Estimation of Time-Averaged Surface Divergence and Vorticity from Satellite Ocean Vector Winds

Abstract

wo methods of computing the time-mean divergence and vorticity from satellite vector winds in rain-free (RF) and all-weather (AW) conditions are investigated. Consequences of removing rain-contaminated winds on the mean divergence and vorticity depend strongly on the order in which the time-average and spatial derivative operations are applied. Taking derivatives first and averages second (DFAS_RF) incorporates only those RF winds measured at the same time into the spatial derivatives. While preferable mathematically, this produces mean fields biased relative to their AW counterparts because of the exclusion of convergence and cyclonic vorticity often associated with rain. Conversely, taking averages first and derivatives second (AFDS_RF) incorporates all RF winds into the time-mean spatial derivatives, even those not measured coincidentally. While questionable, the AFDS_RF divergence and vorticity surprisingly appears qualitatively consistent with the AW means, despite using only RF winds. The analysis addresses whether the AFDS_RF method accurately estimates the AW mean divergence and vorticity.Model simulations indicate that the critical distinction between these two methods is the inclusion of typically convergent and cyclonic winds bordering rain patches in the AFDS_RF method. While this additional information removes some of the sampling bias in the DFAS_RF method, it is shown that the AFDS_RF method nonetheless provides only marginal estimates of the mean AW divergence and vorticity given sufficient time averaging and spatial smoothing. Use of the AFDS_RF method is thus not recommended.