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164 63 rd EASTERN SNOW CONFERENCE Newark, Delaware USA 2006 In case of low resolution passive SSM/I and active QuikSCAT, the ANN model shows satisfactory result in dependent estimation of SWE. Also, adding QuikSCAT-Ku increased the accuracy of the estimated SWE by neural networks. It was concluded that estimating SWE by neural networks is highly dependent on training data. This can become a source of error on model development. In order to resolve this problem a large dataset is necessary. ACKNOWLEDGEMENTS The Authors express their gratitude to Dr Jeff Hurley, a Senior Project Manager in RADARSAT International, for his support in processing and analyzing RADARSAT images. REFERENCES Bernier M, Fortin J (1998).The Potential of Times Series pf C-Band SAR Data to Monitor Dry and Shallow Snow Cover. IEEE Transaction on Geoscinece and remote sensing, 36(1): 226– 242. Bernier M, Fortin J Y.Gauthier, R.Gauthier, R.Roy, and P. Vincent (1999i).Determination of Snow Water Equivalent using RADARSAT SAR data in eastern Canada..Hydrological Processes,13:3041–3051. Chang, A. T. C, L. Foster, D. K. Hall. (1987). Nimbus-7 SMMR derived global snow cover parameters. Annals Glaciology, 9:39–44. Derksen, E. LeDrew, A.Walker, and B. Goodison (2001). Evaluation of a Multi-Algorithm Approach to Passive Microwave Monitoring of Central North American Snow Water Equivalent. IEEE, 952–954. Hallikainen, M. T. (1984). Retrieval of snow water equivalent from Nimbus-7 SSMR data: effect of land cover categories and weather conditions. IEEE Oceanic Engineering, 9(5): 372–376. Hallikainene, M. T,P.Halme,M Takala, J.Pulliainen (2003). Combined Active and Passive Mirowave Remote Sensing of Snow in Finland. IEEE:830–832. Hallikainene, M. T,P.Halme,P.Lahtinen, M Takala, J.Pulliainen (2004). Retrieval of Snow Characteristics from Scapeborne Scatteromter Data. IEEE:1849–1852. Sokol. J, T.J. Pultz, and A.E. Walker (2003). Passive and Active microwave remote sensing of snow cover. INT. J. Remote Sensing, 24(24):5327–5344. Tedesco, J. Pullianinen, M. Takala, M Hallikainen, P. Pampaloni. (2004). Artificial neural network-based techniques for the retrieval of SWE and snow depth from SSM/I data. Remote Sensing of Environment 90:76–85. Walker, G. B. E. a. A. E. (1995). Canadian development and use of snow cover information from passive microwave satellite data. Passive microwave remote sensing of land–atmosphere interactions. B. J. Choudhury, Y. H. Kerr, E. G. Njoku and P. Pampaloni. The Netherlands, VSP BV 245–262.
165 63 rd EASTERN SNOW CONFERENCE Newark, Delaware USA 2006 Enhancements and Forthcoming Developments to the Interactive Multisensor Snow and Ice Mapping System (IMS) ABSTRACT SEAN R. HELFRICH 1 , DONNA MCNAMARA 1 , BRUCE H. RAMSAY 2 , THOMAS BALDWIN 1 , AND TIM KASHETA 3 The national oceanic and atmospheric administration’s national environmental satellite data, and information service (noaa/nesdis) interactive multisensor snow and ice mapping system (ims) has undergone substantial changes since the inception in 1997. These changes include the data sources used to generate the product, methodology of product creation, and even changes in the output. Among the most notable of the past upgrades to the ims are a 4 kilometer resolution grid output, ingest of an automated snow detection algorithm, expansion to a global extent, and a static digital elevation model for mapping based on elevation. Further developments to this dynamic system will continue as noaa strives to improve snow parameterization for weather forecast modeling. Several future short term enhancements will be evaluated for possible transition to operations before the northern hemisphere winter of 2006–07. Current and historical data will be adobted to a geographic information systems (GIS) format before 2007, as well. Longer-term enhancements are also planned to account for new snow datasources, mapping methodologies and user requirements. These modifications are being made with care to preserve the integrity of the long standing satellite derived snow record that is vital to global change detection. Keywords: satellite remote sensing; environmental data; snow cover; ice cover; geographic information systems; climate INTRODUCTION The interactive multisensor snow and ice mapping system (IMS) has been the main operational snow and ice charting tool of the National Oceanic and Atmospheric Administration’s National Environmental Satellite Data, and Information Service (NOAA/NESDIS) for almost a decade. This product was primarily created to improve the quality and timeliness of Northern Hemisphere snow maps for National Centers for Environmental Prediction (NCEP) numerical forecasting (Ramsay, 1998). Prior to the IMS’s operational inception in 1997, snow charts were constructed manually once per week. The IMS is produced daily using geographic information systems (GIS) technology. This system had substantial impacts on production speed, product spatial accuracy, and time between observations. A comparison and validation review of the product transition from 1 NOAA/NESDIS/OSDPD – NOAA Science Center, 5200 Auth Rd., Camp Springs Maryland, 20746 sean.helfrich@noaa.gov 2 NOAA/NESDIS/STAR/CORP – NOAA Science Center, 5200 Auth Rd., Camp Springs Maryland, 20746 3 Riverside Technology, Inc. – 2290 East Prospect Road, Suite 1, Fort Collins, Colorado 80525
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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
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
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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 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
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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
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Page 225 and 226: 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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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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