Point-Wise Quality Assessment
Having a larger swath than its predecessor NSCAT, SeaWinds affords
us near global coverage of surface winds on a daily basis. Along
with the larger swath comes many challenges. SeaWinds wind retrieval
utilizes the nadir region which was formerly ignored by NSCAT. Due
to insufficient azimuth variation among multiple measurements, forming
realistic wind aliases in the nadir region is more difficult. Measurements
from low wind speed regions also promote unreliable wind estimates
due to a low signal to noise ratio. Noise along with model function
inaccuracies and errors due to rain can spawn severe problems in
selecting a true solution to the wind. Thus, the traditional point-wise
algorithm of selecting a unique wind vector for each measurement
cell is prone to error.
Using the KL model to verify consistancy of the wind fields
Wind field models are used to reduce the effects of noise in wind
retrieval. The Karhunen-Loeve (KL) model is especially effective
because truncating high order parameters minimizes the basis restriction
error, rendering a noise-suppressed model fit to the wind. Utilizing
this characteristic, noisy areas and ambiguity removal problems
can be located by comparing a wind field to a KL model fit. Cells
or regions that contain sufficiently large errors between the point-wise
field and the model fit can be flagged as unrealistic. These regions
are likely to contain ambiguity removal problems.
An example of a KL model fit to a wind field
The following figure shows the observed wind to an 8x8 region
containing ambiguity removal errors. All the aliases are also shown
along with the model fit and the difference field between the model
fit and the observed wind.
A simple overview of the Quality Assurance Algorithm
The quality assurance algorithm divides the swath into wind fields
overlapping by half in the cross-track and along-track directions.
A set of initial criteria is used to preselect regions. A model
fit is made for each preselected region. Each individual wind vector
cell (wvc) is compared to the corresponding model-fit cell. If the
error between the observed wind vector and the model fit vector
exceeds an angle or vector threshold, it is flagged as ``poor.''
If the number of poor cells in a region exceeds a region threshold,
the entire region is flagged as containing possible ambiguity removal
errors. This algorithm is summarized in the following figure.
Accuracy and results of the algorithm
The thresholds to the algorithm were tuned to give a constant false
alarm rate for RMS wind speed and cross track position on a set
of manually flagged regions. The results on the tuned data set showed
a 1.5% false alarm rate and a 3% missed detection rate. The percent
of regions flagged by the algorithm for the tuning set was 5%. This
means that the point wise ambiguity removal algorithm is at least
95% effective. Further research is underway to find ways to correct
regions that are flagged as ambiguity removal errors.
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