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The height of the anemometer at each station was assumed to be at 6m, and the height of the gauge orifice was assumed to be at 2m. The wind speed was converted from a 6m height to a 2 m height, as per Goodison et al. (1998) using the snowpack aerodynamic roughness of 0.005 m (Fassnacht et al., 1999). For solid precipitation, wetting losses were assumed to be small as compared to wind induced losses, and for monthly computations were assumed to be minimal. Similarly, evaporation losses were assumed to be negligible (Goodison et al., 1998). Undercatch equations were derived from daily data for snowfall when the daily air temperature (Ta) was colder than freezing and for mixed precipitation when Ta was between 0 and 3 degrees C (Goodison et al., 1998). The specific equations derived by Yang et al. (1998b) for the unshielded 8" NWS Standard precipitation gauge with respect to the Double Fence Intercomparison Reference gauge (DFIR) were used for all sites except Syracuse (Table 1). This station used a Belfort Universal gauge. As per Groisman et al. (1999), the Yang et al. (1998b) equation was used for snowfall undercatch of the Belfort Universal gauge. Mixed precipitation undercatch was increased by 7% for the Belfort Universal gauge used at Syracuse (Groisman et al., 1999). As per Fassnacht (2004) and Bogart et al. (2006), a maximum wind speed of 6.5 m/s was used in the undercatch equations due to the increased uncertainty at the higher wind speeds. Sublimation and the occurrence of blowing snow are episodic and were thus computed using hourly data. Sublimation was estimated using the bulk transfer approach for the latent heat flux as a function of humidity (RH), air temperature, wind speed, and station pressure (PR): ( RH, T , U , PR) FE = f a z , (3) as initially formulated by Sverdrup (1936). The occurrence of blowing snow (BSY/N) was initially estimated as a function of wind speed and different temperature considerations, as per Li and Pomeroy (1997): Y / N = . (4) ( U T ) BS f , z a Once blowing snow was determined to have initiated, the quantity of blowing snow was computed as a function of wind speed: BS ( U ) q = f , (5) z using the equation derived by Pomeroy et al. (1991). Sublimation and blowing snow quantities were summed to yield net snowpack loss estimates. The NWS denotes trace events (PT) as precipitation amounts less than 0.01 inches or 0.254 mm per hour or day (NWS, 2005), while Legates et al. (2005) called all measurements less than half the measurable precipitation depth (0.005 inches or 0.127 mm) as a trace event. In this paper, daily trace events will be assigned a value of 0.127 mm. RESULTS Hourly, daily and monthly meteorological data were used to compute PU (Figure 1) and snowpack losses (FE plus qBS) (Figure 2). The estimated difference between losses and undercatch using the time step specific to the derived equations versus using a monthly time step is presented in Figure 3 for monthly totals, and Figure 4 for seasonal totals. For the difference comparison, the same net result would appear along the 1:1 line, while data at the origin would indicate that measured precipitation could be used as an estimate of snow on the ground. Below the x-axis or to the left of the y-axis, more snow is actually accumulating than estimated from the measured precipitation alone. The difference between the two time step estimates and gauge precipitation for individual months is illustrated in Figure 5, and for each season is illustrated in Figure 6. 222
monthly probability adjusted data 40 [% of δt cumulative] 90 80 70 60 50 30 20 10 0 100 0 0 10 20 30 40 50 daily data 60 70 80 90 100 [% of δt cumulative] 100 223 90 80 70 60 average monthly data [% of δt cumulative] 50 Pullman WA Rawlins WY Leadville CO Rhinelander WI Syracuse NY Figure 1. Comparison of monthly precipitation undercatch estimates using daily and monthly data. Each estimate is presented as a percentage of the total of the three different datasets. The values for undercatch have been derived for daily data. The average monthly data were derived from the mean of the daily data, whereas the monthly probability adjusted data were derived using the monthly precipitation distribution (snow, mixed precipitation, or rain) and the average wind speed during each type of precipitation. average monthly data 40 [% of δt cumulative] 90 80 70 60 50 30 20 10 0 100 0 0 10 20 30 40 50 60 70 80 90 100 hourly data [% of δt cumulative] 100 90 80 70 60 50 40 40 30 30 20 20 10 Pullman WA Rawlins WY Leadville CO Rhinelander WI Syracuse NY daily data [% of δt cumulative] Figure 2. Comparison of relative monthly snowpack loss (sublimation plus blowing snow) estimates using hourly, daily, and monthly data. Each estimate is presented as a percentage of the total of the three different time step estimates. The values have been derived for hourly data. 10
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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
Page 183 and 184: imagery, the higher latitudes rely
Page 185 and 186: MODIS Land Science Team, NASA Godda
Page 187 and 188: FUTURE OF IMS Enhancements to the I
Page 189 and 190: information never present before on
Page 191 and 192: Brubaker, K.L., R. Pinker and E. De
Page 193 and 194: 181 63 rd EASTERN SNOW CONFERENCE N
Page 195 and 196: The Sierra Nevada del Cocuy region
Page 197 and 198: Colombia Venezuela Glacier Series R
Page 199 and 200: Table 4. Glacier Areas of the Ruiz-
Page 201 and 202: Pico Bonpland Massif-Sinigüis Glac
Page 203 and 204: Linder W, Jordan E, 1991. Ice-mass
Page 205 and 206: Snowpack Processes 193
Page 207 and 208: 63 rd EASTERN SNOW CONFERENCE Newar
Page 209 and 210: In the following paragraph the main
Page 211 and 212: Figure 1: Variation of the season a
Page 213 and 214: Figure 3: 8-day composite maps (24
Page 215 and 216: The third pair (Figure 5) represent
Page 217 and 218: Figure 9: Time evolution of SWE ave
Page 219 and 220: melting occurs, whereas at the high
Page 221 and 222: Colbeck SC, Anderson EA. 1982. The
Page 223 and 224: ABSTRACT 211 63 rd EASTERN SNOW CON
Page 225 and 226: A B Figure 1. Low-magnification sec
Page 227 and 228: GB ridge GB groove Figure 3. Higher
Page 229 and 230: CONCLUSION Figure 5. Indexed electr
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Page 233: Meteorological data for the winter
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
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 and 284: 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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