Download the entire proceedings as an Adobe PDF - Eastern Snow ...

More documents

Recommendations

Info

154 63 rd EASTERN SNOW CONFERENCE Newark, Delaware USA 2006 still underestimates SWE in densely forested areas. Tedesco et al. (2004) developed and tested an inversion technique for retrieval of SWE and dry snow depths based on artificial neural networks (ANN) by using 19- and 37-GHz SSM/I measured brightness temperatures. Hallikainen et al 2003 combined active (QuikSCAT/SeaWinds) and passive (SSMI/DMSP) data for monitoring key snow parameters in Finland. The results show that combined active and passive microwave sensors provide useful diurnal and seasonal information. These results are more accurate than those obtained by only passive microwave. In another research Hallikainen showed that using space borne scatterometer (QuikSCAT onboard SeaWinds) for dry snow conditions, the backscattering coefficient increases with increasing SWE. For wet snow condition backscattering coefficient decreases with increasing SWE. Ku-band scatterometer were used successfully to determine the onset and the end of snow melt, and to derive time series for the fraction of snow-free ground during the seasonal snow melt period (Hallikainen et al 2004). Synthetic Aperture Radar (SAR) particularly C-band SAR has shown the potential for monitoring snow and ice for more than two decades. The high spatial resolution and the independence of the sensors from sun illumination and cloud cover make SAR an ideal tool for snow studies. Launched in 1995, Radarsat-1 offers spatial resolutions between 10m to 100m and a swath up to 500km. To estimate SWE using C-band SAR, Bernier et al. (1998) introduced an approach based on the fact that snow cover characteristics influence the underlying soil. The snow influence on soil temperature affects the dielectric properties of the soil which has a major role on the backscattered signal. To recover the SWE from SAR data an algorithm made of two equations was used. The first equation defines a linear relationship between the snow thermal resistance and the backscattering ratio between a winter image and a reference (snow-free) image in DB. The snow-free image helps to eliminate the radiometric distortion due to topography as well as to minimize the effect of soil roughness on the signal. The second equation is a linear relationship between thermal resistance and the SWE. To estimate SWE from thermal resistance the mean density of the snowpack has to be derived. This approach has been applied for cold winter conditions and dry snow (Bernier et al. 1999). The critical variables influencing the algorithm are variety of land cover, specifically forest density, Snowpack properties (depth>2m), and severe topography. In a research on passive and active airborne microwave remote sensing of snow cover Sokol et al. (2003) showed that SAR sensors are highly sensitive to changes in the dielectric constant and have better spatial resolution than their passive counterparts. They concluded that passive techniques estimate SWE most accurately under dry snow conditions with minimal stratified snow structures (Sokol et al. 2003). The focus of this research is estimating Snow Water Equivalent (SWE) in Great Lakes area by using active and passive microwaves. Different approaches were examined for SWE estimations by RADARSAT SAR and also QuikSCAT-Ku along and passive SSM/I. STUDY AREA It is also located on the transitional zone for snow meaning that the northern part of the study area is covered by snow for the whole winter season however for the southern part there is a pattern of snow-fall and snow melt within the season. In addition to snow pattern, the land cover type varies a wide range including, Evergreen Needle leaf forest, Deciduous Broadleaf forest, cropland, woodland and dry land (Figure 1).
DATA USED SSM/I & QuikSCAT Coverage LANDCOVER Figure 1. Study area and SSM/I and RADARSAT coverage 155 63 rd EASTERN SNOW CONFERENCE Newark, Delaware USA 2006 RADARSAT Images SSM/I SSM/I passive microwave radiometer with seven channels is operating at five frequencies (19, 35, 22, 37, and 85.5 GHz) and dual- polarization (except at 22 GHz which is V-polarization only). The sensor’s spatial resolution varies for different channel frequencies. In this study we used Scalable Equal Area Earth Grid EASE-Grid SSM/I products distributed by National Snow and Ice Data Center (NSIDC). EASE-Grid spatial resolution is slightly more than 25km (25.06) for all the channels (NSIDC) although the recorded resolution of the sensor for longer wavelengths is more than 50km. The three EASE-Grid projections comprise two azimuthal equal-area projections for the Northern or Southern hemispheres, respectively and a global cylindrical equal area projection. In our study we used a Northern hemisphere azimuthal equal-area. The study area is covered by 980 (28 by 35) SSM/I EASE-Grid pixels. Ku-Band The QuikSCAT/SeaWinds scatterometer provides normalized radar cross section measurements of the Earth’s surface at unprecedented coverage and resolution. The QuikSCAT sensor on the SeaWinds satellite operates at 13.4 GHz vertical and horizontal channels. The sigma (0) browse product of QuikSCAT has the grid size of 5 pixels per degree or about 22.5km at the equator. In order to match SSM/I grid size the QuikSCAT images were averaged to 25km resolution for the study area. Normalized Difference Vegetation Index (NDVI) NDVI is used to represent the variety of land cover in the study area. The NDVI data obtained from the NOAA/NASA Pathfinder AVHRR is distributed at Goddard Space Flight Center (GSFC). The spatial resolution is 8km by 8km obtained within a 10 day period that has the fewest cloud. To match the with RADARSAT images, NDVI image was resampled and projected to UTM.
Page 1 and 2:
Proceedings of the 63rd ANNUAL EAST
Page 3 and 4:
FOREWORD T his proceedings volume c
Page 5 and 6:
CONTENTS Foreword..................
Page 7 and 8:
STATEMENT OF PURPOSE The Eastern Sn
Page 9 and 10:
EXECUTIVES FOR THE 63rd EASTERN SNO
Page 11 and 12:
THE PRESIDENT’S PAGE The 63rd ann
Page 13 and 14:
Weisnet Medal for Best Student Pape
Page 15 and 16:
3 63 rd EASTERN SNOW CONFERENCE New
Page 17 and 18:
Figure 1. Layer 1 represents the so
Page 19 and 20:
Figure 4. The general layout of the
Page 21 and 22:
lue color represented no convection
Page 23 and 24:
Total Density of Water Profiles The
Page 25 and 26:
Figure 11(a). Simulated snow grain
Page 27 and 28:
Jordan R, 1991. A one-dimensional t
Page 29 and 30:
Campbell Scientific Award for Best
Page 31 and 32:
19 63 rd EASTERN SNOW CONFERENCE Ne
Page 33 and 34:
Page 35 and 36:
Page 37 and 38:
Page 39 and 40:
Page 41 and 42:
R ∑i ∑ n 2 = 1 NS = − n i = 1
Page 43 and 44:
Page 45 and 46:
Page 47 and 48:
Page 49 and 50:
Snow and Climate 37
Page 51 and 52:
Page 53 and 54:
Spatial distributions of changes in
Page 55 and 56:
Snow and Climate Posters 43
Page 57 and 58:
Page 59 and 60:
Figure 2 presents correlation maps
Page 61 and 62:
CONCLUSIONS Three regional-continen
Page 63 and 64:
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 77 and 78:
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 87 and 88:
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 and 102:
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 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
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: ABSTRACT 153 63 rd EASTERN SNOW CON
Page 175 and 176: 180 160 140 120 100 80 60 40 20 0 1
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
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
Page 231 and 232:
219 63rd EASTERN SNOW CONFERENCE De
Page 233 and 234:
Meteorological data for the winter
Page 235 and 236:
monthly probability adjusted data 4
Page 237 and 238:
225 Pullman WA Rawlins WY Leadville
Page 239 and 240:
The daily wind speed can exceed 6.5
Page 241 and 242:
NCDC. 2006. National Climate Data C
Page 243 and 244:
ABSTRACT 231 63 rd EASTERN SNOW CON
Page 245 and 246:
and mass balances for 540 environme
Page 247 and 248:
Temperature, precipitation, windspe
Page 249 and 250:
the left in response to snow compac
Page 251 and 252:
Pinched-cone dimensions for each sl
Page 253 and 254:
Terrain slope (degrees) Terrain slo
Page 255 and 256:
ACKNOWLEDGEMENTS This work was fund
Page 257 and 258:
Glacial and Periglacial Processes 2
Page 259 and 260:
247 63 rd EASTERN SNOW CONFERENCE N
Page 261 and 262:
From linear regression the constant
Page 263 and 264:
Page 265 and 266:
DATA COLLECTION Figure 1. Location
Page 267 and 268:
Table 1. Comparison of GPS measured
Page 269 and 270:
Transverse Velocity Profiles Surfac
Page 271 and 272:
is shown in Figure 4. This variatio
Page 273 and 274:
Table 4. The calculated volume flux
Page 275 and 276:
Page 277 and 278:
Figure 1. Cordillera Blanca regiona
Page 279 and 280:
MATERIALS AND METHODS Field Measure
Page 281 and 282:
Data analysis techniques We synthes
Page 283 and 284:
season, but unacceptable for the dr
Page 285 and 286:
significant, although more informat
Page 287 and 288:
(a) Liquid Water (mm) Dry Period: M
Page 289 and 290:
Future Work We are installing addit
Page 291 and 292:
Shuttleworth, W.J., and J.S. Wallac
Page 293 and 294:
Snow and Periglacial Processes Post
Page 295 and 296:
Page 297 and 298:
----- -------------- ------- ------
Page 299 and 300:
No one to date has shown why snowfl
Page 301 and 302:
APPENDIX A Northern Hemisphere Year
Page 303 and 304:
62nd ESC Papers 291
Page 305 and 306:
62 nd EASTERN SNOW CONFERENCE Water
Page 307 and 308:
each other; and, they both decrease
Page 309 and 310:
297 62 nd EASTERN SNOW CONFERENCE W
Page 311 and 312:
comes in the form of precipitation.
Page 313 and 314:
averaged to reduce bias in this var
Page 315 and 316:
RESULTS Figure 2. Flow chart of reg
Page 317 and 318:
Table 6: Actual-Conditions SWE, dep
Page 319 and 320:
307 Table 9: Regression results bet
Page 321 and 322:
Goita, K., A. Walker, B. Goodison,
Page 323:
TO ERR IS HUMAN, TO FORGET IT WOULD
show all

Download the entire proceedings as an Adobe PDF - Eastern Snow ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?