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Figure 1: Pan-Arctic land mass (north of 45 ◦ N, dark gray), the arctic drainage basin (light gray), and locations of 179 river basins used in the study. Dot sizes are scaled by basin area. A total of 39,926 EASE-Grid cells comprise the approximately 25 million km 2 drainage basin. Areas for the 179 river basins range from 20,000 km 2 to 486,000 km 2 . respective the basin. Satellite-borne remote sensing at microwave wavelengths can be used to monitor landscape freeze/thaw state (Ulaby et al., 1986; Way et al., 1997; Frolking et al., 1999; Kimball et al., 2001). A step edge detection scheme applied to SSM/I brightness temperatures (McDonald et al., 2004) was used to identify the predominant springtime thaw transition event for each EASE-Grid cell. As with SWE, we derived a basin average date of thaw by averaging thaw event dates across the basin grid cells. Snow thaw across arctic basins often can occur over a period of weeks or months. Therefore, for large watersheds, our timing estimates derived from SSM/I brightness temperatrures must be interpreted with caution. Nonetheless they provide a general approximation of the timing in landscape thaw for use in estimating pre-melt SWE and spring Q. As an illustration, monthly river discharge, SWE, and thaw date for the Yukon basin are shown in Figure 2a–d). A simulated topological network (Vörösmarty et al., 2000), recently implemented at 6 minute resolution, defines river basins over the approximately 25 million km 2 of the Pan-Arctic basin. The degree to which SWE and Q covary over the period 1988-2000 is evaluated using the coefficient of determination, R 2 (squared correlation). Throughout our analysis we assume a significance level of 0.05 (5%) as the cutoff to determine whether a given SWE vs. Q comparison is statistically significant, and not due to chance. For a sample size of 13 years this correspondes to R 2 ≥ 0.22. RESULTS Interannual variability in basin averaged, pre-melt SWE is compared with spring Q for 179 basins 124
over the period 1988–2000. With the exception of SWE derived from SSM/I data, interannual variability in pre-melt SWE agrees well with spring Q variability across the Yukon basin in Alaska (Figure 2). Variability in basin SWE from the CCIN analysis scheme explains nearly 75% of the variability in spring Q. When reanalysis data drives the PWBM (PWBM/ERA-40 or PWBM/NNR), pre-melt SWE explains well over 50% of the variability in spring Q. Across the Yukon basin, the greater SWE variability and magnitude (among all SWE products) is noted for LaD SWE, along with a lower R 2 (Figure 2c–d). Basin averaged SWE derived from SSM/I, however, shows little interannual variability and relatively low magnitude. For snow packs above 100 mm, the bias in SWE estimated from Scanning Multichannel Microwave Radiometer (SMMR) data was shown to be linearly related to the snow pack mass, with root-mean-square errors approaching 150 mm (Dong et al., 2005). In contrast to the result over the Yukon basin, strong agreements in pre-melt SWE and spring Q variability are not noted across many of the study basins. Although R 2 s for a majority of the North American basins are significant (mean values between 0.26 and 0.36, Table 1), agreements across eastern Eurasia are generally low (Figure 3, Table 1). Of the comparisons involving SWE from PWBM, more than half (130 of 231) are significant. Mean R 2 s from comparisons using the PWBM are comparable to those involving CCIN SWE estimates, which were developed using observed snow depth observations (Brown et al., 2003). Across all basins analyzed, the highest proportion of negative correlations (very poor agreement) and lowest overall R 2 are associated with SSM/I SWE. The algorithm used to produce these estimates, like many of the early passive-microwave SWE algorithms, tends to underestimate SWE in forested regions. Models which account for the differing influences on the microwave signature have shown promise in reducing errors in forested regions (Goita et al., 2003). The best agreements involving SSM/I SWE are found across the prairies of south-central Canada. This is expected, as the SSM/I SWE algorithm was developed for application across the non-forested prairie provinces of Canada. Comparisons using SWE from the PWBM simulations (PWBM/ERA-40, PWBM/NNR, and PWBM/WM), produce similar R 2 values across each region, with mean value by region ranging from 0.15 to 0.36. Given that water budget models like PWBM are most sensitive to time-varying climatic inputs (Rawlins et al., 2003), small differences in R 2 among these SWE estimates suggest similar spatial and temporal variability among the underlying precipitation data. Basin R 2 s obtained from comparisons using LaD SWE are comparable with those from the comparisons using PWBM SWE across North America, while lower correlations are noted for Eurasia. Mean R 2 sarehigher across eastern Eurasia (east of longitude 90 ◦ E) as compared with western Eurasia. The better agreement across Siberia is likely attributable to the higher fraction of precipitation which falls as snow and the higher discharge/precipitation ratios across the colder east. When the PWBM is driven with precipitation data from a new gauge-corrected archive for the former USSR (“Daily and Sub-daily Precipitation for the Former USSR”) (National Climatic Data Center, 2005), basin R 2 s are generally no higher (figure not shown). This suggests that precipitation-gauge undercatch is not a significant influence on the computed SWE vs. Q agreements. Snowmelt and subsequent rises in river Q begins in southerly regions of the terrestrial arctic and progresses northward each spring. Comparisons of winter SWE storage and Q over a fixed interval (e.g. April–June) are complicated when inputs from rainfall are significant, or a large fraction of the snowmelt occurs outside of the April–June period. A more meaningful comparison of SWE and river Q wouldberestrictedtothatfractionofQ which is attributable to the melting of snow. For example, simulated spring Q from PWBM—driven by ERA-40 data—explains a much higher proportion of observed spring Q than does the pre-melt SWE across the study basins (Figure 4, Table 1). The 125
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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
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 and 132: RMSE Non linear Azar Chang Goodison
Page 133 and 134: 63 nd EASTERN SNOW CONFERENCE Newar
Page 135: http://www.ccin.ca); from simulatio
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
Page 183 and 184: imagery, the higher latitudes rely
Page 185 and 186: 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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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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