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Table 1: Remote sensing data used within this study Data Set Label Spatial Resolution Description SWE estimates derived from AMSR-E TB re-sampled to 12.5 km 1) 12.5k_AMSR-E SWE 12.5 km EASE-Grid 18.7v TB obtained from AMSR-E 12.5k_AMSR-E 18.7v 12.5 km re-sampled to 12.5 km EASE-Grid 36.5v TB obtained from AMSR-E 12.5k_AMSR-E 36.5v 12.5 km re-sampled to 12.5 km EASE-Grid SWE estimates derived from AMSR-E TB re-sampled to 25 km 2) 25k_AMSR-E SWE 25 km EASE-Grid 18.7v TB obtained from AMSR-E 25k_AMSR-E 18.7v 25 km re-sampled to 25 km EASE-Grid 36.5v TB obtained from AMSR-E 25k_AMSR-E 36.5v 25 km re-sampled to 25 km EASE-Grid SWE estimates derived from AMSR-E TB that have not been re- 3) Swath_AMSR-E SWE 24 km x 12 km sampled 18.7v TB obtained from AMSR-E Swath_AMSR-E 18.7v 24 km that have not been re-sampled 36.5v TB obtained from AMSR-E Swath_AMSR-E 36.5v 12 km that have not been re-sampled SWE estimates derived from SSM/I TB re-sampled to 25 km 4) 25k_SSM/I SWE 25 km EASE-Grid METHODS The analysis procedure was divided into four steps: i) collection of the ground-truth observations, ii) processing of the data sets, iii) comparison with the remotely sensed SWE estimates, and iv) statistical tests. These are discussed in the following subsections. In-situ Data Collection The study area was systematically divided into 25 km square grid cells, with the centre of each cell located at 5 km intervals. Field measurements were made nearest the centre of each grid cell as possible. The field campaign was concentrated into a three-day period to minimize changes in snow pack conditions due to melt or fresh snowfall. Two teams, of 3–4 surveyors each, collected a total of 88 ground observations from 84 sampling sites that covered an area of 1600 km 2 . As a control, four of the sites were sampled on consecutive days to ensure data consistency. Included in the 84 sampling sites were 20 sites coincident with an established MSC validation/calibration snow course data archive (Flight Line 603). Comparisons among these data will be the subject of a future communication. The land coverage of the sampling sites included pastures and shelter belts, as well as fallow and stubble fields. The ground-truth data collected from each sampling site included: geographic locations, snow pack profiles, depth measurements, core samples, air and ground temperatures, site photographs, sampling dates and times, and land cover and weather observations. Sampling site locations were recorded using global positioning system (GPS) handsets. Since data for differential corrections were not available, the positions have an approximate accuracy of 10 metres. Approximately 90% of the sampling sites were found to have patchy snow coverage. As such, one of the first observations made at each site was a visual assessment of the percentage of snow cover. Each team member made this assessment independently and these values were then 300
averaged to reduce bias in this variable. For sites with complete snow cover, a total of 4 snow core samples were collected. This number was reduced proportionally for partially covered sites. For example, only 2 cores were collected from sites determined as having 50% snow coverage, and just 1 core was collected from sites with 25% snow cover. This rationale was used to satisfy the requirement that the cores be taken randomly within each site. Thus, if a site was found to have 50% snow cover, then the probability of randomly selecting a sampling location containing snow is only 50%. In this situation, the 2 core samples that were not actually collected were simply given zero values for their core lengths, depths, weights, and densities. The core samples were obtained using Eastern Snow Conference (ESC-30) snow core tubes. Measurements from the core samples include the actual depths of the snow packs from where the cores were removed, the lengths of the cores, and their weights. The lengths and weights of the cores were used to calculate the core densities and ground SWE measurements. Snow densities, represented as g/cm 3 , were based on the average of the 0 to 4 core samples. A snow pit was dug at each sampling site and a detailed snow pack profile was made that included the snow pack’s total depth, the number of layers and ice lenses within the snow pack, the depth and snow grain size of each layer (using Sears snow crystal screens, labeled with 1–3 mm grids), a qualitative description of each layer, and the air and snow/ground interface temperatures. A total of 16 depth measurements were made around each snow pit using 15-metre long ropes as guides for the purpose of consistently collecting the depth measurements from 30metre diameter circles. Depth measurements to the nearest one-half centimetre were made using 1metre long depth probes. The depth measurements from each site were used to calculate the average depths within the sites, which were then used as representative values for the sampling sites. The average depths are based on the 16 random depth measurements recorded from the circle around the snow pit along with the 0 to 4 depth measurements recorded from the snow core samples. Other data recorded included the sampling dates and times, weather observations, and land cover types. In-situ Data Processing Two data sets were created from the ground sampled data in order to better understand how snow properties over a partial snow cover are manifested in the remotely sensed SWE estimates. In the “Snow-Only” data set, only those snow depth measurements that were greater than zero were included in the average depth, density, and ground SWE calculations. For example, if a site was found to have snow depth readings of 3, 4, 0, 1, and 4 cm, then the average depth for that site was recorded as 3.0 cm ( (3 + 4 + 1 + 4) / 4 ), excluding the zero value. Conversely, the same site in the “Actual-Conditions” data set would have a mean depth of 2.4 cm ( (3 + 4 + 0 + 1 + 4 / 5) ). From each of these data sets, four SWE estimates were calculated. The first ground SWE value, “Core_SWE,” represents the SWE calculated by using the mean SWE from the snow cores only. The second SWE value, “Derived_SWE,” is representative of the mean value for the Core_SWE plus the SWE derived from the 16 depth measurements using the average density for each sampling site. The remaining SWE values, “Fractional_Core_SWE” and “Fractional_Derived_SWE” are represented by the previous SWE values weighted by the Snow_Cover_Percent, respectively. Four SWE values were deemed necessary to investigate the most accurate way of representing SWE over a patchy snow cover. Remote Sensing Data The remote sensing images were re-projected to a UTM projection for analysis in ArcGIS. Each pixel centroid was assumed to be a point, and pixel footprints were created using Thiessen polygons. The Thiessen polygon algorithm segments the measurement space into polygons such that every polygon encloses the region closest to each pixel centroid (O’Sullivan and Unwin, 2003). Although the algorithm does not produce perfectly squared pixels, the fact that the algorithm measures the mid-points between the pixel centroids ensures that the spatial resolutions of the remote sensing data sets are preserved. 301
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Proceedings of the 63rd ANNUAL EAST
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FOREWORD T his proceedings volume c
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CONTENTS Foreword..................
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STATEMENT OF PURPOSE The Eastern Sn
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EXECUTIVES FOR THE 63rd EASTERN SNO
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THE PRESIDENT’S PAGE The 63rd ann
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Weisnet Medal for Best Student Pape
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3 63 rd EASTERN SNOW CONFERENCE New
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Figure 1. Layer 1 represents the so
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Figure 4. The general layout of the
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lue color represented no convection
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Total Density of Water Profiles The
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Figure 11(a). Simulated snow grain
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Jordan R, 1991. A one-dimensional t
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Campbell Scientific Award for Best
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19 63 rd EASTERN SNOW CONFERENCE Ne
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R ∑i ∑ n 2 = 1 NS = − n i = 1
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Snow and Climate 37
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Spatial distributions of changes in
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Snow and Climate Posters 43
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Figure 2 presents correlation maps
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CONCLUSIONS Three regional-continen
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Discharge (mm) 20 18 16 14 12 10 8
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ABSTRACT 55 63 rd EASTERN SNOW CONF
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This paper analyzes the synoptic pa
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Cyclones that track west of the App
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The synoptic-scale atmospheric circ
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CONCLUSIONS The record snowfall in
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correlations between April-May snow
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Figure 3. Linear correlations betwe
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winter/early spring could contribut
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Snow Remote Sensing 73
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height averaged 11.4 m with an aver
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strings were buried 20 - 30 cm belo
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Although the λE/Rn fraction was on
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estimated snow depth based on the 2
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important to note that the average
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Schmidt, R. A., C. A. Troendle, and
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METHODS The IMS Product The IMS was
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Figure 1. Microwave spectral charac
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snow depth and SWE on a weekly basi
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Figure 4. Example of blended SWE pr
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Evaluation of the global distributi
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Figure 9. Inter-comparison plots of
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Helfrich, S.R., D. McNamara, B.H.Ra
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105 63 rd EASTERN SNOW CONFERENCE N
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and validated regionally so they ca
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109 Test Site Figure 2. Variation o
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Correlation Coe. Correlation Coe. C
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Figure 8. SSM/I scattering signatur
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Considering the facts mentioned abo
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Besides the temporal validation, th
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RMSE Non linear Azar Chang Goodison
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63 nd EASTERN SNOW CONFERENCE Newar
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http://www.ccin.ca); from simulatio
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over the period 1988-2000. With the
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Figure 3: Explained variance (R 2 )
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correspondence between simulated an
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values. Continued development of ne
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National Climatic Data Center (2005
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Snow Remote Sensing Posters 135
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ABSTRACT 63 rd EASTERN SNOW CONFERE
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day and has a mean pixel resolution
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Table 1. Wheaton River basin EASE-G
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The SSM/I snowmelt onset algorithm
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coarse-resolution pixel with the hi
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Figure 5. (a) Scatterplot of snowpa
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For both 2004 and 2005, the AMSR-E
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threshold of ±18 K that reflects t
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ABSTRACT 153 63 rd EASTERN SNOW CON
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DATA USED SSM/I & QuikSCAT Coverage
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180 160 140 120 100 80 60 40 20 0 1
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independent of regions. This greatl
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slightly vary the IMS with this pro
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imagery, the higher latitudes rely
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MODIS Land Science Team, NASA Godda
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FUTURE OF IMS Enhancements to the I
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information never present before on
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Brubaker, K.L., R. Pinker and E. De
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The Sierra Nevada del Cocuy region
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Colombia Venezuela Glacier Series R
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Table 4. Glacier Areas of the Ruiz-
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Pico Bonpland Massif-Sinigüis Glac
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Linder W, Jordan E, 1991. Ice-mass
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Snowpack Processes 193
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63 rd EASTERN SNOW CONFERENCE Newar
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In the following paragraph the main
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Figure 1: Variation of the season a
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Figure 3: 8-day composite maps (24
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The third pair (Figure 5) represent
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Figure 9: Time evolution of SWE ave
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melting occurs, whereas at the high
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Colbeck SC, Anderson EA. 1982. The
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A B Figure 1. Low-magnification sec
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GB ridge GB groove Figure 3. Higher
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CONCLUSION Figure 5. Indexed electr
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219 63rd EASTERN SNOW CONFERENCE De
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Meteorological data for the winter
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monthly probability adjusted data 4
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225 Pullman WA Rawlins WY Leadville
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The daily wind speed can exceed 6.5
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NCDC. 2006. National Climate Data C
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and mass balances for 540 environme
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Temperature, precipitation, windspe
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the left in response to snow compac
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Pinched-cone dimensions for each sl
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Terrain slope (degrees) Terrain slo
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ACKNOWLEDGEMENTS This work was fund
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Glacial and Periglacial Processes 2
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Page 261 and 262: From linear regression the constant
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Page 265 and 266: DATA COLLECTION Figure 1. Location
Page 267 and 268: Table 1. Comparison of GPS measured
Page 269 and 270: Transverse Velocity Profiles Surfac
Page 271 and 272: is shown in Figure 4. This variatio
Page 273 and 274: Table 4. The calculated volume flux
Page 277 and 278: Figure 1. Cordillera Blanca regiona
Page 279 and 280: MATERIALS AND METHODS Field Measure
Page 281 and 282: Data analysis techniques We synthes
Page 283 and 284: season, but unacceptable for the dr
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Page 287 and 288: (a) Liquid Water (mm) Dry Period: M
Page 289 and 290: Future Work We are installing addit
Page 291 and 292: Shuttleworth, W.J., and J.S. Wallac
Page 293 and 294: Snow and Periglacial Processes Post
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Page 299 and 300: No one to date has shown why snowfl
Page 301 and 302: APPENDIX A Northern Hemisphere Year
Page 303 and 304: 62nd ESC Papers 291
Page 305 and 306: 62 nd EASTERN SNOW CONFERENCE Water
Page 307 and 308: each other; and, they both decrease
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Page 311: comes in the form of precipitation.
Page 315 and 316: RESULTS Figure 2. Flow chart of reg
Page 317 and 318: Table 6: Actual-Conditions SWE, dep
Page 319 and 320: 307 Table 9: Regression results bet
Page 321 and 322: Goita, K., A. Walker, B. Goodison,
Page 323: TO ERR IS HUMAN, TO FORGET IT WOULD
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