scholarly journals Evaluation of Remote Sensing and Reanalysis Snow Depth Datasets over the Northern Hemisphere during 1980–2016

2020 ◽  
Vol 12 (19) ◽  
pp. 3253
Author(s):  
Lin Xiao ◽  
Tao Che ◽  
Liyun Dai
Keyword(s):  
Remote Sensing ◽  
Snow Depth ◽  
Snow Accumulation ◽  
Climate Change Projections ◽  
Mountain Areas ◽  
Earth Observing System ◽  
The Earth ◽  
Snow Condition ◽  
Modern Era ◽  
Key Parameter

Snow cover is a key parameter of the climate system and its significant seasonal and annual variability have significant impacts on the surface energy balance and global water circulation. However, current snow depth datasets show large inconsistencies and uncertainties, which limit their applications in climate change projections and hydrological processes simulations. In this study, a comprehensive assessment of five hemispheric snow depth datasets was carried out against ground observations from 43,391 stations. The five snow depth datasets included three remote sensing datasets, i.e., Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), Advanced Microwave Scanning Radiometer-2 (AMSR2), Global Snow Monitoring for Climate Research (GlobSnow), and two reanalysis datasets, i.e., ERA-Interim and the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). Assessment results imply that the spatial distribution of GlobSnow and ERA-Interim exhibit overall better agreements with ground observations than other datasets. GlobSnow and ERA-Interim exhibit less uncertainty during the snow accumulation and ablation periods, respectively. In plain and forested regions, GlobSnow, ERA-Interim and MERRA-2 show better performances, while in mountain and forested mountain areas, GlobSnow exhibits the best performance. AMSR-E and AMSR2 agree better with ground observations in shallow snow condition (0–10 cm), while MERRA-2 shows more satisfying performance when snow depth exceeds 50 cm. These systematic and integrated understanding of the five representative snow depth datasets provides information on data selection and data refinement, as well as data fusion, which is our next work of interest.

A multiangle imaging spectroradiometer for terrestrial remote sensing from the earth observing system

1991 ◽  
Vol 3 (2) ◽  
pp. 92-107 ◽  
Author(s):  
David J. Diner ◽  
Carol J. Bruegge ◽  
John V. Martonchik ◽  
Graham W. Bothwell ◽  
Eric D. Danielson ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Earth Observing System ◽  
The Earth ◽  
Multiangle Imaging ◽  
Multiangle Imaging Spectroradiometer

scholarly journals Spatial and temporal variability of snow accumulation for the South-Western Greenland Ice Sheet

2019 ◽  
Author(s):  
Achim Heilig ◽  
Olaf Eisen ◽  
Martin Schneebeli ◽  
Michael MacFerrin ◽  
C. Max Stevens ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Depth ◽  
Regional Scale ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Temporal Changes ◽  
Snow Accumulation ◽  
Small Scale ◽  
Spatial And Temporal Variability ◽  
Accumulation Pattern

Abstract. The Greenland ice sheet (GrIS) has experienced significant changes in recent decades. Data confirming those changes are derived from remote sensing, regional climate models (RCMs), firn cores and automatic weather stations (AWSs) on the ice sheet. Data sources comprise different extents in area coverage. While remote sensing and RCMs cover at least regional scales with an extent ranging from 1–10 km, AWS data and firn cores are point observations. To link such regional scales with point measurements, we investigate the spatial variability of snow accumulation within areas of approximately 1–4 km2 and its temporal changes. At three different sites of the southwestern GrIS (Swiss Camp, KAN-U, Dye-2), we performed extensive ground-penetrating radar (GPR) transects and numerous snow pits. In dry snow conditions, radar-measured two-way travel time can be converted to snow depth and snow accumulation if the density is known. Density variations per site for snow pits within distances of up to 1 km are found to be consistently within ±5 %. GPR transects were further filtered to remove small scale surface-related noise. The combined uncertainty of density variations and spatial filtering of radar transects is at 7–8 % per regional scale. To link point observations with regional scales, we analyze for spatial representativeness of snow pits. It occurs that with a probability of p = 0.8 (KAN-U) to p > 0.95 (Swiss Camp and Dye-2), randomly selected snow pits are representative in snow accumulation for entire regions with an offset of ±10 % from arithmetic means. However, to achieve such high representativeness of snow pits, it is required to average snow depth for an area of at least 20 m x 20 m. Interannual accumulation pattern at Dye-2 are very persistent for two subsequent accumulation seasons with similarity probabilities of p > 0.95, if again an error of ±10 % is included. Using target reflectors placed at respective end-of-summer-melt horizons, we additionally analyzed for occurrences of lateral redistribution within one melt season. In this study, we show that at Dye-2 lateral flow of meltwater cannot be evidenced in the current climate. Such studies of spatial representativeness and temporal changes in accumulation are inevitable to assess reliability of the linkage between point measurements and regional scale data and predictions, which are used for validation and calibration of remote sensing data and RCM outputs.

scholarly journals Snow depth monitoring with GNSS reflectometry: Results from Antarctica and selected geodetic ground stations

2021 ◽  
Author(s):  
Tzvetan Simeonov ◽  
Markus Ramatschi ◽  
Sibylle Vey ◽  
Jens Wickert
Keyword(s):  
Solar Radiation ◽  
Snow Depth ◽  
Snow Accumulation ◽  
Phase Changes ◽  
New Technique ◽  
Depth Measurement ◽  
Ice Caps ◽  
Single Antenna ◽  
The Earth ◽  
Gnss Reflectometry

<p>The permanent and seasonal snow covers are an important element of the global hydrological cycle and have substantial influence on global climate. Currently around 10% of the Earth’s land surface is covered by glaciers, ice caps and snow cover. Snow and ice cover play important role in the Earth’s climate by reflecting solar radiation and thus decreasing the average Earth temperature. Glaciers and ice caps participate in a positive feedback loop in the Earth’s climate. By contracting due to increasing temperatures, they reflect less solar radiation, further contributing to the global temperatures increase.</p><p>Using the single antenna ground-based GNSS Reflectometry (GNSS-R) method for snow depth estimation is an emerging application. A new technique for snow depth measurement using the phase changes in the observed SNR data, rather than the height estimates, is validated in a GNSS-R setup in Antarctic station Neumayer III. The new technique shows improved characteristics to the classical single antenna ground-based GNSS-R snow depth determination method. The validation is done in an environment of constant snow accumulation. The results from new technique show high correlation of the de-trended datasets between the GNSS-R and in-situ snow buoy measurements of 0.85. The de-trended classical height estimations of the SNR show lower correlation to the snow buoys of 0.60.</p><p>A screening of the International GNSS Service (IGS) global network shows, that snow depth observations are possible in only 7 of the 506 available stations. The main limitations on the stations are the local topography and climate. The snow depth observations from these seven stations are compared with the ERA5 snow depth estimations, local measurements and climate normals. The analysis of the data for station Visby, following the new GNSS-R analysis technique, shows very high correlation of 0.91 and low RMSE of 2.26cm, while the classical GNSS-R estimation has RMSE of 2.48cm and ERA5 shows RMSE of 4.2cm when compared to local meteorological observations.</p>

scholarly journals Relating regional and point measurements of accumulation in southwest Greenland

2020 ◽  
Vol 14 (1) ◽  
pp. 385-402
Author(s):  
Achim Heilig ◽  
Olaf Eisen ◽  
Martin Schneebeli ◽  
Michael MacFerrin ◽  
C. Max Stevens ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Snow Depth ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Scale ◽  
Greenland Ice Sheet ◽  
Temporal Changes ◽  
Snow Accumulation ◽  
Small Scale ◽  
Surface Mass

Abstract. In recent decades, the Greenland ice sheet (GrIS) has frequently experienced record melt events, which have significantly affected surface mass balance (SMB) and estimates thereof. SMB data are derived from remote sensing, regional climate models (RCMs), firn cores and automatic weather stations (AWSs). While remote sensing and RCMs cover regional scales with extents ranging from 1 to 10 km, AWS data and firn cores are point observations. To link regional scales with point measurements, we investigate the spatial variability of snow accumulation (bs) within areas of approximately 1–4 km2 and its temporal changes within 2 years of measurements. At three different sites on the southwestern GrIS (Swiss Camp, KAN-U, DYE-2), we performed extensive ground-penetrating radar (GPR) transects and recorded multiple snow pits. If the density is known and the snowpack dry, radar-measured two-way travel time can be converted to snow depth and bs. We spatially filtered GPR transect data to remove small-scale noise related to surface characteristics. The combined uncertainty of bs from density variations and spatial filtering of radar transects is at 7 %–8 % per regional scale of 1–4 km2. Snow accumulation from a randomly selected snow pit is very likely representative of the regional scale of 1–4 km2 (with probability p=0.8 for a value within 10 % of the regional mean for KAN-U, and p>0.95 for Swiss Camp and DYE-2). However, to achieve such high representativeness of snow pits, it is required to determine the average snow depth within the vicinity of the pits. At DYE-2, the spatial pattern of snow accumulation was very similar for 2 consecutive years. Using target reflectors placed at respective end-of-summer-melt horizons, we additionally investigated the occurrences of lateral redistribution within one melt season. We found no evidence of lateral flow of meltwater in the current climate at DYE-2. Such studies of spatial representativeness and temporal changes in accumulation are necessary to assess uncertainties of the linkages of point measurements and regional-scale data, which are used for validation and calibration of remote-sensing data and RCM outputs.

Forecast of manifestations of dangerous geological processes in Dnipropetrovsk with the use of methods of aerospace remote sensing of the Earth

2004 ◽  
Vol 10 (5-6) ◽  
pp. 194-196
Author(s):  
V.I. Voloshin ◽  
◽  
A.S. Levenko ◽  
N.N. Peremetchik ◽  
◽  
...  
Keyword(s):  
Remote Sensing ◽  
Geological Processes ◽  
The Earth

Algorithm for planning of realization of thematic programs of the Earth remote sensing

2007 ◽  
Vol 13 (2) ◽  
pp. 39-42
Author(s):  
A.I. Kirillov ◽  
◽  
Ye.I. Kapustin ◽  
N.A. Kirillova ◽  
E.I. Makhonin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Earth Remote Sensing ◽  
The Earth
Sign in / Sign up

Export Citation Format

Share Document