Publications

2019

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


2018

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 Zhou, W. (2018), ESM-SnowMIP: Assessing models and quantifying snow-related climate feedbacks, Geosci. Model Dev., 11(12), 5027–5049, doi:10.5194/gmd-11-5027-2018.


2017

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, 44, doi:10.1002/2016GL071999.

Cristea, N.C., I. Breckheimer, M.S. Raleigh, J. HilleRisLambers, 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, 53, doi: 10.1002/2017WR020799.


2016

Raleigh, M. S., B. Livneh, K. Lapo, and J. D. Lundquist (2016), How does availability of meteorological forcing data impact physically-based snowpack simulations?, Journal of Hydrometeorology, 17(1), 99-120, doi:10.1175/J HM-D-14-0235.1.


2015


Raleigh, M.S.
, J.D. Lundquist, 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, 19, 3153-3179, doi:10.5194/hess-19-3153-2015

Dickerson-Lange, S.E., J.A. Lutz, K.A. Martin, M.S. Raleigh, R. Gersonde, and J.D. Lundquist (2015), Evaluating observational methods to quantify snow duration under diverse forest canopies, Water Resources Research, 51, doi: 10.1002/2014WR015744

Lapo, K.E., L.M. Hinkelman, M.S. Raleigh, 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


2014

Landry, C.C., K.A. Buck, M.S. Raleigh, 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, 50, doi: 10.1002/2013WR013711


2013

Raleigh, M.S., C. C. Landry, M. Hayashi, W. L. Quinton, 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, 49, doi: 10.1002/2013WR013958

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

Henn, B, M.S. Raleigh, A. Fisher, and J.D. Lundquist (2013), A comparison of methods for filling gaps in hourly near-surface air-temperature data, J. Hydrometeor, 14, 929-945, doi: 10.1175/JHM-D-12-027.1

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

Ford, K.R., A.K. Ettinger, J.D. Lundquist, M.S. Raleigh, 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, 8(6): e65008, doi: 10.1371/journal.pone.0065008


2012

Raleigh, M.S., and J.D. Lundquist (2012), Comparing and combining SWE estimates from the SNOW-17 model using PRISM and SWE reconstruction, Water Resour. Res., 48, W01506, doi:10.1029/2011WR010542.


Non-peer reviewed publications

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.

Raleigh, M. S., and J. S. Deems (2016), Investigating the response of an operational snowmelt model to unusual snow conditions and melt drivers, in Proc. 84th Western Snow Conference, pp. 89-100, Seattle, WA.

Raleigh, M. S., and M. P. Clark (2014), Are temperature-index models appropriate for assessing climate change impacts on snowmelt?, in Proc. 82nd Western Snow Conference, pp. 45–51, Durango, CO.

Raleigh, M. S., K. Rittger, 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, in Proc. 79th Western Snow Conf., pp. 3–14, Stateline, Nevada.


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.

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.

If you have difficulty finding any of my papers, please contact me directly.