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 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 A Methodology for Fitting the Time Series of Snow Depth on the Arctic Sea Ice

2019 ◽  
Vol 36 (8) ◽  
pp. 1449-1462
Author(s):  
Yuzhe Wang ◽  
Haidong Pan ◽  
Daosheng Wang ◽  
Xianqing Lv
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Sea Ice ◽  
Snow Depth ◽  
Spline Interpolation ◽  
Optimal Number ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Earth Observing System ◽  
Arctic Sea

AbstractSnow depth is an important geophysical variable for investigating sea ice and climate change, which can be obtained from satellite data. However, there is a large number of missing data in satellite observations of snow depth. In this study, a methodology, the periodic functions fitting with varying parameter (PFF-VP), is presented to fit the time series of snow depth on Arctic sea ice obtained from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). The time-varying parameters are obtained by the independent point (IP) scheme and cubic spline interpolation. The PPF-VP is validated by experiments in which part of the observations are artificially removed and used to compare with the fitting results. Results indicate that the PPF-VP performs better than three traditional fitting methods, with its fitting results closer to observations and with smaller errors. In the practical experiments, the optimal number of IPs can be determined by only considering the fraction of missing data, particularly the length of the longest gaps in the snow-depth time series. All the experimental results indicate that the PPF-VP is a feasible and effective method to fit the time series of snow depth and can provide continuous data of snow depth for further study.

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
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