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Following previous research by Goodison and Walker (1995) and Derksen et al. (2002, 2003b), remotely sensed SWE estimates were compared to ground SWE measurements. The remotely sensed estimates found to be within ±20 mm (the previously determined accuracy of the MSC SWE algorithm) of ground measurements were considered as equivalent. SWE estimates found not to be within the ±20 mm threshold were considered as anomalous. Statistical Comparison Tests Z-tests were performed between the results of the Snow-Only and Actual-Conditions data sets to determine if there were significant differences in algorithm performance between the two groundtruth representations. Linear regression models were developed between the remote sensing data (as the dependant variables) and the ground observations from the coincident sampling sites (as the independent variables). The linear regression outputs were interpreted following the systematic procedure proposed by Gupta (2000) (Figure 2). The significance of the model fit is analyzed. The model significance explains the deviations of the dependent variables (eg. the SWE estimates and TB). We used a model significance of 0.10 (90% confidence level) as the cutoff for model acceptance. Models with levels below the 90% confidence level were removed from further analysis. The next step in interpreting the regression output is to analyze the Adjusted R 2 value from the model summary. This value is sensitive to the addition of irrelevant variables, and is a measure of the proportion of the variance in the dependent variables that are explained by the variations of the independent variables. For example, an Adjusted R 2 value of 0.500 suggests that 50% of the variance in a SWE estimate is explained by the variation in the ground-truth measurements. The third interpretation step involves identifying the reliability of the individual coefficients for the independent variables. The Beta values included in the coefficients output indicate the predicted coefficients for the model along with their standard errors and significances. Similar to the significance of the model fit, if a coefficient results in a significance value above 0.10, then it can be concluded that the independent variable is not significant at a 90% level of confidence. Table 2 provides an instructive example of how an output coefficient table is analyzed. The SWE predicted by the model is specified by the Constant’s Beta coefficient, 30.5 mm. The standard error of this prediction is 1.9 mm of SWE. The next step is to identify the significance of the independent variables’ Beta coefficients. In this example, a coefficient of .149 for the average snow pack depth is found to be significant at a 95% level of confidence (sig. = .043), but the coefficient of –2.899 for the average density is irrelevant (sig. = .776) towards the predicted SWE value. Table 2: Example coefficient table output Unstandardized Coefficients Model Beta St. Error Significance Constant (predicted SWE value) 30.5 1.9 — Depth 0.149 0.111 0.043 Density –2.899 10.148 0.776 302
RESULTS Figure 2. Flow chart of regression analyses The results are presented in two parts. First, the remotely sensed SWE estimates are compared with the SWE measurements obtained from the coincident sampling sites. This is followed by an analysis of the linear regression models. In-situ vs. Remotely Sensed SWE Estimates The SSM/I and swath AMSR-E SWE data provided the closest estimates to both in-situ data sets. This was expected, because: i) the MSC algorithm used to derive SWE estimates was actually developed for SSM/I TB, and ii) the swath AMSR-E TB have not been re-sampled, thus, they are truer representations of the interaction between the sensor and the ground surface. Tables 3 and 4 illustrate the number (and percentage in brackets) of sampling sites that were found to be equivalent (i.e. within ±20 mm SWE) to the remotely sensed SWE estimates. Table 3 shows the results of the Snow-Only data set, while Table 4 presents the results of the Actual-Conditions data set. These results also show a slight increase in algorithm agreement when only the amount of snow found at each sampling site is included in the ground-truth observations (i.e. Core_SWE from Table 3 vs. Table 4). 303
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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
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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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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
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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
Page 305 and 306: 62 nd EASTERN SNOW CONFERENCE Water
Page 307 and 308: each other; and, they both decrease
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Page 311 and 312: comes in the form of precipitation.
Page 313: averaged to reduce bias in this var
Page 317 and 318: Table 6: Actual-Conditions SWE, dep
Page 319 and 320: 307 Table 9: Regression results bet
Page 321 and 322: Goita, K., A. Walker, B. Goodison,
Page 323: TO ERR IS HUMAN, TO FORGET IT WOULD
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