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 Topographic and vegetation effects on snow accumulation in the southern Sierra Nevada: a statistical summary from lidar data

2016 ◽  
Vol 10 (1) ◽  
pp. 257-269 ◽  
Author(s):  
Z. Zheng ◽  
P. B. Kirchner ◽  
R. C. Bales
Keyword(s):  
Sierra Nevada ◽  
Snow Depth ◽  
Point Cloud ◽  
Accumulation Point ◽  
Snow Accumulation ◽  
Slope Aspect ◽  
Conifer Forest ◽  
Vegetation Effects ◽  
Cloud Data ◽  
Depth Measurement

Abstract. Airborne light detection and ranging (lidar) measurements carried out in the southern Sierra Nevada in 2010 in the snow-free and peak-snow-accumulation periods were analyzed for topographic and vegetation effects on snow accumulation. Point-cloud data were processed from four primarily mixed-conifer forest sites covering the main snow-accumulation zone, with a total surveyed area of over 106 km2. The percentage of pixels with at least one snow-depth measurement was observed to increase from 65–90 to 99 % as the sampling resolution of the lidar point cloud was increased from 1 to 5 m. However, a coarser resolution risks undersampling the under-canopy snow relative to snow in open areas and was estimated to result in at least a 10 cm overestimate of snow depth over the main snow-accumulation region between 2000 and 3000 m, where 28 % of the area had no measurements. Analysis of the 1 m gridded data showed consistent patterns across the four sites, dominated by orographic effects on precipitation. Elevation explained 43 % of snow-depth variability, with slope, aspect and canopy penetration fraction explaining another 14 % over the elevation range of 1500–3300 m. The relative importance of the four variables varied with elevation and canopy cover, but all were statistically significant over the area studied. The difference between mean snow depth in open versus under-canopy areas increased with elevation in the rain–snow transition zone (1500–1800 m) and was about 35 ± 10 cm above 1800 m. Lidar has the potential to transform estimation of snow depth across mountain basins, and including local canopy effects is both feasible and important for accurate assessments.

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.

Prediction of the solar radiation on the Earth using support vector regression technique

2015 ◽  
Vol 68 ◽  
pp. 179-185 ◽  
Author(s):  
Jamshid Piri ◽  
Shahaboddin Shamshirband ◽  
Dalibor Petković ◽  
Chong Wen Tong ◽  
Muhammad Habib ur Rehman
Keyword(s):  
Solar Radiation ◽  
Support Vector Regression ◽  
Regression Technique ◽  
Support Vector ◽  
The Earth

scholarly journals Seasonal influence of snow conditions on Dall’s sheep productivity in Wrangell-St Elias National Park and Preserve

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0244787
Author(s):  
Christopher L. Cosgrove ◽  
Jeff Wells ◽  
Anne W. Nolin ◽  
Judy Putera ◽  
Laura R. Prugh
Keyword(s):  
Snow Depth ◽  
National Park ◽  
Meteorological Data ◽  
Multiple Regression Model ◽  
Snow Accumulation ◽  
Population Declines ◽  
Climate Conditions ◽  
Remote Cameras ◽  
Snow Conditions ◽  
Dall’S Sheep

Dall’s sheep (Ovis dalli dalli) are endemic to alpine areas of sub-Arctic and Arctic northwest America and are an ungulate species of high economic and cultural importance. Populations have historically experienced large fluctuations in size, and studies have linked population declines to decreased productivity as a consequence of late-spring snow cover. However, it is not known how the seasonality of snow accumulation and characteristics such as depth and density may affect Dall’s sheep productivity. We examined relationships between snow and climate conditions and summer lamb production in Wrangell-St Elias National Park and Preserve, Alaska over a 37-year study period. To produce covariates pertaining to the quality of the snowpack, a spatially-explicit snow evolution model was forced with meteorological data from a gridded climate re-analysis from 1980 to 2017 and calibrated with ground-based snow surveys and validated by snow depth data from remote cameras. The best calibrated model produced an RMSE of 0.08 m (bias 0.06 m) for snow depth compared to the remote camera data. Observed lamb-to-ewe ratios from 19 summers of survey data were regressed against seasonally aggregated modelled snow and climate properties from the preceding snow season. We found that a multiple regression model of fall snow depth and fall air temperature explained 41% of the variance in lamb-to-ewe ratios (R2 = .41, F(2,38) = 14.89, p<0.001), with decreased lamb production following deep snow conditions and colder fall temperatures. Our results suggest the early establishment and persistence of challenging snow conditions is more important than snow conditions immediately prior to and during lambing. These findings may help wildlife managers to better anticipate Dall’s sheep recruitment dynamics.

scholarly journals Direct Solar Radiation and Atmospheric Turbidity in Mikołajki in the Years 1971–1980 and 1991–2000

2009 ◽  
Vol 2 (1) ◽  
pp. 19-33
Author(s):  
Joanna Uscka-Kowalkowska
Keyword(s):  
Solar Radiation ◽  
Solar Irradiance ◽  
Direct Solar Radiation ◽  
Spectral Structure ◽  
Solar Constant ◽  
Air Masses ◽  
Atmospheric Turbidity ◽  
The Earth

Abstract The present study deals with the changing amount of incoming direct solar radiation and the optical state of the atmosphere in Mikołajki in the years 1971-1980 and 1991-2000. The highest level of solar irradiance in these two decades occurred on 23rd June 1977 and amounted to 1043.9 W·m-2. Compared to the first decade analysed, the percentage of the solar constant reaching the Earth in the second decade was higher. The spectral structure of the radiation also changed - the share of the shortest waves (λ<525 nm) increased, whereas the amount of waves with a wavelength of 710 nm or more decreased. In both study periods the annual course of solar extinction (expressed in terms of Linke’s turbidity factor) turned out to have been typical, with the highest values in summer and the lowest in winter. In the years 1991-2000, in all seasons, a lower atmospheric turbidity was observed in comparison with the years 1971-1980. The atmospheric turbidity was also analysed with relation to the air masses. In both decades in question the lowest turbidity occurred in arctic air masses and the highest in tropical air masses. An improved optical state of the atmosphere was observed in all considered air masses, though the biggest decrease in turbidity was found in polar air masses, particularly in the polar maritime old air (TLAM2 dropped by 0.75) and polar continental air (by 0.70).

scholarly journals High resolution grid of potential incoming solar radiation for Serbia

2015 ◽  
Vol 19 (suppl. 2) ◽  
pp. 427-435 ◽  
Author(s):  
Jelena Lukovic ◽  
Branislav Bajat ◽  
Milan Kilibarda ◽  
Dejan Filipovic
Keyword(s):  
Solar Radiation ◽  
Driving Force ◽  
Total Solar Radiation ◽  
Surface Solar Radiation ◽  
Natural Processes ◽  
Web Map ◽  
Complex Interactions ◽  
The Earth ◽  
Digital Elevation ◽  
Elevation Model

Solar radiation is a key driving force for many natural processes. At the Earth?s surface solar radiation is the result of complex interactions between the atmosphere and Earth?s surface. Our study highlights the development and evaluation of a data base of potential solar radiation that is based on a digital elevation model (DEM) with a resolution of 90 m over Serbia. The main aim of this paper is to map solar radiation in Serbia using DEM. This is so far the finest resolution being applied and presented using DEM. The final results of the potential direct, diffuse and total solar radiation as well as duration of insolation databases of Serbia are portrayed as thematic maps that can be communicated and shared easily through the cartographic web map-based service.

scholarly journals CryoSat-2 delivers monthly and inter-annual surface elevation change for Arctic ice caps

2015 ◽  
Vol 9 (3) ◽  
pp. 2821-2865 ◽  
Author(s):  
L. Gray ◽  
D. Burgess ◽  
L. Copland ◽  
M. N. Demuth ◽  
T. Dunse ◽  
...  
Keyword(s):  
Snow Accumulation ◽  
Surface Elevation ◽  
Radar Altimeter ◽  
Elevation Change ◽  
Ice Caps ◽  
Ice Cap ◽  
Winter Snow ◽  
Surface Elevation Change ◽  
Devon Ice Cap ◽  
Arctic Ice

Abstract. We show that the CryoSat-2 radar altimeter can provide useful estimates of surface elevation change on a variety of Arctic ice caps, on both monthly and yearly time scales. Changing conditions, however, can lead to a varying bias between the elevation estimated from the radar altimeter and the physical surface due to changes in the contribution of subsurface to surface backscatter. Under melting conditions the radar returns are predominantly from the surface so that if surface melt is extensive across the ice cap estimates of summer elevation loss can be made with the frequent coverage provided by CryoSat-2. For example, the average summer elevation decreases on the Barnes Ice Cap, Baffin Island, Canada were 2.05 ± 0.36 m (2011), 2.55 ± 0.32 m (2012), 1.38 ± 0.40 m (2013) and 1.44 ± 0.37 m (2014), losses which were not balanced by the winter snow accumulation. As winter-to-winter conditions were similar, the net elevation losses were 1.0 ± 0.2 m (winter 2010/2011 to winter 2011/2012), 1.39 ± 0.2 m (2011/2012 to 2012/2013) and 0.36 ± 0.2 m (2012/2013 to 2013/2014); for a total surface elevation loss of 2.75 ± 0.2 m over this 3 year period. In contrast, the uncertainty in height change results from Devon Ice Cap, Canada, and Austfonna, Svalbard, can be up to twice as large because of the presence of firn and the possibility of a varying bias between the true surface and the detected elevation due to changing year-to-year conditions. Nevertheless, the surface elevation change estimates from CryoSat for both ice caps are consistent with field and meteorological measurements. For example, the average 3 year elevation difference for footprints within 100 m of a repeated surface GPS track on Austfonna differed from the GPS change by 0.18 m.

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