AMERICAN METEOROLOGICAL SOCIETY

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1 AMERICAN METEOROLOGICAL SOCIETY Journal of Atmospheric and Oceanic Technology EARLY ONLINE RELEASE This is a preliminary PDF of the author-produced manuscript that has been peer-reviewed and accepted for publication. Since it is being posted so soon after acceptance, it has not yet been copyedited, formatted, or processed by AMS Publications. This preliminary version of the manuscript may be downloaded, distributed, and cited, but please be aware that there will be visual differences and possibly some content differences between this version and the final published version. The DOI for this manuscript is doi: /JTECH-D The final published version of this manuscript will replace the preliminary version at the above DOI once it is available. If you would like to cite this EOR in a separate work, please use the following full citation: S., S., M. Ravichandran, and G. M. S, 2013: Evaluation of ASCAT based daily gridded winds in the tropical Indian Ocean. J. Atmos. Oceanic Technol. doi: /jtech-d , in press American Meteorological Society

2 Manuscript (non-latex) Click here to download Manuscript (non-latex): Siva_DASCAT_JAOT_revised_27Dec2012.docx 1 Evaluation of ASCAT based daily gridded winds in the tropical Indian Ocean S. Siva Reddy, M. Ravichandran, and M. S. Girishkumar Indian National Centre for Ocean Information Services, Hyderabad , India Corresponding author address: Dr. M. Ravichandran, Indian National Centre for Ocean Information Services, Hyderabad , Abstract 1

3 The quality of daily gridded ASCAT (DASCAT) blended winds is examined in the Tropical Indian Ocean using 3-day running mean gridded QuikSCAT (QSCAT) winds, and in-situ daily winds from the Research Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA). The primary objective of this study is to examine whether DASCAT is a reliable replacement for the widely-used QSCAT wind products. Spatial distributions of DASCAT and QSCAT winds show good agreement in speed and direction, except over a few localized regions. We find a significant spatial coherence between rainfall and the regions of discrepancy between DASCAT and QSCAT. Comparison of DASCAT and QSCAT wind products with RAMA moorings indicates that DASCAT better captures the overall wind variability compared to QSCAT, especially during rainy and low-wind (< 5 ms -1 ) conditions. The root mean square of the RAMA-DASCAT (RAMA-QSCAT) difference during rain fall in the zonal and meridional wind is 1.4 and 1.6 ms -1 (2.7 and 2.0 ms -1 ) respectively. The present study indicates that the DASCAT blended wind product is a reliable alternative to QSCAT in the tropical Indian Ocean. 39 Key words: ASCAT, QuikSCAT, Gridded winds, Ocean and Atmospheric modelling Introduction 2

4 The availability of accurate and high frequency vector winds over the ocean is very important for various fields of oceanographic, meteorological, and climatic studies. It is a key component for forcing numerical models, potential fishing zone advisories, search and rescue missions, and initial conditions for numerical weather prediction models. The use of satellitebased measurements makes it possible to produce long time series of wind data with better spatial and temporal resolution. Earlier studies have shown that high temporal and spatial resolution of wind fields obtained from satellites result in significant improvements of ocean general circulation model simulations (Agarwal et al. 2007; Ravichandran et al. 2012), particularly for the realization of realistic subsurface features such as coastal currents and coastal upwelling (Hackert et al. 2001; Dong and Oey 2005; Jiang et al. 2008). Assimilation of winds will improve the forecast of marine weather and both the location and intensity of the storms (Atlas et al. 2001; Chelton et al. 2006). In addition, satellite wind measurements are being used for the calculation of turbulent fluxes at the sea surface (Yu and Weller, 2007) and for near surface circulation (Bonjean et al. 2002). It is important that the satellite-derived or blended wind data is validated rigorously against in-situ surface measurements to characterize the overall accuracy and precision of the dataset. The scatterometers can measure ocean surface wind vectors in both clear and cloudy conditions (Wentz et al. 2001). The wind vector derived by scatterometer has become available for the past two decades (Liu 2002). The National Aeronautics and Space Administration (NASA) launched QuikSCAT on 19 June, The QuikSCAT SeaWinds scatterometer uses a frequency of 13.4 GHz (Ku-band) for measuring wind speed and direction at the ocean surface (Callahan 2006). The mission objective of QuikSCAT is to meet an accuracy of 2 ms -1 and 20º in wind speed and direction, respectively. High-resolution measurements by QuikSCAT reveal a rich diversity of persistent small-scale features in the global wind stress field that cannot be detected by other means (Chelton et al. 2004). The 3

5 QuikSCAT wind data (level-2 and level-3) have been extensively validated in different basins (Ebuchi et al. 2002; Goswami and Sengupta 2003; Goswami and Rajagopal 2003; Pickett 2003; Satheesan et al. 2007; Winterfeldt et al. 2008; Agarwal et al. 2007; Pensieriet al. 2010) and confirm that QuikSCAT estimates both wind speed and direction with reasonably good ( 2 ms -1 and 18 in wind speed and direction respectively) accuracy. Further these studies reported that skill was poor in low wind speed and rainy conditions (Portabella and Stoffelen 2001; Stiles and Yueh 2002; Milliff et al. 2004; Ahmad et al. 2005). QuikSCAT provided more than 10 years continuous time series of accurate global wind product with good spatial and temporal resolution before its normal mission ended on 19 November, The European Meteorological Satellite Organization (EUMETSAT), MetOp-A launched on 19 October, 2006 accommodated the Advanced scatterometer (ASCAT) for measuring winds. ASCAT, operates at 5.3 GHz frequency band (C band) and can estimate the wind vector with an accuracy of ~1.2 ms -1 in speed and ~18 in direction for wind speeds between 4 ms -1 and 24 ms -1 (Verhoef and Stoffelen, 2009). ASCAT measurements are foreseen to run from 2006 to Earlier studies (Bentamy et al. 2008; Vogelzang et al. 2011) validated ASCAT level- 2b products with buoy observation and reported that the root mean square difference (RMSD) of the ASCAT wind measurements with respect to in-situ wind speed and direction is less than 1.72 ms -1 and 18, respectively. The ASCAT data is continuously available till date and has been a good source for climate studies. ASCAT can fill the data gap created by the termination of QuikSCAT scatterometer Numerous studies have evaluated the performance of level-2 scatterometer winds by collocating (within a given temporal and spatial window) scatterometer winds with available in-situ observations (Ebuchi et al. 2002; Satheesan et al. 2007; Bentamy et al. 2008). Bentamy et al. (2012) compared collocated level-2 ASCAT and QuikSCAT wind and showed 4

6 correlations ranging from 0.50 to 0.70 in the tropical regions and biases up to 1 ms -1 during rain events. However, evaluating the performance of gridded satellite-based winds is crucial, as it is widely used in operational weather forecasting and is a key component for understanding the physics and dynamics of air-sea interaction processes (Chelton et al. 2006). A daily gridded level-4 wind product (DASCAT) using ASCAT level-2b winds and European Centre for Medium range Weather Forecast (ECMWF) wind analyses was generated based on the Krigging method by Bentamy and Fillon (2012). The ECMWF wind fields are considered as external drift for the Krigging method. Bentamy and Fillon (2012) reported on the quality of the wind product over the world ocean based on various buoy data (National Data Buoy Centre (NDBC), Meteo-France and U.K. Metoffice, Tropical Atmosphere Ocean project (TAO), Research moored Array for African-Asian-Australian Monsoon Analysis and prediction (RAMA) and Pilot Research Moored Array in the Atlantic project (PIRATA)). They also performed spatial comparison between DASCAT and a Numerical Weather Prediction model's wind product in the global ocean. However, the spatial comparison is not done, to the best of our knowledge, with the high resolution QuikSCAT based gridded wind product, which is generally considered to be the most accurate wind dataset for the last decade. The study by Bentamy et al. (2002) showed that the accuracy of scatterometer derived wind varies over different oceans due to atmospheric and marine peculiarities of each basin. These peculiarities include seasonal variability of wind and rainy conditions. Such studies emphasize the need for validation in the tropical Indian Ocean region which is known to have peculiar variabilities in the ocean-atmospheric parameters. It is clear from the above discussion that a consolidated report on the accuracy of DASCAT in the tropical Indian Ocean based on surface observations is missing. This is our main motivation for comparing the wind product in the tropical Indian Ocean with satellite 5

7 derived gridded wind and the RAMA surface wind observations. In the present study, we use 3-day running mean daily QuikSCAT gridded wind product (QSCAT) and buoy wind measurements to answer the following questions. 1) How accurate is the DASCAT?, and 2) can it act as a replacement product for the QuikSCAT gridded product over the Indian Ocean? The result of this study will prove to be useful for the oceanographic and meteorological community. To meet the above objective, we chose the analysis period of 1 April, 2009 to 15 November, The selection of the study period is constrained by the simultaneous availability of the DASCAT product, which uses 12.5 km resolution ASCAT level-2b data from 1 April, 2009 (25km resolution ASCAT level-2b data is used before this date), and QSCAT product, which was terminated a few days after 15 November, We also investigated the qualitative and quantitative nature of these gridded winds with respect to the RAMA buoy wind data. This study is organized as follows. In section 2, we describe the data and methodology followed in this study. Results from validation of DASCAT with QSCAT wind and RAMA buoy measurements, and plausible causes for the errors are discussed in section 3. Section 4 provides summary and conclusions Data and Methods DASCAT wind product is obtained from ftp server of Institut Fançais de Recherche pour l'exploitation de la MER (IFREMER), France. It is daily averaged wind fields at a spatial resolution of 0.25 in both longitude and latitude. The calculation of daily estimates uses ascending as well as descending passes with available and valid retrievals. More details about data product can be found in Bentamy and Fillon (2012). Daily averaged QSCAT level-3 gridded winds (Wentz et al. 2001) at 0.25 spatial resolution are obtained from Remote Sensing System. The QSCAT product is selected because of it is widely used in earlier studies (e.g. Goswami and Sengupta 2003; Sengupta et al. 2007; Ravichandran et al. 6

8 ). Further, Sengupta et al. (2007) have shown insignificant differences between 3-day wind stress and interpolated daily gridded wind stress fields obtained from QuikSCAT for simulating the ocean parameters using Ocean general circulation model. The daily averaged in-situ wind data used for validation of DASCAT and QSCAT are obtained from RAMA buoys (McPhaden et al. 2009) which is the moored buoy component of the Indian Ocean Observing System (IndOOS). RAMA buoys measure wind speed and direction at an accuracy of 0.3 ms -1 and 5, respectively (McPhaden et al. 2009). Tropical Rainfall Measuring Mission (TRMM) precipitation radar 3B42 daily composite rainfall data obtained from the Asia Pacific Data Research Centre (APDRC) (Huffman et al., 2007) is used to identify the location of intense rainfall and its relation to scatterometer performance. Gridded winds of DASCAT and QSCAT are equivalent neutral surface winds representing the winds at 10m height. RAMA measures winds at 4 m height. Thus to compare the winds from the gridded wind products with RAMA, winds from the latter should be brought to 10 m height. We use a simple approach of assuming logarithmic wind profiles so that the corrected wind speed at height z is given by U(z) = U(z m ) * ln(z / z 0 ) / ln(z / z m ), where U (z) is the wind speed at height z, z 0 the roughness length, and z m the measurement height. This expression can be derived using a mixing length approach assuming neutral stability (Peixoto and Oort 1992). The typical oceanic value for z 0 is 1.52 x 10-4 m (Peixoto and Oort 1992; Mears et al. 2001) and it is used in this study. In the present study, statistical comparison has been performed using scatter diagrams and standard parameters such as mean, standard deviation, RMSD, and correlation coefficient. All the statistical calculations are done only during the period when both the data sets are available Results and Discussion a. Comparison of DASCAT wind with QSCAT 7

9 The main objective of this analysis is to understand the ability of DASCAT to capture large scale wind variability in the tropical Indian Ocean. In the period between 01-April-2009 to 15-November-2009, both DASCAT and QSCAT wind data are simultaneously available and it provides a unique opportunity to validate the performance of DASCAT with respect to QSCAT. The bi-monthly averages of DASCAT and QSCAT wind vectors and the difference (QSCAT-DASCAT) between wind speeds and wind vectors of these two products are shown in Figure 1a, 1b and 1c, respectively. Figure 1 clearly shows that the DASCAT accurately reproduced the location of the maxima, minima and direction of wind as seen in QSCAT products. Even though both products show strong spatial correspondence in magnitude and direction, DASCAT underestimates the mean strength of the wind by up-to 1 ms -1 with respect to QSCAT, except for a few localised regions in the Southern tropical Indian Ocean, where DASCAT overestimates the wind speed. However, there are certain regions, particularly in the central and eastern equatorial Indian Ocean and the Bay of Bengal, where the difference between DASCAT and QSCAT wind speed measurements are relatively large (1-2.5 ms -1 ). For example, during spring (April-May) and fall (October-November), QSCAT product shows strong westerly wind in the central and eastern equatorial Indian Ocean which was not present in DASCAT. Further, compared to QSCAT, DASCAT shows relatively weak south-westerly in the south-eastern Arabian Sea and central Bay of Bengal. The probable reason for this difference will be examined later (see section 1). Figure 2a and 2b shows the standard deviation of wind speed measurements from DASCAT and QSCAT, respectively. Figure 2 clearly shows that standard deviation for both winds is comparable. However, over the region south of equator, the standard deviation is much higher (by 0.5 ms -1 ) in the DASCAT as compared to QSCAT. The correlation and RMSD between QSCAT and DASCAT wind speeds during the study period are shown in Figure 2c and 2d, respectively. The correlation is more than 0.7 over most of the region. 8

10 Relatively small correlation ( ) is observed in the regions of eastern equatorial Indian Ocean and south-eastern Arabian Sea (Figure 2c). The RMSD shows a value of 1.5 ms -1 over most of the region. However, it is higher in regions where the correlation is relatively low. Generally, RMSD is less than the standard deviation in the entire tropical Indian Ocean. It is well known that the accuracy of scatterometer wind retrievals is affected by particular environmental conditions such as rain, low and high wind speed conditions (Quilfen et al. 2004; Satheesan et al. 2007). It is interesting to note that the wind speed underestimation in DASCAT, compared to QSCAT, is more or less stable with respect to magnitude of the wind (compare figures 1a, 1b and 1c). Hence, we speculate that the discrepancy between QSCAT and DASCAT may be associated with rain fall activity in that region. Figure 1d shows a bi-monthly distribution of the number of rainy days. The figure shows a broad geographic correspondence between the occurrences of large rainfall activity and the discrepancy between QSCAT and DASCAT measurements. It indicates that rain is a potential factor for discrepancy between QSCAT and DASCAT wind product. It is well known that difference in the C-band (employed in ASCAT) and Ku-band (employed in QuikSCAT) sensitivities to the rain lead to comparatively better estimates of wind in the former as indicated by earlier studies (Fernandez et al. 2003; Tournadre and Quilfen 2003). It is worth mentioning here that DASCAT is constructed based on both ASCAT and ECMWF wind fields. However, it is not clear, which way these two factors affects the gridded DASCAT wind fields. Thus from the above discussion one cannot establish the conclusion on whether the DASCAT is a safe replacement to QSCAT or not. Hence, in the following section the QSCAT and DASCAT wind products are compared with in-situ wind measurement obtained from RAMA buoy in the tropical Indian Ocean b. Validation of DASCAT and QSCAT wind with RAMA buoy observations 9

11 In order to understand the influence of rain and wind speed conditions based on the nature of uncertainties in QSCAT and DASCAT wind product, the in-situ wind data obtained from 16 RAMA buoys in the tropical Indian Ocean are used (Figure 3). For this analysis, QSCAT and DASCAT winds at their original grid points nearest to the location of RAMA buoy are used. Relative performance of DASCAT and QSCAT wind products with respect to RAMA observation on different wind speed and rain conditions will be examined in the following two sections ) Influence of wind magnitude on DASCAT and QSCAT winds. The influence of wind magnitude on the DASCAT and QSCAT winds are investigated using RAMA buoy wind speed measurements in no-rain conditions. No-rain (rain) is defined to be the condition when TRMM rain fall shows absolute (greater than) "zero". Table-1 presents the statistical parameters estimated with respect to buoy wind speed ranges (0-4 ms -1, 4-10 ms -1 and > 10 ms -1 and are referred to as low, medium and high wind speed conditions, respectively). The standard deviation and mean of both satellite wind products are comparable with the buoy wind speed measurements in all the wind speed ranges (Table-1). However, standard deviation of DASCAT product shows better agreement with RAMA compared to QSCAT during low wind speed condition. The correlation is relatively low (high) during low (medium and high) wind speed conditions in both the satellite products. It is interesting to note that the correlation between RAMA and QSCAT is always lower than the correlation between RAMA and DASCAT in all the wind speed conditions. The RMSD estimated with respect to RAMA shows relatively large values for QSCAT in low wind speed conditions compared to DASCAT. Further, the RMSD, between QSCAT and RAMA, significantly improved when the wind speed increases. For instance, RMSDs between QSCAT and RAMA are varied from 1.64 to 0.98 ms -1 in wind speed, from 1.87 to 1.31 ms -1 in zonal 10

12 winds, and from 1.69 to 1.35 ms -1 in meridional winds when going from low to medium wind speed conditions. However, this kind of improvement is not observed with DASCAT product, since DASCAT consistently maintains low RMSD values in all wind speed conditions (Table 1). Further, as seen in RMSD, the correlation of buoy winds with QSCAT is always lower than DASCAT in all the wind speed conditions. For further clarity, the RMSD between DASCAT, QSCAT and buoy for wind speed, zonal and meridional winds for each 1 ms -1 wind speed bin are shown in Figure 4. The RMSD between DASCAT and buoy shows relatively low values (~ 1 ms -1 ) in all wind speed regimes without any significant change. However, the figure clearly shows the inability of QSCAT to perform well in low wind speed conditions (Figure 4, green line). The RMSD goes above 2 ms -1 in the low wind speed conditions (0-3 ms -1 ) and gradually decreases as the wind speed increases. DASCAT consistently maintains a relatively low RMSD (1 ms -1 ) under all wind speed conditions when compared to QSCAT. This analysis clearly shows that QSCAT wind product shows a relatively poor performance in low wind speed conditions. Further, the analysis clearly depicts the better performance of DASCAT wind product in all wind speed conditions compared to QSCAT. 2) The influence of rain on ASCAT and QSCAT winds. The influence of rain on the DASCAT and QSCAT winds is further investigated using RAMA buoy wind measurements under rain and no-rain conditions. Figure 5a and 5b show the scatter plots of QSCAT and DASCAT with respect to RAMA for wind speed (zonal and meridional) during rain and norain conditions respectively. The statistics of this comparison are given in Table-2. The analysis indicates that QSCAT has a tendency to overestimate the strength of the wind during rain and for winds of magnitude less than 5 ms -1 (Figure 5a and 5b, Table-2). During no-rain conditions both QSCAT and DASCAT have high correlation with RAMA, though the magnitude is relatively small in QSCAT (Table-2). During rainy conditions, both 11

13 scatterometer wind products show slight decrease in the correlation with RAMA buoy as expected. This might be due to the inability to rule out the uncertainties in the wind estimation caused because of the unwanted roughness at the ocean surface which is being hit by rain drops (Chelton and Freilich 2005). The standard deviation for both the satellite wind products is comparable with buoy wind measurements. However, during rainy days, the discrepancies in standard deviation of the wind speed measurements are relatively large in QSCAT with respect to RAMA observation. This is more evident in QSCAT zonal wind speed (Table-2). During no-rain conditions, the RMSD between QSCAT and RAMA (DASCAT and RAMA) is 1.25 ms -1, 1.44 ms -1 and 1.42 ms -1 (0.88 ms -1, 1.12 ms -1 and 1.12 ms -1 ) for wind speed, zonal and meridional winds, respectively. During rainy days, the RMSD between RAMA and both satellite wind products shows higher value as compared to the no-rain conditions. However, the difference in RMSD between rain and no-rain conditions is larger in QSCAT's zonal and meridional wind speed measurements as compared to DASCAT. In a nutshell, the analysis clearly depicts that DASCAT wind product shows a better performance than QSCAT, particularly in rain conditions. Further, the analysis demonstrates that gridded wind field requirement of providing wind speed with an RMSD 2 ms -1 is met for DASCAT in the tropical Indian Ocean. The analysis from the above sections, however, does not provide combined effect of rain and different wind speed conditions on QSCAT and DASCAT products. In order to examine this, frequency distribution of wind speed (with 1 ms -1 bin interval) from RAMA, DASCAT and QSCAT during no-rain (Figure 6a) and rainy conditions (Figure 6b) is 294 examined. In no-rain conditions, the frequency distribution of QSCAT and DASCAT matches well with RAMA observations (Figure 6a). It clearly indicates that in no-rain conditions, both QSCAT and DASCAT provide reasonably good estimation of the wind speed. In rainy conditions, QSCAT's wind speed is shifted to high wind speed, particularly 12

14 when the wind magnitude is less than 10 ms -1 (Figure 6b). This kind of discrepancy is not visible at higher wind speeds (> 10 ms -1 ). It indicates that rain significantly influences QSCAT wind product by inflating the estimates, particularly for winds weaker than 10 ms -1. However, DASCAT measurements show a good comparison with RAMA observations even under rainy conditions. This clearly indicates the ability of DASCAT to be accurate even in rainy conditions. The better performance of DASCAT compared to QSCAT during low wind speed and rain events may be partly associated with dependency of the wind estimation on the frequency band selection in the corresponding scatterometer instruments. In general, difference in the C-band and Ku-band sensitivities to the rain and wind lead to better estimates of wind in ASCAT than QuikSCAT during rain and low wind events as indicated by earlier studies (Fernandez et al. 2003; Tournadre and Quilfen 2003). The higher accuracy of DASCAT in comparison with QSCAT may be partially associated with the use of ECMWF analysis fields in the Krigging method for constructing DASCAT (Bentamy and Fillon, 2012). Another possible source of the difference, when comparing daily averaged buoy winds with the same from a satellite is the difference in their sampling. Satellite data are a spatial average of instantaneous measurements, roughly equivalent to an 8-10 minutes mean surface wind, while the buoy data is the temporal average of instantaneous measurements at a fixed point. So, the difference between the buoy and scatterometer wind estimation may not be due to the errors in the scatterometer measurements alone Summary and conclusions In this study, the quality of the DASCAT wind product has been evaluated in the tropical Indian Ocean by comparing it with the QSCAT wind product and in-situ wind data available from RAMA buoys. The main objective of this study is to examine whether 13

15 DASCAT can be used as a reliable replacement for QSCAT. The analysis shows that, even though both QSCAT and DASCAT have a strong spatial correspondence, the mean wind speed is underestimated in DASCAT (by up-to 1 ms -1 ) with respect to QSCAT. The discrepancies between DASCAT and QSCAT winds are comparatively large over the eastern parts of north Indian Ocean (with biases and RMSDs of greater than 2 ms -1 and 1.5 ms -1 respectively). We also find a strong spatial coherence between the number of rainy days and the differences between these two wind products. Further, analysis of the source of these discrepancies is performed in terms of different wind speed and rain regimes using in-situ wind speed from the RAMA buoy. This analysis clearly shows that the accuracy of QSCAT winds show wind speed dependence and does not compare well under low wind speed conditions (< 4 ms -1 ). DASCAT, on the other hand, shows no significant change in accuracy with wind speed. Rainfall significantly influences the QSCAT wind product: wind speed estimates are biased high, particularly for winds weaker than 10 ms -1. The DASCAT product compares well with RAMA observations even in rainy conditions. Thus, DASCAT provides reliable estimates of wind speed under all weather conditions. In other words, our study reveals that the DASCAT product is a good alternative to QSCAT, at least in the tropical Indian Ocean Earlier studies have shown that the ocean general circulation models forced with QSCAT winds produce significantly improved current and subsurface temperature fields (Agarwal et al. 2007; Ravichandran et al. 2012). Hence, it will be interesting to examine the improvement in ocean state simulated by an ocean model forced with the DASCAT wind product. For example, the difference in the wind speed and direction observed in the DASCAT wind field during spring and fall may have some influence on simulation of the oceanic Kelvin and Rossby wave as suggested by earlier studies (Goswami and Sengupta 14

16 ). The Indian Ocean is generally warm, and even small errors can result in significant coupled climate biases (Palmer and Mansfield 1984), since wind forcing is as important as heat flux forcing SST variability at all times-scales (Murtugudde et al. 2000). Efforts in this direction are ongoing and the result will be reported as a separate study Acknowledgments The encouragement and facilities provided by the Director, INCOIS are gratefully acknowledged. Authors thank Prof. R. Murtugudde and Prof. D. Sengupta for their valuable suggestions. We thank IFREMER for providing DASCAT wind fields on their ftp server. QuikSCAT data are produced by Remote Sensing Systems ( and sponsored by the NASA Ocean vector winds science team ( TRMM 3B42 data was downloaded from APDRC ( RAMA wind data was obtained from Authors would like to thank Mr. Surendar, for providing some of the inputs required to construct this manuscript. Graphics are generated using Ferret. The authors gratefully acknowledge the financial support provided by Earth System Science Organization, Ministry of Earth Sciences, Government of India to conduct this research. This is INCOIS contribution No References Ahmad, K. A., W. L. Jones, T. Kasparis, S. W. Vergara, I. S. Adams, and J. D. Park, 2005: Oceanic rain rate estimates from the QuikSCAT radiometer: A Global Precipitation Mission pathfinder. J. Geophys. Res., 110.D11101, doi: /2004jd

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18 Chelton Dudley B., and Michael Freilich, 2005: Scatterometer-based assessment of 10-m wind analyses from the operational ECMWF and NCEP Numerical weather prediction models, Mon. Wea. Rev., Vol. 133, Chelton, Dudley B., Michael H. Freilich, Joseph M. Sienkiewicz, Joan M. Von Ahn, 2006: On the Use of QuikSCAT Scatterometer Measurements of Surface Winds for Marine Weather Prediction. Mon. Wea. Rev., 134, Dong, C., and L.Y. Oey, 2005: Sensitivity of Coastal Currents near Point Conception to Forcing by Three Different Winds: ECMWF, COAMPS, and Blended SSM/I ECMWF Buoy Winds. J. Phys. Oceanogr., 35, Ebuchi, N., H. C. Graber, and M. J. Caruso, 2002: Evaluation of wind vectors observed by QuikSCAT/SeaWinds using ocean buoy data. J. Atmos. Oceanic Technol., vol. 19, no. 12, pp , Fernandez D.E., X. Zhang, J. Carswell, D. McLaughlin, P. Chang, L. Connor, P.G. Black, and F.D. Marks, 2003: Hurricane wind and rain measurements using a dual polarized C/ku band airborne radar profiler. Internation Geoscience and Remote sensing symposium (IGARSS), vol. 2, p Goswami, B. N., and E.N. Rajagopal, 2003: Indian Ocean Surface Winds from NCMRWF Analysis as Compared to QuikSCAT and Moored Buoy Winds. Proc. Indian. Acad. Sci.(Earth & Planetary Sci.), 112, No. 1, March 2003, pp Goswami, B N, and D. Sengupta, 2003: A Note on the Deficiency of NCEP/NCAR Reanalysis Surface Winds over the Equatorial Indian Ocean. J. Geophys. Res., 108(C4), 3124, doi: /2002jc Hackert, E. C., A. J. Busalacchi and R. Murtugudde, 2001: A wind comparison study using an ocean general circulation model for the El Nino. J. Geophys. Res., , C2,

19 Huffman, George J., and Coauthors, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales. J. Hydrometeor, 8, Jiang, C., L. Thompson, and K. Kelly, 2008: Equatorial influence of QuikSCAT winds in an isopycnal ocean model compared to NCEP2 winds. Ocean Modell., 24, 65 71, doi: /j.ocemod Liu W. T., 2002: Progress in scatterometer application. J. Oceanogr., 58, McPhaden, M. J., G. Meyers, K. Ando, Y. Masumoto, V. S. N. Murty, M. Ravichandran, F. Syamsudin, J. Vialard, L. Yu, and W. Yu, 2009: RAMA The Research Moored Array for African Asian Australian Monsoon Analysis and Prediction A new moored buoy array in the historically data-sparse Indian Ocean provides measurements to advance monsoon research and forecasting. Bull. Amer. Meteor. Soc., 90, pp doi: /2008bams Mears C. A., K. M. Smith, and F. J. Wentz, 2001: Comparison of Special Sensor Microwave Imager and buoy-measured wind speeds from 1987 to J. Geophys. Res., VOL. 106, NO. C6, pp 11,719 11,729. Milliff, R. F., J. Morzel, D. B. Chelton, and M. H. Freilich, 2004: Wind stress curl and wind stress divergence biases from rain effects on QSCAT surface wind retrievals. J. Atmos. Oceanic Technol., 21, Murtugudde, R., J.P. McCreary Jr., and A.J. Busalacchi, 2000: Oceanic processes associated with anomalous events in the Indian Ocean with relevance to J. Geophys. Res.,, 105, C2, Palmer, T.N., Mansfield, D.A., 1984: Response of two atmospheric general circulation models to sea surface temperature anomolies in the tropical east and west Pacific. Nature, 310,

20 Peixoto, J. P., and A. H. Oort, 1992: Physics of Climate. Am. Inst. of Phys.,Woodbury, N. Y. Pensieri Sara, Roberto Bozzano, and Maria Elisabetta Schiano, 2010: Comparison between QuikScat and buoy wind data in the Ligurian Sea. J. Mar. Syst. 81, 4, Pickett, M. H., W. Q. Tang, L. K. Rosenfeld, and C. H. Wash, 2003: Quikscat satellite comparisons with nearshore buoy wind data off the US West coast. J. Atmos. Oceanic Technol., 20, no. 12, Portabella, M., and A. Stoffelen, 2001: Rain detection and quality control of SeaWinds. J. Atmos. Oceanic Technol., 18, Ravichandran, M., D. Behringer, S. Siva Reddy, M.S. Girishkumar, Neethu Chacko, R. Harikumar, 2012: Evaluation of Global ocean data assimilation system at INCOIS: The tropical Indian Ocean. Submitted in Ocean modelling. Satheesan, K., A. Sarkar, A. Parekh, M.R. Ramesh Kumar, Y. Kuroda, 2007: Comparison of wind data from QuikSCAT and buoys in the Indian Ocean. Int. J. Remote Sensing 10, Sengupta, D., R. Senan, B. N. Goswami, and J. Vialard, 2007: Intraseasonal variability of Equatorial Indian Ocean zonal currents. J. Climate, 20, doi: /JCLI4166.1, Stiles, B., and S. Yueh, 2002: Impact of rain on spaceborne ku-band wind scatterometer data. IEEE Trans. Geosci. Remote Sens., 40, Tournadre, J., and Y. Quilfen, 2003: Impact of rain cell on scatterometer data: 1. Theory and modeling. J. Geophys. Res.,108, 3225, doi: /2002jc Verhoef, A., and A. Stoffelen, 2009: Validation of ASCAT 12.5 km winds. Technical note, SAF/OSI/CDOP/KNMI/TEC/RP/

21 Vogelzang, Jur, Ad Stoffelen, Anton Verhoef, and Julia Figa-Saldana, 2011: On the quality of high resolution scatterometer winds. J. Geophys. Res., 116, C10033, doi: /2010jc Wentz, F.J., D. K. Smith, C. A. Mears, and C. L. Gentemann, 2001: Advanced algorithms for QuikSCAT and SeaWinds/AMSR. Paper presented in Geoscience and Remote Sensing Symposium, IGARSS Vol.3, IEEE 2001 International, Winterfeldt, J. O., A. Andersson, C. Klepp, S. Bakan and R. Weisse, 2008: Comparison of HOAPS, QuikSCAT and Buoy Wind Speed in the Eastern North Atlantic and the North Sea. IEEE Trans. Geosci. Remote Sens., 48,1, Yu, L., R. A. Weller, 2007: Objectively Analyzed Air Sea Heat Fluxes for the Global Ice- Free Oceans ( ). Bull. Amer. Meteor. Soc., 88,

22 Table with captions Table-1. Summary of statistical parameter (during no-rain condition) of RAMA, QSCAT, DASCAT winds as well as of differences between buoy and scatterometer data. In the table WS, U, and V represents wind speed, zonal and meridional winds, respectively. Average (ms -1 ) Standard deviation (ms -1 ) RMSD w.r.to RAMA (ms -1 ) Correlation w.r.to RAMA RAMA QSCAT DASCAT RAMA QSCAT DASCAT QSCAT DASCAT QSCAT DASCAT RAMA wind speed < 4 m/s ( No. of data points 235 ) WS U V RAMA wind speed 4-10 m/s ( No. of data points 764 ) WS U V RAMA wind speed > 10 m/s ( No. of data points 100 ) WS U V

23 Table-2. Summary of the statistical analysis between RAMA and QSCAT and RAMA and DASCAT under rain and no-rain conditions. In the table WS, U, and V, represent wind speed, zonal and meridional winds, respectively. Average (ms -1 ) Standard deviation (ms -1 ) RMSD w.r.to RAMA (ms -1 ) Correlation w.r.to RAMA RAMA QSCAT DASCAT RAMA QSCAT DASCAT QSCAT DASCAT QSCAT DASCAT No-rain ( No. of data points 1099 ) WS U V Rain ( No. of data points 892 ) WS U V Figures captions Figure1. Bi-monthly (April-May, June-July, August-September and October-November) distribution of mean wind speed and wind vectors (ms -1 ) from (a) DASCAT (b) QSCAT and (c) difference between DASCAT and QSCAT. (d) Bi-monthly distribution of the number of rainy days estimated from TRMM 3B42. Figure 2. The standard deviations (ms -1 ) of (a) DASCAT and (b) QSCAT wind speed. The (c) correlation and (d) RMSD between QSCAT and DASCAT wind products. Figure 3. The location of RAMA buoys (filled circles) in the tropical Indian Ocean which are used for validation of QSCAT and DASCAT. 22

24 Figure 4. The RMSD (ms -1 ) between DASCAT (red line) and RAMA and QSCAT (green line) and RAMA for different wind speed bins of 1 ms -1 interval for (a) wind speed, (b) zonal wind and (c) meridional wind. Please refer to Figure 6b for the number of data points used for the statistical calculation in each wind speed bin. (Note: In the figure x-axis 1 ms -1 indicates 0 to 1 ms -1 bin). Figure 5a. Scatterpolts between RAMA and DASCAT (left panel) and RAMA and QSCAT (right panel) for total, zonal and meridional winds (ms -1 ) during rain events. The red line represents least squares linear fit with slope and intercept. Figure 5b. Scatterpolts between RAMA and DASCAT (left panel) and RAMA and QSCAT (right panel) for total, zonal and meridional winds (ms -1 ) during no-rain events. The red line represents least squares linear fit with slope and intercept. Figure 6. The frequency distribution of wind speed in 1 ms -1 bin interval from RAMA, DSCAT and QSCAT in (a) no-rain and (b) rainy conditions Figures with captions 23

25 Figure1. Bi-monthly (April-May, June-July, August-September and October-November) distribution of mean wind speed and wind vectors (ms -1 ) from (a) DASCAT (b) QSCAT and (c) difference between DASCAT and QSCAT. (d) Bi-monthly distribution of the number of rainy days estimated from TRMM 3B

26 Figure 2. The standard deviations (ms -1 ) of (a) DASCAT and (b) QSCAT wind speed. The (c) correlation and (d) RMSD between QSCAT and DASCAT wind products

27 Figure 3. The location of RAMA buoys (filled circles) in the tropical Indian Ocean which are used for validation of QSCAT and DASCAT

28 Figure 4. The RMSD (ms -1 ) between DASCAT (red line) and RAMA and QSCAT (green line) and RAMA for different wind speed bins of 1 ms -1 interval for (a) wind speed, (b) zonal wind and (c) meridional wind. Please refer to Figure 6b for the number of data points used for the statistical calculation in each wind speed bin. (Note: In the figure x-axis 1 ms -1 indicates 0 to 1 ms -1 bin). 27

29 Figure 5a. Scatterpolts between RAMA and DASCAT(left panel) and RAMA and QSCAT(right panel) for total, zonal and meridional winds (ms -1 ) during rain events. The red line represents least squares linear fit with slope and intercept. 28

30 Figure 5b. Scatterpolts between RAMA and DASCAT (left panel) and RAMA and QSCAT (right panel) for total, zonal and meridional winds (ms -1 ) during no-rain events. The red line represents least squares linear fit with slope and intercept. 29

31 Figure 6. The frequency distribution of wind speed in1 ms -1 bin interval from RAMA, DSCAT and QSCAT in (a) no-rain and (b) rainy conditions

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