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SUMMARY The IMS underwent enhancements to the resolutions, input data, methodology, and output formats that augmented the product’s utility. These changes have been largely beneficial to the NCEP EMC modeler (Mitchell, K., 2006, professional conversation). The changes involve an improved architecture, superior output resolution, expanded input sources, and topographic mapping capabilities. The current system architecture allows IMS analysts to quickly record snow and ice, thus speeding the production time. The advanced resolution product begun in February 2004 allows for greater detail in the snow and ice information to be conveyed. While product evaluations are still ongoing at NCEP, improvements in the resolution are likely to have a positive impact on numerical weather prediction. The expanded imagery from which the user may derive data sources allows for an increase in the likelihood of correct snow and ice identification. The more accurately the surface cryosphere is depicted, the better chance a weather prediction model should have at accurate forecasts. The enhancements made to the IMS were mostly driven by the need for better NCEP EMC initializations. Even the planned changes in the product are geared to improving NCEP EMC forecasts. Unfortunately, the impacts of the enhancements to NCEP CPC climate evaluation remain unknown. The product has likely become more accurate due to improved spatial resolution, enhanced methodologies, and improved input sources. However, the impact of an improved product may cause heterogeneity in the snow and ice data record is still under evaluation. While consultation of change impacts is sought by the CPC and non-federal researchers, formal examinations are to date forthcoming. This concern is not without note and is in need of further investigation. Hopefully, providing the data in new formats and with accompanying products will expand the user community for the products. Further advances in the products are predicated on customer requirements, new sensors, and advanced technologies. The advancements in the IMS, as well as links to past snow mapping, should continue to make this a viable snow mapping system for years to come. ACKNOWLEDGEMENTS The authors thank Dr. Dan Tarpley and Dr. Peter Romanov, Office of Research and Applications, NOAA/NESDIS; and Dr. Cezar Kongoli, QSS Group Inc. for their direct contributions to this work. The authors also wish to acknowledge the important contributions and support of, Dr. Ken Mitchell, NOAA/NWS; Mr. Ricky Irving, PIB NOAA/NESDIS; and Dr. David Robinson, Rutgers University. REFERENCES Armstrong, R. L. and M. J. Brodzik, 2001. ‘Recent Northern Hemisphere snow extent: a comparison of data derived from visible and microwave satellite sensors.’ Geophysical Res. Letters, 28(19), 3673–3676. Balk, B.C., and K. Elder. 2000. ‘Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed.’ Water Resources Research., 36, 13–26. Bamzai, A. S. and L. Marx, 2000: ‘COLA AGCM simulation of the effect of anomalous spring snow over Eurasia on the Indian summer monsoon.’ Quart. J. Roy. Meteor. Soc., 126, 2575. Barrett, A., 2003. National Operational Hydrologic Remote Sensing Center SNOw Data Assimilation System (SNODAS) Products at NSIDC. NSIDC Special Report 11. Boulder, CO, USA: National Snow and Ice Data Center. 19 pages Basist, A., D. Garrett, R. Ferraro, N. Grody, and K. Mitchell, 1996. ‘A comparison between snow cover products derived from visible and microwave satellite observations.’ J. Appl. Meteorol., 35, 163–177. 178
Brubaker, K.L., R. Pinker and E. Deviatova, 2005. ‘Evaluation and Comparison of MODIS and IMS Snow Cover Estimates for the Continental United States Using Station Data’. J. Hydrometeorology, 6(6), 1002–1017. Chang, A.T.C., J.L. Foster and D.K. Hall, 1996. ‘Effects of forest on the snow parameters derived from microwave measurements during the BOREAS winter field campaign.’ Hydrol. Processes, 10, 1565–1574. Clark, M.P., and M.C. Serreze, 2000. ‘Effects of variations in East Asian snow cover on modulating atmospheric circulation over the North Pacific Ocean.’ J. Climate, 13, 3700–3710. Dudhia, J., D. Gill, Y-R. Guo, D. Hansen, K. Manning, and W. Wang, 1999. PSU/NCAR Mesoscale Modeling System Tutorial Class Notes and Users' Guide: MM5 Modeling System Version 2. Mesoscale and Microscale Meteorology Division, NCAR, 182 pages. Foster, J.L., Hall, D.K., Chang, A.T.C., Rango, A., Wergin, W. and Erbe, E. 1999. ‘Effects of snow crystal shape on the scattering of passive microwave radiation.’ IEEE Trans. Geosci. Remote Sens., 37, 1165–1168. Foster, J., C. Sun, J.P. Walker, R. Kelly, J. Dong, and A. Chang, 2004. ‘Mapping random and systematic errors of satellite-derived snow water equivalent observations in Eurasia’, Proc. SPIE Int. Soc. Opt. Eng.,, M. Owe, G. D'Urso, B.T. Gouweleeuw, and A.M. Jochum, Eds., 5568, Maspalomas, Gran Canaria, Spain, September 14–16, 2004, 150–158. Gong, G, D. Entekhabi, and J. Cohen. 2003. ‘Relative impacts of Siberian and North American snow anomalies on the winter Arctic Oscillation’, Geophysical Res. Letters, 30(16), 1848. doi:10.1029/2003GL017749. Grumbine, R. W., 1996. Automated Passive Microwave Sea Ice Concentration Analysis at NCEP OMB Tech. Note 120, 13 pages. Grumbine, R. W., 2005. http://meted.ucar.edu/nwp/pcu2/avice1.htm. Hahn, D. J. and J. Shukla, 1976. ‘An apparent relationship between Eurasian snow cover and Indian monsoon rainfall.’ J. Atmos. Sci., 33, 2461–2462. Hall, D.K., J.L. Foster, V.V. Salomonson, A.G. Klein and J.Y.L. Chien, 2001. "Development of a Technique to Assess Snow-Cover Mapping Errors from Space," IEEE Transactions on Geoscience and Remote Sensing, 39(2), 432–438. Hall, D.K., A.B. Tait, J.L. Foster, A.T.C. Chang and M. Allen, 2000. ‘Intercomparison of satellitederived snow-cover maps.’ Annals of Glaciology, 31, 369–376. Huaqiang, L., S. Zhaobo, W. Ju, and M. Jinzhong. 2004 ‘A Modeling Study of the Effects of Anomalous Snow Cover over the Tibetan Plateau upon the South Asian Summer Monsoon.’ Advances in Atmospheric Sciences, 21(6), 964–975. Kongoli, C, C.A.Dean, S.R. Helfrich, and R.R.Ferraro, 2006. ‘The retrievals of snow cover extent and snow water equivalent from a blended passive microwave – interactive multi-sensor snow product’ Proceedings of the 63rd Eastern Snow Conference. 7–9 June, 2006. Newark, Delaware. Marchand, W-D and Å. Killingtveit, 2001 ‘Analyses of the Relation between Spatial Snow Distribution and Terrain Characteristics.’ Proceedings of the 58th Eastern Snow Conference. 14–17 May, 2001. Ottawa, Canada. Marks, D.G., Winstral, A.H., and Seyfried, M.S. 2002. ‘Simulation of Terrain and Forest Shelter Effects on Patterns of Snow Deposition, Snowmelt and Runoff over a Semi-Arid Mountain Catchment.’ Hydrol. Processes, 16, 3605–3626. Mitchell, K. E., G. J. DiMego, and T. L. Black, 1993. ‘Case study on the impact of improved snow cover and sea ice on wintertime forecasts of NMC’s regional NGM and Eta models.’ Preprints, 13th Conf. of Weather Analysis and Forecasting, Vienna, VA, Amer. Meteor. Soc., 516–517. Ramsay, B., 1998. ‘The interactive multisensor snow and ice mapping system.’ Hydrol. Processes, 12, 1537–1546. Ramsay, B., 2000. ‘Prospects for the Interactive Multisensor Snow and Ice Mapping System.(IMS)’ Proceedings of the 57th Eastern Snow Conference. 18–19 May 2000, Syracuse, New York. 161–170. Robinson, D.A., K.F. Dewey and R.R. Heim Jr., 1993. ‘Global snow cover monitoring: an update.’ Bull. Am. Meteorol. Soc., 74, 1689–1696. 179
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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
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
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: information never present before on
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
Page 239 and 240: 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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