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that defines a second point on the cone. The three points defining the shaped solution do not have to be these specific ordinates because any three points on the cone will define the shape; however, points near these extremes are recommended. The fundamental shape of the solution domain can be used to help plan snow measurement networks. In the absence of model solutions, measurements at a few key locations in a basin might be used to define a cone shape and provide an estimate of the snow properties for any slope–azimuth combination of similar canopy and elevation. Locations in contrasting canopy or elevation can define the change in cone dimension with these environmental gradients. Mathematics on the solution domains provides another interpolation route. Our first application of the shaped solution domains will be to distribute snow depth and density across the landscape of the Ethan Allen Firing Range near Jericho, Vermont, for a vehicle mobility model. Interpolating between the shaped solution domains of the 20 slope–azimuth–canopy–elevation environments permits creation of a continuous snow property map across the landscape if continuous maps of each of the environmental variables are available (slope, azimuth, canopy transmissivity, and elevation). The forest map at Ethan Allen Firing Range is categorical, thus our forest component interpolation will also be categorical. Other examples of solution domain mathematics include snow density computations from snow water equivalent and snow depth cones, and melt rate calculations by differencing snow water equivalent cones over time. Equation 13 (with a shape correction to r=0 at the minimum axis) is a good approximation of the fundamental shape for our intended application. Additional model studies at shorter time steps to improve model consistency and potentially refine the azimuths of the major axes are warranted. An equation that predicts r (Δ snow depth) as a function of z and θ o will be developed for future applications (Melloh et al. in review). The shaped solution domains were developed to map snow properties in a hilly forested terrain typical of New Hampshire and Vermont and over a relatively small basin. The method is likely to be useful in other situations, though adaptations are needed to deal with large basins, topographic shading, and blowing snow. The presented method will not be especially useful on plains and prairies where the topography is flat, and where there is significant blowing snow. Using a cone method over larger basins will require adaptations to account for latitudinal and longitudinal meteorological gradients. The cone does not account for topographic shading, an important consideration in mountainous basins. Also of interest is how the cone shape changes with changing maximum sun angle at lower and higher latitudes. It is also important to recognize that the cone solutions represent mean snow properties, and on any given slope in the field there will be variability about the mean due to microclimate and local deposition. CONCLUSION A shaped solution domain was identified for snow depth, water equivalent, and density. The pinched-cone shape describes the differentiation of the snow properties with slope and azimuth relative to the no-slope case represented by the vertex of the cone. The shaped solution domain is approximated by an analytical equation with only two coefficients. A time series of snow properties over a snow season can be described efficiently by a time series of three numbers: the snow property for the no-slope case, and the two coefficients. Interpolating between the shaped solution domains of slope–azimuth–canopy–elevation environments permits creation of a continuous snow property map across the landscape if continuous maps of each of the environmental variables are available (slope, azimuth, canopy transmissivity, and elevation). Knowledge of the shape of the solution domain can be exploited to portray snow property differentiation across environments and may lead to less model-intensive approaches of estimating snow properties across a landscape. 242
ACKNOWLEDGEMENTS This work was funded by the U.S. Army AT42 High-Fidelity Ground Platform and Terrain Mechanics Modeling Program. The meteorological data used in this publication were obtained by scientists of the Hubbard Brook Ecosystem Study; this publication has not been reviewed by those scientists. The Hubbard Brook Experimental Forest is operated and maintained by the Northeastern Forest Experiment Station, U.S. Department of Agriculture, Radnor, Pennsylvania. We thank Susan Frankenstein and Paul Richmond for their reviews of this manuscript. REFERENCES Anderson EA, Baker DR. 1967. Estimating incident terrestrial radiation under all atmospheric conditions. Water Resources Research 3(4): 975–988. Dingman SL. 1988. Application of kriging to estimating mean annual precipitation in a region of orographic influence. Water Resources Bulletin 24(2): 329–339. Dingman SL. 1993. Physical Hydrology. Englewood Cliffs, NJ: Prentice Hall, 575 pp. Dunne T, Leopold LB. 1978. Water in Environmental Planning. San Francisco, CA: W.H. Freeman and Co. Flerchinger GN, Cooley KR, Deng Y. 1994. Impacts of spatially and temporally varying snowmelt on subsurface flow in a mountainous watershed: 1. Snowmelt simulation. Hydrological Sciences 39(5): 507–520. Goodison BE. 1978. Accuracy of Canadian snow gage measurements. Journal of Applied Meteorology 27: 1542–1548. Jordan R. 1991. A one-dimensional temperature model for a snow cover: Technical documentation for SNTHERM.89. U.S. Army Cold Regions Research and Engineering Laboratory, Special Report 91-16. Marks D, Domingo J, Susong D, Link T, Garen D. 1999. A spatially distributed energy balance snowmelt model for application in mountain basins. Hydrological Processes 13: 1925–1959. Melloh RA, Richmond P, Shoop S, Coutermarsh B, Affleck R. in review. Continuous snow property mapping for mobility models. Cold Regions Science and Technology. Melloh RA, Hall TJ, Bailey RN. 2004. Radiation data corrections for snow-covered sensors: Are they needed for snowmelt modeling? Hydrological Processes 18: 1113–1126. Richmond, P., Reid A, Shoop S, Mason G. 2005. Terrain Surface Codes for an All-Season, Off- Road Ride Motion Simulator, MSIAC M&S Journal On-Line. Shoop S, Coutermarsh B, Reid A. 2004. All-Season Virtual Test Site for a Real-Time Vehicle Simulator, Paper No. 2004-01-2644, SAE Transactions 113(2): 333–343. Tarboton DG, Luce CH. 1996. Utah Energy Balance Snow Accumulation and Melt Model (UEB) Computer Model Technical Description and Users Guide. Utah Water Research Laboratory and USDA Forest Service Intermountain Research Station: 64 p. U.S. Department of Agriculture, Forest Service, Northeastern Research Station, Hubbard Brook. 2003. Web page (http://www.hubbardbrook.org), GIS coverages, and overview (guidebook). 243
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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
Page 203 and 204: Linder W, Jordan E, 1991. Ice-mass
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Page 209 and 210: In the following paragraph the main
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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
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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 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
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Page 251 and 252: Pinched-cone dimensions for each sl
Page 253: Terrain slope (degrees) Terrain slo
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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 and 284: season, but unacceptable for the dr
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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
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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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