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

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AGUSTINUS RIBAL Department of Infrastructure Engineering, University of Melbourne, Parkville, Victoria, Australia, and Department of Mathematics, Faculty of Mathematics and Natural Sciences, Hasanuddin University, Makassar, Indonesia

IAN R. YOUNG Department of Infrastructure Engineering, University of Melbourne, Parkville, Victoria, Australia

(Manuscript received 17 July 2019, in ﬁnal form 19 November 2019)

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.

1. Introduction of multiplatform datasets for both altimeter and radiometer observations, calibrated, and cross validated Accurate and high-resolution ocean wind data are in a consistent manner include Young et al. (2017) and critically important for many applications such as re- Ribal and Young (2019). gional weather forecasting, ocean energy development, Radiometers measure brightness temperature over a validation of numerical models, marine disaster moni- broad swath, which is related to wind speed through a toring and wind climatology (Ribal and Young 2019; complicated radiative transfer equation (Wentz 1983). Yang et al. 2011; Young and Ribal 2019; Young et al. Altimeters measure the radar cross section s (ratio of 2017). Today, with the number of satellite platforms 0 transmitted to received radar energy) along a nadir track that have been launched, high-resolution ocean wind below the satellite, which is then related to wind speed. data are available through altimeters, radiometers, Scatterometers also measure radar cross section but scatterometers, and synthetic aperture radars (Young over a broad swatch that depends on its antenna con- et al. 2017). However, to form consistent long-duration ﬁguration. As a result of the antenna conﬁguration, and records, such satellite data observations must be consis- the fact that measurements are made at a range of azi- tently calibrated, validated, and cross validated. Examples muth angles, scatterometers can measure both wind speed and direction. The resolution of the scatterometer Corresponding author: Ian R. Young, [email protected] measurements within the broad ground-track swath is

DOI: 10.1175/JTECH-D-19-0119.1 Ó 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Unauthenticated | Downloaded 09/28/21 02:44 AM UTC 280 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 37

FIG. 1. Characteristics of the four groups of scatterometers, including antenna conﬁguration, polarization, swath conﬁguration, incidence angles, and missions. typically either 25 or 12.5 km (Crapolicchio et al. 2012; consistent multiplatform dataset is necessary for long- Figa-Saldaña et al. 2002; Spencer et al. 2000). duration climate studies (Young and Ribal 2019; Cross-platform evaluation between scatterometer Young et al. 2011). radar cross section s0 measurements have been under- In this study, the wind speed from seven different scat- taken in a number of studies (Alsabah et al. 2018; terometer missions, namely ERS-1, ERS-2, QuikSCAT, Madsen and Long 2016; Portabella et al. 2007; Stoffelen MetOp-A, OceanSat-2, MetOp-B, and Rapid Scatterometer 1999) while other studies have concentrated on com- (RapidScat) (expressed in the order of launch) have parison of the derived wind speed against buoy data been calibrated, validated and cross validated. The cali- (Accadia et al. 2007; Bentamy 2008; Bentamy et al. 2002; bration is based on National Data Buoy Center (NDBC) Ebuchi et al. 2002; Pensieri et al. 2010; Pickett et al. 2003; buoy data. Once the calibrations have been performed

Satheesan et al. 2007; Verspeek et al. 2008). Note that s0 for all scatterometers, the data are validated at high wind can be related to wind speed at a reference height of speeds using wind data from offshore oil platform ob-

10 m U10 through a geophysical model function (GMF). servations sourced from the Norwegian Meteorological In addition, some calibrations have also been performed Institute. Since scatterometers also measure wind di- against other scatterometer missions—so-called inter- rection, in contrast to radiometers and altimeters, wind calibration (Elyouncha and Neyt 2013a,b; Holbach and direction from the scatterometers and buoy data were Bourassa 2017; Yang et al. 2011). These calibration also compared. Furthermore, the differences between studies tend to be conﬁned to a single scatterometer plat- scatterometer observations and buoy measurements as form or, occasionally a small number of scatterometers. a function of time are also investigated to determine We are unaware of a consistent multimission cali- long-term stability of the measurements. In addition, bration across a large number of platforms. Such a for stability and consistency checks, cross validation

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TABLE 1. Scatterometer operating characteristics for seven scatterometers, including repeat mission period, orbit parameters, antenna properties, launch date, operational time for which data are available, and responsible organization.

Repeat mission Altitude Frequency Frequency Launch Data initial Data end Scatterometer (days) Inclination (km) (GHz) band date date date Organization ERS-1 35 98.58 785 5.3 C 17 Jul 1991 2 Mar 1992 3 Jun 1996 ESA (Europe) ERS-2 35 98.58 785 5.3 C 21 Apr 1995 26 Mar 1996 14 Jul 2011 ESA (Europe) QuikSCAT 4 98.68 803 13.4 Ku 19 Jun 1999 27 Oct 1999 22 Nov 2009 NASA (United States) MetOp-A 29 98.78 817 5.255 C 19 Oct 2006 28 Mar 2007 14 Nov 2018 ESA (Europe) OceanSat-2 2 98.258 720 13.515 Ku 23 Sep 2009 16 Jan 2010 20 Feb 2014 ISRO (India) MetOp-B 29 98.78 817 5.255 C 17 Sep 2012 29 Oct 2012 14 Nov 2018 ESA (Europe) RapidScat Irregular 51.68 375–435 13.4 Ku 20 Sep 2014 8 Oct 2014 19 Aug 2016 NASA (United States) between different scatterometers as well as between MetOp-B were launched by the European Space Agency scatterometers and altimeters were also carried out. (ESA). Ku-band scatterometers include QuikSCAT and This paper is arranged as follows. Following this RapidScat, which were launched by the National introduction, a brief description of scatterometers and Aeronautics and Space Administration (NASA), and the sources of the original datasets are presented in OceanSat-2, which was launched by the Indian Space section 2. This is followed in sections 3 and 4 by a de- Research Organization (ISRO). A further grouping of scription of the NDBC buoy data used and the calibration scatterometers can be made based on antenna con- procedures for the scatterometers against buoy data. figuration, polarization and ground swath configu- Section 5 presents the validation of the calibrated scat- ration. This yields four groups: European Space terometer data with respect to the platform measure- Agency Scatterometer (ESCAT), SeaWinds, Advanced ments. Furthermore, cross validation between different Scatterometer (ASCAT), and OceanSat Scatterometer scatterometers and the cross validation between scat- (OSCAT; scatterometer onboard OceanSat-2). The de- terometers and altimeters are presented in sections 6 tails of each scatterometer group is presented below. and 7, respectively. Finally, conclusions are drawn in The ESA’s ESCAT scatterometer operated in the C section 8. band at a frequency of 5.3 GHz. The ESCAT was carried on both ERS-1 and ERS-2. ERS-1 was launched on 2. Scatterometer description and data 17 July 1991 but scatterometer data from the European Organisation for the Exploitation of Meteorological a. Scatterometer overview Satellites (EUMETSAT) is only available from 1992. In general, the scatterometers used in this work can Similarly, ERS-2 was launched on 21 April 1995 but be classified based on the frequency band in which the data sourced from ESA dates from 1996. The they operated, namely, Ku band and C band. C-band scatterometers carried on ERS-1 and ERS-2 not only scatterometers including ERS-1, ERS-2, MetOp-A, and had the same frequency but also had the same antenna

FIG. 2. Durations of all scatterometer data from the seven satellite missions.

Unauthenticated | Downloaded 09/28/21 02:44 AM UTC 282 JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY VOLUME 37 conﬁguration, incidence antenna angle and polarization as shown in Fig. 1. Moreover, both of them have the same repeat mission, altitude, and inclination angle, which are 35 days, 785 km, and 98.58, respectively, as presented in Table 1. The azimuth angles are 458,908, and 1358 for the forward, sideways, and afterward an- tennas, respectively (Fig. 1)(Crapolicchio et al. 2012). Percentage of outliers The SeaWinds scatterometer was developed by NASA and uses the Ku band with a frequency of 13.4 GHz. SeaWinds was carried on QuikSCAT and RapidScat. n These scatterometers were launched on 19 June 1999 and on 20 September 2014, respectively (Ebuchi et al. 2002). The QuikSCAT mission ended on 23 November is the uncalibrated data. Also shown are the 0.2100.211 4979 25 0830.175 143 9160.106 0.48 0.55 100 338 1.15 1.65 0.279 78 9360.254 47 9340.205 0.43 11 867 1.56 1.15 10 2 2 2 2 2009 due to an age-related mechanical failure. This in- 2 2 2 strument was then replaced by RapidScat. However, the U later scatterometer only has data available until 2016. 0.336 to 0.279 to 0.199 to 0.136 to 0.310 to 0.298 to 0.300 to 2 2 2 2 It should be mentioned that, unlike other scatterometers, 2 2 2 RapidScat is the only scatterometer that was in a non-sun-synchronous orbit, meaning that RapidScat passes over different locations on the ground at the same solar time of day. The antenna conﬁguration of these scatterometers uses a rotating dish with two spot beams is the calibrated value and * 1.003 to 1.019 1.046 to 1.055 1.037 to 1.040 1.023 to 1.027 10 that conically sweep producing a circular pattern on the 1.024 to 1.028 1.025 to 1.030 1.010 to 1.018 U surface as shown in Fig. 1. The swaths for the inner and

the outer beams are 1400 and 1800 km, respectively, ] ] ] 79 79 79 : : : centered on the nadir. The repeat mission for QuikSCAT 0 0 0 is four days, while the repeat mission for RapidScat is 15) 14) 14) , and the percentage of outliers from the robust regression. 2 2 2 n irregular due to its non-sun-synchronous orbit (Madsen 10 10 10 U U U 294 276 253 : : : and Long 2016). Hence, the inclination and the altitude 0 0 0 2 2 2 015( 008( 020( for QuikSCAT and RapidScat are also different, with : : : 0 0 0 10 10 10 U U U the inclination and the altitude for QuikSCAT being 2 2 2 [1 [1 [1

8 026 028 013 : : : 98.6 and 803 km, respectively, while the inclination of 10 10 10 1 1 1 U U U 273 245 187 121

8 : : : : RapidScat is 51.6 and its altitude varied from 375 to 5 5 5 5 5 5 0 0 0 0 * * * 10 10 10 * * * 10 10 10 2 2 2 2

435 km as can be seen from Table 1 (Ebuchi et al. 2002; U U U U U U 10 10 10 10 U U U U

Satheesan et al. 2007). 15, 14, 14, 15, 14, 14, # # # . . . ASCAT was designed based on the ESCAT scatterometer. 011 050 038 025 : : : : 1 1 1 1 10 10 10 10 10 10 U U U

Hence, ASCAT and ESCAT have some common U U U 5 5 5 5 * * * * features such as azimuth angle, polarization, radar 10 10 10 10 U U U U For For For frequency, and the operational product resolution (25 km). However, ASCAT introduced some new key additional features such as an increasing spatial cover- age by using a double swath, an increased incidence angle range, improved instrument design for higher conﬁdence limits on the regression, number of points stability and reliability, and an improved onboard processing concept for lower data rates. Moreover, the incidence angles and the swath size were also increased from 188–598 to 258–658 and from 500 to 550 km, re- 2 Mar 1992–3 Jun 1996 28 Mar 2007–14 Nov 2018 29 Oct 2012–14 Nov 2018 26 Mar 1996–4 Jul 2011 16 Jan 2010–20 Feb 2014 For spectively, as shown in Fig. 1 (Figa-Saldaña et al. 2002). In addition, the altitude of ASCAT was also increased from 785 km for ESCAT to 817 km. Similarly, the inclination of ASCAT was slightly increased from 98.58 for 2. Calibration relationships for scatterometer wind speed, obtained from the RMA regression. ESCAT to 98.78 as summarized in Table 1 (Elyouncha ABLE ERS-1 MetOp-A MetOp-B ERS-2 QuikSCAT 27 Oct 1999–22 NovOceanSat-2 2009 For RapidScat 8 Oct 2014–19 Aug 2016 For T and Neyt 2013a; Figa-Saldaña et al. 2002). The swaths of Scatterometer Period Calibration relation 95% limit slope 95% limit offset

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FIG. 3. Locations of the NODC buoys (blue dots) used in this study in which only buoys more than 50 km offshore are used. Green shaded regions indicate the locations for MetOp-A and MetOp-B cross validations (see Fig. 10a).

ASCAT are separated by approximately 360 km from missions is approximately 27 years, from 1992 until 2018 the satellite ground track while the distance of the swath where the period of each scatterometer is presented in to the satellite track of ESCAT is 200 km (Crapolicchio Fig. 2 and Table 1. The resolution of ERS-2, QuikSCAT, et al. 2012). The ASCAT scatterometer was used on two OceanSat-2, and RapidScat is 12.5 km while the data different satellites, namely, MetOp-A and MetOp-B, resolution of the other scatterometers, namely, ERS-1, which were launched on 19 October 2006 and on MetOp-A, and MetOp-B, is 25 km. 17 September 2012, respectively. These scatterometer Each of the original scatterometer datasets contains a missions are still ongoing. It should be noted that MetOp-B range of data quality flags; however, these flags are not follows MetOp-A with a 49-min delay in a tandem con- consistent across the datasets. With the exception of figuration (Elyouncha and Neyt 2013b). ERS-2 and OceanSat-2, the datasets have three quality The fourth scatterometer group is OSCAT, which was flags for wind speed, defining ‘‘good’’ data, ‘‘low wind developed by the ISRO and launched on OceanSat-2 on speed’’ data, and ‘‘high wind speed’’ data. ERS-2 has 23 September 2009. The OSCAT scatterometer is also only one quality flag for ‘‘good’’ wind speed while carried onboard ScatSat-1. However, since the data from OceanSat-2 has five quality flags for ‘‘good’’ wind speed. this mission are not available in the public domain, it has Low and high wind speeds are defined as wind speeds 2 2 not been included in this present work. The OSCAT that are less than 3 m s 1 and greater than 30 m s 1, re- scatterometer carried on OceanSat-2 operates in the Ku spectively. Even though ERS-1, MetOp-A, and MetOp- band at a frequency of 13.515 GHz. Its altitude and in- B scatterometer data were obtained from different clination are 720 km and 98.258, respectively. The re- sources, they have the same quality flags for ‘‘good,’’ peat cycle is 2 days and the incidence angles are 48.98 and ‘‘low,’’ and ‘‘high’’ wind speed. Similarly, although 57.68 for HH and VV polarization, respectively. The an- QuikSCAT and RapidScat both classify the wind speed tenna configuration of this scatterometer is the same as into three categories, they use different numerical SeaWinds but the outer beam swath has been increased values for the flags. Therefore, care needs to be taken in by 40 km from 1800 to 1840 km where the details can be processing the various datasets. In compiling the present found in Table 1 and Fig. 1 (Chakraborty et al. 2013). dataset, these flags have been simplified to a consistent set across all scatterometer missions (see appendixes A b. Scatterometer data sources and B). It should be noted that high and low wind speed The scatterometer data were sourced from three in the original dataset have been flagged as ‘‘Good_data’’ different public domain sites: QuikSCAT, MetOp-A, in the present database. OceanSat-2, MetOp-B, and RapidScat were obtained from PO.DAAC (https://podaac.jpl.nasa.gov/); ERS-1 3. NDBC buoy data and ERS-2 were sourced from EUMETSAT (http:// archive.eumetsat.int) and ESA (https://earth.esa.int), As shown in Fig. 2, the duration of the combined respectively. The total duration of all scatterometer scatterometer dataset is approximately 27 years.

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FIG. 4. Calibration of scatterometer wind speed against NODC buoy data. Shown are the 1:1 agreement (dashed diagonal line) and the RMA regression (thick solid line). Contours show the density of matchup data points, which has been normalized such that the maximum value is 1.0. Contours are drawn at 0.9, 0.7, 0.5, 0.4, 0.3, 0.2, 0.1, and 0.05. Dots represent outliers excluded from the RMA regression.

For calibration purposes, we require a high-quality database calibration (Young and Donelan 2018). Moreover, of buoy wind speed and direction over this period. In although the validity of high-wind-speed buoy data has addition, the buoy data should be relatively far from been questioned (Alves and Young 2003; Bender et al. land, in order to reduce land/island contamination due 2010; Jensen et al. 2015; Large et al. 1995; Taylor and to the size of scatterometer vector cell (12.5 or 25 km). Yelland 2001; Vinoth and Young 2011; Zeng and The most extensive dataset that satisfies these pa- Brown 1998),thedatahavebeenwidelyusedfor rameters is the NDBC buoy archive. These data validation of model results and calibration of satellite have been quality controlled, and archived by the observation and have been found to be high quality National Oceanographic Data Center (NODC; https:// (Evans et al. 2003; Ribal and Young 2019; Zieger data.nodc.noaa.gov/thredds/catalog/ndbc/cmanwx/ et al. 2015). catalog.html), where they are available in the public do- To avoid land/island contamination, only NODC main under NOAA’s National Centers for Environmental moored buoy data that were more than 50 km from the Information (NCEI). Another reason to choose NDBC coastline were used as a source for calibration wind buoy data is due to the fact that it is a long-duration record speed (Zieger et al. 2009). The NODC data after 2011, and generally regarded as being consistent over time contained a series of quality flags for wind speed, namely (Durrant et al. 2009). Even though the NDBC buoy 0, 1, 2, and 3, which represent quality_good, out_of_range, locations are geographically limited to the Northern sensor_nonfunctional, and questionable, respectively. Hemisphere, it has been shown using altimeter data To obtain high-quality buoy wind speed data, only that this does not have a significant impact of the mean values flagged ‘‘0’’ were used for the calibration of the

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FIG. 5. Q–Q plots between the scatterometer and NODC buoy data for wind speed after the calibration was applied. scatterometers. Although NODC data before 2011 do were made at approximately the same time as there not have quality flags, examination of the data indicates was a satellite overflight—termed a matchup. A matchup few clear outliers (,0.5%; see Table 2). The locations of is considered to have occurred if the following criteria are the buoy data used in the calibration are shown in Fig. 3. satisfied: Buoy wind speeds are measured at a variety of differ- 1) The scatterometer wind vector cell location is ent heights depending on the anemometer height U . z within 50 km of the buoy location and the time For calibration purposes, the wind speed is required difference between the buoy measurement and the at a standard reference height of 10 m U . Assuming a 10 scatterometer overflight is less than 30 min. neutral-stability-logarithm boundary layer (e.g., Priestley 2) Only buoys that are more than 50 km from the 1959; Young 1999), U can be approximated by 10 coastline were used so as to avoid land/islands con- sffiffiffiffiffiffi tamination for both buoy and scatterometer. k2 1 U 5 U , (1) 3) A minimum of five scatterometer wind vector cells 10 z C ln(z/z ) d 0 were required within a 50-km-radius region around the buoy. k á á where is the von K rm n constant, which is approxi- 4) Large variability in scatterometer wind speeds were mately 0.4, Cd is the drag coefficient, and z0 is the excluded. Matchups were excluded if s(U10)/U10 . 0:2, roughness length. Measurements of Cd over the ocean where s(U10)andU10 are the standard deviation yield results with scatter over an order of magnitude, and mean, respectively, of scatterometer wind and much research has focused on the wind speed and speed vector cells, within a 50-km radius around sea-state dependence of Cd (Donelan 1982; Guan and 23 the buoy. Xie 2004; Young 1999). In this work, Cd 5 1.2 3 10 25 and z0 5 9.7 3 10 m have been assumed. As men- For each scatterometer, matchup data across all the tioned in previous studies (Young et al. 2017), a different buoys were pooled and a linear regression analysis assumption of Cd does not have a major impact on the performed between the buoy and scatterometer wind final satellite wind speed (Zieger et al. 2009). For a more speeds U10. It should be noted that wind speeds greater 21 detailed description of NOAA buoy data, one can refer than 60 m s were excluded from the analysis, as such to Zieger (2010). This choice of boundary layer correc- data are questionable (a very rare occurrence in the tion is consistent with previous altimeter calibrations dataset). For calibration purposes, buoy data are usually (Zieger et al. 2009). Alternative approaches to address considered as ‘‘ground truth.’’ However, buoy data are boundary layer correction have been proposed by, for not free from errors due to their sampling variability example, Thomas et al. (2005) and Yang et al. (2011). and instrument accuracy (Young et al. 2017; Zieger et al. 2009). As a result, a conventional linear regression analysis is not appropriate. To this end, a reduced major 4. Calibration against NDBC buoy data axis (RMA) regression was used (Harper 2014). This To calibrate scatterometer wind speed, it is necessary to regression minimizes the triangular area bounded by the determine cases where buoy anemometer measurements vertical and horizontal offsets between the data point

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FIG. 6. Q–Q plots between the scatterometer and NODC buoy data for wind speed after the linear calibration and high-wind correction were applied.

and the regression line and the cord of the regression N 5 1 å 2 line. This is in contrast to a conventional regression, B (Mi Oi), (2) Ni51 which minimizes the vertical axis offset from the re- sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi gression line. In addition, standard least squares re- N 5 1 å 2 2 gression analysis is highly sensitive to outliers. Such RMSE (Mi Oi) , (3) N 5 outliers can be removed by the use of robust regression i 1 (Holland and Welsch 1977). Robust regression assigns a sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi N weight to each point, with values between 0 and 1. Points 1 å 2 2 2 (Mi Oi B) with a value less than 0.01 were designated as outliers Ni51 SI 5 , (4) N and removed from the analysis before applying the 1 å RMA regression analysis. Oi Ni51 To evaluate the performance of the calibration, four different statistical parameters were evaluated, cov(M, O) r 5 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi. (5) namely, bias B, root-mean-square error (RMSE), cov(M)cov(O) Pierson’s correlation coefficient r, and scatter index (SI). These parameters were evaluated using the fol- The matchup data across all the NDBC buoys were lowing relationships where M and O stand for model pooled for each of the seven scatterometers and the and observation (Ribal and Young 2019; Zieger et al. linear RMA regression undertaken. Figure 4 shows 2015), respectively, and N is the number of matchup example results for four cases (ERS-1, MetOp-A, points: QuikSCAT, and OceanSat-2).

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FIG. 7. Comparison between scatterometer and NODC for wind direction. The 1:1 agreement line is shown (thick solid line). Contours show the density of matchup data points, which has been normalized such that the maximum value is 1.0. Contours are drawn at 0.9, 0.7, 0.5, 0.4, 0.3, 0.2, 0.1, and 0.05.

It is clear that the scatterometer values of U10 agree shows Q–Q results for QuikSCAT and RapidScat. well with the buoy data, with only small deviations for Interestingly, all three scatterometers operate in the the 1:1 correlation line. Young et al. (2017) have pre- Ku band. viously investigated the calibration of QuikSCAT Young et al. (2017) noted very similar high-wind- and the result shown in Fig. 4c is almost identical to speed behavior for radiometers and Takbash et al. this previous calibration (see Fig. 6b of Young et al. (2019) proposed the empirical correction: 2017). This occurs despite the fact that the source of : the data and presumably the processing is different * 5 U [1 2 a(U 2 b)0 79], U10 10 10 (6) [PO.DAAC here vs Remote Sensing Systems for

Young et al. (2017)]. where U10 is the scatterometer/radiometer wind speed * Although scatterplots such as those shown in Fig. 4 after linear calibration is applied, U10 is the corrected provide a good overall assessment of platform perfor- wind speed, and a and b are constants that depend on the mance, they offer little insight into the performance at individual scatterometer. The values of a and b were high wind speed, where is a relatively small amount of determined based on the Q–Q plot analysis between buoy data. In these cases, it is more insightful to examine buoy measurement and scatterometer data after the quantile–quantile (Q–Q) plots between the buoy data linear calibration was applied. This relationship was andscatterometerdata.Basedonsuchananalysis, tested for the QuikSCAT, OceanSat-2, and RapidScat it was clear that three of the scatterometers overes- scatterometers, which overestimated wind speed com- timate high wind speed compared to buoys, namely, pared to buoys. Figure 6 shows example Q–Q plots QuikSCAT, OceanSat-2 and RapidScat. Figure 5 for the same four scatterometers shown in Fig. 4. In the

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high-wind-speed correction equations are summarized in Table 2. As noted above, scatterometers also measure wind direction. Hence, comparisons of wind direction between buoy and scatterometer data were also undertaken for each of the scatterometers. The same collocation process used for wind speed was again adopted for wind direction. That is, only wind direction data within 50 km of buoys and overpasses within 30 min of the buoy data recording were used for matchups. For all seven scatterometers, there was excellent agreement with the buoys for wind direction (see statistics on the Fig. 7). Figure 7 again shows scatterplots for the four scatterometers considered earlier (ERS-1, MetOp-A, QuikSCAT, and OceanSat-2). Based on these results, no attempt was made to calibrate or alter the wind direction measurements provided for each of the scatterometers.

5. Validation against platform data measurements To perform an independent validation of the calibrated scatterometer data, we utilized platform data provided by the Norwegian Meteorological Institute for offshore oil platforms at the locations shown in Fig. 8.As can be seen from the ﬁgure, there are 10 different locations in which platform data are available. The time period of the platform measurement is from 1999 until 2016. These data have been extensively studied by FIG. 8. Locations of offshore platforms used for validation of the Norwegian oil industries and found to be reliable scatterometer data. (Takbash et al. 2019). To perform the validation, again, the same matchup criteria as for the buoy data have be cases of QuikSCAT and OceanSat-2 (Figs. 6c,d)the applied. That is, only scatterometer data that are within empirical correction (6) results in much better high- 50 km of the platform and with a time difference less wind-speed agreement with the buoy data. The ﬁnal than 30 min are used. Once, the matchups were ob- scatterometer calibration relations, including the tained, the platform measurement data were compared

FIG. 9. Q–Q plots between the calibrated scatterometer and platform data for wind speed.

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FIG. 10. Cross-validation matchup plots between the scatterometers for wind speed. Shown are the 1:1 agreement (dashed diagonal line) and the RMA regression (thick solid line). Contours show the density of matchup data points, which has been normalized such that the maximum value is 1.0. Contours are drawn at 0.9, 0.7, 0.5, 0.4, 0.3, 0.2, 0.1, and 0.05. Dots represent outliers excluded from the RMA regression.

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FIG. 11. Q–Q plots between the scatterometers for wind speed. (a) MetOp-A–MetOp-B,(b)MetOp-A–RapidScat, (c) MetOp-B–RapidScat, (d) MetOp-B–OceanSat-2,(e)MetOp-A–QuikSCAT, and (f) OceanSat-2–ERS-2. with the calibrated scatterometer data, using the cali- boundary layer correction was applied). In addition, bration relationships as presented in Table 2. platform data do not suffer from issues of sheltering of The advantage of using platform data for this valida- the anemometer in high sea states, which has cast doubts tion is that the characteristics of the data are quite dif- on the accuracy of buoy measurements at high wind ferent to buoy data. Typically, the data are recorded speeds (Bender et al. 2010; Jensen et al. 2015; Large at a much greater height than the buoy data (again a et al. 1995; Taylor and Yelland 2001; Zeng and Brown

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FIG. 12. Scatterometer–scatterometer difference for wind speed as a function of time. (a) MetOp-A–MetOp-B, (b) MetOp-A–RapidScat, (c) MetOp-B–RapidScat, (d) MetOp-B–OceanSat-2, (e) MetOp-A–QuikSCAT, and (f) OceanSat-2–ERS-2.

1998). Platform data do, however, potentially suffer calibrated scatterometers. Based on Fig. 2, there are from blockage effects caused by the platform. In this nine possible cross validations that can be performed: regard, the present dataset has been extensively vali- RapidScat–MetOp-A, RapidScat–MetOp-B, OceanSat-2– dated to reliably deﬁne the 10 m wind speed U10 by the MetOp-A, OceanSat-2–MetOp-B,QuikSCAT–MetOp-A, Norwegian oil industry. QuikSCAT–ERS-2, ERS-2–OceanSat-2, ERS-2–MetOp-A, Scatter and Q–Q plots between the scatterometer and and MetOp-A–MetOp-B. The same matchup criteria as platform data were undertaken for each of the scatter- for the buoys was again applied for the cross validation ometers and Fig. 9 shows example Q–Q plots for MetOp- (i.e., 50-km spatial and 30-min temporal separations). A and MetOp-B. For all cases, the Q–Q plots showed good However, MetOp-A and MetOp-B are in the same tan- agreement between calibrated scatterometer and plat- dem orbit but with a 49-min time delay (Elyouncha and form data. All scatterometers measured slightly higher Neyt 2013b). Therefore, the 30-min separation criteria values than the platform data, indicating that the platform would result in no matchups. To address this issue, the data are generally slightly lower than the buoy data. collocation time between MetOp-A and MetOp-B was Importantly, however, the Q–Q plots show good agree- increased from 30 to 60 min. As the number of resulting ment at high wind speeds for all calibrated scatterometers, matchups between MetOp-A and MetOp-B is enor- indicating the high-wind-speed corrections applied for mous, only eighteen 1583158 locations around the QuikSCAT, OceanSat-2, and RapidScat are appropriate. world were selected to extract matchups (see Fig. 3). After applying all the quality criteria, this resulted in 1 769 942 matchups between MetOp-A and MetOp-B. 6. Cross validation between scatterometers Figure 10 shows example cross validations for MetOp-A– Cross validation between the scatterometers was MetOp-B, MetOp-A–RapidScat, MetOp-B–RapidScat, carried out to check the consistency and stability of the MetOp-B–OceanSat-2, MetOp-A–QuikSCAT, and

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FIG. 13. Cross-validation matchup plots between scatterometers and altimeters for wind speed. Shown are the 1:1 agreement (dashed diagonal line) and the RMA regression (thick solid line). Contours show the density of matchup data points, which has been normalized such that the maximum value is 1.0. Contours are drawn at 0.9, 0.7, 0.5, 0.4, 0.3, 0.2, 0.1, and 0.05. Dots represent outliers excluded from the RMA regression.

OceanSat-2–ERS-2. The agreement between the instru- including at high wind speeds. In addition, the difference ments is generally excellent (see statistics on the Fig. 10). between the scatterometers as a function of time is A further analysis of the corresponding Q–Q plots are shown in Fig. 12, indicating there are no changes in the shown in Fig. 11. Again, the agreement is excellent, calibration as a function of time.

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TABLE 3. Root-mean-square error for cross validation between TABLE 4. Scatter index for cross validation between scatterometers 2 scatterometers and altimeters (m s 1). and altimeters.

Altimeters Altimeters Scatterometers C-2 TP J-1 J-2 J-3 SA S-3 Scatterometers C-2 TP J-1 J-2 J-3 SA S-3 ER 0.822 ER 0.079 MA 0.797 0.891 0.862 0.870 1.052 0.782 MA 0.087 0.085 0.085 0.092 0.073 0.083 MB 0.773 1.000 0.890 0.897 1.042 0.768 MB 0.085 0.087 0.083 0.094 0.071 0.082 OC 1.193 1.301 1.201 OC 0.122 0.113 0.112 QU 0.865 0.834 0.809 QU 0.087 0.084 0.085 RA 0.896 0.969 0.948 RA 0.086 0.082 0.084

case, however, the scatterometers produce slightly It is clear in Figs. 10 and 12 that there is some differ- lower values of wind speed than the altimeters. As ence in mean values of some of the scatterometers. The noted above, it is believed that this is the result of at- cross validations of MetOp-A–RapidScat, MetOp-B– mospheric stability and the fact that the altimeters and OceanSat-2, MetOp-B–RapidScat, and MetOp-A– scatterometers operate in different frequency bands. QuikSCAT all show such a difference. In each case, The performance of the cross validations was ana- these represent comparisons of C-band scatterometers lyzed using four different statistical parameters as given versus Ku-band scatterometers, with the Ku-band scat- in Eqs. (2)–(5). The values of the statistical parameters terometers giving slightly lower wind speeds. As shown for all 22 cross validations are provided in the Tables 3–6. by Young and Donelan (2018), satellite remote sensing Note that C-2,TP,J-1, J-2, J-3, SA,andS-3 are ab- measurements of wind speed are impacted by bound- breviated forms of the altimeters CryoSat-2, TOPEX, ary layer stability, with the magnitude of the impact Jason-1, Jason-2, Jason-3, SARAL, and Sentinel-3A, influenced by the frequency of the instrument under respectively. Similarly, ER, MA, MB, OC, QU, and RA consideration. Although all the scatterometers were are abbreviated forms of scatterometers ERS-1, MetOp- calibrated against an extensive buoy dataset, the A, MetOp-B, OceanSat-2, QuikSCAT, and RapidScat, cross-validation matchups are across a much wider respectively. Moreover, unlike Tables 3–5, Table 6 has range of latitudes and hence a more extensive span to be read from row to column as bias is not a commu- of air and water temperatures (i.e., different atmo- tative parameter as shown in Eq. (2). spheric stability). This accounts for the difference As shown in Tables 3–6, all four statistical parameters seen here, with similar differences previously re- that have been used to justify the performance of the ported for altimeter and radiometer data (Young and cross validation show good results, with the scatter in- Donelan 2018). dex for all cases less than 0.13 and the correlation coefficient more than 0.95. Although some of the RMSE 2 7. Cross validation between scatterometers values are more than 1 m s 1, they are still acceptable and altimeters as the other parameters are still good. This is particu- larly the case for OceanSat-2. There is a consistent In addition to the cross validation between negative bias (scatterometer lower than altimeter). scatterometers, a further consistency and stability check This is consistent with the results shown in Figs. 10 and was undertaken by cross validating against altimeter data. 12 and attributed above to the impact of atmospheric For this purpose, the calibrated altimeter dataset of stability. Ribal and Young (2019) wasused.Therearealarge number of possible validation overlaps between these two satellite datasets. For the present purposes, a total TABLE 5. Correlation coefficient for cross validation between scatterometers and altimeters. of 22 combinations of altimeter and scatterometer were considered. These combinations involved six Altimeters scatterometers: ERS-1, QuikSCAT, MetOp-A, OceanSat-2, Scatterometers C-2 TP J-1 J-2 J-3 SA S-3 MetOp-B, and RapidScat and seven altimeters: TOPEX, ER 0.982 Jason-1, Jason-2, CryoSat-2, SARAL, Jason-3,and MA 0.977 0.977 0.977 0.975 0.978 0.978 Sentinel-3A. The same collocation criteria as previously MB 0.978 0.974 0.977 0.974 0.979 0.979 applied have been used (i.e., 50-km spatial and 30-min OC 0.950 0.953 0.954 temporal separations). Figure 13 shows examples of six QU 0.975 0.976 0.976 combinations, again showing good agreement. In each RA 0.973 0.974 0.970

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21 TABLE 6. Bias for cross validation between scatterometers and altimeters (m s ).

Altimeters Scatterometers C-2 TP J-1 J-2 J-3 SA S-3 ER 20.392 MA 20.138 20.363 20.304 20.266 20.710 20.167 MB 20.112 20.504 20.391 20.312 20.709 20.197 OC 20.457 20.723 20.579 QU 20.348 20.313 20.256 RA 20.375 20.564 20.489

The resulting dataset has been archived with the Acknowledgments. The authors acknowledge ongoing Australian Ocean Data Network (AODN) and is avail- support from the Australian Research Council through able for public domain access. Details of this archive are Grant DP160100738 and the Integrated Marine provided in appendixes A and B. Observing System (IMOS) for support in development of this database. As noted in the paper, the original data were sourced from a range of public archives. These 8. Conclusions repositories are gratefully acknowledged. The platform Global ocean wind speed measured from seven dif- wind data were supplied by Oyvind Breivik of the ferent scatterometers have been calibrated, validated, Norwegian Meteorological Institute. and cross validated in this study, creating a consistent dataset spanning 27 years, from 1992 until 2018. This combined dataset is believed to be the first long- APPENDIX A duration, multimission scatterometer dataset developed. Each scatterometer has been calibrated against Data Description in situ buoy data, from buoys that are more than 50 km In the present database, eight physical parameters are from the coastline to avoid potential land contamina- archived as outlined in the following table. The data tion. To show the robust nature of the calibrations, the have been binned into 18318 bins in a similar manner data have been validated against independent wind to the altimeter database of Ribal and Young (2019). measurements obtained from offshore oil platforms. Unlike altimeter measurements, scatterometers mea- This validation shows that the calibrated scatterometer sure over a swath. As a result, the data in the database data are consistent with the platform data both for mean are stored in the form of a two-dimensional matrix in conditions and extreme wind speeds. which the row index represents the number of points in To check the consistency and long-term stability of the swath in the cross-track direction and the column the data, cross validations between scatterometers as index represents the measurement time (Table A1). well as between scatterometers and altimeters have also In providing the data in netCDF format, we follow been performed. Nine combinations of scatterometers the Integrated Marine Observing System (IMOS) data were cross validation showing consistent measurements protocol upon which the project is based (IMOS between all platforms. Similarly, 22 combinations of 2015a,b). In particular, the quality flags in the database altimeters and scatterometers involving six calibrated have followed the IMOS standard flag system where 1, 2, scatterometers and seven calibrated altimeters were cross 4, and 9 represent Good_data, Probably_good_data, validated. The results show that calibrated scatterometer Bad_data, and Missing_data, respectively. It should data and calibrated altimeter data are in excellent be noted that high- and low-wind-speed data flagged agreement (see statistics on the Fig. 13). Comparisons in the respective source datasets have been flagged as between altimeter and scatterometer were quantified ‘‘Good_data’’ in the present database. using four different statistical parameters, all of The file names follow the format IMOS_SRS-Surface- which showed excellent agreement (see statistics on Waves_M_Wind-SCATTEROMETER_FV02_Lat-Lon- the Fig. 13). DM00.nc, where The combined dataset is a resource that can be used for a variety of purposes, including studies of clima- 1) IMOS is the name of the project tology, long-term trends in wind speed and direction, 2) SRS-Surface-Waves represents the present facility and the determination of extreme wind speeds (see 3) M signifies meteorological related parameters appendixes A and B). 4) Wind represents wind only

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TABLE A1. List of all parameters in the present scatterometer such a server can be found online (https://help.aodn.org.au/ database. downloading-data-from-servers/amazon-s3-servers/; once No. NetCDF variable name Description access to the S3 server is gained, the user should navigate to IMOS/SRS/Surface-Waves/Wind-Scatterometry-DM00). 1 TIME Time 2 LATITUDE Latitude c. AODN thredds 3 LONGITUDE Longitude 4 WSPD_CAL_quality_control Wind vector cell quality ﬂags The data can also be accessed from AODN thredds, which 5 WND_DIR_quality_control Wind direction quality ﬂags is very useful for Linux users (http://thredds.aodn.org.au/ 6 WND_DIR Scatterometer wind direction thredds/catalog/IMOS/SRS/Surface-Waves/Wind- 7 WSPD Uncalibrated scatterometer wind speed Scatterometry-DM00/catalog.html). 8 WSPD_CAL Calibrated scatterometer wind speed REFERENCES

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