An advanced ambiguity selection algorithm for SeaWinds

SeaWinds on QuikSCAT, a spaceborne Ku-band scatterometer, estimates ocean winds via the relationship between the normalized radar backscatter and the vector wind. Scatterometer wind retrieval generates several possible wind vector solutions or ambiguities at each resolution cell, requiring a separate ambiguity selection step to give a unique solution. In processing SeaWinds on QuikSCAT data, the ambiguity selection is “nudged” or initialized using numerical weather prediction winds. We describe a sophisticated new ambiguity selection approach developed at Brigham Young University (BYU) that does not require nudging. The BYU method utilizes a low-order data-driven Karhunen–Loeve (KL) wind field model to promote self-consistency. Ambiguity selected winds from the BYU method and standard SeaWinds processing are compared over a set of 102 revs. A manual examination of the data suggests that the nonnudging BYU method selects a more self-consistent wind field in the absence of cyclonic storms. Over a set of cyclonic storm regions, BYU performs better in 9% of the cases and worse in 20% of the cases. Overall, the BYU algorithm selects 93% of the same ambiguities as the standard dataset. This comparison helps validate both nonnudging and nudging techniques and indicates that SeaWinds ambiguity selection can be generally accomplished without nudging.