An improved wind quality control for the China-France Oceanography Satellite (CFOSAT) scatterometer
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Abstract: Quality control (QC) is an essential procedure in scatterometer wind retrieval, which is used to distinguish good-quality data from poor-quality wind vector cells (WVCs) for the sake of wind applications. The current wind processor of the China-France Oceanography Satellite (CFOSAT) scatterometer (CSCAT) adopts a maximum likelihood estimator (MLE)-based QC method to filter WVCs affected by geophysical noise, such as rainfall and wind variability. As the first Ku-band rotating fan-beam scatterometer, CSCAT can acquire up to 16 observations over a single WVC, giving abundant information with diverse incidence/azimuth angles, as such its MLE statistical characteristics may be different from the previous scatterometers. In this study, several QC indicators, including MLE, its spatially averaged value (MLEm), and the singularity exponents (SE), are analyzed using the collocated Global Precipitation Mission rainfall data as well as buoy data, to compare their sensitivity to rainfall and wind quality. The results show that wind error characteristics of CSCAT under different QC methods are similar to those of other Ku-band scatterometers, i.e., SE is more suitable than other parameters for the wind QC at outer-swath and nadir regions, while MLEm is the best QC indicator for the sweet region WVCs. Specifically, SE is much more favorable than others at high wind speeds. By combining different indicators, an improved QC method is developed for CSCAT. The validation with the collocated buoy data shows that it accepts more WVCs, and in turn, improves the quality control of CSCAT wind data.
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Figure 1. Wind speed bias as a function China-France Oceanography Satellite scatterometer (CSCAT) wind speed and the sorted percentiles by maximum likelihood estimator (MLE) for the near-real-time wind product (a) and the reprocessed wind data (b). The black dashed curve indicates the rejection ratio of the operational MLE-based quality control.
Figure 2. Illustration of the wind vector cells at different swaths. Black, blue, and red circles represent the outer swath, the sweet region, and the nadir region, respectively.
Figure 3. VRMS difference between China-France Oceanography Satellite scatterometer (CSCAT) and numerical weather prediction (NWP) (upper panels), and the percentage of rain-contaminated data (GPM RR > 0 mm/h) (lower panels), as a function of the sorted percentiles by maximum likelihood estimator (MLE, blue), averagred MLE (MLEm, red), and singularity exponents (SE, yellow) for the nadir region (a and d), the sweet region (b and e), and the outer swath (c and f), respectively.
Figure 4. Percentage of rain-contaminated data (GPM RR > 1 mm/h) as a function CSCAT wind speed and the sorted percentiles by maximum likelihood estimator (MLE, a), averaged MLE (MLEm, b), and singularity exponents (SE, c). The black dashed curve indicates the (objective) rejection ratio of the operational MLE-based quality control.
Figure 5. CSCAT wind vectors superimposed on the map of wind speed (a–c) and the collocated GPM rain rate (d). White (black) arrows indicate the winds rejected (accepted) by the maximum likelihood estimator -based quality control (a), the averaged MLE-based quality control (b), and the singularity exponents-based quality control (c).
Figure 6. China-France Oceanography Satellite scatterometer reprocessed winds versus buoy wind speed (a, c, e, and g) and wind direction(b, d, f, and h) accepted by the operational maximum likelihood estimator (MLE)-based quality control, averaged MLE-based quality control, SE-based quality control, and the proposed new quality control, respectively.
Table 1. VRMS difference between China-France Oceanography Satellite scatterometer and numerical weather prediction winds categorized by different quality control (QC) methods under different wind speed conditions
Wind speed/(m·s−1) Statistical results over the QC accepted data/(m·s−1) Statistical results over the QC rejected data/(m·s−1) MLE MLEm SE MLE MLEm SE w ≤ 2 1.96 1.96 1.96 2.24 2.22 2.28 2 < w ≤ 14 1.92 1.91 1.90 3.54 3.60 3.74 w > 14 2.16 2.14 2.08 2.88 2.95 3.19 
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Table 2. VRMS difference between China-France Oceanography Satellite scatterometer and buoy winds categorized by different quality control (QC) methods in different wind speed bins
Wind speed/(m·s−1) Statistical results over the QC accepted data/(m·s−1) Statistical results over the QC rejected data/(m·s−1) MLE MLEm SE MLE MLEm SE w ≤ 2 2.26 2.25 2.23 4.84 4.54 5.71 2 < w ≤ 14 2.56 2.54 2.50 5.49 5.47 5.92 w > 14 5.37 4.87 4.64 6.51 7.40 7.62
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Table 3. VRMS difference between China-France Oceanography Satellite scatterometer and buoy winds categorized by different quality control (QC) methods for WVCs at different swath
Swath Statistical results over the QC accepted data/(m·s−1) Statistical results over the QC rejected data/(m·s−1) MLE MLEm SE MLE MLEm SE Nadir region 2.81 2.87 2.71 4.72 5.31 5.83 Sweet region 2.62 2.73 2.56 5.41 5.85 5.73 Outer swath 2.98 3.11 2.88 5.81 5.79 6.19
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Table 4. Percentage of rain contaminated data (with GPM RR > 0 mm/h or GPM RR >1 mm/h) for the quality control (QC)-rejected data by different quality indicators over the three swath regions
Swath Percentage of GPM RR > 0 mm/h over the QC rejected data/% Percentage of GPM RR > 1 mm/h over the QC rejected data/% MLE MLEm SE MLE MLEm SE Nadir region 51.0 58.5 47.7 24.1 28.1 20.0 Sweet region 54.3 61.3 50.4 26.1 29.8 22.4 Outer swath 48.9 55.2 53.0 23.4 26.8 25.0
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Table 5. Percentage of rain contaminated data (with GPM RR > 0 mm/h or GPM RR >1 mm/h) over the quality control (QC)-rejected data by different QC indicators in different wind speed bins
Wind speed/
(m·s−1)Percentage of GPM RR > 0 mm/h over the QC rejected data/% Percentage of GPM RR > 1 mm/h over the QC rejected data/% MLE MLEm SE MLE MLEm SE w ≤ 2 8.5 23.3 31.5 1.2 4.7 5.8 2 < w ≤ 14 54.5 61.0 50.1 28.1 31.4 23.7 w > 14 52.3 53.3 57.7 17.7 18.7 21.1
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Table 6. Percentage of rain-contaminated data (with GPM RR > 0 mm/h or GPM RR > 1 mm/h) over the quality control-rejected WVCs for different swaths
Swath Percentage of GPM RR > 0 mm/h
over all rain contaminated data/%Percentage of GPM RR > 1 mm/h
over all rain contaminated data/%MLE MLEm SE MLE MLEm SE Nadir region 17.1 19.0 16.1 8.1 9.1 6.7 Sweet region 18.6 20.3 17.3 8.9 9.9 7.7 Outer swath 16.8 18.3 18.2 8.0 8.9 8.6
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Table 7. Percentage of rain contaminated data (with GPM RR > 0 mm/h or GPM RR > 1 mm/h) over the quality control-rejected wind vector cells for different wind speed bins
Wind speed/(m·s−1)Percentage of GPM RR > 0 mm/h
over all rain contaminated data/%Percentage of GPM RR > 1 mm/h
over all rain contaminated data/%MLE MLEm SE MLE MLEm SE w ≤ 2 1.6 3.9 6.1 0.2 0.8 1.2 2 < w ≤ 14 17.7 19.9 16.6 9.1 10.2 7.8 w > 14 19.1 19.0 20.7 6.5 6.7 7.5
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