JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109, C12027, doi: /2004jc002450, 2004

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 109,, doi: /2004jc002450, 2004 Wind jets and wind waves off the Pacific coast of northern Japan under winter monsoon captured by combined use of scatterometer, synthetic aperture radar, and altimeter Teruhisa Shimada and Hiroshi Kawamura Ocean Environment Group, Center for Atmospheric and Oceanic Studies, Graduate School of Science, Tohoku University, Sendai, Japan Received 24 April 2004; revised 2 September 2004; accepted 1 October 2004; published 22 December [1] Wind jets and wind waves off the Pacific coast of northern Japan under the east Asian winter monsoon are investigated using scatterometer, synthetic aperture radar (SAR), and altimeters. First, we depict two prevailing wind flow patterns associated with the northwesterly winter monsoon. They are derived by averaging QuikSCAT wind vectors when the 850-hPa pressure level wind directions are within and , respectively. Wind jets and wakes are formed in different regions depending on the approach angle of wind to the topographic features. Second, using high-resolution satellite observations of QuikSCAT and ERS-2 SAR for wind, and TOPEX/Poseidon and ERS-2 radar altimeter for significant wave height, we present two case studies corresponding to the two prevailing wind flow patterns in order to investigate wind wave development under orographically modified winds. Combined use of QuikSCAT and SAR allows us to capture the surface wind transition from the shore to the offshore. They verify that the wind jets and wakes are extensions of terrestrial gaps and blockages. Variations of significant wave height observed by the altimeters are compared with those of surface wind derived from QuikSCAT and SAR along the altimeter ground tracks. The positions of local maxima and minima of significant wave height and squares of wind speeds coincide with each other. This demonstrates an important role of coastal topography in wind modification and the resulting offshore wind wave development. INDEX TERMS: 4504 Oceanography: Physical: Air/sea interactions (0312); 3339 Meteorology and Atmospheric Dynamics: Ocean/ atmosphere interactions (0312, 4504); 4247 Oceanography: General: Marine meteorology; 3360 Meteorology and Atmospheric Dynamics: Remote sensing; KEYWORDS: air-sea-land interaction, coastal wind, SAR Citation: Shimada, T., and H. Kawamura (2004), Wind jets and wind waves off the Pacific coast of northern Japan under winter monsoon captured by combined use of scatterometer, synthetic aperture radar, and altimeter, J. Geophys. Res., 109,, doi: /2004jc Copyright 2004 by the American Geophysical Union /04/2004JC Introduction [2] The advent of satellite observations has revealed orographically modified winds over the sea surface. Satellite scatterometers provide us with surface vector winds over a wide coverage with spatial resolution of 25 km [Liu, 2002]. They have visualized the low-level wind jet and wake and allowed us to examine the extents of the winds, the formation mechanisms, and the oceanic responses to the wind. We can take the following examples: the low-level wind jets around New Zealand [Laing and Brenstrum, 1996], the wind jet off Vladivostok [Kawamura and Wu, 1998], the gap wind in the Gulf of Mannar [Luis and Kawamura, 2000], three wind jets off the Pacific coast of Central America [Chelton et al., 2000a, 2000b], the wind wake behind the Hawaiian Islands [Xie et al., 2001], and Santa Ana winds [Hu and Liu, 2003]. On the other hand, synthetic aperture radar (SAR) can resolve highresolution wind fields by applying SAR wind retrieval algorithms to SAR images [Scoon et al., 1996]. SAR-derived wind fields are especially valuable in near-coastal regions where scatterometer has observational gaps due to land contamination and the wind observations often are too sparse to give representative pictures of the local wind fields. They reveal complex wind distributions in coastal zones and provide us unique pictures of winds from the shore to the offshore [e.g., Pan and Smith, 1999; Sandvik and Furevik, 2002]. [3] The above mentioned studies demonstrate that orography can have far-reaching effects on wind and that the orographically modified wind can play an important role in regional air-sea interaction. Moreover, they remind us that the orographic modification of wind is ubiquitous in coastal seas. Such coastal winds greatly impact human activities. Viewed in this light, we should have comprehensive views of wind distributions on an area-by-area basis. They will lead to further understanding of air-sea-land interaction and allow us to provide information on the demands of coastal communities. 1of11

2 and wind wave development are also investigated. Section 5 is devoted to discussion, and conclusions are given in section 6. Figure 1. Map of the topography and geographical locations (points A, B, and C) referred to in this paper. Gray color scale indicates the elevation. A dot indicates the location of Mutsu Bay buoy. Points with alphabetical letters A, B, and C indicate the locations at which wind roses shown in Figure 4 are constructed. [4] During the east Asian winter monsoon, strong winds blow constantly over the Sea of Japan from Siberia toward the west coast of Japan. They then pass through the Japanese archipelago and blast out toward the northwestern Pacific. The outbreaks usually persist for periods longer than 1 day. Figure 1 shows a map of topography of northern Japan, the present study area. There exist orographic features, such as straits, valleys, and mountains (Figure 1). Under the outbreaks of the winter monsoon, wind should be modified by such topographic features after passing through the archipelago. However, we have not explicitly revealed the wind field especially in coastal seas by the want of highresolution wind data. Therefore we have not specified the orographic features responsible for the wind modification. [5] In this study, we present comprehensive views of orographically modified winds off the Pacific coast of northern Japan under the winter monsoon by combined use of scatterometer- and SAR-derived winds. Moreover, we relate significant wave height distributions observed by satellite altimeters with the wind fields and discuss effects of coastal topography on wind wave development. The synergetic use of high-resolution satellite observations allows us to examine the relation between winds and wind waves and gives a new perspective of studying air-sea-land interaction. In this study, we use the wording of wind jets and wakes to imply locally strong and weak wind regions, respectively. [6] We give a brief data description in section 2. In section 3 we present statistical characteristics and the diversity of wind jets and wakes. In section 4 we illustrate the complete and close-up wind fields from the coast to the offshore region by combined use of scatterometer and SAR from two case studies. The spatial correlation between wind 2. Data [7] The SeaWinds instrument on the QuikSCAT satellite, which was launched by NASA on 19 June 1999, is a specialized radar that measures near-surface wind speed and direction in an 1800-km swath. Swath data on a 0.25 grid for are used. [8] We use the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/ NCAR) reanalysis for 850-hPa pressure level wind on a 2.5 grid. Outputs are given every 6 hours. [9] ERS-2, carrying Active Microwave Instrument (AMI), had been in operation since 21 April The AMI has a function as C band (5.3 GHz) SAR with vertical transmit and receive polarization. It operates over a fixed range of incidence angles of 20 27, which results in an image width of 100 km. The nominal spatial resolution of the imagery is 30 m. SAR images are processed by Sigma SAR Processor developed by Shimada [1999]. Wind speed maps are derived from the SAR images by applying SAR wind retrieval using CMOD IFR2 scatterometer model function [Quilfen et al., 1998] and wind direction data from NCEP/NCAR reanalysis. It is assumed that wind direction is uniform over the sea in a SAR image. Under high-wind situations, wind directions can reasonably be extracted from the SAR images themselves, and the wind directions are generally consistent with the large-scale wind direction, from NCEP/NCAR reanalysis, for instance [e.g., Wackerman et al., 1996]. [10] We use significant wave height (SWH) observed by TOPEX/Poseidon (T/P) and ERS-2 altimeters along the ground tracks. Their repeat periods are about 10 and 35 days. [11] As in situ time series wind data, buoy observations in the center of Mutsu Bay (Figure 1) are used. The buoy is operated by Aomori Prefectural Aquaculture Research Center, and the wind direction and speed at 4-m height are sampled every hour. Around the study area, only this buoy provides time series of ocean surface vector winds, which can be used as a reference. [12] We also use wind speed and direction over the land accumulated by automatic observation facilities, called Automated Meteorological Data Acquisition System (AMeDAS) operated by Japan Meteorological Agency. They are located at intervals of 21 km around Japan. 3. Statistical Characteristics of the Wind Jets and Wakes 3.1. Winter Mean Wind Fields [13] Figure 2 shows the vector averages of the surface wind field measured by QuikSCAT for three winters (December, January, and February) in Overall, the wind field reflects the northwesterly winter monsoon outbreaks. We can identify strong wind regions with interspersed weak wind regions, both off the Siberian coast and off the Pacific coast of Japan. Over the Japan Sea we can see several strong wind regions (>7 m/s) along the Sikhote 2of11

3 Figure 2. Wind vector averages over three winters (January, February, and December) in at 0.5 grid interval. White regions in the maps represent missing data owing to island, continental land, or sea ice contamination in the QuikSCAT footprints. Alin (mountain range along the Siberian coast), including the most prominent one off Vladivostok [Kawamura and Wu, 1998]. Weak wind regions are also located south of Hamgyong Mountain Range north of the Korean Peninsula. Over the Pacific side we can also identify several strong wind regions higher than 7 m/s and interjacent weak wind regions. The wind map shows that the strong wind regions roughly correspond to low topographic features in northwest-southeast direction. However, it does not sufficiently represent the possible wind flow patterns because wind vectors for all the wind directions are averaged, and only the most distinguished wind distribution appears in the mean wind fields Prevailing Wind Flow Patterns and Land Orographic Effect [14] In order to illustrate prevailing wind patterns we will base our analysis on the notion of local wind systems proposed by Kawamura [1977]. This study demonstrates that surface wind flow patterns can be classified into several dominant patterns on an area-by-area basis from land surface winds over Japan. Each dominant wind flow pattern is referred to as a local wind system. A surface wind flow pattern persists as long as the synoptic wind direction remains within a given range, and transition of the wind flow pattern from one type to another occurs in response to change in wind associated with synoptic weather systems. The synoptic wind direction forming the division between wind flow patterns is determined by the direction of the large-scale topographic features such as mountain ridges and terrestrial troughs. [15] We expand the idea of local wind systems to derive dominant wind flow patterns over the ocean. The 850-hPa pressure level is generally considered to correspond to the upper bound of the atmospheric boundary layer and to represent the synoptic wind field. At that level, wind becomes less under the influence of the surface. First, we compute the collocated and coincident wind direction at 850-hPa pressure level from NCEP/NCAR reanalysis by temporal and spatial interpolation at each grid of QuikSCAT wind observations. Second, we divide the direction into 36 sectors, one for each 10. We classify the QuikSCAT surface wind observations by the corresponding 850-hPa pressure level wind direction into the sectors and average them for every sector. As a result of comparing the wind fields, we can derive two wind flow patterns under northwesterly wind. The ranges of wind direction forming the two wind patterns are and The wind direction of 0 means northerly wind, i.e., wind blowing from the north. Figure 3 shows the prevailing wind flow patterns derived by averaging classified wind vectors over the wind direction ranges. They illustrate that the acceleration and the blockage by the topographic features gives rise to wind jets and wakes in different regions under the different synoptic wind direction. [16] Figure 3a shows the wind flow pattern in the case that the 850-hPa wind directions are between 260 and 290. Wind passing through the Soya Straits (Figure 1) is intensified and forms a strong wind region along the coast facing the Sea of Okhotsk. Off the southeastern coast of Hokkaido, weak wind regions extend toward the east because of the wind blockage by the mountain ranges that run in a north-south direction. The southern boundary of the weak wind region extends from Cape Erimo. No wind jets are formed between the islands of the northern territories. The northwesterly winds blowing along the coast of southwestern Hokkaido rush through the Cape Erimo. Beyond the cape the wind speed reaches a maximum that is higher than that in the ambient by 4 m/s. The northwest monsoonal winds impinge on the mountain range on the south of Hokkaido, and the orographic blockage accelerates the winds on the southern tip of Hokkaido, leading to the formation of wind jet offshore. The Kitakami highland is a mountain range that rises above 1000 m and runs in a north-south direction on the east coast of the Tohoku district. Wind is blocked by the highland and the weak wind region extends to the east and southeast over the ocean. A wind jet adjacent to the weak wind region extends from Sendai Bay. This wind flow pattern typically corresponds to the last stage of the winter monsoon outbreak. About 30% of all the QuikSCAT wind observations in winter fall into this wind flow pattern. [17] Figure 3b shows the wind flow patterns when the wind directions are between 290 and 330. Northwesterly winds are blocked by Sakhalin, and a weak wind region is formed in the lee. The islands of northern territories are roughly oriented perpendicular to the prevailing northwesterly winds. The orography is favorable to form wind jets. Winds passing through the straits are accelerated, and the wind jets extend offshore. A triangular-shaped weak wind region is formed off the southeastern coast of Hokkaido, and it extends 200 km at most toward southeast. The most distinguished wind jet extends from the area between western Hokkaido and northern Tohoku district. It extends more than 500 km, and maximum wind speeds are seen throughout the jet along the axis of the jet. Adjacent weak 3of11

4 Figure 3. Average wind fields of QuikSCAT wind observations classified by 850-hPa pressure level wind direction. Their division ranges of the wind direction are between (a) and (b) wind regions also extend as much as the jet. Above all, in the lee of the Katakami highland, wind speeds are especially low as compared with the adjacent wind speeds. On the other hand, a wind jet extends from the south of the Kitakami highland. Moreover, on the southern side of the jet a weak wind region is formed. These two areas show a considerable contrast to the previous case. This wind flow pattern typically corresponds to the peak of the winter monsoon outbreak. As well as the wind flow pattern in Figure 3a, 30% of all the QuikSCAT wind observations in winter fall into this wind flow pattern Wind Roses of Wind Jets and Wakes [18] The temporal variability of wind in the cores of the wind jets and wakes is represented by wind roses (Figure 4). We use time series of wind vectors observed by QuikSCAT form November 2000 to March 2001, which roughly correspond to 12 hourly wind time series. We have divided the compass into 12 sectors, one for each 30 of the horizon. We use cumulative relative frequencies from 0 m/s up to 8, 12, 15, and 30 m/s for each directional sector in order to show the distributions of wind speeds and the frequency of the varying wind directions. The selected locations are indicated in Figure 1 by A, B and C. In winter the winds generally alternate between sustained strong northerly/westerly wind and periodic and short-duration southerly wind. However, the frequency distributions of wind speed and direction are different at the three locations. Figure 4a shows the wind rose at location A corresponding to off the strait. The northwesterly wind is dominant. Wind speed and direction at location B are plotted in Figure 4b. The location corresponds to the cores of wind wakes off the southeastern coast of Hokkaido under both wind flow patterns shown in Figure 3. Westerly wind is dominant. The two wind roses in Figures 4a and 4b are similar to each other in shape, but the winds attain lower speeds than the winds in either of the other two locations. Figure 4c shows the wind rose at location C corresponding to the region off the Cape Erimo. The winds are northwesterly and highly stable in direction. The wind speeds are generally higher than those shown in Figures 4a and 4b. 4. Case Study Examples of Coastal Wind Jets 4.1. Case Selection and Analysis Methods [19] In this section, we illustrate the spatial correlation between wind and wave distribution from two representative time periods of satellite observations. The two case study examples correspond to the two prevailing wind flow patterns shown in Figure 3, respectively. Table 1 summarizes observational conditions of the two case studies, on 25 February 2000 (case 1) and 4 December 2000 (case 2). Under the winter monsoon outbreaks, wind distributions are directly reflected in wave distribution because the swell energy is a much smaller contribution to the total wave height. Moreover, in the strong wind regions it is verified that wind wave development may be simplified as fetchlimited, one-dimensional, and time-independent [Ebuchi et al., 1992; Ebuchi, 1999]. 4of11

5 Figure 4. Wind roses composed from time series of wind vectors observed by QuikSCAT from November 2000 to March 2001 at the selected locations indicated by solid circles with alphabetical characters ( A, B, and C ) in Figure 1. Latitudes and longitudes of the locations plotted are indicated in each panel. We have divided the compass into 12 sectors, one for each 30 of the horizon. Contours indicate cumulative relative frequencies of wind speeds from 0 m/s up to 8, 12, 15, and 30 m/s for each directional sector, from the inner one to the outer one in order, respectively. Circles are the isolines of cumulative relative frequency of 0.05, 0.1, and 0.2, from the inner one to the outer one in order, respectively. Wind direction is defined as direction toward which the wind blows; that is, the origins of wind vectors correspond to the origin of coordinate system. [20] Figure 5 shows the hourly wind speed and direction recorded from a buoy in Mutsu Bay (Figure 1). The time series wind observations can be considered as representative examples of synoptic wind variations. As will hereinafter be described in detail, we can specify the period of time with rather steady wind condition associated with the winter monsoon outbreak in both case 1 and case 2. The arrows in Figure 5 indicate passing times of QuikSCAT, ERS-2 SAR, T/P, and ERS-2 altimeters. Observation time differences between satellite sensors are unimportant to grasp representative views under the steady winter monsoon outbreaks. Then, ocean and land surface winds measured by QuikSCAT, ERS-2 SAR, and AMeDAS are investigated together. They are shown in Figures 6 and 8. Figures 7 and 9 are enlarged views of Figures 6 and 8, respectively, with a central focus on SAR observation areas. Using SWH data from T/P and ERS altimeters, we then investigate the relation between wind and wave distributions. SWHs are plotted along the altimeter ground tracks in Figures 6, 7, and 8. SWH variations are also compared with squares of wind speeds along the altimeter ground tracks, which are proportional to wind energy Case 1: 25 February 2000 [21] According to the synoptic weather charts at 0000 UT on 25 February 2000 (not shown), a low-pressure system had passed through the southern part of Japan, and areas around Japan were under a winter pressure pattern favorable to the monsoon outbreak. In Figure 5a, wind speed rapidly increases at 1800 UT on 24 February 2000, and higher and steady wind around 10 m/s lasted one entire day. During the period the wind direction is almost constant at 280 (Figure 5b). The observation time of ERS-2 SAR corresponds to the onset of strong wind around the study area, and observations of the other sensors follow within 12 hours (Table 1). This case corresponds to the prevailing wind flow patterns shown in Figure 3b. [22] At 0906 UT, QuikSCAT observed wind around Japan (Figure 6a). In the northwestern Pacific, two distinguished wind jets with speeds above 12 m/s are seen to extend from the proximity of Tsugaru Straits and the south of Kitakami highland. The strongest winds with speeds more than 16 m/s are observed in the jets. Local maximum wind speeds are located at (40 N, 143 E), (39 N, 147 E), and (37 N, 145 E). Between the two jets we can see a lowerwind region extending toward the offshore 500 km from the coast and reaching at least 146 E longitude. [23] Wind speeds are retrieved from SAR images acquired at 0115 UT (at around 39 N and 142 E in Figures 6a and 7a). In the northeastern part of SAR images a part of northern wind jet with speeds of 12 m/s is captured. The wind speeds are lower than 8 m/s in the coastal area 50 km from the coastline. In this region we can see alternative high-/low-wind regions, which can be considered as extensions of the terrestrial wind pattern affected by the upstream land topography. We identify two distinguished lower-wind regions <6 m/s. They connect to the offshore low-wind regions observed by QuikSCAT. According to AMeDAS observations, wind speeds are much lower (2 5 m/s) than those observed over the surrounding seas by QuikSCAT (Figure 7a). This is true to both upstream and downstream sides of Japan (Figure 6a). This wind speed Table 1. Observational Condition of Two Case Studies at Times of Satellite Passes Over the Study Area a Case Date Wind Direction Wind Speed, m/s Time, UTC ERS-2 SAR QuikSCAT T/P ERS Altimeter 1 25 Feb northwest Dec northwest a Wind speed and direction are obtained from buoy observations in Mutsu Bay. 5of11

6 Figure 5. Hourly wind observations recorded at the Mutsu Bay buoy, obtained from (a and b) 23 to 26 February and (c and d) 2 to 5 December Time series of wind speed (Figures 5a and 5c) and wind direction (Figures 5b and 5d) are shown. Arrows with alphabetical letters indicate the time when ERS-2 SAR (S), QuikSCAT (Q), TOPEX/Poseidon (T), and ERS-2 altimeter (E) pass through the study area. contrast is primarily due to the difference in surface roughness between the ocean and the land. From the SAR-derived wind fields it is shown that wind speed transition occurs at around km distances from the coastline. [24] SWH data are obtained from T/P track 60 and ERS track 905 at 1004 and 1223 UT, respectively. The altimeter tracks with SWH are superimposed in Figures 6a and 7a. SWH are also plotted in Figures 6b, 6c, and 7b. In these figures the square of wind speed along the altimeter ground tracks is also shown. The detailed relations between wind and SWH in consideration of fetch effect are discussed in section 5. The short T/P track intersects between the coasts of Hokkaido and Tohoku district. The significant wave height is higher at the center of the track (Figure 6b). This is due to longer fetches from the head of Uchiura Bay and larger wind speeds in the jet (Figure 6b). The ERS ascending track passes near the eastern coast of northern Japan through the SAR observation area. When passing over through the southern jet, local maximum wave height above 3.0 m is observed at around 37.5 N (Figure 6c). South of 37.5 N, SWH is higher than 2.5 m, which may be mainly due to larger fetch. The SWH and the QuikSCAT wind speed along the ERS track have local minima at around 39.5 N, where the low-wind region extends from the coast. At the latitude, significant wave heights are 1.0 m lower than those in neighboring jets. In the northern wind jets, ERS altimeter observes SWH higher than 2.0 m. These local maxima and minima of significant wave height correspond well to those of QuikSCAT wind speeds along the ERS ground track (Figure 6c). [25] Figure 7b shows the wind speed profile of SAR and the SWH profile observed by ERS altimeter along the ERS ground track. The local SWH maxima and minima correspond well to those of the SAR wind speeds. Smaller-scale variations of wind speed and SWH are well observed by both sensors Case 2: 4 December 2000 [26] The synoptic weather chart at 0000 UT on 3 December 2000 (not shown) shows that the area around Japan was under a winter pressure pattern as in case 1. Figure 5c shows that the buoy-observed wind speeds higher than 10 m/s lasted 1.5 days from 0000 UT on December 3. Wind direction (Figure 5d) is almost constant (280 ) during the period. A weak southerly wind associated with the passage of a low-pressure system over Japan was observed from 1800 UT on 4 December until 0600 UT on 5 December. The observation time of ERS-2 SAR corresponds to the midpoint of strong wind around the study area. Observations by the other sensors are obtained in the latter half of the outbreak period ( on 4 December 2000). The steady wind direction prior to the satellite observations implies that the wave field should also be steady and in equilibrium. This case corresponds to the prevailing wind flow pattern shown in Figure 3a. [27] At 0842 UT, QuikSCAT observed a strong wind jet off southern Hokkaido (Figures 8a and 9). The wind jet extends more than 600 km from the vicinity of Tsugaru Straits. In the jet, wind speeds are as great as 16 m/s, and the local maximum wind speed is observed south of Cape Erimo. On both sides of the jets, lower-wind regions 6of11

7 Figure 6. (a) Ocean and land surface winds measured by QuikSCAT, SAR, and AMeDAS. Winds are measured by QuikSCAT, ERS-2 SAR, and AMeDAS at 0906, 0115, and 0100 UT on 25 February 2000, respectively. SWHs measured by T/P and ERS altimeters are also plotted. They are obtained at 0121 and 1252 UT on 25 February 2000, respectively. Color scales indicate the magnitude of wind speed (WS), SWH, and the elevation. SWH and squares of QuikSCAT wind speeds (WE) are plotted along the tracks of (b) T/P and (c) ERS altimeters. Figure 7. (a) Close-up view of Figure 6a with a focus on SAR observations. (b) Wind energy values derived from SAR and ERS altimeter SWHs are plotted along the ERS ground track. 7of11

8 Figure 8. (a) Ocean and land surface winds measured by QuikSCAT, SAR, and AMeDAS. Winds are measured by QuikSCAT, ERS-2 SAR, and AMeDAS at 0842, 0120, and 0100 UT on 4 December 2000, respectively. SWHs measured by T/P and ERS altimeters are also plotted. They are obtained at 1544 and 0123 UT on 4 December 2000, respectively. Color scales indicate the magnitude of wind speed, SWH, and the elevation. SWH and squares of QuikSCAT wind speeds are plotted along the tracks of (b) T/P and (c) ERS altimeters. extend along the jet stream from the southeastern side of Hokkaido and Kitakami highland. Their minimum wind speed is <8 m/s. [28] The wind-speed map is retrieved from SAR images acquired at 0120 UT on 4 December 2000 (at around 42 N and 141 E in Figures 8a and 9). It is obviously shown that there exist three distinguished jets of the northwestern winds in the SAR observation areas (Figure 9). These wind jets over the sea correspond to upstream topographical features. The northern and middle jets have local maximum wind speeds at the opening mouth of the Bay of Uchiura and the exit of the Tsugaru Straits, respectively. The southern jet is seen from the central area of Mutsu Bay to the Pacific across the Shimokita Peninsula. Wind speeds first decrease in the lee side of this peninsula but increase rapidly to form the southern jet. While these jets have wind speed as great as 14 m/s, the lower wind speed region between jets has wind speeds <8 m/s. The jets broaden toward the offshore region and merge into one large jet flow as seen in QuikSCAT surface vector wind field in Figure 8a and also Figure 9. According to AMeDAS observations, wind speeds over land are much lower (2 5 m/s) than observed over the surrounding seas as seen in case 1. [29] SWHs are obtained from T/P track 253 and ERS track 978 at 1544 and 0123 UT, respectively. They are superimposed on the vector winds in Figure 8a. The T/P and ERS tracks pass the Pacific Ocean off the eastern coast of Japan. Local SWH maxima 4 m at around 41 N along both altimeter tracks are observed in the jet region (Figures 8b and 8c). The altimeter track segments with SWH larger than 3.0 m correspond to the regions where squares of QuikSCAT-derived wind speeds are >150 (12.0 m/s in wind speed) as shown in Figures 8b and 8c. In the jet area the longer fetches from the Uchiura Bay and Tsugaru Straits contribute to the generation of the higher SWHs. High SWHs are also observed at around 45 N along the ERS track and at around 44 N along the T/P track, respectively. These are also corresponding to a strong wind region blowing through the Soya Straits between Hokkaido and Sakhalin. 5. Discussion [30] Figure 10 shows comparisons of SAR-derived wind speeds with QuikSCAT and ERS altimeter wind speeds in the overlapping area from both case studies discussed in section 4. They generally agree well with each other. The root mean square error is 1.43 m/s, and the bias is 0.21 m/s. QuikSCAT has higher wind speeds than SAR-derived wind speeds on occasions when QuikSCAT observes a wind speed <12 m/s as in case 1. The differences between QuikSCAT- and SAR-derived wind speeds 8of11

9 become larger as the QuikSCAT wind speeds decrease. In case 2, wind speeds are compared only in strong wind regions. The reason may be that observational time differences become dominant in weak wind regions and contribute the wind speed difference more significantly. However, the difference of wind speeds has no relation to the essence of the study. [31] As well as the orographic effects on wind the response of the near-surface wind to underlying sea surface temperature (SST) gradients should be also considered. Several recent studies have demonstrated the SST-wind covariability by utilizing the microwave remote-sensing data [e.g., Xie et al., 2002; Nonaka and Xie, 2003; Chelton et al., 2004], and their accomplishments are comprehensively reviewed by Xie [2004]. They show positive correlation between SST and wind; that is, increased (decreased) wind speeds are found to be associated with warm (cold) SST anomalies. Therefore it is essential to understand dominant mechanisms for modification of the surface wind with various temporal and spatial scales, concerning the regional air-sea-land interaction. [32] We examine the fetch growth characteristics of wind waves in the Pacific Ocean after the fashion of Ebuchi et al. [1992] and Ebuchi [1999]. In their studies, snapshot altimeter SWH observations are effectively used for investigating fetch growth of wind waves in combination with scatterometer wind measurements. They also compare observations with traditional formulas describing the growth of wind waves with fetch. They are empirical relationships between the nondimensional fetch ( ^F) and the nondimensional SWH ( ^H). The fetch (F) and SHW (H) are normalized using the wind speed at a height of 10 m (U 10 )as ^F ¼ gf=u 2 10 ^H ¼ gh=u 2 10 : ð1þ Figure 9. Close-up view of Figure 8a with a focus on SAR observations. H is calculated from two altimeters along their ground tracks. The fetch F, a distance from the coast to an observed jet or wake, is estimated along the streamline on the scatterometer vector wind field. U 10 is given as an average wind speed obtained by scatterometer along the fetch. [33] Figure 11a shows four altimeter tracks in total shown in the two case studies of section 4. The tracks are discriminated between wind jets and wakes according to the analysis in section 4. Altimeter track segments crossing wind jets and wakes are indicated by solid circles and crosses, respectively. In the domain of Figure 11a we computed pairs of ^F and ^U for all the altimeter observations along the tracks. Figure 11b shows the relatizon between the nondimensional fetch and significant wave height. The data plots are categorized into two groups of observations: in wind jets and in wakes. Empirical formulas proposed by Wilson [1965], Joint North Sea Wave Project (JONSWAP) [Hasselmann et al., 1973], and Mitsuyasu [1968] are also shown in Figure 11b. [34] We omit discussion of discrepancy between data plots and the formulas because it is well discussed by Ebuchi [1999], including the wind speed dependence of the drag coefficient. In the work of Ebuchi et al. [1992] and Ebuchi [1999], fetch growth of wind waves is investigated only in strong wind regions in Japan where one-dimensional wave growth is reasonably assumed. Here we pay attention to the difference in wind wave development in wind jets and wakes. Because most plots of wind jets (solid circles) distribute around the Wilson formula, we can conclude that empirical wave growth with fetch is also applicable to wind wave development even in the Pacific Ocean under the two Figure 10. Comparison of SAR-derived wind speeds with QuikSCAT and ERS altimeter wind speeds in the overlapping areas of the sensor observations of the two case studies. Circles and triangles indicate the comparison between SAR-derived wind speeds and QuikSCAT wind speeds in cases 1 and 2, respectively. Asterisks indicate the comparison between SAR-derived wind speeds and ERS altimeter wind speeds in case 1. 9of11

10 [35] Development of a high-resolution coastal wind observing system is a challenge but essential to further understanding of marine meteorology and coastal oceanography. The high-resolution coastal winds also contribute to improvement in the high-resolution coastal atmospheric and oceanic models including the surface wave model. In the present circumstances, SAR has not reached the stage of operational monitoring. However, it is an indisputable fact that the high-resolution wind capability of SAR is essential to study the coastal winds because of their finer spatial structures. Figure 11. (a) Altimeter ground tracks shown in the two case studies. Their segments crossing wind jets are plotted by solid circles, and wakes are plotted by crosses. (b) Relation between the nondimensional fetch ^F and the nondimensional SWH ^H. Lines with alphabetical letters of W, J, and M show the empirical formulas proposed by Wilson [1965], Joint North Sea Wave Project [Hasselmann et al., 1973], and Mitsuyasu [1968], respectively. cases. On the other hand, data plots of wakes (triangles) scatter around Mitsuyasu s formula though it is not applicable for fetches larger than 10 4 as well as JONSWAP s formula. This means that the SWHs in the weak wind regions are higher than expected by formulae for onedimensional wave growth with fetch, though the SWH variations correspond well to those of wind speed as shown in Figures 6 and 8. This might be related to directional spreading features of wind waves, but the further discussions on the wave growth mechanism under strong forcing of nonuniform wind fields is beyond the scope of the present study and left for future studies. 6. Summary and Conclusions [36] Using high-resolution satellite observations, we describe the spatial characteristics and the statistics of the wind jets and wakes off the Pacific coast of northern Japan under the east Asian winter monsoon. Combined use of scatterometer- and SAR-derived winds allows us to examine the orographically modified winds in detail from the shore to the offshore. Additionally, by using coincident SWH observed by altimeters, we investigated significant wave height variations under the wind fields. The following conclusions are obtained. [37] 1. Under the northwesterly wind we derive two prevailing wind flow patterns off the Pacific coast of northern Japan. They are averaged wind vector fields obtained from QuikSCAT measurements classified by wind direction at 850-hPa pressure level of reanalysis fields in cases that the wind directions are between and When the synoptic wind direction is within one of the specified ranges, the land topography acts to form a similar wind flow pattern. However, when the wind synoptic direction changes into the other range, the land topography acts to form the wind jets and wakes in different regions. [38] 2. The smaller-scale wind variations revealed by SAR extend and broaden downwind and consistently connect to the larger-scale wind jets and wakes observed by scatterometer. This proves that the wind jets and wakes over the ocean observed by scatterometer are extensions of terrestrial gaps and orographic blockages. [39] 3. Significant wave heights along altimeter ground tracks intersecting the regions of wind jets and wakes are well correlated with squares of wind speeds observed by scatterometer and SAR. The locations of SWH local maxima and minima agree with those of wind speed of scatterometer and SAR. This means that terrestrial gaps produce the orographically modified winds, which broaden and extend up to several hundred kilometers downwind, and in turn, such wind fields determine the wind sea and its variation. [40] Acknowledgments. SeaWinds/QuikSCAT data and TOPEX/ POSEIDON Merged Geophysical Data Records were obtained from NASA Physical Oceanography Distributed Active Archive Center at the Jet Propulsion Laboratory. ERS-2 SAR data were provided from National Space Development Agency of Japan. ERS-2 altimeter data were obtained from CERSAT at IFREMER, Plouzané (France) during a collaboration work. NCEP Reanalysis data were downloaded from the Web site of the NOAA-CIRES Climate Diagnostics Center, Boulder, Colorado, United States. AMeDAS data were provided by the Japan Meteorological Agency. Buoy observations in Mutsu Bay were provided by Aomori Prefectural Aquaculture Research Center. We thank two anonymous reviewers for their 10 of 11

11 careful reading of the munuscript and constructive suggestions for improvement. This study is partly supported by the category 7 of MEXT RR2002 Project for Sustainable Coexistence of Human, Nature and the Earth and Special Coordination Funds for Promoting Science and Technology New Generation Sea Surface Temperature. References Chelton, D. B., M. H. Freilich, and S. K. Esbensen (2000a), Satellite observation of the wind jets off the Pacific coast of Central America. Part I: Case studies and statistical characteristics, Mon. Weather Rev., 128, Chelton, D. B., M. H. Freilich, and S. K. Esbensen (2000b), Satellite observation of the wind jets off the Pacific coast of Central America. Part II: Regional relationships and dynamical consideration, Mon. Weather Rev., 128, Chelton, D. B., M. G. Schlax, M. H. Freilich, and R. F. Milliff (2004), Satellite measurements reveal persistent small-scale features in ocean winds, Science, 303, Ebuchi, N. (1999), Growth of wind waves with fetch in the Sea of Japan under winter monsoon investigated using data from satellite altimeter and scatterometer, J. Oceanogr., 55, Ebuchi, N., H. Kawamura, and Y. Toba (1992), Growth of wind waves with fetch observed by the Geosat altimeter in the Japan Sea under winter monsoon, J. Geophys. Res., 97, Hasselmann, K., et al. (1973), Measurement of wind-wave growth and swall decay during the Joint North Sea Wave Project (JONSWAP), Dtsch. Hydrogr. Z., 8, suppl. A., 95 pp. Hu, H., and W. T. Liu (2003), Oceanic thermal and biological responses to Santa Ana winds, Geophys. Res. Lett., 30(11), 1596, doi: / 2003GL Kawamura, H., and P. Wu (1998), Formation mechanism of Japan Sea Proper Water in the flux center off Vladivostok, J. Geophys. Res., 103, 21,611 21,622. Kawamura, T. (1977), Areal distribution of surface winds in Japan, Tech. Rep. Jpn. Meteorol. Agency, 91, Laing, A. K., and E. Brenstrum (1996), Scatterometer observations of lowlevel jets over New Zealand coastal waters, Weather Forecasting, 11, Liu, W. T. (2002), Progress in scatterometer application, J. Oceanogr., 58, Luis, A. J., and H. Kawamura (2000), Wintertime wind forcing and sea surface cooling near the South Indian tip observed using NSCAT and AVHRR, Remote Sens. Environ., 73, Mitsuyasu, H. (1968), On the growth of the spectrum of windgenerated waves (1), Rep. Res. Inst. Appl. Mech. Kyushu Univ., 16, Nonaka, M., and S.-P. Xie (2003), Co-variations of sea surface temperature and wind over the Kuroshio and its extension: Evidence for ocean-toatmospheric feedback, J. Clim., 16, Pan, F., and R. B. Smith (1999), Gap wind and wakes: SAR observations and numerical simulation, J. Atmos. Sci., 56, Quilfen, Y., B. Chapron, T. Elfouhaily, K. Katsaros, and J. Tournadre (1998), Observation of tropical cyclones by high-resolution scatterometry, J. Geophys. Res., 103, Sandvik, A. D., and B. R. Furevik (2002), Case study of a coastal jet at Spitsbergen Comparison of SAR- and model-estimated wind, Mon. Weather Rev., 130, Scoon, A., I. S. Robinson, and P. J. Meadow (1996), Demonstration of an improved calibration scheme for ERS-1 SAR imagery using scatterometer wind model, Int. J. Remote Sens., 17(2), Shimada, M. (1999), Verification processor for SAR calibration and interferometry, Adv. Space Res., 23(8), Wackerman, C. C., C. L. Rufenach, R. A. Shuchman, J. A. Johannessen, and K. L. Davidson (1996), Wind vector retrieval using ERS-1 synthetic aperture radar imagery, IEEE Trans. Geosci. Remote Sens., 34, Wilson, B. W. (1965), Numerical prediction of ocean waves in the North Atlantic for December 1959, Dtsch. Hydrogr. Z., 18, Xie, S.-P., W. T. Liu, Q. Liu, and M. Nonaka (2001), Far-reaching effects of the Hawaiian Islands on the Pacific ocean-atmosphere system, Science, 292, Xie, S., J. Hafner, Y. Tanimoto, W. T. Liu, H. Tokinaga, and H. Xu (2002), Bathymetric effect on the winter sea surface temperature and climate of the Yellow and East China Seas, Geophys. Res. Lett., 29(24), 2228, doi: /2002gl Xie, S.-P. (2004), Satellite observations of cool ocean-atmosphere interaction, Bull. Am. Meteorol. Soc., 85(2), H. Kawamura and T. Shimada, Ocean Environment Group, Center for Atmospheric and Oceanic Studies, Graduate School of Science, Tohoku University, Aramaki Aza Aoba, Aoba-ku, Sendai, Miyagi, Japan (shimada@ocean.caos.tohoku.ac.jp) 11 of 11