Characterization of residual information for SeaWinds quality control

• Published in: IEEE transactions on geoscience and remote sensing Vol. 40; no. 12; pp. 2747 - 2759
• Main Authors: Portabella, M., Stoffelen, A.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.12.2002; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied geophysics; Climatology; Earth sciences; Earth, ocean, space; Economic forecasting; Exact sciences and technology; Format; Internal geophysics; Inversions; Maximum likelihood estimation; Meteorology; Oceans; Quality control; Radar measurements; Rain; Representations; Signal averaging; Simulation; Spaceborne radar; Weather; Weather forecasting; Wind forecasting; inverse problem; rain; SeaWinds; quality control; quality controls; meteorology; simulation; remote sensing; Maximum-likelihood estimation; scatterometer; assimilation; winds; maximum likelihood; prediction; theory
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2002.807750

Recent work has shown the important properties of the wind inversion residual or maximum-likelihood estimator (MLE) for quality Control (QC) of QuikSCAT Hierarchical Data Format (HDF) observations. Since March 2000, the QuikSCAT near-real-time (NRT) Binary Universal Format Representation (BUFR) product is available. As this product is used for numerical weather prediction (NWP) assimilation purposes, a QC procedure for the BUFR product is needed. We study the behavior of the MLE in order to determine whether the HDF QC procedure is appropriate for BUFR data. A comparison using real HDF and BUFR data reveals that the MLE distributions of HDF and BUFR differ and are actually poorly correlated. One important difference between BUFR and HDF is the amount of signal averaging prior to wind inversion. The averaging reduces the number of observations used in the wind retrieval for the BUFR product as compared to HDF. We show with a simple example that different MLE distributions are indeed expected due to this averaging. We also run a simulation in order to link theory and reality and better understand the behavior of the MLE. Despite the different MLE behavior in BUFR and HDF, the quality of the retrieved winds, as compared with the European Centre for Medium-Range Weather Forecasts winds, is very similar. We develop an MLE-based QC procedure for BUFR, similarly to the one in HDF, and we compare both. The skill of the QC in BUFR is again very similar to the one in HDF, showing that despite the different MLE behavior in both formats, the properties of the MLE as a QC indicator remain very similar.