scholarly journals Temporal and spatial variability in surface roughness and accumulation rate around 88° S from repeat airborne geophysical surveys

2020 ◽  
Author(s):  
Michael Studinger ◽  
Brooke C. Medley ◽  
Kelly M. Brunt ◽  
Kimberly A. Casey ◽  
Nathan T. Kurtz ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Accumulation Rate ◽  
Snow Accumulation ◽  
Small Scale ◽  
Spatial And Temporal Variability ◽  
Temporal And Spatial Variability ◽  
Geophysical Surveys ◽  
Slope Wind ◽  
Temporal And Spatial ◽  
Airborne Geophysical Data

Abstract. We use repeat high-resolution airborne geophysical data consisting of laser altimetry, snow and Ku-band radar and optical imagery acquired in 2014, 2016 and 2017 to analyze the spatial and temporal variability in surface roughness, slope, wind deposition, and snow accumulation at 88° S as this is a bias validation site for ICESat-2 and may be a potential validation site for CryoSat-2. We find significant small–scale variability (

scholarly journals Temporal and spatial variability in surface roughness and accumulation rate around 88° S from repeat airborne geophysical surveys

2020 ◽  
Vol 14 (10) ◽  
pp. 3287-3308
Author(s):  
Michael Studinger ◽  
Brooke C. Medley ◽  
Kelly M. Brunt ◽  
Kimberly A. Casey ◽  
Nathan T. Kurtz ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Wind Direction ◽  
Accumulation Rate ◽  
Snow Accumulation ◽  
Small Scale ◽  
Temporal And Spatial Variability ◽  
Geophysical Surveys ◽  
Slope Wind ◽  
Temporal And Spatial ◽  
Ku Band

Abstract. We use repeat high-resolution airborne geophysical data consisting of laser altimetry, snow, and Ku-band radar and optical imagery acquired in 2014, 2016, and 2017 to analyze the spatial and temporal variability in surface roughness, slope, wind deposition, and snow accumulation at 88∘ S, an elevation bias validation site for ICESat-2 and potential validation site for CryoSat-2. We find significant small-scale variability (<10 km) in snow accumulation based on the snow radar subsurface stratigraphy, indicating areas of strong wind redistribution are prevalent at 88∘ S. In general, highs in snow accumulation rate correspond with topographic lows, resulting in a negative correlation coefficient of r2=-0.32 between accumulation rate and MSWD (mean slope in the mean wind direction). This relationship is strongest in areas where the dominant wind direction is parallel to the survey profile, which is expected as the geophysical surveys only capture a two-dimensional cross section of snow redistribution. Variability in snow accumulation appears to correlate with variability in MSWD. The correlation coefficient between the standard deviations of accumulation rate and MSWD is r2=0.48, indicating a stronger link between the standard deviations than the actual parameters. Our analysis shows that there is no simple relationship between surface slope, wind direction, and snow accumulation rates for the overall survey area. We find high variability in surface roughness derived from laser altimetry measurements on length scales smaller than 10 km, sometimes with very distinct and sharp transitions. Some areas also show significant temporal variability over the course of the 3 survey years. Ultimately, there is no statistically significant slope-independent relationship between surface roughness and accumulation rates within our survey area. The observed correspondence between the small-scale temporal and spatial variability in surface roughness and backscatter, as evidenced by Ku-band radar signal strength retrievals, will make it difficult to develop elevation bias corrections for radar altimeter retrieval algorithms.

Small-scale temporal and spatial variability in the erosion threshold and properties of cohesive intertidal sediments

2006 ◽  
Vol 26 (3) ◽  
pp. 351-362 ◽  
Author(s):  
T.J. Tolhurst ◽  
E.C. Defew ◽  
J.F.C. de Brouwer ◽  
K. Wolfstein ◽  
L.J. Stal ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Small Scale ◽  
Intertidal Sediments ◽  
Erosion Threshold ◽  
Temporal And Spatial Variability ◽  
Temporal And Spatial

scholarly journals Spatial and temporal variability of clouds and precipitation over Germany: multiscale simulations across the "gray zone"

2015 ◽  
Vol 15 (21) ◽  
pp. 12361-12384 ◽  
Author(s):  
C. Barthlott ◽  
C. Hoose
Keyword(s):  
Cloud Formation ◽  
Small Scale ◽  
Spatial And Temporal Variability ◽  
Gray Zone ◽  
Model Resolution ◽  
Eddy Simulation ◽  
Cosmo Model ◽  
Weather Events ◽  
Temporal And Spatial Variability ◽  
Convergence Zones

Abstract. This paper assesses the resolution dependance of clouds and precipitation over Germany by numerical simulations with the COnsortium for Small-scale MOdeling (COSMO) model. Six intensive observation periods of the HOPE (HD(CP)2 Observational Prototype Experiment) measurement campaign conducted in spring 2013 and 1 summer day of the same year are simulated. By means of a series of grid-refinement resolution tests (horizontal grid spacing 2.8, 1 km, 500, and 250 m), the applicability of the COSMO model to represent real weather events in the gray zone, i.e., the scale ranging between the mesoscale limit (no turbulence resolved) and the large-eddy simulation limit (energy-containing turbulence resolved), is tested. To the authors' knowledge, this paper presents the first non-idealized COSMO simulations in the peer-reviewed literature at the 250–500 m scale. It is found that the kinetic energy spectra derived from model output show the expected −5/3 slope, as well as a dependency on model resolution, and that the effective resolution lies between 6 and 7 times the nominal resolution. Although the representation of a number of processes is enhanced with resolution (e.g., boundary-layer thermals, low-level convergence zones, gravity waves), their influence on the temporal evolution of precipitation is rather weak. However, rain intensities vary with resolution, leading to differences in the total rain amount of up to +48 %. Furthermore, the location of rain is similar for the springtime cases with moderate and strong synoptic forcing, whereas significant differences are obtained for the summertime case with air mass convection. Domain-averaged liquid water paths and cloud condensate profiles are used to analyze the temporal and spatial variability of the simulated clouds. Finally, probability density functions of convection-related parameters are analyzed to investigate their dependance on model resolution and their impact on cloud formation and subsequent precipitation.

Small-scale temporal and spatial variability in the abundance of plastic pellets on sandy beaches: Methodological considerations for estimating the input of microplastics

2016 ◽  
Vol 102 (1) ◽  
pp. 114-121 ◽  
Author(s):  
Fabiana Tavares Moreira ◽  
Alessandro Lívio Prantoni ◽  
Bruno Martini ◽  
Michelle Alves de Abreu ◽  
Sérgio Biato Stoiev ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Sandy Beaches ◽  
Small Scale ◽  
Temporal And Spatial Variability ◽  
Temporal And Spatial ◽  
Methodological Considerations ◽  
Plastic Pellets

The value of a broad temporal and spatial perspective in understanding dynamics of kelp forest ecosystems

2016 ◽  
Vol 67 (1) ◽  
pp. 14 ◽  
Author(s):  
Daniel C. Reed ◽  
Andrew R. Rassweiler ◽  
Robert J. Miller ◽  
Henry M. Page ◽  
Sally J. Holbrook
Keyword(s):  
Forest Ecosystems ◽  
Spatial Scales ◽  
Kelp Forest ◽  
Small Scale ◽  
Spatial And Temporal Variability ◽  
Short Term ◽  
Spatial Perspective ◽  
Ecological Responses ◽  
Temporal And Spatial

Many ecological processes play out over longer time scales and larger spatial scales than can be studied in a traditional 2–4-year grant cycle. Uncertainties in future funding hinder efforts to implement comprehensive research programs that integrate coupled time series observations of physical variables and ecological responses, manipulative experiments and synthetic analyses over the long term. Such research is essential for advancing our understanding of ecological responses associated with climate change, and the physical and biological processes that control them. This need is perhaps greatest for ecosystems that display highly dynamic and spatially complex patterns that are difficult to explain with short-term, small-scale studies. Such is the case for kelp forest ecosystems, which often show tremendous spatial and temporal variability in resource supply, consumer control and physical disturbance across spatial scales of metres to hundreds of kilometres and temporal scales of hours to decades. Here we present four examples from the Santa Barbara Coastal Long-term Ecological Research project that demonstrate the value of a broad temporal and spatial perspective in understanding the causes and ecological consequences of short-term local dynamics of giant kelp forests of California, USA.

scholarly journals Temporal and spatial variability of snow accumulation in central Greenland

1997 ◽  
Vol 102 (D25) ◽  
pp. 30059-30068 ◽  
Author(s):  
H. Kuhns ◽  
C. Davidson ◽  
J. Dibb ◽  
C. Stearns ◽  
M. Bergin ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Snow Accumulation ◽  
Temporal And Spatial Variability ◽  
Temporal And Spatial

scholarly journals Retrieval of Spatial and Temporal Variability in Snowpack Depth over Glaciers in Svalbard Using GPR and Spaceborne POLSAR Measurements

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 21
Author(s):  
Gulab Singh ◽  
Ivan I. Lavrentiev ◽  
Andrey F. Glazovsky ◽  
Akshay Patil ◽  
Shradha Mohanty ◽  
...  
Keyword(s):  
Snow Depth ◽  
Spatial And Temporal Variability ◽  
Inversion Algorithm ◽  
Sar Images ◽  
Polarimetric Synthetic Aperture Radar ◽  
Temporal And Spatial Variability ◽  
Temporal And Spatial ◽  
Ground Penetrating ◽  
Snow Precipitation ◽  
Precipitation Events

The highly dynamic nature of snow requires frequent observations to study its various properties. Keeping this in mind, the present investigation presents results from the analysis of fully polarimetric synthetic aperture radar (POLSAR) parameters for the development of a snow depth (SD) inversion model for SD retrieval. Snow depth retrieved using ground penetrating radar (GPR) at 500 MHz over Austre Grønfjordbreen in the Svalbard region was used to understand the behaviour of certain polarimetric parameters. A significant correlation was found between field-measured SD and POLSAR parameters, namely coherence and normalized volume scattering power (R2 = 0.84 and R2 = 0.73, respectively.) Using the POLSAR scattering powers obtained from the six-component model-based decomposition (6SD), the heterogeneity and anisotropic behaviour in the firn areas are also explained. Further, based on the analyses shown in this work, a polarimetric parameter-based SD inversion algorithm have been proposed and validated. The univariate model with co-polarization coherence has the highest correlation (R2 = 0.84, Root Mean Square Error (RMSE) = 0.18). We have even tested several multivariate models for the same, to conclude that a combination of coherence, normalized volume and double-bounce scattering have a high correlation with SD (R2 = 0.84, RMSE = 0.18). Additionally, temporal and spatial variability in SD was also observed from three polarimetric SAR images acquired between 4 April 2015 and 15 May 2015 over the Western Nordenskiöld Land region. Increase in snow depth corresponding to snow precipitation events were also detected using the POLSAR data.

scholarly journals Spatial and temporal variability of snow accumulation in East Antarctica from traverse data

2005 ◽  
Vol 51 (172) ◽  
pp. 113-124 ◽  
Author(s):  
Massimo Frezzotti ◽  
Michel Pourchet ◽  
Onelio Flora ◽  
Stefano Gandolfi ◽  
Michel Gay ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Temporal Variability ◽  
Accumulation Rate ◽  
Ice Core ◽  
Snow Accumulation ◽  
East Antarctica ◽  
Spatial And Temporal Variability ◽  
Volcanic Event ◽  
Terra Nova Bay ◽  
Order Of Magnitude

AbstractRecent snow accumulation rate is a key quantity for ice-core and mass-balance studies. Several accumulation measurement methods (stake farm, fin core, snow-radar profiling, surface morphology, remote sensing) were used, compared and integrated at eight sites along a transect from Terra Nova Bay to Dome C, East Antarctica, to provide information about the spatial and temporal variability of snow accumulation. Thirty-nine cores were dated by identifying tritium/b marker levels (1965_66) and non-sea-salt (nss) SO42_ spikes of the Tambora (Indonesia) volcanic event (1816) in order to provide information on temporal variability. Cores were linked by snow radar and global positioning system surveys to provide detailed information on spatial variability in snow accumulation. Stake-farm and ice-core accumulation rates are observed to differ significantly, but isochrones (snow radar) correlate well with ice-core derived accumulation. The accumulation/ablation pattern from stake measurements suggests that the annual local noise (metre scale) in snow accumulation can approach 2 years of ablation and more than four times the average annual accumulation, with no accumulation or ablation for a 5 year period in up to 40% of cases. The spatial variability of snow accumulation at the kilometre scale is one order of magnitude higher than temporal variability at the multi-decadal/secular scale. Stake measurements and firn cores at Dome C confirm an approximate 30% increase in accumulation over the last two centuries, with respect to the average over the last 5000 years

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