Wind Stress Curl and Wind Stress Divergence Biases from Rain Effects on QSCAT Surface Wind Retrievals

Abstract

Abstract Surface vector wind datasets from scatterometers provide essential high-resolution surface forcing information for analyses and models of global atmosphere–ocean processes affecting weather and climate. The importance of realistic amplitude, high-wavenumber, surface wind forcing from scatterometer data has been demonstrated in a variety of ocean modeling applications. However, the radar backscatter signal from which surface vector wind estimates are retrieved is attenuated and/or contaminated in heavy rain. The QuikSCAT (QSCAT) dataset flags rain-contaminated wind vector cells where retrievals are either highly uncertain or not available. Zonal and annual averages of wind stress curl and divergence for 2000, 2001, and 2002 are derived and compared across three surface wind datasets: QSCAT only, reanalysis winds from the National Centers for Environmental Prediction (NCEP reanalysis), and blended QSCAT+NCEP. Missing QSCAT surface wind retrievals due to rain contamination lead to statistically sign...