Water from the mountains, The Fourth Paradigm, and the color of snow<br />Jeff Dozier<br />University of California, Santa...
1/5th of Earth’s population gets water from snow and ice<br />1978<br />SierraNevada<br />2002<br />Changes in the QoriKal...
Thousand years ago —experimental science<br />Description of natural phenomena<br />Last few hundred years —theoretical sc...
Snow is one of nature’s most colorful materials (Landsat Thematic Mapper snow & cloud)<br />Bands 3 2 1 RGB(0.66, 0.57, 0....
Automated measurement with snow pillow<br />Measures the snow water equivalent (SWE)<br />amount of water that would resul...
Snow-pillow data for Leavitt Lake, 2929 m, Sierra Nevada<br />
Manual measurement started in the Sierra Nevada in 1910<br />
April-July 2011 forecast, Tuolumne River<br />
Distribution of errors in the April-July forecast<br />(R. Rice, UC Merced)<br />
[Chapman & Davis,2010]<br />Historical record during a period of climate change<br />
[D. Marks]<br />
Snow course RBV, elev 1707m, 38.9°N 120.4°W (American R) <br />
Orographic effect varies (Tuolumne-Merced River basins example)<br />
Sierra Nevada, trends in 220 long-term snow courses(> 50 years, continuing to present)<br />
Daily integrated solar radiation is more heterogeneous when Sun is lower<br />(40°N, 30° slope)<br />[Lundquist & Flint, 2...
Optical properties of ice and water<br />𝐼𝑠𝐼0=exp−4𝜋𝑘𝑠𝜆<br /> <br />
[Erbe et al., 2003]<br />“Snowflakes are hieroglyphs sent from the sky” <br />UkichiroNakaya<br />
Snow spectral reflectivity (albedo) is sensitive to the absorption coefficient of ice<br />[Wiscombe  & Warren, 1980]<br />
Dust<br />algae<br />(M. Skiles)<br />
Spectral reflectivity of dirty snow and snow with red algae (Chlamydomonasnivalis)<br />[Painter et al., 2001]<br />
For clean snow, net solar radiation is greatest in the near-IR wavelengths<br />
Seasonal solar radiation (Mammoth Mtn, 2005)<br />
Terra satellite 705 km altitudeorbit 98 minutes<br />MODIS instrument sees all of Earth’s surface in 2 days (almost all in...
(moderate resolution imaging spectroradiometer)MODIS spectral bands<br />
Spectra with 7 MODIS “land” bands(500 m resolution, daily coverage)<br />
MODIS image<br />
Fractional snow-covered area, Sierra Nevada (MODIS images available daily)<br />
Not just snow cover, but also its reflectivity<br />
Spatially distributed snow water equivalent<br />SWE, mm<br />4500<br />2500<br />1900<br />1300<br />600<br />(N. Molotch...
Interpolation<br />statistical 3D interpolation from snow pillows and snow courses, constrained by remotely sensed snow-co...
Snow redistribution and drifting<br />(D. Marks)<br />
Reconstruction of heterogeneous snow in a grid cell<br />z<br />x<br />y<br />Daily potential melt<br />fSCA<br />Reconstr...
Solar radiation at 1 hr time steps – details<br />Cosgrove et al., 2003;<br />Pinker et al., 2003;<br />Mitchell et al., 2...
Comparison of modeled and observed SWE, April 1, 2006<br />
“All models are wrong, but some are useful” – G. Box<br />Interpolation<br />SNODAS<br />Reconstruction<br />2006<br />Apr...
km3<br />mm<br />Interpolation<br />SNODAS<br />Reconstruction<br />
Persistent, high-elevation snowpack  not measured by surface stations<br />
Data Acquisition & Modeling<br />Archiving & Preservation<br />(J. Frew, T. Hey)<br />Collaboration & Visualization<br />D...
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Dozier 2011 Microsoft LATAM

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Water from the mountains, The Fourth Paradigm, and the color of snow

Microsoft 2011 Latin America Faculty Summit, Cartagena, Colombia, 20 May 2011

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  • Fundamental properties of measuring snow on the ground start with the optical properties of ice (and water)
  • Dozier 2011 Microsoft LATAM

    1. 1. Water from the mountains, The Fourth Paradigm, and the color of snow<br />Jeff Dozier<br />University of California, Santa Barbara<br />
    2. 2. 1/5th of Earth’s population gets water from snow and ice<br />1978<br />SierraNevada<br />2002<br />Changes in the QoriKalis Glacier, Quelccaya Ice Cap, Peru<br />(Barnett et al., 2005)<br />(L. Thompson)<br />
    3. 3. Thousand years ago —experimental science<br />Description of natural phenomena<br />Last few hundred years —theoretical science<br />Newton’s Laws, Maxwell’s Equations . . .<br />Last few decades — computational science<br />Simulation of complex phenomena<br />Today — data-intensive science<br />Model/data integration<br />Data mining<br />Higher-order products, sharing<br />Jim Gray, 1944-2007<br />http://fourthparadigm.org<br />
    4. 4. Snow is one of nature’s most colorful materials (Landsat Thematic Mapper snow & cloud)<br />Bands 3 2 1 RGB(0.66, 0.57, 0.48 μm)<br />Bands 5 4 2(1.65, 0.83, 0.57 μm)<br />
    5. 5. Automated measurement with snow pillow<br />Measures the snow water equivalent (SWE)<br />amount of water that would result if the snow melted<br />snow  depth = kg m–2 (mass/area)<br />(snow  depth)/water = depth of water equivalent<br />1 kg m–2= 1 mm depth<br />(R. Julander)<br />
    6. 6. Snow-pillow data for Leavitt Lake, 2929 m, Sierra Nevada<br />
    7. 7. Manual measurement started in the Sierra Nevada in 1910<br />
    8. 8. April-July 2011 forecast, Tuolumne River<br />
    9. 9. Distribution of errors in the April-July forecast<br />(R. Rice, UC Merced)<br />
    10. 10. [Chapman & Davis,2010]<br />Historical record during a period of climate change<br />
    11. 11. [D. Marks]<br />
    12. 12. Snow course RBV, elev 1707m, 38.9°N 120.4°W (American R) <br />
    13. 13. Orographic effect varies (Tuolumne-Merced River basins example)<br />
    14. 14. Sierra Nevada, trends in 220 long-term snow courses(> 50 years, continuing to present)<br />
    15. 15. Daily integrated solar radiation is more heterogeneous when Sun is lower<br />(40°N, 30° slope)<br />[Lundquist & Flint, 2006]<br />
    16. 16. Optical properties of ice and water<br />𝐼𝑠𝐼0=exp−4𝜋𝑘𝑠𝜆<br /> <br />
    17. 17. [Erbe et al., 2003]<br />“Snowflakes are hieroglyphs sent from the sky” <br />UkichiroNakaya<br />
    18. 18. Snow spectral reflectivity (albedo) is sensitive to the absorption coefficient of ice<br />[Wiscombe & Warren, 1980]<br />
    19. 19. Dust<br />algae<br />(M. Skiles)<br />
    20. 20. Spectral reflectivity of dirty snow and snow with red algae (Chlamydomonasnivalis)<br />[Painter et al., 2001]<br />
    21. 21. For clean snow, net solar radiation is greatest in the near-IR wavelengths<br />
    22. 22. Seasonal solar radiation (Mammoth Mtn, 2005)<br />
    23. 23. Terra satellite 705 km altitudeorbit 98 minutes<br />MODIS instrument sees all of Earth’s surface in 2 days (almost all in 1 day) <br />Path of Satellite<br />
    24. 24. (moderate resolution imaging spectroradiometer)MODIS spectral bands<br />
    25. 25. Spectra with 7 MODIS “land” bands(500 m resolution, daily coverage)<br />
    26. 26. MODIS image<br />
    27. 27. Fractional snow-covered area, Sierra Nevada (MODIS images available daily)<br />
    28. 28. Not just snow cover, but also its reflectivity<br />
    29. 29. Spatially distributed snow water equivalent<br />SWE, mm<br />4500<br />2500<br />1900<br />1300<br />600<br />(N. Molotch)<br />10<br />04/10/05<br />
    30. 30. Interpolation<br />statistical 3D interpolation from snow pillows and snow courses, constrained by remotely sensed snow-covered area<br />SNODAS – the U.S. “national snow model”<br />assimilate numerical weather & snowmelt models with surface data & remote sensing<br />Reconstruction (after the snow is gone)<br />from remotely sensed snow cover, estimate rate of snowmelt from energy input, and back-calculate how much snow there was.<br />Three independent ways<br />
    31. 31. Snow redistribution and drifting<br />(D. Marks)<br />
    32. 32. Reconstruction of heterogeneous snow in a grid cell<br />z<br />x<br />y<br />Daily potential melt<br />fSCA<br />Reconstructed SWE<br />A. Kahl<br />[Homan et al., 2010]<br />
    33. 33. Solar radiation at 1 hr time steps – details<br />Cosgrove et al., 2003;<br />Pinker et al., 2003;<br />Mitchell et al., 2004<br />Erbs et al., 1982;<br />Olyphant et al., 1984<br />Dozier and Frew, 1990<br />Dubayah and Loechel., 1997<br />Link and Marks, 1999; <br />Garren and Marks, 2005<br />Painter et al., 2009;<br />Dozier et al., 2008<br />
    34. 34. Comparison of modeled and observed SWE, April 1, 2006<br />
    35. 35. “All models are wrong, but some are useful” – G. Box<br />Interpolation<br />SNODAS<br />Reconstruction<br />2006<br />April<br />May<br />June<br />July<br />August<br />
    36. 36. km3<br />mm<br />Interpolation<br />SNODAS<br />Reconstruction<br />
    37. 37. Persistent, high-elevation snowpack not measured by surface stations<br />
    38. 38. Data Acquisition & Modeling<br />Archiving & Preservation<br />(J. Frew, T. Hey)<br />Collaboration & Visualization<br />Dissemination & Sharing<br />http://fourthparadigm.org<br />Analysis & Data Mining<br />
    39. 39. Information about water is more useful as we climb the value ladder<br />Forecasting<br />Reporting<br />Done poorly,but a few notablecounter-examples<br />Analysis<br />Integration<br />Data >>> Information >>> Insight<br />>>> Increasing value >>><br />Distribution<br />Done poorly to moderately,not easy to find<br />Aggregation<br />Quality assurance<br />Sometimes done well,generally discoverable and available,butcould be improved<br />Collation<br />Monitoring<br />(I. Zaslavsky & CSIRO, BOM, WMO)<br />
    40. 40. (MOD for Terra/MYD for Aqua)<br />
    41. 41. Finis<br />“the author of all books”– James Joyce, Finnegan’s Wake<br />http://www.slideshare.net/JeffDozier<br />
    42. 42.
    43. 43. References<br />Bales, R. C., N. P. Molotch, T. H. Painter, M. D. Dettinger, R. Rice, and J. Dozier (2006), Mountain hydrology of the western United States, Water Resour. Res., 42, W08432, doi: 10.1029/2005WR004387.<br />Chapman, D. S., and M. G. Davis (2010), Climate change: Past, present, and future, Eos. Trans. AGU, 91, 325-326.<br />Hall, D. K., G. A. Riggs, V. V. Salomonson, N. E. DiGirolamo, and K. J. Bayr (2002), MODIS snow-cover products, Remote Sens. Environ., 83, 181-194, doi: 10.1016/S0034-4257(02)00095-0.<br />Dozier, J., T. H. Painter, K. Rittger, and J. E. Frew (2008), Time-space continuity of daily maps of fractional snow cover and albedo from MODIS, Adv. Water Resour., 31, 1515-1526, doi: 10.1016/j.advwatres.2008.08.011.<br />Homan, J. W., C. H. Luce, J. P. McNamara, and N. F. Glenn (2010), Improvement of distributed snowmelt energy balance modeling with MODIS-based NDSI-derived fractional snow-covered area data, Hydrol. Proc., doi: 10.1002/hyp.7857.<br />Kapnick, S., and A. Hall (2010), Observed climate-snowpack relationships in California and their implications for the future, J. Climate, 23, 3446-3456, doi: 10.1175/2010JCLI2903.1.<br />Lundquist, J. D., and A. L. Flint (2006), Onset of snowmelt and streamflow in 2004 in the western United States: How shading may affect spring streamflow timing in a warmer world, J. Hydrometeorol., 7, 1199-1217, doi: 10.1175/JHM539.1.<br />Martinec, J., and A. Rango (1981), Areal distribution of snow water equivalent evaluated by snow cover monitoring, Water Resour. Res., 17, 1480-1488, doi: 10.1029/WR017i005p01480.<br />Nolin, A. W., and J. Dozier (2000), A hyperspectral method for remotely sensing the grain size of snow, Remote Sens. Environ., 74, 207-216, doi: 10.1016/S0034-4257(00)00111-5.<br />Painter, T. H., K. Rittger, C. McKenzie, R. E. Davis, and J. Dozier (2009), Retrieval of subpixel snow-covered area, grain size, and albedo from MODIS, Remote Sens. Environ., 113, 868–879, doi: 10.1016/j.rse.2009.01.001.<br />Painter, T. H., J. S. Deems, J. Belnap, A. F. Hamlet, C. C. Landry, and B. Udall (2010), Response of Colorado River runoff to dust radiative forcing in snow, Proc. Natl. Acad. Sci. U. S. A.,doi: 10.1073/pnas.0913139107.<br />Rosenthal, W., J. Saleta, and J. Dozier (2007), Scanning electron microscopy of impurity structures in snow, Cold Regions. Sci. Technol., 47, 80-89, doi: 10.1016/j.cold.regions.2006.08.006.<br />Serreze, M. C., M. P. Clark, R. L. Armstrong, D. A. McGinnis, and R. S. Pulwarty (1999), Characteristics of the western United States snowpack from snowpack telemetry (SNOTEL) data, Water Resour. Res., 35, 2145-2160, doi: 10.1029/1999WR900090.<br />Wiscombe, W. J., and S. G. Warren (1980), A model for the spectral albedo of snow, I, Pure snow, J. Atmos. Sci., 37, 2712-2733.<br />

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