Calibration and Cross Validation of Global Ocean Wind Speed Based on Scatterometer Observations

Abstract

Global ocean wind speed observed from seven different scatterometers, namely, ERS-1, ERS-2, QuikSCAT, MetOp-A, OceanSat-2, MetOp-B, and Rapid Scatterometer (RapidScat) were calibrated against National Data Buoy Center (NDBC) data to form a consistent long-term database of wind speed and direction. Each scatterometer was calibrated independently against NDBC buoy data and then cross validation between scatterometers was performed. The total duration of all scatterometer data is approximately 27 years, from 1992 until 2018. For calibration purposes, only buoys that are greater than 50 km offshore were used. Moreover, only scatterometer data within 50 km of the buoy and for which the overpass occurred within 30 min of the buoy recording data were considered as a “matchup.” To carry out the calibration, reduced major axis (RMA) regression has been applied where the regression minimizes the size of the triangle formed by the vertical and horizontal offsets of the data point from the regression line and the line itself. Differences between scatterometer and buoy data as a function of time were investigated for long-term stability. In addition, cross validation between scatterometers and independent altimeters was also performed for consistency. The performance of the scatterometers at high wind speeds was examined against buoy and platform measurements using quantile–quantile (Q–Q) plots. Where necessary, corrections were applied to ensure scatterometer data agreed with the in situ wind speed for high wind speeds. The resulting combined dataset is believed to be unique, representing the first long-duration multimission scatterometer dataset consistently calibrated, validated and quality controlled.

Linking Chlorophyll Concentration and Wind Patterns Using Satellite Data in the Central and Northern California Current System

“…A comparison of the mean of NH station values for each sampling date with the mean of satellite values over the Newport region for the corresponding 8-day period shows good agreement (r 2 = 0.34, p < 0.01; Figure 4B). For satellite wind validation, a recent study by Ribal and Young (2020) found that 10-m wind speed and direction measurements from both QuikSCAT and ASCAT match well with in situ 10-m wind speed and direction measurements from buoys located at least 50 km offshore from the National Data Buoy Center (ρ = 0.9593 and 0.9404, respectively), though QuikSCAT had a tendency to overestimate high wind speeds (>15 m/s). This study also compared wind speed measurements from four other satellite scatterometers and found generally good agreement among instruments, so we would expect similar results with these other satellite wind products.…”

Improving Altimeter Wind Speed Retrievals Using Ocean Wave Parameters

“…The U10 and SWH data are from altimeters. 2 The angle between wind direction and MWP. 3 The difference between SST and SSAT.…”

“…Spaceborne active microwave remote sensors, including radar altimeter, scatterometer, and Synthetic Aperture Radar (SAR), can all be used to retrieve 10-m sea surface wind speed (U10, henceforth) because the Radar Cross-Sections (RCSs) are sensitive to sea surface roughness (SSR) [1], and the SSR is closely correlated to U10. Among these remote sensors, scatterometers have the widest swath and the best overall accuracy (with a typical error of ~1 m/s) [2], making them an irreplaceable data source of U10. Meanwhile, radar altimeters, with a typical error of wind speed of 1.5 m/s (e.g., [3,4]), have a better accuracy in high wind speeds [1] and can provide global coverage of U10 and significant wave height (SWH) data simultaneously, making them also a unique tool for studies in wind-waves (e.g., [5][6][7]).…”

Improving Altimeter Wind Speed Retrievals Using Ocean Wave Parameters

“…used to retrieve 10-m sea surface wind speed (U10, henceforth) because the Radar Cross-Sections (RCSs) are sensitive to sea surface roughness (SSR) [1], and the SSR is closely correlated to U10. Among these remote sensors, scatterometers have the widest swath and the best overall accuracy (with a typical error of ∼1 m/s) [2], making them an irreplaceable data source of U10. Meanwhile, radar altimeters, with a typical error of wind speed of 1.5 m/s (e.g., [3], [4]), have a better accuracy in high wind speeds [1] and can provide global coverage of U10 and significant wave height (SWH) data simultaneously, making them also a unique tool for studies in wind-waves (e.g., [5]- [7]).…”

“…When establishing the retrieving algorithm, however, the RCSs are usually directly mapped to U10 obtained from other instruments, such as buoys or scatterometers [4]. Although the derived GMFs generally have a good performance in measuring U10 (e.g., [1], [2], [4]), it is noted that U10 is not the only impact factor for SSR. For example, surface ocean currents and atmospheric instability can all have a direct impact on the SSR response to U10 (e.g., [8], [9]), thus impacting the wind retrieval.…”