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Comparison of Oceansat-2 Scatterometer Wind Data with Global Moored Buoys and ASCAT Observation

Comparison of Oceansat-2 Scatterometer Wind Data with Global Moored Buoys and ASCAT Observation Hindawi Advances in Meteorology Volume 2019, Article ID 1651267, 9 pages https://doi.org/10.1155/2019/1651267 Research Article Comparison of Oceansat-2 Scatterometer Wind Data with Global Moored Buoys and ASCAT Observation Jungang Yang and Jie Zhang e First Institute of Oceanography, Ministry of Natural Resources of China, Qingdao 266061, China Correspondence should be addressed to Jungang Yang; yangjg@fio.org.cn Received 14 October 2018; Revised 30 December 2018; Accepted 21 February 2019; Published 19 March 2019 Academic Editor: Anthony R. Lupo Copyright © 2019 Jungang Yang and Jie Zhang. -is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. -e Oceansat-2 satellite was launched on 23 September 2009 by the Indian Space Research Organization (ISRO). In this study, the historic archived OSCAT wind vectors are compared with the global moored buoys’ wind observations, including the U.S. National Data Buoy Center (NDBC), the Tropical Atmosphere Ocean (TAO), the Pilot Research Moored Array in the Tropical Atlantic (PIRATA), the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA), and Advanced Scatterometer (ASCAT) wind data in the same period of OSCAT by calculating the statistical parameters, namely, the root mean square error (RMSE), bias (mean of residuals), and correlation coefficient (R) between the collocated data. -e comparisons with the global moored buoys show that the OSCAT wind vectors are consistent with buoys’ wind measurements. -e average errors of the OSCAT wind vectors are 1.20 m/s and 17.7 . -e analysis of the OSCAT wind vector errors at different buoy wind speeds in bins of 1 m/s indicates that the accuracy of the OSCAT wind speed first increases and then decreases with the increasing wind speed. -e comparisons of OSCAT wind vectors and ASCAT wind vectors show that the average RMSEs of their differences are 1.27 m/s and 20.17 . In general, the accuracies of the OSCAT wind vectors satisfy the general scatterometer’s mission requirement and are consistent with ASCAT wind data. OSCAT wind vectors can be used in the global change study by the combination with other scatterometer data. ADEOS-1 NSCAT, QuikScat Seawinds, ADEOS-2 Seawinds, 1. Introduction Metop-A/B ASCAT, Oceansat-2 Scatterometer (OSCAT), Ocean surface wind is an important meteorological factor HY-2A SCAT, ISS-RapidSCAT, and so on. Scatterometers for driving seawater movement. Ocean surface wind affects measure the radar cross section of the ocean surface, and almost all oceanic dynamic processes from sea surface numerical inversion of the geophysical model function microscale waves to ocean circulation. So, ocean surface yields the scatterometer wind measurement. Because scat- wind is of vital importance for studies of oceanic processes terometer does not measure ocean surface wind directly and and improvement of marine and weather forecasting by data accurate observations of ocean surface wind over global assimilation in the operational prediction models [1]. Ocean oceans are required for a wide range of meteorological and surface wind is very important geophysical variable to ac- oceanographic studies and global climate change study, curately measure. Traditional anemometer winds cover the validation of scatterometer wind vectors is necessary for the spatial and temporal domains poorly. Satellite scatterometer applications of the scatterometer wind data [2, 3]. For the is a widely used technique for measuring global ocean existing scatterometers, many validation works have been surface winds from space synchronously. Seasat-A Satellite carried out such as NSCAT [4], AMI [5], QuikSCAT [6], Scatterometer (SASS) as the first satellite scatterometer was ASCAT [7], and HY-2A SCAT [8, 9] by comparing scat- launched in 1978; from then on, many space-based scat- terometer winds with in situ measurements from buoys and terometers appeared successively, such as ERS-1/2 AMI, vessels. 2 Advances in Meteorology -e Oceansat-2 satellite was launched on 23 September wind data are evaluated over different oceans and in the 2009 by the Indian Space Research Organization different ranges of wind speed. (ISRO) with a Ku-band pencil beam scatterometer (OSCAT) into a near polar sun-synchronous orbit with an 2. Data and Methods altitude of 720 km, inclination of 98.25 , and the local time of equatorial crossing in the descending node at 12 2.1. Data noon± 10 minutes. OSCAT experienced an irrecoverable malfunction and stopped working on 20 February 2014. 2.1.1. OSCAT Wind Data. Archived OSCAT version 2 Level -e resolution of OSCAT global wind vector data is 50 km, 2B ocean wind vectors during 16 January 2010 to 20 Feb- 25 km (since July 2013), and 12.5 km. -e goals of OSCAT ruary 2014 are used in this study. -is level 2B dataset is mission were to provide wind data between 4 and 24 m/s, produced by the Jet Propulsion Laboratory (JPL) QuikSCAT with an accuracy of 2 m/s and 20 . Several validation studies Project in cooperation with the Indian Space Research of OSCAT wind data are performed by using in situ data Organization (ISRO). -e ocean wind vectors are provided from buoys and other data. OSCAT wind vectors from on a nonuniform grid with the swath at 12.5 km pixel November 2009 to December 2010 are compared with resolution. -is resolution is achieved through a slice RAMA and TRITON buoys data in the Indian Ocean and composite technique in which high resolution slice mea- the Pacific Ocean, and the results show the accuracies of surements from L1B data are composited into a 12.5 km wind speed are within the mission requirement, but the wind vector cell. OSCAT is a Ku-band (13.515 GHz) pencil wind direction errors are higher than the mission re- beam scatterometer with two differently polarized beams quirement [10]. OSCAT surface winds for the monsoon which scan the ocean surface in a circular manner at in- ° ° period (June–September) of 2011 over the Arabian Sea were cidence angles of 48.9 (HH-polarization) and 57.6 (VV- compared with two moored buoys, and the results show polarization). -is corresponds to overlapping HH and VV that the errors of wind speed and direction are less than swaths of 1400 km and 1840 km width, respectively [18]. 2.5 m/s and 20 , respectively [11]. -e comparisons between OSCAT has a repeat cycle of 2 days. Mission design accu- surface wind vectors form OSCAT and global moored racies of OSCAT wind observation are 2 m/s of wind speed buoys during the period from November 1, 2009 to July 31, and 20 of wind direction in the wind speed range of 2010 show that the RMSE of about 1.5 m/s in wind speed and 4–24 m/s. about 20 in wind direction for the speed range 4–24 m/s [12]. OSCAT winds are validated using in situ buoy observations 2.1.2. Buoy Wind Data. Wind observation data during the and short-term model forecasts for the monsoon in 2011, and the results show OSCAT winds are within the mission same period of OSCAT wind data obtained by 96 moored buoys from NDBC, 66 moored buoys from TAO/TRITON, goal [13]. OSCAT ocean surface winds over the Indian Ocean were validated against the RAMA buoy winds in 18 moored buoys from PIRATA, and 26 moored buoys from RAMA were used in this study. -e location of these buoys is 2011, and the results show that the wind speeds and di- rections derived from OSCAT agree with RAMA buoy shown in Figure 1. -e buoy winds are measured by av- eraging the wind speed and direction over 10 minutes at the winds [14]. Ocean surface winds of OSCAT were validated with equivalent neutral wind observations from 87 global different heights from the sea surface. So, the buoy winds were converted to 10 m neutral winds using the LKB model buoys and winds from ECMWF Numerical Weather Prediction (NWP) model using triple collocation from 1 [19] to make the buoys data comparable with OSCAT. November 2009 to 31 July 2010 [15]. OSCAT wind data in 2010 were compared with wind speeds estimated from Jason Altimeters [16]. Wu and Chen [17] validated OSCAT 2.1.3. ASCAT Wind Data. -e Advanced Scatterometer wind data from January 2012 to August 2013. All existing (ASCAT) 25 km Level 2 ocean surface wind product during validation studies are about the parts of OSCAT wind data the same period of OSCAT data was used in this study. -e for the period no more than 2 years. ASCAT equipped on MetOp-A and MetOp-B satellite was More than 4 years OSCAT wind data are obtained during launched on 19 October 2006 and 17 September 2012 by the the whole lifetime of OSCAT from 23 September 2009 to 20 European Organization for the Exploitation of Meteoro- February 2014. -ese data are useful for the global ocean logical Satellites (EUMETSAT), respectively. -e MetOp surface wind studies. Long-term series multisource scat- satellite is in a circular orbit with a period of approximately terometer ocean surface wind data are also needed by the 101 minutes, an inclination of 98.59 , and an orbit height of global change study and Climate Data Record (CDR) which 800 km. ASCAT has two swaths 550 km wide, located on requires the consistency of accuracy. So, it is very important each side of the satellite track, separated by 700 km. ASCAT to carry out data accuracy assessment of OSCAT ocean wind data are produced by the Royal Netherlands Meteo- surface wind by comparing with in situ measurement of rological Institute (KNMI) in the Ocean and Sea Ice Satellite wind for the data application in the global change study. In Application Facility European Organization for the Ex- this paper, allover archived OSCAT Level 2B ocean wind ploitation of Meteorological Satellites (OSI-SAF EUMET- vectors in 12.5 km slice composites from 16 January 2010 to SAT) projects. -e validation of ASCAT wind data showed 20 February 2014 are compared with the global moored that the wind speed bias was between −0.3∼0.3 m/s at dif- buoys and ASCAT wind data, and the accuracies of OSCAT ferent times, and average standard deviation of zonal and Advances in Meteorology 3 NDBC (96) 60°N TAO (66) PIRATA (18) RAMA (26) 40°N 20°N 0° 20°S 0°E 20°E 40°E 60°E 80°E 100°E 120°E 140°E 160°E 180°E 160°W 140°W 120°W 100°W 80°W 60°W 40°W 20°W 0°W Figure 1: Distribution of the NDBC, TAO, PIRATA, and RAMA moored buoys. from 18 PIRATA buoys, 8001 from 26 RAMA buoys, and meridional wind speed component of ASCAT wind data by buoy winds was less than 1.6 m/s [20]. 39,495 from 66 TAO buoys are collocated by the method introduced in Section 2.2. e nearest collocated NDBC buoys is about 12.4 km from the coast, which is close to the 2.2. Methods. e comparisons of OSCAT wind data with spatial resolution 12.5 km of OSCAT wind vector. 14.6% of global moored buoys and ASCAT observation are mainly the collocated NDBC buoys o—shore distance is less than based on the data collocation in space and time. First, the 25 km. e separate comparisons of the collocated OSCAT OSCAT wind data are collocated with moored buoy and and these buoys data show that there is no inŸuence of land ASCAT wind data to obtain quasisynchronous collocated on the OSCAT wind data. e scatter diagrams of com- data. For the collocation between OSCAT and moored buoy parisons of wind speed and direction are shown in Figure 2. data, the OSCAT wind vector measurement is the spatial In general, OSCAT wind speeds and directions are con- average of the 12.5 km × 12.5 km cell, and the buoy mea- sistent to those of the moored buoys except for a little surement is the 10-minute average wind data at the location overestimation of OSCAT wind speeds. of the buoy. Neither of these measurements have spatial or e statistical parameters of errors between OSCAT and temporal synchronization. OSCAT wind vector cells closest the moored buoys are given in Table 1. e biases and to the buoy locations in space (within 12.5 km) and the buoy RMSEs of OSCAT wind speeds compared with di—erent data closest to the OSCAT observations in time (within moored buoys are 0.22∼0.34 m/s and 1.11∼1.24 m/s, and 30 min) are chosen. For the collocation between OSCATand ° ° those of OSCAT wind directions are −0.61∼0.68 and ° ° ASCAT data, the spatially closest wind cells and observation 16.7∼18.4 . e overall average biases and RMSEs are within 10 min are collocated. e maximum possible dis- ° ° 0.27 m/s and 1.20 m/s for wind speed and 0.37 and 17.7 for tance between OSCAT and ASCAT wind vector cells is half wind direction, respectively. e correlation coeŽcients the spatial grid size, i.e., 6.25 km. Second, according to the show that OSCAT wind vectors and all moored buoys wind collocation data, statistical parameters—the root mean data are consistent. e OSCAT wind speed in the Paci£c square error (RMSE), bias (mean of residuals), and corre- Ocean (area of the TAO buoys) and Atlantic Ocean (area of lation coeŽcient (R) [2, 3]—are computed and presented to the PIRATA buoys) is better than that in the coastal area evaluate the accuracy of OSCAT wind data through the (area of the NDBC buoys) and Indian Ocean (area of the comparison of OSCAT data with moored buoy and ASCAT RAMA buoys). A possible reason for this di—erence is that wind data directly. the wind is more stable in the area of TAO and PIRATA buoys, and the land has an e—ect on the higher variability of wind in the area of NDBC buoys which is not sensed by 3. Results and Discussion OSCAT. In the comparison of the wind direction, the wind 3.1. Comparison with Moored Buoys. e historical archived direction bias is negligibly small for all buoy observations OSCAT Level 2B 12.5 km ocean surface wind vectors during relative to the wind direction accuracy of 2 . e positive 16 January 2010 to 20 February 2014 are compared with biases indicate that the OSCAT-derived wind direction is global moored buoys’ wind data. Because OSCAT retrievals right to the wind direction observed by buoys. In general, produce the 10 m equivalent neutral winds, wind speeds OSCAT wind data have similar accuracy for di—erent oceans. recorded by the moored buoys need to be converted from In order to analyze the comparisons between OSCAT the observation heights to a height of 10 m in a neutrally wind vectors and the moored buoy wind at di—erent wind equivalent atmosphere. Using the method mentioned in speeds, binwise variations of the bias, RMSE, and residuals Section 2.2, the wind speed measured by the moored buoys (OSCATminus buoy) of wind speed and direction in bins of at di—erent heights above the sea surface is converted to the buoy wind speed of 1 m/s are shown in Figure 3. Squares and equivalent neutral wind speed at a height of 10 m. e wind vertical lines are biases and RMSEs in the £gure, respectively. direction is assumed to not change with this conversion. e collocated wind data are mainly distributed at the speed Data pairs totaling 49,414 from 96 NDBC buoys, 14,569 range of 4∼12 m/s. In the case of wind speed comparisons, 4 Advances in Meteorology 30 200 30 200 180 180 25 25 160 160 140 140 20 20 120 120 15 100 15 100 80 80 10 10 60 60 40 40 5 5 20 20 0 0 0 5 10 15 20 25 30 0 5 10 15 20 25 30 NDBC buoy wind speed (m/s) TAO buoy wind speed (m/s) 30 200 30 200 180 180 25 25 160 160 140 140 20 20 120 120 15 15 100 100 80 80 10 10 60 60 40 40 5 5 20 20 0 0 0 5 10 15 20 25 30 10 15 20 25 30 PIRATA buoy wind speed (m/s) RAMA buoy wind speed (m/s) 360 200 360 200 180 180 300 300 160 160 140 140 240 240 120 120 180 100 180 100 80 80 120 120 60 60 40 40 60 60 20 20 0 0 0 60 120 180 240 300 360 0 60 120 180 240 300 360 NDBC buoy wind direction (°) TAO buoy wind direction (°) 360 200 360 200 180 180 300 300 160 160 140 140 240 240 120 120 180 100 180 100 80 80 120 120 60 60 40 40 60 60 20 20 0 0 0 60 120 180 240 300 360 0 60 120 180 240 300 360 PIRATA buoy wind direction (°) RAMA buoy wind direction (°) Figure 2: Scatterplots for the comparisons of wind speed and direction between OSCAT and di—erent moored buoys. the overall biases of wind speed are close to 0. e positive or 5 m/s) and large wind speed (more than 13∼19 m/s for negative biases show an overestimation or underestimation di—erent buoys). e RMSEs of wind speed decrease with of the wind speed by OSCAT at low wind speed (less than increasing wind speed when wind speed is lower than 9 m/s Oceansat-2 wind direction (°) Oceansat-2 wind direction (°) Oceansat-2 wind speed (m/s) Oceansat-2 wind speed (m/s) Number of collocations Number of collocations Number of collocations Number of collocations Oceansat-2 wind direction (°) Oceansat-2 wind direction (°) Oceansat-2 wind speed (m/s) Oceansat-2 wind speed (m/s) Number of collocations Number of collocations Number of collocations Number of collocations 50 Advances in Meteorology 5 Table 1: Error statistics of the comparison between the OSCAT and buoys wind data. Speed Direction Buoy Number ° ° Bias (m/s) RMSE (m/s) R Bias ( ) RMSE ( ) R NDBC 49414 0.28 1.24 0.93 0.68 18.4 0.81 PIRATA 14569 0.34 1.11 0.87 −0.60 17.6 0.80 RAMA 8001 0.27 1.24 0.90 −0.61 18.4 0.78 TAO 39495 0.22 1.17 0.87 0.53 16.7 0.79 ALL 111479 0.27 1.20 0.91 0.37 17.7 0.82 NDBC TAO PIRATA RAMA 5 5 5 –5 –5 –2 –10 –5 05 10 15 20 25 05 10 15 05 10 15 0 5 10 15 10 10 10 10 0 0 0 –10 –10 –10 –10 05 10 15 20 25 05 10 15 05 10 15 0 5 10 15 50 50 50 0 0 0 –50 –50 –50 –50 05 10 15 20 25 05 10 15 05 10 15 0 5 10 15 100 100 100 100 0 0 0 0 –100 –100 –100 –100 05 10 15 20 25 05 10 15 05 10 15 0 5 10 15 10000 10000 4000 2000 5000 5000 2000 1000 0 0 0 0 05 10 15 20 25 05 10 15 05 10 15 0 5 10 15 Buoy wind speed (m/s) Buoy wind speed (m/s) Buoy wind speed (m/s) Buoy wind speed (m/s) Figure 3: Dependence of statistical parameters of wind speed and direction residuals (OSCAT-buoy) on the buoy wind speed. and then increase as the wind speed exceeds 9 m/s. -e -e distribution behavior of PDFs of OSCAT and buoys for accuracy of the OSCAT wind speed first increases and then wind speed and direction implies their consistencies, and decreases with the increasing wind speed. -e wind speed they have the similar characteristics. -e wind speed his- residuals in the coastal area (area of the NDBC buoys) are tograms of OSCATand buoys at speeds less than 10 m/s have larger than that in the open sea (area of the remaining a slight offset for NDBC, PIRATA, and RAMA buoys, which buoys). -e wind speed residuals decrease with the in- indicates the positive biases of OSCAT wind speed. OSCAT overestimates the buoy wind at low wind speed as proofed creasing wind speed. In general, the OSCAT wind speed is higher than the moored buoy wind speed. In the case of the above. wind direction comparisons, the wind direction biases are very close to 0 , showing the consistency between OSCAT and buoy wind directions. -e RMSE of the wind direction 3.2. Comparison with ASCAT. In order to analyze the ac- curacy of OSCAT wind vectors on the global oceans area overall decreases with increasing wind speed. -e wind direction residuals in the coastal area (area of NDBC where there are no in situ wind observations, OSCAT wind vectors are compared with ASCAT wind vectors which has buoys) are larger than that in the open sea (area of the remaining buoys), but it decreases with the increasing wind been proved to be highly accurate during the period from 16 January 2010 to 20 February 2014. -e collocated data pairs speed. In conclusion, the accuracy of the OSCAT wind speed generally first increases and then deceases, while the of OSCAT and ASCAT wind vectors by the method in- troduced in Section 2.2 are more than 1.4 million, and they accuracy of the OSCAT wind direction generally decreases locate on high latitude areas as shown in Figure 6, and the with increasing wind speed. -e accuracies of the OSCAT scatterplots of comparisons are shown in Figure 7. wind data are consistent for different moored buoys. In It is shown in Figure 7 that wind speed and direction addition, the statistical parameters of wind speed and di- rection residuals (OSCAT-buoy) at different times are derived by OSCAT are consistent with those of ASCAT. Scatterplots of wind direction comparison show that there shown in Figure 4. It is shown that the OSCAT wind data are stable over time. are some differences of 180 between OSCAT and ASCAT wind direction. -e reason for 180 difference is the exis- To understand the abundance of the collocated data for an entire possible range of ocean surface wind, probability tence of 180 ambiguities in some derived wind directions by scatterometer. -e 180 ambiguity of wind direction means distribution functions (PDFs) of the collocated data are plotted for wind speed and direction as shown in Figure 5. the wind direction retrieved by scatterometer is opposite to Occurrence Wind direction residual (°) count Wind speed residual (m/s) 6 Advances in Meteorology NDBC PIRATA RAMA TAO 2 2 2.5 2 1.5 1.5 1.5 1.5 1 1 1 0.5 0.5 0.5 0.5 0 0 0 Bias Bias Bias Bias RMSE RMSE RMSE RMSE 20 20 10 10 –5 Bias Bias Bias Bias RMSE RMSE RMSE RMSE Figure 4: Statistical parameters of wind speed and direction residuals (OSCAT-buoy) at di—erent times. –3 ×10 0.15 8 0.2 0.01 0.008 6 0.15 0.1 0.006 0.1 0.004 0.05 0.05 0.002 0 0 0 0 0 5 10 15 20 0 100 200 300 0 5 10 15 20 0 100 200 300 Wind speed (m/s) Wind direction (°) Wind speed (m/s) Wind direction (°) Oceansat-2 Oceansat-2 Oceansat-2 Oceansat-2 NDBC NDBC PIRATA PIRATA 0.15 0.012 0.2 0.012 0.01 0.01 0.15 0.1 0.008 0.008 0.006 0.1 0.006 0.05 0.004 0.004 0.05 0.002 0.002 0 0 0 0 0 5 10 15 20 0 100 200 300 0 5 10 15 20 0 100 200 300 Wind speed (m/s) Wind direction (°) Wind speed (m/s) Wind direction (°) Oceansat-2 Oceansat-2 Oceansat-2 Oceansat-2 RAMA RAMA TAO TAO Figure 5: PDFs of the collocated OSCAT and moored buoy wind speeds and directions in the bin of 1 m/s and 10 . the true wind direction. e di—erences between OSCATand ×10 ASCAT wind vectors (OSCAT minus ASCAT) in di—erent months are shown in Figure 8. e biases and RMSEs of wind speed between OSCAT 4 and ASCAT are −1.23∼−0.17 m/s and 0.62∼1.71 m/s and ° ° those of wind direction are −10.00 ∼16.23 and ° ° 17.56∼22.94 . e overall average biases and RMSEs of wind –80 –60 –40 –20 0 20 40 60 80 ° ° speed and direction are −0.64 m/s, 1.27 m/s and 2.87 , 20.17 , Latitude (°) which basically satis£es the mission speci£cation of less than 2 m/s and 20 . e negative wind speed bias means that Figure 6: Number distribution of collocated data between OSCAT OSCAT wind speed is less than that of ASCAT, which is and ASCAT on the latitude. Wind speed (m/s) Wind direction (°) PDF PDF 10/01 10/01 11/01 11/01 12/01 12/01 13/01 13/01 14/01 14/01 Wind speed (m/s) Wind direction (°) 10/01 10/01 11/01 11/01 12/01 12/01 13/01 13/01 14/01 14/01 Wind speed (m/s) Wind direction (°) 10/01 10/01 11/01 11/01 12/01 12/01 13/01 13/01 14/01 14/01 Wind speed (m/s) Wind direction (°) 10/01 10/01 11/01 11/01 12/01 12/01 13/01 13/01 14/01 14/01 Advances in Meteorology 7 30 60 360 60 55 55 25 300 50 50 45 45 20 40 240 40 35 35 15 180 30 30 25 25 10 120 20 20 15 15 5 60 10 10 5 5 0 0 0 5 10 15 20 25 30 0 60 120 180 240 300 360 OSCAT wind speed (m/s) OSCAT wind direction (°) (a) (b) Figure 7: Scatterplots for wind speed and direction of the comparisons between OSCAT and ASCAT. –1 –2 –3 –20 ×10 2010-01 2010-06 2010-12 2011-06 2011-12 2012-06 2012-12 2013-06 2013-12 Year-month Figure 8: Statistical parameters of di—erence between OSCATand ASCAT wind vectors at di—erent months from January 2010 to February because of the sea surface temperature e—ects on the OSCAT 4. Conclusion Ku-band wind retrievals. Ku-band radar backscatter is known to be sensitive to SST, and this results in lower re- Satellite scatterometry is one of the important means to achieve quasisynchronous acquisition of the global ocean trieved wind speeds over colder ocean areas [21] (OSCAT- ASCAT collocated data locate on high latitude areas). ese surface wind, and long-term sequence global ocean surface wind data are one of the important data sources for global indicate the consistency of OSCAT and ASCAT wind vec- climate change research. OSCAT can provide global ocean tors, and they have the same accuracies of wind vectors. e surface wind vectors. 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Prasad, “In- tercomparison of oceansat-2 and ASCAT winds with in situ Acknowledgments buoy observations and short-term numerical forecasts,” At- mosphere-Ocean, vol. 52, no. 1, pp. 92–102, 2014. -is study was supported by the National Key R&D Program [14] S. I. Rani and M. D. Gupta, “Oceansat-2 and RAMA buoy of China (2016YFA0600102 and 2016YFC1401800). -e au- winds: a comparison,” Journal of Earth System Science, thors would like to thank the JPL’s Physical Oceanography vol. 122, no. 6, pp. 1571–1582, 2013. Distributed Active Data Center (PO. DAAC) of NASA for the [15] A. Chakraborty, R. Kumar, and A. Stoffelen, “Validation of ocean surface winds from the OCEANSAT-2 scatterometer distribution of the OSCAT data, KNMI for the distribution of using triple collocation,” Remote Sensing Letters, vol. 4, no. 1, ASCAT data, and the NOAA Pacific Marine Environmental pp. 85–94, 2013. Laboratory (PMEL) for NDBC, TAO, PIRATA, and RAMA [16] T. Mathew, A. Chakraborty, A. Sarkar, and R. Kumar, buoy data. “Comparison of oceanic winds measured by space-borne scatterometers and altimeters,” Remote Sensing Letters, References vol. 3, no. 8, pp. 715–720, 2012. [17] Q. Wu and G. Chen, “Validation and intercomparison of HY- [1] J. Vogelzang and A. Stoffelen, “Scatterometer wind vector 2A/MetOp-A/Oceansat-2 scatterometer wind products,” products for application in meteorology and oceanography,” Chinese Journal of Oceanology and Limnology, vol. 33, no. 5, Journal of Sea Research, vol. 74, no. 11, pp. 16–25, 2012. pp. 1181–1190, 2015. Advances in Meteorology 9 [18] R. M. Parmar, R. K. Arora, and M. Venkata Rao, “OCEANSAT 2 mission and its applications,” in Proceedings of the GEOSS and Next-Generation Sensors and Missions, Goa, India, November 2006. [19] W. T. Liu and W. Q. Tang, Equivalent Neutral Wind, Jet Propulsion Laboratory Publication, Pasadena, CA, USA, 1996, https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/ 19970010322.pdf. [20] A. Verhoef, J. Vogelzang, and A. Stoffelen, “ASCAT L2 winds data record validation report,” Ocean and Sea Ice SAF Technical Note SAF/OS/CDOP2/KNMI/TEC/RP/239, Royal Netherlands Meteorological Institute, De Bilt, Netherlands, [21] Z. Wang, A. Stoffelen, F. Fois et al., “SST dependence of Ku- and C-band backscatter measurements,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 10, no. 5, pp. 2135–2146, 2016. 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Comparison of Oceansat-2 Scatterometer Wind Data with Global Moored Buoys and ASCAT Observation

Yang, Jungang; Zhang, Jie
Advances in Meteorology , Volume 2019 – Mar 19, 2019
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Copyright © 2019 Jungang Yang and Jie Zhang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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10.1155/2019/1651267
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Hindawi Advances in Meteorology Volume 2019, Article ID 1651267, 9 pages https://doi.org/10.1155/2019/1651267 Research Article Comparison of Oceansat-2 Scatterometer Wind Data with Global Moored Buoys and ASCAT Observation Jungang Yang and Jie Zhang e First Institute of Oceanography, Ministry of Natural Resources of China, Qingdao 266061, China Correspondence should be addressed to Jungang Yang; yangjg@fio.org.cn Received 14 October 2018; Revised 30 December 2018; Accepted 21 February 2019; Published 19 March 2019 Academic Editor: Anthony R. Lupo Copyright © 2019 Jungang Yang and Jie Zhang. -is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. -e Oceansat-2 satellite was launched on 23 September 2009 by the Indian Space Research Organization (ISRO). In this study, the historic archived OSCAT wind vectors are compared with the global moored buoys’ wind observations, including the U.S. National Data Buoy Center (NDBC), the Tropical Atmosphere Ocean (TAO), the Pilot Research Moored Array in the Tropical Atlantic (PIRATA), the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA), and Advanced Scatterometer (ASCAT) wind data in the same period of OSCAT by calculating the statistical parameters, namely, the root mean square error (RMSE), bias (mean of residuals), and correlation coefficient (R) between the collocated data. -e comparisons with the global moored buoys show that the OSCAT wind vectors are consistent with buoys’ wind measurements. -e average errors of the OSCAT wind vectors are 1.20 m/s and 17.7 . -e analysis of the OSCAT wind vector errors at different buoy wind speeds in bins of 1 m/s indicates that the accuracy of the OSCAT wind speed first increases and then decreases with the increasing wind speed. -e comparisons of OSCAT wind vectors and ASCAT wind vectors show that the average RMSEs of their differences are 1.27 m/s and 20.17 . In general, the accuracies of the OSCAT wind vectors satisfy the general scatterometer’s mission requirement and are consistent with ASCAT wind data. OSCAT wind vectors can be used in the global change study by the combination with other scatterometer data. ADEOS-1 NSCAT, QuikScat Seawinds, ADEOS-2 Seawinds, 1. Introduction Metop-A/B ASCAT, Oceansat-2 Scatterometer (OSCAT), Ocean surface wind is an important meteorological factor HY-2A SCAT, ISS-RapidSCAT, and so on. Scatterometers for driving seawater movement. Ocean surface wind affects measure the radar cross section of the ocean surface, and almost all oceanic dynamic processes from sea surface numerical inversion of the geophysical model function microscale waves to ocean circulation. So, ocean surface yields the scatterometer wind measurement. Because scat- wind is of vital importance for studies of oceanic processes terometer does not measure ocean surface wind directly and and improvement of marine and weather forecasting by data accurate observations of ocean surface wind over global assimilation in the operational prediction models [1]. Ocean oceans are required for a wide range of meteorological and surface wind is very important geophysical variable to ac- oceanographic studies and global climate change study, curately measure. Traditional anemometer winds cover the validation of scatterometer wind vectors is necessary for the spatial and temporal domains poorly. Satellite scatterometer applications of the scatterometer wind data [2, 3]. For the is a widely used technique for measuring global ocean existing scatterometers, many validation works have been surface winds from space synchronously. Seasat-A Satellite carried out such as NSCAT [4], AMI [5], QuikSCAT [6], Scatterometer (SASS) as the first satellite scatterometer was ASCAT [7], and HY-2A SCAT [8, 9] by comparing scat- launched in 1978; from then on, many space-based scat- terometer winds with in situ measurements from buoys and terometers appeared successively, such as ERS-1/2 AMI, vessels. 2 Advances in Meteorology -e Oceansat-2 satellite was launched on 23 September wind data are evaluated over different oceans and in the 2009 by the Indian Space Research Organization different ranges of wind speed. (ISRO) with a Ku-band pencil beam scatterometer (OSCAT) into a near polar sun-synchronous orbit with an 2. Data and Methods altitude of 720 km, inclination of 98.25 , and the local time of equatorial crossing in the descending node at 12 2.1. Data noon± 10 minutes. OSCAT experienced an irrecoverable malfunction and stopped working on 20 February 2014. 2.1.1. OSCAT Wind Data. Archived OSCAT version 2 Level -e resolution of OSCAT global wind vector data is 50 km, 2B ocean wind vectors during 16 January 2010 to 20 Feb- 25 km (since July 2013), and 12.5 km. -e goals of OSCAT ruary 2014 are used in this study. -is level 2B dataset is mission were to provide wind data between 4 and 24 m/s, produced by the Jet Propulsion Laboratory (JPL) QuikSCAT with an accuracy of 2 m/s and 20 . Several validation studies Project in cooperation with the Indian Space Research of OSCAT wind data are performed by using in situ data Organization (ISRO). -e ocean wind vectors are provided from buoys and other data. OSCAT wind vectors from on a nonuniform grid with the swath at 12.5 km pixel November 2009 to December 2010 are compared with resolution. -is resolution is achieved through a slice RAMA and TRITON buoys data in the Indian Ocean and composite technique in which high resolution slice mea- the Pacific Ocean, and the results show the accuracies of surements from L1B data are composited into a 12.5 km wind speed are within the mission requirement, but the wind vector cell. OSCAT is a Ku-band (13.515 GHz) pencil wind direction errors are higher than the mission re- beam scatterometer with two differently polarized beams quirement [10]. OSCAT surface winds for the monsoon which scan the ocean surface in a circular manner at in- ° ° period (June–September) of 2011 over the Arabian Sea were cidence angles of 48.9 (HH-polarization) and 57.6 (VV- compared with two moored buoys, and the results show polarization). -is corresponds to overlapping HH and VV that the errors of wind speed and direction are less than swaths of 1400 km and 1840 km width, respectively [18]. 2.5 m/s and 20 , respectively [11]. -e comparisons between OSCAT has a repeat cycle of 2 days. Mission design accu- surface wind vectors form OSCAT and global moored racies of OSCAT wind observation are 2 m/s of wind speed buoys during the period from November 1, 2009 to July 31, and 20 of wind direction in the wind speed range of 2010 show that the RMSE of about 1.5 m/s in wind speed and 4–24 m/s. about 20 in wind direction for the speed range 4–24 m/s [12]. OSCAT winds are validated using in situ buoy observations 2.1.2. Buoy Wind Data. Wind observation data during the and short-term model forecasts for the monsoon in 2011, and the results show OSCAT winds are within the mission same period of OSCAT wind data obtained by 96 moored buoys from NDBC, 66 moored buoys from TAO/TRITON, goal [13]. OSCAT ocean surface winds over the Indian Ocean were validated against the RAMA buoy winds in 18 moored buoys from PIRATA, and 26 moored buoys from RAMA were used in this study. -e location of these buoys is 2011, and the results show that the wind speeds and di- rections derived from OSCAT agree with RAMA buoy shown in Figure 1. -e buoy winds are measured by av- eraging the wind speed and direction over 10 minutes at the winds [14]. Ocean surface winds of OSCAT were validated with equivalent neutral wind observations from 87 global different heights from the sea surface. So, the buoy winds were converted to 10 m neutral winds using the LKB model buoys and winds from ECMWF Numerical Weather Prediction (NWP) model using triple collocation from 1 [19] to make the buoys data comparable with OSCAT. November 2009 to 31 July 2010 [15]. OSCAT wind data in 2010 were compared with wind speeds estimated from Jason Altimeters [16]. Wu and Chen [17] validated OSCAT 2.1.3. ASCAT Wind Data. -e Advanced Scatterometer wind data from January 2012 to August 2013. All existing (ASCAT) 25 km Level 2 ocean surface wind product during validation studies are about the parts of OSCAT wind data the same period of OSCAT data was used in this study. -e for the period no more than 2 years. ASCAT equipped on MetOp-A and MetOp-B satellite was More than 4 years OSCAT wind data are obtained during launched on 19 October 2006 and 17 September 2012 by the the whole lifetime of OSCAT from 23 September 2009 to 20 European Organization for the Exploitation of Meteoro- February 2014. -ese data are useful for the global ocean logical Satellites (EUMETSAT), respectively. -e MetOp surface wind studies. Long-term series multisource scat- satellite is in a circular orbit with a period of approximately terometer ocean surface wind data are also needed by the 101 minutes, an inclination of 98.59 , and an orbit height of global change study and Climate Data Record (CDR) which 800 km. ASCAT has two swaths 550 km wide, located on requires the consistency of accuracy. So, it is very important each side of the satellite track, separated by 700 km. ASCAT to carry out data accuracy assessment of OSCAT ocean wind data are produced by the Royal Netherlands Meteo- surface wind by comparing with in situ measurement of rological Institute (KNMI) in the Ocean and Sea Ice Satellite wind for the data application in the global change study. In Application Facility European Organization for the Ex- this paper, allover archived OSCAT Level 2B ocean wind ploitation of Meteorological Satellites (OSI-SAF EUMET- vectors in 12.5 km slice composites from 16 January 2010 to SAT) projects. -e validation of ASCAT wind data showed 20 February 2014 are compared with the global moored that the wind speed bias was between −0.3∼0.3 m/s at dif- buoys and ASCAT wind data, and the accuracies of OSCAT ferent times, and average standard deviation of zonal and Advances in Meteorology 3 NDBC (96) 60°N TAO (66) PIRATA (18) RAMA (26) 40°N 20°N 0° 20°S 0°E 20°E 40°E 60°E 80°E 100°E 120°E 140°E 160°E 180°E 160°W 140°W 120°W 100°W 80°W 60°W 40°W 20°W 0°W Figure 1: Distribution of the NDBC, TAO, PIRATA, and RAMA moored buoys. from 18 PIRATA buoys, 8001 from 26 RAMA buoys, and meridional wind speed component of ASCAT wind data by buoy winds was less than 1.6 m/s [20]. 39,495 from 66 TAO buoys are collocated by the method introduced in Section 2.2. e nearest collocated NDBC buoys is about 12.4 km from the coast, which is close to the 2.2. Methods. e comparisons of OSCAT wind data with spatial resolution 12.5 km of OSCAT wind vector. 14.6% of global moored buoys and ASCAT observation are mainly the collocated NDBC buoys o—shore distance is less than based on the data collocation in space and time. First, the 25 km. e separate comparisons of the collocated OSCAT OSCAT wind data are collocated with moored buoy and and these buoys data show that there is no inŸuence of land ASCAT wind data to obtain quasisynchronous collocated on the OSCAT wind data. e scatter diagrams of com- data. For the collocation between OSCAT and moored buoy parisons of wind speed and direction are shown in Figure 2. data, the OSCAT wind vector measurement is the spatial In general, OSCAT wind speeds and directions are con- average of the 12.5 km × 12.5 km cell, and the buoy mea- sistent to those of the moored buoys except for a little surement is the 10-minute average wind data at the location overestimation of OSCAT wind speeds. of the buoy. Neither of these measurements have spatial or e statistical parameters of errors between OSCAT and temporal synchronization. OSCAT wind vector cells closest the moored buoys are given in Table 1. e biases and to the buoy locations in space (within 12.5 km) and the buoy RMSEs of OSCAT wind speeds compared with di—erent data closest to the OSCAT observations in time (within moored buoys are 0.22∼0.34 m/s and 1.11∼1.24 m/s, and 30 min) are chosen. For the collocation between OSCATand ° ° those of OSCAT wind directions are −0.61∼0.68 and ° ° ASCAT data, the spatially closest wind cells and observation 16.7∼18.4 . e overall average biases and RMSEs are within 10 min are collocated. e maximum possible dis- ° ° 0.27 m/s and 1.20 m/s for wind speed and 0.37 and 17.7 for tance between OSCAT and ASCAT wind vector cells is half wind direction, respectively. e correlation coeŽcients the spatial grid size, i.e., 6.25 km. Second, according to the show that OSCAT wind vectors and all moored buoys wind collocation data, statistical parameters—the root mean data are consistent. e OSCAT wind speed in the Paci£c square error (RMSE), bias (mean of residuals), and corre- Ocean (area of the TAO buoys) and Atlantic Ocean (area of lation coeŽcient (R) [2, 3]—are computed and presented to the PIRATA buoys) is better than that in the coastal area evaluate the accuracy of OSCAT wind data through the (area of the NDBC buoys) and Indian Ocean (area of the comparison of OSCAT data with moored buoy and ASCAT RAMA buoys). A possible reason for this di—erence is that wind data directly. the wind is more stable in the area of TAO and PIRATA buoys, and the land has an e—ect on the higher variability of wind in the area of NDBC buoys which is not sensed by 3. Results and Discussion OSCAT. In the comparison of the wind direction, the wind 3.1. Comparison with Moored Buoys. e historical archived direction bias is negligibly small for all buoy observations OSCAT Level 2B 12.5 km ocean surface wind vectors during relative to the wind direction accuracy of 2 . e positive 16 January 2010 to 20 February 2014 are compared with biases indicate that the OSCAT-derived wind direction is global moored buoys’ wind data. Because OSCAT retrievals right to the wind direction observed by buoys. In general, produce the 10 m equivalent neutral winds, wind speeds OSCAT wind data have similar accuracy for di—erent oceans. recorded by the moored buoys need to be converted from In order to analyze the comparisons between OSCAT the observation heights to a height of 10 m in a neutrally wind vectors and the moored buoy wind at di—erent wind equivalent atmosphere. Using the method mentioned in speeds, binwise variations of the bias, RMSE, and residuals Section 2.2, the wind speed measured by the moored buoys (OSCATminus buoy) of wind speed and direction in bins of at di—erent heights above the sea surface is converted to the buoy wind speed of 1 m/s are shown in Figure 3. Squares and equivalent neutral wind speed at a height of 10 m. e wind vertical lines are biases and RMSEs in the £gure, respectively. direction is assumed to not change with this conversion. e collocated wind data are mainly distributed at the speed Data pairs totaling 49,414 from 96 NDBC buoys, 14,569 range of 4∼12 m/s. In the case of wind speed comparisons, 4 Advances in Meteorology 30 200 30 200 180 180 25 25 160 160 140 140 20 20 120 120 15 100 15 100 80 80 10 10 60 60 40 40 5 5 20 20 0 0 0 5 10 15 20 25 30 0 5 10 15 20 25 30 NDBC buoy wind speed (m/s) TAO buoy wind speed (m/s) 30 200 30 200 180 180 25 25 160 160 140 140 20 20 120 120 15 15 100 100 80 80 10 10 60 60 40 40 5 5 20 20 0 0 0 5 10 15 20 25 30 10 15 20 25 30 PIRATA buoy wind speed (m/s) RAMA buoy wind speed (m/s) 360 200 360 200 180 180 300 300 160 160 140 140 240 240 120 120 180 100 180 100 80 80 120 120 60 60 40 40 60 60 20 20 0 0 0 60 120 180 240 300 360 0 60 120 180 240 300 360 NDBC buoy wind direction (°) TAO buoy wind direction (°) 360 200 360 200 180 180 300 300 160 160 140 140 240 240 120 120 180 100 180 100 80 80 120 120 60 60 40 40 60 60 20 20 0 0 0 60 120 180 240 300 360 0 60 120 180 240 300 360 PIRATA buoy wind direction (°) RAMA buoy wind direction (°) Figure 2: Scatterplots for the comparisons of wind speed and direction between OSCAT and di—erent moored buoys. the overall biases of wind speed are close to 0. e positive or 5 m/s) and large wind speed (more than 13∼19 m/s for negative biases show an overestimation or underestimation di—erent buoys). e RMSEs of wind speed decrease with of the wind speed by OSCAT at low wind speed (less than increasing wind speed when wind speed is lower than 9 m/s Oceansat-2 wind direction (°) Oceansat-2 wind direction (°) Oceansat-2 wind speed (m/s) Oceansat-2 wind speed (m/s) Number of collocations Number of collocations Number of collocations Number of collocations Oceansat-2 wind direction (°) Oceansat-2 wind direction (°) Oceansat-2 wind speed (m/s) Oceansat-2 wind speed (m/s) Number of collocations Number of collocations Number of collocations Number of collocations 50 Advances in Meteorology 5 Table 1: Error statistics of the comparison between the OSCAT and buoys wind data. Speed Direction Buoy Number ° ° Bias (m/s) RMSE (m/s) R Bias ( ) RMSE ( ) R NDBC 49414 0.28 1.24 0.93 0.68 18.4 0.81 PIRATA 14569 0.34 1.11 0.87 −0.60 17.6 0.80 RAMA 8001 0.27 1.24 0.90 −0.61 18.4 0.78 TAO 39495 0.22 1.17 0.87 0.53 16.7 0.79 ALL 111479 0.27 1.20 0.91 0.37 17.7 0.82 NDBC TAO PIRATA RAMA 5 5 5 –5 –5 –2 –10 –5 05 10 15 20 25 05 10 15 05 10 15 0 5 10 15 10 10 10 10 0 0 0 –10 –10 –10 –10 05 10 15 20 25 05 10 15 05 10 15 0 5 10 15 50 50 50 0 0 0 –50 –50 –50 –50 05 10 15 20 25 05 10 15 05 10 15 0 5 10 15 100 100 100 100 0 0 0 0 –100 –100 –100 –100 05 10 15 20 25 05 10 15 05 10 15 0 5 10 15 10000 10000 4000 2000 5000 5000 2000 1000 0 0 0 0 05 10 15 20 25 05 10 15 05 10 15 0 5 10 15 Buoy wind speed (m/s) Buoy wind speed (m/s) Buoy wind speed (m/s) Buoy wind speed (m/s) Figure 3: Dependence of statistical parameters of wind speed and direction residuals (OSCAT-buoy) on the buoy wind speed. and then increase as the wind speed exceeds 9 m/s. -e -e distribution behavior of PDFs of OSCAT and buoys for accuracy of the OSCAT wind speed first increases and then wind speed and direction implies their consistencies, and decreases with the increasing wind speed. -e wind speed they have the similar characteristics. -e wind speed his- residuals in the coastal area (area of the NDBC buoys) are tograms of OSCATand buoys at speeds less than 10 m/s have larger than that in the open sea (area of the remaining a slight offset for NDBC, PIRATA, and RAMA buoys, which buoys). -e wind speed residuals decrease with the in- indicates the positive biases of OSCAT wind speed. OSCAT overestimates the buoy wind at low wind speed as proofed creasing wind speed. In general, the OSCAT wind speed is higher than the moored buoy wind speed. In the case of the above. wind direction comparisons, the wind direction biases are very close to 0 , showing the consistency between OSCAT and buoy wind directions. -e RMSE of the wind direction 3.2. Comparison with ASCAT. In order to analyze the ac- curacy of OSCAT wind vectors on the global oceans area overall decreases with increasing wind speed. -e wind direction residuals in the coastal area (area of NDBC where there are no in situ wind observations, OSCAT wind vectors are compared with ASCAT wind vectors which has buoys) are larger than that in the open sea (area of the remaining buoys), but it decreases with the increasing wind been proved to be highly accurate during the period from 16 January 2010 to 20 February 2014. -e collocated data pairs speed. In conclusion, the accuracy of the OSCAT wind speed generally first increases and then deceases, while the of OSCAT and ASCAT wind vectors by the method in- troduced in Section 2.2 are more than 1.4 million, and they accuracy of the OSCAT wind direction generally decreases locate on high latitude areas as shown in Figure 6, and the with increasing wind speed. -e accuracies of the OSCAT scatterplots of comparisons are shown in Figure 7. wind data are consistent for different moored buoys. In It is shown in Figure 7 that wind speed and direction addition, the statistical parameters of wind speed and di- rection residuals (OSCAT-buoy) at different times are derived by OSCAT are consistent with those of ASCAT. Scatterplots of wind direction comparison show that there shown in Figure 4. It is shown that the OSCAT wind data are stable over time. are some differences of 180 between OSCAT and ASCAT wind direction. -e reason for 180 difference is the exis- To understand the abundance of the collocated data for an entire possible range of ocean surface wind, probability tence of 180 ambiguities in some derived wind directions by scatterometer. -e 180 ambiguity of wind direction means distribution functions (PDFs) of the collocated data are plotted for wind speed and direction as shown in Figure 5. the wind direction retrieved by scatterometer is opposite to Occurrence Wind direction residual (°) count Wind speed residual (m/s) 6 Advances in Meteorology NDBC PIRATA RAMA TAO 2 2 2.5 2 1.5 1.5 1.5 1.5 1 1 1 0.5 0.5 0.5 0.5 0 0 0 Bias Bias Bias Bias RMSE RMSE RMSE RMSE 20 20 10 10 –5 Bias Bias Bias Bias RMSE RMSE RMSE RMSE Figure 4: Statistical parameters of wind speed and direction residuals (OSCAT-buoy) at di—erent times. –3 ×10 0.15 8 0.2 0.01 0.008 6 0.15 0.1 0.006 0.1 0.004 0.05 0.05 0.002 0 0 0 0 0 5 10 15 20 0 100 200 300 0 5 10 15 20 0 100 200 300 Wind speed (m/s) Wind direction (°) Wind speed (m/s) Wind direction (°) Oceansat-2 Oceansat-2 Oceansat-2 Oceansat-2 NDBC NDBC PIRATA PIRATA 0.15 0.012 0.2 0.012 0.01 0.01 0.15 0.1 0.008 0.008 0.006 0.1 0.006 0.05 0.004 0.004 0.05 0.002 0.002 0 0 0 0 0 5 10 15 20 0 100 200 300 0 5 10 15 20 0 100 200 300 Wind speed (m/s) Wind direction (°) Wind speed (m/s) Wind direction (°) Oceansat-2 Oceansat-2 Oceansat-2 Oceansat-2 RAMA RAMA TAO TAO Figure 5: PDFs of the collocated OSCAT and moored buoy wind speeds and directions in the bin of 1 m/s and 10 . the true wind direction. e di—erences between OSCATand ×10 ASCAT wind vectors (OSCAT minus ASCAT) in di—erent months are shown in Figure 8. e biases and RMSEs of wind speed between OSCAT 4 and ASCAT are −1.23∼−0.17 m/s and 0.62∼1.71 m/s and ° ° those of wind direction are −10.00 ∼16.23 and ° ° 17.56∼22.94 . e overall average biases and RMSEs of wind –80 –60 –40 –20 0 20 40 60 80 ° ° speed and direction are −0.64 m/s, 1.27 m/s and 2.87 , 20.17 , Latitude (°) which basically satis£es the mission speci£cation of less than 2 m/s and 20 . e negative wind speed bias means that Figure 6: Number distribution of collocated data between OSCAT OSCAT wind speed is less than that of ASCAT, which is and ASCAT on the latitude. Wind speed (m/s) Wind direction (°) PDF PDF 10/01 10/01 11/01 11/01 12/01 12/01 13/01 13/01 14/01 14/01 Wind speed (m/s) Wind direction (°) 10/01 10/01 11/01 11/01 12/01 12/01 13/01 13/01 14/01 14/01 Wind speed (m/s) Wind direction (°) 10/01 10/01 11/01 11/01 12/01 12/01 13/01 13/01 14/01 14/01 Wind speed (m/s) Wind direction (°) 10/01 10/01 11/01 11/01 12/01 12/01 13/01 13/01 14/01 14/01 Advances in Meteorology 7 30 60 360 60 55 55 25 300 50 50 45 45 20 40 240 40 35 35 15 180 30 30 25 25 10 120 20 20 15 15 5 60 10 10 5 5 0 0 0 5 10 15 20 25 30 0 60 120 180 240 300 360 OSCAT wind speed (m/s) OSCAT wind direction (°) (a) (b) Figure 7: Scatterplots for wind speed and direction of the comparisons between OSCAT and ASCAT. –1 –2 –3 –20 ×10 2010-01 2010-06 2010-12 2011-06 2011-12 2012-06 2012-12 2013-06 2013-12 Year-month Figure 8: Statistical parameters of di—erence between OSCATand ASCAT wind vectors at di—erent months from January 2010 to February because of the sea surface temperature e—ects on the OSCAT 4. Conclusion Ku-band wind retrievals. Ku-band radar backscatter is known to be sensitive to SST, and this results in lower re- Satellite scatterometry is one of the important means to achieve quasisynchronous acquisition of the global ocean trieved wind speeds over colder ocean areas [21] (OSCAT- ASCAT collocated data locate on high latitude areas). ese surface wind, and long-term sequence global ocean surface wind data are one of the important data sources for global indicate the consistency of OSCAT and ASCAT wind vec- climate change research. OSCAT can provide global ocean tors, and they have the same accuracies of wind vectors. e surface wind vectors. Validation of OSCAT wind data is biases of wind speed and direction di—erence between important to the data application. is study comprehen- OSCAT and ASCAT at di—erent months show that there is sively evaluates the overall historical archived OSCAT wind an annual periodic signal regarding the wind speed and vectors from 16 January 2010 to 20 February 2014 by direction errors, as shown in Figure 8. e possible reasons for these results are the annual cycle variation of ocean winds comparing global moored buoys and MetOp-A/B ASCAT wind data. Firstly, OSCAT wind vectors are compared to 206 causing the annual error variations. ASCAT wind speed (m/s) Wind direction Wind speed Occurrence count Number of collocations ASCAT wind direction (°) Number of collocations 8 Advances in Meteorology [2] J. Yang and J. Zhang, “Evaluation of ISS-RapidScat wind global moored buoys (96 NDBC buoys, 18 PIRATA buoys, vectors using buoys and ASCAT data,” Remote Sensing, 26 RAMA buoys, and 66 TAO buoys) wind data, and more vol. 10, no. 4, p. 648, 2018. than 110,000 data pairs are collocated within the spatial and [3] J. Yang and J. Zhang, “Accuracy assessment of HY-2A temporal scales of 12.5 km and 30 min. -e results show that scatterometer wind measurements during 2011-2017 by the overall average biases and RMSEs of wind speed are comparison with buoys, ASCAT, and ERA-interim data,” 0.27 m/s and 1.20 m/s and those of wind direction are 0.37 IEEE Geoscience and Remote Sensing Letters, pp. 1–5, 2018. and 17.7 . -is indicates that the consistency between [4] M. H. Freilich and R. S. Dunbar, “-e accuracy of the NSCAT OSCAT wind vectors and buoy wind data. -e analyses of 1 vector winds: comparisons with national data buoy center OSCAT wind vector errors in the different buoys’ wind buoys,” Journal of Geophysical Research: oceans, vol. 104, speeds show that the accuracy of the OSCAT wind speed first no. 5, pp. 11231–11246, 1999. increases and then decreases with the increasing wind speed. [5] Y. Quilfen, B. Chapron, and D. Vandemark, “-e ERS Secondly, OSCAT wind vectors are compared with ASCAT scatterometer wind measurement Accuracy: evidence of seasonal and regional biases,” Journal of Atmospheric and wind vectors within the spatial and temporal scales of Oceanic Technology, vol. 18, no. 10, pp. 1684–1697, 2001. 12.5 km and 10 min. More than 1.4 million data pairs of [6] N. Ebuchi, H. C. Graber, and M. J. Caruso, “Evaluation of OSCAT and ASCAT wind data are collocated. -e differ- wind vectors observed by QuikSCAT/seawinds using ocean ences (OSCAT minus ASCAT) are analyzed at the different buoy data,” Journal of Atmospheric and Oceanic Technology, months from January 2010 to February 2014. -e results vol. 19, no. 12, pp. 2049–2062, 2002. show that the overall average biases and RMSEs of wind [7] A. Bentamy, D. Croize-Fillon, and C. Perigaud, “Character- speed and direction difference are between −0.64 m/s and ization of ASCAT measurements based on buoy and ° ° 1.27 m/s and 2.87 and 20.17 , respectively. -is indicates the QuikSCAT wind vector observations,” Ocean Science, vol. 4, consistency of OSCAT and ASCAT wind vectors. no. 4, pp. 265–274, 2008. In general, comparisons of OSCAT wind vectors with [8] H. Wang, J. Zhu, M. Lin et al., “First six months quality moored buoy wind data and ASCAT wind vectors show that assessment of HY-2A SCAT wind products using in situ OSCAT wind vectors satisfy the general scatterometer measurements,” Acta Oceanologica Sinica, vol. 32, no. 11, pp. 27–33, 2013. mission requirements (<2 m/s and 20 ), and they are con- [9] D. Li and H. Shen, “Evaluation of wind vectors observed by sistent with ASCAT wind data. Consequently, OSCAT wind HY-2A scatterometer using ocean buoy observations, ASCAT vectors can be used in the oceanic numerical forecast and the measurements, and numerical model data,” Chinese Journal of global change study by the combination with other scat- Oceanology and Limnology, vol. 33, no. 5, pp. 1191–1200, 2015. terometers’ data. [10] A. K. Sudha and C. V. K. Prasada Rao, “Comparison of oceansat-2 scatterometer winds with buoy observations over the Indian ocean and the Pacific Ocean,” Remote Sensing Data Availability Letters, vol. 4, no. 2, pp. 171–179, 2013. [11] S. Gadad and P. C. Deka, “Comparison of Oceansat-2 scat- -e Oceansat-2 scatterometer, ASCAT, and buoy data and terometer- to buoy-recorded winds and spatial distribution their collocation data used to support the findings of this over the Arabian Sea during the monsoon period,” In- study are available from the corresponding author upon ternational Journal of Remote Sensing, vol. 36, no. 18, request. pp. 4632–4651, 2015. [12] R. Kumar, A. Chakraborty, A. Parekh, R. Sikhakolli, B. S. Gohil, and A. S. Kiran Kumar, “Evaluation of oceansat-2-derived Conflicts of Interest ocean surface winds using observations from global buoys -e authors declare that there are no conflicts of interest and other scatterometers,” IEEE Transactions on Geoscience and regarding the publication of this paper. Remote sensing, vol. 51, no. 5, pp. 2571–2576, 2013. [13] S. I. Rani, M. Das Gupta, P. Sharma, and V. S. Prasad, “In- tercomparison of oceansat-2 and ASCAT winds with in situ Acknowledgments buoy observations and short-term numerical forecasts,” At- mosphere-Ocean, vol. 52, no. 1, pp. 92–102, 2014. -is study was supported by the National Key R&D Program [14] S. I. Rani and M. D. Gupta, “Oceansat-2 and RAMA buoy of China (2016YFA0600102 and 2016YFC1401800). -e au- winds: a comparison,” Journal of Earth System Science, thors would like to thank the JPL’s Physical Oceanography vol. 122, no. 6, pp. 1571–1582, 2013. Distributed Active Data Center (PO. DAAC) of NASA for the [15] A. Chakraborty, R. Kumar, and A. Stoffelen, “Validation of ocean surface winds from the OCEANSAT-2 scatterometer distribution of the OSCAT data, KNMI for the distribution of using triple collocation,” Remote Sensing Letters, vol. 4, no. 1, ASCAT data, and the NOAA Pacific Marine Environmental pp. 85–94, 2013. Laboratory (PMEL) for NDBC, TAO, PIRATA, and RAMA [16] T. Mathew, A. Chakraborty, A. Sarkar, and R. 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