Impact of Wind/Wind-Stress Field in the North Pacific Constructed by ADEOS/NSCAT Data

Transcription

1 Journal of Oceanography, Vol. 54, pp. 443 to Impact of Wind/Wind-Stress Field in the North Pacific Constructed by ADEOS/NSCAT Data KUNIO KUTSUWADA School of Marine Science and Technology, Tokai University, , Orido, Shimizu, Shizuoka , Japan (Received 31 March 1998; in revised form 25 May 1998; accepted 27 May 1998) Data sets of surface wind and wind-stress fields in the North Pacific from September 1996 to June 1997 have been constructed using NASA Scatterometer (NSCAT) data on-board ADEOS to investigate their variability and implications for the wind-driven oceanic circulation. Using a weighting function decreasing with the distance between each grid and data points, and of Gaussian type for time, daily, 10-day and monthly averages are calculated for each 1 1 grid. Products are validated by comparison with those calculated from in-situ measurement data at oceanic buoys around Japan (JMA) and in the equatorial area (TAO). The RMS differences for wind direction and speed never exceed 20 and 2 m s 1, respectively, for the TAO buoys. This does not hold for data taken by JMA buoys, suggesting that the reliability in the mid-latitudes is not good for time averages shorter than several days. Zonal integration of the Sverdrup transport in a zone of N calculated from the monthly-mean products ranges between 25 and m 3 s 1 (Sv) around its mean of 38 Sv. These are not so different from the Kuroshio transport values calculated from oceanic measurements. Keywords: ADEOS/NSCAT, wind-stress, North Pacific, scatterometer, validation. 1. Introduction The wind stress on the sea surface is one of the main external forces driving the motion of sea water. For an instance, the wind-driven ocean circulation theory has provided evidence that the general circulation in the upper ocean is governed by the wind stress curl on the sea surface (e.g. Stommel, 1948). The surface wind stress has been calculated using wind data which have been measured routinely on ships-of-opportunity, and some data sets have been constructed (e.g. Hellerman and Rosenstein, 1983; Kutsuwada and Teramoto, 1987). These ship wind data can cover a long-term period of several decades, so they have been used practically for climatological research relating to the ocean and atmosphere. However, there are some problems with the data sets constructed by ship-measured winds. The first is that the ship observations tend to be confined to areas along the main merchant shipping routes, so the data density has an inhomogeneous spatial distribution. Further, some recent studies have pointed out that these ship-measured wind data are not especially reliable (e.g. Piarson, 1990). For example, the anemometer height in many volunteer ships is much higher than 10-m above the sea surface (Cardone et al., 1990). This produces some overestimation of wind speed; because the wind speed in the lower atmosphere generally has a logarithmic profile in the vertical direction, and increases monotonically with the height. Microwave scatterometer sensors on board satellites can supply data of wind speed and direction over the open ocean with a short time interval. The first attempt to obtain such data was made by the SEASAT scatterometer (SASS) which was launched in However, the SASS supplied data of surface wind vectors only for a period of about three months. The next scatterometer sensor was the Active Microwave Instrument (AMI) on board the European Remote Sensing Satellites 1 (ERS-1) which was launched in July 1991 by the European Space Agency (ESA). This was succeeded by ERS-2 at the end of 1995, which has been supplying data to the present. The newest sensor is the National Aeronautics and Space Administration (NASA) scatterometer (NSCAT) which was aboard the Advanced Earth Observation Satellite (ADEOS). The ADEOS was launched in August 1996 as a collaboration between NASA and the National Space Development Agency of Japan (NASDA). Unfortunately, ADEOS stopped operating at the end of June 1997, when an accident occurred due to the breakdown of the solar panel. Although the NSCAT lifetime was only about 9 months, it has supplied surface wind vectors with highest resolution in both time and space. Thus, in this study we use a data set obtained by NSCAT. The spaceborne scatterometer is an active microwave radar measuring the normalized radar backscatter coefficient Copyright The Oceanographic Society of Japan. 443

2 (σ 0 ) of the ocean surface. The principle of deriving the wind speed and wind direction from σ 0 is based on the mechanism that capillary waves generated by surface winds cause changes in surface roughness and these roughness changes are related to the radar cross-section of ocean surface, namely the magnitude of the backscattered power. The NSCAT wind derivation procedures have been described in detail in various papers (e.g. Naderi et al., 1991). In general, σ 0 is taken to be a function of the surface wind speed, the incidence angle, and the azimuth angle between incident microwave radiation and the wind direction. This function, called the geophysical model function, also involves the effects of some non-wind variables such as atmospheric stratification, temperature, frequency and polarization of the incident radiation. Since a single measurement of σ 0 is not sufficient to determine a set of wind direction and wind speed data, the scatterometers measure two to three σ 0 - values for a point from different angles. The SASS with two antennas generally had a four-fold ambiguity in wind direction, while the NSCAT was designed to remove the ambiguity by having three antennas per side of the subsatellite track. Although the NSCAT instrument produced better information on the ocean surface wind compared with the AMI/ERS-1 and SASS, the ambiguity of NSCAT wind direction is not completely resolved. Hence we need to examine the reliability of NSCAT wind speed and direction by comparison with in-situ measurement data. In this study, we attempt to construct a data set of surface wind and wind-stress vectors in the North Pacific and the northern part of the South Pacific (north of 30 S). The resolution is taken to be a 1 1 grid in space and 1 day in time. To construct the data set with such a high resolution, we adopt an averaging method using a weighting function which varies with time and space. The construction procedure is described in Section 3. The reliability of the constructed products is examined by comparison with data from buoy measurements in the tropical Pacific and the seas around Japan (Section 4). We have produced not only the wind and wind-stress vectors but also the wind-stress curl and the Sverdrup transport. Some examples of the time series and their time and space characters are described in Section Data We use the Level 2.0 NSCAT data which have been distributed by the NASA Physical Oceanography Distributed Active Archive Center (PO.DAAC) in the Jet Propulsion Laboratory (JPL), California Institute of Technology. This data set involves the wind speed and wind direction, which are converted from the NSCAT-measured σ 0 through the model function constructed by JPL. The NSCAT products were refined through various workshops for the purpose of calibration/validation of NSCAT data after the ADEOS launch. The geophysical model function was newly constructed. To remove the ambiguity of wind direction found in the standard products, Fig. 1. Wind vectors from NSCAT Level 2.0 data set in an area around Japan during one day on 2 October K. Kutsuwada

3 Fig. 2. Location map of oceanic buoys in the TOGA/TAO array. Buoys where surface meteorological data are used in this study are indicated by black symbols. Fig. 3. Location map of JMA buoys around Japan. the best selection of wind direction was determined by comparison with products from the numerical weather prediction (NWP) model in the National Centers for Environmental Prediction (NCEP). The data set of level 2.0 used in this study is based on the new model function (called NSCAT-1) and products which were nudged by the NCEP- NWP model. The wind speed is taken to be a value at 10 m above the sea surface. Accuracies for the data set in the final version are remarked to ±2 m s 1 for wind speed over range between 3 to 20 m s 1 and ±20 for wind direction over range between 5 to 20 m s 1. An example of wind vectors plotted for a certain day around Japan is shown in Fig. 1. To validate our products, we use surface meteorological data measured at oceanic buoys, which are the Tropical Atmosphere Ocean (TAO) array operated by the Tropical Ocean-Global Atmosphere (TOGA) Program, and buoys operated by the Japan Meteorological Agency (JMA). The TAO arrays were set in the tropical Pacific region (Fig. 2) in order to monitor the basin-wide surface atmospheric changes and associated oceanic response in the surface layer. The array was developed as a mooring system call ATLAS (Autonomous Temperature Line Acquisition System) by the Pacific Marine Environmental Laboratory (PMEL) of the National Oceanic and Atmospheric Administration (NOAA) (Hayes et al., 1991; McPhaden, 1995). The AT- LAS buoy system supplies hourly data of numerous surface meteorological parameters: wind components, air temperature, relative humidity, solar radiation, and water temperature at sea surface and some subsurface depths. In this study, time series of hourly data of zonal and meridional winds are used. For the comparison, we converted the ATLAS wind speeds into those at 10-m level, because the anemometer on the ATLAS buoys is installed at 3.8 m above the sea surface. We made a height correction using data of the other meteorological values measured at the buoys. This procedure is described in the Appendix. The JMA buoys are deployed in the areas around Japan. We use data obtained at three buoys which are located in the south of Japan, the East China Sea and the Japan Sea (Fig. 3). These buoys are much stronger than TOGA/TAO buoys, because the weather conditions around Japan are very severe, especially in winter. The JMA buoy system is almost similar to TOGA/TAO one, except that the scalar-averaged wind data are measured every 3 hours and the anemometer is 7.5 m above the sea surface (Suzuki, 1993). These data were processed similarly to the TOGA/TAO data. The magnitude of the wind-stress vector τ = (τ x, τ y ) on the sea surface is calculated from the wind speed W at 10-m level obtained from the scatterometer wind vector W = (U, V) by the bulk formula, τ = ρc D W 2, () 1 τ x = ρc D WU, τ y = ρc D WV. ( 1a) ( 1b) Air density, ρ, is taken to be a function of latitude only. The drag coefficient C D changes only with wind speed under the assumption of neutral stability (see Appendix). The drag coefficient has been determined for each wind data point. So, in this study the stress components are calculated from Impact of Wind Field in the North Pacific by NSCAT Data 445

4 each NSCAT wind by (1a) and (1b), and they are vectoraveraged for each grid point by a procedure described in the next section. 3. Construction Procedure for Grid Data The ADEOS had a circular orbit with a period of about minutes, inclination and nominal height 796 km. Its repeat cycle was 41 days. NSCAT had two swaths of 600 km width on each side of the satellite track, which are separated by 300 km. The resulting spatial resolution of σ 0 measurement on the earth surface is about 25 km. Each wind vector cell involves at most 24 values of σ 0, providing wind speed and wind direction (Science Data Product User s Manual, NASA, 1995). The distribution of NSCAT data depends upon the orbital motion of ADEOS. As shown in Fig. 1, NSCAT data can never cover the whole area in one day. On the other hand, the NSCAT instrument was in poor condition a few times during the mission days. The data distribution in December 1996 (not shown here) exhibits not only inhomogeneity of data density due to the orbits but also low density attributed to the instrument conditions in areas off the west coast of North America. A similar tendency is also confirmed for October 1996, caused by no data being supplied from 21st to 25th. We expect that these characteristics of the NSCAT wind field produce spatial inhomogeneity of the product reliability, especially in calculation of the curl of the wind stress. For this problem, we adopt an averaging method using a weighting function varying with space and time. This method was originally proposed by Levy and Brown (1986), who designed a procedure for calculating the spatial field from scatterometer data. The weighting function P for space is give by ( ) n ( ) n r i R P i ( r)= R2 2 r i R r i ( ) 2a ( ) P i ( r)= 0 ( r i > R) ( 2b) where r is the distance between the center of each grid point and each data point, R is the radius of influence (Fig. 4a), and n is taken to be a value selected from 1, 2, and 4. The value of R is related to a spatial scale which is dominant in a constructed field. In this sense, the R value is stated to be relevant to the decorrelation scale. Recently, Kubota and Yokota (1998), using the successive correction method based on a weighting function given by (2), constructed the wind stress field in the North Pacific from ERS-1 wind data. They calculated wind stress fields by the different weighting functions, namely different values of n in (2a), and, by comparison of the constructed fields with pseudo-observed ones, examined their significance. They concluded that the field constructed by n = 4 is the best, and thus in our calculation the weighting function given by n = 4 is used. Fig. 4. Weighting functions used in calculations of grid data products for space (a) and time (b). A spatial weight (a) is a function which decreases with the distance r from the center of each grid with maximum value (=1) and has zero values at points where the distance is larger than the radius of influence R. Since R takes on different values between the zonal and meridional directions in this study (see text), an area where the weight has non-zero value is given by the ellipsoidal area in (a). The temporal weight (b) is a Gaussiantype function depending on the time difference from noon of each day and having non-zero values for three days including the previous and subsequent days. 446 K. Kutsuwada

5 The radius of influence R should be determined by the decorrelation scale which is dominant in the constructed field, but unknown here. Kubota and Yokota (1998) calculated the decorrelation scale for the monthly mean field based on the ship wind data set which is called the COADS (Comprehensive Ocean Atmosphere Data Set). They used the decorrelation value which changes in the zonal and meridional directions, but it is difficult to find the validity of their values for constructing the daily field. On the other hand, we can easily expect that the decorrelation scale has a larger value in the zonal direction than in the meridional one. In the present study we adopted 600 and 300 km for the values of R in the zonal and meridional directions, respectively, which are almost equal to averages of the values adopted by Kubota and Yokota. This means that the weighting function has non-zero values (given by (2a)) within an ellipsoid with the center of the grid point. The weighting function is taken to be dependent on time as well as space. This is Gaussian-type with non-zero values for three days including each calculated day (Fig. 4b), and given by, ()= exp t t 0 Q i t ( ) 2 2T 2 ( t t 0 36 hours) ( 3a) the buoy wind. For comparison, the following statistical values are processed. First, the buoy wind speed is converted to that at 10-m level (see Appendix). Next, 10-m winds are vector-averaged for time intervals of a month and 10 days as well as a day. These procedures are conducted for the zonal (U) and meridional (V) components and scalar wind (W), and their mean difference, root-mean-square (RMS) difference and correlation coefficient are calculated for each station. Further, a difference of the wind directions is calculated from the wind components for each hour as well as for the entire period to derive its RMS difference. For each calculation of RMS differences, mean difference between the buoy and NSCAT winds are removed in advance. We have available in-situ measurement data at numerous buoy stations in the TOGA/TAO array over the tropical Pacific (Fig. 2), but unfortunately in our study period data were missing, either continuously or intermittently for a few months at many stations. So, the data of only 7 buoys indicated by solid symbols in Fig. 2 are used. The statistical values are listed in Tables 1 and 2, for 10-day and daily average products, respectively. The RMS differences for the wind components and scalar wind never exceed 1.5 m s 1 at all the stations, and the correlation coefficients for them are significantly high, with the exception of the 10-day averaged meridional component at 2 S, 155 W. This exception Q i ()= t 0 ( t t 0 > 36 hours) ( 3b) where t 0 is the time of the noon on each day and T is taken to be 12 hours. This function has values between unity at noon and exp( 1/2) = at t t 0 = 12 hours corresponding to midnight on each day. On the previous and subsequent days, the function rapidly decreases with the increase of t t 0. The final weight for each grid is given by S i ( r,t)= P i ( r) Q i () t P i ( r) Q i t { ()} i. ( 4) Using the weighting function (4), we construct some products in each 1 1 grid over the Pacific basins of 60 N- 30 S and 120 E-70 W. 4. Comparison with Buoy Observations 4.1 Tropical area To evaluate the quality of our products, we calculate wind vectors from in-situ observations by some oceanic buoys and compare them with those at their closest grid points to the buoy locations. Figure 5 shows time series of the daily data for the zonal and meridional winds at 2 S, 155 W. The variation of NSCAT wind is similar to that of Fig. 5. Time series of zonal (a) and meridional (b) components of the wind and scalar wind (c) at 2 S, 155 W. Solid and broken lines are those from buoy observations and NSCAT, respectively. Impact of Wind Field in the North Pacific by NSCAT Data 447

6 448 K. Kutsuwada Table 1. Comparison between NSCAT and TAO buoy winds (10-day mean). Table 2. Comparison between NSCAT and TAO buoy winds (daily mean).

7 is due to the fact that the meridional wind at the station is weak throughout most of the period (Fig. 5b). The RMS differences for wind direction are at most 15, which is smaller than 20 of the sensor accuracy. Thus, the reliability of our NSCAT products is good for the tropical Pacific region. It should be noted that the mean differences for the wind direction have negative values at all the stations, which may suggest the possibility that the buoy wind sensors have some bias. 4.2 Area around Japan Statistics of the comparison using the JMA buoy data are indicated in Tables 3 and 4 for 10-day and daily averages, respectively. In an example of time series for 10-day averages at 29 N, 135 E (Fig. 6), we can see that changes with a period of days are dominant for the two wind components. The buoy wind and NSCAT wind are almost similar to each other. The correlation coefficients between them are and for the zonal and meridional winds, respectively, which are significantly high. On the other hand, the RMS differences are 2.1 and 1.3, close to the sensor accuracy, and furthermore the RMS difference for wind direction also has a value of 46.9, which is much larger than the sensor accuracy (Table 3). Similar features are found in the statistical values for the other two stations. In comparisons of daily data products (Table 4), the RMS differences become larger, while the correlation coefficients are lower at all the stations. This means that our products have relatively low reliability in areas surrounding Table 3. Comparison between NSCAT and JMA buoy winds (10 day mean). Buoy No. Year Longitude Latitude Element Mean diff. RMS diff. Cor. coeff E N U (m/s) Japan Sea V (m/s) W (m/s) Dir. ( ) E N U (m/s) South of Japan V (m/s) W (m/s) Dir. ( ) E N U (m/s) East China Sea V (m/s) W (m/s) Dir. ( ) Table 4. Comparison between NSCAT and JMA buoy winds (daily). Buoy No. Year Longitude Latitude Element Mean diff. RMS diff. Cor. coeff E N U (m/s) Japan Sea V (m/s) W (m/s) Dir. ( ) E N U (m/s) South of Japan V (m/s) W (m/s) Dir. ( ) E N U (m/s) East China Sea V (m/s) W (m/s) Dir. ( ) Impact of Wind Field in the North Pacific by NSCAT Data 449

8 Fig. 6. Time series of zonal (a) and meridional (b) components of the wind and scalar wind (c) at 29 N, 135 E. Solid and broken lines are those from buoy observations and NSCAT, respectively. Japan, especially for wind direction. This may be due to the low reliability of wind direction in many of the original wind data in the NSCAT Level 2.0 data set. The above results do not necessarily mean that our products have generally low reliability in the mid- and/or high-latitudinal areas, because our validation is confined only to areas around Japan. Unfortunately, we have no insitu data, such as buoy station data, that allow us to validate NSCAT wind data. 5. Character of Products 5.1 Wind field This section describes some examples of our products. Figure 7 shows distributions of mean and standard deviation (SD) for the zonal wind during the entire period (16 Sep., Jun., 1997). In these figures we can see some general features in the surface wind field. For example, the mean zonal wind is eastward north of about 30 N and westward south of it, corresponding to the westerly and trade wind areas, respectively. The maximum in the trade wind region reaches about 7 m s 1 in a zone of N, while that in the westerly wind region is only about 3 m s 1 in the central portion of mid-latitude area. The SD is relatively large in mid- and high-latitudes, with a maximum exceeding 6 m s 1 north of 40 N. This reflects the fact that the zonal wind changes with several time scales including seasonal and intraseasonal ones, associated with a reversal of wind direction. Other regions with large SD are found in low latitude areas. One is the eastern part of a zone around 10 N where the SD exceeds 4 m s 1. In this area, the SD for the meridional wind (not shown) also has a maximum, reflecting the meridional shift of the Inter-Tropical Convergence Zone (ITCZ). The other is found at the northeast side of Australia, which is related to a seasonal movement of the South Pacific Convergence Zone (SPCZ). Next, an example of daily-mean wind field on 26 December, 1996 is shown in Fig. 8. The most interesting feature is found in the western equatorial Pacific where there are two cyclonic wind fields with their centers around 10 N, 140 E and 10 S, 160 E. These are located in almost the same latitude, and are thus called twin cyclones. We can see an enhancement of westerly wind along the equator between these cyclones. It is considered that this is an important phenomenon at an onset phase of the present (97 98) El Niño event, because there is evidence that the surface oceanic current in the area became strongly eastward, which may be driven by the westerly wind burst (Kutsuwada et al., 1998). In the mid-latitude region, the westerly or northwesterly winds are dominant over areas east of Japan. Some complicated features are found in the central portion; northwesterly winds between 170 E and 160 W and southerly winds between 160 W and 150 W. Parts of them may not represent real phenomena in this period, because the results presented in the last section have revealed that the wind direction is questionable in these latitudes. We have no insitu data around the areas, so it is difficult to validate these wind fields at present. A final example is attributed to time series of wind components on a selected grid off the Sanriku coast (Fig. 9). Although some peaks in the time series of daily data (thin line) are somewhat questionable, changes with bimonthly to monthly time scales are recognized in 5-day running mean time series (thick line). Frequency spectra for these time series (Fig. 10) exhibit significant peaks at the periods around 35 days for the zonal wind and 25 days for the meridional wind. Similar spectral features are also recognized for many time series of the zonal wind in the western North Pacific. 5.2 Wind stress and Sverdrup transport The zonal (τ x ) and meridional (τ y ) components of wind stress are constructed for each 1 1 grid. The mean field of the zonal wind stress has a similar distribution to that of the zonal wind (Fig. 7a), except that maximum in the westerly wind region has almost the same magnitude as that in the trade wind region (about 0.08 N m 2 ). The vertical component of the wind-stress curl (curl z τ) 450 K. Kutsuwada

9 Fig. 7. Distributions of mean (a) and standard deviation (b) of the zonal wind from September 1996 to June 1997 constructed by NSCAT data. Positive and negative values for mean field denote westerly and easterly winds, respectively. Units are m s 1. for each grid is calculated from the stress components in four surrounding grids by the center finite difference. Further, the meridional Sverdrup transport (V s ) is calculated by V s = ( 1/ρ 0 β )curl z τ ( 5) where ρ 0 and β are the oceanic density and the meridional derivative of the Coriolis parameter, respectively. V s is integrated westward from the eastern boundary along each latitudinal zone. Figure 11 shows a distribution of the zonally-integrated values calculated from the mean field averaged over the entire period. In a temperate area between about 15 N and 40 N, there are negative values, meaning that transport is southward. This corresponds to the anticyclonic subtropical gyre including the Kuroshio as its western boundary current. The wind-driven ocean current theory explains that the Sverdrup balance given by (6) is applicable to the interior oceanic region and the transport of the western boundary current is derived as a compensated flow for zonal integration of V s. In Fig. 11, we can find a value of about m 3 s 1 (Sv) in the western boundary of about 30 N and relate it to the Kuroshio transport. This value is almost the same as that derived from the mean wind field in previous studies (e.g. Kutsuwada and Teramoto, 1987). Similar calculations are also made for each monthlymean wind field using NSCAT data. Figure 12 shows time series of the zonally-integrated values of V s at a zone of N, corresponding to the Kuroshio transport south of Japan. Unfortunately we have no available information on the Kuroshio transport, derived from oceanic data in our Impact of Wind Field in the North Pacific by NSCAT Data 451

10 Fig. 8. Map of wind vector on 26 December Fig. 10. Frequency spectra of time series of the zonal wind (solid line) and the meridional wind (broken line) at a grid of 40 N, 143 E. Vertical bar denotes 90% confidence limit. Fig. 9. Time series of the zonal wind (a) and the meridional wind (b) at a grid of 40 N, 143 E. Daily and 5-day running mean values are denoted by thin and thick lines, respectively. study period. Some intensive measurements of the Kuroshio transport were made in the south of Japan, and have given evidence that the transport ranges between 30 and 70 Sv (e.g. Imawaki et al., 1997). The transport value in Fig. 12 ranges from 25 and 60 Sv around its average of 38 Sv with a maximum in winter and minimum in summer, which is no great discrepancy from those in the previous studies. Thus, it may be concluded that the transports estimated from our product are not inconsistent with the wind-driven ocean circulation theory, at least in the mid-latitude area. 452 K. Kutsuwada

11 Fig. 11. Distribution of meridional Sverdrup volume transport calculated from mean wind field in September 1996 to June 1997 constructed by NSCAT data. Positive and negative values denote southward and northward, respectively, transports in the interior ocean. Contour interval is m 3 s 1 (Sv). Fig. 12. Time series of zonally-integrated Sverdrup transport in a zone of N estimated from monthly-mean products. 6. Summary and Discussion In this study, data sets of surface wind and wind-stress fields over the North Pacific have been constructed from ADEOS/NSCAT data for September 1996 to June The products have a high spatial resolution of 1 1 grid and time resolutions of monthly, 10-day and daily periods. To investigate their reliability, we have compared the products with those estimated from meteorological data measured by oceanic buoys. Comparisons with TOGA/ TAO buoy data have revealed that the RMS differences for wind speed and direction are both smaller than the sensor accuracies (2 m s 1 and 20 ) in the daily product as well as the 10-day one. Thus we can conclude that our products have desirable reliability in the tropical area. Although the products have short time coverage of about 9 months, they can be considered to be instructive for examining the variability of various time scales in the tropical wind field. The first impact is due to the fact that the most intense (97/98) El Niño event occurred in the study period. In the onset phase of the event, a typical phenomenon, characterized by a strong westerly wind (burst), is recognized over the western equatorial Pacific. In the first half of 1997, similar characteristic wind changes are observed at some times (Kutsuwada et al., 1998). The present products can supply much information for these studies. On the other hand, comparisons with the JMA buoy data have revealed that even in the 10-day product the RMS differences exceed the sensor accuracies. This means that the product has no desirable reliability in areas surrounding Japan, and suggests that the reliabilities in mid-latitudes are not good for time averages shorter than several days. The result in this study do not necessarily mean that the product and NSCAT Level 2.0 data are not useful in areas outside of the tropical region, because we have used data only at the three buoy stations around Japan to validate our products. There are many moored and drifting buoys in the open oceans which are operated by the National Data Buoy Center (NDBC). According to the other validation studies for NSCAT data (e.g. Ebuchi et al., personal communication), the NSCAT winds have no large RMS differences from these NDBC buoy winds. This may suggest that the low reliability of our product is confined to an area surrounding Japan and is not general in mid- and high-latitude areas. In fact, we can find some noticeable signals with intraseasonal time scale in the time series of daily product (Fig. 9). These signals will be examined in future studies. Impact of Wind Field in the North Pacific by NSCAT Data 453

12 The wind stress on the sea surface, its curl and the Sverdrup transport fields have also been constructed. The reliability of the daily products might not be good in the midand high-latitude areas. Zonally-integrated values of the Sverdrup transport relating to the transport of the western boundary current may have some reliability, because random error in each grid can be compensated by zonal integration. The result has shown that the estimations in a zone of N, calculated from the monthly-mean products, ranges between 25 and m 3 s 1 (Sv) around its mean of 38 Sv. When we compare these with the Kuroshio transport derived from oceanic data such as hydrographic and surface sea level data, they are not so different from each other. This suggests that the monthly-mean product constructed by the present study is reliable in the mid-latitude area, and can give an important impact to oceanic studies. Acknowledgements The author would like to express his grateful thanks for much encouragement to Dr. Harunobu Masuko and the other members of the Japanese ADEOS/NSCAT Science team, which was organized by the National Space Development Agency of Japan. The data set of the NASA Scatterometer used in this study was kindly provided by the NASA Physical Oceanography Distributed Active Archive Center (PO.DAAC) at the Jet Propulsion Laboratory, California Institute of Technology. The author also express his appreciation to Dr. M. J. McPhaden, the director of the TOGA-TAO Project Office for kindly supplying the surface meteorological data from the ATLAS buoys, and to the Oceanographic Division of the Marine Department of the Japan Meteorological Agency for providing the buoy data sets. This study was made possible by funding support from a Grant-in-Aid for Scientific Research on Priority Areas (No ) of the Japanese Ministry of Education, Science, Sports and Culture. Part of it was also supported by the ADEOS Field Campaign off Sanriku/North Pacific organized by the National Space Development Agency of Japan. Appendix: Method of Height Correction for Measured Wind Data We calculate the wind speed W 10 at 10-m level above the sea surface from the wind speed W(z) measured by an anemometer at height z using the following procedure, based on the formula given by Large and Pond (1981). Assuming that the wind is measured within the atmospheric boundary layer, the wind speed W(z) at z (m) above the sea surface is given by, where W * is the friction velocity, κ is the von Kármán constant (=0.41), z 0 is the surface roughness parameter, ψ is the effect of atmospheric stability, and L is the Monin-Obukhov length. The friction velocity W * is given by W = ( τ / ρ) 1/2, ( A2) where τ is the wind stress on the sea surface and ρ is the air density. The wind stress τ is calculated from wind speed at 10-m level U 10 using the following bulk formula, 2 τ = ρ C D ( 10) W 10 ( A3) where C D (10) is the drag coefficient for 10-m wind speed W 10. Substitution of (A3) into (A2) gives W 2 = C D ( 10) W ( A4) The buoy wind data are not measured at 10-m level. For wind speed W(z) measured at height z (m) above the sea surface, the drag coefficient C DN (z) in the neutral stability condition is defined as C DN ( z)= C DN ( 10) ( ) { 1 + ( C ( DN 10 ) / κ ) ln( z /10)} 2 A5 where C DN (10) is the drag coefficient at 10-m level in the neutral condition and is given by C DN ( 10)= ( ) W m s 1 ( U 10 ) < W m s ( W 10 > 26 m s 1 ). ( ) ( A6) Further, the drag coefficient C D (z) in the non-neutral stability condition is given by C D ( z)= C DN ( z) 1 ( C ( DN 10 ) / κ ) ψ z / L Insertion of C D (z) into (A4) gives { ( )} 2. ( A7) Wz ( )= W κ ln z ψ z L z 0 ( A1) W 2 = C D ( z) Wz ( ) 2. ( A8) 454 K. Kutsuwada

13 Representation of the stability factor ψ is divided into the following two types based on atmospheric stability condition: 5 z z > 0: stable L L ψ z L = 2ln ( 1 + X) 2 + ln 1 + X 2 ( ) 2tan 1 X + π 2 2 z < 0: unstable L ( A9) with X = (1 16z/L) 1/4. The bulk stability parameter (z/l) is calculated from wind speed W(z) and air temperature T z at the measurement height and sea surface temperature T s. When it is assumed that the saturation over sea water is 98% and the relative humidity at the measurement height z is 75%, the absolute humidity on the sea surface Q s and one at the height Q z are given by Q s = 0.98Sat(T s ) Q z = 0.75Sat(T z ) (A10-1) (A10-2) where saturated water vapor pressure Sat(T) is a function of temperature T (K): Sat(T) = exp( /T). The bulk stability parameter (z/l) is calculated by z L = κc TC 3/2 θ D zg Wz ( ) 2 1 T T 0 Q θ (A11) ( A12) where C T and C D is the drag coefficient and Stanton number, respectively, g is the gravitational acceleration, and the difference of the specific humidity Q (=Q s Q z ) is calculated by (A10-1) and (A10-2). C T is taken to be and for unstable and stable conditions, and C D to be a constant value of The difference of the potential temperature θ is given by θ = T s θ z = T s (T z + γ z) (A13) where the adiabatic lapse rate in the air is assumed to be 0.01 K m 1. Local averaged temperature T 0 is approximately represented by T 0 = T z + T z 2 Q z (A14) The procedures for the height correction and derivation of the drag coefficient at the anemometer height z (m) above the sea surface are as follows: 1) Calculation of θ from the T s, T z and z by (A13) 2) Calculation of T 0 by (A14) 3) Calculation of the specific humidity difference Q by (A10-1) and (A10-2) 4) Calculation of the bulk stability parameter (z/l) by (A12) 5) Calculation of the stability factor ψ(z/l) by (A9) 6) Assuming W 10 = W(z), calculation of C DN (10) by (A6) 7) Calculation of C DN (z) by (A5) 8) Calculation of C D (z) by (A7) 9) Calculation of W * by (A8) 10) Calculation of C D (10) by (A7) 11) Calculation of W 10 by (A7) 12) Using a value of W 10 derived by step 11), calculation of C DN (10) by (A6) 13) Recalculation of step 7) 11) 14) If recalculated value of W 10 has no difference larger than 0.1 m s 1 from the previous one, the value of W 10 is adopted. The process of step 13) is repeated until the condition of step 14) is satisfied. Resultant steps are times. References Cardone, V. J., J. A. Greenwood and M. A. Cane (1990): On trends in historical marine wind data. J. Climate, 3, Hayes, S. P., L. J. Mangum, J. Picaut, A. Sumi and K. Takeuchi (1991): TOGA-TAO: A moored array for real time measurements in the tropical Pacific Ocean. Bull. Amer. Meteor. Soc., 72, Hellerman, S. and M. Rosenstein (1983): Normal monthly wind stress over the world ocean with error estimates. J. Phys. Oceanogr., 13, Imawaki, S., H. Uchida, H. Ichikawa, M. Fukasawa, S. Umatani and ASUKA Group (1997): Time series of the Kuroshio transport derived from field observations and altimetry data. International WOCE Newsletter, No. 20. Kubota, M. and H. Yokota (1998): Construction of surface wind stress fields with high temporal resolution by using the ERS- 1 scatterometer data. J. Oceanogr., 54, Kutsuwada, K. and T. Teramoto (1987): Monthly maps of surface wind stress fields over the North Pacific during Bull. of Ocean Res. Inst., Univ. of Tokyo, No. 24, 100 pp. Kutsuwada, K., I. Ueki, M. Kondoh and Y. Kuroda (1998): Investigation of surface wind variability in the tropical Pacific using satellite scatterometer data. Proceeding of PORSEC 98 - Qingdao, Large, W. G. and S. Pond (1981): Open ocean momentum flux measurements in moderate to strong winds. J. Phys. Oceanogr, 11, Levy, G. and R. Brown (1986): A simple, objective analyses Impact of Wind Field in the North Pacific by NSCAT Data 455

14 scheme for scatterometer data. J. Geophys. Res., 91, McPhaden, M. J. (1995): The Tropical Atmosphere Ocean array is completed. Bull. Amer. Soc., 76, Naderi, F. M., M. H. Freilich and D. G. Long (1991): Spaceborne radar measurement of wind velocity over the ocean-an overview of the NSCAT scatterometer system. Proceedings of the IEEE, 79-6, National Aeronautics and Space Administration (1995): Science data product user s guide, overview and geophysical products. Jet Propulsion Laboratory. Cal. Inst. of Tech., 66 pp. Pierson, W. J., Jr. (1990): Examples of, reasons for, and consequences of the poor quality of wind data from ships for the marine boundary layer: implications for remote sensing. J. Geophys. Res., 95-C8, Stommel, H. (1948): The westward intensification of wind-driven ocean currents. Trans. Amer. Geophys. Union, 99, Suzuki, M. (1993): An introductory note on the Ocean Data Buoys of the Japan Meteorological Agency. Kishou, 37-6, (in Japanese). 456 K. Kutsuwada