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Snow Depth (mm) Snow Depth (cm) Snow Depth (cm) Snow Depth (cm) 100 90 80 70 60 50 40 30 20 10 Goodison-Walker Alg. Chang Alg. New Alg. Winter 03–04 (SWE) Winter 03–04 (SWE) Winter 03–04 (SWE) GOODISON-WALKER Linear Algorithm R=0.7734 RMSE=63mm Bias= -47mm MAX(E)=32mm MAX(S)=173mm 0 0 10 20 30 40 50 60 70 80 90 100 Estimated (mm) Snow Depth (mm) CHANG Linear Algorithm 100 90 R=0.80306 80 RMSE=58mm 70 Bias= -44mm 60 MAX(e)=43mm 50 40 30 20 10 MAX(s)=173mm 0 0 10 20 30 40 50 60 70 80 90 100 Estimated SWE (mm) 116 Snow Depth (mm) AZAR Non-linear Algorithm 100 90 R=0.7734 80 RMSE=30mm 70 Bias= 3mm 60 MAX(E)=127mm 50 40 30 20 10 MAX(S)=173mm 0 0 10 20 30 40 50 60 70 80 90 100 Estimated (mm) Winter 03–04 (SD) Winter 03–04 (SD) Winter 03–04 (SD) GOODISON-WALKER Linear Algorithm 100 90 R=0.50634 80 RMSE=18mm 70 Bias= -13mm 60 MAX(E)=24mm 50 40 30 20 10 MAX(S)=50mm 0 0 10 20 30 40 50 60 70 80 90 100 Estimated (mm) Snow Depth (cm) CHANG Linear Algorithm 100 90 R=0.4396 80 RMSE=13mm 70 Bias= 7mm 60 MAX(e)=43mm 50 40 30 20 10 MAX(s)=36mm 0 0 10 20 30 40 50 60 70 80 90 100 Estimated (cm) Snow Depth (cm) AZAR Non-linear Algorithm 100 90 R=0.50634 80 RMSE=14mm 70 Bias= -8mm 60 MAX(E)=33mm 50 40 30 20 10 MAX(S)=50mm 0 0 10 20 30 40 50 60 70 80 90 100 Estimated (mm) Winter 02–03 (SD) Winter 02–03 (SD) Winter 02–03 (SD) GOODISON-WALKER Linear Algorithm 100 90 R=0.69708 80 RMSE=9mm 70 Bias= -4mm 60 MAX(E)=27mm 50 40 30 20 10 MAX(S)=36mm 0 0 10 20 30 40 50 60 70 80 90 100 Estimated (mm) Snow Depth (cm) CHANG Linear Algorithm 100 90 R=0.4396 80 RMSE=13mm 70 Bias= 7mm 60 MAX(e)=43mm 50 40 30 20 10 MAX(s)=36mm 0 0 10 20 30 40 50 60 70 80 90 100 Estimated (cm) Snow Depth (cm) AZAR Non-linear Algorithm 100 90 R=0.69708 80 RMSE=8mm 70 Bias= 1mm 60 MAX(E)=38mm 50 40 30 20 10 MAX(S)=36mm 0 0 10 20 30 40 50 60 70 80 90 100 Estimated (mm) Winter 01–02 (SD) Winter 01–02 (SD) Winter 01–02 (SD) AZAR Non-linear Algorithm 100 90 R=0.62132 80 RMSE=7mm 70 Bias= 1mm 60 MAX(E)=36mm 50 40 30 20 10 MAX(S)=38mm 0 0 10 20 30 40 50 60 70 80 90 100 Estimated (mm) Snow Depth (cm) CHANG Linear Algorithm 100 90 R=0.53409 80 RMSE=16mm 70 Bias= 12mm 60 MAX(e)=51mm 50 40 30 20 10 MAX(s)=38mm 0 0 10 20 30 40 50 60 70 80 90 100 Estimated (cm) Snow Depth (cm) AZAR Non-linear Algorithm 100 90 R=0.62132 80 RMSE=7mm 70 Bias= 1mm 60 MAX(E)=36mm 50 40 30 20 10 MAX(S)=38mm 0 0 10 20 30 40 50 60 70 80 90 10 Estimated (mm) Figure 11. Comparison of the results for different algorithms for test site 10 (Lat = 48.6N, Lon = –88.46W, and NDVI = 0.2)
Besides the temporal validation, the new algorithm was spatially validated for the whole study area (Latitudes: 41N to 49N & Longitudes: –87W to –98W). There were eleven days (3 days December, 4 days January, and 4 days February) in winter 2003–2004 selected. For those days the full coverage of the study area from SSM/I data was available. The ground truth data was obtained by averaging NOHRC SNODAS dataset. Figure 12 shows the ground truth and estimated SWE for January 25, 2004. 30 25 20 15 10 5 Chang Estimated SWE (mm) Ground Truth NOHRSC SWE (mm) 0 0 5 10 15 20 25 30 35 30 25 20 15 10 5 Estimated SWE CHANG Algorithm (mm),Jan 25,04 180 160 140 120 100 80 60 40 20 0 -20 30 25 20 15 10 117 5 SNODAS , Jan25,04 0 0 5 10 15 20 25 30 35 Goodison-Walker Estimated SWE (mm) New Algorithm Estimated SWE (mm) Estimated SWE AZAR Algorithm (mm),Jan 25,04 0 0 5 10 15 20 25 30 35 180 160 140 120 100 80 60 40 20 0 30 25 20 15 10 5 Estimated SWE Goodison Walker Algorithm (mm),Jan 25,04 0 0 5 10 15 20 25 30 35 Figure 12. Comparison of estimated SWE by various algorithms with ground truth data for January 25, 2004 for the study area (Lat: 41N to 49N & Lon: –87W to –98W) The NDVI image of the study area (Fig 13) shows higher values of NDVI around the lake. This is the area that both Chang and Goodison-Walker algorithms highly underestimate the SWE (Fig 12). In contrast, the new non-linear algorithm can estimate SWE in the area in the vicinity of the lake with much higher accuracy (Fig 13, 14). The calculated RMSE and correlation coefficient (R 2 ) are shown for all the three algorithms. The use of NDVI in the new algorithm results in a decrease of the RMSE and the increase of the correlation coefficient. It also increases the range for the estimated SWE. Figure 15 demonstrates a consistent improvement in the accuracy of the estimated SWE for the winter season of 2003–2004. For all days, application of the new developed algorithm results in the highest correlation coefficient between SSM/I and SWE. At the same time, the RMSE of SWE derived with the new algorithm is lower for all days but one. There 80 60 40 20 0 180 160 140 120 100 80 60 40 20 0 180 160 140 120 100
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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
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 and 126: Figure 8. SSM/I scattering signatur
Page 127: Considering the facts mentioned abo
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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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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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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