OSU CryoSIGHT authors are in bold

In review or revision

Roberts-Pierel, B.M., M.S. Raleigh, and R.E. Kennedy (in revision). Tracking the evolution of snow drought in the U.S. Pacific Northwest at variable scales. Submitted to Water Resources Research.

Herbert, J.N., M.S. Raleigh, and E.E. Small (in review). Reanalyzing the spatial representativeness of snow depth at automated monitoring stations using airborne Lidar data. Submitted to The Cryosphere. Preprint

Berkelhammer, M., G.F. Page, F. Zurek, C. Still, M.S. Carbone, W. Talavera, L. Hildebrand, J. Byron, K. Inthabandith, K. Foss, W. Brown, R.W.H. Carroll, A. Simonpietri, M. Worsham, I. Breckheimer, A. Ryken, R. Maxwell, D. Gochis, M.S. Raleigh, E.E. Small, and K.H. Williams (submitted). Canopy structure modulates the sensitivity of subalpine forest stands to interannual snowpack and precipitation variability. Submitted to Hydrology and Earth Systems Sciences. Preprint

Collao-Barrios, G., V. Favier, F. Gillet-Chaulet, X. Fettweis, H. Gallée, M. Santolaria-Otín, L. Davaze, and M.S. Raleigh (submitted). Regional climate and surface mass balance of the Northern Patagonia Icefield and linkages with climate indices. Submitted to Journal of Glaciology.


33. Steele, H., E.E. Small, and M.S. Raleigh (accepted). Demonstrating a Hybrid Machine Learning Approach for Snow Characteristic Estimation throughout the Western United States. Water Resources Research.

32. Meehan, T.G., A. Hojatimalekshah, H.P. Marshall, E.J. Deeb, S. O’Neel, D. McGrath, R.W. Webb, R. Bonnell, M.S. Raleigh, C. Hiemstra, and K. Elder (accepted). Spatially distributed snow depth, bulk density, and snow water equivalent from ground-based and airborne sensor integration at Grand Mesa, Colorado, USA. The Cryosphere. Preprint

31. Jensen, A., K. Rittger, and M.S. Raleigh (2024). Spatio-temporal patterns and trends in MODIS-retrieved radiative forcing by snow impurities over the Western U.S. from 2001 – 2022. Environmental Research: Climate, doi: 10.1088/2752-5295/ad285a. Paper

30. Girotto, M., G. Formetta, S. Azimi, C. Bachand, M. Cowherd, G. De Lannoy, H. Lievens, S. Modanesi, M.S. Raleigh, R. Rigon, and C. Massari (2024). Identifying Snowfall Elevation Gradients by Assimilating Satellite- Based Snow Depth Observations. Science of the Total Environment. doi: 10.1016/j.scitotenv.2023.167312. Paper


29. Feldman, D.R., A.C. Aiken, W. R. Boos, R.W.H. Carroll, V. Chandrasekar, S. Collis, J. M. Creamean, G. de Boer, J. Deems, P. J. DeMott, J. Fan1, A. N. Flores, D. Gochis, M. Grover, T.C. J. Hill, A. Hodshire, E. Hulm, C. C. Hume, R. Jackson, F. Junyent, A. Kennedy, M. Kumjian, E. J. T. Levin, J. D. Lundquist, J. O’Brien, M.S. Raleigh, J. Reithel, A. Rhoades, K. Rittger, W. Rudisill, Z. Sherman, E. Siirila-Woodburn, S. M. Skiles, J. N. Smith, R. C. Sullivan, A. Theisen, M. Tuftedal, A. C. Varble, A. Wiedlea, S. Wielandt, K. Williams, Z. Xu (2023). The Surface Atmosphere Integrated Field Laboratory (SAIL) Campaign. Bulletin of the American Meteorological Society. doi: 10.1175/BAMS-D-22-0049.1. Paper

28. K. Johnson, A. Harpold, R.W.H. Carroll, H. Barnard, M.S. Raleigh, C. Segura, L. Li, K.H. Williams, W. Dong, P.L. Sullivan (2023). Leveraging groundwater dynamics to improve predictions of summer low flow discharges. Water Resources Research. doi: 10.1029/2023WR035126. Paper

27. Stillinger, T., Rittger, K., Raleigh, M.S., Michell, A., Davis, R.E., and E.H. Bair (2023). Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets. The Cryosphere. doi: 10.5194/tc-17-567-2023. Paper


26. Bonner, H.M., Smyth, E., Raleigh, M.S. and E.E. Small (2022). A meteorology and snow dataset from adjacent forested and meadow sites at Crested Butte, CO, USA. Water Resources Research. doi: 10.1029/2022WR033006. Paper Data

25. Jaeger, D.M., Looze, A.M.C., Raleigh, M.S., Miller, B.W., Friedman, J.M., and C.A. Wessman (2022). From flowering to foliage: Accelerometers track tree sway to provide high-resolution insights to tree phenology. Agricultural and Forest Meteorology, doi: 10.1016/j.agrformet.2022.108900. Paper News

24. Smyth, E.J., Raleigh, M.S., and E.E. Small (2022). Data assimilation of snow depth reduces model SWE error and parameter sensitivity in forests. Water Resources Research, doi: 10.1029/2021WR030563. Paper

23. Teich, M., Becker, K.M.L., Raleigh, M.S., and J.A. Lutz (2022). Large-diameter trees affect snow duration in post-fire old-growth forests. Ecohydrology, doi: 10.1002/eco.2414. Paper News

22. Raleigh, M.S., Gutmann, E.D., Van Stan II, J.T., Burns, S.P., Blanken, P.D., and E.E. Small (2022). Challenges and capabilities in estimating snow mass intercepted in conifer canopies with tree sway monitoring. Water Resources Research, doi: 10.1029/2021WR030972. Paper Data Data CodePreprint News News News News

21. Bonner, H. M., Raleigh, M. S., & Small, E. E. (2022). Isolating forest process effects on modelled snowpack density and snow water equivalent. Hydrological Processes, doi: 10.1002/hyp.14475. Paper

20. Wrzesien, M.L., Kumar, S., Vuyovich, C., Gutmann, E.D., Kim, R.S., Forman, B.A., Durand, M., Raleigh, M.S., Webb, R., and P. Houser (2022). Development of a “nature run” for observing system simulation experiments (OSSEs) for snow mission development. Journal of Hydrometeorology, doi: 10.1175/JHM-D-21-0071.1. Paper


19. Webb, R.W., Raleigh, M.S., McGrath, D., Molotch, N.P., Elder, K., Hiemstra, C., Brucker, L., and H.P. Marshall (2020). Within-stand boundary effects of snow water equivalent distribution in forested areas, Water Resources Research, doi: 10.1029/2019WR024905. Paper

18. Smyth E., Raleigh M.S., and E.E. Small (2020), Improving SWE estimation with data assimilation: the influence of snow depth observation timing and uncertainty, Water Resources Research, doi: 10.1029/2019WR026853. Paper

17. Rittger K., Raleigh M.S., Dozier J., Hill A.F., Lutz J.A., and T.H. Painter (2020), Canopy adjustment and improved cloud detection for remotely sensed snow cover mapping, Water Resources Research, doi: 10.1029/2019WR024914. Paper 


16. Ménard C. B., Essery R., Barr A., Bartlett P., Derry J., Dumont M., Fierz C., Kim H., Kontu A., Lejeune Y., Marks D., Niwano M., Raleigh M.S., Wang L., and N. Wever (2019), Meteorological and evaluation datasets for snow modelling at ten reference sites: description of in situ and bias-corrected reanalysis data, Earth System Science Data, doi: 10.5194/essd-11-865-2019. Paper  Data 

15. Smyth E., Raleigh M.S., and E.E. Small (2019), Particle filter data assimilation of monthly snow depth observations improves estimation of snow density and SWE, Water Resources Research, doi: 10.1029/2018WR023400. Paper


14. Krinner G., Derksen C., Essery R., Flanner M., Hagemann S., Clark M., Hall A., Rott H., Brutel-Vuilmet C., Kim H., Ménard C. B., Mudryk L., Thackeray C., Wang L., Arduini G., Balsamo G., Bartlett P., Boike J., Boone A., Chéruy F., Colin J., Cuntz M., Dai Y., Decharme B., Derry J., Ducharne A., Dutra E., Fang X., Fierz C., Ghattas J., Gusev Y., Haverd V., Kontu A., Lafaysse M., Law R., Lawrence D., Li W., Marke T., Marks D., Nasonova O., Nitta T., Niwano M., Pomeroy J., Raleigh M.S., Schaedler G., Semenov V., Smirnova T., Stacke T., Strasser U., Svenson S., Turkov D., Wang T., Wever N., Yuan H., and W. Zhou (2018), ESM-SnowMIP: Assessing models and quantifying snow-related climate feedbacks, Geoscientific Model Development, doi:10.5194/gmd-11-5027-2018. Paper 


13. Raleigh, M. S., and E.E. Small (2017), Snowpack density modeling is the primary source of uncertainty when mapping basin-wide SWE with lidar, Geophysical Research Letters, doi:10.1002/2016GL071999. Paper 

12. Cristea, N.C., Breckheimer I., Raleigh M.S., HilleRisLambers J., and J.D. Lundquist (2017), An evaluation of terrain-based downscaling of fractional snow covered area data sets based on LiDAR-derived snow data and orthoimagery, Water Resources Research, doi: 10.1002/2017WR020799. Paper


11. Raleigh, M.S., Livneh B., Lapo K., and J.D. Lundquist (2016), How does availability of meteorological forcing data impact physically-based snowpack simulations?, Journal of Hydrometeorology, doi:10.1175/J HM-D-14-0235.1. Paper


10. Raleigh, M.S., Lundquist J.D., and M.P. Clark (2015), Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework, Hydrology and Earth System Sciences, doi:10.5194/hess-19-3153-2015. Paper 

9. Dickerson-Lange, S.E., Lutz J.A., Martin K.A., Raleigh M.S., Gersonde R., and J.D. Lundquist (2015), Evaluating observational methods to quantify snow duration under diverse forest canopies, Water Resources Research, doi: 10.1002/2014WR015744. Paper 

8. Lapo, K.E., Hinkelman L.M., Raleigh M.S., and J.D. Lundquist (2015), Impact of errors in the downwelling irradiances on simulations of snow water equivalent, snow surface temperature, and the snow energy balance, Water Resources Research, 51, doi: 10.1002/2014WR0162591. Paper 


7. Landry, C.C., Buck K.A., Raleigh M.S., and M.P. Clark (2014), Mountain system monitoring at Senator Beck Basin, San Juan Mountains, Colorado: A new integrative data source to develop and evaluate models of snow and hydrologic processes, Water Resources Research, doi: 10.1002/2013WR013711. Paper 


6. Raleigh, M.S., Landry C.C. , Hayashi M., Quinton W.L., and J.D. Lundquist (2013), Approximating snow surface temperature from standard temperature and humidity data: New possibilities for snow model and remote sensing evaluation, Water Resources Research, doi: 10.1002/2013WR013958. Paper 

5. Raleigh, M.S., Rittger K., Moore C.E., Henn B., Lutz J.A., and J.D. Lundquist (2013), Ground-based testing of MODIS fractional snow cover in subalpine meadows and forests of the Sierra Nevada, Remote Sensing of Environment, doi: 10.1016/j.rse.2012.09.016. Paper 

4. Henn, B, Raleigh M.S., Fisher A., and J.D. Lundquist (2013), A comparison of methods for filling gaps in hourly near-surface air-temperature data, Journal of Hydrometeorology, doi: 10.1175/JHM-D-12-027.1. Paper 

3. Slater, A.G., Barrett A.P., Clark M.P., Lundquist J.D., and M.S. Raleigh (2013), Uncertainty in seasonal snow reconstruction: relative impacts of model forcing and image availability, Advances in Water Resources, doi: 10.1016/j.advwatres.2012.07.006. Paper 

2. Ford, K.R., Ettinger A.K., Lundquist J.D., Raleigh M.S., and J. Hille Ris Lambers (2013), Spatial heterogeneity in ecologically important climate variables at coarse and fine scales in a high-snow mountain landscape, PLoS ONE, doi: 10.1371/journal.pone.0065008. Paper 


1. Raleigh, M.S., and J.D. Lundquist (2012), Comparing and combining SWE estimates from the SNOW-17 model using PRISM and SWE reconstruction, Water Resources Research, doi:10.1029/2011WR010542. Paper 

Conference Proceedings

Raleigh, M.S., & J.S. Deems (2018), Filling the holes in the space-time cube of snowpack evolution with lasers, cameras, computers, and snow shovels, 86th Western Snow Conference, Albuquerque, New Mexico. Paper

Raleigh, M.S., and J.S. Deems (2016), Investigating the response of an operational snowmelt model to unusual snow conditions and melt drivers, 84th Western Snow Conference, Seattle, Washington. Paper 

Raleigh, M.S., and M.P. Clark (2014), Are temperature-index models appropriate for assessing climate change impacts on snowmelt?, 82nd Western Snow Conference, Durango, Colorado. Paper 

Raleigh, M.S., Rittger K., and J.D. Lundquist (2011), What lies beneath? Comparing MODIS fractional snow covered area against ground-based observations under forest canopies and in meadows of the Sierra Nevada, 79th Western Snow Conference, Stateline, Nevada. Paper 

Academic Research

Raleigh, M. S. (2013), Quantification of uncertainties in snow accumulation, snowmelt, and snow disappearance dates, PhD Thesis, Department of Civil and Environmental Engineering, University of Washington. Thesis 

Raleigh, M.S. (2009), A statistical evaluation of a snow water equivalent reconstruction method using three snowmelt models at daily and hourly time steps, Masters Thesis, Department of Civil and Environmental Engineering, University of Washington.

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