Divergence and vorticity are estimated for the stratocumulus topped boundary layer using aircraft data from the DYCOMS-II field campaign. The estimates are based on a new method which assumes that the large-scale wind field varies linearly in space and time and that the air-relative aircraft path is on average circular. Using a least-mean-square error method, the measured wind speed is regressed on to a model for a linearly varying wind field. The coefficients which result from such a regression are shown to represent the components of divergence and vorticity. Values of divergence and vorticity derived using this method are compared to estimates based on satellite retrieved winds from the SeaWinds instrument on Quickscat, as well predictions by the NCEP-AVN and ECMWF forecast models.

Using synthetic data and idealized noise the regression method is also compared to the path integral method (Lenschow et al. 1999, J. Atmos Oceanic Tech, 14:1394-1400). The regression method is shown to be less dependent on the circle being closed, and more capable of estimating divergence and vorticity for time varying flows. Sources of error are also explored and it is shown that the greatest limitation of the method arises from sampling errors associated with mesoscale variability in the winds. These errors are shown to depend on both the amplitude and the integral scale of the variability.