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(a) Wind Direction (°) Wind Speed (m/s) Air Temperature (°C) 360 300 240 180 120 60 (a) (a) 0 3.5 2.5 1.5 0.5 20 16 12 8 4 0 3 2 1 0 Dry Period Diurnal Variation of Wind Speed and Direction 0:00 2:00 Wind Dir. Wind Speed 4:00 6:00 8:00 10:00 12:00 Time (hours) 14:00 16:00 18:00 20:00 22:00 3 2.5 2 1.5 1 0.5 0 Wind Speed (m/s) Wind Direction (°) 272 360 300 240 180 120 60 0 Wet Period Diurnal Variation of Wind Speed and Direction 0:00 2:00 4:00 6:00 8:00 10:00 12:00 Time (hours) Wind Dir. Wind Speed 14:00 16:00 18:00 20:00 22:00 Figure 10. Composite diurnal cycle of wind speed and direction for dry (a) and wet (b) periods. Dry Period Wind Speed versus Direction Valley Orientation 55° => 235° 0 45 90 135 180 225 270 315 360 Wind Direction (°) Wind Speed (m/s) 3.5 3 2.5 2 1.5 1 0.5 0 Wet Period Wind Speed versus Direction Valley Orientation 55° => 235° 0 45 90 135 180 225 270 315 360 Wind Direction (°) Figure 11: Composite wind speed versus direction for dry (a) and wet (b) periods. Dry Period Air Temperature versus Wind Direction Valley Orientation 55° => 235° 0 45 90 135 180 225 270 315 360 Wind Direction (°) Air Temperature (°C) 20 16 12 8 4 0 Wet Period Air Temperature versus Wind Direction Valley Orientation 55° => 235° 0 45 90 135 180 225 270 315 360 Wind Direction (°) Figure 12: Composite wind speed versus air temperature for dry (a) and wet (b) periods. iButton temperatures Table 2 summarizes the iButton temperature profile observed during the wet and dry periods. With exception of the iButton at 3948 m, which is cooler during the wet season due to shading from the south wall off the valley, the general trend is for stronger seasonal contrasts at higher elevation. Although the up-valley “lapse rate” is not the conventional atmospheric lapse rate, it is a strong indicator of sensible heat flux within the surface boundary layer, thereby affecting the rates of ice and snow ablation. Figs. 13 provides a unique perspective on the diurnal variability of temperature as function of elevation. The strong nocturnal inversion below the level of the meltwater lakes during the dry period is not present during the wet period. In general, the lapse rate is greater below the lakes than above for both seasons, with greatest diurnal variability during the dry period. Fig. 14 shows the composite lapse rate for both seasons. The average dry period zero degree isotherm is at 5377 m above sea-level, 344 m higher than the wet period freezing level (Fig. 14). Hence, the local valley impact on ablation rate of the glacial tongue is possibly 3 2.5 2 1.5 1 0.5 0 Wind Speed (m/s)
significant, although more information on the global and synoptic forcing is necessary to make conclusions at this time. Table 2. Average temperature recorded by iButtons during the dry and wet periods. Note the seasonal impact of “lapse rate” on the elevation of the freezing line (0°C). Elevation (m) (a) 4800 4600 4400 4200 4000 3800 3600 3400 Dry Period Diurnal Change of Temperature Profiles iButton Dry Wet Dry/ Elevation (m) (°C) (°C) Wet 4742 2.84 1.77 1.60 4559 4.39 3.06 1.43 4344 5.31 3.56 1.49 4148 6.68 5.66 1.18 3948 8.12 5.48 1.48 3871 8.59 7.45 1.15 3862 7.49 6.24 1.20 3469 9.49 9.48 1.00 Lapse Rate (°C/km) 5.3 5.9 0.90 0°C Elevation (m) 5377 5033 1.07 6:00 7:00 8:00 9:00 12:00 15:00 16:00 17:00 18:00 21:00 0:00 3:00 -5 0 5 10 15 20 25 Temperature (°C) Elevation (m) 273 4800 4600 4400 4200 4000 3800 3600 3400 Wet Period Diurnal Change of Temperature Profiles 6:00 7:00 8:00 9:00 12:00 15:00 16:00 17:00 18:00 21:00 0:00 3:00 -5 0 5 10 15 20 25 Temperature (°C) Figure 13. Diurnal variability of near-surface air temperature profiles, “lapse rate,” for dry (a) and wet (b) periods. Elevation (m) 5400 5200 5000 4800 4600 4400 4200 4000 3800 3600 3400 Average Temperature Profiles: Dry and Wet Periods Dry Wet Linear (Wet) Linear (Dry) Tdry = -0.0053(Z) + 28.5 R 2 = 0.95 Twet = -0.0059(Z) + 29.7 R 2 = 0.97 0.0 2.0 4.0 6.0 8.0 10.0 Air Temperature (°C) Figure 14. Composite iButton temperature profiles for dry (a) and wet (b) periods.
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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
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Snow Remote Sensing 73
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height averaged 11.4 m with an aver
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strings were buried 20 - 30 cm belo
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Although the λE/Rn fraction was on
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estimated snow depth based on the 2
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important to note that the average
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Schmidt, R. A., C. A. Troendle, and
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METHODS The IMS Product The IMS was
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Figure 1. Microwave spectral charac
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snow depth and SWE on a weekly basi
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Figure 4. Example of blended SWE pr
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Evaluation of the global distributi
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Figure 9. Inter-comparison plots of
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Helfrich, S.R., D. McNamara, B.H.Ra
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105 63 rd EASTERN SNOW CONFERENCE N
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and validated regionally so they ca
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109 Test Site Figure 2. Variation o
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Correlation Coe. Correlation Coe. C
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Figure 8. SSM/I scattering signatur
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Considering the facts mentioned abo
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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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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
Page 233 and 234: Meteorological data for the winter
Page 235 and 236: monthly probability adjusted data 4
Page 237 and 238: 225 Pullman WA Rawlins WY Leadville
Page 239 and 240: The daily wind speed can exceed 6.5
Page 241 and 242: NCDC. 2006. National Climate Data C
Page 243 and 244: ABSTRACT 231 63 rd EASTERN SNOW CON
Page 245 and 246: and mass balances for 540 environme
Page 247 and 248: Temperature, precipitation, windspe
Page 249 and 250: the left in response to snow compac
Page 251 and 252: Pinched-cone dimensions for each sl
Page 253 and 254: Terrain slope (degrees) Terrain slo
Page 255 and 256: ACKNOWLEDGEMENTS This work was fund
Page 257 and 258: Glacial and Periglacial Processes 2
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Page 261 and 262: From linear regression the constant
Page 265 and 266: DATA COLLECTION Figure 1. Location
Page 267 and 268: Table 1. Comparison of GPS measured
Page 269 and 270: Transverse Velocity Profiles Surfac
Page 271 and 272: is shown in Figure 4. This variatio
Page 273 and 274: Table 4. The calculated volume flux
Page 277 and 278: Figure 1. Cordillera Blanca regiona
Page 279 and 280: MATERIALS AND METHODS Field Measure
Page 281 and 282: Data analysis techniques We synthes
Page 283: season, but unacceptable for the dr
Page 287 and 288: (a) Liquid Water (mm) Dry Period: M
Page 289 and 290: Future Work We are installing addit
Page 291 and 292: Shuttleworth, W.J., and J.S. Wallac
Page 293 and 294: Snow and Periglacial Processes Post
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Page 299 and 300: No one to date has shown why snowfl
Page 301 and 302: APPENDIX A Northern Hemisphere Year
Page 303 and 304: 62nd ESC Papers 291
Page 305 and 306: 62 nd EASTERN SNOW CONFERENCE Water
Page 307 and 308: each other; and, they both decrease
Page 309 and 310: 297 62 nd EASTERN SNOW CONFERENCE W
Page 311 and 312: comes in the form of precipitation.
Page 313 and 314: averaged to reduce bias in this var
Page 315 and 316: RESULTS Figure 2. Flow chart of reg
Page 317 and 318: Table 6: Actual-Conditions SWE, dep
Page 319 and 320: 307 Table 9: Regression results bet
Page 321 and 322: Goita, K., A. Walker, B. Goodison,
Page 323: TO ERR IS HUMAN, TO FORGET IT WOULD
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