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9 results for “snow accumulation”
1-km Antarctic net snow accumulation predictions
<p><strong>Overview</strong></p> <p>We provide static predictions of net snow accumulation over the Antarctic Ice Sheet derived using a machine learning approach. Here, we train random forest models to predict variability in net accumulation using atmospheric variables and topographic characteristics as predictors at 1 km resolution. Observations of net snow accumulation from both in situ and airborne radar data provide the input observable targets needed to train the random forest models. The data file includes all the predictors and predictands, an independent stake transect used for evaluation, four random forest gridded accumulation anomalies and their uncertainties, and a combined accumulation anomalies and its uncertainty.</p> <p>For a thorough description of how the predictions were generated generated see Medley et al. (2022). </p>
DATASET: In situ measurements of meltwater flow through snow and firn in the accumulation zone of the SW Greenland Ice Sheet
<p>This repository contains all the data and code used to analyse these data related to the paper "In situ measurements of meltwater flow through snow and firn in the accumulation zone of the SW Greenland Ice Sheet" by Clerx et al. (2022), published in "The Cryosphere".</p>
Snow accumulation patterns in a high mountain Andean catchment from optical tri-stereoscopic remote sensing
<p><strong>1) DBSM_Data_RioYeso'</strong> = Automatic weather station (AWS) data from Yeso Embalse and Termas del Plomo meteorological stations (available from Chilean Water Directorate, 'Dirección General de Aguas' or 'DGA' http://www.arcgis.com/apps/OnePane/basicviewer/index.html?appid=d508beb3a88f43d28c17a8ec9fac5ef0), used to force a distributed blowing snow model of Essery et al. (1999) to derive spatial snow depth of the Rio del Yeso catchment, Chile. The format is as follows:</p> <p><em>{'Year','Month','Day','Hour','Incoming shortwave radiation (Wm2)','Incoming longwave radiation (Wm2)','SnowfallRate(mm/hr)','RainfallRate(mm/hr)','Air temperature (celsius)','Relative humidity (%)','Wind speed (m s-1)','Compass wind direction','Air pressure (hPa)'};</em></p> <p><strong>2) 'snowHeightPleiadesREG' </strong>= A snow depth map (horizontal resolution 4m) derived from triplets of high resoution stereo optical satellite images (Pléiades) following the methodology of Marti et al. (2016). The snow depth map is derived for a high mountain catchment (Rio del Yeso) of the central Chilean Andes (see Burger et al., 2018).</p> <p><strong>3) 'L2_LiDAR_4m'</strong> = A LiDAR (Light detection and Ranging) spatial snow depth map at a horizontal resolution of 4 m. The data were captured by a Reigl VZ-6000 LiDAR scanner and generated from the difference of two constructed digital elevation models (DEMs) between the dates 13th September, 2017 (with snow) and 12th December, 2017 (without snow). </p> <p><strong>4) 'L2_Pleiades_SDLidar_NEW' </strong>= The Pléiades snow depth map as described in <strong>2)</strong>, extracted by the areas of LiDAR scan described in <strong>3)</strong>. </p> <p><strong>5) 'SnowDepthResults'</strong> = A folder containing a corrected and gap-filled Pléiades snow depth map (<strong>'SD_PleiadesCORR'</strong>) and for comparison: <strong>'SD_TOPO'</strong>, a statistical estimation of snow depth using topographic parameters and the regression equation of Grünewald et al. (2013) and; The physically based estimates of snow depth using the DBSM model as in <strong>1)</strong> without snow transport for the 4th September, 2017 (<strong>'SD_EXTP_Sep04'</strong>) and 13th September, 2017 ('<strong>SD_EXTP_Sep13'</strong>) and with snow transport for those dates (<strong>'SD_Wind_Sep04','SD_Wind_Sep13'</strong>).</p> <p><strong>6) 'rdyDEM'</strong> = An independent ASTER GDEM (https://asterweb.jpl.nasa.gov/gdem.asp) cut to the area of the study catchment (horizontal resolution = 30 m). </p> <p><strong>7) '</strong><strong>PlanetScope_20170907_TPK' </strong>= An stitched optical PlanetScope image of the catchment (horizontal resolution of 3.25 m) derived from access under the research and teaching iniative (planet.com). </p> <p><strong>Cited work:</strong></p> <p><strong>Burger, F. et al.</strong> (2018) ‘Interannual variability in glacier contribution to runoff from a high ‐ elevation Andean catchment : understanding the role of debris cover in glacier hydrology’, Hydrological Processes, pp. 1–16. doi: 10.1002/hyp.13354.</p> <p><strong>Essery, R</strong>., Li, L. and Pomeroy, J. (1999) ‘A distributed model of blowing snow over complex terrain’, Hydrological Processes, 13(14–15), pp. 2423–2438. doi: 10.1002/(SICI)1099-1085(199910)13:14/15<2423::AID-HYP853>3.0.CO;2-U.</p> <p><strong>Grünewald, T. et al.</strong> (2013) ‘Statistical modelling of the snow depth distribution in open alpine terrain’, Hydrology and Earth System Sciences, 17(8), pp. 3005–3021. doi: 10.5194/hess-17-3005-2013.</p> <p><strong>Marti, R. et al</strong>. (2016) ‘Mapping snow depth in open alpine terrain from stereo satellite imagery’, The Cryosphere, pp. 1361–1380. doi: 10.5194/tc-10-1361-2016.</p>
Antarctic Snow Radar-Derived Relative Accumulation Product
<p>Small-scale Antarctic accumulation rates relative to MERRA-2 precipitation-evaporation as derived from NASA Operation IceBridge snow radar. </p>
Fresh snow, peak accumulation, and ablation-Sun Cup snow surface datasets for evaluation of geometry aerodynamic roughness code
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Data from: Temporal variability in snow accumulation and density at Summit Camp, Greenland ice sheet
<p>A 3-year record of weekly snow water equivalent (SWE) accumulation at Summit Camp, central Greenland ice sheet, obtained by direct sampling, is presented. While the overall SWE accumulation of 24.2 cm w.e. a<sup>−1</sup> matches long-term ice core estimates, variability increases at shorter timescales. Half of the annual SWE accumulation occurs during a few large events, with the average accumulation rate decreasing 35% between the first and second halves of the record coinciding with exceptional anticyclonic conditions in the spring and summer of 2019. No seasonality in accumulation is detected. Rather, local accumulation rates appear to be significantly impacted by wind redistribution that obscures temporal patterns in snowfall. Surface snow density is consistent, on average, with previously measured values but does not correlate with near surface temperature or wind speed. Two surface mass balance reanalysis models significantly underestimate accumulation rates at Summit Camp. This is concerning because such models are often used to estimate ice-sheet mass loss.</p>
Data from: Temporal variability in snow accumulation and density at Summit Camp, Greenland ice sheet
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Data from: impacts of El Niño and La Niña on interannual snow accumulation in the Andes: results from a high-resolution 31 year reanalysis
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Data for "Century-long West Antarctic Snow Accumulation Changes Induced by Tropical Teleconnections"
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OpenNeuro
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