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Ray Bell SOES6026 Ian Robinson

TROPICAL CYCLONE ANALYSIS WITH SATELLITE RADARS

INTRODUCTION

Tropical cyclones are amongst the most devastating natural disasters; they can cause substantial loss of life, damage properties and shape financial markets every year. This has recently been highlighted by hurricane Katrina which in August 2005 displaced hundreds of thousands of people and damaged major oil refineries which caused gasoline prices in the USA to soar (Sampe and Xie, 2007). High wind speed events play an important role in earth’s climate. They remove heat and moisture from the ocean which affects the formation of deep water to drive ocean circulation (Bourassa, 2009). The strong air-sea exchange affects gas levels such as carbon dioxide and mixes water masses. They have a noticeable influence on pumping up nutrients from the deep and increasing the number of plankton. Tropical cyclones are defined as storm systems characterised by a low pressure centre with strong winds and heavy rains. There are subtle differences between the terminology of tropical cyclones, hurricanes and typhoons based on location but all have wind speeds of greater than approximately 20 m/s.

Satellite radars have an advantage over conventional observations of tropical cyclones due there global scale sampling (Smith et al, 2009). The intensities of tropical cyclones often make it a hostile environment for in-situ observations. They provide an advantage over other remote sensing satellites as they can penetrate through cloud, which are largely present in tropical cyclones. They have provided additional data about tropical cyclones which have shown a marked improvement in forecasting. There have been many case studies from satellite radars of scatterometers, altimeters and Synthetic

Aperture Radars (SARs) which have flown over the cyclone or in very close proximity and obtained results. However, the satellite radars present problems in areas with high rain fall and the calibration of extreme wind speeds.

Figure. 1. QuickSCAT wind speeds and direction of hurricane Katrina in 2005. The eye of the hurricane is visible within the purple swirls and wind speed decreases radiating outward. The barbs reveal wind direction and the white barbs show heavy rainfall. To convert knots to m/s divide by approximately 2. NASA (2006).

SCATTEROMETERS

Scatterometers work by sending out a radar signal obliquely and measuring the normalised radar cross section (σ°) which is related to surface roughness. Scatterometers give surface wind speeds and also offer the ability to measure wind direction as they measure several σ° from different azimuths angles. The values of σ° are placed into an empirical model to obtain surface wind speed and direction (Nadari et al, 1991). They offer a common spatial resolution of approximately 25-50km2, large swath width of e.g. 1800km

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for QuikSCAT and a repeat cycle of 4 days, which gives unprecedented synoptic observations of surface wind and can reveal cyclone structure with good accuracy. Figure. 1. shows the scatterometer QuikSCAT observations as it flew over hurricane Katrina in 2005.

Although scatterometers have a fairly good spatial coverage of tropical cyclones a main limitation is their calibration against very high winds, for example the QSCAT-1 geophysical model function (GMF) in which the QuikSCAT scatterometer obtains wind speed is known to be limited at 30m/s (Hennon et al, 2006) (Figure. 2.). Understanding the maximum wind speed of tropical cyclones is very important to assess the likely damage it will cause. Fernandez et al (2006) noted that when using airborne backscatter measurements of C and Ku bands to obtain moderate to high wind speeds (25-65 m/s) the σ° response became saturated and stopped increasing at hurricane force winds for both frequency bands and polarisations. Other studies also show the scatterometers underestimate high winds as the σ° response becomes saturated or due to the shortcomings of the GMF (Zeng and Brown, 1998; Weissman et al. 2002; Yuan, 2004). Improved GMF have been suggested to measure these high wind speeds which give a more accurate wind field. However, validations of high wind speeds are hard to come by due to sparse observations especially in data poor regions such as the Southern Ocean.

To improve the GMFs which provide wind speed and direction from scatterometers, parametric equations are now given as wind speed becomes greater than a certain threshold, specifically for tropical cyclones (Yueh, 2003; Draper and Long, 2004; Atlas et al, 2005; Adams et al, 2005). Hennon et al

(2006) validated surface wind speeds by using Stepped Frequency Microwave Radiometers (SFMR), onboard a National Oceanic and Atmospheric Administration aircraft and analysed winds for nine hurricanes and one tropical storm. It should be noted the choice of these in-situ wind products for their study are also limited (Uhlhorn and Black 2003). Fernandez et al (2006) gives new GMF for individual C and Ku bands up to wind speeds of 65 m/s. Although most of these GMFs give better results in areas around the cyclone, the strongest winds speeds near the eye can reach > 70m/s. In addition, the GMFs still obtain poor data due the presence of heavy rain near the core. This is the greatest limitation of measuring tropical cyclones from radars.

Figure. 2. Observation of hurricane Fabian in 2003 at 2149 UTC. Retrievals of objective smoothing and empirically tuned wind speeds in the Ku-2001 GMF Vs. Analysed winds. It is also post-processed to be a 2.5 km Ultra High Resolution product (UHR KU). Hennon et al (2006)

Rain interferes with σ° as it affects small-scale surface roughness, attenuation and scattering of the radar signal in the atmosphere; therefore it is complex and hard to take into account (Weissman et al, 2002). Measurements with the Ku band are known to be strongly affected by rain much more than the C band, especially at high incidence angles (Nie and Long, 2008). Sea surface roughness is affected further by the airflow associated with the rain event. Nie and Long (2008) found that additional

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scattering from rain causes wind speeds to appear higher than expected. They created a simultaneous wind/rain retrieval (SWRR) using the C band with the ERS scatterometer, ESCAT and collocated with the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), and numerical predicted wind velocities from the European Centre for Medium-Range Weather Forecasts (ECMWF) for incidence angles > 40°. It is shown to significantly improve wind velocity estimates and the surface rain rate in moderate to heavy rain cases. Heavy rain can also bias the wind direction towards along track, regardless of the true wind direction and therefore disrupt the wind field of a tropical cyclone, which is not improved by the SWRR, and results become noisier.

The scatterometers offer the lowest spatial resolution in comparison to the other radar satellites. Tropical cyclones are complex features, which have small-scale features especially in the eye which regular scatterometer products cannot resolve (Quilfen et al, 1998). Recently higher resolution scatterometer products have become available with resolutions of 2.5 km x 2.5 km. Williams (2008) uses a wind field model of a tropical cyclone to correct rain contamination which provides better observations for the eye of the cyclone and wind direction. However, these are inherently noisier than the standard 25 km products and again underestimate high wind speeds.

ALTIMETERS

Altimeters offer a greater spatial resolution than the scatterometer with a footprint of approximately 5 km, however they are only nadir directed, have a longer repeat cycle and rarely catch the strongest wind speeds of a tropical cyclone. The altimeter works by sending out a nadir directed pulse of

microwave radiation continuously along its track which reflects back from the sea surface as backscattered return. The return can then be used to obtain geophysical properties of the ocean (Fu and Cazenave, 2001). The backscattered power (σo) is given by the amplitude of the return pulse which is used to estimate wind speed and the slope of the waveform is related to significant wave height (Hs). Figure. 3. shows when Envisat measured Hurricane Juan in 2003. In addition to studying wind speed to measure tropical cyclones the altimeters have an advantage in offering the sea surface height anomaly (SSHa) and Hs which can also be related to the tropical cyclone and sparsely observed from in-situ measurements.

Figure. 3. Combined sensor output for a single Envisat pass across Hurricane Juan at 14:37 UT on 27th Sept. 2003. (left and right) Swath information derived from the AATSR, with the black lines indicating the nadir track. (middle) The derived attenuation from the RA-2 (in black when statistically significant and related to rain rate) along with the Brightness Temperature values from the MWR-2 channels (pink 24 GHz, red 36 GHz). Quartley et al, 2007.

Similar to the scatterometer most research has been to investigate the effect of high wind speeds and heavy rain on altimeter wave forms in tropical cyclones (Young, 1993; Quartley, 1997; Quilfen et al, 2006; Quartley and Guymer, 2007). Quilfen et al (2006)

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investigated the effects of winds, waves and rain rates on altimeter signals from tropical cyclone Isabel in 2003. The dual frequency altimeters on board Jason-1 are used to correct for ionospheric effects, which affects the radar signal path delay, but also flag measurements affected by rain. The effects of rain on σo are fairly similar to scatterometers. An increase in wind speed is responsible for the large drops in both C and Ku band σo, but larger attenuation is found for the Ku band σo in the presence of rain. They studied two altimeter tracks within 100 km from the observed eye of the storm and had wind speeds >25m/s. They created a corrected C band σo and fed it back to create a corrected Ku band for observations of extreme conditions. The corrected bands give much better results for the surface wind speed and are larger than the Young (1993) algorithm. The rainfall asymmetry was found to shift with increasing intensity; the stronger the tropical cyclone the more axisymetric the inner core. Satellite altimeters have shown that the maximum rainfall remains in front of the tropical cyclone at all speeds (Lonfat et al, 2004). These are some examples how satellite radars have verified the theory of tropical cyclones.

Altimeters provide Hs as the stretching effect of the signal by the time delay of returns from the wave crest first and the wave trough later. Quilfen et al (2006) showed Hs reached up to 11m and 14.7m either side of the centre by using its own tracking algorithm to process C band waveforms little affected by rain (Quartley, 1997) in tropical cyclone Isabel (Figure. 4.). This verified the theory of tropical cyclones that high wind speeds and sea states are more likely to be larger to the right of the direction of tropical cyclones (Willoughby and Rahn, 2004).

Altimeters have an advantage over the other radars as they provide SSHa which qualitatively gives the integrated water column temperature and therefore the amount of potential energy. This is important to investigate the formation of tropical cyclones by cyclogenesis, as is known to exist in areas with sea surface temperature greater than 26°C, as well as favourable atmospheric conditions.

Figure. 4. Ku and C band Hs in meters for Jason orbit 50 (top) and 152 (bottom). Quilfen et al (2006).

Important processes at the air-sea interface are a key component in driving tropical cyclones and therefore it is essential to improve our knowledge about them. Shay et al (2000) showed the intensification of hurricane Opal in 2005 was likely due to a warm eddy from the loop current in the Gulf of Mexico. Other results indicate that in 31 out of 36 cases hurricane intensification can be linked to an increase in the values of hurricane heat potential of approximately 30 kJ/cm2 under the storm track (Goni et al, 2003). Lin et al (2005) showed that the intensity of supertyphoon Maemi in 2003 went from having 1 minute sustained winds of 41 m/s to its peak of 77 m/s as it passed over a warm eddy (Figure. 5.). The incorporation of this eddy activity yielded an evident improvement on Maemi’s intensity evolution. Algorithms have been produced to map the 26°C isotherm using altimeter SSHa

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which is shown to influence the track of a hurricane (Goni et al, 2003). Although to fully understand a hurricanes track the atmospheric conditions must be taken into account but cannot be provided with satellite radars. Altimeters like all radars are unfortunately limited when they reach the coast where tropical cyclones have a direct impact on the population. This is currently a large focus of research.

Figure. 5. Detailed SSHa distribution from a composite of TOPEX/Poseidon and Jason-1 measurements during the cycle (30 Aug–8 Sep) before Maemi’s encounter of the eddy WOE-2. Maemi’s intensity (in Saffir–Simpson scale) and radius of maximum wind are also shown. The storm position is denoted every 6 h. Lin et al (2005)

SARs

SARs offer the highest spatial resolution of the radars as they can measure down to 25m x 25m pixels. SARs offer wind speed and wind direction from images and have potential for observing hurricanes. SARs can retrieve extreme ocean surface winds using the same GMF C band - CMOD5 as scatterometers (Shen et al, 2006). The maximum wind speed can be obtained from SAR images by fitting an appropriate Holland (1980) model given by the parametric model with wind speed below or

above 20m/s with an accuracy of 10% (Reppuci et al, 2008). However the results become underestimated at high wind speeds (Pichel et al, 2007; Nie and Long, 2008b). A problem with this method is the SAR uses a priori of Hurricane Research Division (HRD) wind directions upon which to base their GMF. Additionally, errors arise from rain (Weissmann and Bourassa, 2008) and the GMF applied.

Wind direction is obtained by observing wind streaks at the surface, however there is a 180° ambiguity. High surface winds speeds can dampen the σo at certain incident angles compared to moderate winds, which can cause ambiguity in the wind speed, especially for the near range and range travelling winds. Shen et al (2009) introduced a model to reduce this ambiguity and improve the accuracy of the radius of maximum hurricane winds of hurricane Rita in 2005 (Figure. 6.) and it shows good potential to study other intense storms and hurricanes.

FORECASTING

The coherent spatial coverage of tropical cyclones from satellite radars has great benefits for modelling tropical cyclones. There has been a marked improvement in the prediction of the tropical cyclones, especially in areas where

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Figure. 6. (Left) Simulated SAR image based on the HRD wind, and (Right) the final retrieved wind speed with application of the speed ambiguity removal method. Shen et al (2009)there are fewer in-situ measurements such as the Southern hemisphere (Isaksen and Stoffelen, 2000; Bourassa, 2009). Satellites also have the advantage of providing high resolution data whereas tropical cyclone models are usually limited by their resolution (Kurihara et al, 1990). This can cause the location of the tropical cyclone eye to be out of position by a few grid points which is likely to amplify in the forecast.

Atlas et al (2005) showed that using the QuikSCAT scatterometer by studying the hurricanes of Bonnie (1998), Cindy (1999) and Floyd (1999) helped improve the location of the hurricane eye in the model. The centre of the hurricane was displaced by approximately 200 km without the assimilation of the scatterometer (Figure. 7.). Scatterometers have provided information of the spatial variability of wind speeds and can give the distance at which winds of various speeds extend from the storm centre. This provides critical information in emergency scenarios. They are also crucial for more accurate short term forecasting. The model improvements were largest in the storm track regions with the most variable winds (Chang et al, 2009).

The SSHa from altimeters has improved modelling the intensity of tropical cyclones as simulated using the Coupled Hurricane Intensity Prediction System (Lin et al, 2005). Altimeter winds have been assimilated into the ECMWF re-analysis models since the earlier 1990s and have improved previous results (Uppala et al, 2005).

The high resolution SARs have potential for modelling the fine scale structure of tropical cyclone cores but because of the complex processing have yet to be implemented (Perrie et al, 2008). The most destructive inner core winds could be more accurately diagnosed with other high resolution satellite products such as the 2.5 x 2.5 km scatterometer data. This would allow for more highly refined forecasts (Hennon et al, 2006).

Figure. 7. Sea-level pressure analysis (hPa) at 12 UTC 23 August 1999 and QuikSCAT winds super-imposed, without QuikSCAT (top panel) and with QuikSCAT (bottom panel). The winds are in much improved agreement in the QuikSCAT case. Atlas et al (2005).

CONCLUSION

Satellite radars offer a unique opportunity to study the spatial scale of tropical cyclones with fairly good accuracy once additional algorithms are applied to the data. There are enough satellite radars in orbit to capture most of the tropical cyclones occurring (Liu et al, 2008). The tropical cyclone forecasts

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have seen a marked improvement especially forecasting immediate events. In reverse order of what I believe is the most important satellite radar for observing tropical cyclones firstly SARs. Results are not easy to obtain without complex processing and using a priori of model data. Next, altimeters have been used to achieve fairly good wind, wave and rain rate in tropical cyclones. They also offer additional parameters of Hs and SSHa which can be related to the tropical cyclone. However, the nadir direction footprint often misses the most intense part of the tropical cyclone. I believe scatterometers offer the greatest opportunities of observing tropical cyclones outside of the intense convection of the inner core, but are still limited in the core (Hennon et al, 2006). They can also provide accurate winds in some rain conditions and are quite consistent at measuring extreme winds (Nie and Long, 2008; Bourassa et al, 2009).

The limitations of measuring tropical cyclones with satellite radars are caused by the large amounts of foam, sea spray and wave breaking present, which the satellite radars measure rather than the sea surface (Yang et al, 2008). This has been a major challenge as the effects of rain and foam on σo are similar and hard to separate. These are likely to be the focus of future research. Satellites only provide detail on surface wind speeds and to fully understand the tropical cyclones we would require the change in wind speeds with height which are not provided by satellite radars and analysed winds are often used. To obtain the most accurate results of tropical cyclones from satellite radars the best approach to take is synergy between the different instruments. If a tropical cyclone is measured by two or more different types of radars the comparison between wind speeds and rain

rates will improve calibration along with collocation of other data such as radiometers and models.

There will be a continuation of satellites measuring surface winds which are likely to give more accurate results for extreme winds. Future missions include Extended Ocean Vector Wind Mission scatterometer. The resolution will be increased to 5 km with the Ku band by using a SAR technique. The scatterometer will also have a C band pencil beam to mitigate rain contamination of extreme wind speeds typical of tropical cyclones (Gaston and Rodríguez 2008). There is currently much interest in the future of tropical cyclones due to global warming especially from the re-insurance industry. I believe the observations by satellites radar will help assess this once a longer time series has been obtained.

REFERNCES

Adams, I. S., Jones, W. L. Vasudevan, S.and Soisuvarn, S. (2005) Hurricane wind retrievals using the SeaWinds scatterometer on QuikSCAT. Proc. of MTS/IEEE OCEANS 2005, Washington, D.C., Marine Technology Society and the Oceanic Engineering Society of the IEEE, pp. 2148-2150.

Atlas, R., Hou, A.Y. and Reale, O. (2005) Applications of SeaWinds scatterometer and TMI-SSM/I rain rates to hurricane analysis and forecasting. Journal of Photogrammetry & Remote Sensing, 59. pp. 233-243.

Bourassa, M.A. (2009) Remotely sensed winds and wind stresses for marine forecasting and ocean modeling.

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Community White paper, OceanObs ’09: Satellite winds, p. 24.

Chang, P.S., Jelenak, Z. Sienkiewicz, J. Knabb, R. and Brennan, M. (2009) Operational utilization and impact of satellite remotely-sensed ocean surface vector winds in the marine warning and forecasting environment, Oceanography magazine, in press.

Draper, D. W., and Long, D. G. (2004) Evaluating the effect of rain on SeaWinds scatterometer measurements, Journal of Geophysical Researcg, 109, C02005, doi:10.1029/2002JC001741.

Fernandez, D. E., Carswell, J., Frasier, S., Chang, P. S., Black P. G. and Marks, F. D. (2006) Dual-polarized C and Ku-Band ocean backscatter response to hurricane force winds. Journal of Geophysical Research, 11, C08013, doi:10.1029/2005JC003048.

Fu, L. and Cazenave, A. (2001) Satellite altimetry and earth sciences: A handbook of techniques and applications. P. 463.

Gaston, R. and Rodrìguez, E. (2008) Quikscat follow-on concept study. Tech. Rep. JPL Publication 08-18, Jet Propulsion Laboratory [Available online at http://winds.jpl.nasa.gov/publications/QFO_MissionConceptReport_JPL_08-18_2.pdf.]

Goni, G J., Black, P. and Trinanes, J. (2003) Using satellite altimetry to identify regions of hurricane intensification.

AVISO Newsletter, No. 9, CNES, Paris, France, 19–20.

Hennon, C. C., Long, D. G. and Wentz, F. J. (2006) Validation of QuikScat wind retrievals in tropical cyclone environments. 27th Conference on Hurricanes and Tropical Meteorology, Monterey, CA, American Meteorological Society, JP1.1.

Kurihara, Y.M., Bender, A., Tuleya, R.E., Ross, R. (1990) Prediction experiments of hurricane Gloria (1985) using a multiply nested movable mesh model. Monthly Weather Review. 118(10), pp. 2185– 2198.

Lin, I-I., Wu, C-C., Emanuel, K.A., Lee, I-H., Wu, C-R. and Pun, I-F. (2005) The interaction of super typhoon Maemi with a warm ocean eddy. Monthly weather review, 113, pp. 2635-2649.

Liu, W. T., Tang, W. Xie, X. Navalgund, R. and Xu, K. (2008) Power density of ocean surface wind-stress from International scatterometer tandem missions. International Journal of Remote Sensing, 29, pp. 6109-6116.

Lonfat, M., Matks, F. D. and Chen, S. S. (2004) Precipitation distribution in tropical cyclones using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager: a global perspective. Monthly Weather Review, 132, pp. 1645-1660,

Naderi, F., Freilich, M. H. and Long, D. G. (1991) Spaceborne radar measurement of wind velocity over the ocean: an overview of the NSCAT scatterometer system. Proceedings of the IEEE, 79(6), pp. 850-866.

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NASA (2006) Hurricane Katrina image. Available online from: http://visibleearth.nasa.gov/view_rec.php?id=20206. Accessed 27th April 2010.

Nie, C. and Long, D.G. (2008) A C-band Scatterometer Simultaneous Wind/Rain Retrieval Method. IEEE Transactions on Geoscience and Remote Sensing, 46(11). Pp. 3618-3632.

Nie, C., and D. G. Long, (2008b) RADARSAT ScanSAR wind retrieval and rain effects on ScanSAR measurements under hurricane conditions. Proc. of the International Geoscience and Remote Sensing Symposium, Boston, MA, IEEE, pp. 493-496.

Perrie, W., W. Zhang, M. Bourassa, H. Shen, and P. W. Vachon, (2008) Impact of satellite winds on marine wind simulations. Weather and Forecasting, 23, pp. 290–303.

Pichel, W.G., Li, X., Monaldo, F., Wackerman, C., Jackson, C., Zou, C.Z., Zheng, W., Friedman, K.S., Clemente-Colo, N.P. (2007) ENVISAT ASAR applications demonstrations: Alaska SAR demonstration and Gulf of Mexico hurricane studies. In Proceedings of Envisat

Quartly, G. D. (1997) Achieving accurate altimetry across storms: Improved wind and wave estimates from C-band, Journal of Atmospheric and Oceanic Technology, 14, pp. 705– 715.

Quartley, G. D. and Guymer, T.H. (2007) Realizing Envisat’s potential for rain cloud studies. Geophysical Research Letters. 38, L09807. doi:10.1029/2006GL028996

Quilfen, Y., Tournadre, J. and Chapron, B. (2006) Altimeter dual-frequency observations of surface winds, waves, and rain rate in tropical cyclone Isabel. Journal of Geophysical Research, 111, CO1004. doi:10.1029/2005JC003068

Reppucci, A., Lehner, S., Schulz-Stellenfleth, J. and Yang, S. (2008) Extreme wind conditions observed by satellite synthetic aperture radar in the North West Pacific, International journal of remote sensing, 29(21), pp. 6129-6144.

Said, F., and Long, D.G. (2008) Effectiveness of QuikSCAT's ultra high resolution images in determining tropical storm eye location. Proc. of the International Geoscience and Remote Sensing Symposium, Boston, MA, IEEE, 351-354.

Sampe, T. and Xie, S.-P. (2007) Mapping high sea winds from space. Bulletin of the American Meteorological Society, 88, pp. 1965-1978.

Shay, L.K., Goni, G.J. Black, P.G. (2000) Effect of a warm ocean ring on hurricane Opal, Monthly Weather Review., 128, pp. 1366-1383.

Shen, H., He, Y. and Perrie, W. (2009) Speed ambiguity in hurricane wind retrieval from SAR imagery. International Journal of Remote Sensing. 30(11), pp. 2827-2836.

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Shen, H. Perrie, W. and He, Y. (2006) A new hurricane wind retrieval algorithm for SAR images. Geophysical Research Letters, 33, L21812, doi:10.1029/2006GL027087.

Smith, D.K., Hilburn, K.A. and Wentz, F.J. (2009) Characterization of a regional wind using SSM/I and QuikSCAT data, AMS 89th Annual Meeting, Paper P3.6, Phoenix, AZ

Uhlhorn, E.W. and Black, P.G. (2003) Verification of remotely sensed sea surface winds in hurricanes. Journal of Atmospheric and Oceanic Technology, 20, pp. 99 – 116.

Uppala, S.M., Kallberg, P.W., Simmons, A.J., Andrae, U., Da Costa Bechtold, V., Fiorino, M., Gibson, J.K., Haseler, J., Hernandez, A., Kelly, G.A., Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R.P., Andersson, E., Arpe, K., Balmaseda, M.A., Beljaars, A.C.M., Vande Bergy, L., Bidlot, J., Bormass, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M., Fisher, M., Fuentes, M., Hagemann, S., Ho’lm, E., Hoskins, B.J., Isaksen, L., Janssen, P.A.E.M., Jenne, R., McNally, A.P., Mahfour, J-F., Morcrette, J-J., Rayner, N.A., Saunders, R.W., Simon, P., Sterl, A., Trenberth, K.E., Untch, A., Vasiljevic, D., Viterbo, P. and Woollen, J. (2005) The ERA-40 re-analysis. Quarterly Journal of Royal Meteorological Society, 131, pp. 2961-3012.

Weissman, D.E., Bourassa, M. A. and Tongue, J. (2002) Effects of rain rate and wind magnitude on Sea Winds scatterometer wind speed errors. Journal of Atmospheric and. Oceanic Technology., 19, pp. 738-746.

Weissman, D. E., and Bourassa, M. A. (2008) Measurements of the effect of rain induced sea surface roughness on the satellite scatterometer radar cross section. IEEE Transactions on Geoscience and Remote Sensing, 46, pp. 2882-2894.

Willoughby, H. E. and Rahn, M. E. (2004) Parametric representation of the primary hurricane vortex. Part I: Observations and evaluation of the Holland (1980) model. Monthly Weather Review, 132, pp. 3033-3045.

Williams, B.A. (2008) Estimation of Hurricane Winds From SeaWinds at Ultrahigh Resolution. IEEE Transactions on Geoscience and Remote Sensing, 46(10). Pp. 2924-2935.

Yang, L., Zou, J., Lin, M. and Pan, D. (2008) Method to correct both foam and rain effects on dual frequency Jason1 wind measurements in typhoon Shanshan. Proceedings of SPIE, the International Society for Optical Engineering, 710. p. 12.

Young, I.R, (1993) An estimate of the Geosat altimeter wind speed algorithm at high wind speeds. Journal of Geophysical Research. 98(C11), pp. 22,275-20,285.

Yuan, X. (2004) High-wind-speed evaluation in the Southern Ocean. Journal of Geophysical Research, 109, D13101, doi:10.1029/2003JD004179.

Yueh, S. H., Stiles, B. W. and Liu, W. T. (2003) QuikSCAT wind retrievals for

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tropical cyclones. IEEE Transactions on Geoscience and Remote Sensing, 41, pp. 2616-2628.

Zeng, L. and Brown, R. A. (1998) Scatterometer observations at high wind speeds. Journal of Applied Meteorology, 37, pp. 1412-1420.

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