Application of Singularity Analysis on ASCAT Wind Quality Control
Portabella, Marcos1; Lin, Wenming1; Stoffelen, Ad2; Verhoef, Anton2; Turiel, Antonio1
1Institut de Cičncies del Mar (ICM-CSIC), SPAIN; 2Royal Netherlands Meteorological Institute (KNMI), NETHERLANDS

The Advanced Scatterometer (ASCAT) wind quality generally degrades with increasing inversion residual or Maximum Likelihood Estimator (MLE) value. As such, MLE-based quality control (QC) procedures, like those developed for previous scatterometer missions (e.g., European Remote-Sensing Satellite ERS, NSCAT, and SeaWinds), are generally very effective in screening poor-quality wind retrievals [1], [2]. A limitation of such technique is found though in rainy conditions. That is, although the rain can substantially impact the radar backscatter (NRCS) measurements, leading to poor quality wind retrievals, the resulting MLE value is often low.

To better screen poor-quality winds, an image processing method, named as singularity analysis, is proposed to complement the current ASCAT QC methodology [3], [4]. Singularity analysis is a welcome technique used to assess the presence of multi-fractal structure associated with turbulent flows. It has been successfully applied to derive the streamlines of ocean surface circulation from remote-sensing data of scalar variables. It can also be used to detect acquisition and/or processing errors in remote-sensing maps, therefore opening the way to improve quality control procedures. Singularity maps of the ASCAT-derived parameters, such as the backscatter measurements, the MLE, and the retrieved wind components (i.e., U, V, speed and direction) are examined independently for several typical wind field cases. It shows that by taking the lowest (most negative) singularity exponent of the set of wind speed/direction and MLE exponents, the detection of rain-induced artefacts in the ASCAT wind fields is optimized (see correspondence between rainy areas and low singularity exponent values in Figure 1a).

To assess the performance of singularity analysis on filtering poor-quality winds, both the European Centre for Medium-range Weather Forecasts (ECMWF) and the moored buoy winds are used as reference. The results show that the Vector Root-Mean-Square (VRMS) difference between ASCAT and ECMWF/buoy winds is substantially high for wind vector cells (WVCs) with low singularity exponent values, even for very low absolute MLE values. Therefore, WVCs with large negative singularity exponents should be filtered (quality controlled) according to the requirements of data processing. On the other hand, poor quality winds with high MLE values and small negative singularity exponent values also appear, indicating the complementarity of both QC techniques (see Figure 1b). As such, a combined MLE and singularity exponent based QC is being developed for ASCAT. This method is generic and can therefore be applied to other scatterometer systems such as in Oceansat-2 and HY-2A missions.

Fig. 1. a) Illustration of the singularity exponents map (see greyscale legend) for the ASCAT wind field observed at 20:30 UTC on September 24, 2008. The collocated TMI rain rate (RR) is represented by the greyscale contour lines, in which white lines correspond to RR= 0.5 mm/hr, and black lines correspond to RR as high as 10 mm/hr. b) Mean VRMS difference between ASCAT and ECMWF winds as a function of singularity exponent and MLE values. The labels indicate the VRMS value of the contour lines; the blank area is due to lack of data in the corresponding bins. Note that the density of points (not shown) increases with decreasing MLE and increasing singularity exponent values.


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[3] A. Turiel, H. Yahia, and C. Pérez-Vicente, "Microcanonical multifractal formalism: a geometrical approach to multifractal systems. Part I: Singularity analysis," Journal of Physics A, 41:015501, 2008.

[4] M. Portabella, A. Stoffelen, W. Lin, A. Turiel, A. Verhoef, J. Verspeek and J. Ballabrera-Poy, "Rain effects on ASCAT-retrieved winds: toward an improved quality control," IEEE Trans. Geosci. And Remote Sens, 50(7), 2495-2506., 2012.