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Figure 4: (a) R 2 for PWBM simulated spring total Q vs. observed spring total Q. The vertical line in colorbar is level at which R 2 is significant. Minimum, maximum, and mean R 2 sacrossall basins, North America (NA), western Eurasia (WE), and eastern Eurasia (EE) are shown in Table 1. The ’X’s mark basins with a negative correlation. (b) Standard deviation of spring (April–June) discharge for the period 1988-2000. 130
values. Continued development of new regional schemes which account for microwave emmission from forests should improve large-scale SWE estimates. Poor agreements among all SWE products —particularly across parts of western Eurasia—are noted in areas with low discharge variability. Pre-screening to eliminate basins with low flow or insufficient variability would likely improve the SWE vs. Q agreements. Results of the covariance analysis using alternate temporal integrations to derive pre-melt SWE (or Q) suggest that our choice of a fixed interval for spring, ie. April–June, is not the primary cause of the relatively low R 2 s. Furthermore, we conclude that much of the interannual variability in river discharge must be influenced by factors other than basin SWE storage variations. The unexplained variability is likely due to a combination of effects from physical processes (sublimation, infiltration) and errors in spatial SWE. Relatively good agreement between simulated and observed spring Q suggests that hydrological models can be useful in understanding the SWE-to-Q linkages. Our results provide a benchmark of the relationship between the solid precipitation input and spring discharge flux, and demonstrate that hydrological models driven with reanalysis data can provide SWE estimates sufficient for use in validation of remote-sensing and GCM SWE fields. Additional studies using daily discharge data to better quantify snowmelt runoff will further facilitate SWE product evaluations and the understanding of linkages in arctic hydrological system. ACKNOWLEDGEMENTS The authors thank Richard Lammers, Ernst Linder, and Dominik Wisser (University of New Hampshire) for frequent useful discussions, and Kyle McDonald (Jet Propulsion Laboratory) for the thaw timing data. We also thank Chris Milly (NOAA/GFDL) for providing the LaD SWE estimates, and Ross Brown (Environnement Canada) for the CCIN data. This study was supported by the NSF ARCSS program and NSF grants OPP-9910264, OPP-0230243, OPP-0094532, and NASA grant NAG5-9617. REFERENCES Armstrong, R. L. and Brodzik, M. J. 1995. An earth-gridded SSM/I data set for cryospheric studies and global change monitoring. Advances in Space Research, 16(10):155–163. Armstrong, R. L. and Brodzik, M. J. 2001. Recent Northern Hemisphere snow extent: a comparison of data derived from visible and microwave sensors. Geophys. Res. Lett., 28(19):3673–3676. Armstrong, R. L. and Brodzik, M. J. 2002. Hemispheric-scale comparison and evaluation of passivemicrowave snow algorithms. Annals of Glaciology, 34(1):38–44. Armstrong, R. L., Knowles K. W., Brodzik M. J., Hardman M. A. 2006. DMSP SSM/I Pathfinder daily EASE-Grid brightness temperatures. Technical report, National Snow and Ice Center. CDROM, Boulder, CO, USA. http://nsidc.org/data/nsidc-0032.html. Brodzik, M. J. (2004). personal communication, 2 March, 2004. Brodzik, M. J. and Knowles K. 2002. EASE-Grid: A versatile set of equal-area projections and grids. in M. Goodchild (Ed.) Discrete Global Grids. Santa Barbara, CA, USA: National Center for Geographic Information and Analysis. Brown, R. D., Brasnett, B., Robinson, D. (2003). Gridded North American monthly snow depth and snow water equivalent for GCM evaluation. Atmosphere–Ocean, 41(1):1–14. 131
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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
Page 91 and 92: strings were buried 20 - 30 cm belo
Page 93 and 94: Although the λE/Rn fraction was on
Page 95 and 96: estimated snow depth based on the 2
Page 97 and 98: important to note that the average
Page 99 and 100: Schmidt, R. A., C. A. Troendle, and
Page 101 and 102: 89 63 rd EASTERN SNOW CONFERENCE Ne
Page 103 and 104: METHODS The IMS Product The IMS was
Page 105 and 106: Figure 1. Microwave spectral charac
Page 107 and 108: snow depth and SWE on a weekly basi
Page 109 and 110: Figure 4. Example of blended SWE pr
Page 111 and 112: Evaluation of the global distributi
Page 113 and 114: Figure 9. Inter-comparison plots of
Page 115 and 116: Helfrich, S.R., D. McNamara, B.H.Ra
Page 117 and 118: 105 63 rd EASTERN SNOW CONFERENCE N
Page 119 and 120: and validated regionally so they ca
Page 121 and 122: 109 Test Site Figure 2. Variation o
Page 123 and 124: Correlation Coe. Correlation Coe. C
Page 125 and 126: Figure 8. SSM/I scattering signatur
Page 127 and 128: Considering the facts mentioned abo
Page 129 and 130: Besides the temporal validation, th
Page 131 and 132: RMSE Non linear Azar Chang Goodison
Page 133 and 134: 63 nd EASTERN SNOW CONFERENCE Newar
Page 135 and 136: http://www.ccin.ca); from simulatio
Page 137 and 138: over the period 1988-2000. With the
Page 139 and 140: Figure 3: Explained variance (R 2 )
Page 141: correspondence between simulated an
Page 145 and 146: National Climatic Data Center (2005
Page 147 and 148: Snow Remote Sensing Posters 135
Page 149 and 150: ABSTRACT 63 rd EASTERN SNOW CONFERE
Page 151 and 152: day and has a mean pixel resolution
Page 153 and 154: Table 1. Wheaton River basin EASE-G
Page 155 and 156: The SSM/I snowmelt onset algorithm
Page 157 and 158: coarse-resolution pixel with the hi
Page 159 and 160: Figure 5. (a) Scatterplot of snowpa
Page 161 and 162: For both 2004 and 2005, the AMSR-E
Page 163 and 164: threshold of ±18 K that reflects t
Page 165 and 166: ABSTRACT 153 63 rd EASTERN SNOW CON
Page 167 and 168: DATA USED SSM/I & QuikSCAT Coverage
Page 175 and 176: 180 160 140 120 100 80 60 40 20 0 1
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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181 63 rd EASTERN SNOW CONFERENCE N
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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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ABSTRACT 211 63 rd EASTERN SNOW CON
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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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ABSTRACT 231 63 rd EASTERN SNOW CON
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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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From linear regression the constant
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DATA COLLECTION Figure 1. Location
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Table 1. Comparison of GPS measured
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Transverse Velocity Profiles Surfac
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is shown in Figure 4. This variatio
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Table 4. The calculated volume flux
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Figure 1. Cordillera Blanca regiona
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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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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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