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manual weekly to IMS daily charts was conducted between 1997 and 1999. Preliminary examination of the data between these periods suggests the IMS output to be superior to the weekly manual snow charts (Ramsay, 2000). In June 1999, the manual charting of snow extent was supplanted operationally with the daily IMS. Since the charts are now constructed digitally, their distribution has increased, with hundreds of known users viewing data each month from the NESDIS site and an unknown number of users obtaining the IMS data from other sources. While there are potentially many uses, the primary function of the product is to provide cryospheric input for environmental modeling. There are two operational government customers for this product, the NCEP / Environmental Modeling Center (EMC) and the NCEP / Climate Prediction Center (CPC). These customers help support and influence the direction of the product. The feedback from the NCEP modeling agencies and the preliminary NOAA Program Observational Requirements have led to advancements in the product and point toward continued improvement. The EMC applies the models for each three hour modeling run for North America and temporally coarser models for the entire planet. Snow plays an important role in model input and can lead to substantial error in forecast results based on incorrect representations of snow distribution, age, depth, snow water equivalent (SWE), and snow density (Mitchell et al., 1993; Sheffeld et al., 2003). Along with serving as an initial state of the surface cryosphere for the Northern Hemisphere for weather prediction, NOAA’s snow maps serve as a 40 year environmental monitoring record for hemispheric snow cover. This is considered the longest continuous satellite-derived record of any environmental variable (Robinson et al., 1993). It is vital for climate change detection and a key element in NOAA’s Mission Goals to “understand climate variability and change to enhance society’s ability to plan and respond” (USDOC/NOAA, 2005). Given the importance of this record, changes in the record should be considered with great care to preserve the integrity of the product for climate monitoring. Consultation within the snow and ice climate monitoring community has been sought before the integration of changes to safeguard the IMS’s environmental monitoring role. The IMS was designed to allow meteorologists to chart snow cover interactively on a daily basis using a variety of data sources within a common geographic system. Since first outlined by Ramsay (1998), there has been additional information discerned about the production methodology, and there have been noticeable changes in the input data sources, production techniques, and output format. This paper will cover changes in the input, production techniques, and output files since 1997, including statistics regarding the production methodology. The paper will also discuss the future enhancements and pending developments to the product, both short and long term plans. The conclusion will summarize the present and future of the product and what this means to the user community. IMS PRODUCT EVOLUTION System architecture enhancements A limiting factor of the original IMS system architecture was the inability of analysts to draw while looping imagery. This adversely affected the areas covered by geostationary satellites where imagery animation distinguishes snow and ice from fog and clouds. This limitation was changed in February 2004 to allow IMS analysts the freedom to loop imagery while drawing, erasing, or using any of the other IMS features. With this enhancement and other features such as image enhancement, product overlays and terrain mapping, the analysts can deduct snow and ice without relying on looking at a nearby system with looping imagery. Another feature modified within the system architecture in February 2004 was enhancing how the geographic area assignments of imagery were made within the system. The system before the change applied fixed areas for each satellite. These fixed geographic areas often only covered the best viewed regions. These regions also had set screen boundaries which did not allow analysts to recenter. The current IMS allows analysts to pan globally and select different datasets/satellite data 166
independent of regions. This greatly enhanced the flexibility of the IMS to optimize the snow mapping display. Improved Resolution One of the largest changes in the product since the creation of the IMS has been the improvement in output resolution introduced in February 2004. This increased resolution was made to improve model input for the EMC, providing greater detail of snow and ice cover. These improvements were possible due to advancements in computer speed and imagery resolution that produced a higher resolution product at approximately 4km resolution (6144 × 6144 grid). Changes were made to all ancillary data layers such as coastlines, elevation, and vegetation cover to support the improved resolution. Impacts of this change are still under evaluation but positive feedback has been noted from National Weather Service (NWS) field stations in regard to snow season temperature predictions. This is in part due to the improvements in snow distribution mapping, as well as the resolving of the many water bodies, not possible on larger scale products. When not correctly mapped there can be significant forecast errors where sea or lake ice cover affects heat and moisture fluxes to the atmosphere (Grumbine, 2005). Figures 1A and 1B demonstrate the difference in resolutions over northern North America. This easily demonstrates the noticeable differences in the inclusion of interior lakes and more detailed coastlines. Snow on mountainous terrain is also better represented using the 4km versus lower resolution products. The 4km product is also upscaled to the original previous resolution of approximately 24km resolution (1024 × 1024 grid). This is to maintain the satellite snow cover historical dataset. As previously mentioned, the IMS record is an important climate monitoring element and careful consideration must be taken to preserve the integrity of this snow cover record. Validation and monitoring of the IMS product at the 24km resolution is carried out under a joint effort by NESDIS and Rutgers University (Robinson, 2003). Added input data sources The IMS was designed to allow meteorologists to chart snow cover interactively on a daily basis using a variety of data sources within a common geographic system. The original input satellite data sources were outlined as NOAA polar orbiters (POES), NOAA geostationary (GOES) data, Japanese geostationary meteorological satellites (GMS), European geostationary meteorological satellites (METEOSAT), and US Department of Defense (DOD) polar orbiters (DMSP). Indirect satellite sources also include a weekly National Ice Center (NIC) chart and the US Air Force (USAF) daily snow depth & ice cover product (Ramsay, 1998). Several additional input products have been added to the IMS over the past decade. A few of these enhancements were outlined as prospects before, but have since become operational input options (Ramsay, 2000). These products include the Advanced Very High Resolution Radiometer (AVHRR) channel 3A, added in February 2001, the MOderate Resolution Imaging Spectroradiometer (MODIS) channel 1 added in February 2004, and an experimental automated snow mapping system over North America added in February 2004. Other product enhancements and their impacts are outlined in the following paragraphs. Meteosat 5 for INDOEX The original primary geostationary coverage left large portions of Siberia, central Asia, Himalayas, and the Tibetan Plateau unobserved by looping imagery. This is an important and difficult area for snow charting. Snow cover in this vast area has been identified as a significant influence on the Asian Monsoon (Hahn and Shukla, 1976; Huaqiang et al, 2004), global circulation (Bamzai and Marx, 2000; Clark and Serreze, 2000; Gong et al., 2003), and regional river discharge (Yang et al., 2003; Shaman et al., 2005). While non-geostationary satellite data sources such as polar orbiting imagery and microwave measurements provide mapping snow input, they are not the preferred data source by analysts. Microwave retrievals over the area are often erroneous in the winter due to atmospheric signal distortion, high elevation bare ground low temperatures, and/or soil grain scattering (Basist et al., 1996; Armstrong and Brodzik, 2001). Polar orbiters have limited over pass times, thus providing only a limited number of observations 167
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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
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 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 175 and 176: 180 160 140 120 100 80 60 40 20 0 1
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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-
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
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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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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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