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SUMMARY Main research objective of this study was to evaluate the accuracy of a new SWE product derived from the blending of a passive microwave SWE product based on AMSU instrument 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. The paper described data and evaluation methodology. The blending procedure between the AMSU SWE and IMS snow cover extent products was also described. Next, snow cover extent retrievals by the AMSU, IMS and the blended product were evaluated for the 2005–2006 snow season. Despite good inter-product agreement, it was shown that the IMS had better skills than the AMSU snow cover extent product in retrieving new snow cover in early winter and in correctly identifying snow-free land, e.g., over Mongolia, that was otherwise reported as “snow” by the AMSU product. These problem areas were also evaluated using other observation sources, e.g., the MODIS snow cover product and the AMSU snowfall product. In a separate investigation, the paper also described the evaluation of the microwave SWE product globally and over central Europe (Slovakia). Qualitative evaluation of the large-scale, global SWE patterns showed dependence of AMSU retrieved SWE on land surface temperature: the lower the land surface temperature, the higher the SWE retrieved. This temperature bias was attributed in part to temperature effects on snow properties that impact microwave response. Therefore, AMSU SWE algorithm modifications are needed, e.g., adjustment of algorithm coefficients for changing snow properties. Quantitative evaluation over Slovakia for a limited period in 2006 showed reasonably good agreement for SWE less than 100 mm and snow depth less than 30 cm, associated with low elevation terrain (less than 400 m). Sensitivity to snow deeper than 100 mm in SWE decreased significantly. ACKNOWLEDGEMENT The authors wish to thank Dr. Yan Kanak and Joseph Pecho from the Slovak National Institute of Hydrometeorology for their cooperation and for graciously providing valuable station data over Slovakia. REFERENCES Armstrong, R., M.J. Brodzik, and M. Savoie, 2003: Multisensor approach to mapping snow cover using data from NASA’s Terra and Aqua Spacecraft (AMSR-E and MODIS), Eos Trans. AGU, 84(46). Basist, A., D. Garrett, R. Ferraro, N. Grody and K. Mitchell, 1996: A comparison between snow cover products derived from visible and microwave observations, J. Appl. Meteorol., 35:163– 177. Dong, J., J.P. Walker, and P. R. Houser, 2005: Factors affecting remotely sensed snow water equivalent uncertainty, Remote Sensing of Environment 97: 68–82. Ferraro, R., C. Kongoli, and P. Pellegrino, 2004: An Evaluation of a New AMSU-Derived Falling Snow Retrieval Algorithm (Preprint), 13 th Conference on Satellite Meteorology and Oceanography, American Meteorological Society, 20–23 September, 2004, Norfolk, VA. Ferraro, R. R., F. Weng, N.C. Grody, I. Guch, C. Dean, C. Kongoli, H. Meng, P. Pellegrino, and L. Zhao, 2002: NOAA satellite-derived hydrological products prove their worth, Eos Trans. AGU, 83: 429–437. Foster J.L., C. Sun, J.P. Walker, R. Kelly, A. Chang, J. Dong, and H. Powell, 2005: Quantifying the uncertainty in passive microwave snow water equivalent observations, Remote Sensing of Environment 94: 187–203. Grody, N.C., 1991: Classification of snow cover and precipitation using the special sensor microwave imager, J. Geophys. Res., 96(D4): 7423–7435. Grody, N.C., and A.N. Basist, 1996: Global identification of snow cover using SSM/I Measurements, IEEE Trans. Geosci. Remote Sens. 34(1): 237–249. 102
Helfrich, S.R., D. McNamara, B.H.Ramsay, T. Baldwin, and T. Kasheta (2006), Enhancements and forthcoming developments to the Interactive Multisensor Snow and Ice Mapping System (IMS), Hydrological Processes (this issue). Kongoli, C., N.C. Grody, and R. Ferraro, 2004: Interpretation of AMSU measurementsfor the retrievals of snow water equivalent and snow depth, J. Geophys. Res, 109: do:10.1029/2004JD004836. Kongoli, C., and R. Ferraro, 2004: Development and evaluation of the AMSU-based SWE product (Preprint), 13 th Conference on Satellite Meteorology and Oceanography, American Meteorological Society, 20–23 September, 2004, Norfolk, VA. Kongoli, C., R. Ferraro, P. Pellegrino, and H. Meng, 2005 (Preprint): Snow Microwave Products from the NOAA’s Advanced Microwave Sounding Unit, 19 th Conference on Hydrology, American Meteorological Society, 8–14 January, 2005, San Diego, CA. Kongoli, C., P. Pellegrino, R.R. Ferraro, C. Grody, and H. Meng, 2003: A new snowfall detection algorithm over land using measurements from the Advanced Microwave Sounding Unit (AMSU), Geophys. Res. Let. 30(14): 1756–1759. Matzler, C., 1994: Passive microwave signatures of landscapes in winter, Meteorol. Atmos. Phys. 54: 241–260. Meng H., L. Zhao, R. Ferraro, F. Weng, Q. Liu, 2006 (Preprint): Calibration andvalidation of N- 18 AMSU-A and MHS, 14 th Conference on Satellite Meteorology and Oceanography, American Meteorological Society, 28 January–2 February, 2006, Atlanta, GA. Ramsay, B.H., 1998: The interactive multisensor snow and ice mapping system, Hydrological Processes, 12:1537–1546. Ramsay, B.H., 2000: Prospects for the Interactive Multisensor Snow and Ice Mapping System (IMS), Proceedings of the 57 th Eastern Snow Conference, 18–19 May 2000, Syracuse,NY, 161–170. Romanov, P. G. Goodman and I. Csiszar, 2000: Automated monitoring of snow cover over North America with multispectral satellite data, J. Appl. Meteorol., 39: 1866–1880. Sturm, M., J. Holmgren, and G.E. Liston, 1995: A seasonal snow cover classification system for local to global applications, Journal of Climate, 8(5): 1261–1283. 103
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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
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 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: Figure 9. Inter-comparison plots of
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
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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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