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ANALYSIS OF THE DATA The study area is located in Great Lakes area between latitudes 41N and 49N and longitudes 87W and 98W. It covers parts of Minnesota, Wisconsin and Michigan states. The area has various land covers (Fig 1). The area is covered by 980 (28 by 35) EASE-Grid pixels. To do the time series analysis 19 test sites were selected. Each site, 25km*25km, represents an SSM/I pixel. The sites were selected based on their latitude and their land cover type along with the annual snow accumulation. In order to avoid wet snow conditions we used the data starting December 1 of each year to the February 28 of the year after. Three 90 day sets of data were derived for each winter. Table 1 shows geographical location of the selected sites and their NDVI characteristics including the mean value and variance. High variance of NDVI indicates substantial changes. Table 1. Coordinates of selected pixels along with NDVI values Test Site SSM/I Pixels Latitude Longitude NDVI= (P–128) × 0.008 P value P mean P variance 1 42.33 –93.62 123.00 123.86 1.07 2 42.89 –91.97 123.00 123.20 0.45 3 43.63 –91.43 124.00 124.86 2.27 4 44.14 –90.57 145.00 143.11 6.19 5 44.39 –89.12 128.00 132.00 4.24 6 45.12 –89.11 130.00 132.22 5.14 7 46.07 –88.19 162.00 155.33 13.53 8 45.59 –88.21 152.00 157.67 9.06 9 46.09 –88.79 160.00 161.44 9.10 10 46.80 –88.46 166.00 152.56 10.63 11 46.80 –88.16 150.00 157.22 7.95 12 46.83 –89.69 159.00 158.89 10.53 13 45.36 –91.18 132.00 132.78 7.50 14 45.56 –92.68 125.00 130.89 3.66 15 47.26 –92.78 149.00 143.33 5.10 16 48.01 –91.88 148.00 154.56 9.82 17 47.92 –94.08 135.00 137.89 6.55 18 48.40 –95.99 135.00 132.33 4.36 19 47.47 –97.47 125.00 124.78 0.67 108 Dry land and Cropland Cropland/Grassland Cropland /Woodland Mixed Forest Deciduous Broadleaf Evergreen Needleleaf Urban and Built-up Land Water Body Figure 1: Land Cover Image According to USGS National Atlas of Land Cover Characteristics
109 Test Site Figure 2. Variation of SWE for winter season 2003–2004 and the corresponding Box plot In this study we verify the correlation between SSM/I channels and snow depth and SWE for different types of land cover located in different latitudes. Three series (2002–2004) of SSM/I channels versus snow depth and SWE for each of the selected sites were derived. The SSM/I data were obtained from the descending pass of Defense Meteorological Satellite Program (DMSP) satellites. There are three SSM/I scattering signatures used in this analysis. The first scattering signature named GTH (19H–37H) is the difference between 19 and 37 GHz in horizontal polarization. The second signature, GTVN (19V–37V) shows the discrepancy between vertically polarized 19 and 37 GHz. Finally, SSI (22V–85V) presents the difference between 22 and 85 GHz in vertical polarization. SSI can be used to identify shallow snowcover. The Box plot of the signatures GTH and GTVN for winter season 2003–2004 is shown in figure 3. The outliers in the Box plots range from –20 to 20 are due to either sensor or data processing errors which need to be eliminated. GTVN mean ranges from 5 to 15 for all the pixels except for the test site 12 which is very close to the lake. GTH Box plot and mean has the same pattern as GTVN. The test site 12 has a mean around –5 for GTVN (19V–37V) indicating of the fact that part of SSM/I pixel is water. Since the SSM/I sensor have different spatial resolution for various channel, the scattering from pixel 12 is disturbed in channel 19GHz (69km resolution) by the lake however it is not disturbed in channel 37GHz (37km resolution). The low scattering of the water and high scattering of the land make the difference of 19V–37V a negative number. GTVN (19H-37H) (k) Box Plot of GTVN for Points 1-19 Box Plot of GTH for Points 1-19 Test Site GTH (19H-37H) (k) Test site Figure 3. Box-Whiskers plot of GTVN and GTH for winter season 2003–2004
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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
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 77 and 78: 65 63 rd EASTERN SNOW CONFERENCE Ne
Page 79 and 80: 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 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 validated regionally so they ca
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 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
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159 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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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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