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Rain Identification in ASCAT Winds Using Singularity Analysis

Fig. 1. (a) Map of collocated ASCAT-TMI data. The gray scale square areas superimposed correspond to different TMI RRs (see the legend, mm/h). Note that the white background corresponds to no TMI RR data. The acquisition date was September 24, 2008, at 20:30 UTC; (b) the corresponding MLE distribution and (c) Kp (mean value of fore and aft beams, %) distribution; (d) Singularity map of the ASCAT-retrieved wind field shown in Fig. 1(a). At every grid point, the minimum SE value from the wind speed, wind direction, and MLE SEs is used to generate the map. The RR contour lines depict the rainy areas. Note that there are no data around [163 ◦W 18 ◦S].
W Lin, M Portabella, A Stoffelen, A Turiel, A Verhoef

The Advanced Scatterometer (ASCAT) onboard the Metop satellite series is designed to measure the global ocean surface wind vector. Generally, ASCAT provides wind products at excellent quality. Occasionally, though, ASCAT-derived winds are degraded near rain. Therefore, identification of rain can help to better understand the rain impact on scatterometer wind quality, and to develop a proper quality control (QC) approach for scatterometer data processing. In this letter, an image processing method, known as singularity analysis (SA), is used to detect the presence of rain such that rain-contaminated wind vector cells (WVCs) are flagged. The performance of SA for rain detection is validated using ASCAT Level-2 data collocated with satellite radiometer rain data. The rain probability as a function of SA singularity exponent is calculated and compared with other rain sensitive parameters, such as the wind inversion residual or maximum-likelihood estimator (MLE). The results indicate that the SA is effective in detecting ASCAT rain areas. Moreover, SA is a complementary rain indicator to the MLE parameter, thus showing great potential for an improved scatterometer QC.

Bibliografische gegevens

W Lin, M Portabella, A Stoffelen, A Turiel, A Verhoef. Rain Identification in ASCAT Winds Using Singularity Analysis
published, IEEE Gosci. Remote Sensing Letters, 2014, 11

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