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Figure 2. Example of AMSU measurements over snow covered land Blending Procedure Primary satellite data are not used in the blended product. Instead, the data inputs include AMSU SWE product files and IMS snow cover product files. The SWE data are obtained from the MSPPS Level 2 swath geographical products available in the Hierarchical Data Format-Earth Observing System (HDF-EOS) at NOAA/NESDIS. These swath files are generated on an orbital basis from NOAA-15, 16, 17 and more recently the NOAA-18 satellites. Spatial resolution of the AMSU SWE swath data is that of AMSU-B: 16 km at nadir viewing angle. Resolution degrades as viewing angle increases. The IMS snow cover data are obtained daily as ASCII files in 1/16 th mesh PS Projection, or approximately 24 km in resolution. The data flow and rules for generating merged product are as follows: - Obtain daily IMS snow cover product file, - Obtain the AMSU SWE swath product files. The NOAA-18 instrument was selected as the most recent instrument, and the descending (night-time) was selected to minimize SWE retrieval errors due to surface melting during day-time, - Convert the AMSU swath SWE data into gridded data compatible with the IMS snow cover product file. - Inter-compare the AMSU SWE and IMS snow cover values on a grid cell-by-cell basis. If SWE value is missing or zero over snow cover as identified by IMS, retrieve previous day SWE value (two-day compositing). If previous-day SWE value is positive assign that SWE value to grid cell. Otherwise, flag as “indeterminate” the grid cell that corresponds to missing or zero AMSU SWE and IMS snow-covered land. Also, label as “indeterminate” instances of positive AMSU SWE value that correspond to IMS snowfree land. At this point, no revision of SWE values over these “indeterminate” grid cells is made. This is reserved for future development, - Generate blended SWE output file, a statistics file with grid cell confusion data, and image input and blended output grid files for product visualization and monitoring Ground SWE measurements over Slovakia The SWE retrievals were quantitatively evaluated against SWE measurements over Slovakia during February–March 2006. The SWE data and maps were provided by the Slovak National Hydro-meteorological Institute which maintains a dense network of ground stations that record 94
snow depth and SWE on a weekly basis (Figure 3). Dotted green points denote station locations, whereas in red are depicted grid points spaced 0.25 by 0.25 degrees Latitude and Longitude. The areas depicted in brown in Central and North Slovakia denotes higher elevation terrain. To statistically evaluate SWE retrievals, station SWE data were matched up with the AMSU observations and retrieved SWE. Station SWE data were averaged over a cell of 0.5 X 0.5 degree in latitude and longitude centered at the centroid of AMSU FOV. This size represents the average resolution of the AMSU instrument. Along with the average SWE, average values of snow depth and elevation, the number of stations per cell, as well as standard deviations of SWE, depth and elevation were computed. Only cells with over 5 stations were included in the analysis. Due to the higher density of stations, cells with over five stations were predominant. RESULTS AND DISCUSSION Figure 3. Map of Slovakia and locations of ground SWE stations Inter-comparison of blended snow cover with that retrieved from the AMSU and IMS products The blended product system has been automated and running experimentally on a UNIX machine since November 2005. Examples of blended SWE product are shown in Figure 4. Figure 4 displays merged SWE (left) and inter-comparison of AMSU and IMS snow cover extent (right) on November 7, 2005 (top panels) and on February 15, 2006 (bottom panels). The intercomparison map depicts the blended SWE area in blue, the underestimated microwave SWE area in green (AMSU-SWE values are zero but IMS snow cover is positive), and the overestimated microwave SWE area in brown (AMSU-SWE is positive but IMS snow cover is zero). The red color depicts areas when AMSU-SWE is labeled as “undetermined” due to confusion with rain, and is displayed for diagnostic purposes. Note the green-coded area over Eastern Canada on November 7, 2005 (top right), denoting underestimation of snow cover and hence SWE by the AMSU snow cover extent product. In the blended SWE product, these undetermined SWE areas are denoted as “white”. Examination of ground observations from several Canadian meteorological stations indicated continuous snowfall occurrences prior to November 7. The deposited new snow was not captured by the AMSU snow cover extent product, but was identified by the IMS snow cover product. Interestingly, these snowfall occurrences were retrieved by the AMSU snowfall product. An example of the AMSU snowfall product retrievals on November 2, 2005 is given in Figure 5. The AMSU snowfall algorithm utilizes frequencies at 89 GHz and above (AMSU-B channels 16-20 in Table 1) to identify precipitation in the form of snowfall 95
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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
Page 55 and 56: Snow and Climate Posters 43
Page 57 and 58: 45 63 rd EASTERN SNOW CONFERENCE Ne
Page 59 and 60: Figure 2 presents correlation maps
Page 61 and 62: CONCLUSIONS Three regional-continen
Page 65 and 66: Discharge (mm) 20 18 16 14 12 10 8
Page 67 and 68: ABSTRACT 55 63 rd EASTERN SNOW CONF
Page 69 and 70: This paper analyzes the synoptic pa
Page 71 and 72: Cyclones that track west of the App
Page 73 and 74: The synoptic-scale atmospheric circ
Page 75 and 76: CONCLUSIONS The record snowfall in
Page 79 and 80: correlations between April-May snow
Page 81 and 82: Figure 3. Linear correlations betwe
Page 83 and 84: winter/early spring could contribut
Page 85 and 86: Snow Remote Sensing 73
Page 89 and 90: height averaged 11.4 m with an aver
Page 91 and 92: strings were buried 20 - 30 cm belo
Page 93 and 94: Although the λE/Rn fraction was on
Page 95 and 96: estimated snow depth based on the 2
Page 97 and 98: important to note that the average
Page 99 and 100: Schmidt, R. A., C. A. Troendle, and
Page 103 and 104: METHODS The IMS Product The IMS was
Page 105: Figure 1. Microwave spectral charac
Page 109 and 110: Figure 4. Example of blended SWE pr
Page 111 and 112: Evaluation of the global distributi
Page 113 and 114: Figure 9. Inter-comparison plots of
Page 115 and 116: Helfrich, S.R., D. McNamara, B.H.Ra
Page 117 and 118: 105 63 rd EASTERN SNOW CONFERENCE N
Page 119 and 120: and validated regionally so they ca
Page 121 and 122: 109 Test Site Figure 2. Variation o
Page 123 and 124: 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
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
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coarse-resolution pixel with the hi
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Figure 5. (a) Scatterplot of snowpa
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For both 2004 and 2005, the AMSR-E
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threshold of ±18 K that reflects t
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ABSTRACT 153 63 rd EASTERN SNOW CON
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DATA USED SSM/I & QuikSCAT Coverage
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157 63 rd EASTERN SNOW CONFERENCE N
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180 160 140 120 100 80 60 40 20 0 1
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independent of regions. This greatl
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slightly vary the IMS with this pro
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imagery, the higher latitudes rely
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MODIS Land Science Team, NASA Godda
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FUTURE OF IMS Enhancements to the I
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information never present before on
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Brubaker, K.L., R. Pinker and E. De
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The Sierra Nevada del Cocuy region
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Colombia Venezuela Glacier Series R
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Table 4. Glacier Areas of the Ruiz-
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Pico Bonpland Massif-Sinigüis Glac
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Linder W, Jordan E, 1991. Ice-mass
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Snowpack Processes 193
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63 rd EASTERN SNOW CONFERENCE Newar
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In the following paragraph the main
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Figure 1: Variation of the season a
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Figure 3: 8-day composite maps (24
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The third pair (Figure 5) represent
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Figure 9: Time evolution of SWE ave
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melting occurs, whereas at the high
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Colbeck SC, Anderson EA. 1982. The
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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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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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