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land cover is not considered as part of equation in the algorithm, the model is not very accurate. Chang et al. (1987) related the difference between the SMMR brightness temperatures in 37 GHz and 18 GHz channels to derive snow depth – brightness temperature relationship for a uniform snow field, SD=1.59 [Tb 18H–Tb37H]. Goodison and Walker (1995) introduced another algorithm to estimate SWE using SSM/I channels. They used vertical gradient (GTV) between brightness temperatures at 37 GHz and 19 GHz and defined a linear relationship between SWE and GTV. This gradient value is obtained by subtracting the brightness temperature, Tb at frequencies of 37 and 19 GHz and dividing it by a constant (Goodison, Walker 1995). Grody (1996) developed an image classification algorithm to generate global snow map from Special Sensor Microwave Imager (SSM/I) data. The algorithm employs a decision tree technique and uses thresholds to filter out precipitation, warm desert, cold desert and frozen surfaces. De Seve et al. (1997) applied two previously developed models by Hallikainen and Goodison-Walker to James Bay area in La Grande River watershed, Quebec, Canada to estimate SWE using SSM/I images. The investigations revealed that both models tend to underestimate SWE especially when SWE was more than 200mm. A modified version of Goodison-Walker algorithm was suggested. Foster et al. (1999) have modeled various snow crystals shapes in different sizes and concluded that the shape of the crystal has little effect on the scattering in microwave. A physically based snow emission model was introduced by Pulliainen et al. (1999) of Helsinki University of technology (HUT snow emission model). The model assumes that scattering of the microwave radiation inside the medium is mostly in forward direction. The scattering coefficient is weighted by an empirical factor. The brightness temperature is computed by solving the radiative transfer equation. A boreal forest canopy model proposed by Kurvonen et al. (1994) was used to account for the influence of vegetation on the brightness temperature. Atmospheric effects were neglected and the snow grain size was allowed to vary in the model. Derksen (2004) carried out a detailed evaluation of SWE and SCE derived using SMMR and SSM/I data over the south Central part of Canada. The new technique to infer SWE from satellite data incorporated different algorithms, open environments, deciduous, coniferous, and spars forest cover and calculated SWE as weighted average of all four estimates. SWE = FDSWED + FC SWEC + FS SWES + FOSWEO, where (F) is the fraction of each land cover type within a pixel, D, C, S, and O correspondingly represent deciduous forest, coniferous forest, S sparse forest, and O open prairie environments. Passive microwave dataset and in situ SWE observation were compared and showed that the SMMR brightness temperature adjustments are required to produce SWE that would fit SWE inferred from SSM/I. SWE and SCE time series for December through March for a period of 88 years were analyzed to examined the variability of SWE and SCE (Derksen, 2004). Tedesco et al. (2004) proposed an Artificial Neural Network (ANN) technique for the retrieval of SWE from SSM/I data. They have used a multilayer perceptron (MLP) with various inputs to estimate SWE. First, brightness temperatures simulated by means of HUT snow estimation model were employed. The second approach made use of a subset of measured values. The input layer consists of four neurons, made up of 19 and 37 GHz vertical and horizontal brightness temperatures and the output was snow parameters. The results showed higher performance of ANN model compare to other methods. In 2005 Derksen conducted a study to assess the accuracy of an inter-annually consistent zone of high passive microwave derived SWE retrievals co-located with the Canadian northern boreal forest, using extended transects of in situ snow cover measurements (Derksen, 2005). The research conducted by Kelly et al. (2001) was focused at the development of a global snow monitoring for The Advanced Microwave Scanning Radiometer – EOS (AMSR-E) onboard Aqua satellite. The proposed algorithm had the following form: SWE (mm) = B*(TbH18–TbH37), where TbH18 and TbH37 are horizontal polarized brightness temperature at 18 and 37 GHz and B coefficient has been calibrated as 4.8 mm K –1 for SMMR data. Latter Kelly et al. (2003) described the development and testing of an algorithm to estimate global snow cover volume from spaceborne passive microwave, AMSR-E. The above algorithms used the spectral difference between microwave channels from various sensors to estimate SWE or snow depth. However other snow or land parameters such as snow grain size and land cover type and conditions have effects on scattering in microwave. Although, some researchers introduced land cover type to their models but their algorithms were developed 106
and validated regionally so they can not be used for other study areas. In addition, these algorithms use a multi-regression approach to account for the land cover type variation. Then, development of an algorithm which considers variation of land cover quantitatively and can be used and validated for different areas is necessitated. Normalized Difference Vegetation Index (NDVI) has been widely used to represent the health and greenness of the vegetation. In this study a non-linear algorithm is proposed, which estimates SWE using spectral difference between SSM/I channels along with NDVI data. DATA USED SSM/I Data SSM/I passive microwave radiometer with seven channels is operating at five frequencies (19, 35, 22, 37.0, and 85.5 GHz) and dual-polarization (except at 22GHz which is V-polarization only). The sensor spatial resolution varies for different channels frequencies. In this study Scalable Equal Area Earth Grid EASE-Grid SSM/I products distributed by National Snow and Ice Data Center (NSIDC) were used. EASE-Grid spatial resolution is slightly more than 25km (25.06km) for all the channels (NSIDC) although the recorded resolution of the microwave spectrum with longer wavelengths is more than 50km. The three EASE-Grid projections comprise two azimuthal equalarea projections for the Northern or Southern hemisphere, respectively and a global cylindrical equal area projection. In this we study have used a Northern hemisphere azimuthal equal-area. Normalized Difference Vegetation Index (NDVI) NDVI is typically used to represent the vegetation cover properties and it defines as a difference between reflectance in visible and near infrared spectral bands divided by their sum (NDVI = (NIR – VIS)/(NIR + VIS)). The NDVI data for this study were obtained from the NOAA/NASA Pathfinder Advanced Very High Resolution Radiometer (AVHRR) which is distributed at Goddard Space Flight Center (GSFC). The spatial resolution is 8km * 8km obtained within a 10 day period that has the fewest cloud. To facilitate the comparison and matching of the two datasets (NDVI and SSM/I) NDVI data resampled and were brought to the same EASE-Grid projection at 25km spatial resolution. Ground Truth Snow Data Point Gauge Measurements Surface observations of snow depth data for the study were obtained from point the National Climate Data Center (NCDC). The point measurements were averaged and gridded to 25km spatial resolution to match EASE-Grid SSM/I spatial resolution. To increase the reliability and avoid errors due to interpolation, we have used only those pixels where station data were available. If more than one station data were available in a given SSM/I pixel, the station data were averaged. SNODAS SWE Snow products generated by the Snow Data Assimilation System (SNODAS) of NOAA National Weather Service's National Operational Hydrologic Remote Sensing Center (NOHRSC) are available since October 2003. SNODAS includes a procedure to assimilate airborne gamma radiation and ground-based observations of snow covered area and snow water equivalent, downscaled output from Numerical Weather Prediction (NWP) models combined in a physically based, spatially distributed energy and mass balance model. The output products have 1km spatial and hourly temporal resolution. In order to match the EASE-Grid pixels the SNODAS SWE data were averaged to 25km. 107
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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
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 77 and 78: 65 63 rd EASTERN SNOW CONFERENCE Ne
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 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: 105 63 rd EASTERN SNOW CONFERENCE N
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
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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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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ABSTRACT 211 63 rd EASTERN SNOW CON
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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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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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