2024-03-29T07:12:16Zhttp://digital.csic.es/dspace-oai/requestoai:digital.csic.es:10261/1350922021-06-09T08:51:59Zcom_10261_123com_10261_8col_10261_376
Lin, Wenming
Portabella, Marcos
Turiel, Antonio
Stoffelen, Ad
Verhoef, Anton
2016-07-28T06:30:51Z
2016-07-28T06:30:51Z
2016-07
IEEE Transactions on Geoscience and Remote Sensing 54(7): 3890-3898 (2016)
http://hdl.handle.net/10261/135092
10.1109/TGRS.2016.2529700
http://dx.doi.org/10.13039/501100003329
http://dx.doi.org/10.13039/501100010560
Singularity analysis has proven to be a complementary tool to the Advanced Scatterometer (ASCAT) inversion residual (or maximum likelihood estimator) in terms of wind quality control (QC). In this paper, a new implementation scheme of singularity exponent (SE) is developed for ASCAT data analysis. It combines the wavelet projections of the gradient measurements of multiple parameters into the analysis, ensuring that the analyzed parameters contribute equally to the final singularity map. Therefore, the underlying geophysical phenomena in the different ASCAT-derived parameters can be effectively revealed simultaneously on a unique map of SEs. The validation using both buoy winds and European Centre for Medium-Range Weather Forecasting forecast wind output shows that the newly derived SE significantly improves the current ASCAT wind QC. In particular, poor-quality ASCAT measurements at low-wind and high-variability conditions (w <; 4 m/s) can be effectively screened using the new SE
eng
openAccess
Advanced scatterometer
Maximum likelihood estimator
Quality control
Singularity analysis
Wind variability
SA
ASCAT
MLE
An Improved Singularity Analysis for ASCAT Wind Quality Control: Application to Low Winds
artÃculo