73 Eastern Snow Conference Scientific Program & Abstracts

73rd Eastern Snow Conference Scientific Program & Abstracts

❄ Airborne and spaceborne remote sensing of snow and ice ❄

Highbanks Metro Park, Columbus, Ohio, February 2015.

June 14-16, 2016, at the Byrd Polar & Climate Research Center ❄ ❄ and the Wexner Center for the Arts, The Ohio State University

73rd Eastern Snow Conference

The Eastern Snow Conference

The Eastern Snow Conference (ESC) is a joint Canadian/U.S. organization. The Eastern snow conference is described in the first published Eastern Snow Conference Proceedings as a relatively small organization operating quietly since its founding in 1940 by a small group of individuals originally from eastern North America. The conference met eight times between 1940 and 1951. The first Eastern Snow Conference Proceedings contained papers from its 9th Annual Meeting held February 14 and 15, 1952, in Springfield, Massachusetts. Today, its membership is drawn from Europe, Japan, the Middle East, as well as North America. Our current membership includes scientists, engineers, snow surveyors, technicians, professors, students and professionals involved in operations and maintenance. The western counterpart to this organization is the Western Snow Conference (WSC), also a joint Canadian/US organization. Every fifth year or so, the ESC and WSC hold joint meetings.

At its annual meeting, the Eastern Snow Conference brings the research and operations communities together to discuss recent work on scientific, engineering and operational issues related to snow and ice. The location of the conference alternates yearly between the United States and Canada, and attendees present their work by giving talks or presenting posters. Authors submit their manuscripts for publication in our yearly Proceedings of the Eastern Snow Conference. Volumes of the Eastern Snow Proceedings can be found in libraries throughout North America and Europe; papers can also be found through the National Technical Information Service (NTIS) in the United States and CISTI in Canada and issues since 2000 are available on the conferences website at www.easternsnow.org.

In recent years, the ESC meetings have included presentations on snow physics, management and hydrology, snow and ice loads on structures, river ice, winter survival of animals, remote sensing of snow and ice, glacier processes, snow science as a teaching tool and socio-political impacts of winter.

Corporate Members and Sponsors

The ESC could not operate without the support of its corporate membership over the years and 2016 sponsor. This year the ESC would like to thank Geonor (www.geonor.com), and Campbell Scientific Canada (https://www.campbellsci.ca). Thanks to the Byrd Polar & Climate Research Center for their support of the 73rd meeting!

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The ESC encourages student research through its Wiesnet Medal. This medal is presented annually to the best student paper presented at the conference. Campbell Scientific Canada also graciously awards a cash prize to the student research showing the most innovative use of technology in the gathering of data. Finally, the David Miller Award is awarded to the best student poster at the annual Conference.

Year Winner Affiliation 2015 Nicolas Leroux University of Saskatchewan 2014 Justin Hartnett Syracruse University, Syracruse, NY 2013 Andreas Dietz Earth Observation Center / DFD, Germany 2012 Elizabeth Burakowski University of New Hampshire, NH 2011 Kathryn Semmens Lehigh University 2010 Simon von de Wall University of Victoria, BC 2009 Si Chen Dartmouth College 2008 Chris Furhman University of North Carolina at Chapel Hill, NC 2007 not awarded 2006 Y.C. Chung University of Michigan 2005 M. Javan-Mashmool Université du Québec à Chicoutimi, Chicoutimi QC 2004 J. Farzaneh-Dehkordi Université du Québec à Chicoutimi, Chicoutimi QC 2003 Alexandre Langlois Université de Sherbrooke, Sherbrooke QC 2002 Patrick Ménard Université de Laval, Ste Foy, QC 2001 C. Tavakoli Université du Québec à Chicoutimi, Chicoutimi QC 2000 not awarded 1999 S. Brettschneider Université du Québec à Chicoutimi, Chicoutimi QC 1998 Andrew Grundstein University of Delaware, Newark, DE 1997 Newell Hedstrom University of Saskatchewan, Saskatoon SK 1996 Suzanne Hartley University of Denver, Denver CO 1995 Paul Wolfe Wilfred Laurier University, Waterloo ON 1994 G.E. Mann University of Michigan, Ann Arbor MI 1993 G. Devarennes Université de Québec à Québec, QC 1992 D.W. Cline University of Colorado, Boulder CO 1991 D. Samelson Cornell University, Ithaca NY 1990 A.K. Abdel-Zaher University of New Brunswick, Fredericton NB 1989 A. Giguere McGill University, Montréal QC 1988 Mauri Pelto University of Maine, Orono ME 1987 Cameron Wake Wilfred Laurier University, Waterloo ON 1986 Craig Allan Trent University, Peterborough ON 1985 Robert Speck Rensselaer Polytechnic Institute, Troy NY 1984 N.K. Kalliomaki Laurentian University, Sudbury, ON 1983 David Beresford Trent University, Peterborough ON

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1982 not awarded 1981 Jeffrey Patch University of New Brunswick, Fredericton NB 1980 Bryan Wolfe Trent University, Peterborough ON 1979 Margaret Leech McGill University, Montréal QC 1978 Michael English Trent University, Peterborough ON 1977 Don McLaughlin & Rensselaer Polytechnic Institute, Troy NY George Duggan 1976 Dwayne McMurter Trent University, Peterborough ON 1975 Nigel Allan Syracuse University, Syracuse NY 1974 not awarded 1973 Stan Mathewson Trent University, Peterborough ON

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Life Members The Eastern Snow conference gratefully recognizes individuals who have made lifelong contributions to the study of snow and for their support of this organization. Our current life members are:

Peter Adams Art Eschner Barry Goodison Gerry Jones John Metcalfe Hilda Snelling Donald Wiesnet

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The Eastern Snow Conference annually bestows upon a distinguished snow scientist who, in striving for excellence in snow research, contributes to an event of notable humor the highly coveted Sno-foo Award.

Year Winner Affiliation 2015 Kevin Coté Université de Sherbrooke, Sherbrooke, QC 2014 Dorothy Hall NASA-Goddard, MD 2013 Benoit Montpetit Université de Sherbrooke, Sherbrooke, QC 2012 Don Pierson NYC DEP, NY 2011 Ken Rancourt Mount Washington Observatory, North Conway, NH 2010 Kyung-Kuk (Kevin) Kang University of Waterloo, Waterloo, ON 2009 Rob Hellström Bridgewater State University, Bridgewater, MA 2008 Steven Fassnacht Colorado State University, Fort Collins, CO 2007 the group of 9* U. Saskatchewan, UBC, Alberta Environment, U. Calgary 2006 Andrew Klein Texas A&M University, College Station, TX 2005 Claude Duguay University of Alaska-Fairbanks, Fairbanks, AK 2004 Chris Derksen Meteorological Service of Canada, Toronto, ON 2003 Miles Ecclestone Trent University, Peterborough ON 2002 Danny Marks U.S.D.A., Boise ID 2001 Brenda Toth University of Saskatchewan, Saskatoon SK 2000 Mauri Pelto Nichols College, Dudley MA 1999 Ross Brown Meteorological Service of Canada, Montréal, PQ 1998 Mary Albert CRREL, Hanover, NH 1997 Jean Stein Université de Laval, Ste Foy, QC 1996 Colin Taylor Trent University, Peterborough ON 1995 Mike Demuth N.H.R.I., Saskatoon SK 1994 Bert Davis CRREL, Hanover, NH 1993 John Pomeroy N.H.R.I., Saskatoon SK 1992 Tom Niziol N.W.S., Buffalo, NY 1991 Terry Prowse N.H.R.I., Saskatoon SK 1990 Kersi Davar University of New Brunswick, Fredericton, NB 1989 Gerry Jones INRS-EAU, Saint Foy, QC 1988 Robert Sykes SUNY, Syracuse NY 1987 John Metcalfe Meteorological Service of Canada, Toronto, ON 1986 Peter Adams Trent University, Peterborough ON 1985 Don Wiesnet National Weather Service, Minneapolis, MN 1984 Barry Goodison Meteorological Service of Canada, Toronto, ON * Jimmy MacDonald (U. Sask.), Bill Floyd (UBC), Chris DeBeer (U. Sask.), Wendell Koenig (AB Env.), Jaime Hood (U. Calgary), Dankia Muir (U. Calgary), John Jackson (U. Calgary), Sarah Forte (U. Calgary), Prof. Masaki Hayashi (U. Calgary)

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73rd ESC Executive Committee 2015-2016

Past President: Baker Perry, Elks Park, NC President: Alain Royer, Sherbrooke, QC Vice President and Program Chair: Michael Durand, Columbus, Ohio Treasurer and 1st Secretary, CA: Miles Ecclestone, Peterborough, ON 2nd Secretary, CA: Alexander Langlois, Sherbrooke, QC 1st Secretary, US: Kenneth Rancourt, Conway, NH 2nd Secretary, US: Derrill Cowing, Monmouth, ME Editor, ESC Proceedings: Alexander Langlois, Sherbrooke, QC Editors, Physical Geography: Mauri Pelto, Dudley, MA, Chair Robert Hellstrom, Bridgewater, MA

Steering Committee: Allan Frei, New York, NY, Chair Janet Hardy, Hanover, NH George Riggs, Gambrills, MD Rae Melloh, Hanover, NH Chris Fuhrman, Chapel Hill, NC Craig Smith, Saskatoon, SK Alex Roy, Sherbrooke, QC Steve Howell, Toronto, ON Laura Thomson, Ottawa, ON John Sugg, Boone, NC

Research Committee: Sean Helfrich, Suitland, MD, Chair James Brylawski, Augusta, NJ Rick Fleetwood, Fredericton, NB Kevin Kang, Waterloo, ON Krys Chutko, North Bay, ON Bart Forman, College Park, MD

Web Master: Andrew Klein, College Station, TX Local Arrangements: Michael Durand & Bryan Mark, Columbus, OH

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Welcome Tuesday, June 14 ❄ Wexner Center Café

5:00-7:00 pm Registration and Ice breaker reception Session 1. Recent Advances in Remote Sensing Wednesday, June 15 ❄ Byrd Polar Climate & Research Center (BPCRC) ❄ Chair: Bart Forman

8:00 am Welcome: Ellen Mosley-Thompson, Director of the BPCRC 8:10 am David Robinson: 50 Years of Satellite Snow Cover Extent Mapping Over Northern Hemisphere Lands 8:30 am Chris Derksen et al.: User requirements, algorithm readiness, and modeling studies in support of terrestrial snow mass radar mission concepts 8:50 am Brian Henn et al.: Comparison of high-elevation LiDAR snow measurements with distributed streamflow observations 9:10 am Manuela Girotto et al: A Landsat-era (1985-2015) Sierra Nevada (USA) Snow Reanalysis Dataset (Invited) 9:30 am Noah Molotch et al.: Development of Universal Relationships between Snow Depth, Snow Covered Area and Terrain Roughness from NASA Airborne Snow Observatory data (Invited) Session 2. Advances in Remote Sensing Theory and Methods Wednesday, June 15 ❄ Byrd Polar Climate & Research Center (BPCRC) ❄ Chair: Bryan Mark

10:00 am Ed Kim et al.: The NASA SnowEx airborne snow campaign 10:30 am Leung Tsang et al.: Snow Microstructure Characterization and Numerical Simulation of Maxwell’s Equation in 3D Applied to Snow Microwave Remote Sensing (Invited) 10:50 am Alain Royer et al.: Comparison of three microwave radiative transfer models for simulating snow brightness temperature 11:10 am Eli Deeb et al. Characterizing Satellite-Based Passive Microwave Estimates of Snow Water Equivalent at Sub-Grid Resolution 11:30 am Bart Forman: Seeing and Feeling Snow from Space: A Unified Radiometric and Gravimetric Approach

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11:50 am 3-minute theses (3MT, go.osu.edu/3mt): Recent advances in passive microwave remote sensing methods, with presentations by current graduate students (or recent graduates): Yonghwan Kwon, Dongyue Li, Mohammad Mousavi, Olivier Saint-Jean-Rondeau and Yuan Xue. Session 3. Posters Wednesday, June 15, 1:30 – 3:00 ❄ Mershon Auditorium Lobby

1. Miles Ecclestone et al.: A pictorial history of changes in polar science and technology: an example from glacier measurements on Axel Heiberg Island, Nunavut, Canada, 1959-2015. 2. Dongyue Li et al.: How much western United States streamflow originates as snow? 3. Eric Burton et al.: Airflow Associated with Snowfall Events on the Quelccaya Icecap of Peru During the 2014-2015 Wet Season 4. Jill Coleman and Robert Schwartz: An Updated U.S. Blizzard Climatology: 1959-2014 5. Kelsey Cartwright et al.: Terrain Characteristic Influence on Snow Accumulation and Persistence: Case Study 6. Reed Parsons & Christopher Hopkinson: In-situ Light Emitting Diode Detection and Ranging for the Mapping of Snow Surface Topography and Depth 7. Roger de Roo et al.: Inexpensive in-situ snow pack sensors for temperature, density and grain size: First light 8. Krystopher Chutko: Seasonal and interannual variability in snow and streamflow δ18O signatures 9. Yonghwan Kwon et al.: Can assimilation of microwave radiance data improve continental-scale snow water storage estimates? (Invited) 10. Mohammad Mousavi et al.: Elevation Angular Dependence of Wideband Autocorrelation Radiometric (WiBAR) Remote Sensing of Dry Snowpack and Lake Icepack 11. Olivier Saint-Jean-Rondeau et al.: Parameterization of snow microstructure for passive microwave radiometry 12. Julie Miller et al.: Mapping firn aquifers on the Greenland and Antarctic ice sheets from space using C-band satellite-borne scatterometry 13. Alexandra Bringer et al.: Observations of snow packs with an Ultra Wide Band Radiometer 14. Yuna Duan et al.: A Bayesian retrieval of Greenland ice sheet internal temperature from ultra- wideband software-defined microwave radiometer (UWBRAD) measurements 15. Eunsang Cho et al.: Comparison between AMSR2 and AMSR-E Snow Water Equivalent using SSM/I over the North Central U.S. 16. Ryan Crumley et al.: Analyzing Seasonal Snow Cover Frequency Using the MODIS/Terra Daily Snow Cover Product with Google Earth Engine in the Pacific Northwest and California

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17. Carrie Vuyovich et al.: Sensitivity Analysis of passive microwave brightness temperatures to distributed snowmelt 18. Elizabeth Dyer & Joan Ramage: Investigating the interplay between warm winter anomalies and glacial melting in the Arctic: do early warming events matter? 19. Dorothy Hall et al.: Comparison of MODIS and VIIRS snow-cover products to study data-product continuity in the Catskill Mountain watersheds, New York 20. Richard Kelly et al.: The GCOM-W1 Satellite-based Microwave Snow Algorithm (SMSA) 21. Joan Ramage et al.: MELT ON THE MARGINS: Calibrated Enhanced-Resolution Brightness Temperatures to Map Melt Onset Near Glacier Margins and Transition Zones 22. Yuan Xue and Barton Forman: Decoupling atmospheric- and forest-related radiance emissions from satellite-based passive microwave observations over forested and snow-covered land in North America Session 4. Posters Wednesday, June 15, 3:15 – 4:45 ❄ Mershon Auditorium Lobby

23. Jason Endries et al.: Vertical structure and character of precipitation in the tropical high Andes of southern Peru and northern Bolivia 24. James Feiccabrino: Using cloud base height to decrease misclassified precipitation in hydrological models 25. Johnathan Kirk: Large precipitation events at SNOTEL sites and streamflow variability in the Upper Colorado River Basin 26. Andrew Klein: Daily snow depth at Palmer Station, Antarctica, 2007-2014: an initial analysis 27. Sebastian Schlögl et al.: How do stability corrections perform over snow? 28. Aaron Thompson et al.: Spatial variability of snow at Trail Valley Creek, NWT 29. Melissa Wrzesien et al.: Consideration of Mountain Snow Storage from Global Data Products 30. Kelly Elder & Matthew Sturm: 3rd Winter course for field snowpack measurements NASA Snow Working Group - Remote sensing (iSWGR) 31. Martin Schneebeli & Juha Lemmetyinen: 2nd European Snow Science Winter School 32. Qinghuan Li & Richard Kelly: Terrestrial laser scanning observations of tree canopy intercepted snow 33. Sean Helfrich et al.: Evaluation of Algorithm Alternatives for Blended Snow Depth in the IMS 34. Adam Hunsaker et al.: Evaluation of satellite-based observations for capturing early winter snowmelt within mid-latitude basins 35. Rhae Sung Kim et al.: Spectral analysis of airborne passive microwave measurements for classification of alpine snowpack

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36. Alex Langlois et al.: Rain-on-snow and ice layer formation detection using passive microwave radiometry: An arctic perspective 37. Dongyue Li et al.: Estimating snow water equivalent in a mountainous Sierra Nevada watershed with spaceborne radiance data assimilation 38. Jinmei Pan and Michael Durand: Formulation of a Bayesian SWE retrieval algorithm using X- and Ku- measurements 39. George Riggs et al.: Status of the MODIS C6 Snow Cover and NASA Suomi-NPP VIIRS Snow Cover Data Products 40. Saberi Nastaran et al.: Snow Properties Retrieval using DMRT-ML in a Statistical Framework Using Passive Microwave Airborne Observations 41. Shurun Tan et al.: Modeling polar ice sheet emission from 0.5-2.0GHz with a partially coherent model of layered media with random permittivities and roughness (Invited) 42. Oliver Wigmore et al.: UAV Mapping of Debris Covered Glacier Change, Llaca Glacier, Cordillera Blanca, Peru 43. Yuan Xue and Barton Forman: Can regional-scale snow water equivalent estimates be enhanced through the integration of a machine learning algorithm, passive microwave brightness temperature observations, and a land surface model? Banquet Wednesday, June 15 ❄ OSU Faculty Club 6:00-8:00 pm† The banquet agenda includes presentation of awards. The banquet keynote speaker, Prof Lonnie Thompson, is presenting on: “Global Climate Change: a perspective from the World's Highest Mountains.” † Happy hour begins at 5pm on the Faculty Club patio. Session 5. Remote sensing applications for cryospheric science: From the ice sheets to the mid-latitudes Thursday, June 16 ❄ Byrd Polar Climate & Research Center (BPCRC) ❄ Chair: Joan Ramage

8:30 am Nathan Amador: Assessing a Depth-retrieval method for determining Supraglacial Melt Lake Volume 8:50 am Kyung In Huh et al.: Evaluating 50 years of tropical Peruvian glacier volume change from multi-temporal digital elevation models (DEMs) and glacier flow and hydrology in the Cordillera Blanca, Peru (Invited) 9:10 am Caroline Dolant et al.: Detection of Rain-On-Snow events in the Canadian Arctic Archipelago between 1980-2014 using Passive Microwave Radiometry 9:30 am Jessica Cherry et al.: Recent airborne measurements of snow and ice in Interior Alaska

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9:50 am Rune Solberg et al.: Single and multi-sensor snow wetness mapping by Sentinel-1 and MODIS data 10:10 am Samuel Tuttle et al.: Comparison of Satellite Passive Microwave, Airborne Gamma Radiation Survey, and Ground Survey Snow Water Equivalent Estimates in the Northern Great Plains Session 6. Snow and ice processes, hydroclimatology, and change Thursday, June 16 ❄ Byrd Polar Climate & Research Center (BPCRC) ❄ Chair: Krystopher Chutko

10:45 am Ross Brown et al.: Northern Hemisphere winter thaw events – characteristics, trends and projected changes 11:05 am Aaron Wilson et al.: Improving atmospheric circulation and turbulent heat fluxes with the Arctic System Reanalysis (Invited) 11:25 am Sebastian Schlögl: Energy balance and melt over a patchy snow cover

12 73rd Eastern Snow Conference Assessing a Depth-retrieval method for determining Supraglacial Melt Lake Volume

Nathanael Amador

Ohio Wesleyan University, Department of Geology and Geography, Delaware, OH

2000 – 2012 Landsat-7 imagery is used to monitor the evolution of five supraglacial melt lakes in the ablation zone north of Jakobshavn Isbræ to relate melt lake volume and the required sensible energy to produce the meltwater. I utilize the cumulative positive degree day (cPDD) metric for melt production and a depth-reflectance methodology to estimate melt lake depths, and thus derive total melt lake volume. In 71% of instances when the annual peak melt lake volume occurs, the calculated volume exceeds the Krawczynski et al. (2009) threshold for hydrofracturing. The volume results for these lakes indicate that they have the potential to hydrofracture multiple times over the study period, which can affect nearby ice flow velocity via basal lubrication. The interannual variability in melt lake volume, when compared to cPDD, suggests that meltwater production is less important to melt lake size (area and volume) than the local ice sheet surface topography. When relating lake depths using the depth-reflectance methodology, there is minimal difference in the maximum melt-lake depth between in-situ measurements and the depth-reflectance methodology (~9%), suggesting that the depth-reflectance methodology accurately estimates melt-lake inundation depth for supraglacial lakes.

13 73rd Eastern Snow Conference Observations of snow packs with an Ultra Wide Band Radiometer

A. Bringer1, J. Johnson1, K. Jezek2, M. Durand2

1 ElectroScience Laboratory, Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH 2 School of Earth Sciences & Byrd Polar Research Center, The Ohio State University, Columbus, OH

Microwave radiometers are often used for cryospheric studies and especially to observe snow packs. They usually operate at a single frequency, 18 GHz or 37 GHz, as high frequencies are sensitive to the internal structure of snow (layering, grain size, density). However, recent studies have shown the potential of using lower frequencies such as L Band (1.4 GHz) to retrieve information about the freeze/thaw state of the soil beneath the snow pack. The brightness temperature at such frequencies shows sensitivity to the thickness of the frozen soil and the snow thickness. We are presently developing a radiometer for cryospheric studies, called the Ultra Wide Band Software Defined Radiometer (UWBRAD). It measures thermal emission over frequencies from 0.5 to 2 GHz in 12 frequency channels. Because of the dielectric contrast between the snow permittivity and the frozen soil one, we investigate whether UWBRAD microwave spectra can be used to measure the snow thickness. The soil is modeled as a two layer medium: a frozen layer on top and a thawed layer below. The snowpack is considered as a planar layered media with variations in temperature and density. Because the electrical properties are temperature dependent, we adopt a simple, linear model for the temperature profile in the snow. We use a coherent radiative transfer model to calculate the snowpack brightness temperature. In our preliminary studies, we observe an oscillating pattern with frequency which also varies with snow thickness. This indicates that UWBRAD may be used to infer snow thickness over frozen soil.

14 73rd Eastern Snow Conference Northern Hemisphere winter thaw events – characteristics, trends and projected changes

Ross Brown, Libo Wang, Peter Toose, and Chris Derksen

Climate Processes Section, Environment and Climate Change Canada, Montréal, Québec

Winter melt/refreeze events modify the physical properties of snow with potentially significant impacts on the surface energy budget, hydrology and soil thermal regime. The refreezing of melt water can also create ice layers that adversely impact the ability of ungulate travel and foraging, and exert uncertainties in snow water retrieval from passive microwave satellite data. The conventional wisdom is that the frequency of these events increases under a warming climate. This hypothesis is evaluated from an analysis of winter thaw events from atmospheric reanalyzes, satellite passive microwave data and climate models. The analysis shows that trends in winter thaw events are strongly dependent on the analysis method, and that the use of a fixed seasonal window can generate artificial increases in winter thaw frequencies from a temporal shift in the period of the year where these events are typically observed. The analysis also shows that the frequency of winter thaw events is significantly correlated to the length of the snow accumulation season over large areas of the NH snow covered area, which implies decreases in winter thaw frequencies in response to warming. Projected changes in thaw frequency are presented for some of the models participating in the CMIP5 and CORDEX experiments.

15 73rd Eastern Snow Conference Airflow Associated with Snowfall Events on the Quelccaya Icecap of Peru During the 2014- 2015 Wet Season

Eric J. Burton1, L. Baker Perry1, Anton Seimon1,2, Jason L. Endries1, Maxwell Rado3, Sandro Arias4

1 Department of Geography and Planning, Appalachian State University, Boone, NC 2 Climate Change Institute, University of Maine, Orono 3 Universidad Nacional de San Antonio Abád de Cusco, Perú 4 Servicio Nacional de Meteorología e Hidrología (SENAMHI), Perú

The Quelccaya Icecap, located in the Cordillera Vilcanota of Southern Peru, is the largest glacier in the tropics from where ice cores dating back nearly 2,000 years provide one of the most important records of late-Holocene climates. This poster analyzes the timing, trajectories, and synoptic patterns associated with precipitation events during the 2014-2015 wet season. A meteorological station installed at 5,650 m asl near the Quelccaya summit in October 2014 provides meteorological data including precipitation amount, type and intensity, snow depth, insolation, relative humidity, and wind speed and direction. NOAA’s Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) provides 72-hour backward air trajectories for each precipitation event using GDAS data with 0.5° resolution, and ERA-Interim data are used to examine synoptic-scale patterns of various meteorological variables for precipitation events. Results suggest that trajectories associated with precipitation events come predominantly from the north and northwest (63%) with another maximum from the southeast (25%). Northwest trajectories have the highest net contribution (34% of annual total), while those from the Pacific produce the largest snowfall events on average (6.3 cm). Composite plots of vector winds support the trajectory analysis. Temperature and wind speed varied little throughout the initial study period, and the present weather sensor shows that frozen precipitation, in particular graupel, was the dominant precipitation type. Of the 250 precipitation events that occurred during the study period, 88% had a source region in the Amazon Basin. A nighttime maximum in precipitation is inferred to be predominantly stratiform in nature, while an afternoon maximum is inferred to be predominantly convective.

16 73rd Eastern Snow Conference Terrain Characteristic Influence on Snow Accumulation and Persistence: Case Study

Kelsey Cartwright, N. Reed Parsons, Gerrard Biggins, Joshua Baerg, Christopher Hopkinson

Department of Geography, University of Lethbridge, Lethbridge, AB

Mountain snowmelt contributes 70-90% of streamflow in Western Canada. An enriched understanding of snowpack dynamics in headwater regions is essential to water resource management in the face of unpredictable climatic patterns associated with climate change. Current snowpack monitoring in the Oldman Watershed to approximate SWE for water supply and flood risk predictions do not provide an accurate representation of true snow water equivalency due to the large spatial variation in mountainous terrain attributes, for example slope, aspect, substrate and forest cover across ~26,000 km2. As a result of these dynamic terrain characteristics, snow depth exhibits an even greater spatial variation in comparison to snow density. Field work was carried out in the West Castle watershed, the second highest yielding sub-watershed of the Oldman drainage, at a ski hill where our hydrometeorological stations occur along an elevational gradient as part of a Government of Alberta funded water resource monitoring research. Depth measurements were collected in areas representative of various terrain attributes and ecotones. Using regional in-situ meteorological station data, field validation measurements and LiDAR remote sensing data (September, February 2014; April 2016) collected mid-winter and at the onset of spring melt, relationships between the various macro and micro scale catchment processes provide an improved understanding of the terrain characteristic influence on snow accumulation and persistence. Both the identification and quantification of the terrain characteristic influence on snow accumulation and persistence, enable the modelling of depth across larger areas thus providing the precise data required to make informed water resource management decisions.

17 73rd Eastern Snow Conference Recent airborne measurements of snow and ice in Interior Alaska

Jessica Cherry

International Arctic Research Center, University of Alaska, Fairbanks

This talk will discuss results from recent airborne measurements of snow and ice in Interior Alaska from imaging (optical, near infra-red and thermal infra-red) and microwave sensors using Structure from Motion and other techniques. Impacts of GPS accuracy on snow-related phenomena will be described, including the positional error budget. Our group has two modified Cessnas for this effort and will also discuss the economics of airborne measurements from unmanned versus manned systems.

18 73rd Eastern Snow Conference Comparison between AMSR2 and AMSR-E Snow Water Equivalent using SSM/I over the North Central U.S.

Eunsang Cho, Samuel Tuttle, and Jennifer M. Jacobs

Civil and Environmental Engineering, University of New Hampshire, Durham

Satellite-based passive microwave sensors enable spatially distributed snowpack monitoring at a global scale. The Advanced Microwave Scanning Radiometer 2 (AMSR2) is a relatively new passive microwave satellite that provides estimates of snow depth (SD) and snow water equivalent (SWE). AMSR2 continues the legacy of the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), which stopped operation in October 2011. However, the quality of AMSR2 SWE retrievals has not yet been evaluated in comparison with its predecessor. This study compared the weekly maximum AMSR2 and AMSR-E SWE products over twelve winter seasons (AMSR-E period: 2002-2011, AMSR2 period: 2012-2015) to SSM/I SWE estimates over 1176 watersheds in the North Central United States. For consistency, both the AMSR2 and AMSR-E satellite SWE products used the Kelly algorithm (Kelly et al., 2009). Results show that the two satellite-based SWE retrievals have temporally reasonable agreement with SSM/I SWE estimates (Chang algorithm; Chang et al., 1987). However, yearly bias maps between AMSR2 and SSM/I SWE are clearly different than between AMSR-E and SSM/I. Particularly in forested areas, the magnitude of AMSR2 SWE estimates is much larger than SSM/I, unlike AMSR-E. When using the normalized SWE anomaly, the spatial pattern of bias shows good agreement between AMSR2 and AMSR-E. The differing SWE magnitudes may be related to the calibration of AMSR2 brightness temperatures.

19 73rd Eastern Snow Conference Seasonal and interannual variability in snow and streamflow δ18O signatures

Krystopher J. Chutko1 and April L. James2

1 Department of Geography and Planning, University of Saskatchewan, Saskatoon, SK 2 Department of Geography, Nipissing University, North Bay, ON

Seasonal snowpacks often play a large and important role in hydrological processes, typically manifested as a spring freshet. From an isotopic standpoint, this freshet marks the “lightest” water of the year, being fed by isotopically light water derived from spring snowmelt. Seasonal and interannual variations in the isotopic composition of streamflow are strongly related to the isotopic conditions of the winter snowpack and have implications on hydrological analyses and modeling. Four years of regional snow and streamflow isotope measurements (2013 – 2016) in the Lake Nipissing region of Ontario, Canada, illustrate this variability in snowpack isotopic composition and its impact on streamflow isotopic composition. Much of this variability is derived from winter air temperature. Average winter (DJFM) air temperature has varied from -5.3 °C in 2016 to -13.5 °C in 2014, a variability that is mirrored in the snowpack isotopic signature in each year as well as in the isotopic signature of streamflow. Snowpack signatures were measured using bulk core samples and snow melt signatures were measured with a combination of snowmelt lysimeters and passive wicks. The interannual variability in snowpack δ18O is shown to impact streamflow isotopic signatures. For 9 catchments reported here in the Lake Nipissing region (35 to 6875 km2), spring (MAM) streamflow δ18O was 0.67 ‰ lighter and summer (JAS) streamflow δ18O was 0.82 ‰ lighter, on average in 2014 vs. 2013, for example. However, the seasonal amplitude of δ18O remained consistent between years, varying by just 0.15 ‰.

20 73rd Eastern Snow Conference An Updated U.S. Blizzard Climatology: 1959- 2014

Jill S. M. Coleman1 and Robert M. Schwartz2

1Department of Geography, Ball State University, Muncie, IN 2Center for Emergency Management and Homeland Security Policy Research, University of Akron, OH

21 73rd Eastern Snow Conference Analyzing Seasonal Snow Cover Frequency Using the MODIS/Terra Daily Snow Cover Product with Google Earth Engine in the Pacific Northwest and California

Ryan Crumley, Anne W. Nolin, and Eugene Mar

College of Earth, Ocean, and Atmospheric Sciences, Oregon State University, Corvallis

New snow metrics are needed to characterize changing snow cover in a warming world. For this project, we compute the frequency of remotely sensed snow cover during the winter season, for each pixel in our maritime West Coast study region and explore spatio-temporal trends. Remote sensing of snow covered area using the MODIS/Terra Snow Cover Daily L3 500m (MOD10A1) product is now available to scientists using Google Earth Engine (GEE). GEE stores and provides access to a multi- petabyte catalog of satellite images for geospatial analysis, employing both Javascript and Python APIs. The MOD10A1 Snow Cover Product along with the GEE cloud computing infrastructure allows for regional to global-scale data processing to be performed quickly and efficiently, without having to download massive amounts of data. Specifically, the objectives are to: 1) calculate Snow Cover Frequency (SCF) from October to July over a 16-year period (2001 to 2015) for the Cascades mountain range in Oregon and Washington and the Sierra Nevada mountain range in California; 2) evaluate multi-year trends; 3) disseminate the GEE scripts and code so that this processing can easily be readily reproduced for any location, geometry, or region on Earth. Snow Cover Frequency is computed as the number of times during the snow season that a pixel is snow covered divided by the number of valid observations for that pixel. Trends are computed using the Mann-Kendall statistic and are examined by region. The results of this research serve as a valuable tool for water managers and policy makers that rely on snow measurements for seasonal streamflow estimates and who would like to supplement the traditional metric of April 1 Snow Water Equivalent.

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Inexpensive in-situ snow pack sensors for temperature, density and grain size: First light

Roger De Roo, Eric Haengel, Steve Rogacki, Adam Schneider, Chandler Ekins and Seyedmohammad Mousavi

Department of Atmospheric, Oceanic, and Space Sciences, University of Michigan, Ann Arbor

A suite of small, battery operated devices for implantation in a snow pack has made its first measurements in the Winter of 2016. They measure and log sensor outputs related to snow pack parameters of temperature, density, moisture and grain size. The temperature is provided by an electronic thermometer; snow density and moisture affect an open resonant circuit operating near 950 MHz; grain size and density affect the scattering of an optical link operating at 880 nm. Five units were deployed in the roughly 30 cm deep snow pack at the University of Michigan Biological Station in two snow pits, where they made measurements every 5 minutes for approximately 10 days. Upon extraction, temperature, density and grain size of the snow pack were observed manually. Three more units were involved in an inter-comparison experiment at the US Army's Cold Regions Research and Engineering Laboratory. Two samples of old snow pack from their archives, two fresh snow samples, and one artificially grown snow sample were also characterized with micro-computed tomography, infrared reflectometry, such as University of Michigan's Near Infrared Emitting Reflectance Dome (NERD), and manual methods. In April 2016, the measurements are being analyzed and calibrated. We will report on results, and lessons learned, at the June conference.

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Characterizing Satellite-Based Passive Microwave Estimates of Snow Water Equivalent at Sub-Grid Resolution

E. J. Deeb, C.A. Hiemstra, S.F. Daly, C.M. Vuyovich, and J.B. Eylander

US Army Cold Regions Research and Engineering Laboratory (CRREL), Hanover, NH

Snow water equivalent (SWE) is the amount of water contained within the snowpack if melted. The accurate assessment of this snow parameter is crucial in estimating spring runoff as it relates to water resource management, flood hazard mitigation, drought monitoring, and climate change impacts. Satellite-based passive microwave estimates of SWE offer the only operational platform for which a near real-time, global SWE product is available. In general, satellite-based passive microwave SWE estimates are possible due to the naturally emitted microwave signal from the soil being attenuated by the snowpack. This microwave energy is relatively small; therefore, the satellite-based products are often at very coarse resolution (tens of kilometers) in order to detect the signal. For hydrology applications, passive microwave estimates of SWE are particularly difficult to interpret when only a handful of pixels represent a single hydrologic basin. Moreover, passive microwave retrieval algorithms are subject to difficulties in both deep and shallow snow (depending on the microwave frequencies available on the satellite platform) as well as uncertainties due to forest fraction, snow microstructure, and snow wetness. Here, a spatially-distributed, snow-evolution modeling system (SnowModel) is used to simulate 14 years (water years 2000 through 2013) of snow properties for the Hubbard Brook Long Term Ecological Research site (New Hampshire, USA) at fine resolution (50 meters). These data are used to generate snow depth climatology over the satellite-based passive microwave pixels that encompass the Hubbard Brook watershed. This climatology is then used in conjunction with the daily passive microwave estimate of SWE to appropriately distribute the satellite-based observation at coarse resolution to a sub-grid, finer resolution. The methodology and results of the model technique are presented; and when compared to an independent snow depth observation within the basin show better agreement and improved model efficiency (R2 = 0.76 and Nash-Sutcliffe model efficiency = 0.70) when compared to simply the satellite-based passive microwave estimates (R2 = 0.61 and Nash- Sutcliffe model efficiency = 0.40) . Potential benefits of using this model technique in snow hydrology applications are also discussed.

24 73rd Eastern Snow Conference User requirements, algorithm readiness, and modeling studies in support of terrestrial snow mass radar mission concepts

Chris Derksen1, Stephane Belair2, Joshua King1, Camille Garnaud2, Lawrence Mudryk3, Yves Crevier4, Melanie Lapointe4, and Ralph Girard4

1Climate Research Division, Environment and Climate Change Canada, Toronto 2Meteorological Research Division, Environment and Climate Change Canada, Montréal, Québec 3Department of Physics, University of Toronto 4Canadian Space Agency, Saint-Hubert

The snow remote sensing community has long grappled with how to prioritize observational requirements and technological solutions due to differing needs related to snow extent (SE) versus snow water equivalent (SWE), and the tradeoffs between spatial resolution and revisit time which differ for alpine hydrological applications versus hemispheric climate and operational land surface modeling needs. A single observing strategy simply cannot meet all these requirements. Environment and Climate Change Canada (ECCC) recently identified moderate resolution (~ 1 km), daily hemispheric SWE as a priority observational gap which limits operational environmental monitoring, services, and prediction. This presentation will provide an overview of current science activities at ECCC in support of the development of radar mission concepts in partnership with the Canadian Space Agency (CSA) to address this observational gap: 1. An assessment of currently available gridded hemispheric SWE products was performed to establish the baseline of current capabilities. These datasets (from passive microwave remote sensing, modern reanalysis, and physical snow models) are available only at a coarse spatial resolution (25 km or greater), exhibit a high degree of spread between products, and have poorly constrained uncertainty due to systematic (bias) and random errors when evaluated with in situ observations. 2. Experimental airborne datasets are being utilized to identify snow volume and stratigraphic influences on radar signatures at X- and Ku-band. Analysis of data from experimental campaigns in Canada show radar sensitivity to SWE, but first guess model derived information on snow microstructure is required as a retrieval input. 3. An Observing System Simulation Experiment (OSSE), performed using the Canadian Land Data Assimilation System (CaLDAS), is being utilized to identify critical resolution, revisit, and retrieval accuracy thresholds in order to ensure the user requirements of the operational land surface modeling community can be addressed with a radar concept. 25 73rd Eastern Snow Conference

Emerging international partnership opportunities will also be presented, including how a spaceborne radar designed to address needs related to terrestrial snow would also provide suitable measurements for sea ice, land ice, and ocean vector wind applications.

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Detection of Rain-On-Snow events in the Canadian Arctic Archipelago between 1980- 2014 using Passive Microwave Radiometry

C. Dolant 1, 2, A. Langlois 1, 2, L. Brucker 3, 4, B. Montpetit1 and A. Royer 1, 2

1Centre d’Applications et de Recherches en Télédétection, Université de Sherbrooke, Quebec 2Centre d’Études Nordiques, Quebec 3NASA Goddard Space Flight Center, Cryospheric Sciences Laboratory, Greenbelt, MD 4Universities Space Research Association, Goddard Earth Sciences Technology and Research Studies and Investigations, Columbia, MD

Climate change impacts in northern environments are significant, especially in tundra areas. Rising temperatures, changes in the precipitation regime are amongst the strongest consequences of climate warming and variability in the Arctic since the early 1980’s (Liston and Hiemstra, 2011). Of particular relevance, rain-on-snow (ROS) events increase the presence of liquid water content (LWC) in the snowpack and are responsible for the formation of ice crusts (Dolant et al., 2016, Hydrological Processes) that have a strong impact on ecology, hydrology and energy balance of the snowpack by changing the internal structure of the different snow layers. The spatial and temporal distribution of ROS across the Canadian Arctic Archipelago (CAA) remains poorly understood owing to their sporadic nature in time and space. The use of remote sensing, in particular passive microwaves (PMW), allow us to obtain information on the different layers of the snowpack, thus representing an interesting avenue for tracking and studying ROS events in the Arctic. In this study, we highlight the distribution and evolution of ROS occurrences inventoried since 1980 at 14 weather stations in the CAA, and introduce an adaptation of the algorithm of ROS detection using passive microwave radiometry proposed by Dolant et al. 2016, in order to establish patterns of temporal and spatial evolution of ROS events. Furthermore, simulating the effects of ROS using a radiative transfer model (i.e. MEMLS (Wiesmann and Matzlër, 1999) driven with snowpit measurements and variation of LWC threshold) will improve the understanding of this complex phenomenon. Across the 14 weather stations, 700 ROS events were surveyed since 1980, where more than 80% occurred during the spring season.

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A Bayesian retrieval of Greenland ice sheet internal temperature from ultra-wideband software-defined microwave radiometer (UWBRAD) measurements

Yuna Duan1, Michael Durand1*, Ken Jezek1, Caglar Yardim2, Alexandra Bringer2, Mustafa Aksoy2, Joel Johnson2

1 School of Earth Sciences and Byrd Polar and Climate Research Center, Ohio State University, Columbus 2 Electroscience Laboratory and Department of Electrical Engineering, Ohio State University, Columbus

Ice sheet internal temperature is an important factor in understanding glacier dynamics. The ultra- wideband software-defined microwave radiometer (UWBRAD) is designed to provide ice sheet internal temperature by measuring low frequency microwave emission. Twelve channels ranging from 0.5 to 2.0 GHz are covered by the instrument. A four channel prototype of UWBRAD was completed and operated in Antarctic ice sheet at Dome-C from a tower. A Bayesian framework is designed to retrieve the ice sheet internal temperature from simulated UWBRAD brightness temperature (Tb) measurements for the Greenland air-borne demonstration scheduled for September 2016. A 1-D heat-flow model, the Robin Model, is used to generate the ice sheet internal temperature profile. It requires surface temperature ice, sheet thickness, snow accumulation rate and geothermal heat flux as input and calculates steady state temperatures as a function of depth. The coherent radiation transfer model, which neglects scattering, utilizes the Robin model temperature profile and vertical density profile as input and calculates Tb. At lower frequencies, deeper and warmer ice contribute to the emission and higher brightness temperature can be measured; While at higher frequency bands, the resulting brightness temperature is lower, thus provides the basis of retrieval. The effective surface temperature, geothermal heat flux and the variance of upper layer ice density are least-well known and are treated as unknown random variables within the retrieval framework. For each unknown parameter, a range of possible values was identified. The coherent model was used to generate a look-up table between the unknown parameters and the Tb. A set of synthetic UWBRAD observations was generated and corrupted with white noise to mimic the UWBRAD observational error. A Bayesian framework was developed to estimate the three unknown parameters, using the Metropolis algorithm, a Markov Chain Monte Carlo (MCMC) approach. We examined the results using the three science goals: estimation of the 10-m firn temperature, the average temperature integrated with depth, and the entire temperature profile. We conduct a random walk between the sampling

28 73rd Eastern Snow Conference space defined by the priors. At each step, we evaluate each new iteration of the three unknown parameters based on how well it explains UWBRAD data. Our goals are to investigate whether the priors can be improved and the temperature can be estimated. The 10 m temperatures are all estimated within ± 1 K, and mostly within ± 0.5 K despite the prior estimate being precise to ± 1.0 K. The RMS error of the UWBRAD estimates are all within 3.3 K; 28/47 points show improvement over the prior. For the 100 m averaged temperature estimation, the estimation uncertainty increases with depth and stays below 1 K up to about 1500 m. Along the flight line, a consistent high correlation, over 0.75, between surface temperature and density variation is observed, which means that multiple combinations of density variations and surface temperatures in the sample space would produce the exact same Tb. Yet the 10m temperature can still be well estimated. The Bayesian framework is capable of constrain the parameters within reasonable region by trading off among the parameters.

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Investigating the interplay between warm winter anomalies and glacial melting in the Arctic: do early warming events matter?

Elizabeth Dyer and Joan Ramage

Earth and Environmental Science Department, Lehigh University, Bethlehem, PA

The winter of 2015-2016 was the warmest winter on record, breaking several global temperature records. From the end of December 2015 to the beginning of January 2015, many areas in the Russian High Arctic (RHA) and Svalbard experienced temperatures above 0 °C; precipitation fell as rain. These types of events can disrupt the overall pattern of accumulation during winter and melting during summer, and they are predicted to increase in frequency due to climate change. This study examines the effects of unusual warm winter events on the melting and mass loss of glaciers and ice caps in Svalbard and the RHA, particularly Novaya Zemlya. The events during which air temperature was above freezing are studied in detail; the main datasets are microwave remote sensing observations, including the Special Sensor Microwave Imager/Sounder (SSMIS) from the National Snow and Ice Data Center (NSIDC). Using the 19 and 37 Ghz channels, the period following the warm events is evaluated to see where and when a melting event was triggered, and what aspect of the storm caused it. To understand the full dynamics of the responses to these warm events, the microwave observations are compared with other datasets, including sea surface temperature from the Moderate-resolution Imaging Spectrometer (MODIS), and sea ice extent from the NSIDC. Anomalous warm winter events are expected to have an impact on subsequent glacial melting and negative mass balance.

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A pictorial history of changes in polar science and technology: an example from glacier measurements on Axel Heiberg Island, Nunavut, Canada, 1959-2015

Peter Adams, Miles Ecclestone, Graham Cogley

Department of Geography, Trent University, Peterborough, Ontario

Changes in modes of transportation, instrumentation as well as in personnel make-up have dramatically changed the nature of polar science in the half century since the McGill expeditions began research on Axel Heiberg Island, Nunavut, Canada, in 1959. These changes have intensified and extended research on glaciers and lakes and they have also produced marked changes in the way polar science is conducted. During this same period there have been equally dramatic changes in the glaciers of the region. These themes are presented here through a series of annotated images.

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3rd Winter course for field snowpack measurements: NASA Snow Working Group - Remote sensing (iSWGR)

K. Elder1 and M. Sturm2

1 U.S. Department of Agriculture Forest Service 2 University of Alaska, Fairbanks

As our ability to characterize and model the hydrologic regime in snow-dominated ecosystems continues to improve, there is a parallel need to make meaningful and accurate measurements of snowpack properties. Practitioners often collect and use field data for their own purposes. Modelers and remote sensers often obtain the snowpack data from field practitioners or other researchers, but have little knowledge of meaning or richness of the data they are using. This course is aimed at teaching skills to practitioners and modelers in order to increase the quality of the results for all users. The course introduced students to standard and specialized, quantitative and qualitative, methods for the characterization of the snowpack. The 3rd winter course for field snowpack measurements from the NASA snow remote sensing group took place on January 12-14 2016 at the Fraser Experimental Forest, Colorado, USA. Numerous international students participated to the school and lecturers provided courses on remote sensing, and field measurements of various snow properties. These state-of-the-art snow remote sensing techniques will be taught in the 4th iSWGR snow school which is expected to occur in February-March 2017.

❄ Page 32 73rd Eastern Snow Conference Vertical structure and character of precipitation in the tropical high Andes of southern Peru and northern Bolivia

Jason L. Endries1, L. Baker Perry1, Sandra Yuter2, Anton Seimon1,3, Marcos Andrade4, Guido Mamani5, Marti Bonshoms6, Fernando Velarde4, Ronald Winkelmann4, Nilton Montoya5, Nelson Quispe6

1Department of Geography and Planning, Appalachian State University, Boone, NC 2Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC 3Climate Change Institute, University of Maine, Orono 4Universidad Mayor de San Andres, Bolivia 5Universidad Nacional de San Antonio de Abád de Cusco, Perú 6Servicio Nacional de Meteorología e Hidrología (SENAMHI), Perú

Glaciers that provide critical freshwater to the tropical high Andes of southern Peru and northern Bolivia are currently threatened by rising temperatures and changing precipitation patterns. In this study, we evaluate the vertical structure, character, and melting layer heights (snow levels) during precipitation events in the region.. A vertically pointing K-band Micro Rain Radar (MRR) in Cusco, Peru (3,350 m asl) and La Paz, Bolivia (3,440 m asl) from August 2014 to February 2015 and from October 2015 to the present, respectively, provided continuous 1-min profiles of reflectivity and Doppler velocity. Vertical data were also collected from several mid- precipitation balloon launches, collocated with the La Paz MRR. Hourly observations of various meteorological variables were collected from stations at the Cusco International Airport (3,350 m asl) and the Universidad Mayor de San Andres (3,440 m asl), on the Quelccaya Icecap (5,650 m asl) and Nevado Chacaltaya (5,540 m asl), and from Murmurani Alto (5,050 m asl). MRR signatures reveal a bimodal precipitation pattern, with afternoon convective and nighttime stratiform events. Hourly median melting layer heights over Cusco (La Paz) ranged from 4,025 (4,115) to 5,975 (5,990) m asl with an overall median value of 4,775 (4,865) m asl. The mean echo top height in Cusco (La Paz) was 6,773 (7,019) m asl, well above the altitude of surrounding glaciers. Precipitation processes in the region are therefore likely to play an important role in determining glacier behavior; an increase in future melting layer heights could further accelerate glacier recession.

❄ Page 33 73rd Eastern Snow Conference Using cloud base height to decrease misclassified precipitation in hydrological models

James M Feiccabrino

Department of Water Resources Engineering, Lund University, Sweden

Surface air (AT), dew-point (DP) and wet-bulb (WB) temperature thresholds are used in hydrological models to determine if precipitation is rain or snow. It is preferential to use AT thresholds due to the widespread availability of the data compared to DP or WB. AT, unlike DP and WB, does not take into account the important secondary role of humidity in the melting, evaporation, and sublimation processes. However, the height of a cloud base above the ground could be used to give the depth of an unsaturated atmospheric layer which has much different melting, evaporation, and sublimation rates than a saturated cloud layer. Cloud base height could therefore be used as a proxy for atmospheric humidity when using AT thresholds. Using hourly observations from 12 manually augmented meteorological stations in the mid- western United States, surface AT thresholds for the following cloud bases were found; 0.0°C for under 100m, 0.6°C for 100 and 200m, 1.1°C for 300 and 400m, 1.7°C for 500 and 600m, and 2.2°C for 700-1000m. These cloud height AT thresholds reduced misclassified precipitation from a single AT threshold (1.1°C) by 15% from 14.0% to 11.9% total error. Cloud height AT thresholds resulted in a 1.5% decrease in total error from the DP threshold (0.0°C), and was within 0.2% of the WB threshold (0.6°C). This indicates cloud height AT thresholds may be used in place of WB and DP thresholds to improve surface based precipitation phase categorization in hydrological models.

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Seeing and Feeling Snow from Space: A Unified Radiometric and Gravimetric Approach

Barton A. Forman

Department of Civil and Environmental Engineering, University of Maryland, College Park

The Gravity and Recovery Climate Experiment (GRACE) has revolutionized large-scale remote sensing of the Earth’s hydrologic cycle. However, GRACE is a vertically-integrated measure of terrestrial water storage (TWS) and provides no direct mechanism for stating that a given portion of TWS is associated with snow, or that a given portion of TWS is associated with soil moisture, or that a given portion of TWS is associated with groundwater. It is hypothesized here that GRACE information can be effectively downscaled into its constituent components (e.g., snow, soil moisture, groundwater) via Bayesian merger with an advanced land surface model as part of a multi-variate, multi-sensor data assimilation framework. This study introduces a novel approach to merge passive microwave (PMW) measurements of brightness temperature (Tb) over snow-covered terrain with GRACE-based gravimetric retrievals of TWS across regional and continental scales. The simultaneous PMW Tb + GRACE TWS assimilation framework will employ the NASA Goddard Earth Observing System Version 5 (GEOS-5) land surface model and leverage a suite of measurements from past and on-going satellite missions. A set of both “synthetic” and “real” experiments have been designed to quantify the added utility to SWE estimation using the multi-sensor, multi-variate assimilation approach. It is hypothesized that this new assimilation framework will improve estimates of global SWE as well as help bridge the gap between the temporal and spatial resolutions of PMW Tb observations and GRACE-based TWS retrievals.

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A Landsat-era (1985-2015) Sierra Nevada (USA) Snow Reanalysis Dataset

Manuela Girotto1, Steven A. Margulis2, Gonzalo Cortés2, Laurie S. Huning2, Dongyue Li3, Michael Durand3

1 Cryospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD 2 Department of Civil and Environmental Engineering, University of California, Los Angeles 3 School of Earth Sciences and Byrd Polar & Climate Research Center, The Ohio State University, Columbus

This work presents a newly developed state-of-the-art snow water equivalent (SWE) reanalysis dataset over the Sierra Nevada (USA) based on the assimilation of remotely sensed fractional snow covered area data over the Landsat 5-8 record (1985-2015). The method (fully Bayesian), resolution (daily, 90-meter), temporal extent (31 years), and accuracy provide a unique dataset for investigating snow processes to ultimately improve real-time streamflow predictions of snow-dominated regions. The reanalysis dataset was used to characterize SWE climatology to provide a basic accounting of the stored snowpack water in the Sierra Nevada over the last 31 years. The ongoing California drought contains the lowest snowpack years (water years 2014 and 2015) and three of the four driest years over the reanalysis record. In particular, water year 2015 was a truly extreme (dry) year, with range-wide peak snow volume characterized by a return period of over 600 years.

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Comparison of MODIS and VIIRS snow- cover products to study data-product continuity in the Catskill Mountain watersheds, New York

Dorothy K. Hall1, Allan Frei2, George A. Riggs3, Nicolo E. Digirolamo3, James H. Porter4, and Miguel O. Román5

1 Under contract to NASA Goddard Space Flight Center, Greenbelt, MD 2 Institute for Sustainable Cities, Hunter College, City University Of New York, NY 3 SSAI, Lanham, MD 4 NYC Environmental Protection, Bureau Of Water Supply, Reservoir Operations, Grahmsville, NY 5 Terrestrial Information Systems Laboratory, Nasa Goddard Space Flight Center, Greenbelt, MD

Runoff emanating from the Catskill Mountains supplies water to approximately nine million people in New York City and to other municipalities in New York State. The NYC Water Supply System consists of three subsystems: the Catskill, the Delaware, and the Croton. NYC relies heavily on the six basins of the Catskill/Delaware subsystems: Ashokan, Schoharie, Rondout, Neversink, Cannonsville and Pepacton. The goal of this work is to investigate the continuity of the Moderate-resolution Imaging Spectroradiometer (MODIS) and Suomi-National Polar Partnership (NPP) Visible Infrared Imager Radiometer Suite (VIIRS) NASA snow-cover products for development of a snow-cover climate-data record (CDR) and to study snowmelt timing in concert with meteorological and streamflow data. We use the two types of NASA snow maps to develop snowpack build-up and depletion curves for the six Catskill/Delaware watersheds to enable comparison of results of the two independently-created snow maps. These include daily Collection 5 MODIS standard snow-cover products at 500-m resolution, and the new NASA VIIRS snow-cover products at 375-m resolution along with air temperature, precipitation and streamflow data. We focus our evaluation on similarities and differences in snow-cover depletion timing in the six Catskill/Delaware watersheds using the two snow-cover products during the MODIS-VIIRS overlap period from 2011 – 2015, to include the four water years: 2011-12, 2012-13, 2013-14 and 2014-15.

❄ Page 37 73rd Eastern Snow Conference Evaluation of Algorithm Alternatives for Blended Snow Depth in the IMS

Sean R. Helfrich1, Cezar Kongoli, Lawrence Vulis3, Milton Martinez4, Christopher Grassotti2, and Naresh Devineni3

1NOAA/NESDIS/OSPO/NIC, Suitland, MD 2NOAA/NESDIS/STAR, College Park, MD 3Environmental Engineering, City College of New York, New York 4University of Puerto Rico, Mayaguez, PR

Since December 2014, the Interactive Multisensor Snow and Ice Mapping System (IMS) has generated snow depth estimates over the Northern Hemisphere at a 4 km resolution. The algorithm applies optimal interpolation with an elevation nudging technique to generate a snow depth over locations within 800 km of the snow observing site. This data is further blended using a weighting schema with passive microwave based estimates from the Advanced Technology Microwave Sounder (ATMS) instrument and a snow depth elevation climatology. Improvements in the blended snow depth were sought to improve performance. Several methods were tested to improve snow depth estimates by refining microwave estimate of snow depth, promoting application of prior day estimates, developing regional snow depth/elevation relationships, altering the source of snow depth in-situ observations, and adjusting the weighting schema based on elevation ranges. Testing of these algorithm enhancements are presented in this poster to demonstrate the methodology of the enhancements and provide an evaluation of algorithm performance compared to the current algorithm baseline.

❄ Page 38 73rd Eastern Snow Conference Comparison of high-elevation LiDAR snow measurements with distributed streamflow observations

Brian Henn1, Thomas H. Painter2, Bruce McGurk3, Greg Stock4, Nicoleta Cristea1 and Jessica D. Lundquist1

1Civil and Environmental Engineering, University of Washington, Seattle 2NASA/JPL, Pasadena, CA 3McGurk Hydrologic 4National Park Service, Yosemite National Park

High-elevation spatial and temporal distributions of snow water equivalent (SWE) and precipitation are difficult to detect due to the relatively sparse coverage of existing meteorological stations. Airborne LiDAR provides remotely sensed, high-resolution observations of snow depth that are capable of resolving these patterns. However, there are uncertainties in the estimation of SWE from LiDAR due to uncertain snow density, the effects of forest canopy coverage on snow depth estimates and uncertain baselines in areas with glaciers and permanent snowfields. Streamflow observations offer another perspective on the distributions of SWE, as streamflow integrates the basin’s snowmelt response. By comparing distributed streamflow observations from multiple nested and adjacent basins with LiDAR- based SWE estimates, we can identify places and times where these two estimates of the basins’ water budgets agree or disagree. In this study, we use LiDAR observations from the NASA Airborne Snow Observatory (ASO) over the upper Tuolumne River basin in Yosemite National Park, over water years 2013-2015. Streamflow time series from multiple sub-basins are available from the Yosemite Hydroclimate Network. For each sub-basin in the Tuolumne domain, we compare ASO SWE volumes from each LiDAR flight with streamflow volumes for the remainder of the snowmelt season. This allows for an evaluation of the effectiveness of LiDAR SWE estimates in streamflow forecasting. We also consider how evapotranspiration and rainfall – basin water balance components that are reflected in streamflow but not in SWE volumes – influence skill in snowmelt-driven streamflow forecasting.

❄ Page 39 73rd Eastern Snow Conference Evaluating 50 years of tropical Peruvian glacier volume change from multi-temporal digital elevation models (DEMs) and glacier flow and hydrology in the Cordillera Blanca, Peru

Kyung In Huh1, Bryan G. Mark2, Michele Baraer3, Yushin Ahn4 ,Chris Hopkinson5

1Department of Geography and Anthropology, California State Polytechnic University, Pomona 2Department of Geography, The Ohio State University, Columbus 3Département de génie de la construction, École de technologie supérieure (ÉTS), Montréal, Québec 4School of Technology, Michigan Technological University, Houghton 5Department of Geography, University of Lethbridge, Water & Environmental, Alberta

Although far smaller than large polar ice caps, mountain glaciers are significant contributors to sea level rise and tropical glaciers in particular are sources of critical water resources to regional societies. The glaciers in Cordillera Blanca, Peru, have environmental and economic importance as regional water supplies to communities in the arid western part of the country under continued global climate change. We quantify glacier volume change in the Cordillera Blanca by intercomparing digital surface elevations derived from three sources of remotely sensed image data spanning almost 50 years: ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer, 2000-08); airborne LiDAR (Light Detection and Ranging, 2008); and stereo aerial photography (1962). We characterize the limitations inherent in processing historic aerial photography with different viewing geometries over highly rugged terrain relief and uncertainties in the processing stage as well as DEM comparison by analyzing DEM over non-glacierized terrain. We confirm volume changes from previous studies in the Cordillera Blanca and extend temporal resolution in time series by adding the first acquisition of high-resolution airborne LiDAR achieved in 2008. We assess the historical contribution of glacier ice volume loss to stream flow based on reconstructed volume changes through Little Ice Age (LIA) can be directly related to the understanding of glacier-hydrology in the current epoch of rapid glacier ice loss that has disquieting implications for water resources in the Cordillera Blanca of the Peruvian Andes. We compute the rate and magnitude of glacier volume changes for Yanamarey and Queshque

❄ Page 40 73rd Eastern Snow Conference glaciers between the LIA and modern defined by 2011 ASTER Global Digital Elevation Model Version 2 (GDEM V2) from the Cordillera Blanca.

❄ Page 41 73rd Eastern Snow Conference Evaluation of satellite-based observations for capturing early winter snowmelt within mid-latitude basins

Adam Hunsaker2, Carrie M. Vuyovich1, Douglas Osborne2, Jennifer M. Jacobs2

1 Cold Regions Research and Engineering Laboratory, Hanover, NH 2 University of New Hampshire, Durham, NH

Over the past fifty years global climate change has altered various environmental processes. Due to global climate change early snowmelt is occurring much more frequently throughout much of the world (Semmens, Ramage, Bartsch, & Liston, 2013). The increasing frequency of these events is a relatively new phenomena and it is challenging the effectiveness of current water resource management and flood forecasting best practices. Early snowmelt events are caused by a brief period of unusually high air temperature, high humidity, or rain-on-snow (Semmens, Ramage, Bartsch, & Liston, 2013). This research focuses on the detection and distribution of rain-on-snow events using remote sensing approaches to identify and quantify the frequency, extent and magnitude of early melt events. The analysis highlights several recent flood events occurring in North America. Early melt events, driven by heavy rainfall with the presence of snow, are identified from the Dartmouth Flood Observatory archives. Passive microwave data from the AMSR-E and SSMI instruments are compared with MODIS imagery and field observations to assess the microwave products’ reliability in capturing these events. Early melt detection algorithms that use passive microwave retrievals for northern latitude areas, primarily Canada were evaluated in the continental United States. These algorithms failed to capture mid latitude early snow melt events primarily due to climatological differences between northern and mid latitude areas. This research developed an alternative, more reliable algorithm using the passive microwave signature that reflects the inherent characteristics of mid latitude rain-on-snow events. The two algorithms are used to compare their relative value for detecting mid latitude rain-on-snow events as compared to northern latitude events for several different frequency and linking performance to climatological signatures of observed rain-on-snow events.

❄ Page 42 73rd Eastern Snow Conference The GCOM-W1 Satellite-based Microwave Snow Algorithm (SMSA)

Richard Kelly, Nastaran Saberi and Qinghuan Li

Interdisciplinary Centre on Climate Change and Department of Geography and Environment Management, University of Waterloo, Waterloo, ON

The Satellite-based Microwave Snow Algorithm (SMSA) for estimating snow depth (SD) and snow water equivalent (SWE) is described. Calibrated for use with the Advanced Microwave Scanning Radiometer – 2 (AMSR2) aboard the Global Change Observation Mission – Water, the SMSA standard SD product for AMSR2 has been updated in two ways, from the existing algorithm. First, the detection algorithm screens various non-snow surface targets (water bodies [including freeze/thaw state], rainfall, high altitude plateau regions [e.g. Tibetan plateau]) before detecting moderate and shallow snow. Second, the implementation of the Dense Media Radiative Transfer model (DMRT) originally developed by Tsang et al. (2000) and more recently adapted by Picard et al. (2011) is used to estimate SWE and SD. The implementation combines a parsimonious snow grain size and density approach originally developed by Kelly et al. (2003). Snow grain size is estimated from the tracking of estimated air temperatures that are used to drive an empirical grain growth model. Snow density is estimated from the Sturm et al. (2010) scheme. Results are presented from recent winter seasons since 2012 to illustrate the performance of the new approach in comparison with the initial AMSR2 algorithm.

❄ Page 43 73rd Eastern Snow Conference The NASA SnowEx airborne snow campaign

Edward Kim1, Charles Gatebe1, Dorothy Hall1, Matthew Sturm2 and many others

1NASA Goddard Space Flight Center, Greenbelt, MD 2University of Alaska, Fairbanks

NASA is planning a multi-year airborne snow campaign called “SnowEx,” beginning the northern hemisphere winters of 2016-2017. The primary goal of SnowEx Year 1 is the collection of coincident observations with a suite of sensor types including active and passive optical and active and passive microwave sensors. Detailed ground truth will also be collected for algorithm development. The objective of this presentation is to update the snow community on SnowEx Year 1 plans, and to provide an opportunity for community input to help design the campaign toward the ultimate goal of defining future global-scale snow satellite measurement systems.

❄ Page 44 73rd Eastern Snow Conference Spectral analysis of airborne passive microwave measurements for classification of alpine snowpack

Rhae Sung Kim and Michael Durand

School of Earth Sciences and Byrd Polar & Climate Research Center, The Ohio State University, Columbus

Passive microwave measurements have been widely used and invested in order to obtain information about snowpack properties. Accurate knowledge and understanding the signatures of this remote sensing data from land surfaces are critical to study snow distribution over alpine mountainous area. However, this task often ambiguous due to the large variability of physical conditions and surface object types. Based on the literature, it was hypothesized that snow depth, forest fraction, and liquid water would result in distinct microwave spectra. In this study, we discuss and analyze the spectra of measured brightness temperatures (Tb) and emissivities for the frequency range of 10.7 to 89 GHz. 100m resolution of the Multiband polarimetric Scanning Radiometer (PSR) imagery was used over NASA Cold Land Processes Field Experiment (CLPX) study area with ground-based measurements of snow depth and wetness information. A total of 900 grid cells, each one hectare in size were analyzed, utilizing both a total of 144 snowpits and a total of 900 snow depth transects. In addition, two observation times in February 2003 and March 2003 were considered for normal winter snow pack and spring snow melt. Vegetation interferes with the signal that was received by PSR and therefore, NLCD 2001 percent tree canopy dataset was used for considering the vegetation influence. Snow classes with different snow depth and wetness conditions were created to determine whether microwave spectra bear one-to-one correspondence with snow and landscape properties to enable snow classification. Statistical tests show that snow depth can be distinguished even when the pixels are vegetated when using all PM frequencies instead of using single 37GHz frequency. In addition, emissivity spectra and Tb spectra were qualitatively similar, this enabling us to analyze the Tb spectra. Supervised classification scheme with using derived snow classes from this analysis will be used to classify alpine snowpack under various conditions.

❄ Page 45 73rd Eastern Snow Conference Large precipitation events at SNOTEL sites and streamflow variability in the Upper Colorado River Basin

Johnathan Kirk

Department of Geography, Kent State University, Kent, OH

Declining annual mountain snowpack across the western United States is placing unprecedented strains on regional water supplies. Further complicating seasonal water supply forecasting is the emerging prospect that interannual variation in alpine snow conditions is greatly influenced by the occurrence and magnitude of large precipitation events (LPEs) each year. The occurrence of LPEs can dictate whether a year produces above or below average runoff, underscoring the need for more targeted investigation. Using observational precipitation data recorded at a sample of snow telemetry (SNOTEL) monitoring stations located among significant runoff-producing headwater regions of the Upper Colorado River Basin (UCRB) in Colorado and Wyoming from 1981-2014; this study defines “large precipitation events” and examines their relative influence on yearly streamflow and reservoir inflow, as measured throughout the UCRB. Results indicate that interannual precipitation variability at the SNOTEL sites is significantly correlated with streamflow variability, as are the frequency and magnitude of LPEs. This study then incorporates a synoptic classification of mid-tropospheric circulation patterns associated with LPEs to investigate potential predictive signals. Results suggest that a latitudinal variation exists in the types of circulation patterns which coincide with LPEs between headwater regions, reinforcing anecdotal knowledge of the variable local responses at the SNOTEL sites to synoptic-scale forcings. Such relationships, in addition to the overall characteristics of LPEs in the UCRB, may be further integrated into actionable improvements towards more accurate and representative seasonal water supply forecasts.

❄ Page 46 73rd Eastern Snow Conference Daily snow depth at Palmer Station, Antarctica, 2007-2014: an initial analysis

Andrew G. Klein

Department of Geography, Texas A&M University, College Station

Daily snow depth measurement made at Palmer Station, Antarctica, are available beginning in December 2006. The station’s snow measurement board is currently located just off a boardwalk surrounding the main station buildings. Because it is not positioned as recommended by the National Weather Service definite errors are evident in the time series. However, these measurements do allow detailed analysis of snow accumulation patterns at Palmer Station for the 2007-2014 period. Snow depths from January to early to mid-April to early/mid May are typically less than 10 cm with many days being snow free. Snow depths typically increase irregularly over the austral winter reaching maximum thickness from late September to the first week of November. Considerably variability exists in this relatively short record in (1) maximum snow depths, (2) the date of maximum accumulation and (3) the first snow free day in summer. Maximum annual snow depths vary by a factor of two ranging from 55 to 109 cm. In low accumulation years (maximum depth less than 90 cm), the date of maximum depth occurs from mid-August to the last week in September and the station becomes snow free by November 23rd. In high accumulation years (maximum depth in excess of 90 cm), the date of maximum accumulation is delayed from early October to early November and snow persists into December. To better understand the climatic controls on snow depth at Palmer Station, this snow accumulation record will be analyzed in relation to other meteorological variables which are recorded at Palmer Station at 2 minute intervals. This time series will also be compared to snow observations made at other scientific stations along the Western Antarctic Peninsula. The work is the first step in better understanding patterns and persistence of snow cover near Palmer Station and its possible influences on the spatial distribution of local flora.

❄ Page 47 73rd Eastern Snow Conference Can assimilation of microwave radiance data improve continental-scale snow water storage estimates?

Yonghwan Kwon1, Zong-Liang Yang1, Long Zhao1, Timothy J. Hoar2, Ally M. Toure3, and Matthew Rodell3

1Department of Geological Sciences, Jackson School of Geosciences, The University of Texas at Austin 2National Center for Atmospheric Research, Boulder, CO 3Hydrological Sciences laboratory, NASA Goddard Space Flight Center, Greenbelt, MD

Understanding spatial and temporal variations in snowpack is crucial for climate studies and water resource management. Towards this goal, the climate and hydrological research communities have been working together to improve large-scale snow estimates. This study aims to address the feasibility of using microwave radiance assimilation (RA) methods to estimate continental-scale snow water storage. The RA system used in this study is comprised of the Community Land Model version 4 (CLM4) (for snow energy and mass balance modeling), radiative transfer models (RTMs) (for brightness temperature (TB) estimates), and the Data Assimilation Research Testbed (DART) (for ensemble-based data assimilation). Two snowpack RTMs, the Microwave Emission Model for Layered Snowpacks (MEMLS) and the Dense Media Radiative Transfer–Multi Layers model (DMRT-ML), are used to simulate the snowpack TB. It is hypothesized that the continental-scale RA performance in estimating snow water storage can be improved by simultaneously updating all model physical states and parameters determining TB using a rule-based approach, in which prior estimates are updated depending on their correlations with a prior TB. This hypothesis has been tested through analysis of results from a series of RA experiments. Our results also show that the performance of the RA system can be improved further, especially for vegetated areas, by assimilating the best-performing frequency channels (i.e., 18.7 and 23.8 GHz) and by considering the vegetation single scattering albedo to represent the vegetation effect on TB at the top of the atmosphere.

❄ Page 48 73rd Eastern Snow Conference Rain-on-snow and ice layer formation detection using passive microwave radiometry: An arctic perspective

1,2 1 1,2 3 1,2 4 5 A. Langlois , B. Montpetit , C. Dolant , L. Brucker , F. Ouellet , C. A. Johnson , A. Richards , A. Roy1,2, and A. Royer1,2

1Centre d’Applications et de Recherches en Télédétection (CARTEL), Université de Sherbrooke, Quebec 2Centre d’étude nordiques, Quebec 3NASA Goddard Space Flight Center, Cryospheric Sciences Laboratory, Greenbelt, MD 4Canadian Wildlife Service, Environment Canada, Ottawa, ON 5Climate Research Division, Environment Canada, Toronto, ON

With the current changes observed in the Arctic, an increase in occurrence of rain-on-snow (ROS) events has been reported in the Arctic (land) over the past few decades. Several studies have established that strong linkages between surface temperatures and passive microwaves do exist, but the contribution of snow properties under winter extreme events such as rain-on- snow events (ROS) and associated ice layer formation need to be better understood that both have a significant impact on ecosystem processes. In particular, ice layer formation is known to affect the survival of ungulates by blocking their access to food. Given the current pronounced warming in northern regions, more frequent ROS can be expected. However, one of the main challenges in the study of ROS in northern regions is the lack of meteorological information and in-situ measurements. The retrieval of ROS occurrence in the Arctic using satellite remote sensing tools thus represents the most viable approach. Here, we present here results from 1) ROS occurrence formation in the Peary caribou habitat using an empirically developed ROS algorithm by our group based on the gradient ratio, 2) ice layer formation across the same area using a semi-empirical detection approach based on the polarization ratio spanning between 1978 and 2013. A detection threshold was adjusted given the platform used (SMMR, SSM/I and AMSR-E), and initial results suggest high-occurrence years as: 1981-1982, 1992-1993; 1994-1995; 1999-2000; 2001-2002; 2002-2003; 2003-2004; 2006- 2007; 2007-2008. A trend in occurrence for Banks Island and NW Victoria Island and linkages to caribou population is presented.

❄ Page 49 73rd Eastern Snow Conference Estimating snow water equivalent in a mountainous Sierra Nevada watershed with spaceborne radiance data assimilation

Dongyue Li1, Michael Durand1, Steven A. Margulis2

1 School of Earth Sciences and Byrd Polar & Climate Research Center, The Ohio State University, Columbus 2 Department of Civil and Environmental Engineering, University of California Los Angeles

Given the critical role of the Sierra Nevada mountain snow in the water supply and the ecological system in the western U.S., being able to improve the estimate of snow water equivalent (SWE) in the Sierra Nevada has societal and natural merit. In this study, we demonstrate the accurate retrieval of SWE from spaceborne passive microwave measurements for the sparsely forested Upper Kern watershed (511 km2) in the southern Sierra Nevada. This is accomplished by assimilating AMSR-E 36.5 GHz measurements into model predictions of SWE at 90-m spatial resolution using the Ensemble Batch Smoother (EnBS) data assimilation framework. For each water year (WY) from 2003 to 2008, SWE was estimated for the accumulation season, from October 1st to April 1st and validated against snow courses and snow pillows. On average, the EnBS accumulation season SWE RMSE was 77.4 mm, despite average peak SWE of ~556 mm; the prior model estimate without assimilation had an accumulation season average RMSE of 119.7 mm. After assimilation, the overall bias of the accumulation season SWE estimates was reduced by 84.2%, and their RMSE reduced by 35.4%. The assimilation also reduced the bias and the RMSE of the April 1st SWE estimates by 80.9% and 45.4%, respectively. Sensitivity experiments indicated optimal results when the raw observations are assimilated, rather than first averaging over the watershed. This method is expected to work well above treeline, and for dry snow.

❄ Page 50 73rd Eastern Snow Conference How much western United States streamflow originates as snow?

Dongyue Li 1, Melissa Wrzesien1, Michael Durand1, Jennifer Adam2, Dennis Lettenmaier3

1 School of Earth Sciences and Byrd Polar & Climate Research Center, The Ohio State University 2 Department of Civil and Environmental Engineering, Washington State University 3 Department of Geography, University of California, Los Angeles

Snow is a vital component of the water supply in the western United State. Quantifying the fraction of streamflow that originates as snow is critical for assessing the availability and vulnerability of water resources, particularly in a changing climate. Although many estimates of this fundamental quantity have been suggested, none of them (to our knowledge) has been based upon a systematic study. Here, we examine the ratio of the snow-derived streamflow to the total streamflow over the western United States for the period of 1950 to 2100. By using a new method for tracing snowmelt fate within a macroscale hydrological model, we show that snow accounts for 53% of the total streamflow in the western United States, despite only 37% of the total precipitation being snowfall. In the mountain ranges of the west, 71% of the streamflow comes from snow, and the snowmelt charges 66% of the major reservoirs in the western United States; such reservoir storage is critical to meet the peak water demands in the summer and fall. Further, we demonstrate that the contribution of snowmelt to streamflow will likely decrease in a warmer climate, especially in the Cascades and the Sierra Nevada where the ratio could decline by 33% by 2100 in comparison with the historical record.

❄ Page 51 73rd Eastern Snow Conference Terrestrial laser scanning observations of tree canopy intercepted snow

Qinghuan Li, Richard Kelly

Department of Geography and Environmental Management, University of Waterloo, ON

The distribution of snow in forest canopies is important for both the water mass and energy budgets of forested environments. Snow accumulation in forest canopies can be significant for the tree water demand whilst canopy snow can also act as a buffer to the understory with through-fall during the winter season occurring sporadically. Moreover, significant amounts of water equivalent can also be lost through sublimation from the canopy snow. Understanding canopy snow dynamics is important for understanding forest hydrology but also for understanding the remote sensing response of forest canopies, especially at microwave wavelengths which are sensitive to forest canopy volume scattering processes. The overall goal of the study was to estimate the snow volume intercepted in a coniferous canopy using a terrestrial laser scanner (TLS). The study was performed on two coniferous trees in southern Ontario on the University of Waterloo campus. The laser scanner, a Leica MS50 multi-station, was used to scan the tree when snow was present and then when snow was removed. Snow properties in the canopy and on the ground were evaluated using traditional measurements of grain size and bulk properties. The paper demonstrates the utility of high resolution TLS and shows how the simplicity of time-differencing TLS measurement approaches are complicated by the need to account for the mechanics of snow loading and unloading which are a function of the tree biophysical properties (e.g. elasticity).

❄ Page 52 73rd Eastern Snow Conference Development of Universal Relationships between Snow Depth, Snow Covered Area and Terrain Roughness from NASA Airborne Snow Observatory data

Noah Molotch and Dominik Schneider

Department of Geography, INSTAAR, and CWEST, University of Colorado Boulder

Snowmelt is the primary water source in the Western United States and mountainous regions globally. Forecasts of streamflow and water supply rely heavily on snow measurements from sparse observation networks that may not provide adequate information during abnormal climatic conditions. Using observations LiDAR and Hyperspectral observations from the NASA Airborne Snow Observatory, we have developed transferable functional relationships between terrain roughness, snow covered area, and snow depth. We show that the relationship between snow covered area and snow depth varies systematically as a function of terrain roughness. Regression analyses that use fractional snow covered as the independent variable to estimate snow depth result in relative mean squared errors between 39% and 58% of measured snow depth for different roughness classifications. Future work will look at the changes in the relationship between snow depth and snow covered area through the ablation season to determine the relationship’s utility to water supply forecasting. The importance of this work is illustrated through examples that estimate snow depth for select alpine regions.

❄ Page 53 73rd Eastern Snow Conference Elevation Angular Dependence of Wideband Autocorrelation Radiometric (WiBAR) Remote Sensing of Dry Snowpack and Lake Icepack

Seyedmohammad Mousavi1, Roger De Roo2, Kamal Sarabandi1, and Anthony W. England3

1 Electrical Engineering and Computer Science Department, University of Michigan, Ann Arbor 2 Climate and Space Sciences and Engineering Department, University of Michigan, Ann Arbor 3 College of Engineering and Computer Science, University of Michigan, Dearborn

In most remote sensing applications, the gross parameter of the target, such as snow depth and snow water equivalent (SWE), are often the parameters of interest. A novel and recently developed microwave radiometric technique, known as wideband autocorrelation radiometry (WiBAR), offers a deterministic method to remotely sense the microwave propagation time of multi-path microwave emission of low loss terrain covers and other layered surfaces such as dry snowpack and freshwater lake icepack. The microwave propagation time through the pack yields a measure of its vertical extent; thus, this technique is a direct measurement of depth. This technique is inherently low-power since there is no transmitter as opposed to active remote sensing techniques. It also works at angles away from nadir. We have confirmed the expected simple dependence of the microwave propagation time on the elevation angle with ground-based WiBAR measurements of the icepack on Douglas Lake in Michigan in early March 2016. The observations are done in the X-band for the icepack. At these frequencies, the volume and surface scattering are small in the pack. The system design parameters and physics of operation of the WiBAR is discussed and it is shown that the microwave propagation time can be readily measured for dry snowpack and lake icepack for observation angles away from nadir to at least 70◦.

❄ Page 54 73rd Eastern Snow Conference Formulation of a Bayesian SWE retrieval algorithm using X- and Ku- measurements

Jinmei Pan, Michael Durand

School of Earth Science and Byrd Polar & Climate Research Center, The Ohio State University, Columbus

When the snow radar was applied for the snow water equivalent retrieval, an advanced algorithm is required to separate the influence of the underlying soil, and taking the penetration depth and the stratigraphy of the natural snowpit into consideration. In this study, the Bayesian-based Markov Chain Monte Carlo method is applied to estimate SWE based on active backscattering coefficient measurements at X- and Ku-bands for taiga snowpits at Sodankyla (Lemmentyinen et al., 2013). This algorithm samples the SWE as well as the snow and soil properties that can reproduce the radar measurements from a set of globally-available prior distributions of these parameters. The active Microwave Emission Model of Layered Snowpacks (MEMLS) converted from the passive MEMLS is used as the observation model. This model separated the equivalent reflectivity (1-emissivitiy) at the snow surface into a specular scattering part and a diffuse scattering part, and later semi-empirically converted them into the corresponding contributions to the backscattering coefficient. Therefore, the computation cost of active MEMLS is similar to passive MEMLS, and thus is suitable for the MCMC application. Based on previous MCMC retrieval studies using passive brightness temperature (TB) as observations, at this time, the observation model will be revised as active MEMLS for SWE estimation, and the retrieval system will be formulated. Besides the parameters already included in passive MEMLS, the active MEMLS introduced three empirical parameters, which are the coefficient to split the cross- and like-pol. backscattering coefficients, the ratio of the specular part in the rough soil reflectivity, and the roughness of the air-snow interface. How these additional parameters will influence the MCMC retrieval performance will be studied.

❄ Page 55 73rd Eastern Snow Conference In-situ Light Emitting Diode Detection and Ranging for the Mapping of Snow Surface Topography and Depth

N. Reed Parsons, Christopher Hopkinson

Department of Geography, University of Lethbridge, AB

The West Castle catchment study site, a mountainous sub-basin of the Oldman River Basin, is a vital hydrological resource as well as an equally ecologically and geomorphologically diverse region in south west Alberta. The ARTeMiS Research Team have installed three meteorological stations at three elevations: valley (1415 m ASL); tree line (1850 m ASL); and alpine ridge (2130 m ASL) within the boundaries of the West Castle Mountain Ski Resort. Current accepted methods of in-situ snow depth monitoring, such as ultrasonic range detection sensors, are only capable of measuring an average accumulation over a small footprint leaving snow surface profile mapping to be conducted manually. Furthermore, in areas in which the primary snow transportation process is aeolian, the depositional and erosional features are not accurately estimated. Thus, under the currently accepted in-situ snow depth measurement regime, the results are often over or under estimated. Leveraging the Meteorological tower infrastructure, a conventional SR50A sonic ranging depth senor is co-located with a Light Emitting Diode Detection and Ranging (LEDDAR) solution provided by Canadian tech start-up, LeddarTech. In this study we map snow accumulation and snow surface topography using LEDDAR, and compare the accuracy, precision, and susceptibility to extreme alpine conditions to that of the SR50A.

❄ Page 56 73rd Eastern Snow Conference Melt on the Margins: Calibrated Enhanced- Resolution Brightness Temperatures to Map Melt Onset Near Glacier Margins and Transition Zones

Joan Ramage,1 Mary J. Brodzik2 and Molly Hardman2

1Earth and Environmental Sciences Department, Lehigh University, Bethlehem, PA 2 University of Colorado/NSIDC/CIRES, Boulder

Passive microwave (PM) observations from Special Sensor Microwave Imager/Sounder (SSMI and SSMIS), and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) at 18-19 GHz and 36-37 GHz channels have been important sources of information about snow melt status in glacial environments, particularly at higher latitudes. PM data are sensitive to the changes in near-surface liquid water that accompany melt onset, melt intensification, and refreezing. Overpasses are frequent enough that in most areas multiple (2-8) observations per day are possible, yielding the potential for determining the dynamic state of the snow pack during transition seasons. Limitations to this approach include glacier-marginal zones where pixels may be only fractionally snow/ice covered, and areas where the glacier is near large bodies of water: even small regions of open water in a pixel severely impact the microwave signal. We use the enhanced-resolution prototype Calibrated Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature Earth System Data Record (CETB) product to evaluate melt characteristics along glacier margins and melt zone boundaries during the melt seasons in 2003- 2004 for the Alaskan Coast Range and Akademii Nauk Ice Cap, Severnaya Zemlya, locations where legacy methods were successful that span a wide range of melt scenarios. Sites include pixels that were previously excluded due to mixed pixel effects. We anticipate that improvement from the original 25 km-scale EASE-Grid pixels to the enhanced resolution of 6.25 km will dramatically improve the ability to evaluate melt timing across gradients in glacier margins and transition zones in glacial environments.

❄ Page 57 73rd Eastern Snow Conference Status of the MODIS C6 Snow Cover and NASA Suomi-NPP VIIRS Snow Cover Data Products

George A. Riggs1, Dorothy K. Hall2 and Miguel O. Román2

1 SSai, Lanham, MD 2NASA Goddard Space Flight Center, Greenbelt, MD

An updated synopsis of the soon-to-be-released NASA Suomi-NPP (S-NPP) Visible Infrared Imager Radiometer Suite (VIIRS) snow cover data products produced in the Land Science Investigator-led Processing System (LSIPS) and the recently released MODIS Collection 6 (C6) data products is presented. The VIIRS snow cover algorithm and data product content are the same as presented at the 72nd ESC however the data product format has changed to HDF5 and NetCDF Climate Forecast (CF) conventions have been adopted for the attributes. Forward processing and reprocessing of the MODIS C6 data products began in April 2016 and products have been released. Notable revisions made in the MODIS C6 snow cover algorithm are the change to normalized difference snow index (NDSI) outputs replacing the thematic and the fractional snow cover maps, changes in data screens to reduce snow commission errors and output of a quality assessment array of bit flags reporting data screen results. Users thus have increased data and information content as compared to MODIS C5 products.

❄ Page 58 73rd Eastern Snow Conference

50 Years of Satellite Snow Cover Extent Mapping Over Northern Hemisphere Lands

David A. Robinson

Global Snow Lab, Department of Geography, Rutgers University

This fall marks a half-century of continuous satellite mapping of snow cover extent (SCE) over Northern Hemisphere lands. NOAA has produced the primary dataset throughout this time, recently in cooperation with the US Navy and Coast Guard at the National Ice Center. Throughout the 50 years, trained analysts have primarily employed visible satellite imagery and interactive means of mapping the SCE on a weekly (1966-1999) and daily (1999- present) basis. The dataset has been carefully evaluated over the years to ensure the best possible continuity in what has emerged as a primary satellite climate data record (CDR). In fact, this CDR is the longest, continuous satellite-derived environmental record in existence. This presentation will discuss the history of the mapping program, trends and variability in SCE over the decades gleaned from the maps, and the utilization of this CDR in numerous climate studies. Special attention will be paid to eastern North America. This will include the first presentation of a short-term climatology (1999-present) based on the 24 km resolution Interactive Multisensor Snow and Ice Mapping System product. A comparison of this product over the coarser spatial resolution one that extends back to 1966 will be included in the discussion.

❄ Page 59 73rd Eastern Snow Conference Comparison of three microwave radiative transfer models for simulating snow brightness temperature

Alain Royer1,2, Alexandre Roy1,2, Benoit Montpetit1, Olivier St-Jean-Rondeau1,2, Ghislain Picard4, Ludovic Brucker5 and Alexandre Langlois1,2

1 CARTEL, Université de Sherbrooke, Québec 2 Centre d'Études Nordiques, Québec 3 LGGE, CNRS-UJF, Grenoble, France 4 NASA GSFC, Greenbelt, MD

This presentation compares three microwave radiative transfer models commonly used for snow brightness temperature (TB) simulations, namely: DMRT-ML, MEMLS and HUT n-layers models. Using the same new comprehensive sets of ground-based measured detailed snowpack physical properties, we compared simulations of TBs at 11, 19 and 37 GHz from these 3 models based on different electromagnetic approaches using three different snow grain metrics, i.e. respectively measured specific surface area (SSA), calculated correlation length using the Deby relationship and measured maximum diameter extent. Comparison with surface-based radiometric measurements for different types of snow (in southern Québec, and in subarctic and arctic areas) shows similar averaged root mean square errors in the range of 10 K or less between measured and simulated TBs when simulations are optimized using scaling factors applied on these metrics. This means that, in practice, the different approaches of these models (physical to empirical) converge to similar results when driven by appropriate scaled in-situ measurements. We discussed the results relatively to the uncertainties in snow microstructure measurements. In particular, we show that the scaling factor to be applied on the SSA measurements in order to minimised the DMRT-ML simulated TBs compared to measured TBs is not due to uncertainty in SSA data.

❄ Page 60 73rd Eastern Snow Conference Snow Properties Retrieval using DMRT-ML in a Statistical Framework Using Passive Microwave Airborne Observations

Nastaran Saberi and Richard Kelly

Interdisciplinary Centre on Climate Change, and Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON

Forward radiative transfer models to estimate the passive microwave brightness temperature from multi-layered snow are increasing in maturity. The challenge now is in the retrievals because an inverse modeling approach should be employed. Inverse approaches include statistical methods and techniques based on machine learning optimization, where a cost function (a function of difference between observed and modeled data) is minimized using linear or non-linear optimization approaches. In this study using the Dense Media Radiative Transfer- Multi Layered (DMRT-ML) model, a model-based inversion algorithm is used to retrieve snow depth with passive microwave observations from airborne radiometer measurements aligned with ground-based snow-surveys in the Arctic Eureka region during April 2011. The acknowledged challenge in passive microwave inversion, that of dealing with underdetermined set of equations, is addressed by exploring the parameterization of physical quantities required to constraint input variables such as grain size, density, physical temperature and stratigraphy also known as a priori information. Based on known emission sensitivity (captured by the models), grain size as an unknown quantity is often used in the cost function minimizing process while snow depth, the variable to be estimated, may be known at some places from in situ measurements and can be used in the cost function approach, perhaps through a maximum likelihood solution to the simulation. This general retrieval approach is used in the Globsnow approach that employs emission model of Helsinki University of Technology (HUT) which is itself based on Pulliainen’s (2011) method.

In contrast with Globsnow, this experimental study employs more detailed characteristics of the snowpack from in-situ measurements and unlike Globsnow, where a background snow depth map is assimilated into the retrieval process to mitigate errors, a range of acceptable snow depth values are considered. In situ snow depth measurements are used to provide insight into the plausibility of the snow depths used. Moreover, grain size is estimated as an optical size of grains (as required by the DMRT-ML). Surface physical temperature estimated from airborne observations is used as a tuning parameter to update the acceptable range for retrieved snow depth. The approach provides insight into the feasibility and applicability of the

❄ Page 61 73rd Eastern Snow Conference proposed methodology globally for spaceborne retrievals since it is a fairly fast statistics-based framework that leverages a physics based model snow radiative transfer model in a parsimonious manner.

❄ Page 62 73rd Eastern Snow Conference Parameterization of snow microstructure for passive microwave radiometry

Olivier Saint-Jean-Rondeau1,2, Alain Royer1,2, Alexandre Roy1,2, Alexandre Langlois1,2, Jean-Benoît Madore1,2

1CARTEL, Université de Sherbrooke, Sherbrooke, Québec 2Centre d'Études Nordiques, Québec, Canada

Passive microwave (PMW) remote sensing has proved to be the most practical approach in characterizing the seasonal snowpack of remote northern regions at the synoptic scale. This is attributed to the availability of a daily surface coverage since 1978 and the sensitivity of PMW to the dielectric properties of snow. The polarized thermal microwave radiation emitted by the ground is transmitted, absorbed and scattered, becoming sensitive to the vertical profile of snow microstructure. Radiative transfer models are used to calculate the brightness temperature as a function of microstructural properties: snow density, grain size, and 3-D grain structure. However, microstructure is difficult to describe with a quantifiable metric; it can be assessed directly or indirectly by various methods and instruments, which provide complementary information. These methods include snow density cutter measurement, infrared reflectometry for specific surface area (SSA) retrieval, micropenetrometry (SMP), thermal conductivity, and visual grain size and classification. This study aims to assess the value of each of these measurements as proxies for microstructural parameters in a physically-based model, namely the Dense Media Radiative Transfer– Multi-Layer (DMRT-ML). For this purpose, measurement campaigns were conducted during the winters of 2015 and 2016 in Southern and Northern Québec. In-situ measurements are compared to DMRT-ML brightness temperatures using either infrared reflectometry or SMP derived SSA as snow grain metric, as well as various density and stratification metrics. Furthermore, an experiment relating the absorption and diffusion coefficients of sampled homogeneous layers of snow to microstructural properties was realised.

❄ Page 63 73rd Eastern Snow Conference Energy balance and melt over a patchy snow cover

Sebastian Schlögl 1,2, Rebecca Mott 1 and Michael Lehning 1,2

1 WSL Institute for snow and avalanche research SLF, Davos, Switzerland 2School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

A patchy snow cover significantly alters the snow surface energy exchange and therefore snowmelt especially due to (i) horizontal advection of warm air from the bare ground to the snow patch and (ii) the development of strong stability close to the ground, which are opposing effects. As snow and hydrological models are typically limited to simulating pointwise vertical exchange between the ground and the atmosphere and do not include lateral transport, melting rates are sufficiently represented exclusively for homogeneous snow covers. For a patchy snow cover, modelled melting rates of snow patches are underestimated at the upwind edge. In this study we assess the relative contribution of the advective heat flux to the total surface energy balance and therefore snow melt using (i) high-resolution measurements of snow depth changes obtained from Terrestrial Laser Scanning, (ii) the atmospheric model Advanced Regional Prediction System ARPS and (iii) the distributed and physics-based snow model Alpine3D. We force Alpine3D with air temperature and wind velocity fields calculated from the non-hydrostatic atmospheric model ARPS. Analysis of measured melt rates have shown a 5 % increase in snow melting due to the effect of the advective heat flux for a typical spring snow distribution. We numerically investigate the effect of atmospheric flow field dynamics over a patchy snow cover on the total surface energy balance by forcing Alpine3D with fully resolved meteorological fields (air temperature and wind velocity) obtained from ARPS close to the surface. As a reference and for comparison, the model is forced with air temperature and wind velocity fields above the blending height. We present quantitative experimental and numerical results that show how the snow melt rate changes with snow cover fraction (SCF) and the mean perimeter of the snow patches and increases with decreasing SCF and decreasing perimeter.

❄ Page 64 73rd Eastern Snow Conference How do stability corrections perform over snow?

Sebastian Schlögl 1,2, Rebecca Mott 1 and Michael Lehning 1,2

Modelling turbulent heat fluxes over snow is a challenging issue. One specific complication is that stability corrections are typically determined over non-snow surfaces but often applied over snow. This study focuses on sensible heat flux parametrizations in stable conditions by testing five well-established and developing two new stability correction functions for two alpine and two polar test sites. The performance test of different stability corrections reveals an overestimation of the turbulent sensible heat flux for high wind velocities and a generally poor performance of all investigated functions for large temperature gradients. The stability parametrizations produce an error between 7 and 12 W m-2 on average. The smallest error of published stability corrections is found for the Holtslag scheme, which is recommended for very stable conditions. The newly developed univariate parametrization (classically dependent on the stability parameter) has its strength for atmospheric conditions near neutral and for moderate wind velocities (2-5 m/s). Our newly developed bivariate parametrization based on a simple linear combination of buoyancy and shear terms was found be to a viable alternative especially in regions with large wind velocities. The bivariate parametrization also avoids known difficulties for large values of ζ.

❄ Page 65 73rd Eastern Snow Conference 2nd European Snow Science Winter School

Schneebeli, M.1, Lemmetyinen, J.2

1 WSL Institute for Snow and Avalanche Research SLF, Switzerland 2 Finnish Meteorological Institute FMI, Finland

The cryosphere forms an integral part of the Earth. The seasonal snow cover extends to 49% of the total land surface in midwinter in the northern hemisphere. Monitoring of seasonal snow cover properties is therefore essential in understanding interactions and feedback mechanisms related to the cryosphere, but also to ecosystems. However, as a complex and highly variable medium, many essential properties of seasonal snow cover have traditionally been difficult to measure. The past 10 years snow science has seen a rapid change from a semi-quantitative to a quantitative science; especially the new methods allow improved quantification of the snow microstructure. Understanding physical and chemical processes in the snowpack requires detailed measurements of the microstructure. The Snow Grain Size Intercomparison Workshop 2014 recently solidified the progress in quantitative measurements. The 2nd European Snow Science Winter School in Preda, Switzerland, in February 2016 aimed at teaching graduate students in modern snow measurement techniques. In addition to the lectures, different measuring instruments were available for the students to get hands-on experience in the field. The list of instruments was long, ranging from hand lenses and crystal plates for traditional snow pits up to high-resolution lasers and penetrometers. Field measurements occurred in small groups and a report is prepared describing the methods, results and interpretation. These state-of-the-art snow measurement techniques will be taught in future in an annual snow school held in various places in Europe. The 2017 will occur in Sodankyla, Finland.

❄ Page 66 73rd Eastern Snow Conference Single and multi-sensor snow wetness mapping by Sentinel-1 and MODIS data

Rune Solberg1, Øystein Rudjord1, Øivind Due Trier1, Gheorghe Stancalie2, Andrei Diamandi2 and Anisoara Irimescu2

1Norwegian Computing Center, Oslo, Norway 2Romanian National Meteorological Administration, Bucharest, Romania

Snow monitoring is essential for prediction of flooding due to rapid snowmelt, to provide snow avalanche risk forecasts and for water resource management – including hydropower production, agriculture, groundwater and drinking water. Snow wetness and snow liquid water are essential variables for monitoring the snow state and providing early warning of flood risk and snow avalanches during the melting season. The presentation shows the first results from the SnowBall project of single-sensor and fusion algorithms applied on Sentinel-1 SAR and MODIS data for frequent monitoring of the snow wetness during the melting seasons in Norway and Romania. Sentinel-1 C-band SAR is sensitive to presence of wet snow. Wet snow can be detected since the radar backscatter drops significantly. However, with C-band SAR it is difficult to quantify how wet the snow is. Wet snow mapping into a set of five categories of wetness has been demonstrated in the past by NR using MODIS data. The combination of surface temperature and the temporal development of the effective snow grain size are used to infer approximately how wet the snow is. In the SnowBall project this approach is now ported to the combined use of the Sentinel-3 OLCI and SLSTR sensors. The previous algorithm is also advanced to enable further discrimination of snow wetness classes quantitatively related to the snow liquid water (volume of liquid water per volume of snow) for the snow surface. Field measurements have been accomplished using spectroradiometer measurements and direct measurements of snow liquid water with a dielectric probe to develop the retrieval model. The retrieval model will also be adapted to Sentinel-3 data and applied in the new algorithm. Furthermore, to utilise the combined capability of Sentinel-1 and MODIS/Sentinel-3 for more accurate retrieval and improved temporal coverage – given that optical sensors are limited by cloud cover and SAR only detects wet snow – we develop a sensor-fusion approach. The algorithm applies a hidden Markov model (HMM) to simulate the snow wetness states the snow surface go through, given the temporal observations of the surface conditions. The most likely current snow state is estimated, giving the current snow liquid water category. The snow products from SAR, optical and the multi-sensor approach are validated against cal/val sites providing frequent snow measurements in Romania and Norway, and additional field campaigns where a significant terrain relief is present providing corresponding significant

❄ Page 67 73rd Eastern Snow Conference gradients in snow wetness during the snowmelt season. Successful algorithms are implemented and demonstrated in a prototype system producing daily wet-snow maps of Romania and Norway. When the system is operationalised, the products will be used in operational hydrological models assisting flood prediction for issuing flood warnings. Similarly, the products will be used by the snow avalanche service providing avalanche warnings.

❄ Page 68 73rd Eastern Snow Conference Modeling polar ice sheet emission from 0.5-2.0GHz with a partially coherent model of layered media with random permittivities and roughness

Shurun Tan1, Leung Tsang1, Tianlin Wang1, Mohammadreza Sanamzadeh1, Joel Johnson2, and Kenneth Jezek3

1 Radiation Laboratory, Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor 2 ElectroScience Laboratory, The Ohio State University, Columbus 3 School of Earth Sciences & Byrd Polar Research Center, The Ohio State University, Columbus

The surface of the polar ice sheet is characterized by rapid density variations on centimeter scales due to the accumulation process. The fluctuation forms layers near the top of the ice sheet as well as introducing interface roughness. The fluctuating permittivities among layers as a result of density variation cause reflections and modulate the ice sheet emission. Interface roughness, on the other hand, can cause angular and polarization coupling. Our interests are the brightness temperatures between 0.5 to 2.0 GHz for the Ultra-wide Band Software Defined Radiometer (UWBRAD) project. The UWBRAD goal is to sense the internal temperature profile of the ice sheet using low frequency ultra-wide band radiometry. Previously incoherent models and coherent models were used to calculate the brightness temperatures of multilayered media consisting of thousands of layers. In this paper, we use a partially coherent approach. When the correlation lengths of the density fluctuations are within a wavelength inside the ice sheet, the coherent interference due to reflections remains even after statistical averages over density profiles. The coherent wave effects are “localized” in random layered media to spatial scales within a few wavelengths. Thus we can divide the entire ice sheet into blocks, with each block on the order of a few wavelengths, and apply fully coherent scattering models within a single block. The blocks are also sized to correspond to the bandwidth of the microwave channel so that interference effects within a channel can be captured. We then incoherently cascade the intensities among different blocks. A smaller number of realizations is then required in the Monte Carlo averaging process for each block due to the smaller number of interfaces. This partially coherent approach has proved to be much more efficient than applying the fully coherent model to the entire ice sheet, and to produce results in agreement with the fully coherent approach.

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The partially coherent approach also enables us to examine interface roughness effects by applying a full wave small perturbation method up to second order (SPM2) to the multi-layered roughness scattering problem within the same block. The SPM2 has the advantage of conserving energy. We report numerical results in checking energy conservation and illustrate the angular and polarization coupling effects arising due to interface roughness.

❄ Page 70 73rd Eastern Snow Conference Spatial variability of snow at Trail Valley Creek, NWT

Aaron Thompson1, Richard Kelly1, Philip Marsh1, Tyler de Jong2

1Interdisciplinary Centre on Climate Change and Department of Geography and Environmental Management, University of Waterloo, ON 2Wilfrid Laurier University, Waterloo, ON

With a renewed focus on large scale, global remote sensing of snow, bolstered by upcoming projects like NASA’s SnowEx campaign, the importance of ground referencing through in situ measurements is emphasized. Recent studies have suggested that microstructural elements of the snowpack may be a critical driver of the radar response at Ku- and X-band frequencies further highlighting the importance of a comprehensive field data set (Thompson et al., in preparation). A field campaign, in April 2016, located at Environment Canada’s Trail Valley Creek research basin in the Northwest Territories focused on in situ snowpack measurements, and lay the foundation for a 3-year study that will combine ground-based radar observations at Ku- and X- band frequencies using UW-SCAT, with differential interferometric SAR techniques aimed at extracting snow volume information, and will therefore require a robust suite of field measurements. These observations allowed us to explore the spatial variability of snow microstructure in a variety of seasonal arctic accumulation environments including a forested site, wind-swept tundra, and drifted snow. Measurements included snow depth, density and temperature profiles, along with snow grain and stratigraphy observations augmented by NIR photography. Employing a series of 5 m by 5 m orthogonal snow trenches at each site, we investigated the spatial variability of these snowpack characteristics over short distance scales. Local meteorological data, collected at two of the sites, provided evidence of the processes that controlled the snowpack development and metamorphosis. Collectively, these measurements not only provided insight into the nature of snow microsctructure variability in these environments, but also helped to identify optimal sites within Trail Valley Creek for future radar acquisitions.

❄ Page 71 73rd Eastern Snow Conference Long-term trends and variability of winter snow accumulation at White Glacier, Nunavut, Canada

Laura Thomson and Luke Copland

Department of Geography, University of Ottawa, Ontario

The measurement of winter snow accumulation has continued as part of the glaciological mass balance observations at White Glacier (90°47’W, 79°29’N, 100-1780 m a.s.l.) since glacier research began on Axel Heiberg Island in 1959. In this study we examine the variability of snow accumulation with elevation over 55 years of observations and consider trends in accumulation over this time period. Declining sea ice extent and duration over the past two decades are expected to lead to corresponding increases in ocean temperatures, evaporation, and precipitation over the Canadian Arctic. This has prompted predictions that snow accumulation will increase over the Queen Elizabeth Islands, as observed at the Eureka Weather Station (85°56’W, 79°59’N, 10 m a.s.l.). However, to date no statistically significant trend of increasing snow accumulation has been observed in the accumulation area of White Glacier. In addition to conducting analysis of spatial and temporal variability in snowfall over the glacier, we consider the impacts on glacier mass balance, which has shown a significant decrease in the past decade, and ice dynamics. Since the mass imbalance between the accumulation and ablation areas of a glacier is the primary driving force for ice motion, we integrate snow accumulation and ice ablation observations at White Glacier to model mass transfer through cross-sectional flux gates at 370, 580, and 870 m a.s.l. Comparison of these modelled ice fluxes with observations of ice motion, which indicate that velocities have decreased on the order of 45%, 15%, and 5% at these respective locations since 1960, enables us to estimate the dynamic response time of White Glacier.

❄ Page 72 73rd Eastern Snow Conference Snow Microstructure Characterization and Numerical Simulation of Maxwell’s Equation in 3D Applied to Snow Microwave Remote Sensing

Leung Tsang1, Shurun Tan1, Jiyue Zhu1, and Xiaolan Xu2

1 Radiation Laboratory, Department of Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor 2 Jet Propulsion Laboratory, Pasadena, CA

In this paper, we review our recent research results on snow microstructure characterization and physical models of microwave remote sensing of terrestrial snow. The study domain is focused on the Snow Cold Land Process experiment (SCLP) that is in the Decadal Study. The SCLP consists of radar backscattering at X- and Ku band and radiometric brightness temperatures at Ku- and Ka band. In snow microstructure, we use correlation function to characterize the snow. We use the bicontinuous media model to generate computer snow. The bicontinuous media has correlation functions dependent on the input parameters. For densely discrete scatterers, we use the pair distribution functions of sticky spheres and multiple size spheres. Recently, we show that the correlation functions can be derived from the pair distribution functions. Thus the correlation function becomes the basis of comparisons of bicontinuous media of computer snow, densely packed spheres, and real snow. The derived correlation functions are distinctly different from the traditional exponential correlation functions. They are exponential near the origin but have tails for longer distances. Thus at least two parameters are needed to characterize the correlation function instead of one. Field measurements of snow microstructure typically provide a visual grain size, which is the maximum extent of the dominant snow grains. On the other hand, the emerging measurements of the specific surface area (SSA) is more sensitive to fine snow grains. The SSA can be converted to an equivalent optical grain size. Knowing the optical grain size and the visual grain size, we will approximate the correlation function of snow microstructure from the pair distribution functions of two-size spheres with varying number densities. The bicontinuous media has been combined with the partially coherent approach of dense media radiative transfer (DMRT) to provide look up tables of backscatter and brightness temperatures of snowpack under various conditions. In DMRT, Maxwell’s equation is solved within several cubic wavelengths of statistically homogeneous snow volume to compute the

❄ Page 73 73rd Eastern Snow Conference phase matrix. The phase matrix, accounting for the coherent near field and intermediate field interactions, is then substituted into the radiative transfer equation to propagate the intensity over the snow volume, accounting for the incoherent far field and volume / surface interactions. Such forward snowpack scattering model has been applied to develop snow water equivalent (SWE) retrieval algorithms and shown to be successful when tested over the Finland SnowScat and SnowSAR dataset. A fully coherent snowpack scattering model is also developed to compute the backscattering coefficients and the brightness temperatures of a snowpack. The model is based on numerically solving the Maxwell’s equation in 3D (NMM3D) directly over the entire domain of snowpack. We use a half-space to represent the soil or sea ice under the snowpack, and use the bicontinuous media to represent the snow volume. The fully coherent approach predicts the complex scattering matrix from the snowpack, including both magnitude and phase. In passive remote sensing, this approach allows arbitrary temperature and layer profiles of the snowpack. The brightness temperatures and backscatters out of the fully coherent model are compared against the results of DMRT for various snowpack configurations. We also illustrate the co- polarization phase difference of an anisotropic snow layer extracted from full wave simulations.

❄ Page 74 73rd Eastern Snow Conference Comparison of Satellite Passive Microwave, Airborne Gamma Radiation Survey, and Ground Survey Snow Water Equivalent Estimates in the Northern Great Plains

Samuel Tuttle1, Eunsang Cho1, Carrie M. Vuyovich1,2, Carrie Olheiser3, Jennifer M. Jacobs1

1 University of New Hampshire, Durham, NH 2 U.S. Army Corps of Engineers Cold Regions Research and Engineering Laboratory, Hanover, NH 3 National Weather Service National Operational Hydrologic Remote Sensing Center, Chanhassan, MN

Remote sensing has the potential to enhance operational river flow forecasting by helping to constrain estimates of snow water equivalent (SWE). Snowmelt contributes significantly to runoff in northern and mountainous areas of North America. In the northern Great Plains, melting snow is a primary driver of spring flooding, so knowledge of the magnitude and spatial distribution of SWE is necessary for accurate flood forecasting. However, ground surveys are relatively sparse in the region and provide only point estimates. Airborne gamma radiation surveys from the U.S. National Weather Service (NWS) provide SWE estimates at larger resolution (approximately 5-7 km2), but are available only 1-4 times per winter. Thus, satellite remote sensing can increase the spatiotemporal coverage of SWE observations available for forecasting purposes. We compare satellite passive microwave estimates to NWS airborne gamma radiation snow survey and U.S. Army Corps of Engineers (USACE) ground snow survey SWE estimates in the northern Great Plains. The three SWE datasets compare favorably in the low relief, low vegetation study area, but the different spatial extents of each measurement complicates the comparison. Additionally, the effect of snow grain size changes and wet snow on the satellite SWE estimates remain limitations of the passive microwave method. Awareness of when and how snowpack physical conditions impact retrievals can optimize the useful information provided by passive microwave SWE observations for operational flow forecasting.

❄ Page 75 73rd Eastern Snow Conference Sensitivity analysis of passive microwave brightness temperatures to distributed snowmelt

C. M. Vuyovich1, J.M. Jacobs2, C.A. Hiemstra3, E. J. Deeb1, J.B. Eylander

1 Cold Regions Research and Engineering Laboratory, Hanover, New Hampshire 2 Civil and Environmental Engineering, University of New Hampshire, Durham 3 Cold Regions Research and Engineering Laboratory, Fairbanks, Alaska 4 HQ AF Weather Agency, Offutt AFB, Nebraska

Global datasets of recorded passive microwave emissions provide non-destructive, daily information on snow processes, and the microwave signal is highly responsive to snow wetness due to the sensitivity of the radiance to changes in the dielectric constant. A key challenge to using the microwave melt signal is that its spatial resolution is quite coarse and not able to explicitly characterize sub-grid scale variations needed for most water resource applications. The objective of this research is to test the sensitivity of brightness temperatures within a microwave pixel as it relates to spatially distributed liquid water content of the snowpack. Daily snow states were simulated for a 14-year period using a high-resolution (50 m) energy balance snow model over a 34x34 km pixel. These data were fed into a microwave emission model to simulate brightness temperatures during wet snow events. A sensitivity analysis was conducted to develop a relationship between the change in microwave brightness temperature and the percent area affected by liquid water content in the snowpack. The model output was also compared to AMSR-E passive microwave satellite data and discharge data at a basin outlet within the study area. The results are used to help understand the hydrological impact of large- scale snowmelt events as detected by passive microwave data.

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UAV Mapping of Debris Covered Glacier Change, Llaca Glacier, Cordillera Blanca, Peru

Oliver Wigmore and Bryan Mark

Department of Geography and Byrd Polar & Climate Research Center, The Ohio State University, Columbus

The glaciers of the Cordillera Blanca Peru are rapidly retreating as a result of climate change, altering timing, quantity and quality of water available to downstream users. Furthermore, increases in the number and size of proglacial lakes associated with these melting glaciers is increasing potential exposure to glacier lake outburst floods (GLOFs). Understanding how these glaciers are changing and their connection to proglacial lake systems is thus of critical importance. Most satellite data are too coarse for studying small mountain glaciers and are often affected by cloud cover, while traditional airborne photogrammetry and LiDAR are costly. Recent developments have made Unmanned Aerial Vehicles (UAVs) viable and potentially transformative method for studying glacier change at high spatial resolution, on demand and at relatively low cost. Using a custom designed high altitude hexacopter we have completed repeat aerial surveys (2014 and 2015) of the debris covered Llaca glacier tongue and proglacial lake system. Analysis of highly accurate 10cm DEM's and orthomosaics reveals highly heterogeneous changes in the glacier surface. The most rapid areas of ice loss were associated with exposed ice cliffs and melt water ponds on the glacier surface. Significant subsidence and low surface velocities were also measured on the sediments within the pro-glacial lake, indicating the presence of extensive regions of buried ice and continued connection to the glacier tongue. Only limited horizontal retreat of the glacier tongue was recorded, indicating that simple measurements of changes in aerial extent are inadequate for understanding actual changes in glacier ice quantity.

❄ Page 77 73rd Eastern Snow Conference Improving atmospheric circulation and turbulent heat fluxes with the Arctic System Reanalysis

Aaron B. Wilson1, David H. Bromwich1,2, Le-Sheng Bai1, G. W. Kent Moore3, Flavio Justino1,4

1 Polar Meteorology Group, Byrd Polar and Climate Research Center, The Ohio State University, Columbus 2 Atmospheric Sciences Program, Department of Geography, The Ohio State University, Columbus 3 Department of Physics, University of Toronto, Toronto, Ontario 4 Department of Agricultural Engineering, Universidade Federal de Viçosa, Viçosa, Brazil

The Arctic System Reanalysis (ASR), a high-resolution regional assimilation of model output, observations, and satellite data across the mid- and high latitudes of the Northern Hemisphere for the period 2000 – 2012 has been performed at 30 km (ASRv1) and 15 km (ASRv2) horizontal resolution. A comparison between the advanced ASRv2 and the global European Centre for Medium Range Forecasting Interim Reanalysis (ERAI) shows the troposphere to be well represented in the ASRv2. Monthly and annual temperature, humidity, pressure, and wind differences compared to surface and upper-air observations are small. The high-resolution land surface description in ASRv2 leads to more accurate representation of topographically-forced wind events, such as tip jets and barrier winds along the southeast coast of Greenland, as well as atmospheric circulation throughout the Arctic. With sensible and latent heat fluxes strongly linked to wind speed and land-surface change, ASR’s high resolution and weekly-updated vegetation from the MODIS lead to much improved turbulent heat fluxes compared to global reanalyses. Analysis of surface evaporation shows that while global reanalyses exhibit weak intraseasonal variability, weekly changes in the snow-albedo feedback and associated changes in the leaf area index produce a better depiction of the seasonality of surface heat fluxes over land. Therefore, the ASR has proven to be an important resource for many Arctic studies including investigations of mesoscale phenomena as well as the diagnosis of change in the coupled Arctic climate system.

❄ Page 78 73rd Eastern Snow Conference Consideration of Mountain Snow Storage from Global Data Products

Melissa L. Wrzesien1, Michael T. Durand1, and Tamlin M. Pavelsky2

1School of Earth Sciences and Byrd Polar and Climate Research Center, the Ohio State University 2Department of Geological Sciences, University of North Carolina at Chapel Hill

Seasonal snow accumulation and ablation are important components in not only the global water balance, but also the energy budget. Despite its importance, we believe an estimate of global snow storage – particularly in montane regions – is not well constrained by current datasets, whether observational or model-based. Here we present estimates of snow storage, both globally and for only regions of complex topography, from multiple global datasets, including satellite products and reanalyses. Global products include AMSR-E, GLDAS, MERRA, and ERA-Interim, all of which have spatial resolution of ~25 km or larger. We consider both April 1 and peak snow water equivalent (SWE) over the period of 1980-2010, or where the data is available. Most products estimate ~2000-4000 km3 of snow storage, globally, when averaged over the period of record, with 30-50% of the snow storage existing in mountains. However, regional climate model simulations for a handful of North America mountain ranges (with spatial resolution of 3-9 km), which are also presented here, suggest > 500 km3 of snow accumulates annually in the Sierra Nevada of California and the Coast Mountains of British Columbia alone. We further discuss the possibility of biases in currently-available global products and whether regional climate model results may present a more reliable global SWE estimate.

❄ Page 79 73rd Eastern Snow Conference Can regional-scale snow water equivalent estimates be enhanced through the integration of a machine learning algorithm, passive microwave brightness temperature observations, and a land surface model?

Yuan Xue, Barton A. Forman

University of Maryland College Park, Department of Civil and Environmental Engineering

To accurately estimate the mass of water within a snowpack (a.k.a., snow water equivalent (SWE)) across regional or continental scales is a challenge, especially in the presence of dense vegetation. In order to overcome some of the limitations imposed by traditional SWE retrieval algorithms and radiative transfer-based snow emission models in forested regions, this study explores the use of a well- trained support vector machine (SVM) en route to merging an advanced land surface model within a radiance emission (i.e., brightness temperature (Tb)) assimilation framework in order to improve model-based SWE (and snow depth) estimates. In an assimilation context, the goal of direct Tb assimilation is preferable as it avoids inconsistencies in the use of ancillary data between the assimilation system and the independently-generated geophysical retrieval. Existing studies also suggest that a SVM-based observation operator is more reliable within an assimilation framework (relative to a snow emission model) without the need to assume a uniform snow pack or fixed snow density or fixed snow grain size. However, it is widely-acknowledged that satellite-based passive microwave (PMW) Tb observations are often contaminated by overlying atmospheric and forest related emission signals. Therefore, the utilization of a SVM-based PMW Tb prediction model trained on decoupled, satellite-based Tb estimates for integration into an existing land data assimilation system is explored in this study. The performance of the original (i.e., coupled) Tb assimilation, and decoupled Tb assimilation procedures are evaluated via comparisons to state- of- the-art SWE (or snow depth) products as well as available ground-based observations. It is shown that SVM performance improves when integrating atmospheric and forest decoupling procedures.

❄ Page 80 73rd Eastern Snow Conference Decoupling atmospheric- and forest-related radiance emissions from satellite-based passive microwave observations over forested and snow-covered land in North America

Yuan Xue, Barton A. Forman

University of Maryland College Park, Department of Civil and Environmental Engineering

This study addresses two significant sources of uncertainty prevalent in snow water equivalent (SWE) retrievals derived from Advanced Microwave Scanning Radiometer (AMSR-E) passive microwave (PMW) brightness temperature (Tb) observations at 18.7 GHz and 36.5 GHz. Namely, atmospheric and overlying forest effects are decoupled from the original AMSR-E PMW Tb observations using relatively simple, first-order radiative transfer models. Comparisons against independent Tb measurements collected during airborne PMW Tb surveys highlight the effectiveness of the proposed AMSR-E atmospheric decoupling procedure. The atmospherically- contributed Tb ranges from 1 K to 3 K depending on the frequency and polarization measured as well as meteorological conditions at the time of AMSR-E overpasses. It is further shown that forest decoupling should be conducted as a function of both land cover type and snow cover class. The exponential decay relationship between the forest structure parameter, namely satellite-scale leaf area index (LAI), and satellite-scale forest transmissivity is fitted across snow-covered terrain in North America. The fitted exponential function can be utilized during forest decoupling activities for evergreen needle leaved forest and woody savanna regions, but remains uncertain in other forest types due to sparse coverage in snow- covered regions. By removing forest-related Tb contributions from the original AMSR-E observations, the results show that Tb spectral difference between 18.7 GHz and 36.5 GHz increases across thinly-vegetated to heavily-vegetated regions, which can be beneficial when using with traditional SWE retrieval algorithms. Comparisons are made between snow depth and SWE estimates, state-of-the-art retrieval products, and independent ground-based observations. When using the decoupled PMW Tb estimates (relative to using the original, coupled AMSR-E Tb observations), snow depth bias is reduced by 60% and SWE bias is reduced by 55%. However, computed RMSE values suggest random errors in the snow depth and SWE retrievals (with or without application of the decoupling procedures) are significant and remains an issue for further study.

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