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ACKNOWLEDMENTS The authors express their gratitude to Dr C. Derksen of Meteorological Service of Canada (MSC) and Dr A. Frei of the City University of New York. The SNODAS datasets produced by NOHRSC, were obtained through NSIDC. Thanks to Dr T. Carrol at NOHRSC and L. Ballagh at NOAA at NSIDC, University of Colorado. REFRENCES Aschbacher, J. (1989). Land surface studies and atmospheric effects by satellite microwave radiometry, university of Innsbruk. Chang A.T.C., J. L. F., and D.K.Hall. (1987). "Nimbus-7 SMMR derived global snow cover parameters." Annals Glaciology 9: 39–44. Derksen, R. B., A. Walker (2004). "Merging Conventional (1915–92) and Passive Microwave (1978–2002) Estimates of Snow Extent and Water Equivalent over Central North America." Journal of Hydrometeorology 5: 850–861. Derksen C., A. W., B.Goodison (2005). "Evaluation of passive microwave snow water equivalent retrievals across the boreal forest/tundra transition of western Canada." Remote Sensing of Environment 96: 315–327. De Seve D., B. M., Fortin J. P. , and Walker A., (1997). "Preliminary analysis of snow microwave radiometry using the SSM/I passive-microwave data: The case of La Grande River watershed (Quebec)." Annals of Geology 25. Foster J. L., H. D. K., Chang A.T., Rango A., Wergin W., and Erbe E., (1999). "Effect of Snow Crystal Shape on the Scattering of Passive Microwave Radiation." IEEE Transaction on Geosciences and Remote sensing 37(2). Grody N., B. N. (1996). "Global Identification of Snowcover Using SSM/I Measurements." IEEE Transaction on Geosciences and Remote sensing 34(1). Hallikainen, M. T. (1984). "Retrieval of snow water equivalent from Nimbus-7 SSMR data: effect of land cover categories and weather conditions." IEEE Oceanic Eng 9(5): 372–376. Kelly R.E., A. T. C., L.Tsang, J.L.Foster (2003). "A prototype AMSR-E Global Snow Area and Snow depth Algorithm." IEEE Transaction on Geosciences and Remote Sensing, 41(2): 230– 242. Kelly R.E.J. , A. T. C. C., J.L. Foster and D.K. Hall (2001). Development of a passive microwave global snow monitoring algorithm for the Advanced Microwave Scanning Radiometer-EOS. Kurvonen, L. (1994). Radiometer measurements of snow in Sodankyla, Helsinki University of Technology, Laboratory of Space Technology. Pullianen, J. T., Grandell J., and Hallikainen M (1999). "HUT snow emission model and its applicability to snow water equivalent retrieval." IEEE Transaction on Geosciences and Remote sensing 37(3): 1378–1390. Tedesco M., P. J., Takala M., Hallikainen M., and Pampaloni P (2004). "Artificial neural networkbased techniques for the retrieval of SWE and snow depth from SSM/I data." Remote sensing of Environment 90. Walker, G. B. E. a. A. E. (1995). Canadian development and use of snow cover information from passive microwave satellite data. Passive microwave remote sensing of land–atmosphere interactions. B. J. Choudhury, Y. H. Kerr, E. G. Njoku and P. Pampaloni. The Netherlands, VSP BV 245–262. 120
63 nd EASTERN SNOW CONFERENCE Newark, Delaware USA 2006 On the Evaluation of Snow Water Equivalent Estimates over the Terrestrial Arctic Drainage Basin ABSTRACT Michael A. Rawlins 1 , Mark Fahnestock 1,2 ,SteveFrolking 1,2 , Charles J. Vörösmarty 1,2 Comparisons between snow water equivalent (SWE) and river discharge estimates are important in evaluating the SWE fields and to our understanding of linkages in the freshwater cycle. In this study we compared SWE drawn from land surface models and remote sensing observations with measured river discharge (Q) across 179 arctic river basins. Over the period 1988-2000, basin-averaged SWE prior to snowmelt explains a relatively small (yet statistically significant) fraction of interannual variability in spring (April–June) Q, as assessed using the coefficient of determination (R 2 ). Over all river basins, mean R 2 s vary from 0.20 to 0.28, with the best agreement noted for SWE drawn from simulations of the Pan-Arctic Water Balance Model (PWBM) that are forced with data from the National Center for Environmental Prediction / National Center for Atmospheric Research (NCEP-NCAR) Reanalysis. Variability and magnitude in SWE derived from Special Sensor Microwave Imager (SSM/I) data are considerably lower than the variability and magnitude in SWE drawn from the land surface models, and generally poor agreement is noted between SSM/I SWE and spring Q. We find that the SWE vs. Q comparisons are no better when alternate temporal integrations—using an estimate of the timing in basin thaw—are used to define pre-melt SWE and spring Q. Thus, a majority of the variability in spring discharge must arise from factors other than basin snowpack water storage. This study suggests that SWE estimated from remote sensing observations or general circulation models (GCMs) can be evaluated effectively using monthly discharge data or SWE from a hydrological model. The relatively small fraction of Q variability explained by basin SWE warrants further investigation using daily discharge observations to more accurately define the snowmelt contribution to river runoff. Keywords: SWE; River Discharge; Remote Sensing; SSM/I 1 Water Systems Analysis Group, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH 03824 (USA) 2 Department of Earth Sciences, University of New Hampshire, Durham, NH 03824 (USA) 121
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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
Page 81 and 82: Figure 3. Linear correlations betwe
Page 83 and 84: winter/early spring could contribut
Page 85 and 86: Snow Remote Sensing 73
Page 87 and 88: 75 63 rd EASTERN SNOW CONFERENCE Ne
Page 89 and 90: 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: RMSE Non linear Azar Chang Goodison
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 and 142: correspondence between simulated an
Page 143 and 144: values. Continued development of ne
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
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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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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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----- -------------- ------- ------
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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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