Derksen, C., Walker, A., LeDrew, E., Goodison, B. (2003). Combining SMMR <strong>an</strong>d SSM/I data for time series <strong>an</strong>alysis of central North Americ<strong>an</strong> snow water equivalent. J. Hydrometeorol., 4(2):304–316. Déry, S. J., Sheffield, J., Wood, E. F. (2005). Connectivity between Eur<strong>as</strong>i<strong>an</strong> snow cover extent <strong>an</strong>d C<strong>an</strong>adi<strong>an</strong> snow water equivalent <strong>an</strong>d river discharge. J. Geophys. Res., 110(D23). D23106, doi:10.1029/2005JD006173. Dong, J., Walker, J. P., Houser, P. R. (2005). Factors affecting remotely sensed snow water equivalent uncertainty. Remote Sens. Environ., 97:68–82. Europe<strong>an</strong> Centre for Medium R<strong>an</strong>ge Wea<strong>the</strong>r Forec<strong>as</strong>ts (ECMWF) (2002). ERA-40 Project Report Series. 3. Workshop om Re-<strong>an</strong>alysis, 5-9 november 2001. Technical report, Europe<strong>an</strong> Centre for Medium R<strong>an</strong>ge Wea<strong>the</strong>r Forec<strong>as</strong>ts. 443 pp. Frappart, F., Ramillien, G., Bi<strong>an</strong>camaria, S., Mognard, N. M., Cazenave, A. (2006). Evolution of high-latitude snow m<strong>as</strong>s derived from <strong>the</strong> GRACE gravimetry mission (2002–2004). Geophys. Res. Lett., 33. L02501, doi:10.10292005GL024778. Frolking, S., McDonald, K. C., Kimball, J., Way, J. B., Zimmerm<strong>an</strong>n, R., Running, S. W. (1999). Using <strong>the</strong> space-borne NASA scatterometer (NSCAT) to determine <strong>the</strong> frozen <strong>an</strong>d thawed se<strong>as</strong>ons of a boreal l<strong>an</strong>dscape. J. Geophys. Res., 104(D22):27,895–27,907. Goita, K., Walker, A. E., Goodison, B. E. (2003). Algorithm development for <strong>the</strong> estimation of snow water equivalent in <strong>the</strong> boreal forest using p<strong>as</strong>sive microwave data. Int. J. Remote Sens., 24(5):1097–1102. Grippa, M., Mognard, N., To<strong>an</strong>a, T. L. (2005). Comparison between <strong>the</strong> inter<strong>an</strong>nual variability of snow parameters derived from SSM/I <strong>an</strong>d <strong>the</strong> Ob river discharge. Remote Sens. Environ., 98:35–44. Hinzm<strong>an</strong>, L. D. <strong>an</strong>d K<strong>an</strong>e, D. L. (1991). <strong>Snow</strong> hydrology of a headwater Arctic b<strong>as</strong>in 2. Conceptual <strong>an</strong>alysis <strong>an</strong>d computer modeling. Water Resour. Res., 27(6):1111–1121. Kalnay, E., K<strong>an</strong>amitsu, M., Kistler, R., Collins, W., Deaven, D., G<strong>an</strong>din, L., Iredell, M., Saha, S., White, G., Woolen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgens, W., J<strong>an</strong>owiak, J., Ropelewski, K. C., W<strong>an</strong>g, J., Leetma, A., Reynolds, R., Jenne, R., Joseph, D. (1996). The NCEP/NCAR 40-year re<strong>an</strong>alysis project. Bull. Am. Meteorol. Soc., 77:437–471. Kimball, J. S., McDonald, K. C., Keyser, A. R., Frolking, S., Running, S. W. (2001). Application of <strong>the</strong> NASA scatterometer (NSCAT) for determining <strong>the</strong> daily frozen <strong>an</strong>d nonfrozen l<strong>an</strong>dscape of Al<strong>as</strong>ka. Remote Sens. Environ., 75:113–126. Lammers, R. B., Rawlins, M., McGuire, D., Clein, J., Kimball, J., Wu, W. (2006). Water budget closure over <strong>the</strong> western arctic <strong>an</strong>d Yukon river b<strong>as</strong>in - a model inter-comparison. Earth Interactions. in preparation. Lammers, R. B., Shiklom<strong>an</strong>ov, A. I., Vörösmarty, C. J., Fekete, B. M., Peterson, B. J. (2001). Assessment of contemporary Arctic river runoff b<strong>as</strong>ed on observational discharge records. J. Geophys. Res., 106(D4):3321–3334. McDonald, K. C., Kimball, J. S., Njoku, E., Zimmerm<strong>an</strong>n, R., Zhao, M. (2004). Variability in springtime thaw in <strong>the</strong> terrestrial high latitudes: Monitoring a major control on biospheric <strong>as</strong>similation of atmospheric CO2 with spaceborne microwave remote sensing. Earth Interactions, 7:1–23. Milly, P. C. D. <strong>an</strong>d Shmakin, A. B. (2002). Global modeling of l<strong>an</strong>d water <strong>an</strong>d energy bal<strong>an</strong>ces: I. The L<strong>an</strong>d Dynamics (LaD) model. J. Hydrometeorol., 3:283–299. 132
National Climatic Data Center (2005). Daily <strong>an</strong>d Sub-daily Precipitation for <strong>the</strong> Former USSR. Technical report. Available from National Geophysical Data Center, http://www.ncdc.noaa.gov/oa/documentlibrary/surface-doc.html9813. Peterson, B. J., Holmes, R. M., McClell<strong>an</strong>d, J. W., Vörösmarty, C. J., Lammers, R. B., Shiklom<strong>an</strong>ov, A. I., Shiklom<strong>an</strong>ov, I. A., Rahmstorf, S. (2002). Incre<strong>as</strong>ing river discharge to <strong>the</strong> Arctic Oce<strong>an</strong>. Science, 298:2171–2173. Rawlins, M. A., Lammers, R. B., Frolking, S., Fekete, B. M., Vörösmarty, C. J. (2003). Simulating p<strong>an</strong>-Arctic runoff with a macro-scale terrestrial water bal<strong>an</strong>ce model. Hydrol. Processes, 17:2521–2539. Serreze, M. C., Clark, M. P., Bromwich, D. H., Etringer, A. J., Zh<strong>an</strong>g, T., Lammers, R. (2003). Monitoring precipitation over <strong>the</strong> Arctic terrestrial drainage system: Data requirements, shortcomings, <strong>an</strong>d applications of atmospheric re<strong>an</strong>alysis. J. Hydrometeorol., 4(2):387–407. Shiklom<strong>an</strong>ov, A. I., Lammers, R. B., Vörösmarty, C. J. (2002). Widespread decline in hydrological monitoring threatens P<strong>an</strong>-Arctic research. Eos Tr<strong>an</strong>s. AGU, 83(2):13–17. Shmakin, A. B., Milly, P. C. D., Dunne, K. (2002). Global modeling of l<strong>an</strong>d water <strong>an</strong>d energy bal<strong>an</strong>ces. part III: Inter<strong>an</strong>nual variability. J. Hydrometeorol., 3(3):311–321. Ulaby, F. T., Moore, R. K., Fung, A. K. (1986). Microwave Remote Sensing: Active <strong>an</strong>d P<strong>as</strong>sive, Vol. III – Volume Scattering <strong>an</strong>d Emission Theory, Adv<strong>an</strong>ced Systems <strong>an</strong>d Applications. Artec House Inc., Dedham, MA. Vörösmarty, C. J., Fekete, B. M., Maybeck, M., Lammers, R. B. (2000). Geomorphometric attributes of <strong>the</strong> gloabl system of rivers at 30-min spatial resolution. J. Hydrol., 237:17–39. Vörösmarty, C. J., Hinzm<strong>an</strong>, L. D., Peterson, B. J., Bromwich, D. H., Hamilton, L. C., Morrison, J., Rom<strong>an</strong>ovsky, V. E., Sturm, M., Webb, R. S. (2001). The hydrologic cycle <strong>an</strong>d its role in arctic <strong>an</strong>d global environmental ch<strong>an</strong>ge: A rationale <strong>an</strong>d strategy for syn<strong>the</strong>sis study. Technical report, Fairb<strong>an</strong>ks, AK: Arctic Research Consortium of <strong>the</strong> U.S. Waliser, D. E., Waliser, S. E., Seo, K., Enjoku, E. (2005). Evaluation <strong>an</strong>d climate ch<strong>an</strong>ge projections of <strong>the</strong> global hydrological cycle in IPCC AR4 model simulations. In Eos Tr<strong>an</strong>s. AGU,volume86. Fall Meet. Suppl., Abstract H51I-07. Way, J. B., Zimmerm<strong>an</strong>n, R., Rignot, E., McDonald, K., Oren, R. (1997). Winter <strong>an</strong>d spring thaw <strong>as</strong> observed with imaging radar at BOREAS. J. Geophys. Res., 102(D24):29673–29684. Willmott, C. J. <strong>an</strong>d Matsuura, K. (2001). Arctic terrestrial air temperature <strong>an</strong>d precipitation: Monthly <strong>an</strong>d <strong>an</strong>nual time series (1930–2000) version 1. available online at: http://climate.geog.udel.edu/ climate/. Y<strong>an</strong>g, D., K<strong>an</strong>e, D. L., Hinzm<strong>an</strong>, L. D. (2002). Siberi<strong>an</strong> Lena River hydrologic regime <strong>an</strong>d recent ch<strong>an</strong>ge. J. Geophys. Res., 107(D23). 4694, doi:10.1029/2002JD002542. 133
- 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:
21 63 rd EASTERN SNOW CONFERENCE Ne
- Page 35 and 36:
23 63 rd EASTERN SNOW CONFERENCE Ne
- Page 37 and 38:
25 63 rd EASTERN SNOW CONFERENCE Ne
- Page 39 and 40:
27 63 rd EASTERN SNOW CONFERENCE Ne
- Page 41 and 42:
R ∑i ∑ n 2 = 1 NS = − n i = 1
- Page 43 and 44:
31 63 rd EASTERN SNOW CONFERENCE Ne
- Page 45 and 46:
33 63 rd EASTERN SNOW CONFERENCE Ne
- Page 47 and 48:
35 63 rd EASTERN SNOW CONFERENCE Ne
- Page 49 and 50:
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 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 63 and 64:
51 63 rd EASTERN SNOW CONFERENCE Ne
- 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:
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 87 and 88:
75 63 rd EASTERN SNOW CONFERENCE Ne
- 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: 89 63 rd EASTERN SNOW CONFERENCE Ne
- 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: values. Continued development of ne
- 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
- Page 169 and 170: 157 63 rd EASTERN SNOW CONFERENCE N
- Page 171 and 172: 159 63 rd EASTERN SNOW CONFERENCE N
- Page 173 and 174: 161 63 rd EASTERN SNOW CONFERENCE N
- Page 175 and 176: 180 160 140 120 100 80 60 40 20 0 1
- Page 177 and 178: 165 63 rd EASTERN SNOW CONFERENCE N
- 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 193 and 194: 181 63 rd EASTERN SNOW CONFERENCE N
- 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:
251 63 rd EASTERN SNOW CONFERENCE N
- 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:
263 63 rd EASTERN SNOW CONFERENCE N
- 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:
283 63 rd EASTERN SNOW CONFERENCE N
- 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