scholarly journals Inter-comparison of snow depth over Arctic sea ice from reanalysis reconstructions and satellite retrieval

2021 ◽  
Vol 15 (1) ◽  
pp. 345-367
Author(s):  
Lu Zhou ◽  
Julienne Stroeve ◽  
Shiming Xu ◽  
Alek Petty ◽  
Rachel Tilling ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Ground Truth ◽  
Snow Accumulation ◽  
Arctic Sea Ice ◽  
Spatial And Temporal Scales ◽  
Airborne Measurements ◽  
Arctic Sea ◽  
Danish Meteorological Institute

Abstract. 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.

scholarly journals Inter-comparison of snow depth over sea ice from multiple methods

2020 ◽  
Author(s):  
Lu Zhou ◽  
Julienne Stroeve ◽  
Shiming Xu ◽  
Alek Petty ◽  
Rachel Tilling ◽  
...  
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Ground Truth ◽  
Significant Trend ◽  
Data Sets ◽  
Snow Density ◽  
Spatial And Temporal Scales ◽  
Danish Meteorological Institute ◽  
The University

Abstract. In this study, we compare eight recently developed snow depth products that 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 buoys (IMBs), snow buoys, snow depth derived from NASA's Operation IceBridge (OIB) flights, as well as snow depth climatology from historical observations. Large snow depth discrepancies between the different snow depth data sets are observed over the Atlantic and Canadian Arctic sectors. Among the products evaluated, the University of Washington snow depth product (UW) produces the overall deepest spring (March-April) snow packs, while the snow product from the Danish Meteorological Institute (DMI) provide the shallowest spring snow depths. There is no significant trend in the mean snow depth among all snow products since the 2000s, despite the great differences in regional snow depth. Two products, SnowModel-LG and the NASA Eulerian Snow on Sea Ice Model (NESOSIM), also provide estimates of snow density. Arctic-wide, these density products show the expected seasonal evolution with varying inter-annual variability, and no significant trend since the 2000s. The snow density in SnowModel-LG is generally higher than climatology, whereas NESOSIM density is generally lower. Both SnowModel-LG and NESOSIM densities have a larger seasonal change than climatology. Inconsistencies in the reconstructed snow parameters among the products, as well as differences between in-situ and airborne observations can in part be 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.

Inter-comparison of snow depth over sea ice from multiple methods

2020 ◽  
Author(s):  
Lu Zhou ◽  
Julienne Stroeve ◽  
Shiming Xu
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Ground Truth ◽  
Significant Trend ◽  
Data Sets ◽  
Snow Density ◽  
Spatial And Temporal Scales ◽  
Danish Meteorological Institute ◽  
The University

<p>In this study, we compare eight recently developed snow depth products that 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 buoys (IMBs), snow buoys, snow depth derived from NASA's Operation IceBridge (OIB) flights, as well as snow depth climatology from historical observations.</p><p>Large snow depth differences between data sets are observed over the Atlantic and Canadian Arctic sectors. Among the products evaluated, the University of Washington snow depth product (UW) produces the overall deepest Spring snow packs, while the snow product from the Danish Meteorological Institute (DMI) provide the shallowest Spring snow depths. There is no significant trend for mean snow depth among all snow products since the 2000s, however, those in regional varies larhely. Two products, SnowModel-LG and the NASA Eulerian Snow on Sea Ice Model: NESOSIM, also provide estimates of snow density. Arctic-wide, these density products show the expected seasonal evolution with varying inter-annual variability, and no significant trend since the 2000s. Compared to climatology, snow density from SnowModel-LG is generally denser, whereas that from NESOSIM is less. Both SnowModel-LG and NESOSIM densities have a larger seasonal change than climatology.</p><p>Inconsistencies in the reconstructed snow parameters among the products, as well as differences and with in-situ and airborne observations can in part be 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.</p>

scholarly journals Snow depth on Arctic sea ice derived from radar: In situ comparisons and time series analysis

2015 ◽  
Vol 120 (6) ◽  
pp. 4260-4287 ◽  
Author(s):  
Benjamin Holt ◽  
Michael P. Johnson ◽  
Dragana Perkovic‐Martin ◽  
Ben Panzer
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
Series Analysis ◽  
Arctic Sea

scholarly journals Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

2019 ◽  
Vol 11 (23) ◽  
pp. 2864 ◽  
Author(s):  
Jiping Liu ◽  
Yuanyuan Zhang ◽  
Xiao Cheng ◽  
Yongyun Hu
Keyword(s):  
Neural Network ◽  
Sea Ice ◽  
Snow Depth ◽  
Deep Neural Network ◽  
Arctic Basin ◽  
Temporal Variations ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Thickness ◽  
Arctic Sea

The accurate knowledge of spatial and temporal variations of snow depth over sea ice in the Arctic basin is important for understanding the Arctic energy budget and retrieving sea ice thickness from satellite altimetry. In this study, we develop and validate a new method for retrieving snow depth over Arctic sea ice from brightness temperatures at different frequencies measured by passive microwave radiometers. We construct an ensemble-based deep neural network and use snow depth measured by sea ice mass balance buoys to train the network. First, the accuracy of the retrieved snow depth is validated with observations. The results show the derived snow depth is in good agreement with the observations, in terms of correlation, bias, root mean square error, and probability distribution. Our ensemble-based deep neural network can be used to extend the snow depth retrieval from first-year sea ice (FYI) to multi-year sea ice (MYI), as well as during the melting period. Second, the consistency and discrepancy of snow depth in the Arctic basin between our retrieval using the ensemble-based deep neural network and two other available retrievals using the empirical regression are examined. The results suggest that our snow depth retrieval outperforms these data sets.

Projected decline in spring snow depth on Arctic sea ice caused by progressively later autumn open ocean freeze-up this century

2012 ◽  
Vol 39 (17) ◽  
pp. n/a-n/a ◽  
Author(s):  
P. J. Hezel ◽  
X. Zhang ◽  
C. M. Bitz ◽  
B. P. Kelly ◽  
F. Massonnet
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Open Ocean ◽  
Arctic Sea Ice ◽  
Arctic Sea
Sign in / Sign up

Export Citation Format

Share Document