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

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 Adivergence for 2000, 2001, and 2002 are derived and compared across three surfacewind 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 significant discrepancies of up to 50% in the implied Sverdrup transports in subtropical and subpolar gyre regions of the Northern and Southern Hemispheres. Dataset-to-dataset wind stress divergence amplitude differences due to rain contamination are also large in the midlatitude storm track regions. Discrepancies occur in the Tropics due to rain contamination effects on QSCAT data and due to high-wavenumber deficiencies in the NCEP reanalysis winds. In addition, NCEP operational forecast model surface wind analyses (NCEP–FNL) have been trilinearly interpolated to the QSCAT wind vector cell locations and sample times. The NCEP–FNL winds are not affected by rain, so it is possible to compare NCEP–FNL-interpolated surface wind fields and related quantities calculated with and without wind vectors at the rain-flagged wind vector cell locations. When all locations are included, Awind stress curl amplitudes are found to be skewed toward cyclonic curl in both Ahemispheres. Vectors at rain-flagged locations in both hemispheres are also skewed toward large-amplitude, cyclonic curls. This is because midlatitude synoptic systems are the meteorological sources of large-amplitude cyclonic curls, as well as the places Awhere the rain-flag bias in wind stress curl is largest. Blended QSCAT+NCEP surface winds ameliorate the rain-flag-induced biases in zonal averages of wind stress curl and Awind stress divergence, while retaining high-wavenumber properties of the scatterometer winds. Evidence to support rain-flag algorithm refinement for high wind speeds is presented.