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characteristics associated with this seasonal snowmelt may provide a better understanding of the sensitivity of snowmelt timing to climatic conditions as well as the effects of the snowmelt upon basin hydrology. The Yukon River extends from the Juneau Icefield, British Columbia, to the Bering Sea, passing through the Yukon Territory and Alaska. It receives drainage from a basin more than 850,000 km 2 in area. The majority of this drainage flows from the Bering Sea northward to provide 8% of the total fluvial freshwater input to the Arctic Ocean (Aagaard and Carmack, 1989). The basin includes a variety of ecosystems as well as a range of elevations and local relief. This region has a heterogeneous terrain marked by boreal forests, mountain ranges, low-lying valleys, and a variety of lakes. Much of the upper basin is seasonally snow covered aside from some glaciated regions near the headwaters. This paper will focus on the timing of the spring snowmelt transition in the Wheaton River subbasin of the Yukon River (Fig. 1), from which the findings may then be applied to larger subbasins and eventually the upper Yukon River basin as a whole. The Wheaton River basin is a small (875 km 2 ) upland basin that has heterogeneous topography and terrain with only a small glacial input. Two passive microwave sensors, the Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E), provide at least twice daily observations in the high latitude regions, and the record of SSM/I observations extends back nearly two decades. Due to differences in brightness temperature emitted from dry versus wet snow, these passive microwave sensors are effective at differentiating between snow that is melting and snow that is not melting, or frozen. In addition, the ability of these sensors to observe snow characteristics through clouds and most other atmospheric conditions, as well as during the night, proves useful for examining snowmelt in high latitude areas that may have increased cloud cover and darkness during the winter and spring months. The purpose of this study is to derive the timing of spring snowmelt in the Wheaton River basin with recently acquired AMSR-E observations and to test the sensitivity of this sensor to the dynamic and varied regional snowpack characteristics. A snowmelt onset algorithm developed by Ramage et al. (2006) for use with SSM/I data is modified for use with AMSR-E data to allow for a direct comparison of snowmelt onset timing from both of these passive microwave sensors. In addition, higher-resolution AMSR-E data and elevation data are used to show improvements of this sensor over the SSM/I sensor in detecting snowmelt within heterogeneous terrain. The algorithm is fine-tuned using hourly near-surface air temperature data and ground-based snow wetness data from within the Wheaton River basin. Due to the greater spatial and temporal resolution of the AMSR-E sensor compared to SSM/I, snowmelt dynamics can be more accurately described with AMSR-E data in the mixed terrain of the upper Yukon River basin. DATA Passive microwave satellites provide a means by which to differentiate between snow that is melting and snow that is frozen. Both AMSR-E and SSM/I brightness temperature data were used for this analysis. In addition, 90 meter digital elevation models of the Yukon Territory were utilized (Environment Yukon, 2000), as well as hourly temperature measurements and snow wetness measurements from within the Wheaton River basin. AMSR-E swath data, in the form of Level-2A Global Swath Spatially-Resampled Brightness Temperatures (Tb) V001, are supplied by the National Snow and Ice Data Center (NSIDC) through the EOS Data Gateway with a record of observation that extends back to 2002 (Ashcroft and Wentz, 2004). The AMSR-E sensor provides two to four observations of the study area per 138
day and has a mean pixel resolution at 36.5 GHz of 12 x 12 km 2 (actual footprint size is 14 x 8 km 2 ). Twice-daily SSM/I data extending back to 1987 come from the Defense Meteorological Satellite Program (DMSP) SSM/I and have a nominal resolution for the 37 GHz channel of 37 x 28 km 2 . SSM/I images gridded to the Equal Area Scalable Earth Grid (EASE-Grid) 25 x 25 km 2 resolution are provided by the NSIDC, and this product separates the ascending and descending passes. The AMSR-E data were gridded first at the EASE-Grid 25 x 25 km 2 resolution to compare directly with the SSM/I data and establish to what degree they are similar with a minimum of complicating factors. The AMSR-E data then were gridded at the finer EASE-Grid 12.5 x 12.5 km 2 resolution to examine improvements in snowmelt detection of AMSR-E upon the SSM/I sensor. Passive microwave sensors have been used to examine characteristics of snow such as snow extent (e.g. Abdalati and Steffen, 1997; Walker and Goodison, 1993; Wang et al., 2005), snow depth (e.g. Josberger and Mognard, 2002; Kelly et al., 2003), and snow water equivalent (e.g. Derksen et al., 2005; Foster et al., 2005; Goita et al., 2003). The SSM/I sensor has been used in previous studies to establish the timing of the spring melt transition in the upper Yukon River basin (Ramage et al., 2006) as well as in the Juneau Icefield (Ramage and Isacks, 2002, 2003). Even though the SSM/I sensor provides twice-daily observations and has been shown to correlate well with ground-based brightness temperature measurements over fairly homogeneous terrain such as the Alaskan North Slope (Kim and England, 2003), the pixel resolution of greater than 25 x 25 km 2 that results from the passive nature of the sensor is a problematic issue in monitoring dynamic changes over heterogeneous terrain. The AMSR-E sensor, recently launched aboard NASA’s Aqua satellite in 2002, provides more observations of the study area per day and, with an improved pixel resolution over SSM/I, can help to provide a more accurate examination of snow characteristics over mixed terrain. A significant difference in brightness temperature (Tb) between dry and wet snow occurs at frequencies greater than 10 GHz. The Tb of a material is related to its surface temperature (Ts) and emissivity (E), Tb = ETs. (1) A rapid increase in emissivity occurs as a result of a small amount (~ 1-2%) of liquid water within the snowpack, causing the Tb to increase for wet snow (Ulaby et al., 1986). The Tb in the 19 and 37 GHz frequencies associated with the SSM/I sensor are useful in detecting melt on glaciers (Ramage and Isacks, 2002, 2003) and on heterogeneous terrain (Ramage et al., 2006) since the Tb transition from dry to wet snow occurs as surface temperatures approach 0°C. From the AMSR-E sensor, the Tb from the vertically polarized 36.5 GHz frequency (wavelength of 0.82 cm) is comparable to the SSM/I sensor for the detection of snowmelt. Here, we focus on comparing and contrasting the Tb from the two sensors and comparing the ability to detect the onset of snowmelt. During the spring snowmelt, the snowpack experiences cyclical daytime melt and nighttime freeze, changes that are manifested as diurnal differences in Tb. As a result of the at least twicedaily observations by the sensors, these high diurnal amplitude variations of Tb (referred to as DAV) can be detected and are useful in identification of these dynamic transition periods. The Wheaton River basin is covered by six EASE-Grid 25 x 25 km 2 pixels (Fig. 1a and Table 1). Due to the coarse pixel size in relation to the size of the basin, some pixels only cover a small percentage of the basin (see Table 1). The Wheaton basin has a range of elevations and land cover that include bare mountain slopes, dense boreal forest, and slopes of varying aspect (Brabets et al., 2000; Ramage et al., 2006). Pixels A02 and A03 cover the upland headwaters of the Wheaton River and have high mean elevations. The rest of the basin is mostly covered by pixels B03 and B02, representing the middle and lower parts of the basin respectively. Pixels C02 and C03 cover a small fraction of the basin but can be used to examine the lowest elevations of the basin. In 139
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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
Page 99 and 100: Schmidt, R. A., C. A. Troendle, and
Page 101 and 102: 89 63 rd EASTERN SNOW CONFERENCE Ne
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
Page 111 and 112: Evaluation of the global distributi
Page 113 and 114: Figure 9. Inter-comparison plots of
Page 115 and 116: Helfrich, S.R., D. McNamara, B.H.Ra
Page 117 and 118: 105 63 rd EASTERN SNOW CONFERENCE N
Page 119 and 120: and validated regionally so they ca
Page 121 and 122: 109 Test Site Figure 2. Variation o
Page 123 and 124: Correlation Coe. Correlation Coe. C
Page 125 and 126: Figure 8. SSM/I scattering signatur
Page 127 and 128: Considering the facts mentioned abo
Page 129 and 130: Besides the temporal validation, th
Page 131 and 132: RMSE Non linear Azar Chang Goodison
Page 133 and 134: 63 nd EASTERN SNOW CONFERENCE Newar
Page 135 and 136: http://www.ccin.ca); from simulatio
Page 137 and 138: over the period 1988-2000. With the
Page 139 and 140: Figure 3: Explained variance (R 2 )
Page 141 and 142: correspondence between simulated an
Page 143 and 144: values. Continued development of ne
Page 145 and 146: National Climatic Data Center (2005
Page 147 and 148: Snow Remote Sensing Posters 135
Page 149: ABSTRACT 63 rd EASTERN SNOW CONFERE
Page 153 and 154: Table 1. Wheaton River basin EASE-G
Page 155 and 156: The SSM/I snowmelt onset algorithm
Page 157 and 158: 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
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-
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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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ABSTRACT 211 63 rd EASTERN SNOW CON
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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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ABSTRACT 231 63 rd EASTERN SNOW CON
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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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247 63 rd EASTERN SNOW CONFERENCE N
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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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