Evaluation of Oceansat-2-Derived Ocean Surface Winds Using Observations From Global Buoys and Other Scatterometers

Kumar, Raj; Chakraborty, Abhisek; Parekh, Anant; Sikhakolli, Rajesh; Gohil, B. S.; Kumar, Akhilesh

doi:10.1109/tgrs.2012.2214785

Cited by 32 publications

(15 citation statements)

References 13 publications

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“…The OSCAT mission requirements for the RMSE in wind speed is less than 2 m/s (for wind direction less than 20°) for the 4-24 m/s speed range. The statistics shown in Table 1 satisfies the mission requirements and hence compares well with the study by Kumar et al [17].…”

Section: Comparison Of Oscat Winds With Ndbc Measurementssupporting

confidence: 86%

Wave Hindcasting Using WAM and WAVEWATCH III: A Comparison Study Utilizing Oceansat-2 (OSCAT) Winds

Swain¹,

Pa²,

Balch³

et al. 2017

J Oceanogr Mar Res

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Section: Comparison Of Oscat Winds With Ndbc Measurementssupporting

confidence: 86%

Wave Hindcasting Using WAM and WAVEWATCH III: A Comparison Study Utilizing Oceansat-2 (OSCAT) Winds

Swain¹,

Pa²,

Balch³

et al. 2017

J Oceanogr Mar Res

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“…Thus, the σ • data from OSCAT are temporally consistent, and the error in the σ • data is up to ±0.5 dB, which corresponds to an error of up to 2 m/s in wind speed and 20 • in direction [11]. This makes the OSCAT σ • data useful for operational usage.…”

Section: (A)] Andmentioning

confidence: 72%

Cross Calibration of the OceanSAT -2 Scatterometer With QuikSCAT Scatterometer Using Natural Terrestrial Targets

Bhowmick

Kumar

2014

IEEE Trans. Geosci. Remote Sensing

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The accuracy of ocean surface wind vectors measured by satellite-borne scatterometers depends on measured backscattering coefficient (σ • ). Hence, an in-flight calibration of a satellite scatterometer is essential as this is not guaranteed by its prelaunch absolute calibration. The postlaunch calibration of σ • is also required to monitor the time evolution of the accuracy of measured σ • . This is performed using relative calibration over land targets with minor spatiotemporal variation of σ • . A few such targets are the Amazon rainforest, Greenland, Antarctica, etc. In this paper, relative calibration of σ • from the OceanSAT-2 Scatterometer (OSCAT) has been carried out by comparing it with a similar quantity from the Quick Scatterometer (QuikSCAT) for November 2009. The differences between the average σ • of QuikSCAT and that of OSCAT have been calculated globally to check the overall consistency. Over the calibration sites, the differences are within ±0.25 dB. Histograms of differences in ascending/descending passes and fore/aft looks of OSCAT have also been analyzed over the calibration sites. These indicate that look bias in OSCAT σ • is within the range of ±0.5 dB. It is also evident that pass biases, i.e., differences between ascending and descending passes, exist over the Amazon rainforest for both QuikSCAT and OSCAT. This diurnal variation in σ • may go up to 1.25 dB in OSCAT. Further, computations of daily average and standard deviation over the calibration site show that mean OSCAT σ • is consistent with mean QuikSCAT σ • , whereas the standard deviation in OSCAT is marginally higher. Further, time-series analysis of OSCAT σ • shows its temporal stability.

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“…In another near-shore study, the performance of QuikSCAT was even worse when data flagged for rain and light wind speeds (<3 m¨s´1) were excluded (the RMSEs of wind speed and direction were 1.4 m¨s´1 and 37˝, respectively) [14]. During 1 November-21 November 2009, the RMSEs of the wind speeds from QuikSCAT and OSCAT were 1.69 and 1.76 m¨s´1, respectively (relative to buoy data), while the respective wind direction counterparts were 34.67˝and 24.69˝ [24]. In comparisons of the QuikSCAT wind data with buoys observations from the Indian Ocean, the RMSEs of the wind speed and direction were 1.57 m¨s´1 and 44.1˝, respectively [16].…”

Section: Discussionmentioning

confidence: 99%

“…Collocated with ASCAT wind vectors, the H2SCAT data were located either below 50˝S or above 50˝N within 1-h timespan, where the RMSEs of the wind speeds and directions are 1.62 m¨s´1 and 47.47˝, respectively. The RMSEs were changed to 2.13 m¨s´1 and 42.31˝when the time span was expanded to 3 h. Likewise, within a half-hour timespan in the polar zone (<55˝S and >50˝N), the proportion of collocated data between OSCAT and ASCAT is only 1.15%, and the RMSEs of the wind speed and direction were 1.47 m¨s´1 and 26˝, respectively [24]. On the other hand, collocations of QuikSCAT and ASCAT are found in the polar zone within 1-h timespan, and the STD between two scatterometers was approximately 1.5-2 m¨s´1 [22].…”

Section: Discussionmentioning

confidence: 99%

Evaluation of HY-2A Scatterometer Wind Vectors Using Data from Buoys, ERA-Interim and ASCAT during 2012–2014

et al. 2016

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Abstract:The first Chinese operational Ku-band scatterometer on board Haiyang-2A (HY-2A), launched in August 2011, is designed for monitoring the global ocean surface wind. This study estimates the quality of the near-real-time (NRT) retrieval wind speed and wind direction from the HY-2A scatterometer for 36 months from 2012 to 2014. We employed three types of sea-surface wind data from oceanic moored buoys operated by the National Data Buoy Center (NDBC) and the Tropical Atmospheric Ocean project (TAO), the European Centre for Medium Range Weather Forecasting (ECMWF) reanalysis data (ERA-Interim), and the advanced scatterometer (ASCAT) to calculate the error statistics including mean bias, root mean square error (RMSE), and standard deviation. In addition, the rain effects on the retrieval winds were investigated using collocated Climate Prediction Center morphing method (CMORPH) precipitation data. All data were collocated with the HY-2A scatterometer wind data for comparison. The quality performances of the HY-2A NRT wind vectors data (especially the wind speeds) were satisfactory throughout the service period. The RMSEs of the HY-2A wind speeds relative to the NDBC, TAO, ERA-Interim, and ASCAT data were 1.94, 1.73, 2.25, and 1.62 m¨s´1, respectively. The corresponding RMSEs of the wind direction were 46.63˝, 43.11˝, 39.93˝, and 47.47˝, respectively. The HY-2A scatterometer overestimated low wind speeds, especially under rainy conditions. Rain exerted a diminishing effect on the wind speed retrievals with increasing wind speed, but its effect on wind direction was robust at low and moderate wind speeds. Relative to the TAO buoy data, the RMSEs without rain effect were reduced to 1.2 m¨s´1 and 39.68˝for the wind speed direction, respectively, regardless of wind speed. By investigating the objective laws between rain and the retrieval winds from HY-2A, we could improve the quality of wind retrievals through future studies.

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Evaluation of Oceansat-2-Derived Ocean Surface Winds Using Observations From Global Buoys and Other Scatterometers

Cited by 32 publications

References 13 publications

Wave Hindcasting Using WAM and WAVEWATCH III: A Comparison Study Utilizing Oceansat-2 (OSCAT) Winds

Wave Hindcasting Using WAM and WAVEWATCH III: A Comparison Study Utilizing Oceansat-2 (OSCAT) Winds

Cross Calibration of the OceanSAT -2 Scatterometer With QuikSCAT Scatterometer Using Natural Terrestrial Targets

Evaluation of HY-2A Scatterometer Wind Vectors Using Data from Buoys, ERA-Interim and ASCAT during 2012–2014

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