An Intercomparison of TOPEX, NSCAT, and ECMWF Wind Speeds: Illustrating and Understanding Systematic Discrepancies

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1 780 MONTHLY WEATHER REVIEW An Intercomparison of TOPEX, NSCAT, and ECMWF Wind Speeds: Illustrating and Understanding Systematic Discrepancies GE CHEN Ocean Remote Sensing Institute, Ocean University of China, Qingdao, China (Manuscript received 18 March 2003, in final form 19 September 2003) ABSTRACT The availability of multiple satellite missions with wind measuring capacity has made it more desirable than ever before to integrate wind data from various sources in order to achieve an improved accuracy, resolution, and duration. A clear understanding of the error characteristics associated with each type of data is needed for a meaningful merging or combination. The two kinds of errors namely, random error and systematic error are expected to evolve differently with increasing volume of available data. In this study, a collocated ocean Topography Experiment (TOPEX) NASA Scatterometer (NSCAT) ECMWF dataset, which covers 66S 66N and spans the entire 10-month lifetime of NSCAT, is compiled to investigate the systematic discrepancies among the three kinds of wind estimates, yielding a number of interesting results. First, the satellite-derived wind speeds are found to have a larger overall bias but a much smaller overall root-mean-square (rms) error compared to ECMWF winds, implying that they are highly converging but are systematically biased. Second, the TOPEX and NSCAT wind speed biases are characterized by a significant phase opposition with latitude, season, and wind intensity, respectively. Third, the TOPEX (NSCAT) bias exhibits a low high low (high low high) pattern as a function of wind speed, whose turning point at 14.2 m s 1 coincides well with the transitional wind speed from swell dominance to wind sea dominance in wave condition, suggesting that the degree of wave development plays a key role in shaping wind speed bias. 1. Introduction Since the 1970s, information on sea surface wind speed has been obtained from a variety of spaceborne microwave instruments including scatterometer (e.g., Naderi et al. 1991), altimeter (e.g., Fu et al. 1994), radiometer (e.g., Hollinger et al. 1990), and synthetic aperture radar (SAR; e.g., Vachon and Dobson 1996). Among them, the satellite scatterometer is a dedicated wind measuring sensor that provides both speed and direction; other sensors usually make wind measurement as a by-product. In the past two decades, it has been convincingly demonstrated that a near-synoptic, allweather view of marine winds from spaceborne microwave instruments has greatly advanced our knowledge in many aspects of meteorology and oceanography. Up until now, each type of the sensors mentioned above, except SAR, has accumulated a decade-long observation database that allows wind climatology to be constructed (e.g., Atlas et al. 1996; Bentamy et al. 1996; Young 1999). Naturally, there is a growing interest in combining or merging wind measurements from various sources to achieve a better coverage and resolution in Corresponding author address: Dr. Ge Chen, Ocean Remote Sensing Institute, Ocean University of China, 5 Yushan Road, Qingdao , China. gechen@public.qd.sd.cn both time and space domains. It is obvious that such an effort would be meaningful only if the accuracy of all datasets involved is above a given level. A number of studies have been carried out to compare wind estimates from various sources and evaluate their consistency (e.g., Halpern et al. 1994; Boutin and Etcheto 1990, 1996; Busalacchi et al. 1993; Rienecker et al. 1996; Boutin et al. 1996, 1999; Bentamy et al. 1999; Queffeulou et al. 1999; Meissner et al. 2001). Most of the comparison statistics indicate that the overall differences among various datasets are within their measurement uncertainties. Meanwhile, they also suggest that the observed deviations are geographically, seasonally, and geophysically dependent. Such sensor-related systematic discrepancies are poorly understood so far. In this study, the consistency of altimeter and scatterometer wind speed measurements are examined, with special attention paid to the spatial and temporal patterns as well as the geophysical dependency of their systematic discrepancies. In doing so, a collocation dataset of ocean Topography Experiment (TOPEX) and National Aeronautics and Space Administration (NASA) Scatterometer (NSCAT) is compiled together with European Centre for Medium-Range Weather Forecasts (ECMWF) winds. These two sensors are selected because the former represents the state of the art in satellite altimetry in terms of data quality and duration (Fu and 2004 American Meteorological Society

2 MARCH 2004 CHEN 781 Cazenave 2001), and the latter is known to be one of the most accurate spaceborne scatterometers flown to date (Freilich and Dunbar 1999). Therefore, the results obtained here are expected to be largely representative of the two kinds of sensors at their corresponding frequencies. The rest of the paper is organized as follows: Section 2 describes the collocated TOPEX NSCAT ECMWF dataset with general statistics. The spatial and temporal patterns of the wind speed deviations among the three data sources are examined in section 3. The dependency of wind speed bias on wind intensity is illustrated in section 4 and is discussed in the context of swell and wind sea influences in section 5. Finally, a summary with conclusions is presented in section Collocation data and general statistics a. Collocation dataset The TOPEX/Poseidon satellite is a joint U.S. French altimetric mission launched on 10 August 1992 (Fu et al. 1994). It samples the ocean surface between 66S and 66N at a 1-s interval (corresponding to a 5.8-km resolution on the ground track) for each of the 254 ascending and descending passes that make up a day cycle. The satellite has so far acquired more than 10 yr of sea level, significant wave height, and radar cross-sectional data. NSCAT was launched on 17 August 1996 as part of the Japanese Advanced Earth Observing Satellite-I (ADEOS-I) mission. It has an array of six antennas that scan two 600-km bands of the ocean on each side of the instrument s orbital path separated by a gap of 329 km. Four collocated backscatter measurements (three vertically polarized and one horizontally polarized) from three azimuth angles throughout each swath allow vector winds to be retrieved with approximately 25-km resolution, and 90% of the global ocean is covered by the swath within 2 days (Naderi et al. 1991). NSCAT acquired vector wind data from mid- September 1996 until the spacecraft suffered a catastrophic failure of the solar panel on 30 June Fortunately, the short-lived NSCAT instrument was fully overlapped with the TOPEX altimetric mission. The legacy of the 10-month coincident scatterometer altimeter data with unprecedented individual quality will be of great benefit to a variety of geophysical applications. Taking advantage of this unique opportunity, a TOPEX NSCAT collocation dataset is compiled together with ECMWF winds. The NSCAT wind speed data used for this study come from the High-Resolution Merged Geophysical Data Product (Dunbar 1997). The TOPEX altimeter data are from the TOPEX/Poseidon Merged Geophysical Data Records (Generation B) (Benada 1997) for the period of the NSCAT mission. Global model outputs of surface wind vector from the ECMWF forecasts are also used. Such wind vectors are estimated from surface analysis for an altitude of 10 m above the ocean under neutral stability. Since the ECMWF data used here are from the forecast rather than reanalysis product, neither NSCAT nor TOPEX data have been incorporated (note that ECMWF only started assimilating satellite wind data operationally with QuikSCAT after January 2002). It has to be noted, however, that the ECMWF forecast models experienced a change from a three-dimensional variational data assimilation (3DVAR) system to a fourdimensional variational data assimilation (4DVAR) system in But this is not expected to cause systematic biases in this analysis. An optimal collocation scheme is desirable in order to achieve a reliable and meaningful comparison. The surface resolution of NSCAT is 25 km 25 km for each wind estimate, a so-called wind vector cell. To keep sampling errors to a minimum, equal sensor surface coverage is sought. The high-resolution NSCAT product is beneficial for this purpose as the TOPEX wind resolution cell is relatively miniscule of the order of 2 km 6 km for each footprint. To bring the TOPEX spatial resolution as near to NSCAT as possible, I include an average over those TOPEX data points that fall within a given NSCAT wind cell. Thus the TOPEX wind cell characteristics become variable from 2 km 6kmto2km 25 km (from one to four footprints). The ECMWF marine wind output is on a by grid every 6 h. For our purpose the model output is interpolated linearly in space and time to derive a wind estimate collocated with our sensor observations. This results in a maximum time lag of 3 h. Ground resolution for ECMWF is then 125 km km, depending on the latitude. Some additional quality controls are performed to ensure a better consistency of the three wind speed datasets. Since scatterometer winds will be used in section 5 as sea state independent measurements to distinguish swell from wind sea, and there is evidence showing that near-nadir scatterometer winds are affected by significant wave height (Queffeulou et al. 1999), the crossover points where the NSCAT midbeam antenna has an incidence angle less than 40 are eliminated from the dataset. For the altimeter data, an additional minimal filtering of outliers removes the points where the TOPEX estimate of backscatter is below 5.0 db or above 30.0 db. Also, the TOPEX and NSCAT data are screened for rain contaminations using the schemes described in Chen et al. (2003) and Hoffman et al. (1994), respectively. Some details of this collocation dataset are given in Table 1, and further information can be found in Gourrion et al. (2000). To provide a sense of the global distribution of the compilation, Fig. 1 presents the data density of the TO- PEX NSCAT ECMWF crossover samples for the 10- month period. It is apparent that the dataset does contain global observations out to the 66 latitude limit of TO- PEX. A salient characteristic of the distribution is the increased likelihood of high-latitude intersections, in

3 782 MONTHLY WEATHER REVIEW TABLE 1. Some details of the TOPEX NSCAT ECMWF collocation dataset, where T, N, and E denote TOPEX, NSCAT, and ECMWF, respectively. Time duration 15 Sep Jun 1997 Spatial coverage 66S 66N No. of collocation data Time window (h) T N T E Space window (km) T N T E contrast to the basically random nature of the geographical pattern in low and midlatitude areas. b. General statistics To have a first overview of the collocation dataset, the histograms of the TOPEX, NSCAT, and ECMWF wind speeds are shown in Fig. 2. It can be seen that the distributions of NSCAT and ECMWF winds are very close to each other (as a result of the fact that the NSCAT wind speed algorithm is partially determined by comparison with the ECMWF winds), while that of the TO- PEX is somewhat different. The TOPEX histogram appears to be less smooth, with its peak shifted toward high wind by 2 m s 1. Moreover, the TOPEX measurements seem to favor both high winds between 10 and 20 m s 1 and low winds below 2 m s 1 compared to NSCAT and ECMWF. Given the large number of data used in generating the histograms, such departures are unlikely to be random. Coincidentally, in a similar comparison between NSCAT and European Remote Sensing Satellite-2 (ERS-2) altimeter winds (Queffeulou et al. 1999), the features mentioned above appeared almost in the same way (see their Fig. 5). Therefore, it can be argued that some systematic discrepancies exist between altimeter- and scatterometer-derived wind speeds, which FIG. 2. Wind speed histogram based on the TOPEX NSCAT ECMWF collocation dataset. The thick, thin, and dashed curves represent the TOPEX, NSCAT, and ECMWF results, respectively. may result from the differences in 1) their measuring mechanisms (specular reflection for the former and Bragg resonant scattering for the latter); 2) the deficiencies of the wind retrieving algorithms employed [note that the Witter and Chelton (1991) algorithm is used for both TOPEX and ERS-2 altimeters]; and 3) the spatial scales represented by each dataset. To understand more details about the observed pattern in Fig. 2, scatter diagrams comparing the three sources of wind measurements are plotted in Fig. 3, and their general statistics are given in the first three columns of Table 2. Going through the three subplots in Fig. 3, it is evident that the degree of scatter is considerably less between the two types of satellite measurements (Fig. 3a) than with the model-predicted winds (Figs. 3b and 3c), as also indicated by the overall variances in Table 2 (1.35 m s 1 versus 1.82 and 1.83 m s 1 ). Meanwhile, FIG. 1. Global distribution of spatial density of the TOPEX NSCAT ECMWF collocation data.

4 MARCH 2004 CHEN 783 a wind speed dependent bias between the altimeter and scatterometer wind estimates is also visible (Fig. 3a). The deviation of the TOPEX histogram in Fig. 2 is confirmed here by the systematic overestimation (underestimation) of its speed for low (high) winds, compared to the scatterometer winds. These characteristics imply that altimeter and scatterometer wind measurements are of higher consistency compared to model predictions, but the ECMWF winds are less biased as a whole compared to satellite measurements (see Fig. 3 and Table 2). As a result, a model-based wind product would be very useful in climate-related studies where global statistics and interannual or decadal variabilities are of major interest, while satellite observations would be most desirable when oceanic wind information with fine spatial and temporal resolution is needed. Of course, the value of absolute biases does not necessarily reflect the quality of the product. An absolute bias can be easily taken out by adding or subtracting a constant value to the retrieved wind speed. What really matters is whether this bias changes over time or if it is different in various geographical regions, as will be discussed in detail in the following sections. As far as the wind speed biases of NSCAT and ECMWF are concerned, Freilich and Dunbar (1999) showed that the former is biased low by 0.3 m s 1, while the latter is almost unbiased when validated against collocated buoy data. This implies that a 0.3 ms 1 bias may exist between NSCAT and ECMWF, which agrees reasonably well with our estimate of 0.23 m s 1 (Table 2). In terms of rms difference between NSCAT and ECMWF, the result of Wentz and Smith (1999) is comparable to ours: 1.78 versus 1.83 ms 1. A slightly larger value of our rms error is probably due to the fact that low-incidence (less than 40) measurements are not included in this collocation dataset. As shown by Queffeulou et al. (1999), the standard deviation of scatterometer winds increases gradually with incidence angle. In the following analysis, we introduce a unified reference wind field that is defined as the mean value of the three kinds of collocated wind estimates. This reference dataset is used because an ideal reference dataset (the so-called sea truth ) with high accuracy, long duration, and global coverage simply does not exist. Some kind of surrogate has to be sought if wind comparisons are to be made on a global basis. As far as the present three data sources are concerned, there are basically two solutions: either use one of the datasets as a reference or use a combination of two or all of FIG. 3. Scatter diagrams of sea surface wind speed based on the TOPEX NSCAT ECMWF collocation dataset: (a) NSCAT vs TO- PEX, (b) ECMWF vs TOPEX, and (c) ECMWF vs NSCAT. The color legend depicts the number of data within a 0.1 m s m s 1 grid box.

5 784 MONTHLY WEATHER REVIEW TABLE 2. Comparison statistics of the collocated TOPEX NSCAT ECMWF wind speeds, where T, N, E, and R denote TOPEX, NSCAT, ECMWF, and the reference wind field, respectively. Bias (m s 1 ) Rms (m s 1 ) T N T E N E T R N R E R them. It was decided to generate a pseudo wind field, defined as the simple average of the three. This choice is not ideal at all, but it has, at least, the virtue of being relatively equal to the three original datasets. Moreover, it allows the TOPEX, NSCAT, and ECMWF winds to be intercompared in a somewhat objective and semi-independent sense. As an alternative, an evaluation using ECMWF wind as a reference dataset is also performed for comparison purposes (see section 3a). The general statistics of the three types of wind estimates with respect to the reference wind field are also included in Table 2 (see the last three columns). As expected, the ECMWF wind is least biased (0.05 m s 1 ) but most scattered (1.13 m s 1 ), while the TOPEX and NSCAT winds are comparable in both mean bias (0.34 versus 0.27 m s 1 ) and rms error (0.89 versus 0.88 m s 1 ), with the latter being slightly better. These statistics are believed to be a realistic reflection of the relative quality and characteristics of the true data from the three independent sources. Therefore the pseudo wind field serves as a reasonable reference for the intercomparison of the wind products concerned. 3. Geographical and seasonal patterns of wind speed discrepancy a. Zonal distribution The bias and rms error of the TOPEX, NSCAT, and ECMWF winds with respect to the reference wind are plotted as a function of latitude in Figs. 4a and 4b, respectively. A striking feature in Fig. 4a is the phase opposition between the TOPEX and NSCAT biases. The absolute value of the bias is smallest near the equator for both products and gets positive (negative) for TOPEX (NSCAT) near the poles. The general pattern of zonal wind bias is well correlated (positively for TO- PEX and negatively for NSCAT) to that of the zonal wind intensity (see Fig. 2 of Chen et al. 2002b), suggesting that the zonal bias is highly dependent on wind speed. Variations corresponding to the trade winds and the horse latitudes can be clearly identified in the TO- FIG. 4. (a), (c) Bias and (b), (d) rms of sea surface wind speed with respect to latitude. The pseudodataset and the ECMWF dataset are used as reference wind fields in (a), (b) and (c), (d), respectively. The thick, thin, and dashed curves represent the TOPEX, NSCAT, and ECMWF results, respectively. The thin line in (a) and (c) denotes zero bias.

6 MARCH 2004 CHEN 785 PEX distribution but are less obvious in the NSCAT distribution. Unlike the systematic biases associated with satellite wind measurements, the zonal variation of the ECMWF bias exhibits a largely random nature, with narrow amplitude between 0.4 m s 1. This random uncertainty leads to a considerably larger rms error of the ECMWF winds at all latitudes compared to the TO- PEX and NSCAT winds (Fig. 4b). The TOPEX wind appears to have the smallest rms error in the tropical areas between 20. For the extratropical areas, however, the TOPEX and NSCAT rms errors are very close to each other. Note that for all three cases, the rms error has a significant minimum around 20 where the trade wind belts are located. Surprisingly to some extent, the equatorial area displays a significant peak in all three rms distributions (Fig. 4b), although it is seen to be the least biased zone for both satellite and model winds (Fig. 4a). A possible cause of the equatorial bumps in Fig. 4b could be due to rain contamination, as their locations seem to coincide with the intertropical convergence zone (ITCZ) (Chen et al. 1997). TOPEX-measured wind speed usually increases with the decreasing backscatter coefficient caused by rain attenuation at Ku band, but the reverse situation may also happen as a result of wave damping by rain (Chen et al. 1998). For NSCAT, rain can either raise or lower the wind speed, depending on the incidence angle. Although the TOPEX and NSCAT data have both been screened for rain contamination, the remaining rain effect may still introduce extra variability in wind speed rms around the ITCZ, as evidenced in Fig. 4b. In order to make sure that the above analyses are not misleading because of the inclusion of the pseudo reference wind field, the wind speed bias and rms of TO- PEX and NSCAT against the ECMWF dataset as a function of latitude are also produced (Figs. 4c and 4d). Clearly, Figs. 4c and 4d confirm nicely the major features of Figs. 4a and 4b, implying that the results and conclusions based on the reference wind field will not be too far from the truth, at least in a qualitative sense. b. Global distribution We now examine the geographical distributions of wind speed bias and rms for TOPEX, NSCAT, and ECMWF, as shown in Figs. 5 and 6, respectively (note that a 2D smoothing is applied to these figures, thereby lowering the extreme values compared to Fig. 4). As far as the mean bias is concerned, the three spatial patterns are very different from one another (Fig. 5). The TOPEX winds are most positively biased with maximum values appearing at 60 (Fig. 5a). Areas with a small negative bias are found in the tropical west Pacific and east Atlantic, as well as in the Arabian Sea and the Bay of Bengal, and near the central America. Conversely, the NSCAT bias is negative for the majority of the world s oceans with a poleward increase of its magnitude (Fig. 5b). A few small areas with marginally positive biases are scattered in the tropical oceans. Unlike the zonally banded and dramatically varying structure of the satellite wind biases, the ECMWF bias has a somewhat meridional orientation with small and homogeneous amplitude. The three maps of rms error in Fig. 6 appear to be highly correlated in terms of regional features. Most of the features are characterized by an anistropic pattern that has a meridional orientation for low latitudes and a zonal orientation for high latitudes. Local maximums are found at 70, 190, and 285E along 60S, as well as at 170 and 310E along 60N, where the wind speed estimates are most inaccurate. By contrast, more reliable winds are obtained within 30 of the Atlantic, the east Pacific, and the west Indian Oceans. Obviously, the relative rms error of the ECMWF winds is considerably larger than the satellite winds for almost everywhere in the ocean. The TOPEX result is slightly more accurate than the NSCAT one for low latitudes, while the reverse is true for high latitudes. It should be mentioned that some of the observed features in Figs. 5 and 6 may result from the sampling mismatch among the three data sources, especially the nadir-pointing and aliasing nature of the altimeter instrument as discussed by many previous investigators (e.g., Schlax and Chelton 1994; Chen and Ezraty 1996; Freilich and Dunbar 1999). c. Seasonal variation The short lifetime of NSCAT makes it impossible to examine the full annual cycle of its bias and rms error. But 10 months of collocation data have already permitted us to identify the general trend of the seasonal variation. Figure 7 presents the wind speed bias and rms as a function of month for the North Pacific and North Atlantic between 40 and 60N. As can be seen, seasonality is distinct for both wind speed bias and its rms. Large discrepancies are found in boreal winter for all three biases. A low bias is already visible during summer months, although data from July and August are unavailable. The seasonal departures of TOPEX and NSCAT biases are also anticorrelated, with the former and latter being largely positive and negative, respectively. As far as the TOPEX bias is concerned, the results obtained here are consistent with a previous study based on Japan Meteorological Agency (JMA) buoy data (Chen et al. 2000), in which a cosine-typed seasonal bias was observed for the North Pacific using the Witter and Chelton (1991) algorithm. Given the well-defined phase opposition between TOPEX and NSCAT wind speed biases, a combination of these two datasets may remove part of the seasonal discrepancy. In this sense, altimeter and scatterometer winds could be rather complementary for climatological studies.

7 786 MONTHLY WEATHER REVIEW FIG. 5. Geographical distribution of wind speed bias based on the TOPEX NSCAT ECMWF collocation dataset: (a) TOPEX, (b) NSCAT, and (c) ECMWF results. The pseudo reference wind field is used to generate the subplots. The color scale is in m s 1. FIG. 6. Geographical distribution of wind speed rms based on the TOPEX NSCAT ECMWF collocation dataset: (a) TOPEX, (b) NSCAT, and (c) ECMWF results. The pseudo reference wind field is used to generate the subplots. The color scale is in m s 1.

8 MARCH 2004 CHEN 787 FIG. 7. (a) Bias and (b) rms of sea surface wind speed with respect to month for the North Pacific and North Atlantic between 40 and 60N. The thick, thin, and dashed curves with triangles, squares, and circles represent the TOPEX, NSCAT, and ECMWF results, respectively. The thin line in (a) denotes zero bias. 4. Wind speed dependency A number of previous studies have shown that the biases of altimeter and scatterometer wind measurements are dependent on wind speed (e.g., Ebuchi et al. 1992; Gower 1996; Freilich and Dunbar 1999; Queffeulou et al. 1999; Wentz and Smith 1999). But factors responsible for such dependencies are not altogether clear. In this section, these dependencies are reexamined using the collocation dataset and are discussed with regard to some of the published results. The wind speed bias and rms of the TOPEX, NSCAT, FIG. 8. (a) Bias and (b) rms of sea surface wind speed with respect to the reference wind speed. The thick, thin, and dashed curves represent the TOPEX, NSCAT, and ECMWF results, respectively. The thin horizontal line in (a) denotes zero bias, and the two thin vertical dashed lines in (a) correspond to wind speeds of 10 and 15 m s 1, respectively. and ECMWF data are plotted against the reference wind speed in Figs. 8a and 8b, respectively. There are at least two features in Fig. 8a that are noteworthy. First, the well-defined phase opposition between TOPEX and NSCAT reappears. Second, the variations of satellite biases are nonmonotonic, showing a low high low (L H L) pattern for TOPEX and a high low high (H L H) pattern for NSCAT. In fact, the TOPEX (NSCAT) residual increases (decreases) with wind speed for low and medium winds (1 U 10 m s 1 ), a weakly fluctuating band is observed between 10 and 15 m s 1 before the trend reverses for high winds (U 15 m s 1 ). The turning point is somewhere between 14 and 15 m s 1. The ECMWF bias decreases with increasing

9 788 MONTHLY WEATHER REVIEW TABLE 3. Summary of the characteristics of satellite wind speed bias based on selected references, where L, H,, and denote low, high, positive, and negative, respectively. Figure numbers in the right column refer to those that appeared in the original references, cited in the left column. Author(s) Dataset A Dataset B Wind range (m s 1 ) A B (m s 1 ) Remark Gower (1996) TOPEX Buoy Freilich and Dunbar (1999) NSCAT Buoy Wentz and Smith (1999) NSCAT Buoy Queffeulou et al. (1999) NSCAT ERS-2 altimeter L H L 10% Fig. 8 Fig. 5 Fig. 20 Fig. 2 wind speed until 7 m s 1 before it levels off. For the rms error (Fig. 8b), the ECMWF shows a systematically larger magnitude as expected. The two satellite results are almost indistinguishable for moderate winds between 5 and 15 m s 1, but a divergent trend is observed for both light and strong winds, with the scatterometer being better at the low end and the altimeter being better at the high end. Note that the bias and rms estimates look noisy at high wind speeds in Fig. 8 because of the decreasing volume of available data. But the results are still expected to be statistically significant given that the minimum number of collocations within each bin (0.1 m s 1 in width) is above 20, and their spatial/ temporal distributions are basically random. Although the nonmonotonic feature of wind speed bias has not been explicitly mentioned for either altimeter or scatterometer in the references available to us, it is actually discernible, upon a close scrutiny, in the results of several previous studies, as summarized in Table 3. In the case of TOPEX, a comprehensive validation of its wind speed was performed by Gower (1996). His Fig. 8 indicates that the altimeter underestimates the wind speed for low winds (0 5 m s 1 ) and overestimates it for medium winds (5 15 m s 1 ), while the degree of overestimation decreases considerably for high winds (15 20 m s 1 ), forming a L H L structure. In contrast, a H L H pattern is identifiable to some extent on similar plots for NSCAT, as shown in Fig. 5 of Freilich and Dunbar (1999) and Fig. 20 of Wentz and Smith (1999). In the study by Queffeulou et al. (1999), NSCAT winds are compared directly with ERS-2 altimeter winds. Interestingly, a H L H signature of the bias as a function of wind speed is apparently visible in their Fig. 2. This may imply that the nonmonotonic feature of wind speed bias could also be shared by altimeters other than TOPEX and scatterometers other than NSCAT. Despite the larger uncertainties associated with high winds, accumulating evidence provided by several previous works as well as the present result in Fig. 8 is clear enough to support the argument that a peak/trough of wind speed bias does exist for TOPEX NSCAT between 10 and 15 m s Discussions In this section, the observed systematic bias of satellite wind speed measurement will be discussed in relation to swell and wind sea impacts. In doing so, evidence of swell- and wind-sea-induced biases in wind speed measurement based on existing literature is presented in section 5a. A quantitative link between the wind speed corresponding to the peak trough bias and the frequency of swell wind sea occurrence is examined in section 5b. a. Evidence of swell- and wind sea induced systematic wind speed bias The scatterometer and altimeter wind algorithms, though based on distinct physical mechanisms, share an implicit assumption of a fully developed sea. In other words, better accuracy of satellite wind measurement can be expected under a mature sea state. This, however, is rarely the case in the real ocean (Chen et al. 2002a). The degradation of algorithm performance (especially those theoretically based) in the presence of either swell or wind sea is, in a sense, inevitable. For satellite altimeters, the poor quality of the data at low winds is well known. Glazman and Pilorz (1990) showed theoretically that the degree of wave development becomes an increasingly important factor of the radar backscatter measurement for wind speeds below 5 ms 1. Furthermore, based on a long-wave modulation theory, Hwang et al. (1998) pointed out that it is basically

10 MARCH 2004 CHEN 789 FIG. 9. A scatter diagram of sea surface wind speed and significant wave height based on the collocated TOPEX NSCAT dataset. The wind speeds are extracted from NSCAT, and the significant wave heights are extracted from TOPEX. The grayscale depicts the number of data within a 0.1 m s m grid box. Also overlaid is the theoretical relationship between wind speed and significant wave height for a fully developed sea according to the WAM model. the swell-induced surface tilting effect that causes the degradation of altimeter wind measurement. These arguments are confirmed by both space and field observations. For example, Queffeulou et al. (1999) found that the ERS-2 altimeter-measured wind speeds show a larger departure from NSCAT under swell or mixed swell and wind wave conditions. Hwang et al. (1998) showed a systematic overestimation of TOPEX-derived sea surface slopes (corresponding to an underestimation of wind speed) compared to field optical observations. They further identified that the range of low wind speed is where the major discrepancy between the radar and optical measurements is observed (see Fig. 6b of Hwang et al. 1998), and these data were found to be under strong influence of swells (Hwang and Shemdin 1988). There are also reasons to speculate that the bias of scatterometer-derived wind speed is swell correlated, though it might be much smaller compared to the case of altimeter. In an attempt to evaluate the accuracy of the ERS scatterometer wind measurement, Quilfen et al. (2001) generated a plot of wind speed difference between ERS scatterometers and Tropical Atmosphere Ocean (TAO) buoys during (see their Fig. 8). In that plot, a well-defined area of large bias (over 1.0 ms 1 ) appeared in the eastern equatorial Pacific with an observable northward preference that they attributed to the impact of near-surface current. But given the coincidence of this area with the core of the Pacific swell pool (see Fig. 2a of Chen et al. 2002a), it is very likely a reflection of the sea state effect. Next, we examine the fetch-dependent biases in remotely sensed oceanic winds. The study of Glazman et al. (1988) demonstrated that a systematic bias in Seasat scatterometer due to the fetch effect is well pronounced. It was found that the scatterometer tends to overestimate the wind speed at long fetch and vice versa. The estimated error trend is roughly 0.5 m s 1 /(100 km) of the generalized wind fetch. Glazman and Pilorz (1990) are among the first who pointed out that the bias of altimeter-derived wind speed is correlated to fetch. According to their result, the bias of Geosat wind speed in a short fetch can be experimentally expressed as f X (X 10 ). (1) Note that X is the nondimensional fetch normalized as X gx/u 2, where X is the dimensional fetch in meters, and g is the acceleration of gravity. Equation (1) suggests that the altimeter underestimates the wind speed by0 5ms 1 for short fetches (see Fig. 10 of Glazman and Pilorz 1990). When Eq. (1) was used to correct the fetch effect of Geosat for the Japan Sea by Ebuchi et al. (1992), who averaged the wind speed along the whole fetch, a positive bias of less than 0.5 m s 1 was obtained. If fetch-related biases do exist, the global wind maps based on scatterometer and altimeter measurements could contain false climate trends. Specifically, wind speed averaged in long fetch zones would be overestimated by the altimeter while underestimated by the scatterometer, thus giving divergent predictions of wind climate for those regions. This could be largely the case in reality, as can be understood by relating Figs. 5a and 5b to Fig. 6 of Chen et al. (2002a), in which dominant wind wave regions are indicated. Since fetch (either long or short) is always important in these regions, they are considered to be largely overlapped with the prevailing fetch zones discussed here. Of course, other factors such as rain, current, and sea surface temperature may also contribute to the overall bias. b. A proposed link between swell/wind sea conditions and speed-dependent discrepancies To have an idea of the sea state condition in the real ocean, a scatter diagram of the coincident NSCAT wind speed (U) and TOPEX significant wave height (H s ) from the collocation dataset is presented in Fig. 9. The grayscale legend depicts density level of the data. Also overlaid on Fig. 9 is the theoretical relationship between wind speed and significant wave height for a fully developed sea based on the Wave Modeling Project (WAM) model (Wamdi Group 1988) which is expressed as 2 2 Hs U 1 (0 U 7.5 m s ), (2a) Hs 10 U U 1 1 (7.5 m s U 50 m s ). (2b)

11 790 MONTHLY WEATHER REVIEW FIG. 10. Frequency of swell (circles) and wind sea (triangles) occurrences as a function of wind speed over the global ocean based on the TOPEX NSCAT ECMWF collocation dataset. The thin horizontal line denotes a 50% frequency, and the dashed vertical line indicates the intersection of the swell and wind sea curves at 14.2 m s 1. A direct implication of this graph is that the theoretical relationship can be used as a dividing line for sea state maturity. Measurements lying below the curve are mostly from a growing sea, while those above the curve are probably swell dominated (Chen et al. 2002a). Of course this division is not supposed to be valid in an absolute sense because of the complexity of the wind wave/swell coupling. But it is expected to give a meaningful classification of the two regimes from a statistical point of view. It is apparent that a large majority of the data points are above the theoretical U H s line that corresponds to a mature sea state, implying a systematic swell dominance in the world s oceans. Figure 9 also suggests, however, that the possibility of encountering an underdeveloped sea increases rapidly with wind speed beyond 10ms 1. In order to quantify the occurrences of swell and wind wave events, two wind speed-related frequency indexes are introduced as (Chen et al. 2002a) F (U) N (U)/N(U), (3a) s F (U) N (U)/N(U), w s w (3b) where N s (U) and N w (U) are the number of swell and wind wave events for a given wind speed, respectively. Note that N(U) N s (U) N w (U), thus F s (U) F w (U) 1. This means that, in a relative sense, any individual sea state is classified as either swell dominated or wind sea dominated. Figure 10 shows the swell and wind sea frequencies as a function of wind speed. Also overlaid is a thin straight line corresponding to a 50% frequency. As expected, the swell (wind sea) frequency decreases (increases) monotonically with wind speed. Interestingly, the intersection of the two curves (i.e., the turning point from swell dominance to wind sea dominance) appears at 14.2 m s 1, which falls into the flat band of the TOPEX NSCAT wind speed biases (Fig. 8a). Figs. 8a and 10 thus seem to suggest that predominant swell or wind sea conditions (i.e., U 10 m s 1 or U 15 m s 1, respectively) have opposite impacts on wind speed bias for both the altimeter and scatterometer. The altimeter bias shows a positive trend under swell dominance and a negative trend under wind sea dominance. The reverse is true for the scatterometer. Consequently, the wind speeds between 10 and 15 m s 1 may represent a band of transition from swell (very long fetch) to wind waves with initially long and then progressively shorter fetches. To further understand this argument, separate plots of wind bias against the reference speed under swell or wind sea dominated conditions are shown in Figs. 11a and 11b, respectively. It is obvious that a monotonically increasing (decreasing) trend is observed for TOPEX (NSCAT) under swell dominance that exists for wind speed below 10 m s 1. In Fig. 11b, the increasing (decreasing) portion of the TOPEX (NSCAT) curve can be related to wind wave, long fetch conditions, whereas the rest of the curves beyond approximately 14.2 m s 1 are produced by wind wave, short fetch conditions. Thus, having a wind wave, long fetch condition is somewhat equivalent to having a predominant influence of swell. In this context, there seems to be a contradiction between the results of Glazman et al. (1988), who find a wind speed overestimate by the Seasat scatterometer for long wind fetches, and Quilfen et al. (2001), who find a negative bias for the ERS scatterometers with predominant swell conditions. My results appear to be in support of those of Quilfen et al. (2001). Note that a few months of Seasat data used by Glazman et al. (1988) might be too short, in a statistical point of view, to reach a reliable conclusion. 6. Summary and conclusions Based on a collocated TOPEX NSCAT ECMWF dataset, a detailed investigation of the characteristics and possible causes of systematic discrepancies between altimeter- and scatterometer-measured as well as modelpredicted wind speeds is carried out. The general statistics show that the TOPEX and NSCAT wind measurements have a larger overall bias but a much smaller overall variance compared to the ECMWF winds (Table 2), implying that errors associated with satellite data are mostly systematic while those associated with model outputs are basically random. This is confirmed by a constant phase reversal between TOPEX and NSCAT wind speed biases as a function of latitude, month, and wind intensity. Maximum departures are found at 60 in terms of latitude (Figs. 4a and 5) and in the Northern Hemisphere winter in terms of season (Fig. 7a), with

12 MARCH 2004 CHEN 791 speed rms is found near the equator for all three types of data (Fig. 4b), which is likely to be a consequence of rain contamination over the ITCZ. The performance of TOPEX and NSCAT are very close to each other in terms of rms error for medium winds, but the latter (former) is seen to be considerably better for low (high) winds (Fig. 8b). The results obtained in this study suggest that the reference wind speed defined in section 2b is a useful surrogate. This can be expected to some extent, given the widely accepted 2 ms 1 accuracy of satellite and model wind estimates. Considering an extreme case, if all the TOPEX, NSCAT, and ECMWF winds are biased high (or low) at a given site, the reference wind will also be biased high (or low), even though the relative magnitude and phase relationship among the three wind speed biases remain unchanged. This argument is supported by the consistency of the results with respect to other published ones, as summarized in Table 3. The rms error, however, might be affected to some degree by the ad hoc nature of the reference dataset. But comparisons between the statistics in Table 2 and those obtained from direct validations against in situ measurements in specific regions suggest that the use of this reference dataset leads to quantitatively meaningful rms results in a statistical sense. To conclude, as a result of sea state effect and algorithm deficiency, systematic bias is a common feature in satellite wind measurements, which needs to be kept in mind and taken into account when altimeter, scatterometer, and model-derived wind speeds are interpreted, compared, or integrated. FIG. 11. Wind speed bias under (a) swell and (b) wind sea conditions with respect to the reference wind speed. The thick solid line, thick dashed line, and thin dashed line represent the TOPEX, NSCAT, and ECMWF results, respectively. The thin solid curves depict the number of collocation data, and the horizontal lines denote zero bias. The vertical dashed line at 14.2 m s 1 in (b) separates regions of (left) long fetch and (right) short fetch. TOPEX and NSCAT winds biased positively and negatively, respectively. Another new finding of this study is the low high low (high low high) pattern of the TO- PEX (NSCAT) wind bias with respect to wind intensity (Fig. 8a), which is speculated to be a consequence of sea state maturity. Swell and wind sea appear to have opposite effects on wind speed bias for both the altimeter and scatterometer, which shows a clear three-band structure corresponding to swell dominance (U 10 m s 1 ), mixed swell and wind sea conditions (10 U 15ms 1 ), and wind sea dominance (U 15ms 1 ), respectively (Figs. 10 and 11). As far as the rms error is concerned, low values are observed around 20 latitude in space (Fig. 4b) and between July and September in time (Fig. 7b) for both satellite and model results. A significant peak of wind Acknowledgments. This work was cosponsored by the Natural Science Foundation of China (Project Numbers and ) and the Teaching and Research Award Program for Outstanding Young Teachers in Higher Education Institutions of MOE, PRC. REFERENCES Atlas, R., R. N. Hoffman, S. C. Bloom, J. C. Jusem, and J. Ardizzone, 1996: A multiyear global surface wind velocity dataset using SSM/I wind observations. Bull. Amer. Meteor. Soc., 77, Benada, R., 1997: Merged GDR (TOPEX/Poseidon) Generation B (user s guide). Rep. D-11007, Jet Propulsion Laboratory, Pasadena, CA, 84 pp. Bentamy, A., Y. Quilfen, F. Gohin, N. Grima, M. Lenaour, and J. Servain, 1996: Determination and validation of average fields from scatterometer measurements. Global Atmos. Ocean Syst., 4, 1 29., P. Queffeulou, Y. Quilfen, and K. Katsaros, 1999: Ocean surface wind fields estimated from satellite active and passive microwave instruments. IEEE Trans. Geosci. Remote Sens., 37, Boutin, J., and J. Etcheto, 1990: Seasat scatterometer versus Scanning Multichannel Microwave Radiometer wind speed: A comparison on a global scale. J. Geophys. Res., 95, , and, 1996: Consistency of Geosat, SSM/I, and ERS-1 global surface wind speeds Comparison with in situ data. J. Atmos. Oceanic Technol., 13,

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