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Ice cover analysis relies on a different approach than snow cover for charting. Ice cover determination must rely less on high albedo, stagnate cover, and meteorological conditions. Sea ice in the Northern Hemisphere winter is primarily located in areas with low solar illumination. New ice formation often has a low albedo until the ice thickens, becomes more opaque, and albedo increases (Wadhams, 2000). Furthermore, sea ice is a dynamic surface making it less discernible from clouds using image loops. The presence of lake and sea ice can be unrelated to current meteorological conditions due to ice transport, ice thickness, water temperatures, among other factors. The prominence of low-level stratus clouds over polar and sub-polar region also preclude the use of visible imagery as the primary source of ice observation. While this reduces the efficiency of albedo-based observation of ice, it is still a valuable input. On average, about 60% of changes in winter ice cover are noted via geostationary, AVHRR or MODIS observations. Much of the higher latitude areas are verified as being ice-covered using microwave-based retrievals. Microwave-based observations often represent 30–35% of the winter and autumn (Sep–Nov) ice cover input. Ice climatology is another tool for estimating ice cover in places where observations are unavailable. Since ice cover often exists in remote and dangerous areas, no station data is currently incorporated into the analysis. The NIC produces a sea ice edge vector file that provides the IMS with an external source for ice cover information. Currently, the NIC ice edge encompasses total polygons with greater than 10% ice cover. The IMS attempts to identify whether each 4km × 4km pixel contains more than 50% ice cover. These products do not correspond directly due to each product requiring different output specifications. Despite the differences between products, the NIC ice edge is used when other sources of data fail to provide any clear input on ice-covered ocean or Great Lakes waters. This represents about 2–10% of the time, depending on the season. Mountainous snow mapping Elevation plays an important role in snow generation due mostly to orographic lifting and temperature decreasing with increasing height. Snow often outlines higher elevation areas during transition season and during the winter in semi-arid, mid-latitude regions. To mimic this effect in mapping snow, the IMS allows analysts to chart areas dynamically as having snow based on a digital elevation model (DEM). The DEM resolution is 4km and matches the IMS, thus providing a direct relationship between elevation and IMS pixels. This provides the analyst with the ability to toggle the pixels within a given polygon to match the outline of snow revealed through imagery. The analyst can optimize the snow cover pattern based on elevation, local geography, and reflectance revealed through imagery. This has become a frequently applied tool in IMS snow mapping. The strengths and shortcomings of this DEM-based mapping are considered by the analyst while applying this tool. The strength is a more detailed and realistic mapping technique than previously available based on a physiographic relationship of snow with elevation. A weakness is the DEM based mapping does not account for other known state or physiographic factors that play a role in snow cover distribution, such as slope and aspect. Nor does it take solar, vegetation, or climatologic wind and storm patterns into account. Studies reveal that physiographic features such as radiation, elevation, slope, and aspect account for between 50–80% of snow depth variability in the Rocky Mountains, Sierra Nevadas, and Alps (Marchand and Killingtveit, 2001; Balk and Elder, 2000). Elevation tends to be the second largest influence on snow cover distribution next to radiation, but the weight of this influence is dependent on scale (Balk and Elder, 2000, Marks et al., 2002). Elevation and radiation appear to be greater factors at increasing scales, likely playing a large role in distribution variability at the 4km scale in semi-arid and mountainous environments. Despite the shortcoming of this tool, it is just a methodology for mapping, with analysts basing snow distribution on numerous input data not merely elevation. Analysts can compensate for inhomogeneous spatial patterns noted with regional elevation due to the other state factors that influence snow cover distribution. 174
FUTURE OF IMS Enhancements to the IMS will continue to push the bounds of cryospheric observation and charting. Requirements for snow and sea ice extent differ for climate verse numerical weather prediction. Since surface cyrospheric climate datasets are constrained by past data with the previous scale limitations, data needs to be maintained at original resolutions to preserve the climate record integrity (Robinson, 2003). Downscaling of older datasets would be required to blend the old and new data into a common 4km grid with interpolated daily values from weekly values. Fox and Ghan (2004) point out another limitation to the current computational limits that fine resolution climate grids would need to overcome as well. While the IMS’s resolution for global climate outlooks and monitoring exceeds the current requirements by using a 4km resolution at one observation per day, improvements in weather prediction are predicated on the prediction grid resolutions and prefer up to date observations. The need for global snow information at improved spatial and temporal resolutions for numerical weather prediction models is driving the advancements in the IMS. A 4km resolution snow and ice cover has the spatial resolution to initialize models such as the NCEP North American Mesoscale (NAM) model with 12km resolution and could even provide improved ice and snow initialization for finer resolution models such as the Fifth-Generation Penn State/National Center for Atmospheric Research Mesoscale Model (MM5) (Dudhia et al., 1999; Rogers et al., 2001). The temporal resolution of once per day could improve model results since afternoon (Eastern Standard Time, EST) model runs may be up to 21 hours removed from the last snow and ice cover initialization. With daily snow depth depletions of over 12 inches reported at SNOTEL stations, spatial distribution of snow cover may change drastically over one day, given ideal weather conditions for ablation. IMS will attempt to respond to this need for more timely information by introducing a second IMS observation over North America at the 4km resolution. This will be changing given time constraints of Satellite Analysis Branch (SAB) analysts and the window of visible imagery available for analysis by the late afternoon EST model run, particularly in the western United States in the winter. In addition to a second IMS daily product, the IMS will be expanding to provide global coverage. While the IMS provides adequate coverage in the Northern Hemisphere, the product failed to capture the snow and ice extent in the Southern Hemisphere. Like the improved temporal resolution North American IMS product, this presents a challenge to the resources required to provide such data. As previously mentioned, the automated snow and ice product is likely to play a large role in the production of southern hemispheric analysis. The completion of the Southern Hemisphere IMS will complete the global snow and ice coverage for model initialization. The IMS currently employs over 15 separate sources of data for input. This number can seem daunting to navigate, but each source is expertly selected to provide an optimal snow analysis. Still, NOAA is looking to exploit new technologies for understanding the current state of the surface cryosphere. To improve the output and to meet future product requirements, several new products are being tested for implementation into the IMS. In the short term, these products include snow and sea ice cover from the Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the Northern and Southern Hemisphere automated snow mapping systems, NASA’s Quick Scatterometer (QuikSCAT), and ESA’s Environmental Satellite (Envisat) Advanced Synthetic Aperture Radar (ASAR) Global Monitoring Mode (GMM), and MetOp’s Advanced Scatterometer (ASCAT). MetOp’s impending launch will also offer an expansion in the platforms carrying the AVHRR and Advanced Microwave Sounding Unit (AMSU) sensors. Recent improvements to the Air Force Weather Agency (AFWA) snow depth and MMAB sea ice products will also be incorporated in the near future. The IMS output product has been available to users for almost ten years now. Archival and archived product dissemination has been done through cooperation with the National Snow and Ice Data Center (NSIDC). The NSIDC currently provides users with American Standard Code for Information Interchange (ASCII) output data at the original 24km product as well as the recently added 4km output. While these products have been a popular data source, the formatting of the output can be complex to novice users. To promote a broader user community, NOAA NESDIS 175
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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
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 and 150: ABSTRACT 63 rd EASTERN SNOW CONFERE
Page 151 and 152: day and has a mean pixel resolution
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
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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: MODIS Land Science Team, NASA Godda
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-
Page 201 and 202: Pico Bonpland Massif-Sinigüis Glac
Page 203 and 204: Linder W, Jordan E, 1991. Ice-mass
Page 205 and 206: Snowpack Processes 193
Page 207 and 208: 63 rd EASTERN SNOW CONFERENCE Newar
Page 209 and 210: In the following paragraph the main
Page 211 and 212: Figure 1: Variation of the season a
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
Page 223 and 224: ABSTRACT 211 63 rd EASTERN SNOW CON
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
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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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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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