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eflectance of snow cover as compared to other surface features, and therefore, they typically provide more accurate mapping of snow cover than the microwave imagery. On the other hand, passive microwaves at specific window frequencies penetrate much deeper into the snow pack due to longer wavelengths, and have shown to provide information on the snow cover properties including SWE. Another advantage of passive microwaves is their ability to penetrate cloudy atmospheres at specific window frequencies and thus to provide retrievals of land surface parameters in near all-weather conditions. Despite the near all-weather capability, the current microwave sensors provide measurements and thus retrievals at much coarser resolution (25 km) than the visible satellite sensors (1 km). Another disadvantage of passive microwave imagery is its limited capability to penetrate wet snow cover. The need for continuous regional and global snow cover mapping for climate, hydrological and weather applications has led, especially in recent years, to the development of snow cover mapping products based on multi-sensor data sources. For example, the National Oceanic and Atmospheric Administration (NOAA), the National Environmental Satellite Data and Information Systems (NESDIS) uses the Interactive Multi-sensor Snow and Ice Mapping System (IMS) operationally to provide Northern Hemispheric (NH) snow and ice mapping as input to environmental prediction models. The IMS system utilizes in an interactive fashion, through the interpretation of an analyst, visible and microwave satellite imagery as well as ground observations. Over the years, as more satellite sensors have become available, the IMS has incorporated more satellite data sources and has evolved into a more efficient snow mapping system (Ramsay, 1997, Helfrich et al., this issue). Another multi-sensor snow and ice mapping product is the NOAA’s AUTOSNOW product (Romanov et al., 2000). It utilizes visible, infrared and microwave satellite data to automatically generate high-resolution and continuous NH snow and ice mapping. Main research objective of this study is to evaluate the accuracy of a new SWE product derived from the blending of a passive microwave SWE product based on the Advanced Microwave Sounding Unit (AMSU) with the IMS snow cover extent product. The blended SWE product is needed (in addition to snow cover extent) as input to environmental prediction models. Besides being delivered in compatible format with the IMS snow cover extent, the blended product would be value-added in that SWE would be improved due to more reliable IMS snow mapping. Similar approaches have been reported in literature, e.g., the blending of a microwave SWE product based on the Special Sensor Microwave Imager (SSM/I) or the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) with the snow cover extent product derived from the Moderate Resolution Imaging Spectrometer (MODIS) (Armstrong et al. (2003). Compared to SSM/I and AMSR-E, the AMSU has some unique characteristics: Despite its coarse resolution, it has a wider spatial coverage than AMSR-E and SSM/I, and has additional microwave channels at frequencies in the window, oxygen and water vapor absorption regions. These additional features could potentially be utilized for the development of more robust SWE retrievals. In this paper, we evaluate the blended SWE product for its accuracy in mapping snow cover and SWE. In terms of snow cover mapping, we inter-compare the AMSU snow cover extent product with that of IMS. Inter-comparability of snow cover extent retrieved from the AMSU and from IMS is important to investigate as it has implications for the accuracy of the blended SWE product. Next, the microwave-derived SWE estimates are also evaluated globally and regionally over Central Europe (Slovakia). 90
METHODS The IMS Product The IMS was designed to allow mapping of snow cover interactively on a daily basis using a variety of data sources within a common geographic system. Such data sources include NOAA Polar Orbiting Environmental Satellites (POES) and Geostationary Environmental Satellites (GOES) data, Japanese Geostationary Meteorological Satellites (GMS), European geostationary meteorological satellites (METEOSAT) and US Department of Defense (DOD) polar orbiters (DMSP). Since its inception in 1997, the IMS system has evolved significantly in terms of input data sources, production technology and output format. For more detailed description of evolution and capabilities of the IMS system, the reader is referred to Helfrich et al., (this issue) and Ramsay (1998; 2000). The IMS snow and ice mapping is currently accomplished once daily over the Northern Hemisphere. The product is generated at 4 km and 24 km resolution in Polar Stereographic (PS) Projection. The AMSU Instrument The AMSU instrument contains two modules: AMSU-A and AMSU-B. The A module has 15 channels in the 23-89 GHz frequency range (1-15, Table 1). The B module has five channels in the 89-183 GHz frequency range (16–20, Table 1). The AMSU-A has an instantaneous field of view (FOV) of 48 km at nadir for all frequency channels, and scans ±48 0 from nadir with a total of 30 measurements across the scan. For AMSU-B, 90 measurements are made across the scan with a nadir resolution of 16 km for all channels. The nadir resolution degrades at larger scan angles. The swath width of both AMSU-A and –B is 2343 km. The AMSU-A and -B is flown on board the NOAA 15, NOAA 16, and NOAA 17 POES. This three-satellite suite offers a near-real time global sampling nearly every 4 hours. The unique combination of channels in the microwave window (23, 31, 89, 150 GHz), opaque water vapor (183±1, ±3, ±7 GHz) and oxygen absorption (50-60 GHz) regions has led to the development of a variety of surface and atmospheric products, generated in near-real time within a system called the Microwave Surface and Precipitation Product System (MSPPS). The suite of operational MSPPS products (Ferraro et al., 2002) includes rain rate, total precipitable water, cloud liquid water, ice water path, snow cover, sea ice concentration and land surface temperature and emissivities at 23, 31 and 50 GHz. Recent additions include a snowfall detection (Ferraro et al., 2004), and the extension of the snow cover extent product to include snow water equivalent (SWE) retrievals (Kongoli and Ferraro, 2004). In May 2005, a new POES satellite, NOAA 18, was launched and was also incorporated into MSPPS. This new satellite contains a new instrument, the Microwave Humidity Sensor (MHS) which replaced the AMSU-B. Similar to AMSU-B, the MHS is a fivechannel radiometer. However, it differs from AMSU-B in that channel 2 and channel 5 (channels 17 and 20 in Table 1 ) are 157 GHz and 190 GHz. Calibration and validation of the new MHS instrument within MSPPS has been completed and is described in Meng et al. (2006). As a result, similar products that are generated from the three satellites are now also generated from NOAA- 18. 91
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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
Page 51 and 52: 39 63 rd EASTERN SNOW CONFERENCE Ne
Page 53 and 54: Spatial distributions of changes in
Page 55 and 56: Snow and Climate Posters 43
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 101: 89 63 rd EASTERN SNOW CONFERENCE Ne
Page 105 and 106: Figure 1. Microwave spectral charac
Page 107 and 108: snow depth and SWE on a weekly basi
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
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Table 1. Wheaton River basin EASE-G
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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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----- -------------- ------- ------
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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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