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Baker I, Cullen D, Iliescu D. 2003. The microstructural location of impurities in ice. Canadian Journal of Physics 81: 1–9. Baker I, Iliescu D, Obbard R, Chang H, Bostick B, Daghlian CP. 2006. Microstructural Characterization of Ice Cores. Annals of Glaciology, in press. Barnes PRF, Mulvaney R, Robinson K, Wolff EW. 2002a. Observations of polar ice from the Holocene and the glacial period using the scanning electron microscope. Annals of Glaciology 35: 559–566. Barnes PRF, Mulvaney R, Wolff EW, Robinson K. 2002b. A technique for the examination of polar ice using the scanning electron microscope. Journal of Microscopy 205: 118–124. Barnes PRJ. 2003. Comment on "Grain boundary ridge on sintered bonds between ice crystals" [J. Appl. Phys. 90, 5782 (2001)]. Journal of Applied Physics 93: 783–785. Barnes PRJ, Wolff EW, Mallard DC, Meider HM. 2003. SEM Studies of the Morphology and Chemistry of Polar Ice. Microscopy Research and Technique 62: 62–69. Barrett S. 2005. Image SXM, Surface Science Research Centre, University of Liverpool, Liverpool, UK. Cullen D, Baker I. 2000. The Chemistry of Grain Boundaries in Greenland Ice. Journal of Glaciology 46: 703–706. Cullen D, Baker I. 2001. Observation of Impurities in Ice. Microscopy Research and Technique 55: 198–207. Cullen D, Baker I. 2002a. Sulfate Crystallites in Vostok Accretion Ice. Materials Characterization 48: 263–270. Cullen D, Baker I. 2002b. Scanning Electron Microscopy of Vostok Acrretion Ice. In Proceedings of Microscopy and Microanalysis; 1546-7CD. Cullen D, Iliescu D, Baker I. 2002. SEM/EDS Studies of Impurities in Natural Ice. In Proceedings of Microscopy and Microanalysis; 1398-9CD. Fukazawa H, Sugiyama K, Mae S, Narita H, Hondoh T. 1998. Acid ions at triple junction of Antarctic ice observed by Raman scattering. Geophysical Research Letters, 25: 2845. Iliescu D, Baker I. 2002. Imaging of Uncoated Snow Crystals Using a Low-Vacuum Scanning Electron Microscope. Journal of Glaciology 48(162): 479–480. Iliescu D, Baker I, Cullen D. 2002. Preliminary Microstructural and Microchemical Observations of Pond and River Accretion Ice. Cold Regions Science and Engineering 35: 81–99. Iliescu D, Baker I. 2004. Characterization of the microstructure and mechanical properties in seasonal lake and river ice. In Proc. 24 th Army Science Conference, Orlando, FL; OS-26. Iliescu D, Baker I, Chang H. 2004. Determining the Orientations of Ice Crystals using Electron Backscatter Patterns. Microscopy Research and Technique 63: 183–187. Iliescu D, Baker I, Daghlian CP. 2005. Orientation Mapping in Polycrystalline Ice Using Electron Backscatter Patterns. In Proceedings of Microscopy & Microanalysis, Honolulu, HI; 1500– 1501. Mulvaney R, Wolff EW, Oates K. 1988. Sulphuric acid at grain boundaries in Antarctic ice. Nature 331: 247. Obbard R, Iliescu D, Cullen D, Chang J, Baker I. 2003a. SEM/EDS Comparison of Polar and Seasonal Temperate Ice. Microscopy Research and Technique 62: 49–61. Obbard R, Iliescu D, Baker I, Cullen D. 2003b. A Technique for the Scanning Electron Microscopy and Microanalysis of Uncoated Ice Crystals. In Electron Microscopy: Its Role in Materials Science, The Mike Meshii Symposium, TMS, Warrendale, PA; 133–140. Obbard R, Baker I, Sieg K. 2006. Using Electron Backscatter Diffraction Patterns To Examine Recystallization in Polar Ice Sheets. Submitted to Journal of Glaciology. Rasband W. S. 1997–2006. NIH ImageJ, http://rsb.info.nih.gov/ij/. Rick U, Albert, M. 2004. Microstructure of West Antarctic Firn and its Effect on Air Permeability. ERDC/CREEL Report TR-04-16. Underwood EE. 1970. Quantitative Stereology, Addision-Wesley Publishing Co., Reading, Mass, U.S.A., p24. Wolff EW, Mulvaney R, Oates K. 1998. The location of impurities in Antarctic ice. Annals of Glaciology, 11: 194. 218
219 63rd EASTERN SNOW CONFERENCE Delaware, DE, USA 2005 Computational Time Steps of Winter Water Balance for Snow Losses at United States Meteorological Stations ABSTRACT S.R. FASSNACHT 1 When estimating the water balance for a cold region watershed, that is one that receive a substantial portion of its annual precipitation as snow, accumulation and other winter hydrological processes must be considered. For many of theses watersheds, all but the most fundamental meteorological data (temperature and precipitation), are either not measured or not measured at a reasonable time step. Of particular importance are wind data, as wind influences underestimates of precipitation due to wind undercatch and losses of snow from the snowpack, specifically, snowpack sublimation, and the occurrence and magnitude of blowing snow. Estimating snow accumulation to yield snowmelt amounts requires summing of gauged precipitation and gauge undercatch, and subtracting minus snowpack sublimation and blowing snow transport. The first two components are computed on a daily time step, while the latter two are computed on an hourly time step. From five National Weather Service meteorological stations, the variations in computed snowpack mass losses minus undercatch using data at different time intervals show that at most sites it is difficult to use monthly time steps for computations derived using hourly or daily data. At the relative dry and cold Leadville, Colorado site the computations were transferable between time steps. Keywords: solid precipitation, meteorological data, undercatch, sublimation, blowing snow INTRODUCTION Snow is disturbed by wind, from snowfall to movement through redistribution to sublimation. Snowfall quantities are underestimated from precipitation gauges due to undercatch from wind, wetting of gauges, and to a lesser degree evaporation (Goodison et al., 1998). Undercatch due to wind is caused by the deformation of the wind field around the gauge orifice. As well, falling snow crystals are more easily blown away from the gauge orifice than rain drops. Snow on the ground can be redistributed based on wind characteristics, upwind and downwind fetch length, and the history of the snowpack surface (Pomeroy et al., 1991). Wind across a snowpack (or across snow held by vegetation) can sublimate snow away from or towards the surface depending upon temperature and humidity profiles (Sverdrup, 1936). A watershed analysis uses the water balance to partition water storage and movement into different components of the hydrological cycle. At the end of the accumulation period, the remaining snow melts to contribute to runoff. Accumulation, given as snow water equivalent (SWE), is typically estimated as the cumulative precipitation occurring at air temperatures colder than freezing (0 degrees Celsius), without correcting for precipitation underestimation due to gauge undercatch, nor snowpack losses due to sublimation or blowing snow. 1 Watershed Science Program, College of Natural Resources, Colorado State University, Fort Collins, Colorado 80523-1472 USA.
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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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Page 179 and 180: independent of regions. This greatl
Page 181 and 182: slightly vary the IMS with this pro
Page 183 and 184: imagery, the higher latitudes rely
Page 185 and 186: MODIS Land Science Team, NASA Godda
Page 187 and 188: FUTURE OF IMS Enhancements to the I
Page 189 and 190: information never present before on
Page 191 and 192: Brubaker, K.L., R. Pinker and E. De
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Page 195 and 196: The Sierra Nevada del Cocuy region
Page 197 and 198: Colombia Venezuela Glacier Series R
Page 199 and 200: Table 4. Glacier Areas of the Ruiz-
Page 201 and 202: Pico Bonpland Massif-Sinigüis Glac
Page 203 and 204: Linder W, Jordan E, 1991. Ice-mass
Page 205 and 206: Snowpack Processes 193
Page 207 and 208: 63 rd EASTERN SNOW CONFERENCE Newar
Page 209 and 210: In the following paragraph the main
Page 211 and 212: Figure 1: Variation of the season a
Page 213 and 214: Figure 3: 8-day composite maps (24
Page 215 and 216: The third pair (Figure 5) represent
Page 217 and 218: Figure 9: Time evolution of SWE ave
Page 219 and 220: melting occurs, whereas at the high
Page 221 and 222: Colbeck SC, Anderson EA. 1982. The
Page 223 and 224: ABSTRACT 211 63 rd EASTERN SNOW CON
Page 225 and 226: A B Figure 1. Low-magnification sec
Page 227 and 228: GB ridge GB groove Figure 3. Higher
Page 229: CONCLUSION Figure 5. Indexed electr
Page 233 and 234: Meteorological data for the winter
Page 235 and 236: monthly probability adjusted data 4
Page 237 and 238: 225 Pullman WA Rawlins WY Leadville
Page 239 and 240: The daily wind speed can exceed 6.5
Page 241 and 242: NCDC. 2006. National Climate Data C
Page 243 and 244: ABSTRACT 231 63 rd EASTERN SNOW CON
Page 245 and 246: and mass balances for 540 environme
Page 247 and 248: Temperature, precipitation, windspe
Page 249 and 250: the left in response to snow compac
Page 251 and 252: Pinched-cone dimensions for each sl
Page 253 and 254: Terrain slope (degrees) Terrain slo
Page 255 and 256: ACKNOWLEDGEMENTS This work was fund
Page 257 and 258: Glacial and Periglacial Processes 2
Page 261 and 262: From linear regression the constant
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
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Data analysis techniques We synthes
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season, but unacceptable for the dr
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significant, although more informat
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(a) Liquid Water (mm) Dry Period: M
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Future Work We are installing addit
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Shuttleworth, W.J., and J.S. Wallac
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Snow and Periglacial Processes Post
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----- -------------- ------- ------
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No one to date has shown why snowfl
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APPENDIX A Northern Hemisphere Year
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62nd ESC Papers 291
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62 nd EASTERN SNOW CONFERENCE Water
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each other; and, they both decrease
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297 62 nd EASTERN SNOW CONFERENCE W
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comes in the form of precipitation.
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averaged to reduce bias in this var
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RESULTS Figure 2. Flow chart of reg
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Table 6: Actual-Conditions SWE, dep
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307 Table 9: Regression results bet
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Goita, K., A. Walker, B. Goodison,
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TO ERR IS HUMAN, TO FORGET IT WOULD
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