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Figure 2. Linear correlations between winter (DJF) (green line) and April–May (blue line) snowfall anomalies and summer moisture anomalies (Z-index) calculated for all 15 yr time periods between 1929 and 1999. Dashed lines indicate the 95% significance level. The relationship between April–May snowfall and summer moisture anomalies is further strengthened if the candidate years are restricted to those occurring during the two periods (1929– 1954 and 1970–1987) when land surface conditions appear to be the dominant source of seasonal climate predictability. During these two periods there were 8 yrs with April–May snowfall anomalies that were more than one standard deviation below the mean. Seven of these 8 yrs were associated with drier than normal moisture conditions during the summer (mean Z-index = –0.73). There were also 8 yrs with April–May snowfall anomalies that were more than one standard deviation above the mean. Seven of the 8 yrs with large positive April–May snowfall anomalies were followed by wetter than normal moisture conditions during the summer (mean Z-index = 0.63). Based on these 16 yrs the linear correlation between April–May snowfall and summer moisture is 0.70 (statistically significant at the 95% confidence level). Significant spatial variability is also evident in the relationship between April–May snowfall and summer moisture anomalies (Figure 3). Linear correlations across the study region are generally positive with the highest correlations (up to 0.63) in southern Manitoba and South Dakota. There is only one grid cell in Wyoming where there is a statistically significant negative correlation (–0.23). Statistically significant correlations between April–May snowfall and summer moisture anomalies are found in approximately 56% of the study area. These grid cells tend to be concentrated on the eastern side of the study region and are notably absent from most of Wyoming, the eastern part of Montana, and southern Saskatchewan. 68
Figure 3. Linear correlations between April–May snowfall and summer moisture anomalies (Z-index). Colored grid cells are those with statistically significant correlations (95% significance level). Based on a composite analysis, the five driest summers between 1929 and 1999 (Table 1) are associated with a mean winter (spring) snowfall anomaly of –66.7 mm (–62.4 mm) (Figure 4). Approximately 85% of the study region received below normal snowfall during the winter and spring seasons prior to the five driest summers. About 28% (25%) of the study region had winter (spring) snowfall anomalies that are more than 100 mm below average and only 2% (1%) received winter (spring) snowfall that was more than 100 mm above normal. The five wettest summers between 1929 and 1999 were associated with a mean winter (spring) snowfall anomaly of 6.2 mm (21.6 mm). Approximately 53% (46%) of the study region received above normal snowfall during the prior winter (spring). About 15% (12.1%) of the study region had winter (spring) snowfall that was greater than 100 mm above normal and only 7% (1%) received winter (spring) snowfall that was more than 100 mm below normal. Results of the composite analysis indicate that anomalously dry (wet) summers are associated with significant negative (positive) snowfall anomalies during the preceding winter and spring, which supports the results of the correlation analysis. However, the composite analysis demonstrated that the winter/spring snowfall anomalies associated with the driest summers are typically greater in magnitude and more spatially extensive than the snowfall anomalies associated with wettest summers. 69
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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
Page 29 and 30: Campbell Scientific Award for Best
Page 31 and 32: 19 63 rd EASTERN SNOW CONFERENCE Ne
Page 41 and 42: R ∑i ∑ n 2 = 1 NS = − n i = 1
Page 49 and 50: Snow and Climate 37
Page 53 and 54: Spatial distributions of changes in
Page 55 and 56: Snow and Climate Posters 43
Page 59 and 60: Figure 2 presents correlation maps
Page 61 and 62: CONCLUSIONS Three regional-continen
Page 65 and 66: Discharge (mm) 20 18 16 14 12 10 8
Page 67 and 68: ABSTRACT 55 63 rd EASTERN SNOW CONF
Page 69 and 70: This paper analyzes the synoptic pa
Page 71 and 72: Cyclones that track west of the App
Page 73 and 74: The synoptic-scale atmospheric circ
Page 75 and 76: CONCLUSIONS The record snowfall in
Page 79: correlations between April-May snow
Page 83 and 84: winter/early spring could contribut
Page 85 and 86: Snow Remote Sensing 73
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 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
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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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157 63 rd EASTERN SNOW CONFERENCE N
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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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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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