第 屆海洋工程研討會論文集國立台灣海洋大學民國 92 年 1 月 Proceedings of the th Ocean Engineering Conference in Taiwan, Republic of China National Taiwan Ocean University October, 23 Comparison between QuikSCAT and Ocean Tower Wind Measurements in Taiwan Strait Min-Chih Huang 1 Yueh-Hao Liu 2 ABSTRACT QuikSCAT wind measurements near coastlines were compared with wind measurements from an ocean tower in the southwestern Taiwan Strait. A collocation data set was constructed of QuikSCAT scans over-passing two grid points, one 28 km southwest of tower and the other 39 km east of tower, within 3 minutes of given tower wind measurements. It is concluded that the deteriorating effect of terrestrial backscatter contamination on QuikSCAT measurements reaches far more than one cell (~ km) of the coastline. Topographic influences limit the availability of QuikSCAT measurements only to once daily in coastal waters (descending pass in this site), where the root mean squared differences of the wind speed and direction are 2.34 m/s and 68 degrees, respectively. The rain contamination effect on QuikSCAT measurements is calculated, appreciable improvement is achieved when these data are removed. 台灣海峽內衛星與觀測平台實測風場之比較 黃明志劉岳豪摘要 本文比較在台灣海峽西南部沿岸利用 QuikSCAT 衛星觀測之風場與海洋觀測平台實測之風場 利用衛星通過臨近觀測平台附近二個觀測網格點, 且時差在 3 分鐘內之風場進行比較研究 其中第一個網格點在平台西南方 28 公里處, 第二個網格點在平台西方 39 公里處 研究結果顯示陸地散射對衛星觀測風場之污染效應大於離岸一個網格距離 (~ 公里 ) 以上 此外陸地與海洋之相對地形位置亦會限制衛星觀測風場之準確性, 以觀測平台所在位置而言, 每天二筆衛星觀測數據中只有在晚上衛星下降路徑時之觀測值較不受陸地影響, 整整二年數據之比較, 風速與風向之均方差為各為 2.34 m/s 與 68 度 此外本文亦計算降雨對衛星觀測風場準確性之影響, 亦如預期中之可觀 -- 1 Introduction 1. Professor, Department of Systems and Naval Mechatronic Engineering, National Cheng Kung University 2. Graduate Student, Department of Systems and Naval Mechatronic Engineering, National Cheng Kung University For years, space-borne scatterometry has been used successfully to retrieve the ocean surface winds and has become an increasingly important tool to monitor the Earth s climate, forecast weather, and study ocean-atmosphere interactions. Scatterometer instruments are active microwave sensors that transmit -63-
a series of microwave pulses to the ocean surface and measure the backscatter power to allow determination of the normalized radar backscatter cross section ( σ ) of the ocean surface using the basic radar equation. Wind stress over the ocean generates ripples and waves, which roughen the sea surface. These waves modify the magnitude of backscatter power and hence the radar backscatter cross section of the ocean surface. The observed radar cross section is therefore a function of wind speed, azimuth (horizontal) angle between the incident radiation and the wind direction, incidence angle measured in the vertical plane, instrument frequency, and instrument polarization. This relationship can be generically represented by a geophysical model function: σ = f ( U, χ, θ, f, pol). In the forward model function, the wind speed and the relative azimuth are unknowns, while the incidence angle, frequency and polarization are known for each measurement. This geophysical model function is then numerically inverted to infer the near-surface wind (Naderi, et al., 1991). There have been six satellite scatterometer missions since 1978. In the United States, most efforts have been focused on Ku-band scatterometry. An experimental scatterometer, called Seasat-A Scatterometer (SASS), was launched into space on board the NASA satellite Seasat in 1978 and flew for three months in 1978. It demonstrated the effectiveness of scatterometry for wind retrieval. The European Space Agency launched the two operational C-band scatterometers on board the European Remote Sensing Satellites ERS-1 and ERS-2 in 1991 and 199, respectively. They have relatively low resolution ( km) and narrow swath ( km). In 1996, the NASA Scatterometer (NSCAT) was launched on board the Japan s Advanced Earth Observing Satellite (ADEOS) to obtain continuous surface wind measurements over the global ocean with km resolutions and two 6 km swaths coverage. NSCAT had provided 1 months of wind observations until ADEOS suddenly lost control on June 3, 1997. The Quick Scatterometer (QuikSCAT) is a "quick recovery" mission to fill the gap created by the loss of data from the NSCAT. Built in record time in just 12 months, the ocean-observing satellite was launched June 19, 1999. It circles Earth at an altitude of 83 kilometers once every 11 minutes, passing close to Earth s north and south poles in a Sun-synchronous 98.6 inclination orbit. The scatterometer instrument it carries is known as SeaWinds. The instrument collects data over ocean, land, and ice in a continuous, 1,8-kilometer swath, making approximately 4, measurements and covering 9% of Earth's surface in one day. The newest one, called SeaWinds on ADEOS 2, was launched December 13, 22 on board the Japan s Advanced Earth Observing Satellite 2. Operational data products are still not available for use by science researchers. A main task for scatterometry investigators is the calibration of the sensor data. A second task consists in validating the accuracy of backscatter coefficients and wind estimates and their comparison with other sources of data (Bentamy et al, 2). Various studies had been conducted to validate the accuracy of wind estimates from QuikSCAT with other sources of data. These collocated data include ERS-2 scatterometer wind estimates (Bentamy et al, 2), RADARSAT SAR-derived wind estimates (Monaldo et al, 21; Monaldo and Thompson, 22), TOPEX and ERS-2 altimeter wind estimates (Queffeulou and Bentamy, 2) and ocean buoy measurements (Monaldo et al, 21; Ebuchi, 21; Jelenak et al, 22). On average, SeaWinds on QuikSCAT compares fairly well other instruments. As a quantitative indication, comparisons with NCEP operational surface wind analyses and NDBC buoys show that the SeaWinds wind measurements have a 1.4m/s rms difference for speeds from 3 to 2 m/s, while direction accuracies vary as a function of wind speed, with overall rms differences of 14 o degree for speeds from to 2 m/s and 18 o for wind speeds from 3 to 2 m/s (JPL, 2). To avoid terrestrial backscatter contamination and topographic influences, some criteria were imposed on the collocation data set; e.g., only those scatterometer data greater than 1 km from any land mass were considered in Connor et al (22), only buoys located offshore and in deep water were selected in Ebuchi (21). This has raised concerns that the km resolution restriction of SeaWinds on QuikSCAT does not capture much of the wind field spatial variability in coastal regions (Monaldo and Thompson, 22). Preliminary analysis also suggests that σ and vector wind measurements from SeaWinds on QuikSCAT are accurate within approximately one cell (~ km) of the coastline (JPL, 2). Two operational objectives of QuikSCAT mission are to improve weather forecasts near coastlines by using wind data in numerical weather and wave prediction models and to improve storm warning and monitoring. It is thus of crucial -64-
importance to study terrestrial backscatter contamination and topographic influences on QuikSCAT measurements. 2 Collocation of QuikSCAT with Ocean Tower Wind Measurements In order to compare with the QuikSCAT wind measurements near coastlines, we collected wind measurements from an ocean tower in the southwestern Taiwan Strait. This tower was installed June 22, 2 off the coast of Tainan in water depth of o ' '' o ' '' 14. m and at a location of 23 37 N,12 E, which is approximately 2 km west from the nearest coastline. Two anemometers and four ultrasonic wave gauges were installed at 1 m height above the mean water level. Hourly meteorological and oceanographic data with record length of 1 minutes were sampled at 2 Hz. The measured wind vectors in this study represent 1 minutes temporal averages. The QuikSCAT orbit is a Sun-synchronous orbit such that the ascending and descending equator crossings are at local (mean sun) times of 6 AM ± 3 minutes and 6 PM ± 3 minutes, respectively. The SeaWinds on QuikSCAT Level 3 data set consists of gridded values of scalar wind speed, meridional and zonal components of wind velocity, wind speed squared and time given in fraction of a day. Rain probability determined using the Multidimensional Histogram (MUDH) Rain Flagging technique is also included as an indicator of wind values which may have degraded accuracy due to the presence of rain. Data are currently available in Hierarchical Data Format (HDF) and exist from 19 July 1999 to present. The Level 3 data were obtained from the Direction Interval Retrieval with Threshold Nudging (DIRTH) wind vector solutions contained in the QuikSCAT Level 2B data and are provided on an approximately. x. degree, global grid. Separate maps are provided for both the ascending pass (6AM LST equator crossing) and descending pass (6PM LST equator crossing). By maintaining the data at nearly the original Level 2B sampling resolution and separating the ascending and descending passes, very little overlap occurs in one day. However, when overlap between subsequent swaths does occur, the values are over-written, not averaged. Therefore, a SeaWinds on QuikSCAT Level 3 file contains only the latest measurement for each day (JPL, 21). To minimize spatial and temporal variations in the wind field between wind vector measurements acquired at different locations and time, the twice daily, gridded ocean wind vectors at the two closest grid points from the ocean tower were retrieved from the SeaWinds on QuikSCAT Level 3 data set. These o o two grid points are located at 22.87 N,119.87 E o o and 23.1 N,119.6 E, respectively. The first grid point is 28 km southwest of tower and the second grid point is 39 km east of tower. A collocation data set was constructed of QuikSCAT scans over-passing these two grid points within 3 minutes of given tower wind measurements. By separating the ascending and descending passes, the variations of wind speed and direction observed by QuikSCAT at both grid points are compared with the ocean tower measurements for a period of two years from 2/7/1 to 22/6/3. At first glimpse, the comparisons are better for the descending pass. Example illustrations are given in Figs.1-2 for the wind speed and direction variations observed by the descending pass of QuikSCAT at the first grid point and ocean tower, respectively. wind speed (m/s) 6/3/ 11/23/ 4/18/1 9/11/1 2/4/2 6/3/2 2 1 nighttime grid point 1 6/3/ 11/23/ 4/18/1 9/11/1 2/4/2 6/3/2 date ( descending pass ) Fig. 1. Wind speed variations of QuikSCAT (descending pass) and ocean tower -6-
6/3/ 11/23/ 4/18/1 9/11/1 2/4/2 6/3/2 36 36 33 wind direction (deg) 3 27 24 21 18 12 night time grid point 1 3 24 18 12 9 6 3 6/3/ 11/23/ 4/18/1 9/11/1 2/4/2 6/3/2 date ( descending pass ) Fig. 2. Wind direction variations QuikSCAT (descending pass) and ocean tower Scatter plots of QuikSCAT and ocean tower measurements are then constructed based on samples where both data exist. The corresponding scatter plots of wind speed and direction comparisons for the descending pass and ascending pass over the first grid point are illustrated in Figs. 3-4, and Figs. -6, respectively. Correlation between the descending pass observations of QuikSCAT and in-situ ocean tower measurements is much better than the ascending pass. The root mean squared differences of the wind speed and direction are 2.34 m/s and 68 degrees, respectively, for the descending pass. Thus the terrestrial backscatter contamination on QuikSCAT measurements during the ascending pass is quite obvious. 6 nighttime ( grid point 1 ) no. of data points = 34 correlation coeff. =.864 bais = -6.94 rms difference = 68.7 6 12 18 24 3 36 Fig. 4. QuikSCAT wind directions over the first grid point (descending pass) versus coincident tower wind directions 2 1 daytime ( grid point 1 ) no. of data points = 34 correlation coeff. =.4494 bias = 1.3194 rms difference = 4.388 1 2 Fig.. QuikSCAT wind speeds over the first grid point (ascending pass) versus coincident tower wind speeds 2 1 nighttime ( grid point 1 ) no. of data points = 34 correlation coeff. =.8337 bias =.616 rms difference = 2.3393 1 2 Fig. 3. QuikSCAT wind speeds over the first grid point (descending pass) versus coincident tower wind speeds 36 3 24 18 12 6 daytime ( grid point 1 ) no. of data points = 34 correlation coeff. =.832 bias = -1.78 rms difference = 61.6 6 12 18 24 3 36 Fig. 6. QuikSCAT wind directions over the first grid point (ascending pass) versus coincident tower wind directions -66-
The corresponding scatter plots of wind speed and direction comparisons for the descending pass and ascending pass over the second grid point are illustrated in Figs. 7-8, and Figs. 9-1, respectively. The correlations deteriorated only slightly when compared with the first grid point data. Thus the effect of spatial separation (28 km versus 39 km) on QuikSCAT measurements at this location is very small. The terrestrial backscatter contamination on QuikSCAT measurements during the ascending pass is still quite obvious. However since the second grid point is more far offshore, the numbers of QuikSCAT observations increase 6% and 41% for the descending pass and ascending pass, respectively, when compared with the first grid point data set. 2 1 daytime ( grid point 2 ) no. of data points = 48 correlation coeff. =.4466 bias = 2.3376 rms difference = 4.86 1 2 Fig. 9. QuikSCAT wind speeds over the second grid point (ascending pass) versus coincident tower wind speeds 36 2 1 nighttime ( grid point 2 ) no. of data points = 474 correlation coeff. =.867 bias = 1.219 rms difference = 2.484 1 2 Fig. 7. QuikSCAT wind speeds over the second grid point (descending pass) versus coincident tower wind speeds 36 3 24 18 12 6 nighttime ( grid point 2 ) no. of data points = 474 correlation coeff. =.844 bais = -7.64 rms difference = 67.17 6 12 18 24 3 36 Fig. 8. QuikSCAT wind directions over the second grid point (descending pass) versus coincident tower wind directions 3 24 18 12 6 daytime ( grid point 2 ) no. of data points = 48 correlation coeff. =.7994 bias = -2.26 rms difference = 68.27 6 12 18 24 3 36 Fig. 1. QuikSCAT wind directions over the second grid point (ascending pass) versus coincident tower wind directions To study the effect of the rain contamination on QuikSCAT measurements, the ascending pass and descending pass collocations were then filtered to eliminate samples where QuikSCAT detected rain. The corresponding scatter plots of wind speed and direction comparisons for the descending pass over the first grid point are illustrated in Figs. 11-12, respectively. The root mean squared differences of the wind speed and direction are 1.3 m/s and 71 degrees, respectively. Appreciable improvement is achieved for the comparison between the QuikSCAT and tower wind speeds, where the bias decrease from.61 m/s to.21 m/s and the root mean squared difference improves approximately 3%. -67-
2 1 nighttime ( grid point 1 ) no. of data points = correlation coeff. =.979 bias =.2149 rms difference = 1.26 1 2 Fig. 11. Filtered QuikSCAT wind speeds over the first grid point (descending pass) versus coincident tower wind speeds 36 3 24 18 12 6 nighttime ( grid point 1 ) no. of data points = correlation coeff. =.898 bais = -9.13 rms difference = 71.48 6 12 18 24 3 36 Fig. 12. Filtered QuikSCAT wind directions over the first grid point (descending pass) versus coincident tower wind directions 3 Conclusions In comparing nearly coincident QuikSCAT and ocean tower wind measurements in the coastal waters of Taiwan Strait, we have tentatively arrived at four conclusions. First, the deteriorating effect of terrestrial backscatter contamination on QuikSCAT measurements reaches far more than one cell (~ km) of the coastline. Second, topographic influences will limit the availability of QuikSCAT measurements only to once daily (ascending pass or descending pass) in coastal waters. Third, direction accuracy of QuikSCAT measurements near coastline are very poor, with root mean squared difference at the order of 7 degrees. Fourth, the rain contamination effect on QuikSCAT measurements is also calculated, appreciable improvement is achieved when these data are removed. Therefore further study of the accuracy of QuikSCAT measurements in coastal regions is required in the future. Acknowledgement The authors gratefully acknowledge the Coastal Ocean Monitoring Center/National Cheng Kung University for providing the wind data of its ocean observation tower and the NASA Physical Oceanography Distributed Active Archive Center at the Jet Propulsion Laboratory/California Institute of Technology for providing the SeaWinds on QuikSCAT Level 3 data. References 1. Bentamy, A. et al, (2) Intercomparison of ERS-2 and QuikSCAT winds, 2 IEEE International Geoscience and Remote Sensing Symposium, Vol. 1, pp. 234-236. 2. Connor, L.N. et al, (22) Buoy valication of ocean surface wind estimates from the TRMM precipitation radar, 22 IEEE International Geoscience and Remote Sensing Symposium, Vol. 2, pp. 74-747. 3. Ebuchi, N., (21) Evaluation of wind vectors observed by QuikSCAT/SeaWinds using ocean buoy data, 21 IEEE International Geoscience and Remote Sensing Symposium, Vol. 3, pp. 182-18. 4. Jelenak, Z, et al, (22) The accuracy of high resolution winds from QuikSCAT, 22 IEEE International Geoscience and Remote Sensing Symposium, Vol. 2, pp. 732-734.. JPL, (2) QuikSCAT project data release notes. 6. JPL, (21) SeaWinds on QuikSCAT level 3 daily, gridded ocean wind vectors. 7. Monaldo, F.M. et al, (21) Comparison of RADARSAT SAR-derived wind speeds with buoy and QuikSCAT measurements, 21 IEEE International Geoscience and Remote Sensing Symposium, Vol. 4, pp. 179-176. -68-
8. Monaldo, F. and Thompson, D., (22) Implications of QuikSCAT and RADARSAT wind comparisons for SAR wind speed model functions, 22 IEEE International Geoscience and Remote Sensing Symposium, Vol. 3, pp. 1881-1883. 9. Naderi, F.M. et al, (1991) Spaceborne radar measurement of wind velocity over the ocean an overview of the NSCAT scatterometer system, Proceedings of the IEEE, 79(6), pp. 8-866. 1. Queffeulou, P. and Bentamy, A., (2) Comparison between QuikSCAT and altimeter wind speed measurements, 2 IEEE International Geoscience and Remote Sensing Symposium, Vol. 1, pp. 269-271. -69-
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