Roughness length of sea, sand, and snow

Chamberlain, A. C.

doi:10.1007/bf02041157

Cited by 100 publications

(54 citation statements)

References 8 publications

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“…[14] Each set of profile data (the paired sets of wind speed and elevation for each profile) was subjected to linear regression analysis, with elevation as the independent Butterfield [1999] sand/wind tunnel 0.004 -0.0455 Chamberlain [1983] sand-snow-water/field 0.0145 Charnock [1955] water/field 0.046 -0.085 Dong et al [2003] sand/wind tunnel 0.00036 -0.026 Ellison [1956] water/field 0.085 Farrell [1999] sand/field 0.24 Farrell [1999] sand/wind tunnel 0.032 Garratt [1977] water/field 0.0144 McEwan [1991] snow/field 0.025 Mikkelsen [1989] sand/wind tunnel 0.014 Okoli [2003] sand/wind tunnel 0.0125 Owen [1964] sand/wind tunnel 0.02 Rasmussen et al [1985] sand/field 0.07 -0.09 Rasmussen [1989] sand/wind tunnel 0.01 Rasmussen and Mikkelsen [1991] sand/wind tunnel 0.011 Rasmussen et al [1996] sand/wind tunnel <0.01 -0.03 Sherman [1992] sand/field and wind tunnel 0.009 Tabler [1980] snow/field 0.026 Wu [1968] water/field 0.012 Wu [1980] water/field 0.0156 -0.0185…”

Section: Methodsmentioning

confidence: 99%

Aerodynamic roughness lengths over movable beds: Comparison of wind tunnel and field data

Sherman¹,

Farrell²

2008

J. Geophys. Res.

100

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[1] We evaluate the increase in apparent roughness length during aeolian saltation. Using a data set comprising 291 wind profiles measured in wind tunnels or in the field, we tested the Charnock (1955), modified Charnock (Sherman, 1992), and Raupach (1991 relationships for the entire data set and for segregated wind tunnel and field data sets. We assessed the abilities of the models to predict enhanced roughness and investigated scaling differences between wind tunnels and field environments. For the entire data set, the Charnock relationship worked best, with an r 2 of 0.25. For the segregated data sets, the Charnock relationship also worked best, with an average r 2 of 0.675, as opposed to 0.66 for the modified Charnock and 0.61 for the Raupach model. For conditions with shear velocity 1.0 ms À1 , the comparable, average values of r 2 are 0.54, 0.625, and 0.645, respectively. There was an order of magnitude difference in the roughness length responses for wind tunnel and field data for each model evaluated, convincing evidence of a fundamental scaling constraint in the wind tunnel data. The scaling differences are attributed to conceptual and bias errors that include the use of the law of the wall versus the wake-defect law or differences in turbulence characteristics, especially regarding the role of large coherent structures. Raupach's (1991) A was found to be an extremely volatile constant, making the modified Charnock relationship most suitable for predicting aerodynamic roughness lengths over movable sand beds.

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Section: Methodsmentioning

confidence: 99%

Aerodynamic roughness lengths over movable beds: Comparison of wind tunnel and field data

Sherman¹,

Farrell²

2008

J. Geophys. Res.

100

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“…Dust particle size distribution is described by eight bins with effective radii of 0.15, 0.25, 0.45, 0.78, 1.3, 2.2, 3.8, and 7.1 µm, according to Tegen and Lacis (1996). The main difference between DREAM and other dust models is that the dust transport parameterization in DREAM includes a viscous sublayer between the surface and the lowest model layer (Janjic, 1994), since there is a physical similarity between massheat-momentum exchanges over surfaces such as ocean with that of mobilized dust particle over desert surfaces (Chamberlain, 1983;Segal, 1990). This parameterizes the turbulent transfer of dust into the lowest model layer accounting for different turbulent regimes (laminar, transient and turbulent mixing), using the simulated surface dust concentration as the lower boundary.…”

Section: Model Description and Dust Sourcesmentioning

confidence: 99%

Numerical simulation of "an American haboob"

et al. 2014

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Abstract.A dust storm of fearful proportions hit Phoenix in the early evening hours of 5 July 2011. This storm, an American haboob, was predicted hours in advance because numerical, land-atmosphere modeling, computing power and remote sensing of dust events have improved greatly over the past decade. High-resolution numerical models are required for accurate simulation of the small scales of the haboob process, with high velocity surface winds produced by strong convection and severe downbursts. Dust productive areas in this region consist mainly of agricultural fields, with soil surfaces disturbed by plowing and tracks of land in the high Sonoran Desert laid barren by ongoing draught.Model simulation of the 5 July 2011 dust storm uses the coupled atmospheric-dust model NMME-DREAM (Nonhydrostatic Mesoscale Model on E grid, Janjic et al., 2001; Dust REgional Atmospheric Model, Nickovic et al., 2001;Pérez et al., 2006) with 4 km horizontal resolution. A mask of the potentially dust productive regions is obtained from the land cover and the normalized difference vegetation index (NDVI) data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The scope of this paper is validation of the dust model performance, and not use of the model as a tool to investigate mechanisms related to the storm. Results demonstrate the potential technical capacity and availability of the relevant data to build an operational system for dust storm forecasting as a part of a warning system. Model results are compared with radar and other satellitebased images and surface meteorological and PM 10 observations. The atmospheric model successfully hindcasted the position of the front in space and time, with about 1 h late arrival in Phoenix. The dust model predicted the rapid uptake of dust and high values of dust concentration in the ensuing storm. South of Phoenix, over the closest source regions (∼25 km), the model PM 10 surface dust concentration reached ∼2500 µg m −3 , but underestimated the values measured by the PM 10 stations within the city. Model results are also validated by the MODIS aerosol optical depth (AOD), employing deep blue (DB) algorithms for aerosol loadings. Model validation included Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), equipped with the lidar instrument, to disclose the vertical structure of dust aerosols as well as aerosol subtypes. Promising results encourage further research and application of high-resolution modeling and satellite-based remote sensing to warn of approaching severe dust events and reduce risks for safety and health.

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“…8). Since temperatures will be 0°C or less, I take the Prandtl number ,5---,--' Schmidt (1982) for blowing snow and those summarized by Chamberlain (1983) for drifting snow and sand are generally in the range reported by Banke et al (1980); hence, we assume that eq 60 is a valid W model for snowfields, too.…”

Section: Scalar Transfer Coefficients %mentioning

confidence: 99%

A theory for the scalar roughness and the scalar transfer coefficients over snow and sea ice

Andreas

1987

Boundary-Layer Meteorol

360

375

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Roughness length of sea, sand, and snow

Cited by 100 publications

References 8 publications

Aerodynamic roughness lengths over movable beds: Comparison of wind tunnel and field data

Aerodynamic roughness lengths over movable beds: Comparison of wind tunnel and field data

Numerical simulation of "an American haboob"

A theory for the scalar roughness and the scalar transfer coefficients over snow and sea ice

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Roughness length of sea, sand, and snow

Cited by 100 publications

References 8 publications

Aerodynamic roughness lengths over movable beds: Comparison of wind tunnel and field data

Aerodynamic roughness lengths over movable beds: Comparison of wind tunnel and field data

Numerical simulation of &quot;an American haboob&quot;

A theory for the scalar roughness and the scalar transfer coefficients over snow and sea ice

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Product

Resources

About

Numerical simulation of "an American haboob"