Quality control (QC) is an essential part of the scatterometer wind retrieval. In the current pencil-beam scatterometer wind processor (PenWP), a maximum likelihood estimator (MLE)-based QC is used to discern between good- and poor-quality winds. MLE QC is generally effective in flagging rain contamination and increased subcell wind variability in the ocean surface wind vectors derived from Ku-band pencil-beam scatterometers, such as the RapidScat (RSCAT) installed on the International Space Station. However, the MLE is not an effective quality indicator over the outer swath where the inversion is underdetermined due to the lack of azimuthal diversity (including lack of horizontal polarized measurements). Besides, it is challenging to discriminate rain contamination from “true” high winds. This paper reviews several wind quality-sensitive indicators derived from the RSCAT data, such as MLE and its spatially averaged value (MLE _m ), and the singularity exponents (SE) derived from an image processing technique, called singularity analysis. Their sensitivities to data quality and rain are evaluated using collocated Advanced Scatterometer wind data, and global precipitation measurement satellite's microwave imager rain data, respectively. It shows that MLEm and SE are the most effective indicators for filtering the poorest-quality winds over RSCAT inner and outer swath, respectively. A simple combination of SE and MLEm thresholds is proposed to optimize RSCAT wind QC. Comparing to the operational PenWP QC, the proposed method mitigates over-rejection at high winds, and improves the classification of good- and poor-quality winds.