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ALGORITHM DEVELOPMENT The study area, Great Lakes area, is located in the transitional zone for snow which experiences snow melting during the winter season. In addition to complexity of snow characteristics, variability of the land cover types makes it more difficult to accurately estimate SWE from passive microwave observations with a single linear model such as the one of Chang or Goodison-Walker (Chang et al, 1987, Goodison-Walker 1995). Figure (8) shows that the slope of the best fitted equations varies for different test sites (2, 9, and 18). These test sites have different land cover type and different NDNI values. In order to quantitatively account for scattering attenuation originating from forested areas, NDVI values are suggested to use. Figure (9) illustrates the variation of slope and NDVI for winter 2003–2004 for all the sites. Both NDVI and the slope have the same trend. This indicates that the increase of NDVI makes the slope of the relationship steeper. 16 14 12 10 Slope (m) 8 6 4 2 Winter 03-04, Variation of NDVI with the slope of SWE vs GTVN Slope NDVI 0 -0.1 0 5 10 15 20 Test site 0.3 0.25 0.2 0.15 NDVI 0.1 0.05 0 -0.05 114 6 5 4 Slope 3 2 1 0 03-04 Slopes SD Slopes and NDVI 0 5 10 15 20 Test site 0.15 NDVI Figure 9. Variations of the slope of the bets fitted line to scatter plots with NDVI for the test sites for winter 03–04, SWE vs. GTVN (left), SD vs. GTVN (right) The scatter plots of slope versus NDVI for SWE and snow depth are illustrated in figure 10. The modified scatter plot for 2002–2003 is for the test sites that snow depth vs scattering signature GTVN (19v–37v) showed correlation coefficient more than 50 percent. Slope slope 16 14 12 10 8 6 4 2 0 4 3.5 3 2.5 2 1.5 1 0.5 0 Winter 03–04 , SWE (mm) Winter 03–04, Snow depth (cm) 0 0.05 0.1 0.15 0.2 0.25 0.3 NDVI slope 6 5 4 3 2 1 0 NDVI 0.3 0.25 0.2 0.1 0.05 0 -0.05 0 0.05 0.1 0.15 0.2 0.25 0.3 Winter 02–03, Snow depth (cm) Winter 02–03, Snow depth (cm), Modified 0 0.05 0.1 0.15 0.2 0.25 0.3 NDVI 4 3.5 3 2.5 2 1.5 1 0.5 NDVI 0 0 0.05 0.1 0.15 0.2 0.25 0.3 Figure 10. Variation of the derived slope from the scatter plots vs. NDVI slope NDVI -0.1
Considering the facts mentioned above we propose a new algorithm which relates SWE and the SSM/I scattering signature GTVN (19v–37v) and accounts for possible variation of NDVI: SWE=F* (A* NDVI* + B)*GTVN, While NDVI >= 0 SWE=C*SSI+D While NDVI
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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
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 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: Figure 8. SSM/I scattering signatur
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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165 63 rd EASTERN SNOW CONFERENCE N
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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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