) snow depth product produces the deepest spring (March–April) snowpacks, while the snow product from the Danish Meteorological Institute (DMI) provides the shallowest spring snow depths. Most snow products show significant correlation with snow depths retrieved from Operational IceBridge (OIB) while correlations are quite low against buoy measurements, with no correlation and very low variability from University of Bremen and DMI products. Inconsistencies in reconstructed snow depth among the products, as well as differences between these products and in situ and airborne observations, can be partially attributed to differences in effective footprint and spatial–temporal coverage, as well as insufficient observations for validation/bias adjustments. Our results highlight the need for more targeted Arctic surveys over different spatial and temporal scales to allow for a more systematic comparison and fusion of airborne, in situ and remote sensing observations.
Lu Zhou↗
Arctic Snow Depth and Sea Ice Thickness From ICESat‐2 and CryoSat‐2 Freeboards: A First Examination
We present a first examination of Arctic sea ice snow depth estimates from differencing satellite lidar (ICESat‐2) and radar (CryoSat‐2) freeboards. These estimates cover the period between 14 October 2018 and the end of April 2019. Snow depth is related to freeboard differences by the refractive index/bulk density of the snow layer—the only free parameter in the approach. Area‐averaged snow depth ranges from 9 cm (on first‐year ice: 5 cm, multiyear ice: 14 cm) in late October to 19 cm (first‐year ice: 17 cm, multiyear ice: 27 cm) in April; on average, this snow is thinner over FYI. Spatial patterns and gradients of snow depth estimates compare well with reconstructions using snowfall from ERA‐Interim and ERA5, although snowfall from ERA5 is systematically higher. For all months, the results suggest that ~50% of the total freeboard is comprised of snow. Retrievals are within a few centimeters of snow depth data acquired by Operation IceBridge in April 2019. Sources of uncertainties associated with this freeboard‐differencing approach are discussed. Further, sea ice thicknesses calculated using the retrieved snow depth and a modified climatology are contrasted. Comparatively, the snow depth and calculated ice thickness using a modified climatology are higher by ~5 cm and 0.33 m, although these differences are not uniform throughout the season. Snow accumulation was slower between October and December but increased between December and January, unlike the modified climatology, which exhibited a monotonic accumulation for all months. Future opportunities for assessment and improvement of these estimates are discussed.
Arctic Ocean↗
Snow Depth Variability in the Northern Hemisphere Mountains Observed from Space
Accurate snow depth observations are critical to assess water resources. More than a billion people rely on water from snow, most of which originates in the Northern Hemisphere mountain ranges. Yet, remote sensing observations of mountain snow depth are still lacking at the large scale. Here, we show the ability of Sentinel-1 to map the snow depth in the Northern Hemisphere mountains at 1 km² resolution using an empirical change detection approach. An evaluation with measurements from ~4,000 sites and reanalysis data demonstrates that the Sentinel-1 retrievals capture the spatial variability between and within mountain ranges, as well as their inter-annual differences. This is showcased with the contrasting snow depths between 2017 and 2018 in the US Sierra Nevada and European Alps. With Sentinel-1 continuity ensured until 2030 and likely beyond, these findings lay a foundation for quantifying the long-term vulnerability of mountain snow-water resources to climate change.
Hans Lievens↗
Potential Basin-Scale Estimates of Arctic Snow Depth with Sea Ice Freeboards from CryoSat-2 and ICESat-2: An Exploratory Analysis
The potential of deriving snow depth estimates using differences in freeboard heights from CryoSat-2 (CS-2) and ICESat-2 (IS-2) is examined. In our analysis, we use lidar freeboard from the Airborne Topographic Mapper (ATM) on Operation IceBridge (OIB) as proxy of IS-2 total (snow plus ice) freeboard. Snow depths are estimates from the OIB snow radar. Differences in height between the total (ATM) and ice (CS-2) freeboards are related to snow depth by the refractive index of the snow layer, which is dependent on snow density. For two years (2014 and 2015), regression of the ATM and CS-2 freeboard differences against OIB snow depth gives correlations of approximately 0.80, estimated eta (sub s) of 1.21, and standard errors of 8 centimeters. The resulting refractive index, eta (sub s), can be compared to that expected of the Arctic snow cover in early spring (1.25 plus or minus 0.05). The expected biases and variability in the regression analysis are discussed. Results suggest that snow depth can be estimated from the freeboard differences. The benefits of adjusting the orbit of CS-2 for providing more optimized overlaps between IS-2 and CS-2 are considered.
Sea ice↗
Large-Scale Surveys of Snow Depth on Arctic Sea Ice from Operation IceBridge
We show the first results of a large ]scale survey of snow depth on Arctic sea ice from NASA fs Operation IceBridge snow radar system for the 2009 season and compare the data to climatological snow depth values established over the 1954.1991 time period. For multiyear ice, the mean radar derived snow depth is 33.1 cm and the corresponding mean climatological snow depth is 33.4 cm. The small mean difference suggests consistency between contemporary estimates of snow depth with the historical climatology for the multiyear ice region of the Arctic. A 16.5 cm mean difference (climatology minus radar) is observed for first year ice areas suggesting that the increasingly seasonal sea ice cover of the Arctic Ocean has led to an overall loss of snow as the region has transitioned away from a dominantly multiyear ice cover.
Kurtz, Nathan T.↗
AMSR-2 Daily Snow Depth Data Product Using a Neural Network Algorithm Trained by Collocated ICESat-2 Measurements
By using diffusion theory and Monte Carlo lidar radiative transfer simulations, Hu 1 et al. (2022b) has derived snow depth from the first-, second- and third-order moments of the lidar backscattering pathlength distribution. Lu 2 et al. (2022,2024) calculated the snow depth by applying the methods to the satellite ICESat-2 lidar measurements over the Arctic sea ice, as well as land surfaces of Northern Hemisphere. In this paper, a neural network (NN) algorithm, employing several channels from AMSR-2 and the humidity vertical profiles GMAO GEOS-IT, is trained to determine snow depth identified by time and geolocation matched 2019 ICESat-2 snow-depth data during winter months over the Arctic sea ice. The trained NN snow-depth was applied to 2014-2020 AMSR-2 clear pixel data, although the algorithms perform reasonably well in thinner clouds. This paper used AMSR-2 data, a passive microwave instrument to generate a wide range of snow depth data, covering extensive spatial areas in the cross-orbit direction.
Neural Network↗
Arctic Snow Depth and Sea Ice Thickness From ICESat‐2 and CryoSat‐2 Freeboards: A First Examination
The snow layer atop Arctic sea ice is an important component of the climate system. In winter, the insulating effects of snow slow the rate of ice growth. In spring, the onset of ice surface melt is delayed until the highly reflective snow layer disappears. During summer, meltwater from snow collects in depressions to form melt ponds, which enhances the absorption of solar radiation leading to more rapid surface warming. Presently, there are no routine measurements of snow depth suitable for assessing the impact of climate changes on the precipitation and accumulation of snow on sea ice.Our current understanding of snow depth is limited and has been based on field measurements conducted in the middle of last century and from airborne surveys conducted over the last decade. In this paper, we combine the measurements from two altimetry missions (ICESat‐2 and CryoSat‐2) to calculate snow depth over the entire Arctic Ocean. ICESat‐2 (a lidar) and CryoSat‐2 (a radar) measure the heights to the top and bottom of the snow layer, respectively. Differencing the two heights provides an estimate of the thickness of the snow layer. This paper describes this novel approach and an assessment of the snow depth estimates.
R. Kwok↗