Open AccessAn empirically derived emission algorithm for wind‐blown dust
Author(s) -
Draxler Roland R.,
Ginoux Paul,
Stein Ariel F.
Publication year - 2010
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.67
H-Index - 298
eISSN - 2156-2202
pISSN - 0148-0227
DOI - 10.1029/2009jd013167
Subject(s) - environmental science , plume , wind speed , mesoscale meteorology , flux (metallurgy) , magnitude (astronomy) , atmospheric sciences , meteorology , shear velocity , panache , atmospheric instability , phoenix , atmosphere (unit) , geology , physics , materials science , medicine , metropolitan area , pathology , astronomy , turbulence , metallurgy
A wind‐blown dust emission algorithm was developed by matching the frequency of high–aerosol optical depth (AOD) events derived from the MODIS Deep Blue algorithm with the frequency of friction velocities derived from National Centers for Environmental Prediction's North American Mesoscale model. The threshold friction velocity is defined as the velocity that has the same frequency of as the 0.75 AOD. The AODs are converted to an emission flux that is used to compute the linear regression slope of the flux to the friction velocity. The slope represents the potential of a particular land surface to produce airborne dust and, in combination with the friction velocity, is used as a predictor for wind‐blown dust emissions. Calculations for a test period of June and July 2007 showed the model prediction to capture the major measured plume events in timing and magnitude, although peak events tended to be overpredicted and many of the near‐background level ambient concentrations were underpredicted. Most of the airborne dust loadings are attributed to locations with relatively low threshold friction velocities (<45 cm s −1 ), although these locations only composed of 9% of the total number of source locations. There was some evidence that the duration of wind‐blown dust plume events was comparable to the 3 day sampling frequency of the IMPROVE monitoring network. Higher temporal frequency AIRNow observations at Phoenix showed a surprisingly good fit with the magnitude of the model‐predicted peak concentrations.