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per day. This amplifies the common problem of misidentified snow and clouds in an already area known for its difficulty in visual identification from satellites (Clark and Serreze, 2000). The placement of Meteosat-5 at the equator and 63°E in 1998, helped fill the void of geostationary data. This satellite was moved in support of the Indian Ocean Experiment (INDOEX), and was first incorporated into IMS in March 2001. This platform was preferred over other geostationary meteorological satellites such as Feng Yun 2 (FY-2) from China and Indian National Satellite System 2 (INSAT 2) from India. The observation footprint is comparable for all these satellites, but only a single geostationary satellite is required. Meteosat-5 was chosen because FY2 is experimental and near the end of its serviceable period, while data from INSAT are available for Indian national use. The inclusion of Meteosat-5 has provided a great boost to Asian snow mapping during the winter season. Multi-functional Transport Satellite (MTSAT) The Japanese Meteorological Agency (JMA) MTSAT series succeed the Geostationary Meteorological Satellite (GMS) series as the next generation satellite series covering East Asia and the Western Pacific. While MTSAT’s offers only marginal improvements in visible resolution over GMS, the improved calibration and correction algorithms provide improved detection of snow. The IMS began using MTSAT in November 2005. National Operational Hydrologic Remote Sensing Center (NOHRSC) The inclusion of NOHRSC maps into IMS analysis began in February 2004. The national analysis provided by NOHRSC, called the SNOw Data Assimilation System (SNODAS), provides a 1 km resolution estimation of snow cover for the conterminous United States. SNODAS is a system that amalgamates NCEP modeling, multisensor, station report, and airborne information into a single daily or sub-daily product (Barrett, 2003). The output timing of this product corresponds to the IMS observation day and serves as an important winter input source when clouds blanket the conterminous United States. While the fine resolution and multi-source data of SNODAS provides reliable data, its spatial extent is limited compared to the IMS, so it provides only a small but nationally important area for snow mapping. MODIS Looping Soon after the inclusion of MODIS visible imagery as an IMS datasource, analysts found the utility of looping recent MODIS overpasses for a given location. Looping of this polar orbiter is available due in part to the Aqua and Terra satellites sharing the identical visible channel at 1km and the poles having multiple daily overpasses. While the time span between images used in the loop is somewhat coarse compared with geostationary observations, these loops allow the analyst greater ability to distinguish between the surface cyrosphere and clouds. MODIS is an experimental satellite and will not be followed by a direct legacy product. The merger of the DoD and NOAA satellites in the future, known as the National Polar-Orbiting Operational Environmental Satellite System (NPOESS), will provide a similar product as that of MODIS and hopefully can be exploited for image looping once NPOESS is launched and declared operational. Marine Modeling and Analysis Branch (MMAB) Sea Ice Analysis The tracking of sea ice cover presents many difficulties. The IMS relies primary on visible imagery but this method is contingent on clear sky or thin cloud during the observing periods. When weather or low illumination obscure visual interpretation of sea ice, microwaves play a greater role. IMS analysts often apply a 1/16 mesh Northern Hemispheric grid of sea ice concentrations from the MMAB to demarcate those locations with >50% ice cover. The MMAB sea ice analysis is solely based on Special Sensor Microwave Imager (SSM/I) and applies a modified version of the National Aeronautics and Space Administration (NASA) team algorithm to derive sea ice concentrations (Grumbine, 1996). All SSM/I based products suffer from melt water attenuation, coastal contamination, poor thin ice detection, and difficulties in identifying concentration along the marginal ice zone. Analysts apply the MMAB product cautiously and will 168
slightly vary the IMS with this product when other external sources suggest the MMAB may be contaminated with a false emissivity. A B Figure 1. The 24km IMS product (A on the left) as remapped from the 4km IMS (B on the right) for May 19, 2006. The 24km represents the same land/sea mask used in previous 24km products prior to February 2004. Note the differences in representations for mountainous snow, inland lakes, and sea ice leads. Daily NIC Ice Edge In its inception, the IMS was designed to exploit the NIC weekly ice charts for the Northern Hemisphere. The NIC produced detailed sea ice maps coded in an ice charting nomenclature known as egg code once per week, usually updating the product on Friday afternoons, until 2001. Since that time the NIC switched from weekly to biweekly hemispheric coverage. This decreased the utility of the charts for daily operational ice mapping. Furthermore, ice charts released on Fridays would use input data typically three to seven days old for the analysis. While the NIC charts could still provide a general outlook of ice thickness and ice distribution, the dynamic nature of ice made the charts too old for the IMS analysis. Since February 2004, a daily vector sea ice edge has been incorporated into the IMS. The NIC daily sea ice vector product applies visible, infrared, passive microwave, and radar data to outline those areas with >10% ice cover. This product differs from the IMS product in three key ways. First, the NIC product is vector-based and attempts to enclose amorphous polygons, while the IMS defines ice cover within predefined pixels. The NIC ice cover has no set size requirements on the polygon size or shape, thus the scale of what areas enclose >10% is at the NIC analyst’s digression. This often leads to smoothing along the ice edge in some areas, while other areas may be more detailed than the IMS. Experience has revealed more of the former than the latter. A second difference in the products is the ice concentrations captured by each product. The NIC outlines areas with >10% ice cover, while the IMS demarcates areas with >50% ice cover. IMS analysts must adjust for this difference when using the NIC daily ice edge for IMS input. A third difference in the ice analysis output is the IMS’s inclusion of lake ice. The NIC vector ice edge only outlines sea ice and lake ice in the Great 169
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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
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
Page 179: independent of regions. This greatl
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
Page 229 and 230: 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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