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Figure 3 shows the correlation maps between gridded SWE (Derksen et al., 2004) and North America snow extent (Brown, 2000). The satellite data confirms the observed inconsistency between snow depth and North America snow extent from station datasets seen in Figure 2. Correlations decline from south to north in central North America region (38N–63N) for three consecutive months (December, January and February). Regions of negative correlation also exist north of snowline during January and February. 30 ° N 30 ° N 30 ° N 45 ° N 45 ° N 45 ° N 60 ° N 60 ° N 60 ° N Dec. 120 ° W 90 ° W 60 ° W Jan. 120 ° W 90 ° W 60 ° W Feb. 120 ° W 90 ° W 60 ° W Figure 3. Correlation maps between gridded SWE (Derksen et al., 2004) and North America snow extent (Brown, 2000). 48 0.6 0.4 0.2 0 -0.2 -0.4 0.6 0.4 0.2 0 -0.2 -0.4 0.6 0.4 0.2 0 -0.2 -0.4
CONCLUSIONS Three regional-continental scale snow depth datasets have been evaluated, derived from station observational data or satellite remote sensing data. All of the datasets consistently exhibit differing behavior between interannual timeseries anomalies of average snow-covered depth and North America snow extent at regional/continental scales, generally consistent with what is expected at local scales. Correlation maps of snow depth vs. snow extent show a broad, continental-scale region is maintained throughout much of the snow season in which snow depth variations are not mutually consistent with North America snow extent variations. Research also recognized that relative magnitude of snow depth anomalies is considerably larger than that for snow extent. The observed differing behavior of the two snowpack parameters at regional/continental scales suggest that snow depth variations may be of sufficiently large magnitude, spatial scope and temporal duration to influence regional-hemispheric climate, in a manner unrelated to the more extensively studied snow extent variations. Such analysis represents the first step towards the goal of identifying explicit snow depth – climate relationships and integrating them with snow extent– climate relationships. REFERENCES Brown, R.D., 2000. Northern Hemisphere snow cover variability and change, 1915–1997. Journal of Climate 13: 2339–2355. Derksen, C., Brown, R. D. and Walker, A., 2004. Merging conventional (1915–1992) and passive microwave (1978–2002) estimates of snow extent and water equivalent over central North America. Journal of Hydrometeorology 5(5): 850–861. Dyer, L.J. and Mote,T.L., 2006. Spatial Variability And Trends In Snow Depth Over North America. Geophysical Research Letters, in press. 49
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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
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 and 26: Figure 11(a). Simulated snow grain
Page 27 and 28: Jordan R, 1991. A one-dimensional t
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: Figure 2 presents correlation maps
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
Page 79 and 80: correlations between April-May snow
Page 81 and 82: Figure 3. Linear correlations betwe
Page 83 and 84: winter/early spring could contribut
Page 85 and 86: Snow Remote Sensing 73
Page 89 and 90: height averaged 11.4 m with an aver
Page 91 and 92: strings were buried 20 - 30 cm belo
Page 93 and 94: Although the λE/Rn fraction was on
Page 95 and 96: estimated snow depth based on the 2
Page 97 and 98: important to note that the average
Page 99 and 100: Schmidt, R. A., C. A. Troendle, and
Page 103 and 104: METHODS The IMS Product The IMS was
Page 105 and 106: Figure 1. Microwave spectral charac
Page 107 and 108: snow depth and SWE on a weekly basi
Page 109 and 110: 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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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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