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has begun to generate GIS GeoTiff compatible output at the 4km resolution. This product will be archived and disseminated at the NSIDC. The GeoTiff archive will span from February 2004 until the latest analysis day when complete. Snow and ice extent and coverage has been the primary output for the IMS. However, this is far from the lone variable needed for modeling snow and ice behavior at regional and global scales. NOAA/NESDIS is at the cusp of introducing new snow products that work in conjunction with the IMS to improve initialization in atmospheric models. A common problem reported with IMS has been continuing to keep snow cover during cloud obscured periods. The IMS analysts apply many tools and images to produce a “best guess” approach to snow observing. However, IMS analysts leave conditions as they were since the last observation when clouds obscure visible observations, snow is too thin for microwave detection, and there are no station reports. This can be problematic when snow has actually melted away. Atmospheric models contain algorithms to estimate snow depth throughout day and predict ablation of snow cover. However, snow ablation in atmospheric models is reinitialized with the current snow extent from IMS. If the snow in the IMS is merely the result of continuance and not observed, this reinitialization can lead to false snow observations and propagate errors throughout the NCEP model. A file of last observation time for the IMS has been developed and is undergoing testing. This will allow modelers to choose to use IMS for snow cover observation or to base snow cover on modeled estimates. Snow water equivalent (SWE) has been produced by SSM/I measurements for many decades, and is a valuable snow variable for atmospheric and hydrologic modelers. NOAA NESDIS is currently testing a combined AMSU and IMS SWE product that will merge the reliable IMS snow cover observations with AMSU’s capacity for estimating SWE (Kongoli et al., 2006). An example of the pre-merged and merged products is demonstrated in Figure 5. Additional snow variables like snow depth and fractional snow covered area are being experimented with to improve model initialization in combination with IMS output (Romanov et al., 2003). Future NOAA efforts for sea and lake ice variables utilizing IMS ice cover such as ice concentration and ice thickness are in the planning stage. The enhancements of input and output are not the only short-term changes planned for the product’s future. Current plans are to relocate the operational production of the IMS from its current location of SAB to the NIC. This transition from one agency within NESDIS to another is being done with the hope of producing an improved product at a reduced cost for NESDIS as a whole. This should lead to less duplication in sea ice monitoring within NESDIS by parallel offices, consolidate network systems and imagery storage, free SAB personnel time for other products, and allow time for NIC personnel trained for IMS analysis to meet NCEP requirements of two IMS observations per day. All NIC personnel will undergo the same training as SAB personnel and meet IMS qualifications before being assigned to IMS product generation. Parallel product generation will be performed at the SAB and NIC until comparable output products are obtained between offices. After evaluation and duplication of the IMS has been achieved, production of the IMS will transition fully to the NIC. No production methods other then personnel will change during this process. Longer-term plans for enhancements to the IMS input data revolve around the future deployment of NPOESS and GOES-R. NPOESS will be a joint military and civilian satellite replacing many of the existing U.S. polar orbiting sensors with improved sensors such as the Visible Infrared Imager Radiometer Suite (VIIRS), Conical Scanning Microwave Imager/Sounder (CMIS), and Advanced Technology Microwave Sounder (ATMS). GOES-R will be the next generation of NOAA geostationary satellites that will aide snow and ice observations. Geostationary satellites are regarded by analysts as the most valuable input source. IMS analysts assessing surface cryospheric coverage will benefit greatly by the advancements in remote sensing provided on GOES-R, particularly the Advanced Baseline Imager (ABI). The ABI will have 16 spectral bands, compared with five on the current GOES imagers. The ABI will improve the spatial coverage from 1km to 0.5km at nadir for broadband visible and from 4km to 2km for the infrared bands. For snow and ice detection this will improve the ability of the analyst to confirm the presence of ice on the surface through recognition of spatial patterns more discernable at higher resolutions, such as dendritic spatial patterns of snow on mountains, ice floe shapes that indicate certain ice thickness, or ice fractures. The ABI also includes spectral 176
information never present before on GOES imagers. One band of particular relevance to snow and ice detection will be centered at 1.61μm, which would expand the ice/cloud discrimination sampling beyond the temporally coarse polar orbiters (Schmit et al., 2005). This should improve the IMS accuracy and reduce the amount of time required for detection. This channel differencing would also improve automated snow and ice detection. GOES-R will increase the coverage acquisition rate by nearly fivefold, allowing closer to real-time observations and increased discrimination of relatively static surface features from highly dynamic atmospheric features. Figure 5. Examples of the experimental NOAA merged AMSU SWE-IMS output (bottom) for February 1, 2006 over the Northern Hemisphere. The pre-merged AMSU SWE (top) has erroneous or questionable signals masked by using the IMS. 177
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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
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
Page 169 and 170: 157 63 rd EASTERN SNOW CONFERENCE N
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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 and 186: MODIS Land Science Team, NASA Godda
Page 187: FUTURE OF IMS Enhancements to the I
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
Page 231 and 232: 219 63rd EASTERN SNOW CONFERENCE De
Page 233 and 234: Meteorological data for the winter
Page 235 and 236: monthly probability adjusted data 4
Page 237 and 238: 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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