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CONCLUSIONS The SSVAT model was validated with data from the CLPX field experiment conducted in late winter and early spring near Fraser, Colorado, in 2003. We show an example where soil/snow fluxes increase the energy stored within the snowpack by as much as 17%. Thermal conduction was the predominant soil/snow energy flux. Other energy fluxes, those associated with air convection and vapor diffusion, were 10 –7 smaller but do contribute to the formation of depth hoar and to grain size growth. Grain sizes in the upper snowpack are increased by soil/snow fluxes. Because radiobrightness scatter darkening at frequencies above 19 GHz increases with grain size, static Snow Water Equivalent (SWE) algorithms that use scatter darkening to estimate SWE become less reliable when the soil/snow vapor fluxes are large as they would be when the soil is wet and unfrozen. The maximum difference between soil temperatures of SSVAT and observations was 0.3 K in late winter. The maximum difference between soil temperatures for SNTHERM in that period was 1.7 K. The maximum difference between snow temperatures of SSVAT and observations was 2.8 K in early spring. The maximum difference between snow temperatures of SNTHERM and observations in that period was 6 K. That is, comparisons of SNTHERM and SSVAT with the CLPX data show that SSVAT provided better estimates of soil temperature by 1.4K in late winter and of snow temperature by 3.2 K in early spring. SSVAT also provides better moisture profiles in both snow and soil. The combination of SSVAT and a new radiobrightness module will enable monitoring of water stored in snow over frozen or unfrozen soil during periods when the snowpack is experiencing thawing and refreezing. It will also contribute to the design of the NASA Cold Lands Processes Pathfinder (CLPP) field experiment planned for the Arctic and help prepare the cold lands community to interpret the 1.4 GHz brightness observations from SMOS. REFERENCES Akitaya, E, 1974. Studies on depth hoar. Contributions from the Institute of Low Temperature Science, Series A, 26:1–67. Boone A, Etchevers P, 2001. An intercomparison of three snow schemes of varying complexity coupled to the same land surface model: Local scale evaluation at an alpine site. J. Hydrometeor., 2: 374–394. Chung YC, England, AW, 2005. A coupled soil–snow–atmosphere transfer model. Proc. of the IEEE International Geoscience and Remote Sensing Symposium, IGARSS'05, Seoul. ` Chung YC, England AW, De Roo RD, Weininger E, 2006(submitted). Effects of vegetation and of heat and vapor fluxes from soil on snowpack evolution and radiobrightness. Proc. of the IEEE International Geoscience and Remote Sensing Symposium, IGARSS'06, Denver, CO. Cline D (Chair, Cold Land Processes Working Group), Armstrong R, Davis R, Elder K, Liston G., 2001. NASA Cold Land Processes Field Experiment Plan Home Page. Cline D, Armstrong R, Davis R, Elder K, and Liston G., 2002, Updated July 2004. CLPX-Ground: ISA Snow Pit Measurements. Edited by M. Parsons and M.J. Brodzik., Boulder, CO: National Snow and Ice Data Center. Digital Media, England AW, 2003. CLPX-Ground: Micrometeorological Data at the Local Scale Observation Site (LSOS). Boulder, CO: National Snow and Ice Data Center. Digital Media. Graf T, Koike T, Fujii H, Brodzik M, Armstrong R, 2003. CLPX-Ground: Ground Based Passive Microwave Radiometer (GBMR-7) Data. Boulder, CO: National Snow and Ice Data Center. Digital Media. Flerchinger GN, Hanson CL, 1989. Modeling soil freezing and thawing on a rangeland watershed. Trans. Amer. Soc. of Agric. Engr., 32(5): 1551–1554. Hardy J, Melloh R, Koenig G, Pomeroy J, Rowlands A, Cline D, Elder K, Davis R, 2002, updated 2004. Sub-canopy energetics at the CLPX Local Scale Observation Site (LSOS). Boulder, CO: National Snow and Ice Data Center. Digital Media. 14
Jordan R, 1991. A one-dimensional temperature model for a snow cover. Technical documentation for SNTHERM.89, Special Technical Report 91-16, US Army CRREL. Jordan R, Andreas E, 1999. Heat budget of snow-covered sea ice at North Pole 4. J. Geophys. Res., 104(C4): 7785–7806. Judge J, 1999. Land surface process and radiobrightness modeling of the Great Plains, Ph.D. thesis, University of Michigan. Lehning et al., 1998. A network of automatic weather and snow stations and supplementary model calculations providing SNOWPACK information for avalanche warning, ISSW 98 International Snow Science Workshop, Sunriver, Oregon. Liou, YA, 1996. Land surface process/ radiobrightness models for northern prairie, Ph.D. thesis, University of Michigan. Su, GW, Geller, JT, Pruess, K, Wen, F, 1999. Experimental studies of water seepage and intermittent flow in unsaturated, rough-walled fractures,” Water Resources Research, 35: 1019–1037. Tao, Y.-X. and Gray, D.M.. 1994. Prediction of Snow-Melt Infiltration into Frozen Soils. Numer. Heat Transfer Part A, 26: 643–645. Waldner, PA, Schneebeli, M, Zimmermann, US, Fluhler, H, 2004. Effect of snow structure on water flow and solute transport. Hydrological Processes, 18: 1271–1290. Yang, ZL, 2004(in press). Description of recent snow models. Book Chapter, in Snow and Climate, E. Martin and R. Armstrong (editors), International Committee on Snow and Ice. Zhang, T, Osterkamp, TE, Stamnes K, 1997. Effects of Climate on the Active Layer and Permafrost on the North Slope of Alaska, U.S.A. Permafrost and Periglacial Processes, 8: 45– 67. 15
Page 1 and 2: Proceedings of the 63rd ANNUAL EAST
Page 3 and 4: FOREWORD T his proceedings volume c
Page 5 and 6: CONTENTS Foreword..................
Page 7 and 8: STATEMENT OF PURPOSE The Eastern Sn
Page 9 and 10: EXECUTIVES FOR THE 63rd EASTERN SNO
Page 11 and 12: THE PRESIDENT’S PAGE The 63rd ann
Page 13 and 14: Weisnet Medal for Best Student Pape
Page 15 and 16: 3 63 rd EASTERN SNOW CONFERENCE New
Page 17 and 18: Figure 1. Layer 1 represents the so
Page 19 and 20: Figure 4. The general layout of the
Page 21 and 22: lue color represented no convection
Page 23 and 24: Total Density of Water Profiles The
Page 25: Figure 11(a). Simulated snow grain
Page 29 and 30: Campbell Scientific Award for Best
Page 31 and 32: 19 63 rd EASTERN SNOW CONFERENCE Ne
Page 41 and 42: R ∑i ∑ n 2 = 1 NS = − n i = 1
Page 49 and 50: Snow and Climate 37
Page 53 and 54: Spatial distributions of changes in
Page 55 and 56: Snow and Climate Posters 43
Page 59 and 60: Figure 2 presents correlation maps
Page 61 and 62: CONCLUSIONS Three regional-continen
Page 65 and 66: Discharge (mm) 20 18 16 14 12 10 8
Page 67 and 68: ABSTRACT 55 63 rd EASTERN SNOW CONF
Page 69 and 70: This paper analyzes the synoptic pa
Page 71 and 72: Cyclones that track west of the App
Page 73 and 74: The synoptic-scale atmospheric circ
Page 75 and 76: CONCLUSIONS The record snowfall in
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65 63 rd EASTERN SNOW CONFERENCE Ne
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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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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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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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