Towards the Real-time use of QuikSCAT Winds

We describe our work on QuikSCAT product validation and inversion of the backscatter data to winds. A new procedure to quality control (QC) SeaWinds scatterometer observations, in particular to screen out rain-contaminated Wind Vector Cells (WVC), and a new procedure to assimilate QuikSCAT observations are developed at KNMI.

The noise in the 25-km SeaWinds wind direction has detrimental effects on usefulness of the winds. We attempted to reduce the noise by providing a coarser resolution product.

The ERS scatterometer two-dimensional variational ambiguity removal procedure, called 2DVAR, is generalised to deal with ambiguous solution sets with more than two solutions of varying probability.

2D-VAR products were validated both objectively and subjectively. The objective validation resulted in a skill comparable to the JPL product. Refined objective tests are recommended to highlight differences between the schemes. KNMI meteorologists subjectively evaluated selected cases where SeaWinds deviates substantially from the numerical weather prediction reference wind field. It is found that the SeaWinds products are reliable in the so-called sweet parts of the swath, but of more variable quality in the nadir region; the outer swath was not considered here.

The real-time use of SeaWinds data can only be reliable if appropriate quality monitoring and control routines are operated. The quality information should be available and provided at the same time as the winds. As was found for ERS, the WVC quality control rejection rate, the normalised inversion residual and the wind speed bias and wind direction standard deviation against a NWP model provide effective instrument and processing monitoring parameters. They can be used to provide guidance as to whether the real-time SeaWinds wind vector data are of nominal quality.

The SeaWinds quality control, inversion, monitoring, and 2D-VAR as described here are an excellent preparation for the assimilation of scatterometer winds in variational meteorological analyses.