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ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2019
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2019
License: CC BY
Data sources: Datacite
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Dataset: Approximating input data to a snowmelt model using Weather Research and Forecasting model outputs in lieu of meteorological measurements

Authors: Havens, Scott; Marks, Danny; FitzGerald, Katelyn; Masarik, Matt; Flores, Alejandor N.; Kormos, Patrick; Hedrick, Andrew;

Dataset: Approximating input data to a snowmelt model using Weather Research and Forecasting model outputs in lieu of meteorological measurements

Abstract

The dataset presented is the companion data to the Journal of Hydrometeorology publication entitled “Approximating input data to a snowmelt model using Weather Research and Forecasting model outputs in lieu of meteorological measurements.” The data that follows contains everything needed to reproduce the spatial inputs for the meteorological station model run using the Spatial Modeling for Resources Framework (SMRF, Havens et al., 2017). Software versions used: Image Processing Workbench v2.2.0 (Marks et al., 2017) Spatial Modeling for Resources Framework v0.5.3 (Havens et al., 2019) NOTE: Reproducing the spatial inputs will generate 10 netCDF files at ~80GB per file. topo.nc – Contains multiple static layers that are required to run SMRF and iSnobal. The netCDF layers are: dem – digital elevation model at 100 meter resolution, aggregated from the 10 meter National Elevation Dataset (Archuleta et al., 2017) mask – basin mask for the Boise River Basin veg_height – vegetation height in meters from the National Land Cover Database (Homer et al., 2015) veg_type – vegetation type from the National Land Cover Database veg_tau – vegetation fractional transmissivity derived from the vegetation type veg_k – vegetation emissivity derived from the vegetation type maxus.nc – maximum upwind slope netCDF that contains 72 images for all wind directions in 5 degree increments using the algorithm described in Winstral and Marks (2002) Station data: Contains hourly meteorological station data downloaded from Mesowest (Horel et al., 2002). Data was cleaned and filtered prior to running SMRF. metadata.csv – metadata for 40 stations air_temp.csv – 38 stations cloud_factor.csv – 7 stations precip.csv – 21 stations vapor_pressure.csv – 19 stations wind_direction.csv – 14 stations wind_speed.csv – 14 stations smrf_config.ini – Configuration file needed to reproduce the spatial inputs using SMRF. The paths will need to be changed to reflect the data location.

{"references": ["Archuleta, C.-A.M., Constance, E.W., Arundel, S.T., Lowe, A.J., Mantey, K.S., Phillips, L.A., 2017. The National Map seamless digital elevation model specifications, Techniques and Methods. https://doi.org/10.3133/tm11b9", "Havens, S., Marks, D., Kormos, P., Hedrick, A., 2017. Spatial Modeling for Resources Framework (SMRF): A modular framework for developing spatial forcing data for snow modeling in mountain basins. Comput. Geosci. 109. https://doi.org/10.1016/j.cageo.2017.08.016", "Havens, S., Marks, D., Kormos, P., Hedrick, A., Johnson, M., Sandusky, M., Robertson, M., 2019. USDA-ARS-NWRC/smrf: v0.5.3. https://doi.org/10.5281/ZENODO.2563226", "Homer, C.G., Dewitz, J.A., Yang, L., Jin, S., Danielson, P., Xian, G., Coulston, J., Herold, N.D., Wickham, J.D., Megown, K., 2015. Completion of the 2011 National Land Cover Database for the conterminous United States-Representing a decade of land cover change information. Photogramm. Eng. Remote Sensing 81, 345\u2013354.", "Horel, J., Splitt, M., Dunn, L., Pechmann, J., White, B., Ciliberti, C., Lazarus, S., Slemmer, J., Zaff, D., Burks, J., 2002. MesoWest: Cooperative mesonets in the western United States. Bull. Am. Meteorol. Soc. 83, 211\u2013225. https://doi.org/10.1175/1520-0477(2002)083<0211:MCMITW>2.3.CO;2", "Marks, D., Havens, S., Johnson, M., 2017. USDA-ARS-NWRC/ipw: v2.2.0. https://doi.org/10.5281/ZENODO.1124680", "Winstral, A., Marks, D., 2002. Simulating wind fields and snow redistribution using terrain-based parameters to model snow accumulation and melt over a semi-arid mountain catchment. Hydrol. Process. 16, 3585\u20133603. https://doi.org/10.1002/hyp.1238"]}

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Keywords

WRF, modeling, snow

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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