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! where θv is the nondimensional gas and vapour content, that is the porosity. Finally, we need to find a relation between the snow temperature and the phase change. We have chosen that the phase change should occur when the temperature in each snow layer is equal to 273.15 K: neither the temperature is allowed to increase as long as ice is present nor can it decrease until liquid water is entirely frozen. Therefore, W " 0 if T = 273.15K ; W = 0 if T " 273.15K (6) Thus the system of equations (1)-(2)-(3)-(5)-(6) is solved with a finite difference method with respect to the unknowns T, W, θw, θi, θv. The snow layers have a varying thickness in accordance with the snow precipitation and snow metamorphism (Anderson, 1976). The algorithms to combine and subdivide snow layers have been taken from Oleson et al. (2004), which makes it possible to describe the density changes of the snow pack. APPLICATION The model has been applied to the upper part of Brenta Basin (Trentino, Italy), in the Eastern Italian Alps, a basin with a surface area of about 250 square kilometres. Its elevation ranges from 340 m a.s.l. to 2303 m a.s.l., and the land cover is very variable. The bottom of the valley is urbanized, and many small villages are present, although there is still a wide countryside area. Two lakes, with total area of about 6 square kilometres, are also present. They play an important role in the regulation of the discharge, but, as here our main interest is to follow the snow cover evolution, they are neglected. As elevation increases, woodland prevails, even if there are some small resort villages and wide grassland and pasture areas. The vegetation limit ranges from 1900 m a.s.l. to 2000 m a.s.l.. The snow cover has been simulated for the season 2003-2004, namely from 16 th October 2003 to 16 th June 2004, using a model grid resolution of 250 m. METEOROLOGICAL DATA For the time considered, hourly precipitation measurement data were available for 15 stations located in the basin or in the nearby areas. Precipitation was then distributed according to kriging techniques, and it was split into liquid and solid form in conformity with the air temperature, defining a threshold air temperature TL above which the precipitation is always liquid and another threshold temperature TS below which the precipitation is always solid, as in U.S. Army Corps of Engineers (1956). These threshold temperatures are considered tuneable and adjustable parameters. In fact, as discussed later, in order to catch the snow events with precision, it results very important to distribute the air temperature in the basin accurately. Hourly air temperature observations performed in 17 meteorological stations in and around the basin were used to find a linear dependence of the temperature on the elevation, which is considered variable in time, but valid all over the basin, and an hourly value of the lapse rate was found. Figure 1 shows the relation between the air temperature measurements averaged on the whole season and the station elevations. The temperature variation presents a clear linear trend with a lapse rate of 3.9 °C/km (regression constant equal to 0.94), which is lower than the lapse rate normally used (6.5 °C/km). This means that at the annual scale the temperature measurements do not exhibit evident and regular variability with the horizontal coordinates. However, some stations located approximately at the same elevation can record temperature differences of some degrees (at the most 2 °C) due to local factors (for example shadowing) and to particular meteorological events. This could be a measure of the error in the temperature estimation. 198
Figure 1: Variation of the season average of the air temperature measurements with the elevations of the meteorological stations (dots), and regression line. Figure 2: Time evolution of the hourly and daily average lapse rate inferred by the measurements. But if the time evolution of the lapse rate, daily and hourly averaged (Figure 2) is plotted, it can be noticed that during the winter there are frequent periods of thermal inversion, with a nearly isotherm atmosphere, but during the spring time the lapse rate is more clearly defined and reaches values around the adiabatic lapse rate. The lapse rate also shows a typical daily cycle, with a marked nocturnal inversion in winter. The definition of a correct value of the lapse rate is critical to capture the correct snow-rain limit. Therefore, we have chosen to use an hourly varying lapse rate as input data for the model. 199
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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
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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Page 175 and 176: 180 160 140 120 100 80 60 40 20 0 1
Page 179 and 180: independent of regions. This greatl
Page 181 and 182: 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 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: In the following paragraph the main
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 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 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 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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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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